Using soil properties to predict in vivo bioavailability of lead in soils.

PubMed

Wijayawardena, M A Ayanka; Naidu, Ravi; Megharaj, Mallavarapu; Lamb, Dane; Thavamani, Palanisami; Kuchel, Tim

2015-11-01

Soil plays a significant role in controlling the potential bioavailability of contaminants in the environment. In this study, eleven soils were used to investigate the relationship between soil properties and relative bioavailability (RB) of lead (Pb). To minimise the effect of source of Pb on in vivo bioavailability, uncontaminated study soils were spiked with 1500 mg Pb/kg soil and aged for 10-12 months prior to investigating the relationships between soil properties and in vivo RB of Pb using swine model. The biological responses to oral administration of Pb in aqueous phase or as spiked soils were compared by applying a two-compartment pharmacokinetic model to blood Pb concentration. The study revealed that RB of Pb from aged soils ranged from 30±9% to 83±7%. The very different RB of Pb in these soils was attributed to variations in the soils' physico-chemical properties. This was established using sorption studies showing: firstly, Freundlich partition coefficients that ranged from 21 to 234; and secondly, a strongly significant (R(2)=0.94, P<0.001) exponential relationship between RB and Freundlich partition coefficient (Kd). This simple exponential model can be used to predict relative bioavailability of Pb in contaminated soils. To the best of our knowledge, this is the first such model derived using sorption partition coefficient to predict the relative bioavailability of Pb. Copyright © 2015 Elsevier Ltd. All rights reserved.

Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran.

PubMed

Mahmoudabadi, Ebrahim; Karimi, Alireza; Haghnia, Gholam Hosain; Sepehr, Adel

2017-09-11

Digital soil mapping has been introduced as a viable alternative to the traditional mapping methods due to being fast and cost-effective. The objective of the present study was to investigate the capability of the vegetation features and spectral indices as auxiliary variables in digital soil mapping models to predict soil properties. A region with an area of 1225 ha located in Bajgiran rangelands, Khorasan Razavi province, northeastern Iran, was chosen. A total of 137 sampling sites, each containing 3-5 plots with 10-m interval distance along a transect established based on randomized-systematic method, were investigated. In each plot, plant species names and numbers as well as vegetation cover percentage (VCP) were recorded, and finally one composite soil sample was taken from each transect at each site (137 soil samples in total). Terrain attributes were derived from a digital elevation model, different bands and spectral indices were obtained from the Landsat7 ETM+ images, and vegetation features were calculated in the plots, all of which were used as auxiliary variables to predict soil properties using artificial neural network, gene expression programming, and multivariate linear regression models. According to R 2 RMSE and MBE values, artificial neutral network was obtained as the most accurate soil properties prediction function used in scorpan model. Vegetation features and indices were more effective than remotely sensed data and terrain attributes in predicting soil properties including calcium carbonate equivalent, clay, bulk density, total nitrogen, carbon, sand, silt, and saturated moisture capacity. It was also shown that vegetation indices including NDVI, SAVI, MSAVI, SARVI, RDVI, and DVI were more effective in estimating the majority of soil properties compared to separate bands and even some soil spectral indices.

Prediction of soil properties using imaging spectroscopy: Considering fractional vegetation cover to improve accuracy

NASA Astrophysics Data System (ADS)

Franceschini, M. H. D.; Demattê, J. A. M.; da Silva Terra, F.; Vicente, L. E.; Bartholomeus, H.; de Souza Filho, C. R.

2015-06-01

Spectroscopic techniques have become attractive to assess soil properties because they are fast, require little labor and may reduce the amount of laboratory waste produced when compared to conventional methods. Imaging spectroscopy (IS) can have further advantages compared to laboratory or field proximal spectroscopic approaches such as providing spatially continuous information with a high density. However, the accuracy of IS derived predictions decreases when the spectral mixture of soil with other targets occurs. This paper evaluates the use of spectral data obtained by an airborne hyperspectral sensor (ProSpecTIR-VS - Aisa dual sensor) for prediction of physical and chemical properties of Brazilian highly weathered soils (i.e., Oxisols). A methodology to assess the soil spectral mixture is adapted and a progressive spectral dataset selection procedure, based on bare soil fractional cover, is proposed and tested. Satisfactory performances are obtained specially for the quantification of clay, sand and CEC using airborne sensor data (R2 of 0.77, 0.79 and 0.54; RPD of 2.14, 2.22 and 1.50, respectively), after spectral data selection is performed; although results obtained for laboratory data are more accurate (R2 of 0.92, 0.85 and 0.75; RPD of 3.52, 2.62 and 2.04, for clay, sand and CEC, respectively). Most importantly, predictions based on airborne-derived spectra for which the bare soil fractional cover is not taken into account show considerable lower accuracy, for example for clay, sand and CEC (RPD of 1.52, 1.64 and 1.16, respectively). Therefore, hyperspectral remotely sensed data can be used to predict topsoil properties of highly weathered soils, although spectral mixture of bare soil with vegetation must be considered in order to achieve an improved prediction accuracy.

What are the most crucial soil factors for predicting the distribution of alpine plant species?

NASA Astrophysics Data System (ADS)

Buri, A.; Pinto-Figueroa, E.; Yashiro, E.; Guisan, A.

2017-12-01

Nowadays the use of species distribution models (SDM) is common to predict in space and time the distribution of organisms living in the critical zone. The realized environmental niche concept behind the development of SDM imply that many environmental factors must be accounted for simultaneously to predict species distributions. Climatic and topographic factors are often primary included, whereas soil factors are frequently neglected, mainly due to the paucity of soil information available spatially and temporally. Furthermore, among existing studies, most included soil pH only, or few other soil parameters. In this study we aimed at identifying what are the most crucial soil factors for explaining alpine plant distributions and, among those identified, which ones further improve the predictive power of plant SDMs. To test the relative importance of the soil factors, we performed plant SDMs using as predictors 52 measured soil properties of various types such as organic/inorganic compounds, chemical/physical properties, water related variables, mineral composition or grain size distribution. We added them separately to a standard set of topo-climatic predictors (temperature, slope, solar radiation and topographic position). We used ensemble forecasting techniques combining together several predictive algorithms to model the distribution of 116 plant species over 250 sites in the Swiss Alps. We recorded the variable importance for each model and compared the quality of the models including different soil proprieties (one at a time) as predictors to models having only topo-climatic variables as predictors. Results show that 46% of the soil proprieties tested become the second most important variable, after air temperature, to explain spatial distribution of alpine plants species. Moreover, we also assessed that addition of certain soil factors, such as bulk soil water density, could improve over 80% the quality of some plant species models. We confirm that soil pH remains one of the most important soil factor for predicting plant species distributions, closely followed by water, organic and inorganic carbon related properties. Finally, we were able to extract three main categories of important soil properties for plant species distributions: grain size distribution, acidity and water in the soil.

Chemical properties of forest soils

Treesearch

Charles H. Perry; Michael C. Amacher

2007-01-01

Why Is Soil Chemistry Important? The soil quality indicator was initially developed as a tool for assessing the current status of forest soil resources and predicting potential changes in soil properties. Soil chemistry data can be used to diagnose tree vigor and document the deposition of atmospheric pollutants (e.g., acid rain). This chapter focuses on two chemical...

Predicting Impact of Biochar Addition on Soil Hydraulic Properties

NASA Astrophysics Data System (ADS)

Nakhli, S. A. A.; Yudi, Y.; Imhoff, P. T.

2017-12-01

Biochar has been proposed as a soil amendment to improve soil hydraulic properties, including water retention and saturated and unsaturated hydraulic conductivity, for agricultural and environmental applications. However, its effect on hydraulic properties is difficult to predict and often with mixed results: in some cases biochar enhances soil hydraulic properties, while in other cases it degrades them. Despite several published observational studies, there are no models that can reliably predict biochar's impact on soil hydraulic properties. In this project we developed models to describe the effect of addition of a commercial wood biochar pyrolyzed at 550° on soil hydraulic properties in laboratory-scale experiments. The effects of biochar addition at 2% and 6% (w/w) on water retention and saturated and unsaturated hydraulic conductivity were evaluated for silt loam, sandy loam, and loamy sand. The addition of 6% (w/w) biochar increased the available water content of silt loam, sandy loam and loamy sand by 25, 20 and 70%, respectively. The impact of biochar addition on water retention was predicted reasonably well using information on the intra particle pore volume of biochar (mercury porosimetry, N2 and CO2 sorption) and the particle size distribution of the soil/biochar mixture. When amended with 6% biochar, saturated hydraulic conductivity increased 17% for loamy sand, but decreased 30% and 54% for silt loam and sandy loam, respectively. The Kozeny-Carman equation modified to account for changes in inter pore volume predicted saturated hydraulic conductivities of the biochar-amended soils reasonably well, with RMSE ranging from 0.06 to 5.06 cm h-1 for silt loam and loamy sand, respectively. While intra particle pore volume of biochar contributed significantly to higher water retention, changes in hydraulic conductivity were correlated instead with changes in inter pore volume - the large pores between biochar and soil particles.

REGIONAL SOIL WATER RETENTION IN THE CONTIGUOUS US: SOURCES OF VARIABILITY AND VOLCANIC SOIL EFFECTS

EPA Science Inventory

Water retention of mineral soil is often well predicted using algorithms (pedotransfer functions) with basic soil properties but the spatial variability of these properties has not been well characterized. A further source of uncertainty is that water retention by volcanic soils...

Prediction of soil organic carbon partition coefficients by soil column liquid chromatography.

PubMed

Guo, Rongbo; Liang, Xinmiao; Chen, Jiping; Wu, Wenzhong; Zhang, Qing; Martens, Dieter; Kettrup, Antonius

2004-04-30

To avoid the limitation of the widely used prediction methods of soil organic carbon partition coefficients (KOC) from hydrophobic parameters, e.g., the n-octanol/water partition coefficients (KOW) and the reversed phase high performance liquid chromatographic (RP-HPLC) retention factors, the soil column liquid chromatographic (SCLC) method was developed for KOC prediction. The real soils were used as the packing materials of RP-HPLC columns, and the correlations between the retention factors of organic compounds on soil columns (ksoil) and KOC measured by batch equilibrium method were studied. Good correlations were achieved between ksoil and KOC for three types of soils with different properties. All the square of the correlation coefficients (R2) of the linear regression between log ksoil and log KOC were higher than 0.89 with standard deviations of less than 0.21. In addition, the prediction of KOC from KOW and the RP-HPLC retention factors on cyanopropyl (CN) stationary phase (kCN) was comparatively evaluated for the three types of soils. The results show that the prediction of KOC from kCN and KOW is only applicable to some specific types of soils. The results obtained in the present study proved that the SCLC method is appropriate for the KOC prediction for different types of soils, however the applicability of using hydrophobic parameters to predict KOC largely depends on the properties of soil concerned.

Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods

NASA Astrophysics Data System (ADS)

Shi, Fang; Peng, Xiang; Liu, Huan; Hu, Yafei; Liu, Zheng; Li, Eric

2018-03-01

Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.

Near infrared spectroscopy for determination of various physical, chemical and biochemical properties in Mediterranean soils.

PubMed

Zornoza, R; Guerrero, C; Mataix-Solera, J; Scow, K M; Arcenegui, V; Mataix-Beneyto, J

2008-07-01

The potential of near infrared (NIR) reflectance spectroscopy to predict various physical, chemical and biochemical properties in Mediterranean soils from SE Spain was evaluated. Soil samples (n=393) were obtained by sampling thirteen locations during three years (2003-2005 period). These samples had a wide range of soil characteristics due to variations in land use, vegetation cover and specific climatic conditions. Biochemical properties also included microbial biomarkers based on phospholipid fatty acids (PLFA). Partial least squares (PLS) regression with cross validation was used to establish relationships between the NIR spectra and the reference data from physical, chemical and biochemical analyses. Based on the values of coefficient of determination (r(2)) and the ratio of standard deviation of validation set to root mean square error of cross validation (RPD), predicted results were evaluated as excellent (r(2)>0.90 and RPD>3) for soil organic carbon, Kjeldahl nitrogen, soil moisture, cation exchange capacity, microbial biomass carbon, basal soil respiration, acid phosphatase activity, β-glucosidase activity and PLFA biomarkers for total bacteria, Gram positive bacteria, actinomycetes, vesicular-arbuscular mycorrhizal fungi and total PLFA biomass. Good predictions (0.81

Soil Systems for Upscaling Saturated Hydraulic Conductivity (Ksat) for Hydrological Modeling in the Critical Zone

USDA-ARS?s Scientific Manuscript database

Successful hydrological model predictions depend on appropriate framing of scale and the spatial-temporal accuracy of input parameters describing soil hydraulic properties. Saturated soil hydraulic conductivity (Ksat) is one of the most important properties influencing water movement through soil un...

Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions

PubMed Central

Hengl, Tomislav; Heuvelink, Gerard B. M.; Kempen, Bas; Leenaars, Johan G. B.; Walsh, Markus G.; Shepherd, Keith D.; Sila, Andrew; MacMillan, Robert A.; Mendes de Jesus, Jorge; Tamene, Lulseged; Tondoh, Jérôme E.

2015-01-01

80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008–2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management—organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15–75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological knowledge from data rich countries to countries with limited soil data. PMID:26110833

Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions.

PubMed

Hengl, Tomislav; Heuvelink, Gerard B M; Kempen, Bas; Leenaars, Johan G B; Walsh, Markus G; Shepherd, Keith D; Sila, Andrew; MacMillan, Robert A; Mendes de Jesus, Jorge; Tamene, Lulseged; Tondoh, Jérôme E

2015-01-01

80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008-2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management--organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15-75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological knowledge from data rich countries to countries with limited soil data.

Can Process Understanding Help Elucidate The Structure Of The Critical Zone? Comparing Process-Based Soil Formation Models With Digital Soil Mapping.

NASA Astrophysics Data System (ADS)

Vanwalleghem, T.; Román, A.; Peña, A.; Laguna, A.; Giráldez, J. V.

2017-12-01

There is a need for better understanding the processes influencing soil formation and the resulting distribution of soil properties in the critical zone. Soil properties can exhibit strong spatial variation, even at the small catchment scale. Especially soil carbon pools in semi-arid, mountainous areas are highly uncertain because bulk density and stoniness are very heterogeneous and rarely measured explicitly. In this study, we explore the spatial variability in key soil properties (soil carbon stocks, stoniness, bulk density and soil depth) as a function of processes shaping the critical zone (weathering, erosion, soil water fluxes and vegetation patterns). We also compare the potential of traditional digital soil mapping versus a mechanistic soil formation model (MILESD) for predicting these key soil properties. Soil core samples were collected from 67 locations at 6 depths. Total soil organic carbon stocks were 4.38 kg m-2. Solar radiation proved to be the key variable controlling soil carbon distribution. Stone content was mostly controlled by slope, indicating the importance of erosion. Spatial distribution of bulk density was found to be highly random. Finally, total carbon stocks were predicted using a random forest model whose main covariates were solar radiation and NDVI. The model predicts carbon stocks that are double as high on north versus south-facing slopes. However, validation showed that these covariates only explained 25% of the variation in the dataset. Apparently, present-day landscape and vegetation properties are not sufficient to fully explain variability in the soil carbon stocks in this complex terrain under natural vegetation. This is attributed to a high spatial variability in bulk density and stoniness, key variables controlling carbon stocks. Similar results were obtained with the mechanistic soil formation model MILESD, suggesting that more complex models might be needed to further explore this high spatial variability.

Visible-near infrared spectroscopy as a tool to improve mapping of soil properties

NASA Astrophysics Data System (ADS)

Evgrafova, Alevtina; Kühnel, Anna; Bogner, Christina; Haase, Ina; Shibistova, Olga; Guggenberger, Georg; Tananaev, Nikita; Sauheitl, Leopold; Spielvogel, Sandra

2017-04-01

Spectroscopic measurements, which are non-destructive, precise and rapid, can be used to predict soil properties and help estimate the spatial variability of soil properties at the pedon scale. These estimations are required for quantifying soil properties with higher precision, identifying the changes in soil properties and ecosystem response to climate change as well as increasing the estimation accuracy of soil-related models. Our objectives were to (i) predict soil properties for nested samples (n = 296) using the laboratory-based visible-near infrared (vis-NIR) spectra of air-dried (<2 mm) soil samples and values of measured soil properties for gridded samples (n = 174) as calibration and validation sets; (ii) estimate the precision and predictive accuracy of an empirical spectral model using (a) our own spectral library and (b) the global spectral library; (iii) support the global spectral library with obtained vis-NIR spectral data on permafrost-affected soils. The soil samples were collected from three permafrost-affected soil profiles underlain by permafrost at various depths between 23 cm to 57.5 cm below the surface (Cryosols) and one soil profile with no presence of permafrost within the upper 100 cm layer (Cambisol) in order to characterize the spatial distribution and variability of soil properties. The gridded soil samples (n = 174) were collected using an 80 cm wide grid with a mesh size of 10 cm on both axes. In addition, 300 nested soil samples were collected using a grid of 12 cm by 12 cm (25 samples per grid) from a hole of 1 cm in a diameter with a distance from the next sample of 1 cm. Due to a small amount of available soil material (< 1.5 g), 296 nested soil samples were analyzed only using vis-NIR spectroscopy. The air-dried mineral gridded soil samples (n = 174) were sieved through a 2-mm sieve and ground with an agate mortar prior to the elemental analysis. The soil organic carbon and total nitrogen concentrations (in %) were determined using a dry combustion method on the Vario EL cube analyzer (Elementar Analysensysteme GmbH, Germany). Inorganic C was removed from the mineral soil samples with pH values higher than 7 prior to the elemental analysis using the volatilization method (HCl, 6 hours). The pH of soil samples was measured in 0.01 M CaCl2 using a 1:2 soil:solution ratio. However, for soil sample with a high in organic matter content, a 1:10 ratio was applied. We also measured oxalate and dithionite extracted iron, aluminum and manganese oxides and hydroxides using inductively coupled plasma optical emission spectroscopy (Varian Vista MPX ICP-OES, Agilent Technologies, USA). We predicted the above-mentioned soil properties for all nested samples using partial least squares regression, which was performed using R program. We can conclude that vis-NIR spectroscopy can be used effectively in order to describe, estimate and further map the spatial patterns of soil properties using geostatistical methods. This research could also help to improve the global soil spectral library taking into account that only few previous applications of vis-NIR spectroscopy were conducted on permafrost-affected soils of Northern Siberia. Keywords: Visible-near infrared spectroscopy, vis-NIR, permafrost-affected soils, Siberia, partial least squares regression.

Electromagnetic Power Attenuation in Soils

DTIC Science & Technology

2005-08-01

based on field measurements of effective conductivity. Previous Soil Property Models Clearly, the problem of predicting EM attenuation in soils...Curtis, J. O. (2001a). “Moisture effects on the dielectric properties of soils,” IEEE Transactions on Geoscience and Remote Sensing 39(1), 125-128... properties of materials by time-domain techniques,” IEEE Transactions on Instrumentation and Measurement IM-19(4), 377-382. Portland Cement Association

Assimilation of optical and radar remote sensing data in 3D mapping of soil properties over large areas.

PubMed

Poggio, Laura; Gimona, Alessandro

2017-02-01

Soil is very important for many land functions. To achieve sustainability it is important to understand how soils vary over space in the landscape. Remote sensing data can be instrumental in mapping and spatial modelling of soil properties, resources and their variability. The aims of this study were to compare satellite sensors (MODIS, Landsat, Sentinel-1 and Sentinel-2) with varying spatial, temporal and spectral resolutions for Digital Soil Mapping (DSM) of a set of soil properties in Scotland, evaluate the potential benefits of adding Sentinel-1 data to DSM models, select the most suited mix of sensors for DSM to map the considered set of soil properties and validate the results of topsoil (2D) and whole profile (3D) models. The results showed that the use of a mixture of sensors proved more effective to model and map soil properties than single sensors. The use of radar Sentinel-1 data proved useful for all soil properties, improving the prediction capability of models with only optical bands. The use of MODIS time series provided stronger relationships than the use of temporal snapshots. The results showed good validation statistics with a RMSE below 20% of the range for all considered soil properties. The RMSE improved from previous studies including only MODIS sensor and using a coarser prediction grid. The performance of the models was similar to previous studies at regional, national or continental scale. A mix of optical and radar data proved useful to map soil properties along the profile. The produced maps of soil properties describing both lateral and vertical variability, with associated uncertainty, are important for further modelling and management of soil resources and ecosystem services. Coupled with further data the soil properties maps could be used to assess soil functions and therefore conditions and suitability of soils for a range of purposes. Copyright © 2016 Elsevier B.V. All rights reserved.

Soil property maps of Africa at 250 m resolution

NASA Astrophysics Data System (ADS)

Kempen, Bas; Hengl, Tomislav; Heuvelink, Gerard B. M.; Leenaars, Johan G. B.; Walsh, Markus G.; MacMillan, Robert A.; Mendes de Jesus, Jorge S.; Shepherd, Keith; Sila, Andrew; Desta, Lulseged T.; Tondoh, Jérôme E.

2015-04-01

Vast areas of arable land in sub-Saharan Africa suffer from low soil fertility and physical soil constraints, and significant amounts of nutrients are lost yearly due to unsustainable soil management practices. At the same time it is expected that agriculture in Africa must intensify to meet the growing demand for food and fiber the next decades. Protection and sustainable management of Africa's soil resources is crucial to achieve this. In this context, comprehensive, accurate and up-to-date soil information is an essential input to any agricultural or environmental management or policy and decision-making model. In Africa, detailed soil information has been fragmented and limited to specific zones of interest for decades. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. AfSIS builds on recent advances in digital soil mapping, infrared spectroscopy, remote sensing, (geo)statistics, and integrated soil fertility management to improve the way soils are evaluated, mapped, and monitored. Over the period 2008-2014, the AfSIS project has compiled two soil profile data sets (about 28,000 unique locations): the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site (new soil samples) database -- the two data sets represent the most comprehensive soil sample database of the African continent to date. In addition a large set of high-resolution environmental data layers (covariates) was assembled. The point data were used in the AfSIS project to generate a set of maps of key soil properties for the African continent at 250 m spatial resolution: sand, silt and clay fractions, bulk density, organic carbon, total nitrogen, pH, cation-exchange capacity, exchangeable bases (Ca, K, Mg, Na), exchangeable acidity, and Al content. These properties were mapped for six depth intervals up to 2 m: 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. Random forests modelling was used to relate the soil profile observations to a set covariates, that included global soil class and property maps, MODIS imagery and a DEM, in a 3D mapping framework. The model residuals were interpolated by 3D kriging, after which the kriging predictions were added to the random forests predictions to obtain the soil property predictions. The model predictions were validated with 5-fold cross-validation. The random forests models explained between 37% (exch. Na) and 85% (Al content) of the variation in the data. Results also show that globally predicted soil classes help improve continental scale mapping of the soil nutrients and are often among the most important predictors. We conclude that the first mapping results look promising. We used an automated modelling framework that enables re-computing the maps as new data becomes arrives, hereby gradually improving the maps. We showed that global maps of soil classes and properties produced with models that were predominantly calibrated on areas with plentiful observations can be used to improve the accuracy of predictions in regions with less plentiful data, such as Africa.

Predicting Key Agronomic Soil Properties with UV-Vis Fluorescence Measurements Combined with Vis-NIR-SWIR Reflectance Spectroscopy: A Farm-Scale Study in a Mediterranean Viticultural Agroecosystem.

PubMed

Vaudour, Emmanuelle; Cerovic, Zoran G; Ebengo, Dav M; Latouche, Gwendal

2018-04-10

For adequate crop and soil management, rapid and accurate techniques for monitoring soil properties are particularly important when a farmer starts up his activities and needs a diagnosis of his cultivated fields. This study aimed to evaluate the potential of fluorescence measured directly on 146 whole soil solid samples, for predicting key soil properties at the scale of a 6 ha Mediterranean wine estate with contrasting soils. UV-Vis fluorescence measurements were carried out in conjunction with reflectance measurements in the Vis-NIR-SWIR range. Combining PLSR predictions from Vis-NIR-SWIR reflectance spectra and from a set of fluorescence signals enabled us to improve the power of prediction of a number of key agronomic soil properties including SOC, N tot , CaCO₃, iron, fine particle-sizes (clay, fine silt, fine sand), CEC, pH and exchangeable Ca 2+ with cross-validation RPD ≥ 2 and R² ≥ 0.75, while exchangeable K⁺, Na⁺, Mg 2+ , coarse silt and coarse sand contents were fairly predicted (1.42 ≤ RPD < 2 and 0.54 ≤ R² < 0.75). Predictions of SOC, N tot , CaCO₃, iron contents, and pH were still good (RPD ≥ 1.8, R² ≥ 0.68) when using a single fluorescence signal or index such as SFR_R or FERARI, highlighting the unexpected importance of red excitations and indices derived from plant studies. The predictive ability of single fluorescence indices or original signals was very significant for topsoil: this is very important for a farmer who wishes to update information on soil nutrient for the purpose of fertility diagnosis and particularly nitrogen fertilization. These results open encouraging perspectives for using miniaturized fluorescence devices enabling red excitation coupled with red or far-red fluorescence emissions directly in the field.

Predicting Key Agronomic Soil Properties with UV-Vis Fluorescence Measurements Combined with Vis-NIR-SWIR Reflectance Spectroscopy: A Farm-Scale Study in a Mediterranean Viticultural Agroecosystem

PubMed Central

Vaudour, Emmanuelle; Cerovic, Zoran G.; Ebengo, Dav M.; Latouche, Gwendal

2018-01-01

For adequate crop and soil management, rapid and accurate techniques for monitoring soil properties are particularly important when a farmer starts up his activities and needs a diagnosis of his cultivated fields. This study aimed to evaluate the potential of fluorescence measured directly on 146 whole soil solid samples, for predicting key soil properties at the scale of a 6 ha Mediterranean wine estate with contrasting soils. UV-Vis fluorescence measurements were carried out in conjunction with reflectance measurements in the Vis-NIR-SWIR range. Combining PLSR predictions from Vis-NIR-SWIR reflectance spectra and from a set of fluorescence signals enabled us to improve the power of prediction of a number of key agronomic soil properties including SOC, Ntot, CaCO3, iron, fine particle-sizes (clay, fine silt, fine sand), CEC, pH and exchangeable Ca2+ with cross-validation RPD ≥ 2 and R² ≥ 0.75, while exchangeable K+, Na+, Mg2+, coarse silt and coarse sand contents were fairly predicted (1.42 ≤ RPD < 2 and 0.54 ≤ R² < 0.75). Predictions of SOC, Ntot, CaCO3, iron contents, and pH were still good (RPD ≥ 1.8, R² ≥ 0.68) when using a single fluorescence signal or index such as SFR_R or FERARI, highlighting the unexpected importance of red excitations and indices derived from plant studies. The predictive ability of single fluorescence indices or original signals was very significant for topsoil: this is very important for a farmer who wishes to update information on soil nutrient for the purpose of fertility diagnosis and particularly nitrogen fertilization. These results open encouraging perspectives for using miniaturized fluorescence devices enabling red excitation coupled with red or far-red fluorescence emissions directly in the field. PMID:29642640

DOE-EPSCOR SPONSORED PROJECT FINAL REPORT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Jianting

Concern over the quality of environmental management and restoration has motivated the model development for predicting water and solute transport in the vadose zone. Soil hydraulic properties are required inputs to subsurface models of water flow and contaminant transport in the vadose zone. Computer models are now routinely used in research and management to predict the movement of water and solutes into and through the vadose zone of soils. Such models can be used successfully only if reliable estimates of the soil hydraulic parameters are available. The hydraulic parameters considered in this project consist of the saturated hydraulic conductivity andmore » four parameters of the water retention curves. To quantify hydraulic parameters for heterogeneous soils is both difficult and time consuming. The overall objective of this project was to better quantify soil hydraulic parameters which are critical in predicting water flows and contaminant transport in the vadose zone through a comprehensive and quantitative study to predict heterogeneous soil hydraulic properties and the associated uncertainties. Systematic and quantitative consideration of the parametric heterogeneity and uncertainty can properly address and further reduce predictive uncertainty for contamination characterization and environmental restoration at DOE-managed sites. We conducted a comprehensive study to assess soil hydraulic parameter heterogeneity and uncertainty. We have addressed a number of important issues related to the soil hydraulic property characterizations. The main focus centered on new methods to characterize anisotropy of unsaturated hydraulic property typical of layered soil formations, uncertainty updating method, and artificial neural network base pedo-transfer functions to predict hydraulic parameters from easily available data. The work also involved upscaling of hydraulic properties applicable to large scale flow and contaminant transport modeling in the vadose zone and geostatistical characterization of hydraulic parameter heterogeneity. The project also examined the validity of the some simple average schemes for unsaturated hydraulic properties widely used in previous studies. A new suite of pedo-transfer functions were developed to improve the predictability of hydraulic parameters. We also explored the concept of tension-dependent hydraulic conductivity anisotropy of unsaturated layered soils. This project strengthens collaboration between researchers at the Desert Research Institute in the EPSCoR State of Nevada and their colleagues at the Pacific Northwest National Laboratory. The results of numerical simulations of a field injection experiment at Hanford site in this project could be used to provide insights to the DOE mission of appropriate contamination characterization and environmental remediation.« less

In Situ Measurement of Some Soil Properties in Paddy Soil Using Visible and Near-Infrared Spectroscopy

PubMed Central

Wenjun, Ji; Zhou, Shi; Jingyi, Huang; Shuo, Li

2014-01-01

In situ measurements with visible and near-infrared spectroscopy (vis-NIR) provide an efficient way for acquiring soil information of paddy soils in the short time gap between the harvest and following rotation. The aim of this study was to evaluate its feasibility to predict a series of soil properties including organic matter (OM), organic carbon (OC), total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), available potassium (AK) and pH of paddy soils in Zhejiang province, China. Firstly, the linear partial least squares regression (PLSR) was performed on the in situ spectra and the predictions were compared to those with laboratory-based recorded spectra. Then, the non-linear least-square support vector machine (LS-SVM) algorithm was carried out aiming to extract more useful information from the in situ spectra and improve predictions. Results show that in terms of OC, OM, TN, AN and pH, (i) the predictions were worse using in situ spectra compared to laboratory-based spectra with PLSR algorithm (ii) the prediction accuracy using LS-SVM (R2>0.75, RPD>1.90) was obviously improved with in situ vis-NIR spectra compared to PLSR algorithm, and comparable or even better than results generated using laboratory-based spectra with PLSR; (iii) in terms of AP and AK, poor predictions were obtained with in situ spectra (R2<0.5, RPD<1.50) either using PLSR or LS-SVM. The results highlight the use of LS-SVM for in situ vis-NIR spectroscopic estimation of soil properties of paddy soils. PMID:25153132

Relating soil geochemical properties to arsenic bioaccessibility through hierarchical modeling.

EPA Science Inventory

Interest in improved understanding of relationships among soil properties and arsenic (As) bioaccessibility has motivated the use of regression models for As bioaccessibility prediction. However, limits in the numbers and types of soils included in previous studies restrict the u...

Estimation of soil hydraulic properties with microwave techniques

NASA Technical Reports Server (NTRS)

Oneill, P. E.; Gurney, R. J.; Camillo, P. J.

1985-01-01

Useful quantitative information about soil properties may be obtained by calibrating energy and moisture balance models with remotely sensed data. A soil physics model solves heat and moisture flux equations in the soil profile and is driven by the surface energy balance. Model generated surface temperature and soil moisture and temperature profiles are then used in a microwave emission model to predict the soil brightness temperature. The model hydraulic parameters are varied until the predicted temperatures agree with the remotely sensed values. This method is used to estimate values for saturated hydraulic conductivity, saturated matrix potential, and a soil texture parameter. The conductivity agreed well with a value measured with an infiltration ring and the other parameters agreed with values in the literature.

Exchangeable lead from prediction models relates to vetiver lead uptake in different soil types.

PubMed

Andra, Syam S; Sarkar, Dibyendu; Saminathan, Sumathi K M; Datta, Rupali

2011-12-01

Prediction models for exchangeable soil lead, published earlier in this journal (Andra et al. 2010a), were developed using a suite of native lead (Pb) paint-contaminated residential soils from two US cities heavily populated with homes constructed prior to Pb ban in paints. In this study, we tested the feasibility and practical applications of these prediction models for developing a phytoremediation design using vetiver grass (Vetiveria zizanioides), a Pb-tolerant plant. The models were used to estimate the exchangeable fraction of Pb available for vetiver uptake in four lead-spiked soil types, both acidic and alkaline, with varying physico-chemical properties and that are different from those used to build the prediction models. Results indicate a strong correlation for predictable exchangeable Pb with the observed fraction and as well with total Pb accumulated by vetiver grass grown in these soils. The correlation coefficient for the predicted vs. observed exchangeable Pb with p < 0.001 was 0.999, 0.996, 0.949, and 0.998 in the Immokalee, Millhopper, Pahokee Muck, and Tobosa soil type, respectively. Similarly, the correlation coefficient for the predicted exchangeable Pb vs. accumulated Pb in vetiver grass with p < 0.001 was 0.948, 0.983, 0.929, and 0.969 for each soil type, respectively. This study suggests that the success of a phytoremediation design could be assessed upfront by predicting the exchangeable Pb fraction in a given soil type based on its properties. This helps in modifying the soil conditions to enhance phytoextraction of Pb from contaminated soils.

A detrimental soil disturbance prediction model for ground-based timber harvesting

Treesearch

Derrick A. Reeves; Matthew C. Reeves; Ann M. Abbott; Deborah S. Page-Dumroese; Mark D. Coleman

2012-01-01

Soil properties and forest productivity can be affected during ground-based harvest operations and site preparation. The degree of impact varies widely depending on topographic features and soil properties. Forest managers who understand site-specific limits to ground-based harvesting can alter harvest method or season to limit soil disturbance. To determine the...

Pharmaceuticals' sorptions relative to properties of thirteen different soils.

PubMed

Kodešová, Radka; Grabic, Roman; Kočárek, Martin; Klement, Aleš; Golovko, Oksana; Fér, Miroslav; Nikodem, Antonín; Jakšík, Ondřej

2015-04-01

Transport of human and veterinary pharmaceuticals in soils and consequent ground-water contamination are influenced by many factors, including compound sorption on soil particles. Here we evaluate the sorption isotherms for 7 pharmaceuticals on 13 soils, described by Freundlich equations, and assess the impact of soil properties on various pharmaceuticals' sorption on soils. Sorption of ionizable pharmaceuticals was, in many cases, highly affected by soil pH. The sorption coefficient of sulfamethoxazole was negatively correlated to soil pH, and thus positively related to hydrolytic acidity and exchangeable acidity. Sorption coefficients for clindamycin and clarithromycin were positively related to soil pH and thus negatively related to hydrolytic acidity and exchangeable acidity, and positively related to base cation saturation. The sorption coefficients for the remaining pharmaceuticals (trimethoprim, metoprolol, atenolol, and carbamazepine) were also positively correlated with the base cation saturation and cation exchange capacity. Positive correlations between sorption coefficients and clay content were found for clindamycin, clarithromycin, atenolol, and metoprolol. Positive correlations between sorption coefficients and organic carbon content were obtained for trimethoprim and carbamazepine. Pedotransfer rules for predicting sorption coefficients of various pharmaceuticals included hydrolytic acidity (sulfamethoxazole), organic carbon content (trimethoprimand carbamazepine), base cation saturation (atenolol and metoprolol), exchangeable acidity and clay content (clindamycin), and soil active pH and clay content (clarithromycin). Pedotransfer rules, predicting the Freundlich sorption coefficients, could be applied for prediction of pharmaceutical mobility in soils with similar soil properties. Predicted sorption coefficients together with pharmaceutical half-lives and other imputes (e.g., soil-hydraulic, geological, hydro-geological, climatic) may be used for assessing potential ground-water contamination. Copyright © 2014 Elsevier B.V. All rights reserved.

Multisensor on-the-go mapping of readily dispersible clay, particle size and soil organic matter

NASA Astrophysics Data System (ADS)

Debaene, Guillaume; Niedźwiecki, Jacek; Papierowska, Ewa

2016-04-01

Particle size fractions affect strongly the physical and chemical properties of soil. Readily dispersible clay (RDC) is the part of the clay fraction in soils that is easily or potentially dispersible in water when small amounts of mechanical energy are applied to soil. The amount of RDC in the soil is of significant importance for agriculture and environment because clay dispersion is a cause of poor soil stability in water which in turn contributes to soil erodibility, mud flows, and cementation. To obtain a detailed map of soil texture, many samples are needed. Moreover, RDC determination is time consuming. The use of a mobile visible and near-infrared (VIS-NIR) platform is proposed here to map those soil properties and obtain the first detailed map of RDC at field level. Soil properties prediction was based on calibration model developed with 10 representative samples selected by a fuzzy logic algorithm. Calibration samples were analysed for soil texture (clay, silt and sand), RDC and soil organic carbon (SOC) using conventional wet chemistry analysis. Moreover, the Veris mobile sensor platform is also collecting electrical conductivity (EC) data (deep and shallow), and soil temperature. These auxiliary data were combined with VIS-NIR measurement (data fusion) to improve prediction results. EC maps were also produced to help understanding RDC data. The resulting maps were visually compared with an orthophotography of the field taken at the beginning of the plant growing season. Models were developed with partial least square regression (PLSR) and support vector machine regression (SVMR). There were no significant differences between calibration using PLSR or SVMR. Nevertheless, the best models were obtained with PLSR and standard normal variate (SNV) pretreatment and the fusion with deep EC data (e.g. for RDC and clay content: RMSECV = 0,35% and R2 = 0,71; RMSECV = 0,32% and R2 = 0,73 respectively). The best models were used to predict soil properties from the field spectra collected with the VIS-NIR platform. Maps of soil properties were generated using natural neighbour (NN) interpolation. Calibration results were satisfactory for all soil properties and allowed for the generation of detailed maps. The spatial variability of RDC was in accordance with the field orthophotography. Areas of high RDC content were corresponding to area of bad plant development. Soil texture has been correctly predicted by VIS-NIR spectroscopy (laboratory or on-the-go) before. However, readily dispersible clay (an important parameter for soil stability) has never been investigated before. This study introduces the possibility of using VIS-NIR for predicting readily dispersible clay at field level. The results obtained could be used in preventing soil erosion. Acknowledgement: This research was financed by a National Science Centre grant (NCN - Poland) with decision number UMO-2012/07/B/ST10/04387

Carbon - Bulk Density Relationships for Highly Weathered Soils of the Americas

NASA Astrophysics Data System (ADS)

Nave, L. E.

2014-12-01

Soils are dynamic natural bodies composed of mineral and organic materials. As a result of this mixed composition, essential properties of soils such as their apparent density, organic and mineral contents are typically correlated. Negative relationships between bulk density (Db) and organic matter concentration provide well-known examples across a broad range of soils, and such quantitative relationships among soil properties are useful for a variety of applications. First, gap-filling or data interpolation often are necessary to develop large soil carbon (C) datasets; furthermore, limitations of access to analytical instruments may preclude C determinations for every soil sample. In such cases, equations to derive soil C concentrations from basic measures of soil mass, volume, and density offer significant potential for purposes of soil C stock estimation. To facilitate estimation of soil C stocks on highly weathered soils of the Americas, I used observations from the International Soil Carbon Network (ISCN) database to develop carbon - bulk density prediction equations for Oxisols and Ultisols. Within a small sample set of georeferenced Oxisols (n=89), 29% of the variation in A horizon C concentrations can be predicted from Db. Including the A-horizon sand content improves predictive capacity to 35%. B horizon C concentrations (n=285) were best predicted by Db and clay content, but were more variable than A-horizons (only 10% of variation explained by linear regression). Among Ultisols, a larger sample set allowed investigation of specific horizons of interest. For example, C concentrations of plowed A (Ap) horizons are predictable based on Db, sand and silt contents (n=804, r2=0.38); gleyed argillic (Btg) horizon concentrations are predictable from Db, sand and clay contents (n=190, r2=0.23). Because soil C stock estimates are more sensitive to variation in soil mass and volume determinations than to variation in C concentration, prediction equations such as these may be used on carefully collected samples to constrain soil C stocks. The geo-referenced ISCN database allows users the opportunity to derive similar predictive relationships among measured soil parameters; continued input of new datasets from highly weathered soils of the Americas will improve the precision of these prediction equations.

The Dokuchaev hypothesis as a basis for predictive digital soil mapping (on the 125th anniversary of its publication)

NASA Astrophysics Data System (ADS)

Florinsky, I. V.

2012-04-01

Predictive digital soil mapping is widely used in soil science. Its objective is the prediction of the spatial distribution of soil taxonomic units and quantitative soil properties via the analysis of spatially distributed quantitative characteristics of soil-forming factors. Western pedometrists stress the scientific priority and principal importance of Hans Jenny's book (1941) for the emergence and development of predictive soil mapping. In this paper, we demonstrate that Vasily Dokuchaev explicitly defined the central idea and statement of the problem of contemporary predictive soil mapping in the year 1886. Then, we reconstruct the history of the soil formation equation from 1899 to 1941. We argue that Jenny adopted the soil formation equation from Sergey Zakharov, who published it in a well-known fundamental textbook in 1927. It is encouraging that this issue was clarified in 2011, the anniversary year for publications of Dokuchaev and Jenny.

GlobalSoilMap France: High-resolution spatial modelling the soils of France up to two meter depth.

PubMed

Mulder, V L; Lacoste, M; Richer-de-Forges, A C; Arrouays, D

2016-12-15

This work presents the first GlobalSoilMap (GSM) products for France. We developed an automatic procedure for mapping the primary soil properties (clay, silt, sand, coarse elements, pH, soil organic carbon (SOC), cation exchange capacity (CEC) and soil depth). The procedure employed a data-mining technique and a straightforward method for estimating the 90% confidence intervals (CIs). The most accurate models were obtained for pH, sand and silt. Next, CEC, clay and SOC were found reasonably accurate predicted. Coarse elements and soil depth were the least accurate of all models. Overall, all models were considered robust; important indicators for this were 1) the small difference in model diagnostics between the calibration and cross-validation set, 2) the unbiased mean predictions, 3) the smaller spatial structure of the prediction residuals in comparison to the observations and 4) the similar performance compared to other developed GlobalSoilMap products. Nevertheless, the confidence intervals (CIs) were rather wide for all soil properties. The median predictions became less reliable with increasing depth, as indicated by the increase of CIs with depth. In addition, model accuracy and the corresponding CIs varied depending on the soil variable of interest, soil depth and geographic location. These findings indicated that the CIs are as informative as the model diagnostics. In conclusion, the presented method resulted in reasonably accurate predictions for the majority of the soil properties. End users can employ the products for different purposes, as was demonstrated with some practical examples. The mapping routine is flexible for cloud-computing and provides ample opportunity to be further developed when desired by its users. This allows regional and international GSM partners with fewer resources to develop their own products or, otherwise, to improve the current routine and work together towards a robust high-resolution digital soil map of the world. Copyright © 2016 Elsevier B.V. All rights reserved.

Characterizing scale- and location-dependent correlation of water retention parameters with soil physical properties using wavelet techniques.

PubMed

Shu, Qiaosheng; Liu, Zuoxin; Si, Bingcheng

2008-01-01

Understanding the correlation between soil hydraulic parameters and soil physical properties is a prerequisite for the prediction of soil hydraulic properties from soil physical properties. The objective of this study was to examine the scale- and location-dependent correlation between two water retention parameters (alpha and n) in the van Genuchten (1980) function and soil physical properties (sand content, bulk density [Bd], and organic carbon content) using wavelet techniques. Soil samples were collected from a transect from Fuxin, China. Soil water retention curves were measured, and the van Genuchten parameters were obtained through curve fitting. Wavelet coherency analysis was used to elucidate the location- and scale-dependent relationships between these parameters and soil physical properties. Results showed that the wavelet coherence between alpha and sand content was significantly different from red noise at small scales (8-20 m) and from a distance of 30 to 470 m. Their wavelet phase spectrum was predominantly out of phase, indicating negative correlation between these two variables. The strong negative correlation between alpha and Bd existed mainly at medium scales (30-80 m). However, parameter n had a strong positive correlation only with Bd at scales between 20 and 80 m. Neither of the two retention parameters had significant wavelet coherency with organic carbon content. These results suggested that location-dependent scale analyses are necessary to improve the performance for soil water retention characteristic predictions.

Effects of fire on chaparral soils in Arizona and California and postfire management implications

Treesearch

Leonard F. DeBano

1989-01-01

Wildfires and prescribed burns are common throughout Arizona and California chaparral. Predicting fire effects requires understanding fire behavior, estimating soil heating, and predicting changes in soil properties. Substantial quantities of some nutrients, particularly nitrogen and phosphorus, are lost directly during combustion. Highly available nutrients released...

Relationship between the erosion properties of soils and other parameters

USDA-ARS?s Scientific Manuscript database

Soil parameters are essential for erosion process prediction and ultimately improved model development, especially as they relate to dam and levee failure. Soil parameters including soil texture and structure, soil classification, soil compaction, moisture content, and degree of saturation can play...

A first attempt to reproduce basaltic soil chronosequences using a process-based soil profile model: implications for our understanding of soil evolution

NASA Astrophysics Data System (ADS)

Johnson, M.; Gloor, M.; Lloyd, J.

2012-04-01

Soils are complex systems which hold a wealth of information on both current and past conditions and many biogeochemical processes. The ability to model soil forming processes and predict soil properties will enable us to quantify such conditions and contribute to our understanding of long-term biogeochemical cycles, particularly the carbon cycle and plant nutrient cycles. However, attempts to confront such soil model predictions with data are rare, although increasingly more data from chronosquence studies is becoming available for such a purpose. Here we present initial results of an attempt to reproduce soil properties with a process-based soil evolution model similar to the model of Kirkby (1985, J. Soil Science). We specifically focus on the basaltic soils in both Hawaii and north Queensland, Australia. These soils are formed on a series of volcanic lava flows which provide sequences of different aged soils all with a relatively uniform parent material. These soil chronosequences provide a snapshot of a soil profile during different stages of development. Steep rainfall gradients in these regions also provide a system which allows us to test the model's ability to reproduce soil properties under differing climates. The mechanistic, soil evolution model presented here includes the major processes of soil formation such as i) mineral weathering, ii) percolation of rainfall through the soil, iii) leaching of solutes out of the soil profile iv) surface erosion and v) vegetation and biotic interactions. The model consists of a vertical profile and assumes simple geometry with a constantly sloping surface. The timescales of interest are on the order of tens to hundreds of thousand years. The specific properties the model predicts are, soil depth, the proportion of original elemental oxides remaining in each soil layer, pH of the soil solution, organic carbon distribution and CO2 production and concentration. The presentation will focus on a brief introduction of the model, followed by a description of novel methods using tracers such as optically stimulated luminescence (OSL) dates and meteoric 10Be to evaluate the modelled processes of bioturbation and surface erosion. We will also discuss comparisons of modelled properties with observations and conclude with implications on our understanding of soil evolution.

Thermal properties of soils: effect of biochar application

NASA Astrophysics Data System (ADS)

Usowicz, Boguslaw; Lukowski, Mateusz; Lipiec, Jerzy

2014-05-01

Thermal properties (thermal conductivity, heat capacity and thermal diffusivity) have a significant effect on the soil surface energy partitioning and resulting in the temperature distribution. Thermal properties of soil depend on water content, bulk density and organic matter content. An important source of organic matter is biochar. Biochar as a material is defined as: "charcoal for application as a soil conditioner". Biochar is generally associated with co-produced end products of pyrolysis. Many different materials are used as biomass feedstock for biochar, including wood, crop residues and manures. Additional predictions were done for terra preta soil (also known as "Amazonian dark earth"), high in charcoal content, due to adding a mixture of charcoal, bone, and manure for thousands of years i.e. approximately 10-1,000 times longer than residence times of most soil organic matter. The effect of biochar obtained from the wood biomass and other organic amendments (peat, compost) on soil thermal properties is presented in this paper. The results were compared with wetland soils of different organic matter content. The measurements of the thermal properties at various water contents were performed after incubation, under laboratory conditions using KD2Pro, Decagon Devices. The measured data were compared with predictions made using Usowicz statistical-physical model (Usowicz et al., 2006) for biochar, mineral soil and soil with addition of biochar at various water contents and bulk densities. The model operates statistically by probability of occurrence of contacts between particular fractional compounds. It combines physical properties, specific to particular compounds, into one apparent conductance specific to the mixture. The results revealed that addition of the biochar and other organic amendments into the soil caused considerable reduction of the thermal conductivity and diffusivity. The mineral soil showed the highest thermal conductivity and diffusivity that decreased in soil with addition of biochar and pure biochar. The reduction of both properties was mostly due to decrease in both particle density and bulk density. Both biochar and the organic amendments addition resulted in a decrease of the heat capacity of the mixtures in dry state and considerable increase in wet state. The lowest and highest reduction in the thermal conductivity with decreasing water content was obtained for pure biochar and mineral soil, respectively. The thermal diffusivity had a characteristic maximum at higher bulk densities and lower water contents. The wetland soil higher in organic matter content exhibit smaller temporal variation of the thermal properties compared to soils lower in organic matter content in response to changes of water content. The statistical-physical model was found to be useful for satisfactory predicting thermal properties of the soil with addition of biochar and organic amendments. Usowicz B. et al., 2006. Thermal conductivity modelling of terrestrial soil media - A comparative study. Planetary and Space Science 54, 1086-1095.

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

PubMed

Forkuor, Gerald; Hounkpatin, Ozias K L; Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

Computer aided modeling of soil mix designs to predict characteristics and properties of stabilized road bases.

DOT National Transportation Integrated Search

2009-07-01

"Considerable data exists for soils that were tested and documented, both for native properties and : properties with pozzolan stabilization. While the data exists there was no database for the Nebraska : Department of Roads to retrieve this data for...

Predicting available water of soil from particle-size distribution and bulk density in an oasis-desert transect in northwestern China

NASA Astrophysics Data System (ADS)

Li, Danfeng; Gao, Guangyao; Shao, Ming'an; Fu, Bojie

2016-07-01

A detailed understanding of soil hydraulic properties, particularly the available water content of soil, (AW, cm3 cm-3), is required for optimal water management. Direct measurement of soil hydraulic properties is impractical for large scale application, but routinely available soil particle-size distribution (PSD) and bulk density can be used as proxies to develop various prediction functions. In this study, we compared the performance of the Arya and Paris (AP) model, Mohammadi and Vanclooster (MV) model, Arya and Heitman (AH) model, and Rosetta program in predicting the soil water characteristic curve (SWCC) at 34 points with experimental SWCC data in an oasis-desert transect (20 × 5 km) in the middle reaches of the Heihe River basin, northwestern China. The idea of the three models emerges from the similarity of the shapes of the PSD and SWCC. The AP model, MV model, and Rosetta program performed better in predicting the SWCC than the AH model. The AW determined from the SWCCs predicted by the MV model agreed better with the experimental values than those derived from the AP model and Rosetta program. The fine-textured soils were characterized by higher AW values, while the sandy soils had lower AW values. The MV model has the advantages of having robust physical basis, being independent of database-related parameters, and involving subclasses of texture data. These features make it promising in predicting soil water retention at regional scales, serving for the application of hydrological models and the optimization of soil water management.

Effects of soil-engineering properties on the failure mode of shallow landslides

USGS Publications Warehouse

McKenna, Jonathan Peter; Santi, Paul Michael; Amblard, Xavier; Negri, Jacquelyn

2012-01-01

Some landslides mobilize into flows, while others slide and deposit material immediately down slope. An index based on initial dry density and fine-grained content of soil predicted failure mode of 96 landslide initiation sites in Oregon and Colorado with 79% accuracy. These material properties can be used to identify potential sources for debris flows and for slides. Field data suggest that loose soils can evolve from dense soils that dilate upon shearing. The method presented herein to predict failure mode is most applicable for shallow (depth 8), with few to moderate fines (fine-grained content <18%), and with liquid limits <40.

Improving the spatial representation of soil properties and hydrology using topographically derived watershed model initialization processes

NASA Astrophysics Data System (ADS)

Easton, Z. M.; Fuka, D.; Collick, A.; Kleinman, P. J. A.; Auerbach, D.; Sommerlot, A.; Wagena, M. B.

2015-12-01

Topography exerts critical controls on many hydrologic, geomorphologic, and environmental biophysical processes. Unfortunately many watershed modeling systems use topography only to define basin boundaries and stream channels and do not explicitly account for the topographic controls on processes such as soil genesis, soil moisture distributions and hydrological response. We develop and demonstrate a method that uses topography to spatially adjust soil morphological and soil hydrological attributes [soil texture, depth to the C-horizon, saturated conductivity, bulk density, porosity, and the field capacities at 33kpa (~ field capacity) and 1500kpa (~ wilting point) tensions]. In order to test the performance of the method the topographical adjusted soils and standard SSURGO soil (available at 1:20,000 scale) were overlaid on soil pedon pit data in the Grasslands Soil and Water Research Lab in Resiel, TX. The topographically adjusted soils exhibited significant correlations with measurements from the soil pits, while the SSURGO soil data showed almost no correlation to measured data. We also applied the method to the Grasslands Soil and Water Research watershed using the Soil and Water Assessment Tool (SWAT) model to 15 separate fields as a proxy to propagate changes in soil properties into field scale hydrological responses. Results of this test showed that the topographically adjusted soils resulted better model predictions of field runoff in 50% of the field, with the SSURGO soils preforming better in the remainder of the fields. However, the topographically adjusted soils generally predicted baseflow response more accurately, reflecting the influence of these soil properties on non-storm responses. These results indicate that adjusting soil properties based on topography can result in more accurate soil characterization and, in some cases improve model performance.

Prediction of compressibility parameters of the soils using artificial neural network.

PubMed

Kurnaz, T Fikret; Dagdeviren, Ugur; Yildiz, Murat; Ozkan, Ozhan

2016-01-01

The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index.

Parameterizing the binding properties of dissolved organic matter with default values skews the prediction of copper solution speciation and ecotoxicity in soil.

PubMed

Djae, Tanalou; Bravin, Matthieu N; Garnier, Cédric; Doelsch, Emmanuel

2017-04-01

Parameterizing speciation models by setting the percentage of dissolved organic matter (DOM) that is reactive (% r-DOM) toward metal cations at a single 65% default value is very common in predictive ecotoxicology. The authors tested this practice by comparing the free copper activity (pCu 2+  = -log 10 [Cu 2+ ]) measured in 55 soil sample solutions with pCu 2+ predicted with the Windermere humic aqueous model (WHAM) parameterized by default. Predictions of Cu toxicity to soil organisms based on measured or predicted pCu 2+ were also compared. Default WHAM parameterization substantially skewed the prediction of measured pCu 2+ by up to 2.7 pCu 2+ units (root mean square residual = 0.75-1.3) and subsequently the prediction of Cu toxicity for microbial functions, invertebrates, and plants by up to 36%, 45%, and 59% (root mean square residuals ≤9 %, 11%, and 17%), respectively. Reparametrizing WHAM by optimizing the 2 DOM binding properties (i.e., % r-DOM and the Cu complexation constant) within a physically realistic value range much improved the prediction of measured pCu 2+ (root mean square residual = 0.14-0.25). Accordingly, this WHAM parameterization successfully predicted Cu toxicity for microbial functions, invertebrates, and plants (root mean square residual ≤3.4%, 4.4%, and 5.8%, respectively). Thus, it is essential to account for the real heterogeneity in DOM binding properties for relatively accurate prediction of Cu speciation in soil solution and Cu toxic effects on soil organisms. Environ Toxicol Chem 2017;36:898-905. © 2016 SETAC. © 2016 SETAC.

A review of the impacts of degradation threats on soil properties in the UK.

PubMed

Gregory, A S; Ritz, K; McGrath, S P; Quinton, J N; Goulding, K W T; Jones, R J A; Harris, J A; Bol, R; Wallace, P; Pilgrim, E S; Whitmore, A P

2015-10-01

National governments are becoming increasingly aware of the importance of their soil resources and are shaping strategies accordingly. Implicit in any such strategy is that degradation threats and their potential effect on important soil properties and functions are defined and understood. In this paper, we aimed to review the principal degradation threats on important soil properties in the UK, seeking quantitative data where possible. Soil erosion results in the removal of important topsoil and, with it, nutrients, C and porosity. A decline in soil organic matter principally affects soil biological and microbiological properties, but also impacts on soil physical properties because of the link with soil structure. Soil contamination affects soil chemical properties, affecting nutrient availability and degrading microbial properties, whilst soil compaction degrades the soil pore network. Soil sealing removes the link between the soil and most of the 'spheres', significantly affecting hydrological and microbial functions, and soils on re-developed brownfield sites are typically degraded in most soil properties. Having synthesized the literature on the impact on soil properties, we discuss potential subsequent impacts on the important soil functions, including food and fibre production, storage of water and C, support for biodiversity, and protection of cultural and archaeological heritage. Looking forward, we suggest a twin approach of field-based monitoring supported by controlled laboratory experimentation to improve our mechanistic understanding of soils. This would enable us to better predict future impacts of degradation processes, including climate change, on soil properties and functions so that we may manage soil resources sustainably.

Relationships between soil properties and toxicity of copper and nickel to bok choy and tomato in Chinese soils.

PubMed

Li, Bo; Zhang, Hongtao; Ma, Yibing; McLaughlin, Mike J

2013-10-01

The toxicity of copper (Cu) and nickel (Ni) to bok choy and tomato shoot growth was investigated in a wide range of Chinese soils with and without leaching with artificial rainwater. The results showed that the variations of Ni toxicity induced by soil properties were wider than those of Cu toxicity to both tomato and bok choy plant growth. Leaching generally decreased the toxicity of Cu and Ni added to soils, which also depended on soils, metals, and test plant species. Soil factors controlling metal phytotoxicity were found to be soil pH and soil organic carbon content for Cu, and soil pH for Ni. It was also found that soil pH had stronger effects on Ni toxicity than on Cu toxicity. Predictive toxicity models based on these soil factors were developed. These toxicity models for Cu and Ni toxicity to tomato plant growth were validated using an independent data set for European soils. These models could be applied to predict the Cu and Ni phytotoxicity in not only Chinese soils but also European soils. © 2013 SETAC.

Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy.

PubMed

Stevens, Antoine; Nocita, Marco; Tóth, Gergely; Montanarella, Luca; van Wesemael, Bas

2013-01-01

Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg(-1) for mineral soils and a root mean square error of 50 g C kg(-1) for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation.

Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy

PubMed Central

Stevens, Antoine; Nocita, Marco; Tóth, Gergely; Montanarella, Luca; van Wesemael, Bas

2013-01-01

Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg−1 for mineral soils and a root mean square error of 50 g C kg−1 for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation. PMID:23840459

Hydromorphic soil development in the coastal temperate rainforest of Alaska

Treesearch

David V. D' Amore; Chien-Lu Ping; Paul A. Herendeen

2015-01-01

Predictive relationships between soil drainage and soil morphological features are essential for understanding hydromorphic processes in soils. The linkage between patterns of soil saturation, reduction, and reductimorphic soil properties has not been extensively studied in mountainous forested terrain. We measured soil saturation and reduction during a 4-yr period in...

Assessing predictability of a hydrological stochastic-dynamical system

NASA Astrophysics Data System (ADS)

Gelfan, Alexander

2014-05-01

The water cycle includes the processes with different memory that creates potential for predictability of hydrological system based on separating its long and short memory components and conditioning long-term prediction on slower evolving components (similar to approaches in climate prediction). In the face of the Panta Rhei IAHS Decade questions, it is important to find a conceptual approach to classify hydrological system components with respect to their predictability, define predictable/unpredictable patterns, extend lead-time and improve reliability of hydrological predictions based on the predictable patterns. Representation of hydrological systems as the dynamical systems subjected to the effect of noise (stochastic-dynamical systems) provides possible tool for such conceptualization. A method has been proposed for assessing predictability of hydrological system caused by its sensitivity to both initial and boundary conditions. The predictability is defined through a procedure of convergence of pre-assigned probabilistic measure (e.g. variance) of the system state to stable value. The time interval of the convergence, that is the time interval during which the system losses memory about its initial state, defines limit of the system predictability. The proposed method was applied to assess predictability of soil moisture dynamics in the Nizhnedevitskaya experimental station (51.516N; 38.383E) located in the agricultural zone of the central European Russia. A stochastic-dynamical model combining a deterministic one-dimensional model of hydrothermal regime of soil with a stochastic model of meteorological inputs was developed. The deterministic model describes processes of coupled heat and moisture transfer through unfrozen/frozen soil and accounts for the influence of phase changes on water flow. The stochastic model produces time series of daily meteorological variables (precipitation, air temperature and humidity), whose statistical properties are similar to those of the corresponding series of the actual data measured at the station. Beginning from the initial conditions and being forced by Monte-Carlo generated synthetic meteorological series, the model simulated diverging trajectories of soil moisture characteristics (water content of soil column, moisture of different soil layers, etc.). Limit of predictability of the specific characteristic was determined through time of stabilization of variance of the characteristic between the trajectories, as they move away from the initial state. Numerical experiments were carried out with the stochastic-dynamical model to analyze sensitivity of the soil moisture predictability assessments to uncertainty in the initial conditions, to determine effects of the soil hydraulic properties and processes of soil freezing on the predictability. It was found, particularly, that soil water content predictability is sensitive to errors in the initial conditions and strongly depends on the hydraulic properties of soil under both unfrozen and frozen conditions. Even if the initial conditions are "well-established", the assessed predictability of water content of unfrozen soil does not exceed 30-40 days, while for frozen conditions it may be as long as 3-4 months. The latter creates opportunity for utilizing the autumn water content of soil as the predictor for spring snowmelt runoff in the region under consideration.

Probability Models Based on Soil Properties for Predicting Presence-Absence of Pythium in Soybean Roots.

PubMed

Zitnick-Anderson, Kimberly K; Norland, Jack E; Del Río Mendoza, Luis E; Fortuna, Ann-Marie; Nelson, Berlin D

2017-10-01

Associations between soil properties and Pythium groups on soybean roots were investigated in 83 commercial soybean fields in North Dakota. A data set containing 2877 isolates of Pythium which included 26 known spp. and 1 unknown spp. and 13 soil properties from each field were analyzed. A Pearson correlation analysis was performed with all soil properties to observe any significant correlation between properties. Hierarchical clustering, indicator spp., and multi-response permutation procedures were used to identify groups of Pythium. Logistic regression analysis using stepwise selection was employed to calculate probability models for presence of groups based on soil properties. Three major Pythium groups were identified and three soil properties were associated with these groups. Group 1, characterized by P. ultimum, was associated with zinc levels; as zinc increased, the probability of group 1 being present increased (α = 0.05). Pythium group 2, characterized by Pythium kashmirense and an unknown Pythium sp., was associated with cation exchange capacity (CEC) (α < 0.05); as CEC increased, these spp. increased. Group 3, characterized by Pythium heterothallicum and Pythium irregulare, were associated with CEC and calcium carbonate exchange (CCE); as CCE increased and CEC decreased, these spp. increased (α = 0.05). The regression models may have value in predicting pathogenic Pythium spp. in soybean fields in North Dakota and adjacent states.

Effects of soil properties on copper toxicity to earthworm Eisenia fetida in 15 Chinese soils.

PubMed

Duan, Xiongwei; Xu, Meng; Zhou, Youya; Yan, Zengguang; Du, Yanli; Zhang, Lu; Zhang, Chaoyan; Bai, Liping; Nie, Jing; Chen, Guikui; Li, Fasheng

2016-02-01

The bioavailability and toxicity of metals in soil are influenced by a variety of soil properties, and this principle should be recognized in establishing soil environmental quality criteria. In the present study, the uptake and toxicity of Cu to the earthworm Eisenia fetida in 15 Chinese soils with various soil properties were investigated, and regression models for predicting Cu toxicity across soils were developed. The results showed that earthworm survival and body weight change were less sensitive to Cu than earthworm cocoon production. The soil Cu-based median effective concentrations (EC50s) for earthworm cocoon production varied from 27.7 to 383.7 mg kg(-1) among 15 Chinese soils, representing approximately 14-fold variation. Soil cation exchange capacity and organic carbon content were identified as key factors controlling Cu toxicity to earthworm cocoon production, and simple and multiple regression models were developed for predicting Cu toxicity across soils. Tissue Cu-based EC50s for earthworm cocoon production were also calculated and varied from 15.5 to 62.5 mg kg(-1) (4-fold variation). Compared to the soil Cu-based EC50s for cocoon production, the tissue Cu-based EC50s had less variation among soils, indicating that metals in tissue were more relevant to toxicity than metals in soil and hence represented better measurements of bioavailability. Copyright © 2015 Elsevier Ltd. All rights reserved.

Potential of EnMAP spaceborne imaging spectroscopy for the prediction of common surface soil properties and expected accuracy

NASA Astrophysics Data System (ADS)

Chabrillat, Sabine; Foerster, Saskia; Steinberg, Andreas; Stevens, Antoine; Segl, Karl

2016-04-01

There is a renewed awareness of the finite nature of the world's soil resources, growing concern about soil security, and significant uncertainties about the carrying capacity of the planet. As a consequence, soil scientists are being challenged to provide regular assessments of soil conditions from local through to global scales. However, only a few countries have the necessary survey and monitoring programs to meet these new needs and existing global data sets are out-of-date. A particular issue is the clear demand for a new area-wide regional to global coverage with accurate, up-to-date, and spatially referenced soil information as expressed by the modeling scientific community, farmers and land users, and policy and decision makers. Soil spectroscopy from remote sensing observations based on studies from the laboratory scale to the airborne scale has been shown to be a proven method for the quantitative prediction of key soil surface properties in local areas for exposed soils in appropriate surface conditions such as low vegetation cover and low water content. With the upcoming launch of the next generation of hyperspectral satellite sensors in the next 3 to 5 years (EnMAP, HISUI, PRISMA, SHALOM), a great potential for the global mapping and monitoring of soil properties is appearing. Nevertheless, the capabilities to extend the soil properties current spectral modeling from local to regional scales are still to be demonstrated using robust methods. In particular, three central questions are at the forefront of research nowadays: a) methodological developments toward improved algorithms and operational tools for the extraction of soil properties, b) up scaling from the laboratory into space domain, and c) demonstration of the potential of upcoming satellite systems and expected accuracy of soil maps. In this study, airborne imaging spectroscopy data from several test sites are used to simulate EnMAP satellite images at 30 m scale. Then, different soil algorithms are examined based on the analyses of chemical-physical features from the soil spectral reflectance and/or multivariate established techniques such as Partial-Least Squares PLS, Support-Vector Machine SVM, to determine common surface soil properties, in particular soil organic carbon (SOC), clay and iron oxide content. Results show that EnMAP is able to predict clay, free iron oxide, and SOC with an RV2 between 0.53 and 0.67 compared to airborne imagery with RV2 between 0.64 and 0.74. The correlation between EnMAP and airborne imagery prediction results is high (Pearson coefficients between 0.84 and 0.91). Furthermore, spatial distribution is coherent between the airborne mapping and simulated EnMAP mapping as shown with a spatial structure analysis. In general, this paper demonstrates the high potential of upcoming spaceborne hyperspectral missions for soil science studies but also shows the need for future adapted strategies to fulfill the entire potential of soil spectroscopy for orbital utilization.

SoilGrids1km — Global Soil Information Based on Automated Mapping

PubMed Central

Hengl, Tomislav; de Jesus, Jorge Mendes; MacMillan, Robert A.; Batjes, Niels H.; Heuvelink, Gerard B. M.; Ribeiro, Eloi; Samuel-Rosa, Alessandro; Kempen, Bas; Leenaars, Johan G. B.; Walsh, Markus G.; Gonzalez, Maria Ruiperez

2014-01-01

Background Soils are widely recognized as a non-renewable natural resource and as biophysical carbon sinks. As such, there is a growing requirement for global soil information. Although several global soil information systems already exist, these tend to suffer from inconsistencies and limited spatial detail. Methodology/Principal Findings We present SoilGrids1km — a global 3D soil information system at 1 km resolution — containing spatial predictions for a selection of soil properties (at six standard depths): soil organic carbon (g kg−1), soil pH, sand, silt and clay fractions (%), bulk density (kg m−3), cation-exchange capacity (cmol+/kg), coarse fragments (%), soil organic carbon stock (t ha−1), depth to bedrock (cm), World Reference Base soil groups, and USDA Soil Taxonomy suborders. Our predictions are based on global spatial prediction models which we fitted, per soil variable, using a compilation of major international soil profile databases (ca. 110,000 soil profiles), and a selection of ca. 75 global environmental covariates representing soil forming factors. Results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices (based on MODIS images), lithology, and taxonomic mapping units derived from conventional soil survey (Harmonized World Soil Database). Prediction accuracies assessed using 5–fold cross-validation were between 23–51%. Conclusions/Significance SoilGrids1km provide an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available. Some of the main limitations of the current version of SoilGrids1km are: (1) weak relationships between soil properties/classes and explanatory variables due to scale mismatches, (2) difficulty to obtain covariates that capture soil forming factors, (3) low sampling density and spatial clustering of soil profile locations. However, as the SoilGrids system is highly automated and flexible, increasingly accurate predictions can be generated as new input data become available. SoilGrids1km are available for download via http://soilgrids.org under a Creative Commons Non Commercial license. PMID:25171179

Soil Management for Hardwood Production

Treesearch

W. M. Broadfoot; B. G. Blackmon; J. B. Baker

1971-01-01

Soil management is the key to successful hardwood management because soil properties are probably the most important determinants of forest productivity. Because of the lack of soil uniformity, however, many foresters have become frustrated with attempts to relate soil to satisfactory growth. Since soil scientists have been unable to predict site quality for trees in...

Predicting As, Cd, Cu, Pb and Zn levels in grasses (Agrostis sp. and Poa sp.) and stinging nettle (Urtica dioica) applying soil-plant transfer models.

PubMed

Boshoff, Magdalena; De Jonge, Maarten; Scheifler, Renaud; Bervoets, Lieven

2014-09-15

The aim of this study was to derive regression-based soil-plant models to predict and compare metal(loid) (i.e. As, Cd, Cu, Pb and Zn) concentrations in plants (grass Agrostis sp./Poa sp. and nettle Urtica dioica L.) among sites with a wide range of metal pollution and a wide variation in soil properties. Regression models were based on the pseudo total (aqua-regia) and exchangeable (0.01 M CaCl2) soil metal concentrations. Plant metal concentrations were best explained by the pseudo total soil metal concentrations in combination with soil properties. The most important soil property that influenced U. dioica metal concentrations was the clay content, while for grass organic matter (OM) and pH affected the As (OM) and Cu and Zn (pH). In this study multiple linear regression models proved functional in predicting metal accumulation in plants on a regional scale. With the proposed models based on the pseudo total metal concentration, the percentage of variation explained for the metals As, Cd, Cu, Pb and Zn were 0.56%, 0.47%, 0.59%, 0.61%, 0.30% in nettle and 0.46%, 0.38%, 0.27%, 0.50%, 0.28% in grass. Copyright © 2014 Elsevier B.V. All rights reserved.

Key indicator tools for shallow slope failure assessment using soil chemical property signatures and soil colour variables.

PubMed

Othman, Rashidi; Hasni, Shah Irani; Baharuddin, Zainul Mukrim; Hashim, Khairusy Syakirin Has-Yun; Mahamod, Lukman Hakim

2017-10-01

Slope failure has become a major concern in Malaysia due to the rapid development and urbanisation in the country. It poses severe threats to any highway construction industry, residential areas, natural resources and tourism activities. The extent of damages that resulted from this catastrophe can be lessened if a long-term early warning system to predict landslide prone areas is implemented. Thus, this study aims to characterise the relationship between Oxisols properties and soil colour variables to be manipulated as key indicators to forecast shallow slope failure. The concentration of each soil property in slope soil was evaluated from two different localities that consist of 120 soil samples from stable and unstable slopes located along the North-South Highway (PLUS) and East-West Highway (LPT). Analysis of variance established highly significant difference (P < 0.0001) between the locations, the total organic carbon (TOC), soil pH, cation exchange capacity (CEC), soil texture, soil chromaticity and all combinations of interactions. The overall CIELAB analysis leads to the conclusion that the CIELAB variables lightness L*, c* (Chroma) and h* (Hue) provide the most information about soil colour and other related soil properties. With regard to the relationship between colour variables and soil properties, the analysis detected that soil texture, organic carbon, iron oxide and aluminium concentration were the key factors that strongly correlate with soil colour variables at the studied area. Indicators that could be used to predict shallow slope failure were high value of L*(62), low values of c* (20) and h* (66), low concentration of iron (53 mg kg -1 ) and aluminium oxide (37 mg kg -1 ), low soil TOC (0.5%), low CEC (3.6 cmol/kg), slightly acidic soil pH (4.9), high amount of sand fraction (68%) and low amount of clay fraction (20%).

Using Multispectral and Elevation Data to Predict Soil Properties for a Better Management of Fertilizers at Field Scale

NASA Astrophysics Data System (ADS)

Drouin, Ariane; Michaud, Aubert; Sylvain, Jean-Daniel; N'Dayegamiye, Adrien; Gasser, Marc-Olivier; Nolin, Michel; Perron, Isabelle; Grenon, Lucie; Beaudin, Isabelle; Desjardins, Jacques; Côté, Noémi

2013-04-01

This project aims at developing and validating an operational integrated management and localized approach at field scale using remote sensing data. It is realized in order to support the competitiveness of agricultural businesses, to ensure soil productivity in the long term and prevent diffuse contamination of surface waters. Our intention is to help agrienvironmental advisors and farmers in the consideration of spatial variability of soil properties in the management of fields. The proposed approach of soil properties recognition is based on the combination of elevation data and multispectral satellite imagery (Landsat) within statistical models. The method is based on the use of the largest possible number of satellite images to cover the widest range of soil moisture variability. Several spectral indices are calculated for each image (normalized brightness index, soil color index, organic matter index, etc.). The assignation of soils is based on a calibration procedure making use of the spatial soil database available in Canada. It includes soil profile point data associated to a database containing the information collected in the field. Three soil properties are predicted and mapped: A horizon texture, B horizon texture and drainage class. All the spectral indices, elevation data and soil data are combined in a discriminant analysis that produces discriminant functions. These are then used to produce maps of soil properties. In addition, from mapping soil properties, management zones are delineated within the field. The delineation of management zones with relatively similar soil properties is created to enable farmers to manage their fertilizers by taking greater account of their soils. This localized or precision management aims to adjust the application of fertilizer according to the real needs of soils and to reduce costs for farmers and the exports of nutrients to the stream. Mapping of soil properties will be validated in three agricultural regions in Quebec through an experimental field protocol (spatial sampling by management zones). Soils will be sampled, but crop yields under different nitrogen rates will also be assessed. Specifically, in each of the management areas defined, five different doses of nitrogen were applied (0, 50, 100, 150, 200 kg N / ha) on corn fields. In fall, the corn is harvested to assess differences in yields between the management areas and also in terms of doses of nitrogen. Ultimately, on the basis of well-established management areas, showing contrasting soil properties, the farmer will be able to ensure optimal correction of soil acidity, nitrogen fertilization, richness of soil in P and K, and improve soil drainage and physical properties. Environmentally, the principles of integrated and localized management carries significant benefits, particularly in terms of reduction of diffuse nutrient pollution.

The Effects of Wideband Complex Electromagnetic Properties of Soils on Geophysical Sensor Performance

NASA Astrophysics Data System (ADS)

North, Ryan Elliot

Common near-surface geophysical methods such as time domain electromagnetic induction (TDEM) metal detectors and ground penetrating radar (GPR) suffer performance degradation as a function of site specific complex electromagnetic soil properties (permittivity, permeability and conductivity). Knowledge of these soil properties from the kHz to the GHz frequency range can be used to predict and improve sensor performance. A prototype permittivity probe was used to measure the complex permittivity and conductivity of the soil and calculate the GPR velocity and attenuation of the from the in-situ measurements. The prototype probe was capable of accurately predicting the GPR velocities when compared with the GPR measurement and could easily predict the attenuation which is difficult to determine from actual GPR data. Unfortunately the prototype probe here has one primarily deficiency which is the assumption that the soils where it is used are non-magnetic. To illustrate the problems with using this probe in magnetic soils I made soil analogues from commercially available magnetite and crushed silica powder then measured them using a common open ended coaxial probe followed by measurements with coaxial air- line fixture which can also calculate magnetic properties. The calculated permittivities are up to twice as high when measured with the coaxial probe as they are when measured with a coaxial airline fixture which will lead to incorrect estimates of GPR velocity and attenuation. To address the performance issues of metal detectors in magnetically viscous soils I created a magnetically viscous soil analogue that could be used in mine detection training lanes instead of importing soil from sites exhibiting magnetic viscosity. Five commercially available iron oxide nano-powders were tested as additives to create the soil analogues by measuring the magnetic viscosity of these iron oxides with a new prototype instrument and compared them to samples of magnetically viscous soils collected at sites around the world. Three of the iron oxides exhibited comparable magnetic viscosities to the naturally occurring soil samples. One was selected to make a soil analogue by mixing it with crushed silica. The resulting magnetic susceptibilities compared favorably with those of the natural soil samples.

Soil property effects on wind erosion of organic soils

NASA Astrophysics Data System (ADS)

Zobeck, Ted M.; Baddock, Matthew; Scott Van Pelt, R.; Tatarko, John; Acosta-Martinez, Veronica

2013-09-01

Histosols (also known as organic soils, mucks, or peats) are soils that are dominated by organic matter (OM > 20%) in half or more of the upper 80 cm. Forty two states have a total of 21 million ha of Histosols in the United States. These soils, when intensively cropped, are subject to wind erosion resulting in loss of crop productivity and degradation of soil, air, and water quality. Estimating wind erosion on Histosols has been determined by USDA-Natural Resources Conservation Service (NRCS) as a critical need for the Wind Erosion Prediction System (WEPS) model. WEPS has been developed to simulate wind erosion on agricultural land in the US, including soils with organic soil material surfaces. However, additional field measurements are needed to understand how soil properties vary among organic soils and to calibrate and validate estimates of wind erosion of organic soils using WEPS. Soil properties and sediment flux were measured in six soils with high organic contents located in Michigan and Florida, USA. Soil properties observed included organic matter content, particle density, dry mechanical stability, dry clod stability, wind erodible material, and geometric mean diameter of the surface aggregate distribution. A field portable wind tunnel was used to generate suspended sediment and dust from agricultural surfaces for soils ranging from 17% to 67% organic matter. The soils were tilled and rolled to provide a consolidated, friable surface. Dust emissions and saltation were measured using an isokinetic vertical slot sampler aspirated by a regulated suction source. Suspended dust was sampled using a Grimm optical particle size analyzer. Particle density of the saltation-sized material (>106 μm) was inversely related to OM content and varied from 2.41 g cm-3 for the soil with the lowest OM content to 1.61 g cm-3 for the soil with highest OM content. Wind erodible material and the geometric mean diameter of the surface soil were inversely related to dry clod stability. The effect of soil properties on sediment flux varied among flux types. Saltation flux was adequately predicted with simple linear regression models. Dry mechanical stability was the best single soil property linearly related to saltation flux. Simple linear models with soil properties as independent variables were not well correlated with PM10E values (mass flux). A second order polynomial equation with OM as the independent variable was found to be most highly correlated with PM10E values. These results demonstrate that variations in sediment and dust emissions can be linked to soil properties using simple models based on one or more soil properties to estimate saltation mass flux and PM10E values from organic and organic-rich soils.

Modeling multidomain hydraulic properties of shrink-swell soils

NASA Astrophysics Data System (ADS)

Stewart, Ryan D.; Abou Najm, Majdi R.; Rupp, David E.; Selker, John S.

2016-10-01

Shrink-swell soils crack and become compacted as they dry, changing properties such as bulk density and hydraulic conductivity. Multidomain models divide soil into independent realms that allow soil cracks to be incorporated into classical flow and transport models. Incongruously, most applications of multidomain models assume that the porosity distributions, bulk density, and effective saturated hydraulic conductivity of the soil are constant. This study builds on a recently derived soil shrinkage model to develop a new multidomain, dual-permeability model that can accurately predict variations in soil hydraulic properties due to dynamic changes in crack size and connectivity. The model only requires estimates of soil gravimetric water content and a minimal set of parameters, all of which can be determined using laboratory and/or field measurements. We apply the model to eight clayey soils, and demonstrate its ability to quantify variations in volumetric water content (as can be determined during measurement of a soil water characteristic curve) and transient saturated hydraulic conductivity, Ks (as can be measured using infiltration tests). The proposed model is able to capture observed variations in Ks of one to more than two orders of magnitude. In contrast, other dual-permeability models assume that Ks is constant, resulting in the potential for large error when predicting water movement through shrink-swell soils. Overall, the multidomain model presented here successfully quantifies fluctuations in the hydraulic properties of shrink-swell soil matrices, and are suitable for use in physical flow and transport models based on Darcy's Law, the Richards Equation, and the advection-dispersion equation.

S-World: A high resolution global soil database for simulation modelling (Invited)

NASA Astrophysics Data System (ADS)

Stoorvogel, J. J.

2013-12-01

There is an increasing call for high resolution soil information at the global level. A good example for such a call is the Global Gridded Crop Model Intercomparison carried out within AgMIP. While local studies can make use of surveying techniques to collect additional techniques this is practically impossible at the global level. It is therefore important to rely on legacy data like the Harmonized World Soil Database. Several efforts do exist that aim at the development of global gridded soil property databases. These estimates of the variation of soil properties can be used to assess e.g., global soil carbon stocks. However, they do not allow for simulation runs with e.g., crop growth simulation models as these models require a description of the entire pedon rather than a few soil properties. This study provides the required quantitative description of pedons at a 1 km resolution for simulation modelling. It uses the Harmonized World Soil Database (HWSD) for the spatial distribution of soil types, the ISRIC-WISE soil profile database to derive information on soil properties per soil type, and a range of co-variables on topography, climate, and land cover to further disaggregate the available data. The methodology aims to take stock of these available data. The soil database is developed in five main steps. Step 1: All 148 soil types are ordered on the basis of their expected topographic position using e.g., drainage, salinization, and pedogenesis. Using the topographic ordering and combining the HWSD with a digital elevation model allows for the spatial disaggregation of the composite soil units. This results in a new soil map with homogeneous soil units. Step 2: The ranges of major soil properties for the topsoil and subsoil of each of the 148 soil types are derived from the ISRIC-WISE soil profile database. Step 3: A model of soil formation is developed that focuses on the basic conceptual question where we are within the range of a particular soil property at a particular location given a specific soil type. The soil properties are predicted for each grid cell based on the soil type, the corresponding ranges of soil properties, and the co-variables. Step 4: Standard depth profiles are developed for each of the soil types using the diagnostic criteria of the soil types and soil profile information from the ISRIC-WISE database. The standard soil profiles are combined with the the predicted values for the topsoil and subsoil yielding unique soil profiles at each location. Step 5: In a final step, additional soil properties are added to the database using averages for the soil types and pedo-transfer functions. The methodology, denominated S-World (Soils of the World), results in readily available global maps with quantitative pedon data for modelling purposes. It forms the basis for the Global Gridded Crop Model Intercomparison carried out within AgMIP.

Beyond clay: Towards an improved set of variables for predicting soil organic matter content

USGS Publications Warehouse

Rasmussen, Craig; Heckman, Katherine; Wieder, William R.; Keiluweit, Marco; Lawrence, Corey R.; Berhe, Asmeret Asefaw; Blankinship, Joseph C.; Crow, Susan E.; Druhan, Jennifer; Hicks Pries, Caitlin E.; Marin-Spiotta, Erika; Plante, Alain F.; Schadel, Christina; Schmiel, Joshua P.; Sierra, Carlos A.; Thompson, Aaron; Wagai, Rota

2018-01-01

Improved quantification of the factors controlling soil organic matter (SOM) stabilization at continental to global scales is needed to inform projections of the largest actively cycling terrestrial carbon pool on Earth, and its response to environmental change. Biogeochemical models rely almost exclusively on clay content to modify rates of SOM turnover and fluxes of climate-active CO2 to the atmosphere. Emerging conceptual understanding, however, suggests other soil physicochemical properties may predict SOM stabilization better than clay content. We addressed this discrepancy by synthesizing data from over 5,500 soil profiles spanning continental scale environmental gradients. Here, we demonstrate that other physicochemical parameters are much stronger predictors of SOM content, with clay content having relatively little explanatory power. We show that exchangeable calcium strongly predicted SOM content in water-limited, alkaline soils, whereas with increasing moisture availability and acidity, iron- and aluminum-oxyhydroxides emerged as better predictors, demonstrating that the relative importance of SOM stabilization mechanisms scales with climate and acidity. These results highlight the urgent need to modify biogeochemical models to better reflect the role of soil physicochemical properties in SOM cycling.

SoilInfo App: global soil information on your palm

NASA Astrophysics Data System (ADS)

Hengl, Tomislav; Mendes de Jesus, Jorge

2015-04-01

ISRIC ' World Soil Information has released in 2014 and app for mobile de- vices called 'SoilInfo' (http://soilinfo-app.org) and which aims at providing free access to the global soil data. SoilInfo App (available for Android v.4.0 Ice Cream Sandwhich or higher, and Apple v.6.x and v.7.x iOS) currently serves the Soil- Grids1km data ' a stack of soil property and class maps at six standard depths at a resolution of 1 km (30 arc second) predicted using automated geostatistical mapping and global soil data models. The list of served soil data includes: soil organic carbon (), soil pH, sand, silt and clay fractions (%), bulk density (kg/m3), cation exchange capacity of the fine earth fraction (cmol+/kg), coarse fragments (%), World Reference Base soil groups, and USDA Soil Taxonomy suborders (DOI: 10.1371/journal.pone.0105992). New soil properties and classes will be continuously added to the system. SoilGrids1km are available for download under a Creative Commons non-commercial license via http://soilgrids.org. They are also accessible via a Representational State Transfer API (http://rest.soilgrids.org) service. SoilInfo App mimics common weather apps, but is also largely inspired by the crowdsourcing systems such as the OpenStreetMap, Geo-wiki and similar. Two development aspects of the SoilInfo App and SoilGrids are constantly being worked on: Data quality in terms of accuracy of spatial predictions and derived information, and Data usability in terms of ease of access and ease of use (i.e. flexibility of the cyberinfrastructure / functionalities such as the REST SoilGrids API, SoilInfo App etc). The development focus in 2015 is on improving the thematic and spatial accuracy of SoilGrids predictions, primarily by using finer resolution covariates (250 m) and machine learning algorithms (such as random forests) to improve spatial predictions.

State-Space Estimation of Soil Organic Carbon Stock

NASA Astrophysics Data System (ADS)

Ogunwole, Joshua O.; Timm, Luis C.; Obidike-Ugwu, Evelyn O.; Gabriels, Donald M.

2014-04-01

Understanding soil spatial variability and identifying soil parameters most determinant to soil organic carbon stock is pivotal to precision in ecological modelling, prediction, estimation and management of soil within a landscape. This study investigates and describes field soil variability and its structural pattern for agricultural management decisions. The main aim was to relate variation in soil organic carbon stock to soil properties and to estimate soil organic carbon stock from the soil properties. A transect sampling of 100 points at 3 m intervals was carried out. Soils were sampled and analyzed for soil organic carbon and other selected soil properties along with determination of dry aggregate and water-stable aggregate fractions. Principal component analysis, geostatistics, and state-space analysis were conducted on the analyzed soil properties. The first three principal components explained 53.2% of the total variation; Principal Component 1 was dominated by soil exchange complex and dry sieved macroaggregates clusters. Exponential semivariogram model described the structure of soil organic carbon stock with a strong dependence indicating that soil organic carbon values were correlated up to 10.8m.Neighbouring values of soil organic carbon stock, all waterstable aggregate fractions, and dithionite and pyrophosphate iron gave reliable estimate of soil organic carbon stock by state-space.

Modeling soil parameters using hyperspectral image reflectance in subtropical coastal wetlands

NASA Astrophysics Data System (ADS)

Anne, Naveen J. P.; Abd-Elrahman, Amr H.; Lewis, David B.; Hewitt, Nicole A.

2014-12-01

Developing spectral models of soil properties is an important frontier in remote sensing and soil science. Several studies have focused on modeling soil properties such as total pools of soil organic matter and carbon in bare soils. We extended this effort to model soil parameters in areas densely covered with coastal vegetation. Moreover, we investigated soil properties indicative of soil functions such as nutrient and organic matter turnover and storage. These properties include the partitioning of mineral and organic soil between particulate (>53 μm) and fine size classes, and the partitioning of soil carbon and nitrogen pools between stable and labile fractions. Soil samples were obtained from Avicennia germinans mangrove forest and Juncus roemerianus salt marsh plots on the west coast of central Florida. Spectra corresponding to field plot locations from Hyperion hyperspectral image were extracted and analyzed. The spectral information was regressed against the soil variables to determine the best single bands and optimal band combinations for the simple ratio (SR) and normalized difference index (NDI) indices. The regression analysis yielded levels of correlation for soil variables with R2 values ranging from 0.21 to 0.47 for best individual bands, 0.28 to 0.81 for two-band indices, and 0.53 to 0.96 for partial least-squares (PLS) regressions for the Hyperion image data. Spectral models using Hyperion data adequately (RPD > 1.4) predicted particulate organic matter (POM), silt + clay, labile carbon (C), and labile nitrogen (N) (where RPD = ratio of standard deviation to root mean square error of cross-validation [RMSECV]). The SR (0.53 μm, 2.11 μm) model of labile N with R2 = 0.81, RMSECV= 0.28, and RPD = 1.94 produced the best results in this study. Our results provide optimism that remote-sensing spectral models can successfully predict soil properties indicative of ecosystem nutrient and organic matter turnover and storage, and do so in areas with dense canopy cover.

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

PubMed Central

Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources. PMID:28114334

Application of spatial pedotransfer functions to understand soil modulation of vegetation response to climate

USDA-ARS?s Scientific Manuscript database

A fundamental knowledge gap in understanding land-atmosphere interactions is accurate, high resolution spatial representation of soil physical and hydraulic properties. We present a novel approach to predict hydraulic soil parameters by combining digital soil mapping techniques with pedotransfer fun...

Using geophysical images of a watershed subsurface to predict soil textural properties

USDA-ARS?s Scientific Manuscript database

Subsurface architecture, in particular changes in soil type across the landscape, is an important control on the hydrological and ecological function of a watershed. Traditional methods of mapping soils involving subjective assignment of soil boundaries are inadequate for studies requiring a quantit...

Quantifying the Interactions Between Soil Thermal Characteristics, Soil Physical Properties, Hydro-geomorphological Conditions and Vegetation Distribution in an Arctic Watershed

NASA Astrophysics Data System (ADS)

Dafflon, B.; Leger, E.; Robert, Y.; Ulrich, C.; Peterson, J. E.; Soom, F.; Biraud, S.; Tran, A. P.; Hubbard, S. S.

2017-12-01

Improving understanding of Arctic ecosystem functioning and parameterization of process-rich hydro-biogeochemical models require advances in quantifying ecosystem properties, from the bedrock to the top of the canopy. In Arctic regions having significant subsurface heterogeneity, understanding the link between soil physical properties (incl. fraction of soil constituents, bedrock depth, permafrost characteristics), thermal behavior, hydrological conditions and landscape properties is particularly challenging yet is critical for predicting the storage and flux of carbon in a changing climate. This study takes place in Seward Peninsula Watersheds near Nome AK and Council AK, which are characterized by an elevation gradient, shallow bedrock, and discontinuous permafrost. To characterize permafrost distribution where the top of permafrost cannot be easily identified with a tile probe (due to rocky soil and/or large thaw layer thickness), we developed a novel technique using vertically resolved thermistor probes to directly sense the temperature regime at multiple depths and locations. These measurements complement electrical imaging, seismic refraction and point-scale data for identification of the various thermal behavior and soil characteristics. Also, we evaluate linkages between the soil physical-thermal properties and the surface properties (hydrological conditions, geomorphic characteristics and vegetation distribution) using UAV-based aerial imaging. Data integration and analysis is supported by numerical approaches that simulate hydrological and thermal processes. Overall, this study enables the identification of watershed structure and the links between various subsurface and landscape properties in representative Arctic watersheds. Results show very distinct trends in vertically resolved soil temperature profiles and strong lateral variations over tens of meters that are linked to zones with various hydrological conditions, soil properties and vegetation types. The interaction between these zones is of strong interest to understand the evolution of the landscape and the permafrost distribution. The obtained information is expected to be useful for improving predictions of Arctic ecosystem feedbacks to climate.

[Development of an analyzing system for soil parameters based on NIR spectroscopy].

PubMed

Zheng, Li-Hua; Li, Min-Zan; Sun, Hong

2009-10-01

A rapid estimation system for soil parameters based on spectral analysis was developed by using object-oriented (OO) technology. A class of SOIL was designed. The instance of the SOIL class is the object of the soil samples with the particular type, specific physical properties and spectral characteristics. Through extracting the effective information from the modeling spectral data of soil object, a map model was established between the soil parameters and its spectral data, while it was possible to save the mapping model parameters in the database of the model. When forecasting the content of any soil parameter, the corresponding prediction model of this parameter can be selected with the same soil type and the similar soil physical properties of objects. And after the object of target soil samples was carried into the prediction model and processed by the system, the accurate forecasting content of the target soil samples could be obtained. The system includes modules such as file operations, spectra pretreatment, sample analysis, calibrating and validating, and samples content forecasting. The system was designed to run out of equipment. The parameters and spectral data files (*.xls) of the known soil samples can be input into the system. Due to various data pretreatment being selected according to the concrete conditions, the results of predicting content will appear in the terminal and the forecasting model can be stored in the model database. The system reads the predicting models and their parameters are saved in the model database from the module interface, and then the data of the tested samples are transferred into the selected model. Finally the content of soil parameters can be predicted by the developed system. The system was programmed with Visual C++6.0 and Matlab 7.0. And the Access XP was used to create and manage the model database.

Estimation of soil sorption coefficients of veterinary pharmaceuticals from soil properties.

PubMed

ter Laak, Thomas L; Gebbink, Wouter A; Tolls, Johannes

2006-04-01

Environmental exposure assessment of veterinary pharmaceuticals requires estimating the sorption to soil. Soil sorption coefficients of three common, ionizable, antimicrobial agents (oxytetracycline [OTC], tylosin [TYL], and sulfachloropyridazine [SCP]) were studied in relation to the soil properties of 11 different soils. The soil sorption coefficient at natural pH varied from 950 to 7,200, 10 to 370, and 0.4 to 35 L/kg for OTC, TYL, and SCP, respectively. The variation increased by almost two orders of magnitude for OTC and TYL when pH was artificially adjusted. Separate soil properties (pH, organic carbon content, clay content, cation-exchange capacity, aluminum oxyhydroxide content, and iron oxyhydroxide content) were not able to explain more than half the variation observed in soil sorption coefficients. This reflects the complexity of the sorbent-sorbate interactions. Partial-least-squares (PLS) models, integrating all the soil properties listed above, were able to explain as much as 78% of the variation in sorption coefficients. The PLS model was able to predict the sorption coefficient with an accuracy of a factor of six. Considering the pH-dependent speciation, species-specific PLS models were developed. These models were able to predict species-specific sorption coefficients with an accuracy of a factor of three to four. However, the species-specific sorption models did not improve the estimation of sorption coefficients of species mixtures, because these models were developed with a reduced data set at standardized aqueous concentrations. In conclusion, pragmatic approaches like PLS modeling might be suitable to estimate soil sorption for risk assessment purposes.

Sample dimensions effect on prediction of soil water retention curve and saturated hydraulic conductivity

USDA-ARS?s Scientific Manuscript database

Soil water retention curve (SWRC) and saturated hydraulic conductivity (SHC) are key hydraulic properties for unsaturated zone hydrology and groundwater. Not only are the SWRC and SHC measurements time-consuming, their results are scale dependent. Although prediction of the SWRC and SHC from availab...

The effect of inclined soil layers on surface vibration from underground railways using a semi-analytical approach

NASA Astrophysics Data System (ADS)

Jones, S.; Hunt, H.

2009-08-01

Ground vibration due to underground railways is a significant source of disturbance for people living or working near the subways. The numerical models used to predict vibration levels have inherent uncertainty which must be understood to give confidence in the predictions. A semi-analytical approach is developed herein to investigate the effect of soil layering on the surface vibration of a halfspace where both soil properties and layer inclination angles are varied. The study suggests that both material properties and inclination angle of the layers have significant effect (± 10dB) on the surface vibration response.

Variability of the soil-to-plant radiocaesium transfer factor for Japanese soils predicted with soil and plant properties.

PubMed

Uematsu, Shinichiro; Vandenhove, Hildegarde; Sweeck, Lieve; Van Hees, May; Wannijn, Jean; Smolders, Erik

2016-03-01

Food chain contamination with radiocaesium (RCs) in the aftermath of the Fukushima accident calls for an analysis of the specific factors that control the RCs transfer. Here, soil-to-plant transfer factors (TF) of RCs for grass were predicted from the potassium concentration in soil solution (mK) and the Radiocaesium Interception Potential (RIP) of the soil using existing mechanistic models. The mK and RIP were (a) either measured for 37 topsoils collected from the Fukushima accident affected area or (b) predicted from the soil clay content and the soil exchangeable potassium content using the models that had been calibrated for European soils. An average ammonium concentration was used throughout in the prediction. The measured RIP ranged 14-fold and measured mK varied 37-fold among the soils. The measured RIP was lower than the RIP predicted from the soil clay content likely due to the lower content of weathered micas in the clay fraction of Japanese soils. Also the measured mK was lower than that predicted. As a result, the predicted TFs relying on the measured RIP and mK were, on average, about 22-fold larger than the TFs predicted using the European calibrated models. The geometric mean of the measured TFs for grass in the affected area (N = 82) was in the middle of both. The TFs were poorly related to soil classification classes, likely because soil fertility (mK) was obscuring the effects of the soil classification related to the soil mineralogy (RIP). This study suggests that, on average, Japanese soils are more vulnerable than European soils at equal soil clay and exchangeable K content. The affected regions will be targeted for refined model validation. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Comparison of Selected Statistical Techniques to Model Soil Cation Exchange Capacity

NASA Astrophysics Data System (ADS)

Khaledian, Yones; Brevik, Eric C.; Pereira, Paulo; Cerdà, Artemi; Fattah, Mohammed A.; Tazikeh, Hossein

2017-04-01

Cation exchange capacity (CEC) measures the soil's ability to hold positively charged ions and is an important indicator of soil quality (Khaledian et al., 2016). However, other soil properties are more commonly determined and reported, such as texture, pH, organic matter and biology. We attempted to predict CEC using different advanced statistical methods including monotone analysis of variance (MONANOVA), artificial neural networks (ANNs), principal components regressions (PCR), and particle swarm optimization (PSO) in order to compare the utility of these approaches and identify the best predictor. We analyzed 170 soil samples from four different nations (USA, Spain, Iran and Iraq) under three land uses (agriculture, pasture, and forest). Seventy percent of the samples (120 samples) were selected as the calibration set and the remaining 50 samples (30%) were used as the prediction set. The results indicated that the MONANOVA (R2= 0.82 and Root Mean Squared Error (RMSE) =6.32) and ANNs (R2= 0.82 and RMSE=5.53) were the best models to estimate CEC, PSO (R2= 0.80 and RMSE=5.54) and PCR (R2= 0.70 and RMSE=6.48) also worked well and the overall results were very similar to each other. Clay (positively correlated) and sand (negatively correlated) were the most influential variables for predicting CEC for the entire data set, while the most influential variables for the various countries and land uses were different and CEC was affected by different variables in different situations. Although the MANOVA and ANNs provided good predictions of the entire dataset, PSO gives a formula to estimate soil CEC using commonly tested soil properties. Therefore, PSO shows promise as a technique to estimate soil CEC. Establishing effective pedotransfer functions to predict CEC would be productive where there are limitations of time and money, and other commonly analyzed soil properties are available. References Khaledian, Y., Kiani, F., Ebrahimi, S., Brevik, E.C., Aitkenhead-Peterson, J. 2016. Assessment and monitoring of soil degradation during land use change using multivariate analysis. Land Degradation and Development. doi: 10.1002/ldr.2541.

Effect of soil property uncertainties on permafrost thaw projections: a calibration-constrained analysis

NASA Astrophysics Data System (ADS)

Harp, D. R.; Atchley, A. L.; Painter, S. L.; Coon, E. T.; Wilson, C. J.; Romanovsky, V. E.; Rowland, J. C.

2016-02-01

The effects of soil property uncertainties on permafrost thaw projections are studied using a three-phase subsurface thermal hydrology model and calibration-constrained uncertainty analysis. The null-space Monte Carlo method is used to identify soil hydrothermal parameter combinations that are consistent with borehole temperature measurements at the study site, the Barrow Environmental Observatory. Each parameter combination is then used in a forward projection of permafrost conditions for the 21st century (from calendar year 2006 to 2100) using atmospheric forcings from the Community Earth System Model (CESM) in the Representative Concentration Pathway (RCP) 8.5 greenhouse gas concentration trajectory. A 100-year projection allows for the evaluation of predictive uncertainty (due to soil property (parametric) uncertainty) and the inter-annual climate variability due to year to year differences in CESM climate forcings. After calibrating to measured borehole temperature data at this well-characterized site, soil property uncertainties are still significant and result in significant predictive uncertainties in projected active layer thickness and annual thaw depth-duration even with a specified future climate. Inter-annual climate variability in projected soil moisture content and Stefan number are small. A volume- and time-integrated Stefan number decreases significantly, indicating a shift in subsurface energy utilization in the future climate (latent heat of phase change becomes more important than heat conduction). Out of 10 soil parameters, ALT, annual thaw depth-duration, and Stefan number are highly dependent on mineral soil porosity, while annual mean liquid saturation of the active layer is highly dependent on the mineral soil residual saturation and moderately dependent on peat residual saturation. By comparing the ensemble statistics to the spread of projected permafrost metrics using different climate models, we quantify the relative magnitude of soil property uncertainty to another source of permafrost uncertainty, structural climate model uncertainty. We show that the effect of calibration-constrained uncertainty in soil properties, although significant, is less than that produced by structural climate model uncertainty for this location.

Covariate selection with iterative principal component analysis for predicting physical

USDA-ARS?s Scientific Manuscript database

Local and regional soil data can be improved by coupling new digital soil mapping techniques with high resolution remote sensing products to quantify both spatial and absolute variation of soil properties. The objective of this research was to advance data-driven digital soil mapping techniques for ...

Preliminary study of soil permeability properties using principal component analysis

NASA Astrophysics Data System (ADS)

Yulianti, M.; Sudriani, Y.; Rustini, H. A.

2018-02-01

Soil permeability measurement is undoubtedly important in carrying out soil-water research such as rainfall-runoff modelling, irrigation water distribution systems, etc. It is also known that acquiring reliable soil permeability data is rather laborious, time-consuming, and costly. Therefore, it is desirable to develop the prediction model. Several studies of empirical equations for predicting permeability have been undertaken by many researchers. These studies derived the models from areas which soil characteristics are different from Indonesian soil, which suggest a possibility that these permeability models are site-specific. The purpose of this study is to identify which soil parameters correspond strongly to soil permeability and propose a preliminary model for permeability prediction. Principal component analysis (PCA) was applied to 16 parameters analysed from 37 sites consist of 91 samples obtained from Batanghari Watershed. Findings indicated five variables that have strong correlation with soil permeability, and we recommend a preliminary permeability model, which is potential for further development.

Sample sizes to control error estimates in determining soil bulk density in California forest soils

Treesearch

Youzhi Han; Jianwei Zhang; Kim G. Mattson; Weidong Zhang; Thomas A. Weber

2016-01-01

Characterizing forest soil properties with high variability is challenging, sometimes requiring large numbers of soil samples. Soil bulk density is a standard variable needed along with element concentrations to calculate nutrient pools. This study aimed to determine the optimal sample size, the number of observation (n), for predicting the soil bulk density with a...

Local plant adaptation across a subarctic elevational gradient

PubMed Central

Kardol, Paul; De Long, Jonathan R.; Wardle, David A.

2014-01-01

Predicting how plants will respond to global warming necessitates understanding of local plant adaptation to temperature. Temperature may exert selective effects on plants directly, and also indirectly through environmental factors that covary with temperature, notably soil properties. However, studies on the interactive effects of temperature and soil properties on plant adaptation are rare, and the role of abiotic versus biotic soil properties in plant adaptation to temperature remains untested. We performed two growth chamber experiments using soils and Bistorta vivipara bulbil ecotypes from a subarctic elevational gradient (temperature range: ±3°C) in northern Sweden to disentangle effects of local ecotype, temperature, and biotic and abiotic properties of soil origin on plant growth. We found partial evidence for local adaption to temperature. Although soil origin affected plant growth, we did not find support for local adaptation to either abiotic or biotic soil properties, and there were no interactive effects of soil origin with ecotype or temperature. Our results indicate that ecotypic variation can be an important driver of plant responses to the direct effects of increasing temperature, while responses to covariation in soil properties are of a phenotypic, rather than adaptive, nature. PMID:26064553

Spatial representation of organic carbon and active-layer thickness of high latitude soils in CMIP5 earth system models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mishra, Umakant; Drewniak, Beth; Jastrow, Julie D.

Soil properties such as soil organic carbon (SOC) stocks and active-layer thickness are used in earth system models (F.SMs) to predict anthropogenic and climatic impacts on soil carbon dynamics, future changes in atmospheric greenhouse gas concentrations, and associated climate changes in the permafrost regions. Accurate representation of spatial and vertical distribution of these soil properties in ESMs is a prerequisite for redudng existing uncertainty in predicting carbon-climate feedbacks. We compared the spatial representation of SOC stocks and active-layer thicknesses predicted by the coupled Modellntercomparison Project Phase 5 { CMIP5) ESMs with those predicted from geospatial predictions, based on observation datamore » for the state of Alaska, USA. For the geospatial modeling. we used soil profile observations {585 for SOC stocks and 153 for active-layer thickness) and environmental variables (climate, topography, land cover, and surficial geology types) and generated fine-resolution (50-m spatial resolution) predictions of SOC stocks (to 1-m depth) and active-layer thickness across Alaska. We found large inter-quartile range (2.5-5.5 m) in predicted active-layer thickness of CMIP5 modeled results and small inter-quartile range (11.5-22 kg m-2) in predicted SOC stocks. The spatial coefficient of variability of active-layer thickness and SOC stocks were lower in CMIP5 predictions compared to our geospatial estimates when gridded at similar spatial resolutions (24.7 compared to 30% and 29 compared to 38%, respectively). However, prediction errors. when calculated for independent validation sites, were several times larger in ESM predictions compared to geospatial predictions. Primaly factors leading to observed differences were ( 1) lack of spatial heterogeneity in ESM predictions, (2) differences in assumptions concerning environmental controls, and (3) the absence of pedogenic processes in ESM model structures. Our results suggest that efforts to incorporate these factors in F.SMs should reduce current uncertainties associated with ESM predictions of carbon-climate feedbacks.« less

Thermal Conductivity Prediction of Soil in Complex Plant Soil System using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Wardani, A. K.; Purqon, A.

2016-08-01

Thermal conductivity is one of thermal properties of soil in seed germination and plants growth. Different soil types have different thermal conductivity. One of soft-computing promising method to predict thermal conductivity of soil types is Artificial Neural Network (ANN). In this study, we estimate the thermal conductivity of soil prediction in a soil-plant complex systems using ANN. With a feed-forward multilayer trained with back-propagation with 4, 10 and 1 on the input, hidden and output layers respectively. Our input are heating time, temperature and thermal resistance with thermal conductivity of soil as a target. ANN prediction demonstrates a good agreement with Mean Squared Error-testing (MSEte) of 9.56 x 10-7 for soils with green beans and those of bare soils is 7.00 × 10-7 respectively Green beans grow only on black-clay soil with a thermal conductivity of 0.7 W/m K with a sufficient water content. Our results demonstrate that temperature, moisture content, colour, texture and structure of soil are greatly affect to the thermal conductivity of soil in seed germination and plant growth. In future, it is potentially applied to estimate more complex compositions of plant-soil systems.

Pedotransfer functions for isoproturon sorption on soils and vadose zone materials.

PubMed

Moeys, Julien; Bergheaud, Valérie; Coquet, Yves

2011-10-01

Sorption coefficients (the linear K(D) or the non-linear K(F) and N(F)) are critical parameters in models of pesticide transport to groundwater or surface water. In this work, a dataset of isoproturon sorption coefficients and corresponding soil properties (264 K(D) and 55 K(F)) was compiled, and pedotransfer functions were built for predicting isoproturon sorption in soils and vadose zone materials. These were benchmarked against various other prediction methods. The results show that the organic carbon content (OC) and pH are the two main soil properties influencing isoproturon K(D) . The pedotransfer function is K(D) = 1.7822 + 0.0162 OC(1.5) - 0.1958 pH (K(D) in L kg(-1) and OC in g kg(-1)). For low-OC soils (OC < 6.15 g kg(-1)), clay and pH are most influential. The pedotransfer function is then K(D) = 0.9980 + 0.0002 clay - 0.0990 pH (clay in g kg(-1)). Benchmarking K(D) estimations showed that functions calibrated on more specific subsets of the data perform better on these subsets than functions calibrated on larger subsets. Predicting isoproturon sorption in soils in unsampled locations should rely, whenever possible, and by order of preference, on (a) site- or soil-specific pedotransfer functions, (b) pedotransfer functions calibrated on a large dataset, (c) K(OC) values calculated on a large dataset or (d) K(OC) values taken from existing pesticide properties databases. Copyright © 2011 Society of Chemical Industry.

Predatory beetles facilitate plant growth by driving earthworms to lower soil layers.

PubMed

Zhao, Chuan; Griffin, John N; Wu, Xinwei; Sun, Shucun

2013-07-01

Theory suggests that predators of soil-improving, plant-facilitating detritivores (e.g. earthworms) should suppress plant growth via a negative tri-trophic cascade, but the empirical evidence is still largely lacking. We tested this prediction in an alpine meadow on the Tibetan Plateau by manipulating predatory beetles (presence/absence) and quantifying (i) direct effects on the density and behaviour of earthworms; and (ii) indirect effects on soil properties and above-ground plant biomass. In the absence of predators, earthworms improved soil properties, but did not significantly affect plant biomass. Surprisingly, the presence of predators strengthened the positive effect of earthworms on soil properties leading to the emergence of a positive indirect effect of predators on plant biomass. We attribute this counterintuitive result to: (i) the inability of predators to suppress overall earthworm density; and (ii) the predator-induced earthworm habitat shift from the upper to lower soil layer that enhanced their soil-modifying, plant-facilitating, effects. Our results reveal that plant-level consequences of predators as transmitted through detritivores can hinge on behaviour-mediated indirect interactions that have the potential to overturn predictions based solely on trophic interactions. This work calls for a closer examination of the effects of predators in detritus food webs and the development of spatially explicit theory capable of predicting the occurrence and consequences of predator-induced detritivore behavioural shifts. © 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.

How to feed environmental studies with soil information to address SDG 'Zero hunger'

NASA Astrophysics Data System (ADS)

Hendriks, Chantal; Stoorvogel, Jetse; Claessens, Lieven

2017-04-01

As pledged by UN Sustainable Development Goal (SDG) 2, there should be zero hunger, food security, improved food nutrition and sustainable agriculture by 2030. Environmental studies are essential to reach SDG 2. Soils play a crucial role, especially in addressing 'Zero hunger'. This study aims to discuss the connection between the supply and demand of soil data for environmental studies and how this connection can be improved illustrating different methods. As many studies are resource constrained, the options to collect new soil data are limited. Therefore, it is essential to use existing soil information, auxiliary data and collected field data efficiently. Existing soil data are criticised in literature as i) being dominantly qualitative, ii) being often outdated, iii) being not spatially exhaustive, iv) being only available at general scales, v) being inconsistent, and vi) lacking quality assessments. Additional field data can help to overcome some of these problems. Outdated maps can, for example, be improved by collecting additional soil data in areas where changes in soil properties are expected. Existing soil data can also provide insight in the expected soil variability and, as such, these data can be used for the design of sampling schemes. Existing soil data are also crucial input for studies on digital soil mapping because they give information on parent material and the relative age of soils. Digital soil mapping is commonly applied as an efficient method to quantitatively predict the spatial variation of soil properties. However, the efficiency of digital soil mapping may increase if we look at functional soil properties (e.g. nutrient availability, available water capacity) for the soil profile that vary in a two-dimensional space rather than at basic soil properties of individual soil layers (e.g. texture, organic matter content, nitrogen content) that vary in a three-dimensional space. Digital soil mapping techniques are based on statistical relations between soil properties and environmental variables. However, in some cases a more mechanistic approach, based on pedological knowledge, might be more convincing to predict soil properties. This study showed that the soil science community is able to provide the required soil information for environmental studies. However, there is not a single solution that provides the required soil data. Case studies are needed to prove that certain methods meet the data requirements, whereafter these case studies function as a lighthouse to other studies. We illustrate data availability and methodological innovations for a case study in Kenya, where the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) aims to contribute to SDG 2.

Mapping soil texture classes and optimization of the result by accuracy assessment

NASA Astrophysics Data System (ADS)

Laborczi, Annamária; Takács, Katalin; Bakacsi, Zsófia; Szabó, József; Pásztor, László

2014-05-01

There are increasing demands nowadays on spatial soil information in order to support environmental related and land use management decisions. The GlobalSoilMap.net (GSM) project aims to make a new digital soil map of the world using state-of-the-art and emerging technologies for soil mapping and predicting soil properties at fine resolution. Sand, silt and clay are among the mandatory GSM soil properties. Furthermore, soil texture class information is input data of significant agro-meteorological and hydrological models. Our present work aims to compare and evaluate different digital soil mapping methods and variables for producing the most accurate spatial prediction of texture classes in Hungary. In addition to the Hungarian Soil Information and Monitoring System as our basic data, digital elevation model and its derived components, geological database, and physical property maps of the Digital Kreybig Soil Information System have been applied as auxiliary elements. Two approaches have been applied for the mapping process. At first the sand, silt and clay rasters have been computed independently using regression kriging (RK). From these rasters, according to the USDA categories, we have compiled the texture class map. Different combinations of reference and training soil data and auxiliary covariables have resulted several different maps. However, these results consequentially include the uncertainty factor of the three kriged rasters. Therefore we have suited data mining methods as the other approach of digital soil mapping. By working out of classification trees and random forests we have got directly the texture class maps. In this way the various results can be compared to the RK maps. The performance of the different methods and data has been examined by testing the accuracy of the geostatistically computed and the directly classified results. We have used the GSM methodology to assess the most predictive and accurate way for getting the best among the several result maps. Acknowledgement: Our work was supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

GEMAS: prediction of solid-solution phase partitioning coefficients (Kd) for oxoanions and boric acid in soils using mid-infrared diffuse reflectance spectroscopy.

PubMed

Janik, Leslie J; Forrester, Sean T; Soriano-Disla, José M; Kirby, Jason K; McLaughlin, Michael J; Reimann, Clemens

2015-02-01

The authors' aim was to develop rapid and inexpensive regression models for the prediction of partitioning coefficients (Kd), defined as the ratio of the total or surface-bound metal/metalloid concentration of the solid phase to the total concentration in the solution phase. Values of Kd were measured for boric acid (B[OH]3(0)) and selected added soluble oxoanions: molybdate (MoO4(2-)), antimonate (Sb[OH](6-)), selenate (SeO4(2-)), tellurate (TeO4(2-)) and vanadate (VO4(3-)). Models were developed using approximately 500 spectrally representative soils of the Geochemical Mapping of Agricultural Soils of Europe (GEMAS) program. These calibration soils represented the major properties of the entire 4813 soils of the GEMAS project. Multiple linear regression (MLR) from soil properties, partial least-squares regression (PLSR) using mid-infrared diffuse reflectance Fourier-transformed (DRIFT) spectra, and models using DRIFT spectra plus analytical pH values (DRIFT + pH), were compared with predicted log K(d + 1) values. Apart from selenate (R(2)  = 0.43), the DRIFT + pH calibrations resulted in marginally better models to predict log K(d + 1) values (R(2)  = 0.62-0.79), compared with those from PSLR-DRIFT (R(2)  = 0.61-0.72) and MLR (R(2)  = 0.54-0.79). The DRIFT + pH calibrations were applied to the prediction of log K(d + 1) values in the remaining 4313 soils. An example map of predicted log K(d + 1) values for added soluble MoO4(2-) in soils across Europe is presented. The DRIFT + pH PLSR models provided a rapid and inexpensive tool to assess the risk of mobility and potential availability of boric acid and selected oxoanions in European soils. For these models to be used in the prediction of log K(d + 1) values in soils globally, additional research will be needed to determine if soil variability is accounted on the calibration. © 2014 SETAC.

[Prediction of soil adsorption coefficients of organic compounds in a wide range of soil types by soil column liquid chromatography].

PubMed

Guo, Rongbo; Chen, Jiping; Zhang, Qing; Wu, Wenzhong; Liang, Xinmiao

2004-01-01

Using the methanol-water mixtures as mobile phases of soil column liquid chromatography (SCLC), prediction of soil adsorption coefficients (K(d)) by SCLC was validated in a wide range of soil types. The correlations between the retention factors measured by SCLC and soil adsorption coefficients measured by batch experiments were studied for five soils with different properties, i.e., Eurosoil 1#, 2#, 3#, 4# and 5#. The results show that good correlations existed between the retention factors and soil adsorption coefficients for Eurosoil 1#, 2#, 3# and 4#. For Eurosoil 5# which has a pH value of near 3, the correlation between retention factors and soil adsorption coefficients was unsatisfactory using methanol-water as mobile phase of SCLC. However, a good correlation was obtained using a methanol-buffer mixture with pH 3 as the mobile phase. This study proved that the SCLC is suitable for the prediction of soil adsorption coefficients.

Bacterial community structure and soil properties of a subarctic tundra soil in Council, Alaska.

PubMed

Kim, Hye Min; Jung, Ji Young; Yergeau, Etienne; Hwang, Chung Yeon; Hinzman, Larry; Nam, Sungjin; Hong, Soon Gyu; Kim, Ok-Sun; Chun, Jongsik; Lee, Yoo Kyung

2014-08-01

The subarctic region is highly responsive and vulnerable to climate change. Understanding the structure of subarctic soil microbial communities is essential for predicting the response of the subarctic soil environment to climate change. To determine the composition of the bacterial community and its relationship with soil properties, we investigated the bacterial community structure and properties of surface soil from the moist acidic tussock tundra in Council, Alaska. We collected 70 soil samples with 25-m intervals between sampling points from 0-10 cm to 10-20 cm depths. The bacterial community was analyzed by pyrosequencing of 16S rRNA genes, and the following soil properties were analyzed: soil moisture content (MC), pH, total carbon (TC), total nitrogen (TN), and inorganic nitrogen (NH4+ and NO3-). The community compositions of the two different depths showed that Alphaproteobacteria decreased with soil depth. Among the soil properties measured, soil pH was the most significant factor correlating with bacterial community in both upper and lower-layer soils. Bacterial community similarity based on jackknifed unweighted unifrac distance showed greater similarity across horizontal layers than through the vertical depth. This study showed that soil depth and pH were the most important soil properties determining bacterial community structure of the subarctic tundra soil in Council, Alaska. © 2014 The Authors. FEMS Microbiology Ecology published by John Wiley & Sons Ltd on behalf of the Federation of European Microbiological Societies.

Indicative capacity of NDVI in predictive mapping of the properties of plow horizons of soils on slopes in the south of Western Siberia

NASA Astrophysics Data System (ADS)

Gopp, N. V.; Nechaeva, T. V.; Savenkov, O. A.; Smirnova, N. V.; Smirnov, V. V.

2017-11-01

The informativeness of NDVI for predictive mapping of the physical and chemical properties of plow horizons of soils on different slope positions within the first (280-310 m a.s.l.) and second (240-280 m a.s.l.) altitudinal steps has been examined. This index is uninformative for mapping soil properties in small hollows, whose factual width is less than the Landsat image resolution (30 m). In regression models, NDVI index explains 52% of variance in the content of humus; 35 and 24% of variance in the contents of total and nitrate nitrogen; 19 and 29% of variance in the contents of total and available phosphorus; 25 and 50% of variance in the contents of exchangeable calcium and manganese; and 30 and 29% of variance in the contents of fine silt and soil water, respectively. On the basis of the models obtained, prognostic maps of the soil properties have been developed. Spatial distribution patterns of NDVI calculated from Landsat 8 images (30-m resolution) serve as the cartographic base and the main indicator of the soil properties. The NDVI values and the contents of humus, physical clay (<0.01 mm) and fine silt particles, total and nitrate nitrogen, total phosphorus, and exchangeable calcium and manganese in the soils of the first altitudinal step are higher than those in the soils of the second altitudinal step. An opposite tendency has been found for the available phosphorus content: in the soils of the second altitudinal step and the hollow, its content is higher than that in the soils of the first altitudinal step by 1.8 and 2.4 times, respectively. Differences in the pH of soil water suspensions, easily available phosphorus, and clay in the soils of the compared topographic positions (first and second altitudinal steps and the hollow) are statistically unreliable.

Thermal characteristics and bacterial diversity of forest soil in the Haean basin of Korea.

PubMed

Kim, Heejung; Lee, Jin-Yong; Lee, Kang-Kun

2014-01-01

To predict biotic responses to disturbances in forest environments, it is important to examine both the thermophysical properties of forest soils and the diversity of microorganisms that these soils contain. To predict the effects of climate change on forests, in particular, it is essential to understand the interactions between the soil surface, the air, and the biological diversity in the soil. In this study, the temperature and thermal properties of forest soil at three depths at a site in the Haean basin of Korea were measured over a period of four months. Metagenomic analyses were also carried out to ascertain the diversity of microorganisms inhabiting the soil. The thermal diffusivity of the soil at the study site was 5.9 × 10(-8) m(2) · s(-1). The heat flow through the soil resulted from the cooling and heating processes acting on the surface layers of the soils. The heat productivity in the soil varied through time. The phylum Proteobacteria predominated at all three soil depths, with members of Proteobacteria forming a substantial fraction (25.64 to 39.29%). The diversity and richness of microorganisms in the soil were both highest at the deepest depth, 90 cm, where the soil temperature fluctuation was the minimum.

Thermal Characteristics and Bacterial Diversity of Forest Soil in the Haean Basin of Korea

PubMed Central

Kim, Heejung; Lee, Jin-Yong; Lee, Kang-Kun

2014-01-01

To predict biotic responses to disturbances in forest environments, it is important to examine both the thermophysical properties of forest soils and the diversity of microorganisms that these soils contain. To predict the effects of climate change on forests, in particular, it is essential to understand the interactions between the soil surface, the air, and the biological diversity in the soil. In this study, the temperature and thermal properties of forest soil at three depths at a site in the Haean basin of Korea were measured over a period of four months. Metagenomic analyses were also carried out to ascertain the diversity of microorganisms inhabiting the soil. The thermal diffusivity of the soil at the study site was 5.9 × 10−8 m2 ·s−1. The heat flow through the soil resulted from the cooling and heating processes acting on the surface layers of the soils. The heat productivity in the soil varied through time. The phylum Proteobacteria predominated at all three soil depths, with members of Proteobacteria forming a substantial fraction (25.64 to 39.29%). The diversity and richness of microorganisms in the soil were both highest at the deepest depth, 90 cm, where the soil temperature fluctuation was the minimum. PMID:25431780

Infiltration in soils with a saturated surface

NASA Astrophysics Data System (ADS)

Hogarth, W. L.; Lockington, D. A.; Barry, D. A.; Parlange, M. B.; Haverkamp, R.; Parlange, J.-Y.

2013-05-01

An earlier infiltration equation relied on curve fitting of infiltration data for the determination of one of the parameters, which limits its usefulness in practice. This handicap is removed here, and the parameter is now evaluated by linking it directly to soil-water properties. The new predictions of infiltration using this evaluation are quite accurate. Positions and shapes of soil-water profiles are also examined in detail and found to be predicted analytically with great precision.

Hyper-temporal remote sensing for digital soil mapping: Characterizing soil-vegetation response to climatic variability

USDA-ARS?s Scientific Manuscript database

Indices derived from remotely-sensed imagery are commonly used to predict soil properties with digital soil mapping (DSM) techniques. The use of images from single dates or a small number of dates is most common for DSM; however, selection of the appropriate images is complicated by temporal variabi...

Predicting surface vibration from underground railways through inhomogeneous soil

NASA Astrophysics Data System (ADS)

Jones, Simon; Hunt, Hugh

2012-04-01

Noise and vibration from underground railways is a major source of disturbance to inhabitants near subways. To help designers meet noise and vibration limits, numerical models are used to understand vibration propagation from these underground railways. However, the models commonly assume the ground is homogeneous and neglect to include local variability in the soil properties. Such simplifying assumptions add a level of uncertainty to the predictions which is not well understood. The goal of the current paper is to quantify the effect of soil inhomogeneity on surface vibration. The thin-layer method (TLM) is suggested as an efficient and accurate means of simulating vibration from underground railways in arbitrarily layered half-spaces. Stochastic variability of the soil's elastic modulus is introduced using a K-L expansion; the modulus is assumed to have a log-normal distribution and a modified exponential covariance kernel. The effect of horizontal soil variability is investigated by comparing the stochastic results for soils varied only in the vertical direction to soils with 2D variability. Results suggest that local soil inhomogeneity can significantly affect surface velocity predictions; 90 percent confidence intervals showing 8 dB averages and peak values up to 12 dB are computed. This is a significant source of uncertainty and should be considered when using predictions from models assuming homogeneous soil properties. Furthermore, the effect of horizontal variability of the elastic modulus on the confidence interval appears to be negligible. This suggests that only vertical variation needs to be taken into account when modelling ground vibration from underground railways.

Using IKONOS Imagery to Estimate Surface Soil Property Variability in Two Alabama Physiographies

NASA Technical Reports Server (NTRS)

Sullivan, Dana; Shaw, Joey; Rickman, Doug

2005-01-01

Knowledge of surface soil properties is used to assess past erosion and predict erodibility, determine nutrient requirements, and assess surface texture for soil survey applications. This study was designed to evaluate high resolution IKONOS multispectral data as a soil- mapping tool. Imagery was acquired over conventionally tilled fields in the Coastal Plain and Tennessee Valley physiographic regions of Alabama. Acquisitions were designed to assess the impact of surface crusting, roughness and tillage on our ability to depict soil property variability. Soils consisted mostly of fine-loamy, kaolinitic, thermic Plinthic Kandiudults at the Coastal Plain site and fine, kaolinitic, thermic Rhodic Paleudults at the Tennessee Valley site. Soils were sampled in 0.20 ha grids to a depth of 15 cm and analyzed for % sand (0.05 - 2 mm), silt (0.002 -0.05 mm), clay (less than 0.002 mm), citrate dithionite extractable iron (Fe(sub d)) and soil organic carbon (SOC). Four methods of evaluating variability in soil attributes were evaluated: 1) kriging of soil attributes, 2) co-kriging with soil attributes and reflectance data, 3) multivariate regression based on the relationship between reflectance and soil properties, and 4) fuzzy c-means clustering of reflectance data. Results indicate that co-kriging with remotely sensed data improved field scale estimates of surface SOC and clay content compared to kriging and regression methods. Fuzzy c-means worked best using RS data acquired over freshly tilled fields, reducing soil property variability within soil zones compared to field scale soil property variability.

Considerations for applying digital soil mapping to ecological sites

USDA-ARS?s Scientific Manuscript database

Recent advancements in the spatial prediction of soil properties are not currently being fully utilized for ecological studies. Linking digital soil mapping (DSM) with ecological sites (ES) has the potential to better land management decisions by improving spatial resolution and precision as well as...

Optimizing the Hydrological and Biogeochemical Simulations on a Hillslope with Stony Soil

NASA Astrophysics Data System (ADS)

Zhu, Q.

2017-12-01

Stony soils are widely distributed in the hilly area. However, traditional pedotransfer functions are not reliable in predicting the soil hydraulic parameters for these soils due to the impacts of rock fragments. Therefore, large uncertainties and errors may exist in the hillslope hydrological and biogeochemical simulations in stony soils due to poor estimations of soil hydraulic parameters. In addition, homogenous soil hydraulic parameters are usually used in traditional hillslope simulations. However, soil hydraulic parameters are spatially heterogeneous on the hillslope. This may also cause the unreliable simulations. In this study, we obtained soil hydraulic parameters using five different approaches on a tea hillslope in Taihu Lake basin, China. These five approaches included (1) Rossetta predicted and spatially homogenous, (2) Rossetta predicted and spatially heterogeneous), (3) Rossetta predicted, rock fragment corrected and spatially homogenous, (4) Rossetta predicted, rock fragment corrected and spatially heterogeneous, and (5) extracted from observed soil-water retention curves fitted by dual-pore function and spatially heterogeneous (observed). These five sets of soil hydraulic properties were then input into Hydrus-3D and DNDC to simulate the soil hydrological and biogeochemical processes. The aim of this study is testing two hypotheses. First, considering the spatial heterogeneity of soil hydraulic parameters will improve the simulations. Second, considering the impact of rock fragment on soil hydraulic parameters will improve the simulations.

The Impact of Soil Moisture Initialization On Seasonal Precipitation Forecasts

NASA Technical Reports Server (NTRS)

Koster, R. D.; Suarez, M. J.; Tyahla, L.; Houser, Paul (Technical Monitor)

2002-01-01

Some studies suggest that the proper initialization of soil moisture in a forecasting model may contribute significantly to the accurate prediction of seasonal precipitation, especially over mid-latitude continents. In order for the initialization to have any impact at all, however, two conditions must be satisfied: (1) the initial soil moisture anomaly must be "remembered" into the forecasted season, and (2) the atmosphere must respond in a predictable way to the soil moisture anomaly. In our previous studies, we identified the key land surface and atmospheric properties needed to satisfy each condition. Here, we tie these studies together with an analysis of an ensemble of seasonal forecasts. Initial soil moisture conditions for the forecasts are established by forcing the land surface model with realistic precipitation prior to the start of the forecast period. As expected, the impacts on forecasted precipitation (relative to an ensemble of runs that do not utilize soil moisture information) tend to be localized over the small fraction of the earth with all of the required land and atmosphere properties.

Predicting root zone soil moisture with soil properties and satellite near-surface moisture data across the conterminous United States

NASA Astrophysics Data System (ADS)

Baldwin, D.; Manfreda, S.; Keller, K.; Smithwick, E. A. H.

2017-03-01

Satellite-based near-surface (0-2 cm) soil moisture estimates have global coverage, but do not capture variations of soil moisture in the root zone (up to 100 cm depth) and may be biased with respect to ground-based soil moisture measurements. Here, we present an ensemble Kalman filter (EnKF) hydrologic data assimilation system that predicts bias in satellite soil moisture data to support the physically based Soil Moisture Analytical Relationship (SMAR) infiltration model, which estimates root zone soil moisture with satellite soil moisture data. The SMAR-EnKF model estimates a regional-scale bias parameter using available in situ data. The regional bias parameter is added to satellite soil moisture retrievals before their use in the SMAR model, and the bias parameter is updated continuously over time with the EnKF algorithm. In this study, the SMAR-EnKF assimilates in situ soil moisture at 43 Soil Climate Analysis Network (SCAN) monitoring locations across the conterminous U.S. Multivariate regression models are developed to estimate SMAR parameters using soil physical properties and the moderate resolution imaging spectroradiometer (MODIS) evapotranspiration data product as covariates. SMAR-EnKF root zone soil moisture predictions are in relatively close agreement with in situ observations when using optimal model parameters, with root mean square errors averaging 0.051 [cm3 cm-3] (standard error, s.e. = 0.005). The average root mean square error associated with a 20-fold cross-validation analysis with permuted SMAR parameter regression models increases moderately (0.082 [cm3 cm-3], s.e. = 0.004). The expected regional-scale satellite correction bias is negative in four out of six ecoregions studied (mean = -0.12 [-], s.e. = 0.002), excluding the Great Plains and Eastern Temperate Forests (0.053 [-], s.e. = 0.001). With its capability of estimating regional-scale satellite bias, the SMAR-EnKF system can predict root zone soil moisture over broad extents and has applications in drought predictions and other operational hydrologic modeling purposes.

Data worth and prediction uncertainty for pesticide transport and fate models in Nebraska and Maryland, United States

USGS Publications Warehouse

Nolan, Bernard T.; Malone, Robert W.; Doherty, John E.; Barbash, Jack E.; Ma, Liwang; Shaner, Dale L.

2015-01-01

CONCLUSIONS: Although the observed data were sparse, they substantially reduced prediction uncertainty in unsampled regions of pesticide breakthrough curves. Nitrate evidently functioned as a surrogate for soil hydraulic data in well-drained loam soils conducive to conservative transport of nitrogen. Pesticide properties and macropore parameters could most benefit from improved characterization further to reduce model misfit and prediction uncertainty.

Modeling of phytoextraction efficiency of microbially stimulated Salix dasyclados L. in the soils with different speciation of heavy metals.

PubMed

Złoch, Michał; Kowalkowski, Tomasz; Tyburski, Jarosław; Hrynkiewicz, Katarzyna

2017-12-02

Bioaugmentation of soils with selected microorganisms during phytoextraction can be the key solution for successful bioremediation and should be accurately calculated for different physicochemical soil properties and heavy metal availability to guarantee the universality of this method. Equally important is the development of an accurate prediction tool to manage phytoremediation process. The main objective of this study was to evaluate the role of three metallotolerant siderophore-producing Streptomyces sp. B1-B3 strains in the phytoremediation of heavy metals with the use of S. dasyclados L. growing in four metalliferrous soils as well as modeling the efficiency of this process based on physicochemical and microbiological properties of the soils using artificial neural network (ANN) analysis. The bacterial inoculation of plants significantly stimulated plant biomass and reduced oxidative stress. Moreover, the bacteria affected the speciation of heavy metals and finally their mobility, thereby enhancing the uptake and bioaccumulation of Zn, Cd, and Pb in the biomass. The best capacity for phytoextraction was noted for strain B1, which had the highest siderophore secretion ability. Finally, ANN model permitted to predict efficiency of phytoextraction based on both the physicochemical properties of the soils and the activity of the soil microbiota with high precision.

The influence of soil organic matter chemistry and site/soil properties in predicting the decomposability of tundra soils

NASA Astrophysics Data System (ADS)

Matamala, R.; Jastrow, J. D.; Fan, Z.; Liang, C.; Calderon, F.; Michaelson, G.; Mishra, U.; Ping, C. L.

2017-12-01

With the increase in high latitude warming, there is a need to better understand the potential vulnerability of soil organic matter (SOM) stored in Arctic regions. In this study, we used mid infrared spectroscopy (MidIR) to determine the influence of soil chemistry and site properties in the short-term mineralization potential of SOM stored in tundra soils. Soils from the active and permafrost layers were collected from four tundra sites on the Coastal Plain, and Arctic Foothills of the North Slope of Alaska and were incubated for 60 days at a range of temperatures. Site and soil properties including acidic versus non-acidic tundra, lowland versus upland areas, total soil organic carbon (TOC) and total nitrogen (TN) concentrations, 60-day carbon mineralization potential (CMP), MidIR spectra and the chemical composition of the SOM stored in these soils were determined. Partial least squares (PLS) models for CMP versus MidIR spectra were produced upon splitting the dataset into site and soil properties categories. We found that SOM composition determined by MidIR spectroscopy was most effective in predicting CMP for tundra soils and it was most relevant for the active-layer mineral and upper permafrost soil horizons and/or soils with C concentrations of 10% or lower. Analysis of the factor loadings and standardized beta coefficients from the CMP PLS models indicated that spectral bands associated with clay contents, phenolic OH, aliphatic, silicates, carboxylic acids, and polysaccharides were influential for lower TOC soils, but these bands were less important for higher TOC soils. High TOC soils were influenced by a combination of other factors. Our results suggest that different factors affect the short-term CMP of SOM in tundra soils depending on the amount of TOC present. We show MidIR as a powerful tool for quickly and reasonably estimating the short-term CMP of tundra soils. Widespread application of MidIR measurements to already collected and archived tundra region soils could provide a quick and reliable assessment of the CMP of these soils, reduce the need for incubation studies, and contribute to upscaling and model benchmarking of SOM mineralization of tundra soils.

Wildfires effects on soils: water repellency, NIR models and post-fire treatments. My personal view (SSS Division Outstanding ECS Award Lecture)

NASA Astrophysics Data System (ADS)

Arcenegui, Victoria

2017-04-01

I first was intrigued by fire, because all summers we had some of them in our location, and then I was involve in fire effects on soils. We had, and also have, a lot of question to answer. I am absolutely sure that soil science was my best choice. Soils are amazing, a lot of things are happening in soils. Soils and fire, are my main research topics. I studied the immediately effect of fire on soils, focus on the effect of fire in soil water repellency and aggregate stability. Two physical properties that are crucial to post-fire soil response. I also construct NIR models to know the maximum temperature reached in soils. It is well known that temperature is a key factor affecting soils properties. Then, it is a really important tool to predict the temperature reached in a soil after a wildfire. Currently, I am involve in a project to investigate what are the best post-fire treatments in our soils and how this treatments affects soil properties.

Multivariate soil fertility relationships for predicting the environmental persistence of 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-tricyclohexane (RDX) among taxonomically distinct soils.

PubMed

Katseanes, Chelsea K; Chappell, Mark A; Hopkins, Bryan G; Durham, Brian D; Price, Cynthia L; Porter, Beth E; Miller, Lesley F

2017-12-01

After nearly a century of use in numerous munition platforms, TNT and RDX contamination has turned up largely in the environment due to ammunition manufacturing or as part of releases from low-order detonations during training activities. Although the basic knowledge governing the environmental fate of TNT and RDX are known, accurate predictions of TNT and RDX persistence in soil remain elusive, particularly given the universal heterogeneity of pedomorphic soil types. In this work, we proposed overcoming this problem by considering the environmental persistence of these munition constituents (MC) as multivariate mathematical functions over a variety of taxonomically distinct soil types, instead of a single constant or parameter of a specific absolute value. To test this idea, we conducted experiments where the disappearance kinetics of TNT and RDX were measured over a >300 h period in taxonomically distinct soils. Classical fertility-based soil measurements were log-transformed, statistically decomposed, and correlated to TNT and RDX disappearance rates (k -TNT and k -RDX ) using multivariate dimension-reduction and correlation techniques. From these efforts, we generated multivariate linear functions for k parameters across different soil types based on a statistically reduced set of their chemical and physical properties: Calculations showed that the soil properties exhibited strong covariance, with a prominent latent structure emerging as the basis for relative comparisons of the samples in reduced space. Loadings describing TNT degradation were largely driven by properties associated with alkaline/calcareous soil characteristics, while the degradation of RDX was attributed to the soil organic matter content - reflective of an important soil fertility characteristic. In spite of the differing responses to the munitions, batch data suggested that the overall nutrient dynamics were consistent for each soil type, as well as readily distinguishable from the other soil types used in this study. Thus, we hypothesized that the latent structure arising from the strong covariance of full multivariate geochemical matrix describing taxonomically distinguished "soil types" may provide the means for potentially predicting complex phenomena in soils. Published by Elsevier Ltd.

Application of VNIR diffuse reflectance spectroscopy to estimate soil organic carbon content, and content of different forms of iron and manganese

NASA Astrophysics Data System (ADS)

Klement, Ales; Jaksik, Ondrej; Kodesova, Radka; Drabek, Ondrej; Boruvka, Lubos

2013-04-01

Visible and near-infrared (VNIR) diffuse reflectance spectroscopy is a progressive method used for prediction of soil properties. Study was performed on the soils from the agricultural land from the south Moravia municipality of Brumovice. Studied area is characterized by a relatively flat upper part, a tributary valley in the middle and a colluvial fan at the bottom. Haplic Chernozem reminded at the flat upper part of the area. Regosols were formed at steep parts of the valley. Colluvial Chernozem and Colluvial soils were formed at the bottom parts of the valley and at the bottom part of the studied field. The goal of the study was to evaluate relationship between soil spectra curves and organic matter content, and different forms iron and manganese content (Mehlich III extract, ammonium oxalate extract and dithionite-citrate extract). Samples (87) were taken from the topsoil within regular grid covering studied area. The soil spectra curves (of air dry soil and sieved using 2 mm sieve) were measured in the laboratory using spectometer FieldSpec®3 (350 - 2 500 nm). The Fe and Mn contents in different extract were measured using ICP-OES (with an iCAP 6500 Radial ICP Emission spectrometer; Thermo Scientific, UK) under standard analytical conditions. Partial least squares regression (PLSR) was used for modeling of the relationship between spectra and measured soil properties. Prediction ability was evaluated using the R2, root mean square error (RMSE) and normalized root mean square deviation (NRMSD). The results showed the best prediction for Mn (R2 = 0.86, RMSE = 29, NRMSD = 0.11), Fe in ammonium oxalate extract (R2 = 0.82, RMSE = 171, NRMSD = 0.12) and organic matter content (R2 = 0.84, RMSE = 0.13, NRMSD = 0.09). The slightly worse prediction was obtained for Mn and Fe in citrate extract (R2 = 0.82, RMSE = 21, NRMSD = 0.10; R2 = 0.77, RMSE = 522, NRMSD = 0.23). Poor prediction was evaluated for Mn and Fe in Mehlich III extract (R2 = 0.43, RMSE = 13, NRMSD = 0.17; R2 = 0.39, RMSE = 13, NRMSD = 0.26). In general, the results confirmed that the measurement of soil spectral characteristics is a promising technology for a digital soil mapping and predicting studied soil properties. Acknowledgment: Authors acknowledge the financial support of the Ministry of Agriculture of the Czech Republic (grant No. QJ1230319) and the Czech Science Foundation (grant No. GA526/09/1762).

Evaluation of interaction properties of geosynthetics in cohesive soils : LTRC reinforced soil test wall : technical summary.

DOT National Transportation Integrated Search

2004-07-01

The test wall was constructed to evaluate the behavior of MSE walls constructed with silty-clay soils through comparison between predicted and field measurements. The primary objectives of the construction of the LTRC reinforced test wall were to mon...

Large scale prediction of soil properties in the West African yam belt based on mid-infrared soil spectroscopy

NASA Astrophysics Data System (ADS)

Baumann, Philipp; Lee, Juhwan; Paule Schönholzer, Laurie; Six, Johan; Frossard, Emmanuel

2016-04-01

Yam (Dioscorea sp.) is an important staple food in West Africa. Fertilizer applications have variable effects on yam tuber yields, and a management option solely based on application of mineral NPK fertilizers may bear the risk of increased organic matter mineralization. Therefore, innovative and sustainable nutrient management strategies need to be developed and evaluated for yam cultivation. The goal of this study was to establish a mid-infrared soil spectroscopic library and models to predict soil properties relevant to yam growth. Soils from yam fields at four different locations in Côte d'Ivoire and Burkina Faso that were representative of the West African yam belt were sampled. The project locations ranged from the humid forest zone (5.88 degrees N) to the northern Guinean savannah (11.07 degrees N). At each location, soils of 20 yam fields were sampled (0-30 cm). For the location in the humid forest zone additional 14 topsoil samples from positions that had been analyzed in the Land Degradation Surveillance Framework developed by ICRAF were included. In total, 94 soil samples were analyzed using established reference analysis protocols. Besides soils were milled and then scanned by fourier transform mid-infrared spectroscopy in the range between 400 and 4000 reciprocal cm. Using partial least squares (PLS) regression, PLS1 calibration models that included soils from the four locations were built using two thirds of the samples selected by Kennard-Stones sampling algorithm in the spectral principal component space. Models were independently validated with the remaining data set. Spectral models for total carbon, total nitrogen, total iron, total aluminum, total potassium, exchangeable calcium, and effective cation exchange capacity performed very well, which was indicated by R-squared values between 0.8 and 1.0 on both calibration and validation. For these soil properties, spectral models can be used for cost-effective, rapid, and accurate predictions. Measures of total silicium, total zinc, total copper, total manganese, pH, exchangeable magnesium, total sulfur, total phosphorus, resin membrane extractable phosphorus, DTPA iron, and DTPA copper were predicted with intermediate accuracy (R-squared of both calibration and validation between 0.5 and 0.8). For these measures, the models can be used to establish a rapid screening in order to distinguish high from low soil fertility status. Generally, soil fertility in West African soils is constrained by low organic C, for example, ranging between 0.2% to 2.5% in this study. The accurate prediction of total soil organic C is an important factor for monitoring soil fertility status. Results of this study showed that soil spectroscopy has a high potential to evaluate soil fertility in the selected locations.

Estimation of effective soil hydraulic properties at field scale via ground albedo neutron sensing

NASA Astrophysics Data System (ADS)

Rivera Villarreyes, C. A.; Baroni, G.; Oswald, S. E.

2012-04-01

Upscaling of soil hydraulic parameters is a big challenge in hydrological research, especially in model applications of water and solute transport processes. In this contest, numerous attempts have been made to optimize soil hydraulic properties using observations of state variables such as soil moisture. However, in most of the cases the observations are limited at the point-scale and then transferred to the model scale. In this way inherent small-scale soil heterogeneities and non-linearity of dominate processes introduce sources of error that can produce significant misinterpretation of hydrological scenarios and unrealistic predictions. On the other hand, remote-sensed soil moisture over large areas is also a new promising approach to derive effective soil hydraulic properties over its observation footprint, but it is still limited to the soil surface. In this study we present a new methodology to derive soil moisture at the intermediate scale between point-scale observations and estimations at the remote-sensed scale. The data are then used for the estimation of effective soil hydraulic parameters. In particular, ground albedo neutron sensing (GANS) was used to derive non-invasive soil water content in a footprint of ca. 600 m diameter and a depth of few decimeters. This approach is based on the crucial role of hydrogen compared to other landscape materials as neutron moderator. As natural neutron measured aboveground depends on soil water content, the vertical footprint of the GANS method, i.e. its penetration depth, does also. Firstly, this study was designed to evaluate the dynamics of GANS vertical footprint and derive a mathematical model for its prediction. To test GANS-soil moisture and its penetration depth, it was accompanied by other soil moisture measurements (FDR) located at 5, 20 and 40 cm depths over the GANS horizontal footprint in a sunflower field (Brandenburg, Germany). Secondly, a HYDRUS-1D model was set up with monitored values of crop height and meteorological variables as input during a four-month period. Parameter estimation (PEST) software was coupled to HYDRUS-1D in order to calibrate soil hydraulic properties based on soil water content data. Thirdly, effective soil hydraulic properties were derived from GANS-soil moisture. Our observations show the potential of GANS to compensate the lack of information at the intermediate scale, soil water content estimation and effective soil properties. Despite measurement volumes, GANS-derived soil water content compared quantitatively to FDRs at several depths. For one-hour estimations, root mean square error was estimated as 0.019, 0.029 and 0.036 m3/m3 for 5 cm, 20 cm and 40 cm depths, respectively. In the context of soil hydraulic properties, this first application of GANS method succeed and its estimations were comparable to those derived by other approaches.

Soil organic carbon dynamics jointly controlled by climate, carbon inputs, soil properties and soil carbon fractions.

PubMed

Luo, Zhongkui; Feng, Wenting; Luo, Yiqi; Baldock, Jeff; Wang, Enli

2017-10-01

Soil organic carbon (SOC) dynamics are regulated by the complex interplay of climatic, edaphic and biotic conditions. However, the interrelation of SOC and these drivers and their potential connection networks are rarely assessed quantitatively. Using observations of SOC dynamics with detailed soil properties from 90 field trials at 28 sites under different agroecosystems across the Australian cropping regions, we investigated the direct and indirect effects of climate, soil properties, carbon (C) inputs and soil C pools (a total of 17 variables) on SOC change rate (r C , Mg C ha -1  yr -1 ). Among these variables, we found that the most influential variables on r C were the average C input amount and annual precipitation, and the total SOC stock at the beginning of the trials. Overall, C inputs (including C input amount and pasture frequency in the crop rotation system) accounted for 27% of the relative influence on r C , followed by climate 25% (including precipitation and temperature), soil C pools 24% (including pool size and composition) and soil properties (such as cation exchange capacity, clay content, bulk density) 24%. Path analysis identified a network of intercorrelations of climate, soil properties, C inputs and soil C pools in determining r C . The direct correlation of r C with climate was significantly weakened if removing the effects of soil properties and C pools, and vice versa. These results reveal the relative importance of climate, soil properties, C inputs and C pools and their complex interconnections in regulating SOC dynamics. Ignorance of the impact of changes in soil properties, C pool composition and C input (quantity and quality) on SOC dynamics is likely one of the main sources of uncertainty in SOC predictions from the process-based SOC models. © 2017 John Wiley & Sons Ltd.

Nitrogen Cycling from Increased Soil Organic Carbon Contributes Both Positively and Negatively to Ecosystem Services in Wheat Agro-Ecosystems

PubMed Central

Palmer, Jeda; Thorburn, Peter J.; Biggs, Jody S.; Dominati, Estelle J.; Probert, Merv E.; Meier, Elizabeth A.; Huth, Neil I.; Dodd, Mike; Snow, Val; Larsen, Joshua R.; Parton, William J.

2017-01-01

Soil organic carbon (SOC) is an important and manageable property of soils that impacts on multiple ecosystem services through its effect on soil processes such as nitrogen (N) cycling and soil physical properties. There is considerable interest in increasing SOC concentration in agro-ecosystems worldwide. In some agro-ecosystems, increased SOC has been found to enhance the provision of ecosystem services such as the provision of food. However, increased SOC may increase the environmental footprint of some agro-ecosystems, for example by increasing nitrous oxide emissions. Given this uncertainty, progress is needed in quantifying the impact of increased SOC concentration on agro-ecosystems. Increased SOC concentration affects both N cycling and soil physical properties (i.e., water holding capacity). Thus, the aim of this study was to quantify the contribution, both positive and negative, of increased SOC concentration on ecosystem services provided by wheat agro-ecosystems. We used the Agricultural Production Systems sIMulator (APSIM) to represent the effect of increased SOC concentration on N cycling and soil physical properties, and used model outputs as proxies for multiple ecosystem services from wheat production agro-ecosystems at seven locations around the world. Under increased SOC, we found that N cycling had a larger effect on a range of ecosystem services (food provision, filtering of N, and nitrous oxide regulation) than soil physical properties. We predicted that food provision in these agro-ecosystems could be significantly increased by increased SOC concentration when N supply is limiting. Conversely, we predicted no significant benefit to food production from increasing SOC when soil N supply (from fertiliser and soil N stocks) is not limiting. The effect of increasing SOC on N cycling also led to significantly higher nitrous oxide emissions, although the relative increase was small. We also found that N losses via deep drainage were minimally affected by increased SOC in the dryland agro-ecosystems studied, but increased in the irrigated agro-ecosystem. Therefore, we show that under increased SOC concentration, N cycling contributes both positively and negatively to ecosystem services depending on supply, while the effects on soil physical properties are negligible. PMID:28539929

Nitrogen Cycling from Increased Soil Organic Carbon Contributes Both Positively and Negatively to Ecosystem Services in Wheat Agro-Ecosystems.

PubMed

Palmer, Jeda; Thorburn, Peter J; Biggs, Jody S; Dominati, Estelle J; Probert, Merv E; Meier, Elizabeth A; Huth, Neil I; Dodd, Mike; Snow, Val; Larsen, Joshua R; Parton, William J

2017-01-01

Soil organic carbon (SOC) is an important and manageable property of soils that impacts on multiple ecosystem services through its effect on soil processes such as nitrogen (N) cycling and soil physical properties. There is considerable interest in increasing SOC concentration in agro-ecosystems worldwide. In some agro-ecosystems, increased SOC has been found to enhance the provision of ecosystem services such as the provision of food. However, increased SOC may increase the environmental footprint of some agro-ecosystems, for example by increasing nitrous oxide emissions. Given this uncertainty, progress is needed in quantifying the impact of increased SOC concentration on agro-ecosystems. Increased SOC concentration affects both N cycling and soil physical properties (i.e., water holding capacity). Thus, the aim of this study was to quantify the contribution, both positive and negative, of increased SOC concentration on ecosystem services provided by wheat agro-ecosystems. We used the Agricultural Production Systems sIMulator (APSIM) to represent the effect of increased SOC concentration on N cycling and soil physical properties, and used model outputs as proxies for multiple ecosystem services from wheat production agro-ecosystems at seven locations around the world. Under increased SOC, we found that N cycling had a larger effect on a range of ecosystem services (food provision, filtering of N, and nitrous oxide regulation) than soil physical properties. We predicted that food provision in these agro-ecosystems could be significantly increased by increased SOC concentration when N supply is limiting. Conversely, we predicted no significant benefit to food production from increasing SOC when soil N supply (from fertiliser and soil N stocks) is not limiting. The effect of increasing SOC on N cycling also led to significantly higher nitrous oxide emissions, although the relative increase was small. We also found that N losses via deep drainage were minimally affected by increased SOC in the dryland agro-ecosystems studied, but increased in the irrigated agro-ecosystem. Therefore, we show that under increased SOC concentration, N cycling contributes both positively and negatively to ecosystem services depending on supply, while the effects on soil physical properties are negligible.

Predicting plant uptake of cadmium: validated with long-term contaminated soils.

PubMed

Lamb, Dane T; Kader, Mohammed; Ming, Hui; Wang, Liang; Abbasi, Sedigheh; Megharaj, Mallavarapu; Naidu, Ravi

2016-10-01

Cadmium accumulates in plant tissues at low soil loadings and is a concern for human health. Yet at higher levels it is also of concern for ecological receptors. We determined Cd partitioning constants for 41 soils to examine the role of soil properties controlling Cd partitioning and plant uptake. From a series of sorption and dose response studies, transfer functions were developed for predicting Cd uptake in Cucumis sativa L. (cucumber). The parameter log K f was predicted with soil pH ca , logCEC and log OC. Transfer of soil pore-water Cd 2+ to shoots was described with a power function (R 2  = 0.73). The dataset was validated with 13 long-term contaminated soils (plus 2 control soils) ranging in Cd concentration from 0.2 to 300 mg kg -1 . The series of equations predicting Cd shoot from pore-water Cd 2+ were able to predict the measured data in the independent dataset (root mean square error = 2.2). The good relationship indicated that Cd uptake to cucumber shoots could be predicted with Cd pore and Cd 2+ without other pore-water parameters such as pH or Ca 2+ . The approach may be adapted to a range of plant species.

HYDRAULIC REDISTRIBUTION OF SOIL WATER IN TWO OLD-GROWTH CONIFEROUS FORESTS: QUANTIFYING PATTERNS AND CONTROLS

EPA Science Inventory

Although hydraulic redistribution of soil water (HR) by roots is a widespread phenomenon, the processes governing spatial and temporal patterns of HR are not well understood. We incorporated soil/plant biophysical properties into a simple model based on Darcy's law to predict sea...

Human Exposure Assessment: Development of methods to assess the bioaccessibility of organic contaminants sorbed to soils and house dusts

EPA Science Inventory

Research task- Are physicochemical properties of soil and house dust predictive of the bioaccessibility of sorbed organic compoundsGoalIdentify dust and soil characteristics that influence the bioaccessibility of organic compounds and provide chemical specific data on the fractio...

3D-Digital soil property mapping by geoadditive models

NASA Astrophysics Data System (ADS)

Papritz, Andreas

2016-04-01

In many digital soil mapping (DSM) applications, soil properties must be predicted not only for a single but for multiple soil depth intervals. In the GlobalSoilMap project, as an example, predictions are computed for the 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, 100-200 cm depth intervals (Arrouays et al., 2014). Legacy soil data are often used for DSM. It is common for such datasets that soil properties were measured for soil horizons or for layers at varying soil depth and with non-constant thickness (support). This poses problems for DSM: One strategy is to harmonize the soil data to common depth prior to the analyses (e.g. Bishop et al., 1999) and conduct the statistical analyses for each depth interval independently. The disadvantage of this approach is that the predictions for different depths are computed independently from each other so that the predicted depth profiles may be unrealistic. Furthermore, the error induced by the harmonization to common depth is ignored in this approach (Orton et al. 2016). A better strategy is therefore to process all soil data jointly without prior harmonization by a 3D-analysis that takes soil depth and geographical position explicitly into account. Usually, the non-constant support of the data is then ignored, but Orton et al. (2016) presented recently a geostatistical approach that accounts for non-constant support of soil data and relies on restricted maximum likelihood estimation (REML) of a linear geostatistical model with a separable, heteroscedastic, zonal anisotropic auto-covariance function and area-to-point kriging (Kyriakidis, 2004.) Although this model is theoretically coherent and elegant, estimating its many parameters by REML and selecting covariates for the spatial mean function is a formidable task. A simpler approach might be to use geoadditive models (Kammann and Wand, 2003; Wand, 2003) for 3D-analyses of soil data. geoAM extend the scope of the linear model with spatially correlated errors to account for nonlinear effects of covariates by fitting componentwise smooth, nonlinear functions to the covariates (additive terms). REML estimation of model parameters and computing best linear unbiased predictions (BLUP) builds in the geoAM framework on the fact that both geostatistical and additive models can be parametrized as linear mixed models Wand, 2003. For 3D-DSM analysis of soil data, it is natural to model depth profiles of soil properties by additive terms of soil depth. Including interactions between these additive terms and covariates of the spatial mean function allows to model spatially varying depth profiles. Furthermore, with suitable choice of the basis functions of the additive term (e.g. polynomial regression splines), non-constant support of the soil data can be taken into account. Finally, boosting (Bühlmann and Hothorn, 2007) can be used for selecting covariates for the spatial mean function. The presentation will detail the geoAM approach and present an example of geoAM for 3D-analysis of legacy soil data. Arrouays, D., McBratney, A. B., Minasny, B., Hempel, J. W., Heuvelink, G. B. M., MacMillan, R. A., Hartemink, A. E., Lagacherie, P., and McKenzie, N. J. (2014). The GlobalSoilMap project specifications. In GlobalSoilMap Basis of the global spatial soil information system, pages 9-12. CRC Press. Bishop, T., McBratney, A., and Laslett, G. (1999). Modelling soil attribute depth functions with equal-area quadratic smoothing splines. Geoderma, 91(1-2), 27-45. Bühlmann, P. and Hothorn, T. (2007). Boosting algorithms: Regularization, prediction and model fitting. Statistical Science, 22(4), 477-505. Kammann, E. E. and Wand, M. P. (2003). Geoadditive models. Journal of the Royal Statistical Society. Series C: Applied Statistics, 52(1), 1-18. Kyriakidis, P. (2004). A geostatistical framework for area-to-point spatial interpolation. Geographical Analysis, 36(3), 259-289. Orton, T., Pringle, M., and Bishop, T. (2016). A one-step approach for modelling and mapping soil properties based on profile data sampled over varying depth intervals. Geoderma, 262, 174-186. Wand, M. P. (2003). Smoothing and mixed models. Computational Statistics, 18(2), 223-249.

Predicting the solubility and lability of Zn, Cd, and Pb in soils from a minespoil-contaminated catchment by stable isotopic exchange

NASA Astrophysics Data System (ADS)

Marzouk, E. R.; Chenery, S. R.; Young, S. D.

2013-12-01

The Rookhope catchment of Weardale, England, has a diverse legacy of contaminated soils due to extensive lead mining activity over four centuries. We measured the isotopically exchangeable content of Pb, Cd and Zn (E-values) in a large representative subset of the catchment soils (n = 246) using stable isotope dilution. All three metals displayed a wide range of %E-values (c. 1-100%) but relative lability followed the sequence Cd > Pb > Zn. A refinement of the stable isotope dilution approach also enabled detection of non-reactive metal contained within suspended sub-micron (<0.22 μm) colloidal particles (SCP-metal). For most soils, the presence of non-labile SCP-metal caused only minor over-estimation of E-values (<2%) but the effect was greater for soils with particularly large humus or carbonate contents. Approximately 80%, 53% and 66% of the variability in Zn, Cd and Pb %E-values (respectively) could be explained by pH, loss on ignition and total metal content. E-values were affected by the presence of ore minerals at high metal contents leading to an inconsistent trend in the relationship between %E-value and soil metal concentration. Metal solubility, in the soil suspensions used to measure E-values, was predicted using the WHAM geochemical speciation model (versions VI and VII). The use of total and isotopically exchangeable metal as alternative input variables was compared; the latter provided significantly better predictions of solubility, especially in the case of Zn. Lead solubility was less well predicted by either version of WHAM, with over-prediction at low pH and under-prediction at high soil pH values. Quantify the isotopically exchangeable fractions of Zn, Cd and Pb (E-values), and assess their local and regional variability, using multi-element stable isotope dilution, in a diverse range of soil ecosystems within the catchment of an old Pb/Zn mining area. Assess the controlling influences of soil properties on metal lability and develop predictive algorithms for metal lability in the contaminated catchment based on simple soil properties (such as pH, organic matter (LOI), and total metal content). Examine the incidence of non-isotopically-exchangeable metal held within suspended colloidal particles (SCP-metal) in filtered soil solutions (<0.22 μm) by comparing E-values from isotopic abundance in solutions equilibrated with soil and in a resin phase equilibrated with the separated solution. Assess the ability of a geochemical speciation model, WHAM(VII), to predict metal solubility using isotopically exchangeable metal as an input variable.

Prediction of resilient modulus from soil index properties.

DOT National Transportation Integrated Search

2004-11-01

Subgrade soil characterization in terms of Resilient Modulus (MR) has become crucial for pavement design. For a new : design, MR values are generally obtained by conducting repeated load triaxial tests on reconstituted/undisturbed cylindrical : speci...

Prediction of resilient modulus from soil index properties

DOT National Transportation Integrated Search

2004-11-01

Subgrade soil characterization in terms of Resilient Modulus (MR) has become crucial for pavement design. For a new design, MR values are generally obtained by conducting repeated load triaxial tests on reconstituted/undisturbed cylindrical specimens...

Impediments to predicting site response: Seismic property estimation and modeling simplifications

USGS Publications Warehouse

Thompson, E.M.; Baise, L.G.; Kayen, R.E.; Guzina, B.B.

2009-01-01

We compare estimates of the empirical transfer function (ETF) to the plane SH-wave theoretical transfer function (TTF) within a laterally constant medium for invasive and noninvasive estimates of the seismic shear-wave slownesses at 13 Kiban-Kyoshin network stations throughout Japan. The difference between the ETF and either of the TTFs is substantially larger than the difference between the two TTFs computed from different estimates of the seismic properties. We show that the plane SH-wave TTF through a laterally homogeneous medium at vertical incidence inadequately models observed amplifications at most sites for both slowness estimates, obtained via downhole measurements and the spectral analysis of surface waves. Strategies to improve the predictions can be separated into two broad categories: improving the measurement of soil properties and improving the theory that maps the 1D soil profile onto spectral amplification. Using an example site where the 1D plane SH-wave formulation poorly predicts the ETF, we find a more satisfactory fit to the ETF by modeling the full wavefield and incorporating spatially correlated variability of the seismic properties. We conclude that our ability to model the observed site response transfer function is limited largely by the assumptions of the theoretical formulation rather than the uncertainty of the soil property estimates.

Ecological optimality in water-limited natural soil-vegetation systems. II - Tests and applications

NASA Technical Reports Server (NTRS)

Eagleson, P. S.; Tellers, T. E.

1982-01-01

The long-term optimal climatic climax soil-vegetation system is defined for several climates according to previous hypotheses in terms of two free parameters, effective porosity and plant water use coefficient. The free parameters are chosen by matching the predicted and observed average annual water yield. The resulting climax soil and vegetation properties are tested by comparison with independent observations of canopy density and average annual surface runoff. The climax properties are shown also to satisfy a previous hypothesis for short-term optimization of canopy density and water use coefficient. Using these hypotheses, a relationship between average evapotranspiration and optimum vegetation canopy density is derived and is compared with additional field observations. An algorithm is suggested by which the climax soil and vegetation properties can be calculated given only the climate parameters and the soil effective porosity. Sensitivity of the climax properties to the effective porosity is explored.

Predicting the Spectral Effects of Soils on Concentrating Photovoltaic Systems

DOE PAGES

Burton, Patrick D.; King, Bruce Hardison; Riley, Daniel M.

2014-12-15

The soiling losses on high concentrating photovoltaic (HCPV) systems may be influenced by the spectral properties of accumulated soil. We predicted the response of an isotype cell to changes in spectral content and reduction in transmission due to soiling using measured UV/vis transmittance through soil films. Artificial soil test blends deposited on glass coupons were used to supply the transmission data, which was then used to calculate the effect on model spectra. Moreover, the wavelength transparency of the test soil was varied by incorporating red and yellow mineral pigments into graded sand. The more spectrally responsive (yellow) soils were predictedmore » to alter the current balance between the top and middle subcells throughout a range of air masses corresponding to daily and seasonal variation.« less

Evaluating the performance of coupled snow-soil models in SURFEXv8 to simulate the permafrost thermal regime at a high Arctic site

NASA Astrophysics Data System (ADS)

Barrere, Mathieu; Domine, Florent; Decharme, Bertrand; Morin, Samuel; Vionnet, Vincent; Lafaysse, Matthieu

2017-09-01

Climate change projections still suffer from a limited representation of the permafrost-carbon feedback. Predicting the response of permafrost temperature to climate change requires accurate simulations of Arctic snow and soil properties. This study assesses the capacity of the coupled land surface and snow models ISBA-Crocus and ISBA-ES to simulate snow and soil properties at Bylot Island, a high Arctic site. Field measurements complemented with ERA-Interim reanalyses were used to drive the models and to evaluate simulation outputs. Snow height, density, temperature, thermal conductivity and thermal insulance are examined to determine the critical variables involved in the soil and snow thermal regime. Simulated soil properties are compared to measurements of thermal conductivity, temperature and water content. The simulated snow density profiles are unrealistic, which is most likely caused by the lack of representation in snow models of the upward water vapor fluxes generated by the strong temperature gradients within the snowpack. The resulting vertical profiles of thermal conductivity are inverted compared to observations, with high simulated values at the bottom of the snowpack. Still, ISBA-Crocus manages to successfully simulate the soil temperature in winter. Results are satisfactory in summer, but the temperature of the top soil could be better reproduced by adequately representing surface organic layers, i.e., mosses and litter, and in particular their water retention capacity. Transition periods (soil freezing and thawing) are the least well reproduced because the high basal snow thermal conductivity induces an excessively rapid heat transfer between the soil and the snow in simulations. Hence, global climate models should carefully consider Arctic snow thermal properties, and especially the thermal conductivity of the basal snow layer, to perform accurate predictions of the permafrost evolution under climate change.

Spatial prediction of near surface soil water retention functions using hydrogeophysics and empirical orthogonal functions

NASA Astrophysics Data System (ADS)

Gibson, Justin; Franz, Trenton E.

2018-06-01

The hydrological community often turns to widely available spatial datasets such as the NRCS Soil Survey Geographic database (SSURGO) to characterize the spatial variability of soil properties. When used to spatially characterize and parameterize watershed models, this has served as a reasonable first approximation when lacking localized or incomplete soil data. Within agriculture, soil data has been left relatively coarse when compared to numerous other data sources measured. This is because localized soil sampling is both expensive and time intense, thus a need exists in better connecting spatial datasets with ground observations. Given that hydrogeophysics is data-dense, rapid, non-invasive, and relatively easy to adopt, it is a promising technique to help dovetail localized soil sampling with spatially exhaustive datasets. In this work, we utilize two common near surface geophysical methods, cosmic-ray neutron probe and electromagnetic induction, to identify temporally stable spatial patterns of measured geophysical properties in three 65 ha agricultural fields in western Nebraska. This is achieved by repeat geophysical observations of the same study area across a range of wet to dry field conditions in order to evaluate with an empirical orthogonal function. Shallow cores were then extracted within each identified zone and water retention functions were generated in the laboratory. Using EOF patterns as a covariate, we quantify the predictive skill of estimating soil hydraulic properties in areas without measurement using a bootstrap validation analysis. Results indicate that sampling locations informed via repeat hydrogeophysical surveys, required only five cores to reduce the cross-validation root mean squared error by an average of 64% as compared to soil parameters predicted by a commonly used benchmark, SSURGO and ROSETTA. The reduction to five strategically located samples within the 65 ha fields reduces sampling efforts by up to ∼90% as compared to the common practice of soil grid sampling every 1 ha.

Quantifying the Effects of Biofilm on the Hydraulic Properties of Unsaturated Soils

NASA Astrophysics Data System (ADS)

Volk, E.; Iden, S.; Furman, A.; Durner, W.; Rosenzweig, R.

2017-12-01

Quantifying the effects of biofilms on hydraulic properties of unsaturated soils is necessary for predicting water and solute flow in soil with extensive microbial presence. This can be relevant to bioremediation processes, soil aquifer treatment and effluent irrigation. Previous works showed a reduction in the hydraulic conductivity and an increase in water content due to the addition of biofilm analogue materials. The objective of this research is to quantify soil hydraulic properties of unsaturated soil (water retention and hydraulic conductivity) using real soil biofilm. In this work, Hamra soil was incubated with Luria Broth (LB) and biofilm-producing bacteria (Pseudomonas Putida F1). Hydraulic conductivity and water retention were measured by the evaporation method, Dewpoint method and a constant head permeameter. Biofilm was quantified using viable counts and the deficit of TOC. The results show that the presence of biofilms increases soil retention in the `dry' range of the curve and reduces the hydraulic conductivity (see figure). This research shows that biofilms may have a non-negligible effect on flow and transport in unsaturated soils. These findings contribute to modeling water flow in biofilm amended soil.

Persistence of soil organic matter as an ecosystem property.

PubMed

Schmidt, Michael W I; Torn, Margaret S; Abiven, Samuel; Dittmar, Thorsten; Guggenberger, Georg; Janssens, Ivan A; Kleber, Markus; Kögel-Knabner, Ingrid; Lehmann, Johannes; Manning, David A C; Nannipieri, Paolo; Rasse, Daniel P; Weiner, Steve; Trumbore, Susan E

2011-10-05

Globally, soil organic matter (SOM) contains more than three times as much carbon as either the atmosphere or terrestrial vegetation. Yet it remains largely unknown why some SOM persists for millennia whereas other SOM decomposes readily--and this limits our ability to predict how soils will respond to climate change. Recent analytical and experimental advances have demonstrated that molecular structure alone does not control SOM stability: in fact, environmental and biological controls predominate. Here we propose ways to include this understanding in a new generation of experiments and soil carbon models, thereby improving predictions of the SOM response to global warming.

Persistence of soil organic matter as an ecosystem property

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schmidt, M.W.; Torn, M. S.; Abiven, S.

2011-08-15

Globally, soil organic matter (SOM) contains more than three times as much carbon as either the atmosphere or terrestrial vegetation. Yet it remains largely unknown why some SOM persists for millennia whereas other SOM decomposes readily—and this limits our ability to predict how soils will respond to climate change. Recent analytical and experimental advances have demonstrated that molecular structure alone does not control SOM stability: in fact, environmental and biological controls predominate. Here we propose ways to include this understanding in a new generation of experiments and soil carbon models, thereby improving predictions of the SOM response to global warming.

Effects of soil spatial variability at the hillslope and catchment scales on characteristics of rainfall-induced landslides

NASA Astrophysics Data System (ADS)

Fan, Linfeng; Lehmann, Peter; Or, Dani

2016-03-01

Spatial variations in soil properties affect key hydrological processes, yet their role in soil mechanical response to hydro-mechanical loading is rarely considered. This study aims to fill this gap by systematically quantifying effects of spatial variations in soil type and initial water content on rapid rainfall-induced shallow landslide predictions at the hillslope- and catchment-scales. We employed a physically-based landslide triggering model that considers mechanical interactions among soil columns governed by strength thresholds. At the hillslope scale, we found that the emergence of weak regions induced by spatial variations of soil type and initial water content resulted in early triggering of landslides with smaller volumes of released mass relative to a homogeneous slope. At the catchment scale, initial water content was linked to a topographic wetness index, whereas soil type varied deterministically with soil depth considering spatially correlated stochastic components. Results indicate that a strong spatial organization of initial water content delays landslide triggering, whereas spatially linked soil type with soil depth promoted landslide initiation. Increasing the standard deviation and correlation length of the stochastic component of soil type increases landslide volume and hastens onset of landslides. The study illustrates that for similar external boundary conditions and mean soil properties, landslide characteristics vary significantly with soil variability, hence it must be considered for improved landslide model predictions.

A Moisture Function of Soil Heterotrophic Respiration Derived from Pore-scale Mechanisms

NASA Astrophysics Data System (ADS)

Yan, Z.; Todd-Brown, K. E.; Bond-Lamberty, B. P.; Bailey, V.; Liu, C.

2017-12-01

Soil heterotrophic respiration (HR) is an important process controlling carbon (C) flux, but its response to changes in soil water content (θ) is poorly understood. Earth system models (ESMs) use empirical moisture functions developed from specific sites to describe the HR-θ relationship in soils, introducing significant uncertainty. Generalized models derived from mechanisms that control substrate availability and microbial respiration are thus urgently needed. Here we derive, present, and test a novel moisture function fp developed from pore-scale mechanisms. This fp encapsulates primary physicochemical and biological processes controlling HR response to moisture variation in soils. We tested fp against a wide range of published data for different soil types, and found that fp reliably predicted diverse HR- relationships. The mathematical relationship between the parameters in fp and macroscopic soil properties such as porosity and organic C content was also established, enabling to estimate fp using soil properties. Compared with empirical moisture functions used in ESMs, this derived fp could reduce uncertainty in predicting the response of soil organic C stock to climate changes. In addition, this work is one of the first studies to upscale a mechanistic soil HR model based on pore-scale processes, thus linking the pore-scale mechanisms with macroscale observations.

Control of embankment settlement field verification on PCPT prediction methods.

DOT National Transportation Integrated Search

2011-07-01

Piezocone penetration tests (PCPT) have been widely used by geotechnical engineers for subsurface investigation and evaluation of different soil properties such as strength and deformation characteristics of the soil. This report focuses on the verif...

Control of embankment settlement field verification on PCPT prediction methods.

DOT National Transportation Integrated Search

2011-07-01

Piezocone penetration tests (PCPT) have been widely used by geotechnical engineers for subsurface : investigation and evaluation of different soil properties such as strength and deformation characteristics of the : soil. This report focuses on the v...

Indirect estimation of the Convective Lognormal Transfer function model parameters for describing solute transport in unsaturated and undisturbed soil.

PubMed

Mohammadi, Mohammad Hossein; Vanclooster, Marnik

2012-05-01

Solute transport in partially saturated soils is largely affected by fluid velocity distribution and pore size distribution within the solute transport domain. Hence, it is possible to describe the solute transport process in terms of the pore size distribution of the soil, and indirectly in terms of the soil hydraulic properties. In this paper, we present a conceptual approach that allows predicting the parameters of the Convective Lognormal Transfer model from knowledge of soil moisture and the Soil Moisture Characteristic (SMC), parameterized by means of the closed-form model of Kosugi (1996). It is assumed that in partially saturated conditions, the air filled pore volume act as an inert solid phase, allowing the use of the Arya et al. (1999) pragmatic approach to estimate solute travel time statistics from the saturation degree and SMC parameters. The approach is evaluated using a set of partially saturated transport experiments as presented by Mohammadi and Vanclooster (2011). Experimental results showed that the mean solute travel time, μ(t), increases proportionally with the depth (travel distance) and decreases with flow rate. The variance of solute travel time σ²(t) first decreases with flow rate up to 0.4-0.6 Ks and subsequently increases. For all tested BTCs predicted solute transport with μ(t) estimated from the conceptual model performed much better as compared to predictions with μ(t) and σ²(t) estimated from calibration of solute transport at shallow soil depths. The use of μ(t) estimated from the conceptual model therefore increases the robustness of the CLT model in predicting solute transport in heterogeneous soils at larger depths. In view of the fact that reasonable indirect estimates of the SMC can be made from basic soil properties using pedotransfer functions, the presented approach may be useful for predicting solute transport at field or watershed scales. Copyright © 2012 Elsevier B.V. All rights reserved.

Indirect estimation of the Convective Lognormal Transfer function model parameters for describing solute transport in unsaturated and undisturbed soil

NASA Astrophysics Data System (ADS)

Mohammadi, Mohammad Hossein; Vanclooster, Marnik

2012-05-01

Solute transport in partially saturated soils is largely affected by fluid velocity distribution and pore size distribution within the solute transport domain. Hence, it is possible to describe the solute transport process in terms of the pore size distribution of the soil, and indirectly in terms of the soil hydraulic properties. In this paper, we present a conceptual approach that allows predicting the parameters of the Convective Lognormal Transfer model from knowledge of soil moisture and the Soil Moisture Characteristic (SMC), parameterized by means of the closed-form model of Kosugi (1996). It is assumed that in partially saturated conditions, the air filled pore volume act as an inert solid phase, allowing the use of the Arya et al. (1999) pragmatic approach to estimate solute travel time statistics from the saturation degree and SMC parameters. The approach is evaluated using a set of partially saturated transport experiments as presented by Mohammadi and Vanclooster (2011). Experimental results showed that the mean solute travel time, μt, increases proportionally with the depth (travel distance) and decreases with flow rate. The variance of solute travel time σ2t first decreases with flow rate up to 0.4-0.6 Ks and subsequently increases. For all tested BTCs predicted solute transport with μt estimated from the conceptual model performed much better as compared to predictions with μt and σ2t estimated from calibration of solute transport at shallow soil depths. The use of μt estimated from the conceptual model therefore increases the robustness of the CLT model in predicting solute transport in heterogeneous soils at larger depths. In view of the fact that reasonable indirect estimates of the SMC can be made from basic soil properties using pedotransfer functions, the presented approach may be useful for predicting solute transport at field or watershed scales.

Microbial community composition explains soil respiration responses to changing carbon inputs along an Andes-to-Amazon elevation gradient

PubMed Central

Whitaker, Jeanette; Ostle, Nicholas; Nottingham, Andrew T; Ccahuana, Adan; Salinas, Norma; Bardgett, Richard D; Meir, Patrick; McNamara, Niall P; Austin, Amy

2014-01-01

1. The Andes are predicted to warm by 3–5 °C this century with the potential to alter the processes regulating carbon (C) cycling in these tropical forest soils. This rapid warming is expected to stimulate soil microbial respiration and change plant species distributions, thereby affecting the quantity and quality of C inputs to the soil and influencing the quantity of soil-derived CO2 released to the atmosphere. 2. We studied tropical lowland, premontane and montane forest soils taken from along a 3200-m elevation gradient located in south-east Andean Peru. We determined how soil microbial communities and abiotic soil properties differed with elevation. We then examined how these differences in microbial composition and soil abiotic properties affected soil C-cycling processes, by amending soils with C substrates varying in complexity and measuring soil heterotrophic respiration (RH). 3. Our results show that there were consistent patterns of change in soil biotic and abiotic properties with elevation. Microbial biomass and the abundance of fungi relative to bacteria increased significantly with elevation, and these differences in microbial community composition were strongly correlated with greater soil C content and C:N (nitrogen) ratios. We also found that RH increased with added C substrate quality and quantity and was positively related to microbial biomass and fungal abundance. 4. Statistical modelling revealed that RH responses to changing C inputs were best predicted by soil pH and microbial community composition, with the abundance of fungi relative to bacteria, and abundance of gram-positive relative to gram-negative bacteria explaining much of the model variance. 5. Synthesis. Our results show that the relative abundance of microbial functional groups is an important determinant of RH responses to changing C inputs along an extensive tropical elevation gradient in Andean Peru. Although we do not make an experimental test of the effects of climate change on soil, these results challenge the assumption that different soil microbial communities will be ‘functionally equivalent’ as climate change progresses, and they emphasize the need for better ecological metrics of soil microbial communities to help predict C cycle responses to climate change in tropical biomes. PMID:25520527

Quantitative assessment on soil enzyme activities of heavy metal contaminated soils with various soil properties.

PubMed

Xian, Yu; Wang, Meie; Chen, Weiping

2015-11-01

Soil enzyme activities are greatly influenced by soil properties and could be significant indicators of heavy metal toxicity in soil for bioavailability assessment. Two groups of experiments were conducted to determine the joint effects of heavy metals and soil properties on soil enzyme activities. Results showed that arylsulfatase was the most sensitive soil enzyme and could be used as an indicator to study the enzymatic toxicity of heavy metals under various soil properties. Soil organic matter (SOM) was the dominant factor affecting the activity of arylsulfatase in soil. A quantitative model was derived to predict the changes of arylsulfatase activity with SOM content. When the soil organic matter content was less than the critical point A (1.05% in our study), the arylsulfatase activity dropped rapidly. When the soil organic matter content was greater than the critical point A, the arylsulfatase activity gradually rose to higher levels showing that instead of harm the soil microbial activities were enhanced. The SOM content needs to be over the critical point B (2.42% in our study) to protect its microbial community from harm due to the severe Pb pollution (500mgkg(-1) in our study). The quantitative model revealed the pattern of variation of enzymatic toxicity due to heavy metals under various SOM contents. The applicability of the model under wider soil properties need to be tested. The model however may provide a methodological basis for ecological risk assessment of heavy metals in soil. Copyright © 2014 Elsevier Ltd. All rights reserved.

Ecological risk assessment on heavy metals in soils: Use of soil diffuse reflectance mid-infrared Fourier-transform spectroscopy

PubMed Central

Wang, Cheng; Li, Wei; Guo, Mingxing; Ji, Junfeng

2017-01-01

The bioavailability of heavy metals in soil is controlled by their concentrations and soil properties. Diffuse reflectance mid-infrared Fourier-transform spectroscopy (DRIFTS) is capable of detecting specific organic and inorganic bonds in metal complexes and minerals and therefore, has been employed to predict soil composition and heavy metal contents. The present study explored the potential of DRIFTS for estimating soil heavy metal bioavailability. Soil and corresponding wheat grain samples from the Yangtze River Delta region were analyzed by DRIFTS and chemical methods. Statistical regression analyses were conducted to correlate the soil spectral information to the concentrations of Cd, Cr, Cu, Zn, Pb, Ni, Hg and Fe in wheat grains. The principal components in the spectra influencing soil heavy metal bioavailability were identified and used in prediction model construction. The established soil DRIFTS-based prediction models were applied to estimate the heavy metal concentrations in wheat grains in the mid-Yangtze River Delta area. The predicted heavy metal concentrations of wheat grain were highly consistent with the measured levels by chemical analysis, showing a significant correlation (r2 > 0.72) with acceptable root mean square error RMSE. In conclusion, DRIFTS is a promising technique for assessing the bioavailability of soil heavy metals and related ecological risk. PMID:28198802

Litter decay controlled by temperature, not soil properties, affecting future soil carbon.

PubMed

Gregorich, Edward G; Janzen, Henry; Ellert, Benjamin H; Helgason, Bobbi L; Qian, Budong; Zebarth, Bernie J; Angers, Denis A; Beyaert, Ronald P; Drury, Craig F; Duguid, Scott D; May, William E; McConkey, Brian G; Dyck, Miles F

2017-04-01

Widespread global changes, including rising atmospheric CO 2 concentrations, climate warming and loss of biodiversity, are predicted for this century; all of these will affect terrestrial ecosystem processes like plant litter decomposition. Conversely, increased plant litter decomposition can have potential carbon-cycle feedbacks on atmospheric CO 2 levels, climate warming and biodiversity. But predicting litter decomposition is difficult because of many interacting factors related to the chemical, physical and biological properties of soil, as well as to climate and agricultural management practices. We applied 13 C-labelled plant litter to soil at ten sites spanning a 3500-km transect across the agricultural regions of Canada and measured its decomposition over five years. Despite large differences in soil type and climatic conditions, we found that the kinetics of litter decomposition were similar once the effect of temperature had been removed, indicating no measurable effect of soil properties. A two-pool exponential decay model expressing undecomposed carbon simply as a function of thermal time accurately described kinetics of decomposition. (R 2  = 0.94; RMSE = 0.0508). Soil properties such as texture, cation exchange capacity, pH and moisture, although very different among sites, had minimal discernible influence on decomposition kinetics. Using this kinetic model under different climate change scenarios, we projected that the time required to decompose 50% of the litter (i.e. the labile fractions) would be reduced by 1-4 months, whereas time required to decompose 90% of the litter (including recalcitrant fractions) would be reduced by 1 year in cooler sites to as much as 2 years in warmer sites. These findings confirm quantitatively the sensitivity of litter decomposition to temperature increases and demonstrate how climate change may constrain future soil carbon storage, an effect apparently not influenced by soil properties. © 2016 Her Majesty the Queen in Right of Canada. Global Change Biology. Published by 2016 John Wiley & Sons Ltd.

Application of the soil perturbation index to evaluate created and restored wetlands

Treesearch

Rebecca Smith Maul; Marjorie M. Holland

2000-01-01

Biogeochemical properties of wetlands have recently been investigated to assess recovery of wetland ecosys-tems following human alteration. Analyses of soil samples have shown that the natural regeneration of timber-harvested wetlands exhibits predictable trends for soil organic matter, total organic carbon, total Kjeldahl nitrogen, and total phosphorus. Incorporating...

[Prediction of 137Cs behaviour in the soil-plant system in the territory of Semipalatinsk test site].

PubMed

Spiridonov, S I; Mukusheva, M K; Gontarenko, I A; Fesenko, S V; Baranov, S A

2005-01-01

A mathematical model of 137Cs behaviour in the soil-plant system is presented. The model has been parameterized for the area adjacent to the testing area Ground Zero of the Semipalatinsk Test Site. The model describes the main processes responsible for the changes in 137Cs content in the soil solution and, thereby, dynamics of the radionuclide uptake by vegetation. The results are taken from predictive and retrospective calculations that reflect the dynamics of 137Cs distribution by species in soil after nuclear explosions. The importance of factors governing 137Cs accumulation in plants within the STS area is assessed. The analysis of sensitivity of the output model variable to changes in its parameters revealed that the key soil properties significantly influence the results of prediction of 137Cs content in plants.

Attributing spatial and temporal changes in soil C in the UK to environmental drivers

NASA Astrophysics Data System (ADS)

Thomas, Amy; Cosby, Bernard; Quin, Sam; Henrys, Pete; Robinson, David; Emmett, Bridget

2015-04-01

The largest terrestrial pool of carbon is found in soils. Understanding how soil C responds to drivers of change (land use and management, atmospheric deposition, climate change) and how these responses are modified by inherent soil properties is crucial if we are to manage soils more sustainably in the future. Here we attempt to attribute spatial and temporal changes in UK soil C to environmental drivers using data from the UK Countryside Survey (CS), a national soil survey across England, Scotland and Wales repeated in 1978, 1998 and 2007. A mixed model approach was used to model soil C concentration (g C kg-1) and density (t C ha-1) and their absolute changes for the time periods 1978-1998, 1998-2007 and 1978-2007 across the CS sites using a variety of explanatory variables: soil (parent material, pH, moisture, Olsen-P, Shannon Diversity Index); atmospheric deposition (nitrogen and sulphur); climate (growing degree days and rain); and land use (aggregate vegetation class). Spatially, prediction of soil C concentration was good; soil moisture, pH, vegetation class and dominant grain size were all significant predictors. Field capacity also appeared to be important; however this data was only collected for a fraction of sites. N% was also strongly related to soil C concentration and density, as would be expected due to coupling of C and N in soil OM pools. Although N may drive soil C through impact on plant productivity, this cannot be separated from correlated C and N losses with OM decomposition, and hence N was not included as a driver for modelling. Predictive power for C density is not as strong as for concentration, which may reflect nonlinear relationships not represented by the modelling approach. Temporally, change in soil C is more difficult to explain, and model predictive power was lower. Change in soil pH was important in explaining change in C concentration and density, along with change in atmospheric deposition; decrease in deposition and associated soil acidity (increase in pH) was associated with a decrease in soil C concentration and density. Change in soil moisture or rainfall was also important. Inherent soil and site properties such as soil texture, vegetation class and parent material appeared to contribute most to the prediction of soil C change through modulation of the relationship between change in soil C and change in pH. Including anthropogenic and natural drivers in models of soil C stocks and changes in the UK enables assessment of the relative importance of each across the UK CS sites, however interactions among the drivers are more difficult to disentangle. Given the statistical significance of a number of drivers and soil variables in predicting soil C stocks and changes in the UK, it is important that these continue to be measured to allow better model development and more reliable predictions of future soil C conditions.

Field Scale Spatial Modelling of Surface Soil Quality Attributes in Controlled Traffic Farming

NASA Astrophysics Data System (ADS)

Guenette, Kris; Hernandez-Ramirez, Guillermo

2017-04-01

The employment of controlled traffic farming (CTF) can yield improvements to soil quality attributes through the confinement of equipment traffic to tramlines with the field. There is a need to quantify and explain the spatial heterogeneity of soil quality attributes affected by CTF to further improve our understanding and modelling ability of field scale soil dynamics. Soil properties such as available nitrogen (AN), pH, soil total nitrogen (STN), soil organic carbon (SOC), bulk density, macroporosity, soil quality S-Index, plant available water capacity (PAWC) and unsaturated hydraulic conductivity (Km) were analysed and compared among trafficked and un-trafficked areas. We contrasted standard geostatistical methods such as ordinary kriging (OK) and covariate kriging (COK) as well as the hybrid method of regression kriging (ROK) to predict the spatial distribution of soil properties across two annual cropland sites actively employing CTF in Alberta, Canada. Field scale variability was quantified more accurately through the inclusion of covariates; however, the use of ROK was shown to improve model accuracy despite the regression model composition limiting the robustness of the ROK method. The exclusion of traffic from the un-trafficked areas displayed significant improvements to bulk density, macroporosity and Km while subsequently enhancing AN, STN and SOC. The ability of the regression models and the ROK method to account for spatial trends led to the highest goodness-of-fit and lowest error achieved for the soil physical properties, as the rigid traffic regime of CTF altered their spatial distribution at the field scale. Conversely, the COK method produced the most optimal predictions for the soil nutrient properties and Km. The use of terrain covariates derived from light ranging and detection (LiDAR), such as of elevation and topographic position index (TPI), yielded the best models in the COK method at the field scale.

Spatial prediction of near surface soil water retention functions using hydrogeophysics

NASA Astrophysics Data System (ADS)

Gibson, J. P.; Franz, T. E.

2017-12-01

The hydrological community often turns to widely available spatial datasets such as SSURGO to characterize the spatial variability of soil across a landscape of interest. This has served as a reasonable first approximation when lacking localized soil data. However, previous work has shown that information loss within land surface models primarily stems from parameterization. Localized soil sampling is both expensive and time intense, and thus a need exists in connecting spatial datasets with ground observations. Given that hydrogeophysics is data-dense, rapid, and relatively easy to adopt, it is a promising technique to help dovetail localized soil sampling with larger spatial datasets. In this work, we utilize 2 geophysical techniques; cosmic ray neutron probe and electromagnetic induction, to identify temporally stable soil moisture patterns. This is achieved by measuring numerous times over a range of wet to dry field conditions in order to apply an empirical orthogonal function. We then present measured water retention functions of shallow cores extracted within each temporally stable zone. Lastly, we use soil moisture patterns as a covariate to predict soil hydraulic properties in areas without measurement and validate using a leave-one-out cross validation analysis. Using these approaches to better constrain soil hydraulic property variability, we speculate that further research can better estimate hydrologic fluxes in areas of interest.

Hydraulic redistribution of soil water in two old-growth coniferous forests: quantifying patterns and controls.

Treesearch

J.M. Warren; F.C. Meinzer; J.R. Brooks; J.-C. Domec; R. Coulombe

2006-01-01

We incorporated soil/plant biophysical properties into a simple model to predict seasonal trajectories of hydraulic redistribution (HR). We measured soil water content, water potential root conductivity, and climate across multiple years in two old-growth coniferous forests. The HR variability within sites (0 to 0.5 mm/d) was linked to spatial patterns of roots, soil...

Cokriging of Electromagnetic Induction Soil Electrical Conductivity Measurements and Soil Textural Properties to Demarcate Sub-field Management Zones for Precision Irrigation.

NASA Astrophysics Data System (ADS)

Ding, R.; Cruz, L.; Whitney, J.; Telenko, D.; Oware, E. K.

2017-12-01

There is the growing need for the development of efficient irrigation management practices due to increasing irrigation water scarcity as a result of growing population and changing climate. Soil texture primarily controls the water-holding capacity of soils, which determines the amount of irrigation water that will be available to the plant. However, while there are significant variabilities in the textural properties of the soil across a field, conventional irrigation practices ignore the underlying variability in the soil properties, resulting in over- or under-irrigation. Over-irrigation leaches plant nutrients beyond the root-zone leading to fertilizer, energy, and water wastages with dire environmental consequences. Under-irrigation, in contrast, causes water stress of the plant, thereby reducing plant quality and yield. The goal of this project is to leverage soil textural map of a field to create water management zones (MZs) to guide site-specific precision irrigation. There is increasing application of electromagnetic induction methods to rapidly and inexpensively map spatially continuous soil properties in terms of the apparent electrical conductivity (ECa) of the soil. ECa is a measure of the bulk soil properties, including soil texture, moisture, salinity, and cation exchange capacity, making an ECa map a pseudo-soil map. Data for the project were collected from a farm site at Eden, NY. The objective is to leverage high-resolution ECa map to predict spatially dense soil textural properties from limited measurements of soil texture. Thus, after performing ECa mapping, we conducted particle-size analysis of soil samples to determine the textural properties of soils at selected locations across the field. We cokriged the high-resolution ECa measurements with the sparse soil textural data to estimate a soil texture map for the field. We conducted irrigation experiments at selected locations to calibrate representative water-holding capacities of each estimated soil textural unit. Estimated soil units with similar water-holding characteristics were merged to create sub-field water MZs to guide precision irrigation of each MZ, instructed by each MZ's calibrated water-holding properties.

Plant attributes explain the distribution of soil microbial communities in two contrasting regions of the globe.

PubMed

Delgado-Baquerizo, Manuel; Fry, Ellen L; Eldridge, David J; de Vries, Franciska T; Manning, Peter; Hamonts, Kelly; Kattge, Jens; Boenisch, Gerhard; Singh, Brajesh K; Bardgett, Richard D

2018-04-19

We lack strong empirical evidence for links between plant attributes (plant community attributes and functional traits) and the distribution of soil microbial communities at large spatial scales. Using datasets from two contrasting regions and ecosystem types in Australia and England, we report that aboveground plant community attributes, such as diversity (species richness) and cover, and functional traits can predict a unique portion of the variation in the diversity (number of phylotypes) and community composition of soil bacteria and fungi that cannot be explained by soil abiotic properties and climate. We further identify the relative importance and evaluate the potential direct and indirect effects of climate, soil properties and plant attributes in regulating the diversity and community composition of soil microbial communities. Finally, we deliver a list of examples of common taxa from Australia and England that are strongly related to specific plant traits, such as specific leaf area index, leaf nitrogen and nitrogen fixation. Together, our work provides new evidence that plant attributes, especially plant functional traits, can predict the distribution of soil microbial communities at the regional scale and across two hemispheres. © 2018 The Authors. New Phytologist © 2018 New Phytologist Trust.

A GIS based method for soil mapping in Sardinia, Italy: a geomatic approach.

PubMed

Vacca, A; Loddo, S; Melis, M T; Funedda, A; Puddu, R; Verona, M; Fanni, S; Fantola, F; Madrau, S; Marrone, V A; Serra, G; Tore, C; Manca, D; Pasci, S; Puddu, M R; Schirru, P

2014-06-01

A new project was recently initiated for the realization of the "Land Unit and Soil Capability Map of Sardinia" at a scale of 1:50,000 to support land use planning. In this study, we outline the general structure of the project and the methods used in the activities that have been thus far conducted. A GIS approach was used. We used the soil-landscape paradigm for the prediction of soil classes and their spatial distribution or the prediction of soil properties based on landscape features. The work is divided into two main phases. In the first phase, the available digital data on land cover, geology and topography were processed and classified according to their influence on weathering processes and soil properties. The methods used in the interpretation are based on consolidated and generalized knowledge about the influence of geology, topography and land cover on soil properties. The existing soil data (areal and point data) were collected, reviewed, validated and standardized according to international and national guidelines. Point data considered to be usable were input into a specific database created for the project. Using expert interpretation, all digital data were merged to produce a first draft of the Land Unit Map. During the second phase, this map will be implemented with the existing soil data and verified in the field if also needed with new soil data collection, and the final Land Unit Map will be produced. The Land Unit and Soil Capability Map will be produced by classifying the land units using a reference matching table of land capability classes created for this project. Copyright © 2013 Elsevier Ltd. All rights reserved.

Relationship between sugarcane rust severity and soil properties in louisiana.

PubMed

Johnson, Richard M; Grisham, Michael P; Richard, Edward P

2007-06-01

ABSTRACT The extent of spatial and temporal variability of sugarcane rust (Puccinia melanocephala) infestation was related to variation in soil properties in five commercial fields of sugarcane (interspecific hybrids of Saccharum spp., cv. LCP 85-384) in southern Louisiana. Sugarcane fields were grid-soil sampled at several intensities and rust ratings were collected at each point over 6 to 7 weeks. Soil properties exhibited significant variability (coefficients of variation = 9 to 70.1%) and were spatially correlated in 39 of 40 cases with a range of spatial correlation varying from 39 to 201 m. Rust ratings were spatially correlated in 32 of 33 cases, with a range varying from 29 to 241 m. Rust ratings were correlated with several soil properties, most notably soil phosphorus (r = 0.40 to 0.81) and soil sulfur (r = 0.36 to 0.68). Multiple linear regression analysis resulted in coefficients of determination that ranged from 0.22 to 0.73, and discriminant analysis further improved the overall predictive ability of rust models. Finally, contour plots of soil properties and rust levels clearly suggested a link between these two parameters. These combined data suggest that sugarcane growers that apply fertilizer in excess of plant requirements will increase the incidence and severity of rust infestations in their fields.

Evidence that Soil Properties and Organic Coating Drive the Phytoavailability of Cerium Oxide Nanoparticles.

PubMed

Layet, Clément; Auffan, Mélanie; Santaella, Catherine; Chevassus-Rosset, Claire; Montes, Mélanie; Ortet, Philippe; Barakat, Mohamed; Collin, Blanche; Legros, Samuel; Bravin, Matthieu N; Angeletti, Bernard; Kieffer, Isabelle; Proux, Olivier; Hazemann, Jean-Louis; Doelsch, Emmanuel

2017-09-05

The ISO-standardized RHIZOtest is used here for the first time to decipher how plant species, soil properties, and physical-chemical properties of the nanoparticles and their transformation regulate the phytoavailability of nanoparticles. Two plants, tomato and fescue, were exposed to two soils with contrasted properties: a sandy soil poor in organic matter and a clay soil rich in organic matter, both contaminated with 1, 15, and 50 mg·kg -1 of dissolved Ce 2 (SO 4 ) 3 , bare and citrate-coated CeO 2 nanoparticles. All the results demonstrate that two antagonistic soil properties controlled Ce uptake. The clay fraction enhanced the retention of the CeO 2 nanoparticles and hence reduced Ce uptake, whereas the organic matter content enhanced Ce uptake. Moreover, in the soil poor in organic matter, the organic citrate coating significantly enhanced the phytoavailability of the cerium by forming smaller aggregates thereby facilitating the transport of nanoparticles to the roots. By getting rid of the dissimilarities between the root systems of the different plants and the normalizing the surfaces exposed to nanoparticles, the RHIZOtest demonstrated that the species of plant did not drive the phytoavailability, and provided evidence for soil-plant transfers at concentrations lower than those usually cited in the literature and closer to predicted environmental concentrations.

Biofilm effect on soil hydraulic properties: Experimental investigation using soil-grown real biofilm

NASA Astrophysics Data System (ADS)

Volk, Elazar; Iden, Sascha C.; Furman, Alex; Durner, Wolfgang; Rosenzweig, Ravid

2016-08-01

Understanding the influence of attached microbial biomass on water flow in variably saturated soils is crucial for many engineered flow systems. So far, the investigation of the effects of microbial biomass has been mainly limited to water-saturated systems. We have assessed the influence of biofilms on the soil hydraulic properties under variably saturated conditions. A sandy soil was incubated with Pseudomonas Putida and the hydraulic properties of the incubated soil were determined by a combination of methods. Our results show a stronger soil water retention in the inoculated soil as compared to the control. The increase in volumetric water content reaches approximately 0.015 cm3 cm-3 but is only moderately correlated with the carbon deficit, a proxy for biofilm quantity, and less with the cell viable counts. The presence of biofilm reduced the saturated hydraulic conductivity of the soil by up to one order of magnitude. Under unsaturated conditions, the hydraulic conductivity was only reduced by a factor of four. This means that relative water conductance in biofilm-affected soils is higher compared to the clean soil at low water contents, and that the unsaturated hydraulic conductivity curve of biofilm-affected soil cannot be predicted by simply scaling the saturated hydraulic conductivity. A flexible parameterization of the soil hydraulic functions accounting for capillary and noncapillary flow was needed to adequately describe the observed properties over the entire wetness range. More research is needed to address the exact flow mechanisms in biofilm-affected, unsaturated soil and how they are related to effective system properties.

Large-extent digital soil mapping approaches for total soil depth

NASA Astrophysics Data System (ADS)

Mulder, Titia; Lacoste, Marine; Saby, Nicolas P. A.; Arrouays, Dominique

2015-04-01

Total soil depth (SDt) plays a key role in supporting various ecosystem services and properties, including plant growth, water availability and carbon stocks. Therefore, predictive mapping of SDt has been included as one of the deliverables within the GlobalSoilMap project. In this work SDt was predicted for France following the directions of GlobalSoilMap, which requires modelling at 90m resolution. This first method, further referred to as DM, consisted of modelling the deterministic trend in SDt using data mining, followed by a bias correction and ordinary kriging of the residuals. Considering the total surface area of France, being about 540K km2, employed methods may need to be able dealing with large data sets. Therefore, a second method, multi-resolution kriging (MrK) for large datasets, was implemented. This method consisted of modelling the deterministic trend by a linear model, followed by interpolation of the residuals. For the two methods, the general trend was assumed to be explained by the biotic and abiotic environmental conditions, as described by the Soil-Landscape paradigm. The mapping accuracy was evaluated by an internal validation and its concordance with previous soil maps. In addition, the prediction interval for DM and the confidence interval for MrK were determined. Finally, the opportunities and limitations of both approaches were evaluated. The results showed consistency in mapped spatial patterns and a good prediction of the mean values. DM was better capable in predicting extreme values due to the bias correction. Also, DM was more powerful in capturing the deterministic trend than the linear model of the MrK approach. However, MrK was found to be more straightforward and flexible in delivering spatial explicit uncertainty measures. The validation indicated that DM was more accurate than MrK. Improvements for DM may be expected by predicting soil depth classes. MrK shows potential for modelling beyond the country level, at high resolution. Large-extent digital soil mapping approaches for SDt may be improved by (1) taking into account SDt observations which are censored and (2) using high-resolution biotic and abiotic environmental data. The latter may improve modelling the soil-landscape interactions influencing soil pedogenesis. Concluding, this work provided a robust and reproducible method (DM) for high-resolution soil property modelling, in accordance with the GlobalSoilMap requirements and an efficient alternative for large-extent digital soil mapping (MrK).

Recovering lost ground: effects of soil burn intensity on nutrients and ectomycorrhiza communities of ponderosa pine seedlings

Treesearch

Ariel D. Cowan; Jane E. Smith; Stephen A. Fitzgerald

2016-01-01

Fuel accumulation and climate shifts are predicted to increase the frequency of high-severity fires in ponderosa pine (Pinus ponderosa) forests of central Oregon. The combustion of fuels containing large downed wood can result in intense soil heating, alteration of soil properties, and mortality of microbes. Previous studies show ectomycorrhizal...

Improvement of shallow landslide prediction accuracy using soil parameterisation for a granite area in South Korea

NASA Astrophysics Data System (ADS)

Kim, M. S.; Onda, Y.; Kim, J. K.

2015-01-01

SHALSTAB model applied to shallow landslides induced by rainfall to evaluate soil properties related with the effect of soil depth for a granite area in Jinbu region, Republic of Korea. Soil depth measured by a knocking pole test and two soil parameters from direct shear test (a and b) as well as one soil parameters from a triaxial compression test (c) were collected to determine the input parameters for the model. Experimental soil data were used for the first simulation (Case I) and, soil data represented the effect of measured soil depth and average soil depth from soil data of Case I were used in the second (Case II) and third simulations (Case III), respectively. All simulations were analysed using receiver operating characteristic (ROC) analysis to determine the accuracy of prediction. ROC analysis results for first simulation showed the low ROC values under 0.75 may be due to the internal friction angle and particularly the cohesion value. Soil parameters calculated from a stochastic hydro-geomorphological model were applied to the SHALSTAB model. The accuracy of Case II and Case III using ROC analysis showed higher accuracy values rather than first simulation. Our results clearly demonstrate that the accuracy of shallow landslide prediction can be improved when soil parameters represented the effect of soil thickness.

Toward Linking Aboveground Vegetation Properties and Soil Microbial Communities Using Remote Sensing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamada, Yuki; Gilbert, Jack A.; Larsen, Peter E.

2014-04-01

Despite their vital role in terrestrial ecosystem function, the distributions and dynamics of soil microbial communities (SMCs) are poorly understood. Vegetation and soil properties are the primary factors that influence SMCs. This paper discusses the potential effectiveness of remote sensing science and technologies for mapping SMC biogeography by characterizing surface biophysical properties (e.g., plant traits and community composition) strongly correlated with SMCs. Using remotely sensed biophysical properties to predict SMC distributions is extremely challenging because of the intricate interactions between biotic and abiotic factors and between above- and belowground ecosystems. However, the integration of biophysical and soil remote sensing withmore » geospatial information about the e nvironment holds great promise for mapping SMC biogeography. Additional research needs invol ve microbial taxonomic definition, soil environmental complexity, and scaling strategies. The collaborative effort of experts from diverse disciplines is essential to linking terrestrial surface biosphere observations with subsurface microbial community distributions using remote sensing.« less

Toward Linking Aboveground Vegetation Properties and Soil Microbial Communities Using Remote Sensing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamada, Yuki; Gilbert, Jack A.; Larsen, Peter E.

2014-04-01

Despite their vital role in terrestrial ecosystem function, the distributions and dynamics of soil microbial communities (SMCs) are poorly understood. Vegetation and soil properties are the primary factors that influence SMCs. This paper discusses the potential effectiveness of remote sensing science and technologies for mapping SMC biogeography by characterizing surface biophysical properties (e.g., plant traits and community composition) strongly correlated with SMCs. Using remotely sensed biophysical properties to predict SMC distributions is extremely challenging because of the intricate interactions between biotic and abiotic factors and between above- and below-ground ecosystems. However, the integration of biophysical and soil remote sensing withmore » geospatial information about the environment holds great promise for mapping SMC biogeography. Additional research needs involve microbial taxonomic definition, soil environmental complexity, and scaling strategies. The collaborative effort of experts from diverse disciplines is essential to linking terrestrial surface biosphere observations with subsurface microbial community distributions using remote sensing.« less

Estimation of liquefaction-induced lateral spread from numerical modeling and its application

NASA Astrophysics Data System (ADS)

Meng, Xianhong

A noncoupled numerical procedure was developed using a scheme of pore water generation that causes shear modulus degradation and shear strength degradation resulting from earthquake cyclic motion. The designed Fast Lagrangian Analysis of Continua (FLAC) model procedure was tested using the liquefaction-induced lateral spread and ground response for Wildlife and Kobe sites. Sixteen well-documented case histories of lateral spread were reviewed and modeled using the modeling procedure. The dynamic residual strength ratios were back-calculated by matching the predicted displacement with the measured lateral spread, or with the displacement predicted by the Yound et al. model. Statistical analysis on the modeling results and soil properties show that most significant parameters governing the residual strength of the liquefied soil are the SPT blow count, fine content and soil particle size of the lateral spread layer. A regression equation was developed to express the residual strength values with these soil properties. Overall, this research demonstrated that a calibrated numerical model can predict the first order effectiveness of liquefaction-induced lateral spread using relatively simple parameters obtained from routine geotechnical investigation. In addition, the model can be used to plan a soil improvement program for cases where liquefaction remediation is needed. This allows the model to be used for design purposes at bridge approaches structured on liquefiable materials.

Variability in soil-water retention properties and implications for physics-based simulation of landslide early warning criteria

USGS Publications Warehouse

Thomas, Matthew A.; Mirus, Benjamin B.; Collins, Brian D.; Lu, Ning; Godt, Jonathan W.

2018-01-01

Rainfall-induced shallow landsliding is a persistent hazard to human life and property. Despite the observed connection between infiltration through the unsaturated zone and shallow landslide initiation, there is considerable uncertainty in how estimates of unsaturated soil-water retention properties affect slope stability assessment. This source of uncertainty is critical to evaluating the utility of physics-based hydrologic modeling as a tool for landslide early warning. We employ a numerical model of variably saturated groundwater flow parameterized with an ensemble of texture-, laboratory-, and field-based estimates of soil-water retention properties for an extensively monitored landslide-prone site in the San Francisco Bay Area, CA, USA. Simulations of soil-water content, pore-water pressure, and the resultant factor of safety show considerable variability across and within these different parameter estimation techniques. In particular, we demonstrate that with the same permeability structure imposed across all simulations, the variability in soil-water retention properties strongly influences predictions of positive pore-water pressure coincident with widespread shallow landsliding. We also find that the ensemble of soil-water retention properties imposes an order-of-magnitude and nearly two-fold variability in seasonal and event-scale landslide susceptibility, respectively. Despite the reduced factor of safety uncertainty during wet conditions, parameters that control the dry end of the soil-water retention function markedly impact the ability of a hydrologic model to capture soil-water content dynamics observed in the field. These results suggest that variability in soil-water retention properties should be considered for objective physics-based simulation of landslide early warning criteria.

Variability and scaling of hydraulic properties for 200 Area soils, Hanford Site

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khaleel, R.; Freeman, E.J.

Over the years, data have been obtained on soil hydraulic properties at the Hanford Site. Much of these data have been obtained as part of recent site characterization activities for the Environmental Restoration Program. The existing data on vadose zone soil properties are, however, fragmented and documented in reports that have not been formally reviewed and released. This study helps to identify, compile, and interpret all available data for the principal soil types in the 200 Areas plateau. Information on particle-size distribution, moisture retention, and saturated hydraulic conductivity (K{sub s}) is available for 183 samples from 12 sites in themore » 200 Areas. Data on moisture retention and K{sub s} are corrected for gravel content. After the data are corrected and cataloged, hydraulic parameters are determined by fitting the van Genuchten soil-moisture retention model to the data. A nonlinear parameter estimation code, RETC, is used. The unsaturated hydraulic conductivity relationship can subsequently be predicted using the van Genuchten parameters, Mualem`s model, and laboratory-measured saturated hydraulic conductivity estimates. Alternatively, provided unsaturated conductivity measurements are available, the moisture retention curve-fitting parameters, Mualem`s model, and a single unsaturated conductivity measurement can be used to predict unsaturated conductivities for the desired range of field moisture regime.« less

Using Remote Sensing Platforms to Estimate Near-Surface Soil Properties

NASA Technical Reports Server (NTRS)

Sullivan, D. G.; Shaw, J. N.; Rickman, D.; Mask, P. L.; Wersinger, J. M.; Luvall, J.

2003-01-01

Evaluation of near-surface soil properties via remote sensing (RS) could facilitate soil survey mapping, erosion prediction, fertilization regimes, and allocation of agrochemicals. The objective of this study was to evaluate the relationship between soil spectral signature and near surface soil properties in conventionally managed row crop systems. High resolution RS data were acquired over bare fields in the Coastal Plain, Appalachian Plateau, and Ridge and Valley provinces of Alabama using the Airborne Terrestrial Applications Sensor (ATLAS) multispectral scanner. Soils ranged from sandy Kandiudults to fine textured Rhodudults. Surface soil samples (0-1 cm) were collected from 163 sampling points for soil water content, soil organic carbon (SOC), particle size distribution (PSD), and citrate dithionite extractable iron (Fed) content. Surface roughness, soil water content, and crusting were also measured at sampling. Results showed RS data acquired from lands with less than 4 % surface soil water content best approximated near-surface soil properties at the Coastal Plain site where loamy sand textured surfaces were predominant. Utilizing a combination of band ratios in stepwise regression, Fed (r2 = 0.61), SOC (r2 = 0.36), sand (r2 = 0.52), and clay (r2 = 0.76) were related to RS data at the Coastal Plain site. In contrast, the more clayey Ridge and Valley soils had r-squares of 0.50, 0.36, 0.17, and 0.57. for Fed, SOC, sand and clay, respectively. Use of estimated eEmissivity did not generally improve estimates of near-surface soil attributes.

Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields

USGS Publications Warehouse

Hively, W. Dean; McCarty, Gregory W.; Reeves, James B.; Lang, Megan W.; Oesterling, Robert A.; Delwiche, Stephen R.

2011-01-01

Soil hyperspectral reflectance imagery was obtained for six tilled (soil) agricultural fields using an airborne imaging spectrometer (400–2450 nm, ~10 nm resolution, 2.5 m spatial resolution). Surface soil samples (n = 315) were analyzed for carbon content, particle size distribution, and 15 agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived reflectance spectra was used to predict analyte concentrations, 13 of the 19 analytes were predicted with R2 > 0.50, including carbon (0.65), aluminum (0.76), iron (0.75), and silt content (0.79). Comparison of 15 spectral math preprocessing treatments showed that a simple first derivative worked well for nearly all analytes. The resulting PLS factors were exported as a vector of coefficients and used to calculate predicted maps of soil properties for each field. Image smoothing with a 3 × 3 low-pass filter prior to spectral data extraction improved prediction accuracy. The resulting raster maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. High-resolution maps of soil analyte concentrations can be used to improve precision environmental management of farmlands.

A new Downscaling Approach for SMAP, SMOS and ASCAT by predicting sub-grid Soil Moisture Variability based on Soil Texture

NASA Astrophysics Data System (ADS)

Montzka, C.; Rötzer, K.; Bogena, H. R.; Vereecken, H.

2017-12-01

Improving the coarse spatial resolution of global soil moisture products from SMOS, SMAP and ASCAT is currently an up-to-date topic. Soil texture heterogeneity is known to be one of the main sources of soil moisture spatial variability. A method has been developed that predicts the soil moisture standard deviation as a function of the mean soil moisture based on soil texture information. It is a closed-form expression using stochastic analysis of 1D unsaturated gravitational flow in an infinitely long vertical profile based on the Mualem-van Genuchten model and first-order Taylor expansions. With the recent development of high resolution maps of basic soil properties such as soil texture and bulk density, relevant information to estimate soil moisture variability within a satellite product grid cell is available. Here, we predict for each SMOS, SMAP and ASCAT grid cell the sub-grid soil moisture variability based on the SoilGrids1km data set. We provide a look-up table that indicates the soil moisture standard deviation for any given soil moisture mean. The resulting data set provides important information for downscaling coarse soil moisture observations of the SMOS, SMAP and ASCAT missions. Downscaling SMAP data by a field capacity proxy indicates adequate accuracy of the sub-grid soil moisture patterns.

Comparison of DGT with traditional extraction methods for assessing arsenic bioavailability to Brassica chinensis in different soils.

PubMed

Dai, Yunchao; Nasir, Mubasher; Zhang, Yulin; Gao, Jiakai; Lv, Yamin; Lv, Jialong

2018-01-01

Several predictive models and methods have been used for heavy metals bioavailability, but there is no universally accepted approach in evaluating the bioavailability of arsenic (As) in soil. The technique of diffusive gradients in thin-films (DGT) is a promising tool, but there is a considerable debate with respect to its suitability. The DGT method was compared with other traditional chemical extractions techniques (soil solution, NaHCO 3 , NH 4 Cl, HCl, and total As method) for estimating As bioavailability in soil based on a greenhouse experiment using Brassica chinensis grown in various soils from 15 provinces in China. In addition, we assessed whether these methods are independent of soil properties. The correlations between plant and soil As concentration measured with traditional extraction techniques were pH and iron oxide (Fe ox ) dependent, indicating that these methods are influenced by soil properties. In contrast, DGT measurements were independent of soil properties and also showed a better correlation coefficient than other traditional techniques. Thus, DGT technique is superior to traditional techniques and should be preferable for evaluating As bioavailability in different type of soils. Copyright © 2017 Elsevier Ltd. All rights reserved.

On the role of "internal variability" on soil erosion assessment

NASA Astrophysics Data System (ADS)

Kim, Jongho; Ivanov, Valeriy; Fatichi, Simone

2017-04-01

Empirical data demonstrate that soil loss is highly non-unique with respect to meteorological or even runoff forcing and its frequency distributions exhibit heavy tails. However, all current erosion assessments do not describe the large associated uncertainties of temporal erosion variability and make unjustified assumptions by relying on central tendencies. Thus, the predictive skill of prognostic models and reliability of national-scale assessments have been repeatedly questioned. In this study, we attempt to reveal that the high variability in soil losses can be attributed to two sources: (1) 'external variability' referring to the uncertainties originating at macro-scale, such as climate, topography, and land use, which has been extensively studied; (2) 'geomorphic internal variability' referring to the micro-scale variations of pedologic properties (e.g., surface erodibility in soils with multi-sized particles), hydrologic properties (e.g., soil structure and degree of saturation), and hydraulic properties (e.g., surface roughness and surface topography). Using data and a physical hydraulic, hydrologic, and erosion and sediment transport model, we show that the geomorphic internal variability summarized by spatio-temporal variability in surface erodibility properties is a considerable source of uncertainty in erosion estimates and represents an overlooked but vital element of geomorphic response. The conclusion is that predictive frameworks of soil erosion should embed stochastic components together with deterministic assessments, if they do not want to largely underestimate uncertainty. Acknowledgement: This study was supported by the Basic Science Research Program of the National Research Foundation of Korea funded by the Ministry of Education (2016R1D1A1B03931886).

Spectral reflectance of surface soils: Relationships with some soil properties

NASA Technical Reports Server (NTRS)

Kiesewetter, C. H.

1983-01-01

Using a published atlas of reflectance curves and physicochemical properties of soils, a statistical analysis was carried out. Reflectance bands which correspond to five of the wavebands used by NASA's Thematic Mapper were examined for relationships to specific soil properties. The properties considered in this study include: Sand Content, Silt Content, Clay Content, Organic Matter Content, Cation Exchange Capacity, Iron Oxide Content and Moisture Content. Regression of these seven properties on the mean values of five TM bands produced results that indicate that the predictability of the properties can be increased by stratifying the data. The data was stratified by parent material, taxonomic order, temperature zone, moisture zone and climate (combined temperature and moisture). The best results were obtained when the sample was examined by climatic classes. The middle Infra-red bands, 5 and 7, as well as the visible bands, 2 and 3, are significant in the model. The near Infra-red band, band 4, is almost as useful and should be included in any studies. General linear modeling procedures examined relationships of the seven properties with certain wavebands in the stratified samples.

Strategies for soil quality assessment using VNIR gyperspectral spectroscopy in a western Kenya Chronosequence

USGS Publications Warehouse

Kinoshita, Rintaro; Moebius-Clune, Bianca N.; van Es, Harold M.; Hively, W. Dean; Bilgilis, A. Volkan

2012-01-01

Visible and near-infrared reflectance spectroscopy (VNIRS) is a rapid and nondestructive method that can predict multiple soil properties simultaneously, but its application in multidimensional soil quality (SQ) assessment in the tropics still needs to be further assessed. In this study, VNIRS (350–2500 nm) was employed to analyze 227 air-dried soil samples of Ultisols from a soil chronosequence in western Kenya and assess 16 SQ indicators. Partial least squares regression (PLSR) was validated using the full-site cross-validation method by grouping samples from each farm or forest site. Most suitable models successfully predicted SQ indicators (R2 ≥ 0.80; ratio of performance to deviation [RPD] ≥ 2.00) including soil organic matter (OMLOI), active C, Ca, cation exchange capacity (CEC), and clay. Moderately-well predicted indicators (0.50 ≤ R2 pwp), and field capacity (Θfc). Poorly predicted indicators (R2 < 0.50; RPD < 1.40) were EC, S, P, available water capacity (AWC), K, Zn, and penetration resistance. Combining VNIRS with selected field- and laboratory-measured SQ indicator values increased predictability. Furthermore, VNIRS showed moderate to substantial agreement in predicting interpretive SQ scores and a composite soil quality index (CSQI) especially when combined with directly measured SQ indicator values. In conclusion, VNIRS has good potential for low cost, rapid assessment of physical and biological SQ indicators but conventional soil chemical tests may need to be retained to provide comprehensive SQ assessments.

Physical and Hydraulic Properties at Recently Burned and Long-Unburned Boreal Forest Areas in Interior Alaska, USA

NASA Astrophysics Data System (ADS)

Ebel, B. A.; Koch, J. C.; Walvoord, M. A.

2017-12-01

Boreal forest regions in interior Alaska, USA are subject to recurring wildfire disturbance and climate shifts. These "press" and "pulse" disturbances impact water, solute, carbon, and energy fluxes, with feedbacks and consequences that are not adequately characterized. The NASA Arctic Boreal Vulnerability Experiment (ABoVE) seeks to understand susceptibility to disturbance in boreal regions. Subsurface physical and hydraulic properties are among the largest uncertainties in cryohydrogeologic modeling aiming to predict impacts of disturbance in Arctic and boreal regions. We address this research gap by characterizing physical and hydraulic properties of soil across a gradient of sites covering disparate soil textures and wildfire disturbance in interior Alaska. Samples were collected in the field within the domain of the NASA ABoVE project and analyzed in the laboratory. Physical properties measured include soil organic matter fraction, soil-particle size distribution, dry bulk density, and saturated soil-water content. Hydraulic properties measured include soil-water retention and field-saturated hydraulic conductivity using tension infiltrometers (-1 cm applied pressure head). The physical and hydraulic properties provide the foundation for site conceptual model development, cryohydrogeologic model parameterization, and integration with geophysical data. This foundation contributes to the NASA ABoVE objectives of understanding the underlying physical processes that control vulnerability in Arctic and Boreal landscapes.

A method of site quality evaluation for red alder.

Treesearch

Constance A. Harrington

1986-01-01

A field guide to predict site index for red alder (Alnus rubra Bong.) was developed for use in western Washington and Oregon. The guide requires the user to evaluate 14 soil-site properties that are grouped into three general factors: (1) geographic and topographic position, (2) soil moisture and aeration during the growing season, and (3) soil fertility and physical...

Soil spectral characterization

NASA Technical Reports Server (NTRS)

Stoner, E. R.; Baumgardner, M. F.

1981-01-01

The spectral characterization of soils is discussed with particular reference to the bidirectional reflectance factor as a quantitative measure of soil spectral properties, the role of soil color, soil parameters affecting soil reflectance, and field characteristics of soil reflectance. Comparisons between laboratory-measured soil spectra and Landsat MSS data have shown good agreement, especially in discriminating relative drainage conditions and organic matter levels in unvegetated soils. The capacity to measure both visible and infrared soil reflectance provides information on other soil characteristics and makes it possible to predict soil response to different management conditions. Field and laboratory soil spectral characterization helps define the extent to which intrinsic spectral information is available from soils as a consequence of their composition and field characteristics.

Comparative evaluation of subgrade resilient modulus from non-destructive, in-situ, and laboratory methods.

DOT National Transportation Integrated Search

2007-08-01

Field and laboratory testing programs were conducted to develop models that predict the resilient modulus of subgrade soils from : the test results of DCP, CIMCPT, FWD, Dynaflect, and soil properties. The field testing program included DCP, CIMCPT, F...

STOCHASTIC SIMULATION OF FIELD-SCALE PESTICIDE TRANSPORT USING OPUS AND GLEAMS

EPA Science Inventory

Incorporating variability in soil and chemical properties into root zone leaching models should provide a better representation of pollutant distribution in natural field conditions. Our objective was to determine if a more mechanistic rate-based model (Opus) would predict soil w...

Field determination and QSPR prediction of equilibrium-status soil/vegetation partition coefficient of PCDD/Fs.

PubMed

Li, Li; Wang, Qiang; Qiu, Xinghua; Dong, Yian; Jia, Shenglan; Hu, Jianxin

2014-07-15

Characterizing pseudo equilibrium-status soil/vegetation partition coefficient KSV, the quotient of respective concentrations in soil and vegetation of a certain substance at remote background areas, is essential in ecological risk assessment, however few previous attempts have been made for field determination and developing validated and reproducible structure-based estimates. In this study, KSV was calculated based on measurements of seventeen 2,3,7,8-substituted PCDD/F congeners in soil and moss (Dicranum angustum), and rouzi grass (Thylacospermum caespitosum) of two background sites, Ny-Ålesund of the Arctic and Zhangmu-Nyalam region of the Tibet Plateau, respectively. By both fugacity modeling and stepwise regression of field data, the air-water partition coefficient (KAW) and aqueous solubility (SW) were identified as the influential physicochemical properties. Furthermore, validated quantitative structure-property relationship (QSPR) model was developed to extrapolate the KSV prediction to all 210 PCDD/F congeners. Molecular polarizability, molecular size and molecular energy demonstrated leading effects on KSV. Copyright © 2014 Elsevier B.V. All rights reserved.

Field-Scale Evaluation of Infiltration Parameters From Soil Texture for Hydrologic Analysis

NASA Astrophysics Data System (ADS)

Springer, Everett P.; Cundy, Terrance W.

1987-02-01

Recent interest in predicting soil hydraulic properties from simple physical properties such as texture has major implications in the parameterization of physically based models of surface runoff. This study was undertaken to (1) compare, on a field scale, soil hydraulic parameters predicted from texture to those derived from field measurements and (2) compare simulated overland flow response using these two parameter sets. The parameters for the Green-Ampt infiltration equation were obtained from field measurements and using texture-based predictors for two agricultural fields, which were mapped as single soil units. Results of the analyses were that (1) the mean and variance of the field-based parameters were not preserved by the texture-based estimates, (2) spatial and cross correlations between parameters were induced by the texture-based estimation procedures, (3) the overland flow simulations using texture-based parameters were significantly different than those from field-based parameters, and (4) simulations using field-measured hydraulic conductivities and texture-based storage parameters were very close to simulations using only field-based parameters.

Soil erodibility for water erosion: A perspective and Chinese experiences

NASA Astrophysics Data System (ADS)

Wang, Bin; Zheng, Fenli; Römkens, Mathias J. M.; Darboux, Frédéric

2013-04-01

Knowledge of soil erodibility is an essential requirement for erosion prediction, conservation planning, and the assessment of sediment related environmental effects of watershed agricultural practices. This paper reviews the status of soil erodibility evaluations and determinations based on 80 years of upland area erosion research mainly in China and the USA. The review synthesizes the general research progress made by discussing the basic concepts of erodibility and its evaluation, determination, and prediction as well as knowledge of its spatio-temporal variations. The authors found that soil erodibility is often inappropriately or inaccurately applied in describing soil loss caused by different soil erosion component processes and mechanisms. Soil erodibility indicators were related to intrinsic soil properties and exogenic erosional forces, measurements, and calculations. The present review describes major needs including: (1) improved definition of erodibility, (2) modified erodibility determinations in erosion models, especially for specific geographical locations and in the context of different erosion sub-processes, (3) advanced methodologies for quantifying erodibilities of different soil erosion sub-processes, and (4) a better understanding of the mechanism that causes temporal variations in soil erodibility. The review also provides a more rational basis for future research on soil erodibility and supports predictive modeling of soil erosion processes and the development of improved conservation practices.

Modeling soil processes - are we lost in diversity?

NASA Astrophysics Data System (ADS)

Vogel, Hans-Joerg; Schlüter, Steffen

2015-04-01

Soils are among the most complex environmental systems. Soil functions - e.g. production of biomass, habitat for organisms, reactor for and storage of organic matter, filter for ground water - emerge from a multitude of processes interacting at different scales. It still remains a challenge to model and predict these functions including their stability and resilience towards external perturbations. As an inherent property of complex systems it is prohibitive to unravel all the relevant process in all detail to derive soil functions and their dynamics from first principles. Hence, when modeling soil processes and their interactions one is close to be lost in the overwhelming diversity and spatial heterogeneity of soil properties. In this contribution we suggest to look for characteristic similarities within the hyperdimensional state space of soil properties. The underlying hypothesis is that this state space is not evenly and/or randomly populated but that processes of self organization produce attractors of physical, chemical and biological properties which can be identified. (The formation of characteristic soil horizons is an obvious example). To render such a concept operational a suitable and limited set of indicators is required. Ideally, such indicators are i) related to soil functions, ii) are measurable and iii) are integral measures of the relevant physical, chemical and biological soil properties. This would allow for identifying suitable attractors. We will discuss possible indicators and will focus on soil structure as an especially promising candidate. It governs the availability of water and gas, it effects the spatial distribution of organic matter and, moreover, it forms the habitat of soil organisms and it is formed by soil biota. Quantification of soil structural properties became possible only recently with the development of more powerful tools for non-invasive imaging. Future research need to demonstrate in how far these tools can be used to identify functional soil types (i.e. attractors) allowing for modeling soil processes at an integral level. We provide an example from the 100-years fertilization experiment in Bad-Lauchstädt.

Variation of Desert Soil Hydraulic Properties with Pedogenic Maturity

NASA Astrophysics Data System (ADS)

Nimmo, J. R.; Perkins, K. S.; Mirus, B. B.; Schmidt, K. M.; Miller, D. M.; Stock, J. D.; Singha, K.

2006-12-01

Older alluvial desert soils exhibit greater pedogenic maturity, having more distinct desert pavements, vesicular (Av) horizons, and more pronounced stratification from processes such as illuviation and salt accumulation. These and related effects strongly influence the soil hydraulic properties. Older soils have been observed to have lower saturated hydraulic conductivity, and possibly greater capacity to retain water, but the quantitative effect of specific pedogenic features on the soil water retention or unsaturated hydraulic conductivity (K) curves is poorly known. With field infiltration/redistribution experiments on three different-aged soils developed within alluvial wash deposits in the Mojave National Preserve, we evaluated effective hydraulic properties over a scale of several m horizontally and to 1.5 m depth. We then correlated these properties with pedogenic features. The selected soils are (1) recently deposited sediments, (2) a soil of early Holocene age, and (3) a highly developed soil of late Pleistocene age. In each experiment we ponded water in a 1-m-diameter infiltration ring for 2.3 hr. For several weeks we monitored subsurface water content and matric pressure using surface electrical resistance imaging, dielectric-constant probes, heat-dissipation probes, and tensiometers. Analysis of these data using an inverse modeling technique gives the water retention and K properties needed for predictive modeling. Some properties show a consistent trend with soil age. Progressively more developed surface and near-surface features such as desert pavement and Av horizons are the likely cause of an observed consistent decline of infiltration capacity with soil age. Other properties, such as vertical flow retardation by layer contrasts, appear to have a more complicated soil-age dependence. The wash deposits display distinct depositional layering that has a retarding effect on vertical flow, an effect that may be less pronounced in the older Holocene soil, where the original depositional structure has a relatively modest influence. Anisotropy at the scale of centimeters is of major importance in the Pleistocene soil, with developed horizons that tend to hold water within about 0.5 m of the surface for a longer duration than in the two younger soils. Correlation of these and related pedogenic features with soil hydraulic properties is a first step toward the estimation of effective hydraulic properties of widely varying Mojave Desert soils, as needed for large-scale evaluation of soil moisture dynamics in relation to ecological habitat quality.

Three-Dimensional Mapping of Soil Chemical Characteristics at Micrometric Scale by Combining 2D SEM-EDX Data and 3D X-Ray CT Images.

PubMed

Hapca, Simona; Baveye, Philippe C; Wilson, Clare; Lark, Richard Murray; Otten, Wilfred

2015-01-01

There is currently a significant need to improve our understanding of the factors that control a number of critical soil processes by integrating physical, chemical and biological measurements on soils at microscopic scales to help produce 3D maps of the related properties. Because of technological limitations, most chemical and biological measurements can be carried out only on exposed soil surfaces or 2-dimensional cuts through soil samples. Methods need to be developed to produce 3D maps of soil properties based on spatial sequences of 2D maps. In this general context, the objective of the research described here was to develop a method to generate 3D maps of soil chemical properties at the microscale by combining 2D SEM-EDX data with 3D X-ray computed tomography images. A statistical approach using the regression tree method and ordinary kriging applied to the residuals was developed and applied to predict the 3D spatial distribution of carbon, silicon, iron, and oxygen at the microscale. The spatial correlation between the X-ray grayscale intensities and the chemical maps made it possible to use a regression-tree model as an initial step to predict the 3D chemical composition. For chemical elements, e.g., iron, that are sparsely distributed in a soil sample, the regression-tree model provides a good prediction, explaining as much as 90% of the variability in some of the data. However, for chemical elements that are more homogenously distributed, such as carbon, silicon, or oxygen, the additional kriging of the regression tree residuals improved significantly the prediction with an increase in the R2 value from 0.221 to 0.324 for carbon, 0.312 to 0.423 for silicon, and 0.218 to 0.374 for oxygen, respectively. The present research develops for the first time an integrated experimental and theoretical framework, which combines geostatistical methods with imaging techniques to unveil the 3-D chemical structure of soil at very fine scales. The methodology presented in this study can be easily adapted and applied to other types of data such as bacterial or fungal population densities for the 3D characterization of microbial distribution.

Three-Dimensional Mapping of Soil Chemical Characteristics at Micrometric Scale by Combining 2D SEM-EDX Data and 3D X-Ray CT Images

PubMed Central

Hapca, Simona; Baveye, Philippe C.; Wilson, Clare; Lark, Richard Murray; Otten, Wilfred

2015-01-01

There is currently a significant need to improve our understanding of the factors that control a number of critical soil processes by integrating physical, chemical and biological measurements on soils at microscopic scales to help produce 3D maps of the related properties. Because of technological limitations, most chemical and biological measurements can be carried out only on exposed soil surfaces or 2-dimensional cuts through soil samples. Methods need to be developed to produce 3D maps of soil properties based on spatial sequences of 2D maps. In this general context, the objective of the research described here was to develop a method to generate 3D maps of soil chemical properties at the microscale by combining 2D SEM-EDX data with 3D X-ray computed tomography images. A statistical approach using the regression tree method and ordinary kriging applied to the residuals was developed and applied to predict the 3D spatial distribution of carbon, silicon, iron, and oxygen at the microscale. The spatial correlation between the X-ray grayscale intensities and the chemical maps made it possible to use a regression-tree model as an initial step to predict the 3D chemical composition. For chemical elements, e.g., iron, that are sparsely distributed in a soil sample, the regression-tree model provides a good prediction, explaining as much as 90% of the variability in some of the data. However, for chemical elements that are more homogenously distributed, such as carbon, silicon, or oxygen, the additional kriging of the regression tree residuals improved significantly the prediction with an increase in the R2 value from 0.221 to 0.324 for carbon, 0.312 to 0.423 for silicon, and 0.218 to 0.374 for oxygen, respectively. The present research develops for the first time an integrated experimental and theoretical framework, which combines geostatistical methods with imaging techniques to unveil the 3-D chemical structure of soil at very fine scales. The methodology presented in this study can be easily adapted and applied to other types of data such as bacterial or fungal population densities for the 3D characterization of microbial distribution. PMID:26372473

Derivation of ecological criteria for copper in land-applied biosolids and biosolid-amended agricultural soils.

PubMed

Lu, Tao; Li, Jumei; Wang, Xiaoqing; Ma, Yibing; Smolders, Erik; Zhu, Nanwen

2016-12-01

The difference in availability between soil metals added via biosolids and soluble salts was not taken into account in deriving the current land-applied biosolids standards. In the present study, a biosolids availability factor (BAF) approach was adopted to investigate the ecological thresholds for copper (Cu) in land-applied biosolids and biosolid-amended agricultural soils. First, the soil property-specific values of HC5 add (the added hazardous concentration for 5% of species) for Cu 2+ salt amended were collected with due attention to data for organisms and soils relevant to China. Second, a BAF representing the difference in availability between soil Cu added via biosolids and soluble salts was estimated based on long-term biosolid-amended soils, including soils from China. Third, biosolids Cu HC5 input values (the input hazardous concentration for 5% of species of Cu from biosolids to soil) as a function of soil properties were derived using the BAF approach. The average potential availability of Cu in agricultural soils amended with biosolids accounted for 53% of that for the same soils spiked with same amount of soluble Cu salts and with a similar aging time. The cation exchange capacity was the main factor affecting the biosolids Cu HC5 input values, while soil pH and organic carbon only explained 24.2 and 1.5% of the variation, respectively. The biosolids Cu HC5 input values can be accurately predicted by regression models developed based on 2-3 soil properties with coefficients of determination (R 2 ) of 0.889 and 0.945. Compared with model predicted biosolids Cu HC5 input values, current standards (GB4284-84) are most likely to be less protective in acidic and neutral soil, but conservative in alkaline non-calcareous soil. Recommendations on ecological criteria for Cu in land-applied biosolids and biosolid-amended agriculture soils may be helpful to fill the gaps existing between science and regulations, and can be useful for Cu risk assessments in soils amended with biosolids. Copyright © 2016 Elsevier Ltd. All rights reserved.

Uncertainty indication in soil function maps - transparent and easy-to-use information to support sustainable use of soil resources

NASA Astrophysics Data System (ADS)

Greiner, Lucie; Nussbaum, Madlene; Papritz, Andreas; Zimmermann, Stephan; Gubler, Andreas; Grêt-Regamey, Adrienne; Keller, Armin

2018-05-01

Spatial information on soil function fulfillment (SFF) is increasingly being used to inform decision-making in spatial planning programs to support sustainable use of soil resources. Soil function maps visualize soils abilities to fulfill their functions, e.g., regulating water and nutrient flows, providing habitats, and supporting biomass production based on soil properties. Such information must be reliable for informed and transparent decision-making in spatial planning programs. In this study, we add to the transparency of soil function maps by (1) indicating uncertainties arising from the prediction of soil properties generated by digital soil mapping (DSM) that are used for soil function assessment (SFA) and (2) showing the response of different SFA methods to the propagation of uncertainties through the assessment. For a study area of 170 km2 in the Swiss Plateau, we map 10 static soil sub-functions for agricultural soils for a spatial resolution of 20 × 20 m together with their uncertainties. Mapping the 10 soil sub-functions using simple ordinal assessment scales reveals pronounced spatial patterns with a high variability of SFF scores across the region, linked to the inherent properties of the soils and terrain attributes and climate conditions. Uncertainties in soil properties propagated through SFA methods generally lead to substantial uncertainty in the mapped soil sub-functions. We propose two types of uncertainty maps that can be readily understood by stakeholders. Cumulative distribution functions of SFF scores indicate that SFA methods respond differently to the propagated uncertainty of soil properties. Even where methods are comparable on the level of complexity and assessment scale, their comparability in view of uncertainty propagation might be different. We conclude that comparable uncertainty indications in soil function maps are relevant to enable informed and transparent decisions on the sustainable use of soil resources.

Modeling snowmelt infiltration in seasonally frozen ground

NASA Astrophysics Data System (ADS)

Budhathoki, S.; Ireson, A. M.

2017-12-01

In cold regions, freezing and thawing of the soil govern soil hydraulic properties that shape the surface and subsurface hydrological processes. The partitioning of snowmelt into infiltration and runoff has also important implications for integrated water resource management and flood risk. However, there is an inadequate representation of the snowmelt infiltration into frozen soils in most land-surface and hydrological models, creating the need for improved models and methods. Here we apply, the Frozen Soil Infiltration Model, FroSIn, which is a novel algorithm for infiltration in frozen soils that can be implemented in physically based models of coupled flow and heat transport. In this study, we apply the model in a simple configuration to reproduce observations from field sites in the Canadian prairies, specifically St Denis and Brightwater Creek in Saskatchewan, Canada. We demonstrate the limitations of conventional approaches to simulate infiltration, which systematically over-predict runoff and under predict infiltration. The findings show that FroSIn enables models to predict more reasonable infiltration volumes in frozen soils, and also represent how infiltration-runoff partitioning is impacted by antecedent soil moisture.

A regionally-adapted implementation of conservation agriculture delivers rapid improvements to soil properties associated with crop yield stability.

PubMed

Williams, Alwyn; Jordan, Nicholas R; Smith, Richard G; Hunter, Mitchell C; Kammerer, Melanie; Kane, Daniel A; Koide, Roger T; Davis, Adam S

2018-05-31

Climate models predict increasing weather variability, with negative consequences for crop production. Conservation agriculture (CA) may enhance climate resilience by generating certain soil improvements. However, the rate at which these improvements accrue is unclear, and some evidence suggests CA can lower yields relative to conventional systems unless all three CA elements are implemented: reduced tillage, sustained soil cover, and crop rotational diversity. These cost-benefit issues are important considerations for potential adopters of CA. Given that CA can be implemented across a wide variety of regions and cropping systems, more detailed and mechanistic understanding is required on whether and how regionally-adapted CA can improve soil properties while minimizing potential negative crop yield impacts. Across four US states, we assessed short-term impacts of regionally-adapted CA systems on soil properties and explored linkages with maize and soybean yield stability. Structural equation modeling revealed increases in soil organic matter generated by cover cropping increased soil cation exchange capacity, which improved soybean yield stability. Cover cropping also enhanced maize minimum yield potential. Our results demonstrate individual CA elements can deliver rapid improvements in soil properties associated with crop yield stability, suggesting that regionally-adapted CA may play an important role in developing high-yielding, climate-resilient agricultural systems.

Utilization of data and modeling at multiple scales to compare varying formulations of the soil resistance term affecting evaporative flux from the soil surface.

NASA Astrophysics Data System (ADS)

Smits, K. M.; Forsythe, L.; Riley, W. J.; Bisht, G.

2016-12-01

Land Surface Models (LSMs) are used to predict heat, energy, and momentum fluxesoccurring at the land surface and the resulting effects in the soil and atmosphere at various scales.Evaporation from bare soil is an integral component of the water balance that is very difficult toaccurately predict since it is complexly affected by the coupled effects of atmospheric conditions andsoil properties. Inaccurate or simplifying assumptions can have drastic effects on regional and globalLSM predictions and cause available LSMs to predict conflicting values for the soil moistureconditions and surface fluxes (e.g. evapotranspiration, infiltration, run off). The goal of this work isto see how heterogeneities in soil properties can be properly represented with a soil resistance termthat accounts for physically based parameters of the soil system at the land-atmosphere interface.Utilizing a comprehensive, experimental dataset generated from a soil with known, heterogeneousproperties under highly controlled atmospheric conditions, we are able to compare the effectivenessof various parameterizations in two different models. The first being a multiphase, non-equilibrium,and non-isothermal model that minimizes the dependence on fitting parameters. The effects ofcertain mechanisms are better understood at this fine scale and incorporated into the land surfacecomponent of the Accelerated Climate Modeling for Energy project (ALM), which is focused oncapturing the interactions between the surface and the atmosphere at larger scales. The formulationsof the resistance parameter, soil water retention curve (SWRC), and diffusivity through partiallysaturated porous media are of particular interest. The fine scale model was used in conjunction withthe experimental data to test formulations before implementing them into the ACME Land Model(ALM). Effects of these alterations were compared to the existing mechanisms in ALM and thentested against lab and field scale data sets. Initial findings suggest the Tang and Riley (2013a) soilresistance more accurately reproduces results lab and field results on multiple scales whereheterogeneity is present. Further understanding of soil resistance will lead to more robust landsurface models which decrease the reliance on such empirical relationships.

Rapid identification of oil-contaminated soils using visible near-infrared diffuse reflectance spectroscopy.

PubMed

Chakraborty, Somsubhra; Weindorf, David C; Morgan, Cristine L S; Ge, Yufeng; Galbraith, John M; Li, Bin; Kahlon, Charanjit S

2010-01-01

In the United States, petroleum extraction, refinement, and transportation present countless opportunities for spillage mishaps. A method for rapid field appraisal and mapping of petroleum hydrocarbon-contaminated soils for environmental cleanup purposes would be useful. Visible near-infrared (VisNIR, 350-2500 nm) diffuse reflectance spectroscopy (DRS) is a rapid, nondestructive, proximal-sensing technique that has proven adept at quantifying soil properties in situ. The objective of this study was to determine the prediction accuracy of VisNIR DRS in quantifying petroleum hydrocarbons in contaminated soils. Forty-six soil samples (including both contaminated and reference samples) were collected from six different parishes in Louisiana. Each soil sample was scanned using VisNIR DRS at three combinations of moisture content and pretreatment: (i) field-moist intact aggregates, (ii) air-dried intact aggregates, (iii) and air-dried ground soil (sieved through a 2-mm sieve). The VisNIR spectra of soil samples were used to predict total petroleum hydrocarbon (TPH) content in the soil using partial least squares (PLS) regression and boosted regression tree (BRT) models. Each model was validated with 30% of the samples that were randomly selected and not used in the calibration model. The field-moist intact scan proved best for predicting TPH content with a validation r2 of 0.64 and relative percent difference (RPD) of 1.70. Because VisNIR DRS was promising for rapidly predicting soil petroleum hydrocarbon content, future research is warranted to evaluate the methodology for identifying petroleum contaminated soils.

Using Remotely-Sensed Estimates of Soil Moisture to Infer Soil Texture and Hydraulic Properties across a Semi-arid Watershed

NASA Technical Reports Server (NTRS)

Santanello, Joseph A.; Peters-Lidard, Christa D.; Garcia, Matthew E.; Mocko, David M.; Tischler, Michael A.; Moran, M. Susan; Thoma, D. P.

2007-01-01

Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soils from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed. In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soils information using simple parameter estimation with few but appropriately timed remote sensing retrievals.

Forms of organic phosphorus in wetland soils

NASA Astrophysics Data System (ADS)

Cheesman, A. W.; Turner, B. L.; Reddy, K. R.

2014-06-01

Phosphorus (P) cycling in freshwater wetlands is dominated by biological mechanisms, yet there has been no comprehensive examination of the forms of biogenic P (i.e. forms derived from biological activity) in wetland soils. We used solution 31P NMR spectroscopy to identify and quantify P forms in surface soils of 28 palustrine wetlands spanning a range of climatic, hydro-geomorphic and vegetation types. Total P concentrations ranged between 51 and 3516 μg P g

Soil food web properties explain ecosystem services across European land use systems.

PubMed

de Vries, Franciska T; Thébault, Elisa; Liiri, Mira; Birkhofer, Klaus; Tsiafouli, Maria A; Bjørnlund, Lisa; Bracht Jørgensen, Helene; Brady, Mark Vincent; Christensen, Søren; de Ruiter, Peter C; d'Hertefeldt, Tina; Frouz, Jan; Hedlund, Katarina; Hemerik, Lia; Hol, W H Gera; Hotes, Stefan; Mortimer, Simon R; Setälä, Heikki; Sgardelis, Stefanos P; Uteseny, Karoline; van der Putten, Wim H; Wolters, Volkmar; Bardgett, Richard D

2013-08-27

Intensive land use reduces the diversity and abundance of many soil biota, with consequences for the processes that they govern and the ecosystem services that these processes underpin. Relationships between soil biota and ecosystem processes have mostly been found in laboratory experiments and rarely are found in the field. Here, we quantified, across four countries of contrasting climatic and soil conditions in Europe, how differences in soil food web composition resulting from land use systems (intensive wheat rotation, extensive rotation, and permanent grassland) influence the functioning of soils and the ecosystem services that they deliver. Intensive wheat rotation consistently reduced the biomass of all components of the soil food web across all countries. Soil food web properties strongly and consistently predicted processes of C and N cycling across land use systems and geographic locations, and they were a better predictor of these processes than land use. Processes of carbon loss increased with soil food web properties that correlated with soil C content, such as earthworm biomass and fungal/bacterial energy channel ratio, and were greatest in permanent grassland. In contrast, processes of N cycling were explained by soil food web properties independent of land use, such as arbuscular mycorrhizal fungi and bacterial channel biomass. Our quantification of the contribution of soil organisms to processes of C and N cycling across land use systems and geographic locations shows that soil biota need to be included in C and N cycling models and highlights the need to map and conserve soil biodiversity across the world.

Soil food web properties explain ecosystem services across European land use systems

PubMed Central

de Vries, Franciska T.; Thébault, Elisa; Liiri, Mira; Birkhofer, Klaus; Tsiafouli, Maria A.; Bjørnlund, Lisa; Bracht Jørgensen, Helene; Brady, Mark Vincent; Christensen, Søren; de Ruiter, Peter C.; d’Hertefeldt, Tina; Frouz, Jan; Hedlund, Katarina; Hemerik, Lia; Hol, W. H. Gera; Hotes, Stefan; Mortimer, Simon R.; Setälä, Heikki; Sgardelis, Stefanos P.; Uteseny, Karoline; van der Putten, Wim H.; Wolters, Volkmar; Bardgett, Richard D.

2013-01-01

Intensive land use reduces the diversity and abundance of many soil biota, with consequences for the processes that they govern and the ecosystem services that these processes underpin. Relationships between soil biota and ecosystem processes have mostly been found in laboratory experiments and rarely are found in the field. Here, we quantified, across four countries of contrasting climatic and soil conditions in Europe, how differences in soil food web composition resulting from land use systems (intensive wheat rotation, extensive rotation, and permanent grassland) influence the functioning of soils and the ecosystem services that they deliver. Intensive wheat rotation consistently reduced the biomass of all components of the soil food web across all countries. Soil food web properties strongly and consistently predicted processes of C and N cycling across land use systems and geographic locations, and they were a better predictor of these processes than land use. Processes of carbon loss increased with soil food web properties that correlated with soil C content, such as earthworm biomass and fungal/bacterial energy channel ratio, and were greatest in permanent grassland. In contrast, processes of N cycling were explained by soil food web properties independent of land use, such as arbuscular mycorrhizal fungi and bacterial channel biomass. Our quantification of the contribution of soil organisms to processes of C and N cycling across land use systems and geographic locations shows that soil biota need to be included in C and N cycling models and highlights the need to map and conserve soil biodiversity across the world. PMID:23940339

Fire impact on forest soils evaluated using near-infrared spectroscopy and multivariate calibration.

PubMed

Vergnoux, A; Dupuy, N; Guiliano, M; Vennetier, M; Théraulaz, F; Doumenq, P

2009-11-15

The assessment of physico-chemical properties in forest soils affected by fires was evaluated using near infrared reflectance (NIR) spectroscopy coupled with chemometric methods. In order to describe the soil properties, measurements were taken of the total organic carbon on solid phase, the total nitrogen content, the organic carbon and the specific absorbences at 254 and 280 nm of humic substances, organic carbon in humic and fulvic acids, concentrations of NH(4)(+), Ca(2+), Mg(2+), K(+) and phosphorus in addition to NIR spectra. Then, a fire recurrence index was defined and calculated according to the different fires extents affecting soils. This calculation includes the occurrence of fires as well as the time elapsed since the last fire. This study shows that NIR spectroscopy could be considered as a tool for soil monitoring, particularly for the quantitative prediction of the total organic carbon, total nitrogen content, organic carbon in humic substances, concentrations of phosphorus, Mg(2+), Ca(2+) and NH(4)(+) and humic substances UVSA(254). Further validation in this field is necessary however, to try and make successful predictions of K(+), organic carbon in humic and fulvic acids and the humic substances UVSA(280). Moreover, NIR coupled with PLS can also be useful to predict the fire recurrence index in order to determine the spatial variability. Also this method can be used to map more or less burned areas and possibly to apply adequate rehabilitation techniques, like soil litter reconstitution with organic enrichments (industrial composts) or reforestation. Finally, the proposed recurrence index can be considered representative of the state of the soils.

Influence of Temperature, Relative Humidity, and Soil Properties on the Soil-Air Partitioning of Semivolatile Pesticides: Laboratory Measurements and Predictive Models.

PubMed

Davie-Martin, Cleo L; Hageman, Kimberly J; Chin, Yu-Ping; Rougé, Valentin; Fujita, Yuki

2015-09-01

Soil-air partition coefficient (Ksoil-air) values are often employed to investigate the fate of organic contaminants in soils; however, these values have not been measured for many compounds of interest, including semivolatile current-use pesticides. Moreover, predictive equations for estimating Ksoil-air values for pesticides (other than the organochlorine pesticides) have not been robustly developed, due to a lack of measured data. In this work, a solid-phase fugacity meter was used to measure the Ksoil-air values of 22 semivolatile current- and historic-use pesticides and their degradation products. Ksoil-air values were determined for two soils (semiarid and volcanic) under a range of environmentally relevant temperature (10-30 °C) and relative humidity (30-100%) conditions, such that 943 Ksoil-air measurements were made. Measured values were used to derive a predictive equation for pesticide Ksoil-air values based on temperature, relative humidity, soil organic carbon content, and pesticide-specific octanol-air partition coefficients. Pesticide volatilization losses from soil, calculated with the newly derived Ksoil-air predictive equation and a previously described pesticide volatilization model, were compared to previous results and showed that the choice of Ksoil-air predictive equation mainly affected the more-volatile pesticides and that the way in which relative humidity was accounted for was the most critical difference.

Using Concentrations of Methane and Gasoline Hydrocarbons in Soil Gas to Predict Vapor Intrusion of Benzene

EPA Science Inventory

Risk management of petroleum vapor intrusion has been a daunting and challenging task for the Underground Storage Tank Program. Because chlorinated solvents do not degrade in soil gas, techniques that focus on their properties and behavior can produce useful estimates. However, t...

Using remote sensing and soil physical properties for predicting the spatial distribution of cotton lint yield

USDA-ARS?s Scientific Manuscript database

Timely reflectance data from cotton (Gossypium hirsutum L.) production fields provide a useful tool for crop health assessment and site-specific crop management decisions. This field study investigated the relationships among site-specific normalized difference vegetation index (NDVI), soil physical...

Thermal properties of degraded lowland peat-moorsh soils

NASA Astrophysics Data System (ADS)

Gnatowski, Tomasz

2016-04-01

Soil thermal properties, i.e.: specific heat capacity (c), thermal conductivity (K), volumetric heat capacity (C) govern the thermal environment and heat transport through the soil. Hence the precise knowledge and accurate predictions of these properties for peaty soils with high amount of organic matter are especially important for the proper forecasting of soil temperature and thus it may lead to a better assessment of the greenhouse gas emissions created by microbiological activity of the peatlands. The objective of the study was to develop the predictive models of the selected thermal parameters of peat-moorsh soils in terms of their potential applicability for forecasting changes of soil temperature in degraded ecosystems of the Middle Biebrza River Valley area. Evaluation of the soil thermal properties was conducted for the parameters: specific heat capacity (c), volumetric heat capacities of the dry and saturated soil (Cdry, Csat) and thermal conductivities of the dry and saturated soil (Kdry, Ksat). The thermal parameters were measured using the dual-needle probe (KD2-Pro) on soil samples collected from seven peaty soils, representing total 24 horizons. The surface layers were characterized by different degrees of advancement of soil degradation dependent on intensiveness of the cultivation practises (peaty and humic moorsh). The underlying soil layers contain peat deposits of different botanical composition (peat-moss, sedge-reed, reed and alder) and varying degrees of decomposition of the organic matter, from H1 to H7 (von Post scale). Based on the research results it has been shown that the specific heat capacity of the soils differs depending on the type of soil (type of moorsh and type of peat). The range of changes varied from 1276 J.kg-1.K-1 in the humic moorsh soil to 1944 J.kg-1.K-1 in the low decomposed sedge-moss peat. It has also been stated that in degraded peat soils with the increasing of the ash content in the soil the value of specific heat has decreased in a non-linear manner. Thermal parameters of the dry mass of the studied soils (Kdry, Cdry) were characterised by the mean value of approximately 0.11±0.028 W.m-1.K-1 and 0.781±0.220 MJ.m-3.K-1. The application of the correlation analysis showed that the most significant predictor of these properties of soils is the soil bulk density which, respectively explains: 54.6% and 67.1% of their variation. The increase of the accuracy in determining Kdry and Cdry was obtained by developing regression models, which apart from the bulk density also include the chemical properties of the peat soils. In the fully saturated soil the Ksat value ranged from 0.47 to 0.63 W.m-1.K-1, and the Csat varied from 3.200 to 3.995 MJ.m-3.K-1. The variation coefficients of these soil thermal features are at the level of approx. 5%. The obtained results allowed to conclude that the significant diversity of studied soils doesn't cause the significant differences in thermal soil parameters in fully saturated soils. The developed statistical relationships indicate that parameters Ksat and Csat were poorly correlated with saturated moisture content.

Critical shear stress measurement of cohesive soils in streams: identifying device-dependent variability using an in-situ jet test device and conduit flume

NASA Astrophysics Data System (ADS)

Mahalder, B.; Schwartz, J. S.; Palomino, A.; Papanicolaou, T.

2016-12-01

Cohesive soil erodibility and threshold shear stress for stream bed and bank are dependent on both soil physical and geochemical properties in association with the channel vegetative conditions. These properties can be spatially variable therefore making critical shear stress measurement in cohesive soil challenging and leads to a need for a more comprehensive understanding of the erosional processes in streams. Several in-situ and flume-type test devices for estimating critical shear stress have been introduced by different researchers; however reported shear stress estimates per device vary widely in orders of magnitude. Advantages and disadvantages exist between these devices. Development of in-situ test devices leave the bed and/or bank material relatively undisturbed and can capture the variable nature of field soil conditions. However, laboratory flumes provide a means to control environmental conditions that can be quantify and tested. This study was conducted to observe differences in critical shear stress using jet tester and a well-controlled conduit flume. Soil samples were collected from the jet test locations and tested in a pressurized flume following standard operational procedure to calculate the critical shear stress. The results were compared using statistical data analysis (mean-separation ANOVA procedure) to identify possible differences. In addition to the device comparison, the mini jet device was used to measure critical shear stress across geologically diverse regions of Tennessee, USA. Statistical correlation between critical shear stress and the soil physical, and geochemical properties were completed identifying that geological origin plays a significant role in critical shear stress prediction for cohesive soils. Finally, the critical shear stress prediction equations using the jet test data were examined with possible suggestions to modify based on the flume test results.

Mapping and predictive variations of soil bacterial richness across France

PubMed Central

Dequietd, Samuel; Saby, Nicolas P. A.; Lelièvre, Mélanie; Nowak, Virginie; Tripied, Julie; Régnier, Tiffanie; Jolivet, Claudy; Arrouays, Dominique; Wincker, Patrick; Cruaud, Corinne; Karimi, Battle; Bispo, Antonio; Maron, Pierre Alain; Chemidlin Prévost-Bouré, Nicolas; Ranjard, Lionel

2017-01-01

Although numerous studies have demonstrated the key role of bacterial diversity in soil functions and ecosystem services, little is known about the variations and determinants of such diversity on a nationwide scale. The overall objectives of this study were i) to describe the bacterial taxonomic richness variations across France, ii) to identify the ecological processes (i.e. selection by the environment and dispersal limitation) influencing this distribution, and iii) to develop a statistical predictive model of soil bacterial richness. We used the French Soil Quality Monitoring Network (RMQS), which covers all of France with 2,173 sites. The soil bacterial richness (i.e. OTU number) was determined by pyrosequencing 16S rRNA genes and related to the soil characteristics, climatic conditions, geomorphology, land use and space. Mapping of bacterial richness revealed a heterogeneous spatial distribution, structured into patches of about 111km, where the main drivers were the soil physico-chemical properties (18% of explained variance), the spatial descriptors (5.25%, 1.89% and 1.02% for the fine, medium and coarse scales, respectively), and the land use (1.4%). Based on these drivers, a predictive model was developed, which allows a good prediction of the bacterial richness (R2adj of 0.56) and provides a reference value for a given pedoclimatic condition. PMID:29059218

Mapping and predictive variations of soil bacterial richness across France.

PubMed

Terrat, Sébastien; Horrigue, Walid; Dequiedt, Samuel; Saby, Nicolas P A; Lelièvre, Mélanie; Nowak, Virginie; Tripied, Julie; Régnier, Tiffanie; Jolivet, Claudy; Arrouays, Dominique; Wincker, Patrick; Cruaud, Corinne; Karimi, Battle; Bispo, Antonio; Maron, Pierre Alain; Chemidlin Prévost-Bouré, Nicolas; Ranjard, Lionel

2017-01-01

Although numerous studies have demonstrated the key role of bacterial diversity in soil functions and ecosystem services, little is known about the variations and determinants of such diversity on a nationwide scale. The overall objectives of this study were i) to describe the bacterial taxonomic richness variations across France, ii) to identify the ecological processes (i.e. selection by the environment and dispersal limitation) influencing this distribution, and iii) to develop a statistical predictive model of soil bacterial richness. We used the French Soil Quality Monitoring Network (RMQS), which covers all of France with 2,173 sites. The soil bacterial richness (i.e. OTU number) was determined by pyrosequencing 16S rRNA genes and related to the soil characteristics, climatic conditions, geomorphology, land use and space. Mapping of bacterial richness revealed a heterogeneous spatial distribution, structured into patches of about 111km, where the main drivers were the soil physico-chemical properties (18% of explained variance), the spatial descriptors (5.25%, 1.89% and 1.02% for the fine, medium and coarse scales, respectively), and the land use (1.4%). Based on these drivers, a predictive model was developed, which allows a good prediction of the bacterial richness (R2adj of 0.56) and provides a reference value for a given pedoclimatic condition.

Solid phase evolution in the Biosphere 2 hillslope experiment as predicted by modeling of hydrologic and geochemical fluxes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dontsova, K.; Steefel, C.I.; Desilets, S.

2009-07-15

A reactive transport geochemical modeling study was conducted to help predict the mineral transformations occurring over a ten year time-scale that are expected to impact soil hydraulic properties in the Biosphere 2 (B2) synthetic hillslope experiment. The modeling sought to predict the rate and extent of weathering of a granular basalt (selected for hillslope construction) as a function of climatic drivers, and to assess the feedback effects of such weathering processes on the hydraulic properties of the hillslope. Flow vectors were imported from HYDRUS into a reactive transport code, CrunchFlow2007, which was then used to model mineral weathering coupled tomore » reactive solute transport. Associated particle size evolution was translated into changes in saturated hydraulic conductivity using Rosetta software. We found that flow characteristics, including velocity and saturation, strongly influenced the predicted extent of incongruent mineral weathering and neo-phase precipitation on the hillslope. Results were also highly sensitive to specific surface areas of the soil media, consistent with surface reaction controls on dissolution. Effects of fluid flow on weathering resulted in significant differences in the prediction of soil particle size distributions, which should feedback to alter hillslope hydraulic conductivities.« less

Uncertainty analysis of the nonideal competitive adsorption-donnan model: effects of dissolved organic matter variability on predicted metal speciation in soil solution.

PubMed

Groenenberg, Jan E; Koopmans, Gerwin F; Comans, Rob N J

2010-02-15

Ion binding models such as the nonideal competitive adsorption-Donnan model (NICA-Donnan) and model VI successfully describe laboratory data of proton and metal binding to purified humic substances (HS). In this study model performance was tested in more complex natural systems. The speciation predicted with the NICA-Donnan model and the associated uncertainty were compared with independent measurements in soil solution extracts, including the free metal ion activity and fulvic (FA) and humic acid (HA) fractions of dissolved organic matter (DOM). Potentially important sources of uncertainty are the DOM composition and the variation in binding properties of HS. HS fractions of DOM in soil solution extracts varied between 14 and 63% and consisted mainly of FA. Moreover, binding parameters optimized for individual FA samples show substantial variation. Monte Carlo simulations show that uncertainties in predicted metal speciation, for metals with a high affinity for FA (Cu, Pb), are largely due to the natural variation in binding properties (i.e., the affinity) of FA. Predictions for metals with a lower affinity (Cd) are more prone to uncertainties in the fraction FA in DOM and the maximum site density (i.e., the capacity) of the FA. Based on these findings, suggestions are provided to reduce uncertainties in model predictions.

Water repellency and organic matter composition after a wildfire: new insights using thermal analysis

NASA Astrophysics Data System (ADS)

Neris, Jonay; Doerr, Stefan

2014-05-01

Water repellency, a key parameter in the hydrological and ecological behaviour of ecosystems, is one of the main soil properties affected by wildfire through its impact on organic matter (Shakesby and Doerr, 2006). This study examines the link between post-fire organic matter quantity and composition, soil water repellency and related hydrological properties in order to (i) examine the influence of different organic matter pools on soil hydrological properties and (ii) to explore the use of these links as a proxy for soil hydrological impacts of fire. Soil samples from five fire-affected burned and unburned control sites in Andisols terrain in Tenerife, previously studied for water repellency and hydrology-related properties (Neris et al., 2013), were selected and thermogravimetric analysis (TG) carried out to evaluate fire impacts on their organic matter composition. A decrease in the organic matter quantity as well as in the relative amount of the labile organic matter pool and an increase in the recalcitrant and/or refractory pool depending was observed in the burned soils. TG data, using 10 ºC temperature range steps, allowed reasonable prediction of soil properties evaluated, with R2 ranging from 0.4 to 0.8. The labile pool showed a broad and positive influence on most soil properties evaluated, whereas the refractory pool and the dehydration range affected the surface water holding capacity and water repellency. These results, in conjunction with the simplicity of the TG analysis suggest that, following a calibration step to link TG data to the site-specific post-fire soil properties, this method may be a useful tool for rapid and cost-effective soil hydrological response evaluation after the fire. References Neris, J., Tejedor, M., Fuentes, J., Jiménez, C., 2013. Infiltration, runoff and soil loss in Andisols affected by forest fire (Canary Islands, Spain). Hydrological Processes 27(19), 2814-2824. Shakesby, R.A., Doerr, S.H., 2006. Wildfire as a hydrological and geomorphological agent. Earth-Science Reviews 74(3-4), 269-307.

Role of vegetation and edaphic factors in controlling diversity and use of different carbon sources in semi-arid ecosystems

NASA Astrophysics Data System (ADS)

Lohse, K. A.; McLain, J. E.; Harman, C. J.; Sivapalan, M.; Troch, P. A.

2010-12-01

Microbially-mediated soil carbon cycling is closely linked to soil moisture and temperature. Climate change is predicted to increase intra-annual precipitation variability (i.e. less frequent yet more intense precipitation events) and alter biogeochemical processes due to shifts in soil moisture dynamics and inputs of carbon. However, the responses of soil biology and chemistry to predicted climate change, and their concomitant feedbacks on ecosystem productivity and biogeochemical processes are poorly understood. We collected soils at three different elevations in the Santa Catalina Mountains, AZ and quantified carbon utilization during pre-monsoon precipitation conditions. Contrasting parent materials (schist and granite) were paired at each elevation. We expected climate to determine the overall activity of soil fungal and bacterial communities and diversity of soil C utilization, and differences in parent material to modify these responses through controls on soil physical properties. We used EcoPlateTM C utilization assays to determine the relative abundance of soil bacterial and fungal populations and rate and diversity of carbon utilization. Additional plates were incubated with inhibitors selective to fungal or bacterial activity to assess relative contribution of these microbial groups to overall C utilization. We analyzed soils for soil organic matter, total C and N, particle size analysis and soil moisture content via both gravimetric and volumetric methods to assess the influences of soil physical and chemical properties on the measured biological responses. Consistent with our expectations, overall microbial activity was highest at the uppermost conifer elevation sites compared to the middle and lower elevation sites. In contrast to our expectations, however, overall activity was lower at the mid elevation oak woodland sites compared to the low elevation desert sites. Also consistent with our expectations was the observation that overall activities were consistently higher in schist parent material compared to granite. Though differences between canopy and intercanopy carbon utilization were subtle, the diversity of carbon utilization differed, suggesting a potential role of root exudates in governing C utilization in these semiarid soils. Findings from this study suggest that soil physical properties due to parent material have primary impacts in constraining microbial growth and carbon utilization under changing climate conditions.

Andic soil features and debris flows in Italy. New perspective towards prediction

NASA Astrophysics Data System (ADS)

Scognamiglio, Solange; Calcaterra, Domenico; Iamarino, Michela; Langella, Giuliano; Orefice, Nadia; Vingiani, Simona; Terribile, Fabio

2016-04-01

Debris flows are dangerous hazards causing fatalities and damage. Previous works have demonstrated that the materials involved by debris flows in Campania (southern Italy) are soils classified as Andosols. These soils have peculiar chemical and physical properties which make them fertile but also vulnerable to landslide. In Italy, andic soil properties are found both in volcanic and non-volcanic mountain ecosystems (VME and NVME). Here, we focused on the assessment of the main chemical and physical properties of the soils in the detachment areas of eight debris flows occurred in NVME of Italy in the last 70 years. Such landslides were selected by consulting the official Italian geodatabase (IFFI Project). Andic properties (by means of ammonium oxalate extractable Fe, Si and Al forms for the calculation of Alo+1/2Feo) were also evaluated and a comparison with soils of VME was performed to assess possible common features. Landslide source areas were characterised by slope gradient ranging from 25° to 50° and lithological heterogeneity of the bedrock. The soils showed similar, i.e. all were very deep, had a moderately thick topsoil with a high organic carbon (OC) content decreasing regularly with depth. The cation exchange capacity trend was generally consistent with the OC and the pH varied from extremely to slightly acid, but increased with depth. Furthermore, the soils had high water retention values both at saturation (0.63 to 0.78 cm3 cm-3) and in the dryer part of the water retention curve, and displayed a prevalent loamy texture. Such properties denote the chemical and physical fertility of the investigated ecosystems. The values of Alo+1/2Feoindicated that the soils had vitric or andic features and can be classified as Andosols. The comparison between NVME soils and those of VME showed similar depth, thickness of soil horizons, and family texture, whereas soil pH, degree of development of andic properties and allophane content were higher for VME soils. Such results are consistent with the different soil environments; indeed, in VME a continuous soil enrichment of weatherable volcanic glass affects both soil pH and formation of short range order clay minerals. In conclusion, the direct relationship between debris flows and Andosols, previously found in the Campania VME, is confirmed in some NVME. These findings highlight the similarity of the materials involved by debris flows both in VME and NVME and suggest the existence of a pedological control on debris flow initiation. Furthermore, these results encourage a further extension of soil studies to other European mountain ecosystems. The evidence that andic soils may play a crucial role in debris flows initiation in Italy enables to develop a new strategy for debris flows forecasting. For the case of Sarno 1998 landslides, we provide an example of innovative approach exploring the results obtained by combining the spatial distribution of these andic soils with "on the fly" simulation modelling of the soil water balance, using real time weather forecasting data. The obtained results enable to develop promising Geospatial Decision Support Systems to improve our ability to predict debris flows on soil-covered slopes.

Soil pH Errors Propagation from Measurements to Spatial Predictions - Cost Benefit Analysis and Risk Assessment Implications for Practitioners and Modelers

NASA Astrophysics Data System (ADS)

Owens, P. R.; Libohova, Z.; Seybold, C. A.; Wills, S. A.; Peaslee, S.; Beaudette, D.; Lindbo, D. L.

2017-12-01

The measurement errors and spatial prediction uncertainties of soil properties in the modeling community are usually assessed against measured values when available. However, of equal importance is the assessment of errors and uncertainty impacts on cost benefit analysis and risk assessments. Soil pH was selected as one of the most commonly measured soil properties used for liming recommendations. The objective of this study was to assess the error size from different sources and their implications with respect to management decisions. Error sources include measurement methods, laboratory sources, pedotransfer functions, database transections, spatial aggregations, etc. Several databases of measured and predicted soil pH were used for this study including the United States National Cooperative Soil Survey Characterization Database (NCSS-SCDB), the US Soil Survey Geographic (SSURGO) Database. The distribution of errors among different sources from measurement methods to spatial aggregation showed a wide range of values. The greatest RMSE of 0.79 pH units was from spatial aggregation (SSURGO vs Kriging), while the measurement methods had the lowest RMSE of 0.06 pH units. Assuming the order of data acquisition based on the transaction distance i.e. from measurement method to spatial aggregation the RMSE increased from 0.06 to 0.8 pH units suggesting an "error propagation". This has major implications for practitioners and modeling community. Most soil liming rate recommendations are based on 0.1 pH unit increments, while the desired soil pH level increments are based on 0.4 to 0.5 pH units. Thus, even when the measured and desired target soil pH are the same most guidelines recommend 1 ton ha-1 lime, which translates in 111 ha-1 that the farmer has to factor in the cost-benefit analysis. However, this analysis need to be based on uncertainty predictions (0.5-1.0 pH units) rather than measurement errors (0.1 pH units) which would translate in 555-1,111 investment that need to be assessed against the risk. The modeling community can benefit from such analysis, however, error size and spatial distribution for global and regional predictions need to be assessed against the variability of other drivers and impact on management decisions.

Topographically-determined soil thickening explained spatial variability of soil carbon and nitrogen in Southern California grasslands

NASA Astrophysics Data System (ADS)

Lin, Y.; Prentice, S., III; Tran, T.; Bingham, N.; King, J. Y.; Chadwick, O.

2015-12-01

At the scale of hillslopes, topography strongly regulates soil formation, affecting hillslope hydrology and biological activities. Topographic control of soil formation is particularly strong for semi-arid landscapes where soil thickening is induced by pedoturbation and soil creep. Thus, terrain attributes hold great potential for modeling full profile soil C and N stocks at the hillslope scale in these landscapes. In this study, we developed predictions of grassland soil C and N stocks using digital terrain attributes scaled to the signal of site-specific hillslope geomorphic processes. We found that soil thickness was the major control of soil organic C and N stocks and was best predicted by mean curvature. This curvature dependency of soil thickness affected prediction of organic C and N stocks because of the C and N added by taking subsoil into account. We also found that curvature was positively correlated with depth to carbonate reflecting drier soil conditions in convex hillslope positions and wetter soil conditions in concave areas. Slope aspect also had a marginal effect on soil C and N stocks; soil organic C and N stocks on the north-facing slope tended to be higher than those on the south-facing slope. We found that terrain attributes at medium resolutions (8 to 16 m) were most effective in modeling soil C and N stocks. Overall, terrain attributes explained 61% of the variation in soil thickness and 49% of the variation in soil organic C stock. Our results suggest that curvature-induced soil thickening, coupled with aspect, likely exerts a first-order control on soil organic C and N accumulation rates, and these changes occur predominantly in subsoil. Thus our data highlight the importance of subsoil in mapping soil C and N stocks and other soil properties. Our model also demonstrates how scale-driven analysis may guide soil C and N prediction in other hillslope dominated regions.

Soil chemistry and phosphorus retention capacity of North Carolina coastal plain swamps receiving sewage effluent

DOE Office of Scientific and Technical Information (OSTI.GOV)

Richardson, C.J.; Walbridge, M.R.; Burns, A.

1988-11-01

Several hundred freshwater swamps in North Carolina currently receive municipal waste-water inputs. In the study researchers examined three Coastal Plain wetlands to (1) characterize their soil chemical properties, (2) determine short-term and long-term effects of effluent additions on soil chemistry, (3) estimate the phosphorus sorption capacities of these swamp soils and determine the relationship between P sorption capacity and soil chemistry, and (4) develop a predictive index to evaluate the P sorption potentials of other N.C. Coastal Plain swamps.

Quantitative modeling of soil genesis processes

NASA Technical Reports Server (NTRS)

Levine, E. R.; Knox, R. G.; Kerber, A. G.

1992-01-01

For fine spatial scale simulation, a model is being developed to predict changes in properties over short-, meso-, and long-term time scales within horizons of a given soil profile. Processes that control these changes can be grouped into five major process clusters: (1) abiotic chemical reactions; (2) activities of organisms; (3) energy balance and water phase transitions; (4) hydrologic flows; and (5) particle redistribution. Landscape modeling of soil development is possible using digitized soil maps associated with quantitative soil attribute data in a geographic information system (GIS) framework to which simulation models are applied.

Environmental stochasticity controls soil erosion variability

PubMed Central

Kim, Jongho; Ivanov, Valeriy Y.; Fatichi, Simone

2016-01-01

Understanding soil erosion by water is essential for a range of research areas but the predictive skill of prognostic models has been repeatedly questioned because of scale limitations of empirical data and the high variability of soil loss across space and time scales. Improved understanding of the underlying processes and their interactions are needed to infer scaling properties of soil loss and better inform predictive methods. This study uses data from multiple environments to highlight temporal-scale dependency of soil loss: erosion variability decreases at larger scales but the reduction rate varies with environment. The reduction of variability of the geomorphic response is attributed to a ‘compensation effect’: temporal alternation of events that exhibit either source-limited or transport-limited regimes. The rate of reduction is related to environment stochasticity and a novel index is derived to reflect the level of variability of intra- and inter-event hydrometeorologic conditions. A higher stochasticity index implies a larger reduction of soil loss variability (enhanced predictability at the aggregated temporal scales) with respect to the mean hydrologic forcing, offering a promising indicator for estimating the degree of uncertainty of erosion assessments. PMID:26925542

Impact of anthropomorphic soil genesis on hydraulic properties: the case of cranberry production

NASA Astrophysics Data System (ADS)

Periard, Yann; José Gumiere, Silvio; Rousseau, Alain N.; Caron, Jean; Hallema, Dennis W.

2014-05-01

The construction of a cranberry field requires the installation of a drainage system which causes anthropic layering of the natural sequence of soil strata. Over the years, the soil hydraulic properties may change under the influence of irrigation and water table control. In fact, natural consolidation (drainage and recharge cycles), filtration and clogging soil pores by colloidal particle accelerated by water management will alter the hydrodynamic behavior of the soil (Gaillard et al., 2007; Wildenschild and Sheppard, 2013; Bodner et al., 2013). Today, advances in the field of tomography imagery allows the study a number of physicals processes of soils (Wildenschilds and Sheppard, 2013) especially for the transport of colloidal particles (Gaillard et al., 2007) and consolidation (Reed et al, 2006; Pires et al, 2007). Therefore, the main objective of this work is to analyze the temporal evolution of hydrodynamic properties of a sandy soil during repeated drainage and recharge cycles using a medical CT-scan. A soil columns laboratory experiment was setup in fall 2013, pressure head, input and output flow, tracer monitoring (KBr and ZrO2) and tomographic analyses have been used to quantify the temporal variation of the soil hydrodynamic properties of these soil columns. The results showed that the water management (irrigation and drainage) has strong effect on soil genesis and causes significant alteration of soil hydraulic properties, which may reduce soil drainage capacity. Knowledge about the mechanisms responsible of anthropic cranberry soil genesis will allow us to predict soil evolution according to several conditions (soil type, drainage system design, water management) to better anticipate and control their future negative effects on cranberry production. References: Bodner, G., P. Scholl and H.P. Kaul. 2013. Field quantification of wetting-drying cycles to predict temporal changes of soil pore size distribution. Soil and Tillage Research 133: 1-9. doi:http://dx.doi.org/10.1016/j.still.2013.05.006. Gaillard, J.-F., C. Chen, S.H. Stonedahl, B.L.T. Lau, D.T. Keane and A.I. Packman. 2007. Imaging of colloidal deposits in granular porous media by X-ray difference micro-tomography. Geophysical Research Letters 34: L18404. doi:10.1029/2007GL030514. Pires, L.F., O.O.S. Bacchi and K. Reichardt. 2007. Assessment of soil structure repair due to wetting and drying cycles through 2D tomographic image analysis. Soil and Tillage Research 94: 537-545. doi:http://dx.doi.org/10.1016/j.still.2006.10.008. Reed, A. H., Thompson, K. E., Zhang, W., Willson, C. S., & Briggs, K. B. (2006). Quantifying consolidation and reordering in natural granular media from computed tomography images. Advances in X-ray Tomography for Geomaterials, 263-268. Wildenschild, D. and A.P. Sheppard. 2013. X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems. Advances in Water Resources 51: 217-246. doi:http://dx.doi.org/10.1016/j.advwatres.2012.07.018.

Updated global soil map for the Weather Research and Forecasting model and soil moisture initialization for the Noah land surface model

NASA Astrophysics Data System (ADS)

DY, C. Y.; Fung, J. C. H.

2016-08-01

A meteorological model requires accurate initial conditions and boundary conditions to obtain realistic numerical weather predictions. The land surface controls the surface heat and moisture exchanges, which can be determined by the physical properties of the soil and soil state variables, subsequently exerting an effect on the boundary layer meteorology. The initial and boundary conditions of soil moisture are currently obtained via National Centers for Environmental Prediction FNL (Final) Operational Global Analysis data, which are collected operationally in 1° by 1° resolutions every 6 h. Another input to the model is the soil map generated by the Food and Agriculture Organization of the United Nations - United Nations Educational, Scientific and Cultural Organization (FAO-UNESCO) soil database, which combines several soil surveys from around the world. Both soil moisture from the FNL analysis data and the default soil map lack accuracy and feature coarse resolutions, particularly for certain areas of China. In this study, we update the global soil map with data from Beijing Normal University in 1 km by 1 km grids and propose an alternative method of soil moisture initialization. Simulations of the Weather Research and Forecasting model show that spinning-up the soil moisture improves near-surface temperature and relative humidity prediction using different types of soil moisture initialization. Explanations of that improvement and improvement of the planetary boundary layer height in performing process analysis are provided.

Reflectance spectroscopy: a tool for predicting the risk of iron chlorosis in soils

NASA Astrophysics Data System (ADS)

Cañasveras, J. C.; Barrón, V.; Del Campillo, M. C.; Viscarra Rossel, R. A.

2012-04-01

Chlorosis due to iron (Fe) deficiency is the most important nutritional problem a plant can have in calcareous soils. The most characteristic symptom of Fe chlorosis is internervial yellowing in the youngest leaves due to a lack of chlorophyll caused by a disorder in Fe nutrition. Fe chlorosis is related with calcium carbonate equivalent (CCE), clay content and Fe extracted with oxalate (Feo). The conventional technique for determining these properties and others, based on laboratory analysis, are time-consuming and costly. Reflectance spectroscopy (RS) is a rapid, non-destructive, less expensive alternative tool that can be used to enhance or replace conventional methods of soil analysis. The aim of this work was to assess the usefulness of RS for the determination of some properties of Mediterranean soils including clay content, CCE, Feo, cation exchange capacity (CEC), organic matter (OM) and pHw, with emphasis on those with a specially marked influence on the risk of Fe chlorosis. To this end, we used partial least-squares regression (PLS) to construct calibration models, leave-one-out cross-validation and an independent validation set. Our results testify to the usefulness of qualitative soil interpretations based on the variable importance for projection (VIP) as derived by PLS decomposition. The accuracy of predictions in each of the Vis-NIR, MIR and combined spectral regions differed considerably between properties. The R2adj and root mean square error (RMSE) for the external validation predictions were as follows: 0.83 and 37 mg kg-1 for clay content in the Vis-NIR-MIR range; 0.99 and 25 mg kg-1 for CCE, 0.80 and 0.1 mg kg-1 for Feo in the MIR range; 0.93 and 3 cmolc kg-1 for CEC in the Vis-NIR range; 0.87 and 2 mg kg-1 for OM in the Vis-NIR-MIR range, 0.61 and 0.2 for pHw in the MIR range. These results testify to the potential of RS in the Vis, NIR and MIR ranges for efficient soil analysis, the acquisition of soil information and the assessment of the risk of Fe chlorosis in soils.

Spatial prediction of soil texture in region Centre (France) from summary data

NASA Astrophysics Data System (ADS)

Dobarco, Mercedes Roman; Saby, Nicolas; Paroissien, Jean-Baptiste; Orton, Tom G.

2015-04-01

Soil texture is a key controlling factor of important soil functions like water and nutrient holding capacity, retention of pollutants, drainage, soil biodiversity, and C cycling. High resolution soil texture maps enhance our understanding of the spatial distribution of soil properties and provide valuable information for decision making and crop management, environmental protection, and hydrological planning. We predicted the soil texture of agricultural topsoils in the Region Centre (France) combining regression and area-to-point kriging. Soil texture data was collected from the French soil-test database (BDAT), which is populated with soil analysis performed by farmers' demand. To protect the anonymity of the farms the data was treated by commune. In a first step, summary statistics of environmental covariates by commune were used to develop prediction models with Cubist, boosted regression trees, and random forests. In a second step the residuals of each individual observation were summarized by commune and kriged following the method developed by Orton et al. (2012). This approach allowed to include non-linear relationships among covariates and soil texture while accounting for the uncertainty on areal means in the area-to-point kriging step. Independent validation of the models was done using data from the systematic soil monitoring network of French soils. Future work will compare the performance of these models with a non-stationary variance geostatistical model using the most important covariates and summary statistics of texture data. The results will inform on whether the later and statistically more-challenging approach improves significantly texture predictions or whether the more simple area-to-point regression kriging can offer satisfactory results. The application of area-to-point regression kriging at national level using BDAT data has the potential to improve soil texture predictions for agricultural topsoils, especially when combined with existing maps (i.e., model ensemble).

Bringing life to soil physical processes

NASA Astrophysics Data System (ADS)

Hallett, P. D.

2013-12-01

When Oklahoma's native prairie grass roots were replaced by corn, the greatest environmental (and social) disaster ever to hit America ensued. The soils lost structure, physical binding by roots was annihilated and when drought came the Great Dust Bowl commenced. This form of environmental disaster has repeated over history and although not always apparent, similar processes drive the degradation of seemingly productive farmland and forests. But just as negative impacts on biology are deleterious to soil physical properties, positive impacts could reverse these trends. In finding solutions to soil sustainability and food security, we should be able to exploit biological processes to improve soil physical properties. This talk will focus on a quantitative understanding of how biology changes soil physical behaviour. Like the Great Dust Bowl, it starts with reinforcement mechanisms by plant roots. We found that binding of soil by cereal (barley) roots within 5 weeks of planting can more than double soil shear strength, with greater plant density causing greater reinforcement. With time, however, the relative impact of root reinforcement diminishes due to root turnover and aging of the seedbed. From mechanical tests of individual roots, reasonable predictions of reinforcement by tree roots are possible with fibre bundle models. With herbaceous plants like cereals, however, the same parameters (root strength, stiffness, size and distribution) result in a poor prediction. We found that root type, root age and abiotic factors such as compaction and waterlogging affect mechanical behaviour, further complicating the understanding and prediction of root reinforcement. For soil physical stability, the interface between root and soil is an extremely important zone in terms of resistance of roots to pull-out and rhizosphere formation. Compounds analogous to root exudates have been found with rheological tests to initially decrease the shear stress where wet soils flow, but after decomposition of these exudates by microbes the shear stress increases. This suggests an initial dispersion, followed by aggregation of the soil, which explains the structural arrangement of soil particles in the rhizosphere observed by microscopy. Dispersion of soil minerals in the root zone is important to release bound nutrients from mineral surfaces. Using fracture mechanics we measured large impacts of biological exudates on the toughness and interparticle bond energy of soils. Now novel tests are being developed to quantify interparticle bonding by biological exudates on single and multiple particle contacts, including mechanical test specimens that can be inoculated with specific bacteria or fungi. This will allow for clay mineralogy, water potential and solution chemistry impacts on interparticle bonding to be quantified directly. Wettability experiments with the same samples measure hydrological properties such as contact angle. Basic information from these tests will help explain biological processes that drive soil structure formation and stabilisation, providing data for models of soil structure dynamics.

Influence of soil properties and soil moisture on the efficacy of indaziflam and flumioxazin on Kochia scoparia L.

PubMed

Sebastian, Derek J; Nissen, Scott J; Westra, Phil; Shaner, Dale L; Butters, Greg

2017-02-01

Kochia (Kochia scoparia L.) is a highly competitive, non-native weed found throughout the western United States. Flumioxazin and indaziflam are two broad-spectrum pre-emergence herbicides that can control kochia in a variety of crop and non-crop situations; however, under dry conditions, these herbicides sometimes fail to control this important weed. There is very little information describing the effect of soil properties and soil moisture on the efficacy of these herbicides. Soil organic matter (SOM) explained the highest proportion of variability in predicting the herbicide dose required for 80% kochia growth reduction (GR 80 ) for flumioxazin and indaziflam (R 2 = 0.72 and 0.79 respectively). SOM had a greater impact on flumioxazin phytotoxicity compared to indaziflam. Flumioxazin and indaziflam kochia phytotoxicity was greatly reduced at soil water potentials below -200 kPa. Kochia can germinate at soil moisture potentials below the moisture required for flumioxazin and indaziflam activation, which means that kochia control is greatly influenced by the complex interaction between soil physical properties and soil moisture. This research can be used to gain a better understanding of how and why some weeds, like kochia, are so difficult to manage even with herbicides that normally provide excellent control. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Using Logistic Regression To Predict the Probability of Debris Flows Occurring in Areas Recently Burned By Wildland Fires

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.

2003-01-01

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity in each basin, particle size sorting, average storm intensity (millimeters per hour), soil organic matter content, soil permeability, and soil drainage. The results of this study demonstrate that logistic regression is a valuable tool for predicting the probability of debris flows occurring in recently-burned landscapes.

Using Logistic Regression to Predict the Probability of Debris Flows in Areas Burned by Wildfires, Southern California, 2003-2006

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.; Michael, John A.; Helsel, Dennis R.

2008-01-01

Logistic regression was used to develop statistical models that can be used to predict the probability of debris flows in areas recently burned by wildfires by using data from 14 wildfires that burned in southern California during 2003-2006. Twenty-eight independent variables describing the basin morphology, burn severity, rainfall, and soil properties of 306 drainage basins located within those burned areas were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows soon after the 2003 to 2006 fires were delineated from data in the National Elevation Dataset using a geographic information system; (2) Data describing the basin morphology, burn severity, rainfall, and soil properties were compiled for each basin. These data were then input to a statistics software package for analysis using logistic regression; and (3) Relations between the occurrence or absence of debris flows and the basin morphology, burn severity, rainfall, and soil properties were evaluated, and five multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combinations produced the most effective models, and the multivariate models that best predicted the occurrence of debris flows were identified. Percentage of high burn severity and 3-hour peak rainfall intensity were significant variables in all models. Soil organic matter content and soil clay content were significant variables in all models except Model 5. Soil slope was a significant variable in all models except Model 4. The most suitable model can be selected from these five models on the basis of the availability of independent variables in the particular area of interest and field checking of probability maps. The multivariate logistic regression models can be entered into a geographic information system, and maps showing the probability of debris flows can be constructed in recently burned areas of southern California. This study demonstrates that logistic regression is a valuable tool for developing models that predict the probability of debris flows occurring in recently burned landscapes.

Climate-soil Interactions: Global Change, Local Properties, and Ecological Sites

USDA-ARS?s Scientific Manuscript database

Global climate change is predicted to alter historic patterns of precipitation and temperature in rangelands globally. Vegetation community response to altered weather patterns will be mediated at the site level by local-scale properties that govern ecological potential, including geology, topograph...

Modelling the Impact of Soil Management on Soil Functions

NASA Astrophysics Data System (ADS)

Vogel, H. J.; Weller, U.; Rabot, E.; Stößel, B.; Lang, B.; Wiesmeier, M.; Urbanski, L.; Wollschläger, U.

2017-12-01

Due to an increasing soil loss and an increasing demand for food and energy there is an enormous pressure on soils as the central resource for agricultural production. Besides the importance of soils for biomass production there are other essential soil functions, i.e. filter and buffer for water, carbon sequestration, provision and recycling of nutrients, and habitat for biological activity. All these functions have a direct feed back to biogeochemical cycles and climate. To render agricultural production efficient and sustainable we need to develop model tools that are capable to predict quantitatively the impact of a multitude of management measures on these soil functions. These functions are considered as emergent properties produced by soils as complex systems. The major challenge is to handle the multitude of physical, chemical and biological processes interacting in a non-linear manner. A large number of validated models for specific soil processes are available. However, it is not possible to simulate soil functions by coupling all the relevant processes at the detailed (i.e. molecular) level where they are well understood. A new systems perspective is required to evaluate the ensemble of soil functions and their sensitivity to external forcing. Another challenge is that soils are spatially heterogeneous systems by nature. Soil processes are highly dependent on the local soil properties and, hence, any model to predict soil functions needs to account for the site-specific conditions. For upscaling towards regional scales the spatial distribution of functional soil types need to be taken into account. We propose a new systemic model approach based on a thorough analysis of the interactions between physical, chemical and biological processes considering their site-specific characteristics. It is demonstrated for the example of soil compaction and the recovery of soil structure, water capacity and carbon stocks as a result of plant growth and biological activity. Coupling of the observed nonlinear interactions allows for modeling the stability and resilience of soil systems in terms of their essential functions.

Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor

PubMed Central

He, Yong; Nie, Pengcheng; Dong, Tao; Qu, Fangfang; Lin, Lei

2017-01-01

Nitrogen is one of the important indexes to evaluate the physiological and biochemical properties of soil. The level of soil nitrogen content influences the nutrient levels of crops directly. The near infrared sensor can be used to detect the soil nitrogen content rapidly, nondestructively, and conveniently. In order to investigate the effect of the different soil water content on soil nitrogen detection by near infrared sensor, the soil samples were dealt with different drying times and the corresponding water content was measured. The drying time was set from 1 h to 8 h, and every 1 h 90 samples (each nitrogen concentration of 10 samples) were detected. The spectral information of samples was obtained by near infrared sensor, meanwhile, the soil water content was calculated every 1 h. The prediction model of soil nitrogen content was established by two linear modeling methods, including partial least squares (PLS) and uninformative variable elimination (UVE). The experiment shows that the soil has the highest detection accuracy when the drying time is 3 h and the corresponding soil water content is 1.03%. The correlation coefficients of the calibration set are 0.9721 and 0.9656, and the correlation coefficients of the prediction set are 0.9712 and 0.9682, respectively. The prediction accuracy of both models is high, while the prediction effect of PLS model is better and more stable. The results indicate that the soil water content at 1.03% has the minimum influence on the detection of soil nitrogen content using a near infrared sensor while the detection accuracy is the highest and the time cost is the lowest, which is of great significance to develop a portable apparatus detecting nitrogen in the field accurately and rapidly. PMID:28880202

Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor.

PubMed

He, Yong; Xiao, Shupei; Nie, Pengcheng; Dong, Tao; Qu, Fangfang; Lin, Lei

2017-09-07

Nitrogen is one of the important indexes to evaluate the physiological and biochemical properties of soil. The level of soil nitrogen content influences the nutrient levels of crops directly. The near infrared sensor can be used to detect the soil nitrogen content rapidly, nondestructively, and conveniently. In order to investigate the effect of the different soil water content on soil nitrogen detection by near infrared sensor, the soil samples were dealt with different drying times and the corresponding water content was measured. The drying time was set from 1 h to 8 h, and every 1 h 90 samples (each nitrogen concentration of 10 samples) were detected. The spectral information of samples was obtained by near infrared sensor, meanwhile, the soil water content was calculated every 1 h. The prediction model of soil nitrogen content was established by two linear modeling methods, including partial least squares (PLS) and uninformative variable elimination (UVE). The experiment shows that the soil has the highest detection accuracy when the drying time is 3 h and the corresponding soil water content is 1.03%. The correlation coefficients of the calibration set are 0.9721 and 0.9656, and the correlation coefficients of the prediction set are 0.9712 and 0.9682, respectively. The prediction accuracy of both models is high, while the prediction effect of PLS model is better and more stable. The results indicate that the soil water content at 1.03% has the minimum influence on the detection of soil nitrogen content using a near infrared sensor while the detection accuracy is the highest and the time cost is the lowest, which is of great significance to develop a portable apparatus detecting nitrogen in the field accurately and rapidly.

Mapping SOC content and bulk density of a disturbed peatland relict with electromagnetic induction and DEM data

NASA Astrophysics Data System (ADS)

Altdorff, Daniel; Bechtold, Michel; van der Kruk, Jan; Tiemeyer, Bärbel; von Hebel, Christian; Huisman, Johan Alexander

2014-05-01

Peatlands represent a huge storage of soil organic carbon (SOC), and there is considerable interest to assess the total amount of carbon stored in these ecosystems. However, reliable field-scale information about peat properties, particularly SOC content and bulk density (BD) necessary to estimate C stocks, remains difficult to obtain. A potential way to acquire information on these properties and its spatial variation is the non-invasive mapping of easily recordable physical variables that correlate with peat properties, such as bulk electrical conductivity (ECa) measured with electromagnetic induction (EMI). However, ECa depends on a range of soil properties, including BD, soil and water chemistry, and water content, and thus results often show complex and site-specific relationships. Therefore, a reliable prediction of SOC and BD from ECa data is not necessarily given. In this study, we aim to explore the usefulness of Multiple Linear Regression (MLR) models to predict the peat soil properties SOC and BD from multi-offset EMI and high-resolution DEM data. The quality of the MLR models is assessed by cross-validation. We use data from a medium-scale disturbed peat relict (approximately 35ha) in Northern Germany. The potential explanatory variables considered in MLR were: EMI data of six different integral depths (approximately 0.25, 0.5, 0.6, 0.9, 1, and 1.80 m), their vertical heterogeneity, as well as several topographical variables extracted from the DEM. Ground truth information for SOC, BD content and peat layer thickness was obtained from 34 soil cores of 1 m depth. Each core was divided into several 5 to 20 cm thick layers so that integral information of the upper 0.25, 0.5, and 1 m as well as from the total peat layer was obtained. For cross-validation of results, we clustered the 34 soil cores into 4 classes using K-means clustering and selected 8 cores for validation from the clusters with a probability that depended on the size of the cluster. With the remaining 26 samples, we performed a stepwise MLR and generated separate models for each depth and soil property. Preliminary results indicate reliable model predictions for SOC and BD (R² = 0.83- 0.95). The RMSE values of the validation ranged between 3.5 and 7.2 vol. % for SOC and 0.13 and 0.37 g/cm³ for BD for the independent samples. This equates roughly the quality of SOC predictions obtained by field application of vis-NIR (visible-near infrared) presented in literature for a similar peatland setting. However, the EMI approach offers the potential to derive information from deeper depths and allows non-invasive mapping of BD variability, which is not possible with vis-NIR. Therefore, this new approach potentially provides a more useful tool for total carbon stock assessment in peatlands.

Determination of the saturated film conductivity to improve the EMFX model in describing the soil hydraulic properties over the entire moisture range

NASA Astrophysics Data System (ADS)

Wang, Yunquan; Ma, Jinzhu; Guan, Huade; Zhu, Gaofeng

2017-06-01

Difficulty in measuring hydraulic conductivity, particularly under dry conditions, calls for methods of predicting the conductivity from easily obtained soil properties. As a complement to the recently published EMFX model, a method based on two specific suction conditions is proposed to estimate saturated film conductivity from the soil water retention curve. This method reduces one fitting parameter in the previous EMFX model, making it possible to predict the hydraulic conductivity from the soil water retention curve over the complete moisture range. Model performance is evaluated with published data of soils in a broad texture range from sand to clay. The testing results indicate that 1) the modified EMFX model (namely the EMFX-K model), incorporating both capillary and adsorption forces, provides good agreement with the conductivity data over the entire moisture range; 2) a value of 0.5 for the tortuosity factor in the EMFX-K model as that in the Mualem's model gives comparable estimation of the relative conductivity associated with the capillary force; and 3) a value of -1.0 × 10-20 J for the Hamaker constant, rather than the commonly used value of -6.0 × 10-20 J, appears to be more appropriate to represent solely the effect of the van der Waals forces and to predict the film conductivity. In comparison with the commonly used van Genuchten-Mualem model, the EMFX-K model significantly improves the prediction of hydraulic conductivity under dry conditions. The sensitivity analysis result suggests that the uncertainty in the film thickness estimation is important in explaining the model underestimation of hydraulic conductivity for the soils with fine texture, in addition to the uncertainties from the measurements and the model structure. High quality data that cover the complete moisture range for a variety of soil textures are required to further test the method.

Lead toxicity thresholds in 17 Chinese soils based on substrate-induced nitrification assay.

PubMed

Li, Ji; Huang, Yizong; Hu, Ying; Jin, Shulan; Bao, Qiongli; Wang, Fei; Xiang, Meng; Xie, Huiting

2016-06-01

The influence of soil properties on toxicity threshold values for Pb toward soil microbial processes is poorly recognized. The impact of leaching on the Pb threshold has not been assessed systematically. Lead toxicity was screened in 17 Chinese soils using a substrate-induced nitrification (SIN) assay under both leached and unleached conditions. The effective concentration of added Pb causing 50% inhibition (EC50) ranged from 185 to >2515mg/kg soil for leached soil and 130 to >2490mg/kg soil for unleached soil. These results represented >13- and >19-fold variations among leached and unleached soils, respectively. Leaching significantly reduced Pb toxicity for 70% of both alkaline and acidic soils tested, with an average leaching factor of 3.0. Soil pH and CEC were the two most useful predictors of Pb toxicity in soils, explaining over 90% of variance in the unleached EC50 value. The relationships established in the present study predicted Pb toxicity within a factor of two of measured values. These relationships between Pb toxicity and soil properties could be used to establish site-specific guidance on Pb toxicity thresholds. Copyright © 2016. Published by Elsevier B.V.

Fingerprinting: Modelling and mapping physical top soil properties with the Mole

NASA Astrophysics Data System (ADS)

Loonstra, Eddie; van Egmond, Fenny

2010-05-01

The Mole is a passive gamma ray soil sensor system. It is designed for the mobile collection of radioactive energy stemming from soil. As the system is passive, it only measures energy that reaches the surface of soil. In general, this energy comes from upto 30 to 40 cm deep, which can be considered topsoil. The gathered energy spectra are logged every second, are processed with the method of Full Spectrum Analysis. This method uses all available spectral data and processes it with a Chi square optimalisation using a set of standard spectra into individual nuclide point data. A standard spectrum is the measured full spectrum of a specific detector derived when exposed to 1 Bq/kg of a nuclide. With this method the outcome of the surveys become quantitative.The outcome of a field survey with the Mole results in a data file containing point information of position, Total Counts and the decay products of 232Th, 238U, 40K and 137Cs. Five elements are therefor available for the modelling of soil properties. There are several ways for the modelling of soil properties with sensor derived gamma ray data. The Mole generates ratio scale output. For modelling a quantitative deterministic approach is used based on sample locations. This process is called fingerprinting. Fingerprinting is a comparison of the concentration of the radioactive trace elements and the lab results (pH, clay content, etc.) by regression analysis. This results in a mathematical formula describing the relationship between a dependent and independent property. The results of the sensor readings are interpolated into a nuclide map with GIS software. With the derived formula a soil property map is composed. The principle of fingerprinting can be applied on large geographical areas for physical soil properties such as clay, loam or sand (50 micron), grain size and organic matter. Collected sample data of previous field surveys within the same region can be used for the prediction of soil properties elsewhere when adding a relatively small number of new calibration samples. For this purpose stratification of data is necessary. All radioactive trace elements play a part in the fingerprinting process for the mapping of physical soil properties. Clay content is best predicted with 232Th. It has a general R2 of 0.75 up to 0,9. The correlation is positive and basically linear. The variation of loam (or sand) content is very well described by 232Th or the combination of 232Th and 238U. It has a comparable R2 to clay. Grain size can be well modelled with 40K, probably due to the fact that this nuclide is positively correlated with matter. 40K is therefor negatively correlated to grain size. The R2 is good: 0,7 to 0,8 on average. The combination of 40K and 137Cs is generally applied for modelling organic matter content with a quality comparable with that of grain size models. Finally, Total Counts turns out to be a very useful parameter for the identification of different types of parent material and of unnatural or non-parent material. Passive gamma ray soil sensors as the Mole are very suitable for high resolution mapping of physical soil properties. The FSA method has the advantage that data from previous surveys becomes applicable in the fingerprinting procedure of new fields. Being able to model the physical soil properties with gamma ray sensors opens the possibility to run pedotransfer function models for a particular survey.

Effects of soil properties on the uptake of pharmaceuticals into earthworms.

PubMed

Carter, Laura J; Ryan, Jim J; Boxall, Alistair B A

2016-06-01

Pharmaceuticals can enter the soil environment when animal slurries and sewage sludge are applied to land as a fertiliser or during irrigation with contaminated water. These pharmaceuticals may then be taken up by soil organisms possibly resulting in toxic effects and/or exposure of organisms higher up the food chain. This study investigated the influence of soil properties on the uptake and depuration of pharmaceuticals (carbamazepine, diclofenac, fluoxetine and orlistat) in the earthworm Eisenia fetida. The uptake and accumulation of pharmaceuticals into E. fetida changed depending on soil type. Orlistat exhibited the highest pore water based bioconcentration factors (BCFs) and displayed the largest differences between soil types with BCFs ranging between 30.5 and 115.9. For carbamazepine, diclofenac and fluoxetine BCFs ranged between 1.1 and 1.6, 7.0 and 69.6 and 14.1 and 20.4 respectively. Additional analysis demonstrated that in certain treatments the presence of these chemicals in the soil matrices changed the soil pH over time, with a statistically significant pH difference to control samples. The internal pH of E. fetida also changed as a result of incubation in pharmaceutically spiked soil, in comparison to the control earthworms. These results demonstrate that a combination of soil properties and pharmaceutical physico-chemical properties are important in terms of predicting pharmaceutical uptake in terrestrial systems and that pharmaceuticals can modify soil and internal earthworm chemistry which may hold wider implications for risk assessment. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

The respective effects of soil heavy metal fractions by sequential extraction procedure and soil properties on the accumulation of heavy metals in rice grains and brassicas.

PubMed

Xiao, Ling; Guan, Dongsheng; Peart, M R; Chen, Yujuan; Li, Qiqi

2017-01-01

This study was carried out to examine heavy metal accumulation in rice grains and brassicas and to identify the different controls, such as soil properties and soil heavy metal fractions obtained by the Community Bureau of Reference (BCR) sequential extraction, in their accumulation. In Guangdong Province, South China, rice grain and brassica samples, along with their rhizospheric soil, were collected from fields on the basis of distance downstream from electroplating factories, whose wastewater was used for irrigation. The results showed that long-term irrigation using the electroplating effluent has not only enriched the rhizospheric soil with Cd, Cr, Cu, and Zn but has also increased their mobility and bioavailability. The average concentrations of Cd and Cr in rice grains and brassicas from closest to the electroplating factories were significantly higher than those from the control areas. Results from hybrid redundancy analysis (hRDA) and redundancy analysis (RDA) showed that the BCR fractions of soil heavy metals could explain 29.0 and 46.5 % of total eigenvalue for heavy metal concentrations in rice grains and brassicas, respectively, while soil properties could only explain 11.1 and 33.4 %, respectively. This indicated that heavy metal fractions exerted more control upon their concentrations in rice grains and brassicas than soil properties. In terms of metal interaction, an increase of residual Zn in paddy soil or a decrease of acid soluble Cd in the brassica soil could enhance the accumulation of Cd, Cu, Cr, and Pb in both rice grains and brassicas, respectively, while the reducible or oxidizable Cd in soil could enhance the plants' accumulation of Cr and Pb. The RDA showed an inhibition effect of sand content and CFO on the accumulation of heavy metals in rice grains and brassicas. Moreover, multiple stepwise linear regression could offer prediction for Cd, Cu, Cr, and Zn concentrations in the two crops by soil heavy metal fractions and soil properties.

Multiscale Bayesian neural networks for soil water content estimation

NASA Astrophysics Data System (ADS)

Jana, Raghavendra B.; Mohanty, Binayak P.; Springer, Everett P.

2008-08-01

Artificial neural networks (ANN) have been used for some time now to estimate soil hydraulic parameters from other available or more easily measurable soil properties. However, most such uses of ANNs as pedotransfer functions (PTFs) have been at matching spatial scales (1:1) of inputs and outputs. This approach assumes that the outputs are only required at the same scale as the input data. Unfortunately, this is rarely true. Different hydrologic, hydroclimatic, and contaminant transport models require soil hydraulic parameter data at different spatial scales, depending upon their grid sizes. While conventional (deterministic) ANNs have been traditionally used in these studies, the use of Bayesian training of ANNs is a more recent development. In this paper, we develop a Bayesian framework to derive soil water retention function including its uncertainty at the point or local scale using PTFs trained with coarser-scale Soil Survey Geographic (SSURGO)-based soil data. The approach includes an ANN trained with Bayesian techniques as a PTF tool with training and validation data collected across spatial extents (scales) in two different regions in the United States. The two study areas include the Las Cruces Trench site in the Rio Grande basin of New Mexico, and the Southern Great Plains 1997 (SGP97) hydrology experimental region in Oklahoma. Each region-specific Bayesian ANN is trained using soil texture and bulk density data from the SSURGO database (scale 1:24,000), and predictions of the soil water contents at different pressure heads with point scale data (1:1) inputs are made. The resulting outputs are corrected for bias using both linear and nonlinear correction techniques. The results show good agreement between the soil water content values measured at the point scale and those predicted by the Bayesian ANN-based PTFs for both the study sites. Overall, Bayesian ANNs coupled with nonlinear bias correction are found to be very suitable tools for deriving soil hydraulic parameters at the local/fine scale from soil physical properties at coarser-scale and across different spatial extents. This approach could potentially be used for soil hydraulic properties estimation and downscaling.

Bidirectional Reflectance Modeling of Non-homogeneous Plant Canopies

NASA Technical Reports Server (NTRS)

Norman, J. M.

1984-01-01

Efforts to develop a three dimensional model to predict canopy, bidirectional reflectance for heterogenous plant stands using incident radiation and canopy structural descriptions as inputs are described. Utility programs were developed to cope with the complex output from the 3 dimensional model. In addition an attempt was made to define leaf and soil properties, which are appropriate to the mode, by measuring leaf and soil bidirectional reflectance distribution functions; since almost no data exist on these distributions. In the process it was realized that most models probably are using the wrong leaf spectral properties, and that off-nadir reflectance measurements are difficult to make because of non-Lambertian properties of reference surfaces. Also, in the visible wavebands, rough soil may not be distinguishable from canopies when viewed from above.

Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties.

PubMed

Sila, Andrew M; Shepherd, Keith D; Pokhariyal, Ganesh P

2016-04-15

We propose four methods for finding local subspaces in large spectral libraries. The proposed four methods include (a) cosine angle spectral matching; (b) hit quality index spectral matching; (c) self-organizing maps and (d) archetypal analysis methods. Then evaluate prediction accuracies for global and subspaces calibration models. These methods were tested on a mid-infrared spectral library containing 1907 soil samples collected from 19 different countries under the Africa Soil Information Service project. Calibration models for pH, Mehlich-3 Ca, Mehlich-3 Al, total carbon and clay soil properties were developed for the whole library and for the subspace. Root mean square error of prediction was used to evaluate predictive performance of subspace and global models. The root mean square error of prediction was computed using a one-third-holdout validation set. Effect of pretreating spectra with different methods was tested for 1st and 2nd derivative Savitzky-Golay algorithm, multiplicative scatter correction, standard normal variate and standard normal variate followed by detrending methods. In summary, the results show that global models outperformed the subspace models. We, therefore, conclude that global models are more accurate than the local models except in few cases. For instance, sand and clay root mean square error values from local models from archetypal analysis method were 50% poorer than the global models except for subspace models obtained using multiplicative scatter corrected spectra with which were 12% better. However, the subspace approach provides novel methods for discovering data pattern that may exist in large spectral libraries.

Multivariate functions for predicting the sorption of 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-tricyclohexane (RDX) among taxonomically distinct soils.

PubMed

Katseanes, Chelsea K; Chappell, Mark A; Hopkins, Bryan G; Durham, Brian D; Price, Cynthia L; Porter, Beth E; Miller, Lesley F

2016-11-01

After nearly a century of use in numerous munition platforms, TNT and RDX contamination has turned up largely in the environment due to ammunition manufacturing or as part of releases from low-order detonations during training activities. Although the basic knowledge governing the environmental fate of TNT and RDX are known, accurate predictions of TNT and RDX persistence in soil remain elusive, particularly given the universal heterogeneity of pedomorphic soil types. In this work, we proposed a new solution for modeling the sorption and persistence of these munition constituents as multivariate mathematical functions correlating soil attribute data over a variety of taxonomically distinct soil types to contaminant behavior, instead of a single constant or parameter of a specific absolute value. To test this idea, we conducted experiments measuring the sorption of TNT and RDX on taxonomically different soil types that were extensively physical and chemically characterized. Statistical decomposition of the log-transformed, and auto-scaled soil characterization data using the dimension-reduction technique PCA (principal component analysis) revealed a strong latent structure based in the multiple pairwise correlations among the soil properties. TNT and RDX sorption partitioning coefficients (KD-TNT and KD-RDX) were regressed against this latent structure using partial least squares regression (PLSR), generating a 3-factor, multivariate linear functions. Here, PLSR models predicted KD-TNT and KD-RDX values based on attributes contributing to endogenous alkaline/calcareous and soil fertility criteria, respectively, exhibited among the different soil types: We hypothesized that the latent structure arising from the strong covariance of full multivariate geochemical matrix describing taxonomically distinguished soil types may provide the means for potentially predicting complex phenomena in soils. The development of predictive multivariate models tuned to a local soil's taxonomic designation would have direct benefit to military range managers seeking to anticipate the environmental risks of training activities on impact sites. Published by Elsevier Ltd.

Selenium deficiency risk predicted to increase under future climate change

PubMed Central

Jones, Gerrad D.; Droz, Boris; Greve, Peter; Gottschalk, Pia; Poffet, Deyan; McGrath, Steve P.; Seneviratne, Sonia I.; Smith, Pete; Winkel, Lenny H. E.

2017-01-01

Deficiencies of micronutrients, including essential trace elements, affect up to 3 billion people worldwide. The dietary availability of trace elements is determined largely by their soil concentrations. Until now, the mechanisms governing soil concentrations have been evaluated in small-scale studies, which identify soil physicochemical properties as governing variables. However, global concentrations of trace elements and the factors controlling their distributions are virtually unknown. We used 33,241 soil data points to model recent (1980–1999) global distributions of Selenium (Se), an essential trace element that is required for humans. Worldwide, up to one in seven people have been estimated to have low dietary Se intake. Contrary to small-scale studies, soil Se concentrations were dominated by climate–soil interactions. Using moderate climate-change scenarios for 2080–2099, we predicted that changes in climate and soil organic carbon content will lead to overall decreased soil Se concentrations, particularly in agricultural areas; these decreases could increase the prevalence of Se deficiency. The importance of climate–soil interactions to Se distributions suggests that other trace elements with similar retention mechanisms will be similarly affected by climate change. PMID:28223487

Selenium deficiency risk predicted to increase under future climate change.

PubMed

Jones, Gerrad D; Droz, Boris; Greve, Peter; Gottschalk, Pia; Poffet, Deyan; McGrath, Steve P; Seneviratne, Sonia I; Smith, Pete; Winkel, Lenny H E

2017-03-14

Deficiencies of micronutrients, including essential trace elements, affect up to 3 billion people worldwide. The dietary availability of trace elements is determined largely by their soil concentrations. Until now, the mechanisms governing soil concentrations have been evaluated in small-scale studies, which identify soil physicochemical properties as governing variables. However, global concentrations of trace elements and the factors controlling their distributions are virtually unknown. We used 33,241 soil data points to model recent (1980-1999) global distributions of Selenium (Se), an essential trace element that is required for humans. Worldwide, up to one in seven people have been estimated to have low dietary Se intake. Contrary to small-scale studies, soil Se concentrations were dominated by climate-soil interactions. Using moderate climate-change scenarios for 2080-2099, we predicted that changes in climate and soil organic carbon content will lead to overall decreased soil Se concentrations, particularly in agricultural areas; these decreases could increase the prevalence of Se deficiency. The importance of climate-soil interactions to Se distributions suggests that other trace elements with similar retention mechanisms will be similarly affected by climate change.

Modelling soil-water dynamics in the rootzone of structured and water-repellent soils

NASA Astrophysics Data System (ADS)

Brown, Hamish; Carrick, Sam; Müller, Karin; Thomas, Steve; Sharp, Joanna; Cichota, Rogerio; Holzworth, Dean; Clothier, Brent

2018-04-01

In modelling the hydrology of Earth's critical zone, there are two major challenges. The first is to understand and model the processes of infiltration, runoff, redistribution and root-water uptake in structured soils that exhibit preferential flows through macropore networks. The other challenge is to parametrise and model the impact of ephemeral hydrophobicity of water-repellent soils. Here we have developed a soil-water model, which is based on physical principles, yet possesses simple functionality to enable easier parameterisation, so as to predict soil-water dynamics in structured soils displaying time-varying degrees of hydrophobicity. Our model, WEIRDO (Water Evapotranspiration Infiltration Redistribution Drainage runOff), has been developed in the APSIM Next Generation platform (Agricultural Production Systems sIMulation). The model operates on an hourly time-step. The repository for this open-source code is https://github.com/APSIMInitiative/ApsimX. We have carried out sensitivity tests to show how WEIRDO predicts infiltration, drainage, redistribution, transpiration and soil-water evaporation for three distinctly different soil textures displaying differing hydraulic properties. These three soils were drawn from the UNSODA (Unsaturated SOil hydraulic Database) soils database of the United States Department of Agriculture (USDA). We show how preferential flow process and hydrophobicity determine the spatio-temporal pattern of soil-water dynamics. Finally, we have validated WEIRDO by comparing its predictions against three years of soil-water content measurements made under an irrigated alfalfa (Medicago sativa L.) trial. The results provide validation of the model's ability to simulate soil-water dynamics in structured soils.

Modification of an Existing In vitro Method to Predict Relative ...

EPA Pesticide Factsheets

The soil matrix can sequester arsenic (As) and reduces its exposure by soil ingestion. In vivo dosing studies and in vitro gastrointestinal (IVG) methods have been used to predict relative bioavailable (RBA) As. Originally, the Ohio State University (OSU-IVG) method predicted RBA As for soils exclusively from mining and smelting sites with a median of 5,636 mg As kg-1. The objectives of the current study were to (i) evaluate the ability of the OSU-IVG method to predict RBA As for As contaminated soils with a wider range of As content and As contaminant sources, and (ii) evaluate a modified extraction procedure's ability to improve prediction of RBA As. In vitro bioaccessible (IVBA) by OSU-IVG and California Bioaccessibility Method (CAB) methods, RBA As, speciation, and properties of 33 As contaminated soils were determined. Total As ranged from 162 to 12,483 mg kg-1 with a median of 731 mg kg-1. RBA As ranged from 1.30 to 60.0% and OSU-IVG IVBA As ranged from 0.80 to 52.3%. Arsenic speciation was predominantly As(V) adsorbed to hydrous ferric oxide (HFO) or iron (Fe), manganese (Mn), and aluminum (Al) oxides. The OSU-IVG often extracted significantly less As in vitro than in vivo RBA As, in particularly for soils from historical gold mining. The CAB method, which is a modified OSU-IVG method extracted more As than OSU-IVG for most soils, resulting in a more accurate predictor than OSU-IVG, especially for low to moderately contaminated soils (<1,500 mg As

Reduction kinetics of hexavalent chromium in soils and its correlation with soil properties.

PubMed

Xiao, Wendan; Zhang, Yibin; Li, Tingqiang; Chen, Bao; Wang, Huan; He, Zhenli; Yang, Xiaoe

2012-01-01

The toxicity of chromium (Cr) to biota is related to its chemical forms and consequently to the redox conditions of soils. Hexavalent Cr[Cr(VI)] may undergo natural attenuation through reduction processes. In this study, the reduction kinetics of Cr(VI) in seven soils and its relationships with soil properties were investigated with laboratory incubation experiments. The results indicate that the reduction of Cr(VI) can be described by a first-order reaction. The reduction rates of Cr(VI) in the seven soils decreased in the order: Udic Ferrisols > Stagnic Anthrosols > Calcaric Regosols > Mollisol > Typic Haplustalf > Periudic Argosols > Ustic Cambosols. Simple correlation analysis revealed that the reduction of Cr(VI) in soils was positively related to organic matter content, dissolved organic matter content, Fe(II) content, clay fraction, and to the diversity index of the bacterial community but negatively correlated with easily reducible Mn content. Using stepwise regression, the reduction of Cr(VI) in soil could be quantitatively predicted by the measurement of dissolved organic matter content, Fe(II) content, pH, and soil particle size distribution, with a fitting level of 95.5%. The results indicated that the reduction of Cr(VI) in natural soils is not controlled by a single soil property but is the result of the combined effects of dissolved organic matter, Fe(II), pH, and soil particle size distribution. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Response to comment on "Climate legacies drive global soil carbon stocks in terrestrial ecosystem".

PubMed

Delgado-Baquerizo, Manuel; Eldridge, David J; Maestre, Fernando T; Karunaratne, Senani B; Trivedi, Pankaj; Reich, Peter B; Singh, Brajesh K

2018-03-01

The technical comment from Sanderman provides a unique opportunity to deepen our understanding of the mechanisms explaining the role of paleoclimate in the contemporary distribution of global soil C content, as reported in our article. Sanderman argues that the role of paleoclimate in predicting soil C content might be accounted for by using slowly changing soil properties as predictors. This is a key point that we highlighted in the supplementary materials of our article, which demonstrated, to the degree possible given available data, that soil properties alone cannot account for the unique portion of the variation in soil C explained by paleoclimate. Sanderman also raised an interesting question about how paleoclimate might explain the contemporary amount of C in our soils if such a C is relatively new, particularly in the topsoil layer. There is one relatively simple, yet plausible, reason. A soil with a higher amount of C, a consequence of accumulation over millennia, might promote higher contemporary C fixation rates, leading to a higher amount of new C in our soils. Thus, paleoclimate can be a good predictor of the amount of soil C in soil, but not necessarily of its age. In summary, Sanderman did not question the validity of our results but rather provides an alternative potential mechanistic explanation for the conclusion of our original article, that is, that paleoclimate explains a unique portion of the global variation of soil C content that cannot be accounted for by current climate, vegetation attributes, or soil properties.

Predicting watershed acidification under alternate rainfall conditions

USGS Publications Warehouse

Huntington, T.G.

1996-01-01

The effect of alternate rainfall scenarios on acidification of a forested watershed subjected to chronic acidic deposition was assessed using the model of acidification of groundwater in catchments (MAGIC). The model was calibrated at the Panola Mountain Research Watershed, near Atlanta, Georgia, U.S.A. using measured soil properties, wet and dry deposition, and modeled hydrologic routing. Model forecast simulations were evaluated to compare alternate temporal averaging of rainfall inputs and variations in rainfall amount and seasonal distribution. Soil water alkalinity was predicted to decrease to substantially lower concentrations under lower rainfall compared with current or higher rainfall conditions. Soil water alkalinity was also predicted to decrease to lower levels when the majority of rainfall occurred during the growing season compared with other rainfall distributions. Changes in rainfall distribution that result in decreases in net soil water flux will temporarily delay acidification. Ultimately, however, decreased soil water flux will result in larger increases in soil- adsorbed sulfur and soil-water sulfate concentrations and decreases in alkalinity when compared to higher water flux conditions. Potential climate change resulting in significant changes in rainfall amounts, seasonal distribution of rainfall, or evapotranspiration will change net soil water flux and, consequently, will affect the dynamics of the acidification response to continued sulfate loading.

Seasonal Patterns of Soil Respiration and Related Soil Biochemical Properties under Nitrogen Addition in Winter Wheat Field.

PubMed

Liang, Guopeng; Houssou, Albert A; Wu, Huijun; Cai, Dianxiong; Wu, Xueping; Gao, Lili; Li, Jing; Wang, Bisheng; Li, Shengping

2015-01-01

Understanding the changes of soil respiration under increasing N fertilizer in cropland ecosystems is crucial to accurately predicting global warming. This study explored seasonal variations of soil respiration and its controlling biochemical properties under a gradient of Nitrogen addition during two consecutive winter wheat growing seasons (2013-2015). N was applied at four different levels: 0, 120, 180 and 240 kg N ha(-1) year(-1) (denoted as N0, N12, N18 and N24, respectively). Soil respiration exhibited significant seasonal variation and was significantly affected by soil temperature with Q10 ranging from 2.04 to 2.46 and from 1.49 to 1.53 during 2013-2014 and 2014-2015 winter wheat growing season, respectively. Soil moisture had no significant effect on soil respiration during 2013-2014 winter wheat growing season but showed a significant and negative correlation with soil respiration during 2014-2015 winter wheat growing season. Soil respiration under N24 treatment was significantly higher than N0 treatment. Averaged over the two growing seasons, N12, N18 and N24 significantly increased soil respiration by 13.4, 16.4 and 25.4% compared with N0, respectively. N addition also significantly increased easily extractable glomalin-related soil protein (EEG), soil organic carbon (SOC), total N, ammonium N and nitrate N contents. In addition, soil respiration was significantly and positively correlated with β-glucosidase activity, EEG, SOC, total N, ammonium N and nitrate N contents. The results indicated that high N fertilization improved soil chemical properties, but significantly increased soil respiration.

Physically-based model of soil hydraulic properties accounting for variable contact angle and its effect on hysteresis

NASA Astrophysics Data System (ADS)

Diamantopoulos, Efstathios; Durner, Wolfgang

2013-09-01

The description of soil water movement in the unsaturated zone requires the knowledge of the soil hydraulic properties, i.e. the water retention and the hydraulic conductivity function. A great amount of parameterizations for this can be found in the literature, the majority of which represent the complex pore space of soils as a bundle of cylindrical capillary tubes of various sizes. The assumption of zero contact angles between water and surface of the grains is also made. However, these assumptions limit the predictive capabilities of these models, leading often to errors in the prediction of water dynamics in soils. We present a pore-scale analysis for equilibrium liquid configuration in angular pores taking pore-scale hysteresis and the effect of contact angle into account. Furthermore, we propose a derivation of the hydraulic conductivity function, again as a function of the contact angle. An additional parameter was added to the conductivity function in order take into account effects which are not included in the analysis. Finally, we upscale our model from the pore to the sample scale by assuming a gamma statistical distribution of the pore sizes. Closed-form expressions are derived for both water retention and conductivity functions. The new model was tested against experimental data from multistep inflow/outflow (MSI/MSO) experiments for a sandy material. They were conducted using ethanol and water as the wetting liquid. Ethanol was assumed to form a zero contact angle with the soil grains. By keeping constant the parameters fitted from the ethanol MSO experiment we could predict the ethanol MSI dynamics based on our theory. Furthermore, by keeping constant the pore size distribution parameters from the ethanol experiments we could also predict very well the water dynamics for the MSO experiment. Lastly, we could predict the imbibition dynamics for the water MSI experiment by introducing a finite value of the contact angle. Most importantly, the predictions for both ethanol and water MSI/MSO dynamics were made by assuming a unique pore-size distribution.

Nutrient stoichiometry of temperate trees and effects on the coupled cycles of carbon, nitrogen, and cations in soil

NASA Astrophysics Data System (ADS)

Mueller, K. E.; Oleksyn, J.; Hobbie, S. E.; Reich, P.; Chorover, J. D.; Freeman, K. H.; Eissenstat, D.

2009-12-01

Nutrient stoichiometry of leaf litter (LL) is a potentially important driver of plant effects on soil biogeochemistry; it is also responsive to environmental perturbations and differs among plant functional groups that may have predictable responses to the environment. Thus variation in LL nutrient stoichiometry may provide a predictive framework for the influence of global change on soil. However, this approach depends on several key, but poorly tested assumptions, including: 1) other plant organs follow similar patterns and have similar effects on soil biogeochemistry, and 2) patterns in leaf traits, functional group dominance, and soil properties across large-spatial scales are predictive at smaller scales. To address these assumptions and test the utility of nutrient stoichiometry as a predictive framework for soil change, we synthesize data on tree stoichiometry and soil biogeochemistry from a long-term (> 30 yr) common garden experiment containing replicated, monoculture plots of 14 temperate tree species. LL nutrient stoichiometry alone is insufficient to explain differences in biogeochemical cycling among tree species, in part due to the dissimilarity of leaf and root traits within species. Notably, different elements and plant organs have independent impacts on soil biogeochemistry. LL nitrogen (N) concentration and lignin:N ratios have small or negligible effects on soil carbon (C), N, and cation cycling, while LL-calcium (Ca) drives differences in litter decomposition and soil pH among species in a manner consistent with nutrient requirements of anecic earthworms. However, LL-Ca effects on C and N cycles in soil appear minor compared to the influences of root N and, unexpectedly, green leaf N, which combine to drive differences in soil N dynamics via unique mechanisms consistent with nutrient requirements of soil microbes and the trees. In turn, soil N dynamics are strongly correlated with soil acidity and C stabilization. By taking into account the stoichiometry of each plant organ, of soil microbes and fauna, and the interactions among C, N, and cation cycles, the predictive capacity of tree nutrient stoichiometry for understanding soil change is apparent, albeit complex.

Cropland Field Monitoring: MMV Page 1 Montana Cropland Enrolled Farm Fields Carbon Sequestration Field Sampling, Measurement, Monitoring, and Verification: Application of Visible-Near Infrared Diffuse Reflectance Spectroscopy (VNIR) and Laser-induced Breakdown Spectroscopy (LIBS)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee Spangler; Ross Bricklemyer; David Brown

2012-03-15

There is growing need for rapid, accurate, and inexpensive methods to measure, and verify soil organic carbon (SOC) change for national greenhouse gas accounting and the development of a soil carbon trading market. Laboratory based soil characterization typically requires significant soil processing, which is time and resource intensive. This severely limits application for large-region soil characterization. Thus, development of rapid and accurate methods for characterizing soils are needed to map soil properties for precision agriculture applications, improve regional and global soil carbon (C) stock and flux estimates and efficiently map sub-surface metal contamination, among others. The greatest gains for efficientmore » soil characterization will come from collecting soil data in situ, thus minimizing soil sample transportation, processing, and lab-based measurement costs. Visible and near-infrared diffuse reflectance spectroscopy (VisNIR) and laser-induced breakdown spectroscopy (LIBS) are two complementary, yet fundamentally different spectroscopic techniques that have the potential to meet this need. These sensors have the potential to be mounted on a soil penetrometer and deployed for rapid soil profile characterization at field and landscape scales. Details of sensor interaction, efficient data management, and appropriate statistical analysis techniques for model calibrations are first needed. In situ or on-the-go VisNIR spectroscopy has been proposed as a rapid and inexpensive tool for intensively mapping soil texture and organic carbon (SOC). While lab-based VisNIR has been established as a viable technique for estimating various soil properties, few experiments have compared the predictive accuracy of on-the-go and lab-based VisNIR. Eight north central Montana wheat fields were intensively interrogated using on-the-go and lab-based VisNIR. Lab-based spectral data consistently provided more accurate predictions than on-the-go data. However, neither in situ nor lab-based spectroscopy yielded even semi-quantitative SOC predictions. There was little SOC variability to explain across the eight fields, and on-the-go VisNIR was not able to capture the subtle SOC variability in these Montana soils. With more variation in soil clay content compared to SOC, both lab and on-the-go VisNIR showed better explanatory power. There are several potential explanations for poor on-the-go predictive accuracy: soil heterogeneity, field moisture, consistent sample presentation, and a difference between the spatial support of on-the-go measurements and soil samples collected for laboratory analyses. Though the current configuration of a commercially available on-the-go VisNIR system allows for rapid field scanning, on-the-go soil processing (i.e. drying, crushing, and sieving) could improve soil carbon predictions. Laser-induced breakdown spectroscopy (LIBS) is an emerging elemental analysis technology with the potential to provide rapid, accurate and precise analysis of soil constituents, such as carbon, in situ across landscapes. The research team evaluated the accuracy of LIBS for measuring soil profile carbon in field-moist, intact soil cores simulating conditions that might be encountered by a probe-mounted LIBS instrument measuring soil profile carbon in situ. Over the course of three experiments, more than120 intact soil cores from eight north central Montana wheat fields and the Washington State University (WSU) Cook Agronomy Farm near Pullman, WA were interrogated with LIBS for rapid total carbon (TC), inorganic carbon (IC), and SOC determination. Partial least squares regression models were derived and independently validated at field- and regional scales. Researchers obtained the best LIBS validation predictions for IC followed by TC and SOC. Laser-induced breakdown spectroscopy is fundamentally an elemental analysis technique, yet LIBS PLS2 models appeared to discriminate IC from TC. Regression coefficients from initial models suggested a reliance upon stoichiometric relationships between carbon (247.8 nm) and other elements related to total and inorganic carbon in the soil matrix [Ca (210.2 nm, 211.3 nm, and 220.9 nm), Mg (279.55-280.4 nm, 285.26 nm), and Si (251.6 nm, 288.1 nm)]. Expanding the LIBS spectral range to capture emissions from a broader range of elements related to soil organic matter was explored using two spectrometer systems to improve SOC predictions. Results for increasing the spectral range of LIBS to the full 200-800 nm found modest gains in prediction accuracy for IC, but no gains for predicting TC or SOC. Poor SOC predictions are likely a function of (1) the lack of a consistent/definable molecular composition of SOC, (2) relatively little variation in SOC across field sites, and (3) inorganic carbon constituting the primary form of soil carbon, particularly for Montana soils.« less

Impacts of single and recurrent wildfires on topsoil moisture regime

NASA Astrophysics Data System (ADS)

González-Pelayo, Oscar; Malvar, Maruxa; van den Elsen, Erik; Hosseini, Mohammadreza; Coelho, Celeste; Ritsema, Coen; Bautista, Susana; Keizer, Jacob

2017-04-01

The increasing fire recurrence on forest in the Mediterranean basin is well-established by future climate scenarios due to land use changes and climate predictions. By this, shifts on mature pine woodlands to shrub rangelands are of major importance on forest ecosystems buffer functions, since historical patterns of established vegetation help to recover from fire disturbances. This fact, together with the predicted expansion of the drought periods, will affect feedback processes of vegetation patterns since water availability on these seasons are driven by post-fire local soil properties. Although fire impacts of soil properties and water availability has been widely studied using the fire severity as the main factor, little research is developed on post-fire soil moisture patterns, including the fire recurrence as a key explanatory variable. The following research investigated, in pine woodlands of north central Portugal, the short-term consequences (one year after a fire) of wildfire recurrence on the surface soil moisture content (SMC) and on effective soil water (SWEFF, parameter that includes actual daily soil moisture, soil field capacity-FC and permanent wilting point-PWP). The study set-up includes analyses at two fire recurrence scenarios (1x- and 4x-burnt since 1975), at a patch level (shrub patch/interpatch) and at two soil depths (2.5 and 7.5 cm) in a nested approach. Understanding how fire recurrence affects water in soil over space and time is the main goal of this research. The use of soil moisture sensors in a nested approach, the rainfall features and analyses on basic soil properties as soil organic matter, texture, bulk density, pF curves, soil water repellency and soil surface components will establish which factors has the largest role in controlling soil moisture behavior. Main results displayed, in a seasonal and yearly basis, no differences on SMC as increasing fire recurrence (1x- vs 4x-burnt) neither between patch/interpatch microsites at both two soil depths. Otherwise, in a yearly basis and during soil drying cycles, it was found less effective water on soil at the surface layers of the 4x-burnt and between shrub interpatches, based on the worst soil hydrological conditions (PWP) and the increasing percentage of abiotic soil surface components as increasing fire recurrence. Our results suggest that the inclusion of soil hydrological properties, as pF-curves, on the soil water effectiveness calculation seems to be a better indicator of water availability that volumetric SM per se. Otherwise, the use of a nested approach methodology, stresses how fire recurrence, expected increases in the summer drought spells and, the increasing dominance of abiotic soil surface components, are the factors that much influence soil eco-hydrological functioning in fire prone ecosystems. Furthermore, this research point out how post-fire soil structural quality into plant interpatches could provoke looping feedback processes triggering desertification situations also in humid Mediterranean forestlands.

Plant-soil feedbacks from 30-year family-specific soil cultures: phylogeny, soil chemistry and plant life stage.

PubMed

Mehrabi, Zia; Bell, Thomas; Lewis, Owen T

2015-06-01

Intraspecific negative feedback effects, where performance is reduced on soils conditioned by conspecifics, are widely documented in plant communities. However, interspecific feedbacks are less well studied, and their direction, strength, causes, and consequences are poorly understood. If more closely related species share pathogens, or have similar soil resource requirements, plants may perform better on soils conditioned by more distant phylogenetic relatives. There have been few empirical tests of this prediction across plant life stages, and none of which attempt to account for soil chemistry. Here, we test the utility of phylogeny for predicting soil feedback effects on plant survival and performance (germination, seedling survival, growth rate, biomass). We implement a full factorial experiment growing species representing five families on five plant family-specific soil sources. Our experiments exploit soils that have been cultured for over 30 years in plant family-specific beds at Oxford University Botanic Gardens. Plant responses to soil source were idiosyncratic, and species did not perform better on soils cultured by phylogenetically more distant relatives. The magnitude and sign of feedback effects could, however, be explained by differences in the chemical properties of "home" and "away" soils. Furthermore, the direction of soil chemistry-related plant-soil feedbacks was dependent on plant life stage, with the effects of soil chemistry on germination success and accumulation of biomass inversely related. Our results (1) suggest that the phylogenetic distance between plant families cannot predict plant-soil feedbacks across multiple life stages, and (2) highlight the need to consider changes in soil chemistry as an important driver of population responses. The contrasting responses at plant life stages suggest that studies focusing on brief phases in plant demography (e.g., germination success) may not give a full picture of plant-soil feedback effects.

Impact of soil properties on selected pharmaceuticals adsorption in soils

NASA Astrophysics Data System (ADS)

Kodesova, Radka; Kocarek, Martin; Klement, Ales; Fer, Miroslav; Golovko, Oksana; Grabic, Roman; Jaksik, Ondrej

2014-05-01

The presence of human and veterinary pharmaceuticals in the environment has been recognized as a potential threat. Pharmaceuticals may contaminate soils and consequently surface and groundwater. Study was therefore focused on the evaluation of selected pharmaceuticals adsorption in soils, as one of the parameters, which are necessary to know when assessing contaminant transport in soils. The goals of this study were: (1) to select representative soils of the Czech Republic and to measure soil physical and chemical properties; (2) to measure adsorption isotherms of selected pharmaceuticals; (3) to evaluate impact of soil properties on pharmaceutical adsorptions and to propose pedotransfer rules for estimating adsorption coefficients from the measured soil properties. Batch sorption tests were performed for 6 selected pharmaceuticals (beta blockers Atenolol and Metoprolol, anticonvulsant Carbamazepin, and antibiotics Clarithromycin, Trimetoprim and Sulfamethoxazol) and 13 representative soils (soil samples from surface horizons of 11 different soil types and 2 substrates). The Freundlich equations were used to describe adsorption isotherms. The simple correlations between measured physical and chemical soil properties (soil particle density, soil texture, oxidable organic carbon content, CaCO3 content, pH_H2O, pH_KCl, exchangeable acidity, cation exchange capacity, hydrolytic acidity, basic cation saturation, sorption complex saturation, salinity), and the Freundlich adsorption coefficients were assessed using Pearson correlation coefficient. Then multiple-linear regressions were applied to predict the Freundlich adsorption coefficients from measured soil properties. The largest adsorption was measured for Clarithromycin (average value of 227.1) and decreased as follows: Trimetoprim (22.5), Metoprolol (9.0), Atenolol (6.6), Carbamazepin (2.7), Sulfamethoxazol (1.9). Absorption coefficients for Atenolol and Metoprolol closely correlated (R=0.85), and both were also related to absorption coefficients of Carbamazepin (R=0.67 and 0.68). Positive correlation was found between Trimetoprim absorption coefficients and Atenolol, Metoprolol or Carbamazepin absorption coefficients. The negative relationship was found between absorption coefficients of Sulfomethoxazol and Clarithromycin (R=-0.80). Sulfamethoxazol absorption coefficient was negatively related to pH_H2O, pH_KCL or sorption complex saturation and positively to the hydrolytic acidity or exchangeable acidity. Trimetoprim absorption coefficient was positively related to the oxidable organic carbon content, cation exchange capacity, basic cation saturation or silt content and negatively to particle density or sand content. Clarithromycin absorption coefficient was positively related to pH_H2O, pH_KCL, CaCO3 content, basic cation saturation or sorption complex saturation and negatively to hydrolytic acidity or exchangeable acidity. Atenolol and Metoprolol absorption coefficients were positively related to the oxidable organic carbon content, cation exchange capacity, basic cation saturation, salinity, clay content or silt content, and negatively to the particle density or sand content. Finally Carbamazepin absorption coefficient was positively related to the oxidable organic carbon content, cation exchange capacity or basic cation saturation, and negatively to the particle density or sand content. Evaluated pedotransfer rules for different pharmaceuticals included different sets of soil properties. Absorption coefficients could be predicted from: the hydrolytic acidity (Sulfamethoxazol), the oxidable organic carbon content (Trimetoprim and Carbamazepin), the oxidable organic carbon content, hydrolytic acidity and cation exchange capacity (Clarithromycin), the basic cation saturation (Atenolol and Metoprolol). Acknowledgement: Authors acknowledge the financial support of the Czech Science Foundation (Project No. 13-12477S).

Boundary Condition Effects on Hillslope Form and Soil Development Along a Climatic Gradient From Semiarid to Hyperarid in Northern Chile

NASA Astrophysics Data System (ADS)

Owen, J. J.; Dietrich, W. E.; Nishiizumi, K.; Bellugi, D.; Amundson, R.

2008-12-01

Modeling the development of hillslopes using mass balance equations has generated many testable hypotheses related to morphology, process rates, and soil properties, however it is only relatively recently that techniques for constraining these models (such as cosmogenic radionuclides) have become commonplace. As such, many hypotheses related to the effects of boundary conditions or climate on process rates and soil properties have been left untested. We selected pairs of hillslopes along a precipitation gradient in northern Chile (24°-30° S) which were either bounded by actively eroding (bedrock-bedded) channels or by stable or aggradational landforms (pediments, colluvial aprons, valley bottoms). For each hillslope we measured soil properties, atmospheric deposition rates, and bedrock denudation rates. We observe significant changes in soil properties with climate: there is a shift from thick, weathered soils in the semiarid south, to the near absence of soil in the arid middle, to salt-rich soils in the hyperarid north. Coincident with these are dramatic changes in the types and rates of processes acting on the soils. We found relatively quick, biotically-driven soil formation and transport in the south, and very slow, salt-driven processes in the north. Additionally, we observe systematic differences between hillslopes of different boundary condition within the same climate zone, such as thicker soils, gentler slopes, and slower erosion rates on hillslopes with a non-eroding boundary versus an eroding boundary. These support general predictions based on hillslope soil mass balance equations and geomorphic transport laws. Using parameters derived from our field data, we attempt to use a mass balance model of hillslope development to explore the effect of changing boundary conditions and/or shifting climate.

Static penetration resistance of soils

NASA Technical Reports Server (NTRS)

Durgunoglu, H. T.; Mitchell, J. K.

1973-01-01

Model test results were used to define the failure mechanism associated with the static penetration resistance of cohesionless and low-cohesion soils. Knowledge of this mechanism has permitted the development of a new analytical method for calculating the ultimate penetration resistance which explicitly accounts for penetrometer base apex angle and roughness, soil friction angle, and the ratio of penetration depth to base width. Curves relating the bearing capacity factors to the soil friction angle are presented for failure in general shear. Strength parameters and penetrometer interaction properties of a fine sand were determined and used as the basis for prediction of the penetration resistance encountered by wedge, cone, and flat-ended penetrometers of different surface roughness using the proposed analytical method. Because of the close agreement between predicted values and values measured in laboratory tests, it appears possible to deduce in-situ soil strength parameters and their variation with depth from the results of static penetration tests.

Soil bacterial community and functional shifts in response to altered snowpack in moist acidic tundra of northern Alaska

NASA Astrophysics Data System (ADS)

Ricketts, Michael P.; Poretsky, Rachel S.; Welker, Jeffrey M.; Gonzalez-Meler, Miquel A.

2016-09-01

Soil microbial communities play a central role in the cycling of carbon (C) in Arctic tundra ecosystems, which contain a large portion of the global C pool. Climate change predictions for Arctic regions include increased temperature and precipitation (i.e. more snow), resulting in increased winter soil insulation, increased soil temperature and moisture, and shifting plant community composition. We utilized an 18-year snow fence study site designed to examine the effects of increased winter precipitation on Arctic tundra soil bacterial communities within the context of expected ecosystem response to climate change. Soil was collected from three pre-established treatment zones representing varying degrees of snow accumulation, where deep snow ˜ 100 % and intermediate snow ˜ 50 % increased snowpack relative to the control, and low snow ˜ 25 % decreased snowpack relative to the control. Soil physical properties (temperature, moisture, active layer thaw depth) were measured, and samples were analysed for C concentration, nitrogen (N) concentration, and pH. Soil microbial community DNA was extracted and the 16S rRNA gene was sequenced to reveal phylogenetic community differences between samples and determine how soil bacterial communities might respond (structurally and functionally) to changes in winter precipitation and soil chemistry. We analysed relative abundance changes of the six most abundant phyla (ranging from 82 to 96 % of total detected phyla per sample) and found four (Acidobacteria, Actinobacteria, Verrucomicrobia, and Chloroflexi) responded to deepened snow. All six phyla correlated with at least one of the soil chemical properties (% C, % N, C : N, pH); however, a single predictor was not identified, suggesting that each bacterial phylum responds differently to soil characteristics. Overall, bacterial community structure (beta diversity) was found to be associated with snow accumulation treatment and all soil chemical properties. Bacterial functional potential was inferred using ancestral state reconstruction to approximate functional gene abundance, revealing a decreased abundance of genes required for soil organic matter (SOM) decomposition in the organic layers of the deep snow accumulation zones. These results suggest that predicted climate change scenarios may result in altered soil bacterial community structure and function, and indicate a reduction in decomposition potential, alleviated temperature limitations on extracellular enzymatic efficiency, or both. The fate of stored C in Arctic soils ultimately depends on the balance between these mechanisms.

Understanding the influence of biofilm accumulation on the hydraulic properties of soils: a mechanistic approach based on experimental data

NASA Astrophysics Data System (ADS)

Carles Brangarí, Albert; Sanchez-Vila, Xavier; Freixa, Anna; Romaní, Anna M.; Fernàndez-Garcia, Daniel

2017-04-01

The distribution, amount, and characteristics of biofilms and its components govern the capacity of soils to let water through, to transport solutes, and the reactions occurring. Therefore, unraveling the relationship between microbial dynamics and the hydraulic properties of soils is of concern for the management of natural systems and many technological applications. However, the increased complexity of both the microbial communities and the geochemical processes entailed by them causes that the phenomenon of bioclogging remains poorly understood. This highlights the need for a better understanding of the microbial components such as live and dead bacteria and extracellular polymeric substances (EPS), as well as of their spatial distribution. This work tries to shed some light on these issues, providing experimental data and a new mechanistic model that predicts the variably saturated hydraulic properties of bio-amended soils based on these data. We first present a long-term laboratory infiltration experiment that aims at studying the temporal variation of selected biogeochemical parameters along the infiltration path. The setup consists of a 120-cm-high soil tank instrumented with an array of sensors plus soil and liquid samplers. Sensors measured a wide range of parameters in continuous, such as volumetric water content, electrical conductivity, temperature, water pressure, soil suction, dissolved oxygen, and pH. Samples were kept for chemical and biological analyses. Results indicate that: i) biofilm is present at all depths, denoting the potential for deep bioclogging, ii) the redox conditions profile shows different stages, indicating that the community was adapted to changing redox conditions, iii) bacterial activity, richness and diversity also exhibit zonation with depth, and iv) the hydraulic properties of the soil experienced significant changes as biofilm proliferated. Based on experimental evidences, we propose a tool to predict changes in the hydraulic properties of bio-amended variably saturated soils. The new mechanistic model provides analytical equations for the water retention curve and the relative permeability. The approach consists in assuming that the porous media behaves as an ensemble of capillary tubes, which may be obtained from the experimental saturation profile. This premise is extended by considering the existence of biofilm bodies composed of bacteria and EPS. These compounds display a channeled geometry that reshapes the pore space at the pore-scale following specific geometrical patterns and changes its volume with suction. The hydraulic properties of the bio-amended soil can then be derived from the integrate contribution of the two biofilm compounds separately. Model can successfully reproduce displacements of the soil-water retention curve towards higher saturations and permeability reductions of distinct orders of magnitude.

Nutrient addition dramatically accelerates microbial community succession.

PubMed

Knelman, Joseph E; Schmidt, Steven K; Lynch, Ryan C; Darcy, John L; Castle, Sarah C; Cleveland, Cory C; Nemergut, Diana R

2014-01-01

The ecological mechanisms driving community succession are widely debated, particularly for microorganisms. While successional soil microbial communities are known to undergo predictable changes in structure concomitant with shifts in a variety of edaphic properties, the causal mechanisms underlying these patterns are poorly understood. Thus, to specifically isolate how nutrients--important drivers of plant succession--affect soil microbial succession, we established a full factorial nitrogen (N) and phosphorus (P) fertilization plot experiment in recently deglaciated (∼3 years since exposure), unvegetated soils of the Puca Glacier forefield in Southeastern Peru. We evaluated soil properties and examined bacterial community composition in plots before and one year after fertilization. Fertilized soils were then compared to samples from three reference successional transects representing advancing stages of soil development ranging from 5 years to 85 years since exposure. We found that a single application of +NP fertilizer caused the soil bacterial community structure of the three-year old soils to most resemble the 85-year old soils after one year. Despite differences in a variety of soil edaphic properties between fertilizer plots and late successional soils, bacterial community composition of +NP plots converged with late successional communities. Thus, our work suggests a mechanism for microbial succession whereby changes in resource availability drive shifts in community composition, supporting a role for nutrient colimitation in primary succession. These results suggest that nutrients alone, independent of other edaphic factors that change with succession, act as an important control over soil microbial community development, greatly accelerating the rate of succession.

Nutrient Addition Dramatically Accelerates Microbial Community Succession

PubMed Central

Knelman, Joseph E.; Schmidt, Steven K.; Lynch, Ryan C.; Darcy, John L.; Castle, Sarah C.; Cleveland, Cory C.; Nemergut, Diana R.

2014-01-01

The ecological mechanisms driving community succession are widely debated, particularly for microorganisms. While successional soil microbial communities are known to undergo predictable changes in structure concomitant with shifts in a variety of edaphic properties, the causal mechanisms underlying these patterns are poorly understood. Thus, to specifically isolate how nutrients – important drivers of plant succession – affect soil microbial succession, we established a full factorial nitrogen (N) and phosphorus (P) fertilization plot experiment in recently deglaciated (∼3 years since exposure), unvegetated soils of the Puca Glacier forefield in Southeastern Peru. We evaluated soil properties and examined bacterial community composition in plots before and one year after fertilization. Fertilized soils were then compared to samples from three reference successional transects representing advancing stages of soil development ranging from 5 years to 85 years since exposure. We found that a single application of +NP fertilizer caused the soil bacterial community structure of the three-year old soils to most resemble the 85-year old soils after one year. Despite differences in a variety of soil edaphic properties between fertilizer plots and late successional soils, bacterial community composition of +NP plots converged with late successional communities. Thus, our work suggests a mechanism for microbial succession whereby changes in resource availability drive shifts in community composition, supporting a role for nutrient colimitation in primary succession. These results suggest that nutrients alone, independent of other edaphic factors that change with succession, act as an important control over soil microbial community development, greatly accelerating the rate of succession. PMID:25050551

Multisurface modeling of Ni bioavailability to wheat (Triticum aestivum L.) in various soils.

PubMed

Zhao, Xiaopeng; Jiang, Yang; Gu, Xueyuan; Gu, Cheng; Taylor, J Anita; Evans, Les J

2018-07-01

Continual efforts have been made to determine a simple and universal method of estimating heavy metal phytoavailability in terrestrial systems. In the present study, a mechanism-based multi-surface model (MSM) was developed to predict the partition of Ni(II) in soil-solution phases and its bioaccumulation in wheat (Triticum aestivum L.) in 19 Chinese soils with a wide range of soil properties. MSM successfully predicted the Ni(II) dissolution in 0.01 M CaCl 2 extracting solution (R 2  = 0.875). The two-site model for clay fraction improved the prediction, particularly for alkaline soils, because of the additional consideration of edge sites. More crucially, the calculated dissolved Ni(II) was highly correlated with the metal accumulation in wheat (R 2  = 0.820 for roots and 0.817 for shoots). The correlation coefficients for the MSM and various chemical extraction methods have the following order: soil pore water > MSM ≈ diffuse gradient technique (DGT) > soil total Ni > 0.43 M HNO 3  > 0.01 M CaCl 2 . The results suggested that the dissolved Ni(II) calculated using MSM can serve as an effective indicator of the bioavailability of Ni(II) in various soils; hence, MSM can be used as an supplement for metal risk prediction and assessment besides chemical extraction techniques. Copyright © 2018 Elsevier Ltd. All rights reserved.

[Simulation of effects of soil properties and plants on soil water-salt movement with reclaimed water irrigation by ENVIRO-GRO model].

PubMed

Lü, Si-Dan; Chen, Wei-Ping; Wang, Mei-E

2012-12-01

In order to promote safe irrigation with reclaimed water and prevent soil salinisation, the dynamic transport of salts in urban soils of Beijing under irrigation of reclaimed water was simulated by ENVIRO-GRO model in this study. The accumulation trends and profile distribution of soil salinity were predicted. Simultaneously, the effects of different soil properties and plants on soil water-salt movement and salt accumulation were investigated. Results indicated that soil salinity in the profiles reached uniform equilibrium conditions by repeated simulation, with different initial soil salinity. Under the conditions of loam and clay loam soil, salinity in the profiles increased over time until reaching equilibrium conditions, while under the condition of sandy loam soil, salinity in the profiles decreased over time until reaching equilibrium conditions. The saturated soil salinity (EC(e)) under equilibrium conditions followed an order of sandy loam < loam < clay loam. Salt accumulations in Japan euonymus and Chinese pine were less than that in Blue grass. The temporal and spatial distributions of soil salinity were also different in these three types of plants. In addition, the growth of the plants was not influenced by soil salinity (except clay loam), but mild soil salinization occurred under all conditions (except sandy loam).

Ground-based Remote Sensing for Quantifying Subsurface and Surface Co-variability to Scale Arctic Ecosystem Functioning

NASA Astrophysics Data System (ADS)

Oktem, R.; Wainwright, H. M.; Curtis, J. B.; Dafflon, B.; Peterson, J.; Ulrich, C.; Hubbard, S. S.; Torn, M. S.

2016-12-01

Predicting carbon cycling in Arctic requires quantifying tightly coupled surface and subsurface processes including permafrost, hydrology, vegetation and soil biogeochemistry. The challenge has been a lack of means to remotely sense key ecosystem properties in high resolution and over large areas. A particular challenge has been characterizing soil properties that are known to be highly heterogeneous. In this study, we exploit tightly-coupled above/belowground ecosystem functioning (e.g., the correlations among soil moisture, vegetation and carbon fluxes) to estimate subsurface and other key properties over large areas. To test this concept, we have installed a ground-based remote sensing platform - a track-mounted tram system - along a 70 m transect in the ice-wedge polygonal tundra near Barrow, Alaska. The tram carries a suite of near-surface remote sensing sensors, including sonic depth, thermal IR, NDVI and multispectral sensors. Joint analysis with multiple ground-based measurements (soil temperature, active layer soil moisture, and carbon fluxes) was performed to quantify correlations and the dynamics of above/belowground processes at unprecedented resolution, both temporally and spatially. We analyzed the datasets with particular focus on correlating key subsurface and ecosystem properties with surface properties that can be measured by satellite/airborne remote sensing over a large area. Our results provided several new insights about system behavior and also opens the door for new characterization approaches. We documented that: (1) soil temperature (at >5 cm depth; critical for permafrost thaw) was decoupled from soil surface temperature and was influenced strongly by soil moisture, (2) NDVI and greenness index were highly correlated with both soil moisture and gross primary productivity (based on chamber flux data), and (3) surface deformation (which can be measured by InSAR) was a good proxy for thaw depth dynamics at non-inundated locations.

Can next-generation soil data products improve soil moisture modelling at the continental scale? An assessment using a new microclimate package for the R programming environment

NASA Astrophysics Data System (ADS)

Kearney, Michael R.; Maino, James L.

2018-06-01

Accurate models of soil moisture are vital for solving core problems in meteorology, hydrology, agriculture and ecology. The capacity for soil moisture modelling is growing rapidly with the development of high-resolution, continent-scale gridded weather and soil data together with advances in modelling methods. In particular, the GlobalSoilMap.net initiative represents next-generation, depth-specific gridded soil products that may substantially increase soil moisture modelling capacity. Here we present an implementation of Campbell's infiltration and redistribution model within the NicheMapR microclimate modelling package for the R environment, and use it to assess the predictive power provided by the GlobalSoilMap.net product Soil and Landscape Grid of Australia (SLGA, ∼100 m) as well as the coarser resolution global product SoilGrids (SG, ∼250 m). Predictions were tested in detail against 3 years of root-zone (3-75 cm) soil moisture observation data from 35 monitoring sites within the OzNet project in Australia, with additional tests of the finalised modelling approach against cosmic-ray neutron (CosmOz, 0-50 cm, 9 sites from 2011 to 2017) and satellite (ASCAT, 0-2 cm, continent-wide from 2007 to 2009) observations. The model was forced by daily 0.05° (∼5 km) gridded meteorological data. The NicheMapR system predicted soil moisture to within experimental error for all data sets. Using the SLGA or the SG soil database, the OzNet soil moisture could be predicted with a root mean square error (rmse) of ∼0.075 m3 m-3 and a correlation coefficient (r) of 0.65 consistently through the soil profile without any parameter tuning. Soil moisture predictions based on the SLGA and SG datasets were ≈ 17% closer to the observations than when using a chloropleth-derived soil data set (Digital Atlas of Australian Soils), with the greatest improvements occurring for deeper layers. The CosmOz observations were predicted with similar accuracy (r = 0.76 and rmse of ∼0.085 m3 m-3). Comparisons at the continental scale to 0-2 cm satellite data (ASCAT) showed that the SLGA/SG datasets increased model fit over simulations using the DAAS soil properties (r ∼ 0.63 &rmse 15% vs. r 0.48 &rmse 18%, respectively). Overall, our results demonstrate the advantages of using GlobalSoilMap.net products in combination with gridded weather data for modelling soil moisture at fine spatial and temporal resolution at the continental scale.

Utilization of FEM model for steel microstructure determination

NASA Astrophysics Data System (ADS)

Kešner, A.; Chotěborský, R.; Linda, M.; Hromasová, M.

2018-02-01

Agricultural tools which are used in soil processing, they are worn by abrasive wear mechanism cases by hard minerals particles in the soil. The wear rate is influenced by mechanical characterization of tools material and wear rate is influenced also by soil mineral particle contents. Mechanical properties of steel can be affected by a technology of heat treatment that it leads to a different microstructures. Experimental work how to do it is very expensive and thanks to numerical methods like FEM we can assumed microstructure at low cost but each of numerical model is necessary to be verified. The aim of this work has shown a procedure of prediction microstructure of steel for agricultural tools. The material characterizations of 51CrV4 grade steel were used for numerical simulation like TTT diagram, heat capacity, heat conduction and other physical properties of material. A relationship between predicted microstructure by FEM and real microstructure after heat treatment shows a good correlation.

Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species?

PubMed Central

Esperón-Rodríguez, Manuel; Baumgartner, John B.; Beaumont, Linda J.

2017-01-01

Background Shrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distributions remains unclear. We evaluated the influence of climate and soil characteristics, and whether including soil variables improved the performance of a species distribution model (SDM), Maxent. Methods This study assessed variation in predictions of environmental suitability for 29 Australian shrub species (representing dominant members of six shrubland classes) due to the use of alternative sets of predictor variables. Models were calibrated with (1) climate variables only, (2) climate and soil variables, and (3) soil variables only. Results The predictive power of SDMs differed substantially across species, but generally models calibrated with both climate and soil data performed better than those calibrated only with climate variables. Models calibrated solely with soil variables were the least accurate. We found regional differences in potential shrub species richness across Australia due to the use of different sets of variables. Conclusions Our study provides evidence that predicted patterns of species richness may be sensitive to the choice of predictor set when multiple, plausible alternatives exist, and demonstrates the importance of considering soil properties when modeling availability of habitat for plants. PMID:28652933

An annual model of SSM/I radiobrightness for dry soil

NASA Technical Reports Server (NTRS)

Liou, Yuei-An; England, A. W.

1992-01-01

An annual model is presented of the temperature structure within a homogeneous, dry soil halfspace that is subject to both diurnal and annual insolation, radiant heating from the atmosphere, sensible heat exchange with the atmosphere, and radiant cooling. The thermal constitutive properties of the soil are assumed to be constant so that the heat flow equation can be solved analytically. For computational economy, a variable time interval Laplace transform method is developed to predict the temperature.

Water and solute transport in agricultural soils predicted by volumetric clay and silt contents

NASA Astrophysics Data System (ADS)

Karup, Dan; Moldrup, Per; Paradelo, Marcos; Katuwal, Sheela; Norgaard, Trine; Greve, Mogens H.; de Jonge, Lis W.

2016-09-01

Solute transport through the soil matrix is non-uniform and greatly affected by soil texture, soil structure, and macropore networks. Attempts have been made in previous studies to use infiltration experiments to identify the degree of preferential flow, but these attempts have often been based on small datasets or data collected from literature with differing initial and boundary conditions. This study examined the relationship between tracer breakthrough characteristics, soil hydraulic properties, and basic soil properties. From six agricultural fields in Denmark, 193 intact surface soil columns 20 cm in height and 20 cm in diameter were collected. The soils exhibited a wide range in texture, with clay and organic carbon (OC) contents ranging from 0.03 to 0.41 and 0.01 to 0.08 kg kg- 1, respectively. All experiments were carried out under the same initial and boundary conditions using tritium as a conservative tracer. The breakthrough characteristics ranged from being near normally distributed to gradually skewed to the right along with an increase in the content of the mineral fines (particles ≤ 50 μm). The results showed that the mineral fines content was strongly correlated to functional soil structure and the derived tracer breakthrough curves (BTCs), whereas the OC content appeared less important for the shape of the BTC. Organic carbon was believed to support the stability of the soil structure rather than the actual formation of macropores causing preferential flow. The arrival times of 5% and up to 50% of the tracer mass were found to be strongly correlated with volumetric fines content. Predicted tracer concentration breakthrough points as a function of time up to 50% of applied tracer mass could be well fitted to an analytical solution to the classical advection-dispersion equation. Both cumulative tracer mass and concentration as a function of time were well predicted from the simple inputs of bulk density, clay and silt contents, and applied tracer mass. The new concept seems promising as a platform towards more accurate proxy functions for dissolved contaminant transport in intact soil.

A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves

NASA Astrophysics Data System (ADS)

Montzka, Carsten; Herbst, Michael; Weihermüller, Lutz; Verhoef, Anne; Vereecken, Harry

2017-07-01

Agroecosystem models, regional and global climate models, and numerical weather prediction models require adequate parameterization of soil hydraulic properties. These properties are fundamental for describing and predicting water and energy exchange processes at the transition zone between solid earth and atmosphere, and regulate evapotranspiration, infiltration and runoff generation. Hydraulic parameters describing the soil water retention (WRC) and hydraulic conductivity (HCC) curves are typically derived from soil texture via pedotransfer functions (PTFs). Resampling of those parameters for specific model grids is typically performed by different aggregation approaches such a spatial averaging and the use of dominant textural properties or soil classes. These aggregation approaches introduce uncertainty, bias and parameter inconsistencies throughout spatial scales due to nonlinear relationships between hydraulic parameters and soil texture. Therefore, we present a method to scale hydraulic parameters to individual model grids and provide a global data set that overcomes the mentioned problems. The approach is based on Miller-Miller scaling in the relaxed form by Warrick, that fits the parameters of the WRC through all sub-grid WRCs to provide an effective parameterization for the grid cell at model resolution; at the same time it preserves the information of sub-grid variability of the water retention curve by deriving local scaling parameters. Based on the Mualem-van Genuchten approach we also derive the unsaturated hydraulic conductivity from the water retention functions, thereby assuming that the local parameters are also valid for this function. In addition, via the Warrick scaling parameter λ, information on global sub-grid scaling variance is given that enables modellers to improve dynamical downscaling of (regional) climate models or to perturb hydraulic parameters for model ensemble output generation. The present analysis is based on the ROSETTA PTF of Schaap et al. (2001) applied to the SoilGrids1km data set of Hengl et al. (2014). The example data set is provided at a global resolution of 0.25° at https://doi.org/10.1594/PANGAEA.870605.

Underestimation of boreal soil carbon stocks by mathematical soil carbon models linked to soil nutrient status

NASA Astrophysics Data System (ADS)

Ťupek, Boris; Ortiz, Carina A.; Hashimoto, Shoji; Stendahl, Johan; Dahlgren, Jonas; Karltun, Erik; Lehtonen, Aleksi

2016-08-01

Inaccurate estimate of the largest terrestrial carbon pool, soil organic carbon (SOC) stock, is the major source of uncertainty in simulating feedback of climate warming on ecosystem-atmosphere carbon dioxide exchange by process-based ecosystem and soil carbon models. Although the models need to simplify complex environmental processes of soil carbon sequestration, in a large mosaic of environments a missing key driver could lead to a modeling bias in predictions of SOC stock change.We aimed to evaluate SOC stock estimates of process-based models (Yasso07, Q, and CENTURY soil sub-model v4) against a massive Swedish forest soil inventory data set (3230 samples) organized by a recursive partitioning method into distinct soil groups with underlying SOC stock development linked to physicochemical conditions.For two-thirds of measurements all models predicted accurate SOC stock levels regardless of the detail of input data, e.g., whether they ignored or included soil properties. However, in fertile sites with high N deposition, high cation exchange capacity, or moderately increased soil water content, Yasso07 and Q models underestimated SOC stocks. In comparison to Yasso07 and Q, accounting for the site-specific soil characteristics (e. g. clay content and topsoil mineral N) by CENTURY improved SOC stock estimates for sites with high clay content, but not for sites with high N deposition.Our analysis suggested that the soils with poorly predicted SOC stocks, as characterized by the high nutrient status and well-sorted parent material, indeed have had other predominant drivers of SOC stabilization lacking in the models, presumably the mycorrhizal organic uptake and organo-mineral stabilization processes. Our results imply that the role of soil nutrient status as regulator of organic matter mineralization has to be re-evaluated, since correct SOC stocks are decisive for predicting future SOC change and soil CO2 efflux.

Distinct respiratory responses of soils to complex organic substrate are governed predominantly by soil architecture and its microbial community.

PubMed

Fraser, F C; Todman, L C; Corstanje, R; Deeks, L K; Harris, J A; Pawlett, M; Whitmore, A P; Ritz, K

2016-12-01

Factors governing the turnover of organic matter (OM) added to soils, including substrate quality, climate, environment and biology, are well known, but their relative importance has been difficult to ascertain due to the interconnected nature of the soil system. This has made their inclusion in mechanistic models of OM turnover or nutrient cycling difficult despite the potential power of these models to unravel complex interactions. Using high temporal-resolution respirometery (6 min measurement intervals), we monitored the respiratory response of 67 soils sampled from across England and Wales over a 5 day period following the addition of a complex organic substrate (green barley powder). Four respiratory response archetypes were observed, characterised by different rates of respiration as well as different time-dependent patterns. We also found that it was possible to predict, with 95% accuracy, which type of respiratory behaviour a soil would exhibit based on certain physical and chemical soil properties combined with the size and phenotypic structure of the microbial community. Bulk density, microbial biomass carbon, water holding capacity and microbial community phenotype were identified as the four most important factors in predicting the soils' respiratory responses using a Bayesian belief network. These results show that the size and constitution of the microbial community are as important as physico-chemical properties of a soil in governing the respiratory response to OM addition. Such a combination suggests that the 'architecture' of the soil, i.e. the integration of the spatial organisation of the environment and the interactions between the communities living and functioning within the pore networks, is fundamentally important in regulating such processes.

Soil Water Characteristics of Cores from Low- and High-Centered Polygons, Barrow, Alaska, 2012

DOE Data Explorer

Graham, David; Moon, Ji-Won

2016-08-22

This dataset includes soil water characteristic curves for soil and permafrost in two representative frozen cores collected from a high-center polygon (HCP) and a low-center polygon (LCP) from the Barrow Environmental Observatory. Data include soil water content and soil water potential measured using the simple evaporation method for hydrological and biogeochemical simulations and experimental data analysis. Data can be used to generate a soil moisture characteristic curve, which can be fit to a variety of hydrological functions to infer critical parameters for soil physics. Considering the measured the soil water properties, the van Genuchten model predicted well the HCP, in contrast, the Kosugi model well fitted LCP which had more saturated condition.

Aggregate stability as an indicator of soil erodibility and soil physical quality: review and perspectives

NASA Astrophysics Data System (ADS)

Le Bissonnais, Yves; Chenu, Claire; Darboux, Frédéric; Duval, Odile; Legout, Cédric; Leguédois, Sophie; Gumiere, Silvio

2010-05-01

Aggregate breakdown due to water and rain action may cause surface crusting, slumping, a reduction of infiltration and interrill erosion. Aggregate stability determines the capacity of aggregates to resist the effects of water and rainfall. In this paper, we evaluated and reviewed the relevance of an aggregate stability measurement to characterize soil physical properties as well as to analyse the processes involved in these properties. Stability measurement assesses the sensitivity of soil aggregates to various basic disaggregation mechanisms such as slaking, differential swelling, dispersion and mechanical breakdown. It has been showed that aggregate size distributions of structural stability tests matched the size distributions of eroded aggregates under rainfall simulations and that erosion amount was well predicted using aggregate stability indexes. It means stability tests could be used to estimate both the erodibility and the size fractions that are available for crust formation and erosion processes. Several studies showed that organic matter was one of the main soil properties affecting soil stability. However, it has also been showed that aggregate stability of a given soil could vary within a year or between years. The factors controlling such changes have still to be specified. Aggregate stability appears therefore as a complex property, depending both on permanent soil characteristics and on dynamic factors such as the crusting stage, the climate and the biological activity. Despite, and may be, because of this complexity, aggregate stability seems an integrative and powerful indicator of soil physical quality. Future research efforts should look at the causes of short-term changes of structural stability, in order to fully understand all its aspects.

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS

PubMed Central

Wang, De-Cai; Zhang, Gan-Lin; Zhao, Ming-Song; Pan, Xian-Zhang; Zhao, Yu-Guo; Li, De-Cheng; Macmillan, Bob

2015-01-01

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data. PMID:26090852

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS.

PubMed

Wang, De-Cai; Zhang, Gan-Lin; Zhao, Ming-Song; Pan, Xian-Zhang; Zhao, Yu-Guo; Li, De-Cheng; Macmillan, Bob

2015-01-01

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data.

Properties of biochar-amended soils and their sorption of imidacloprid, isoproturon, and atrazine.

PubMed

Jin, Jie; Kang, Mingjie; Sun, Ke; Pan, Zezhen; Wu, Fengchang; Xing, Baoshan

2016-04-15

Biochars produced from rice straw, wheat straw and swine manure at 300, 450 and 600°C were added to soil at 1, 5, 10, or 20% levels to determine whether they would predictably reduce the pore water concentration of imidacloprid, isoproturon, and atrazine. The sorption capacity of the mixtures increased with increasing biochar amounts. The enhanced sorption capacity could be attributed to the increased organic carbon (OC) content and surface area (SA) as well as the decreased hydrophobicity. Biochar dominated the overall sorption when its content was above 5%. The OC contents of the mixtures with 10% and 20% biochar were generally lower than the predicted values. This implies possible interaction between soil components and biochar and/or the effect of biochar oxidation. For soils amended with biochars produced at 300°C, the N2 SA (N2-SA) values were underestimated. The predicted CO2 SA (CO2-SA) values of the mixtures at the biochar content of 10% and 20% were generally higher than the experimental values. Sorption of imidacloprid to the soils amended with biochar at 10% and 20% levels, excluding the soils amended with rice (SR300) and wheat (SW300) straw-derived biochar produced at 300°C, was lower than the predicted value. For SR300 and SW300, the intrinsic sorption capacity of biochar was enhanced by 1.3-5.6 times, depending on the biochar, solute concentration, and biochar dose. This study indicates that biochars would be helpful to stabilize the soil contaminated with imidacloprid, isoproturon, and atrazine, but the sorption capacity of the mixtures could exceed or fall short of predicted values without assuming a cross-effect between soil and biochar. Copyright © 2016 Elsevier B.V. All rights reserved.

A new MRI land surface model HAL

NASA Astrophysics Data System (ADS)

Hosaka, M.

2011-12-01

A land surface model HAL is newly developed for MRI-ESM1. It is used for the CMIP simulations. HAL consists of three submodels: SiByl (vegetation), SNOWA (snow) and SOILA (soil) in the current version. It also contains a land coupler LCUP which connects some submodels and an atmospheric model. The vegetation submodel SiByl has surface vegetation processes similar to JMA/SiB (Sato et al. 1987, Hirai et al. 2007). SiByl has 2 vegetation layers (canopy and grass) and calculates heat, moisture, and momentum fluxes between the land surface and the atmosphere. The snow submodel SNOWA can have any number of snow layers and the maximum value is set to 8 for the CMIP5 experiments. Temperature, SWE, density, grain size and the aerosol deposition contents of each layer are predicted. The snow properties including the grain size are predicted due to snow metamorphism processes (Niwano et al., 2011), and the snow albedo is diagnosed from the aerosol mixing ratio, the snow properties and the temperature (Aoki et al., 2011). The soil submodel SOILA can also have any number of soil layers, and is composed of 14 soil layers in the CMIP5 experiments. The temperature of each layer is predicted by solving heat conduction equations. The soil moisture is predicted by solving the Darcy equation, in which hydraulic conductivity depends on the soil moisture. The land coupler LCUP is designed to enable the complicated constructions of the submidels. HAL can include some competing submodels (precise and detailed ones, and simpler ones), and they can run at the same simulations. LCUP enables a 2-step model validation, in which we compare the results of the detailed submodels with the in-situ observation directly at the 1st step, and follows the comparison between them and those of the simpler ones at the 2nd step. When the performances of the detailed ones are good, we can improve the simpler ones by using the detailed ones as reference models.

Predicting soil properties for sustainable agriculture using vis-NIR spectroscopy: a case study in northern Greece

NASA Astrophysics Data System (ADS)

Tsakiridis, Nikolaos L.; Tziolas, Nikolaos; Dimitrakos, Agathoklis; Galanis, Georgios; Ntonou, Eleftheria; Tsirika, Anastasia; Terzopoulou, Evangelia; Kalopesa, Eleni; Zalidis, George C.

2017-09-01

Soil Spectral Libraries facilitate agricultural production taking into account the principles of a low-input sustainable agriculture and provide more valuable knowledge to environmental policy makers, enabling improved decision making and effective management of natural resources in the region. In this paper, a comparison in the predictive performance of two state of the art algorithms, one linear (Partial Least Squares Regression) and one non-linear (Cubist), employed in soil spectroscopy is conducted. The comparison was carried out in a regional Soil Spectral Library developed in the Eastern Macedonia and Thrace region of Northern Greece, comprised of roughly 450 Entisol soil samples from soil horizons A (0-30 cm) and B (30-60 cm). The soil spectra were acquired in the visible - Near Infrared Red region (vis- NIR, 350nm-2500nm) using a standard protocol in the laboratory. Three soil properties, which are essential for agriculture, were analyzed and taken into account for the comparison. These were the Organic Matter, the Clay content and the concentration of nitrate-N. Additionally, three different spectral pre-processing techniques were utilized, namely the continuum removal, the absorbance transformation, and the first derivative. Following the removal of outliers using the Mahalanobis distance in the first 5 principal components of the spectra (accounting for 99.8% of the variance), a five-fold cross-validation experiment was considered for all 12 datasets. Statistical comparisons were conducted on the results, which indicate that the Cubist algorithm outperforms PLSR, while the most informative transformation is the first derivative.

Universal Spatial Correlation Functions for Describing and Reconstructing Soil Microstructure

PubMed Central

Skvortsova, Elena B.; Mallants, Dirk

2015-01-01

Structural features of porous materials such as soil define the majority of its physical properties, including water infiltration and redistribution, multi-phase flow (e.g. simultaneous water/air flow, or gas exchange between biologically active soil root zone and atmosphere) and solute transport. To characterize soil microstructure, conventional soil science uses such metrics as pore size and pore-size distributions and thin section-derived morphological indicators. However, these descriptors provide only limited amount of information about the complex arrangement of soil structure and have limited capability to reconstruct structural features or predict physical properties. We introduce three different spatial correlation functions as a comprehensive tool to characterize soil microstructure: 1) two-point probability functions, 2) linear functions, and 3) two-point cluster functions. This novel approach was tested on thin-sections (2.21×2.21 cm2) representing eight soils with different pore space configurations. The two-point probability and linear correlation functions were subsequently used as a part of simulated annealing optimization procedures to reconstruct soil structure. Comparison of original and reconstructed images was based on morphological characteristics, cluster correlation functions, total number of pores and pore-size distribution. Results showed excellent agreement for soils with isolated pores, but relatively poor correspondence for soils exhibiting dual-porosity features (i.e. superposition of pores and micro-cracks). Insufficient information content in the correlation function sets used for reconstruction may have contributed to the observed discrepancies. Improved reconstructions may be obtained by adding cluster and other correlation functions into reconstruction sets. Correlation functions and the associated stochastic reconstruction algorithms introduced here are universally applicable in soil science, such as for soil classification, pore-scale modelling of soil properties, soil degradation monitoring, and description of spatial dynamics of soil microbial activity. PMID:26010779

Universal spatial correlation functions for describing and reconstructing soil microstructure.

PubMed

Karsanina, Marina V; Gerke, Kirill M; Skvortsova, Elena B; Mallants, Dirk

2015-01-01

Structural features of porous materials such as soil define the majority of its physical properties, including water infiltration and redistribution, multi-phase flow (e.g. simultaneous water/air flow, or gas exchange between biologically active soil root zone and atmosphere) and solute transport. To characterize soil microstructure, conventional soil science uses such metrics as pore size and pore-size distributions and thin section-derived morphological indicators. However, these descriptors provide only limited amount of information about the complex arrangement of soil structure and have limited capability to reconstruct structural features or predict physical properties. We introduce three different spatial correlation functions as a comprehensive tool to characterize soil microstructure: 1) two-point probability functions, 2) linear functions, and 3) two-point cluster functions. This novel approach was tested on thin-sections (2.21×2.21 cm2) representing eight soils with different pore space configurations. The two-point probability and linear correlation functions were subsequently used as a part of simulated annealing optimization procedures to reconstruct soil structure. Comparison of original and reconstructed images was based on morphological characteristics, cluster correlation functions, total number of pores and pore-size distribution. Results showed excellent agreement for soils with isolated pores, but relatively poor correspondence for soils exhibiting dual-porosity features (i.e. superposition of pores and micro-cracks). Insufficient information content in the correlation function sets used for reconstruction may have contributed to the observed discrepancies. Improved reconstructions may be obtained by adding cluster and other correlation functions into reconstruction sets. Correlation functions and the associated stochastic reconstruction algorithms introduced here are universally applicable in soil science, such as for soil classification, pore-scale modelling of soil properties, soil degradation monitoring, and description of spatial dynamics of soil microbial activity.

Seasonal Patterns of Soil Respiration and Related Soil Biochemical Properties under Nitrogen Addition in Winter Wheat Field

PubMed Central

Liang, Guopeng; Houssou, Albert A.; Wu, Huijun; Cai, Dianxiong; Wu, Xueping; Gao, Lili; Li, Jing; Wang, Bisheng; Li, Shengping

2015-01-01

Understanding the changes of soil respiration under increasing N fertilizer in cropland ecosystems is crucial to accurately predicting global warming. This study explored seasonal variations of soil respiration and its controlling biochemical properties under a gradient of Nitrogen addition during two consecutive winter wheat growing seasons (2013–2015). N was applied at four different levels: 0, 120, 180 and 240 kg N ha-1 year-1 (denoted as N0, N12, N18 and N24, respectively). Soil respiration exhibited significant seasonal variation and was significantly affected by soil temperature with Q10 ranging from 2.04 to 2.46 and from 1.49 to 1.53 during 2013–2014 and 2014–2015 winter wheat growing season, respectively. Soil moisture had no significant effect on soil respiration during 2013–2014 winter wheat growing season but showed a significant and negative correlation with soil respiration during 2014–2015 winter wheat growing season. Soil respiration under N24 treatment was significantly higher than N0 treatment. Averaged over the two growing seasons, N12, N18 and N24 significantly increased soil respiration by 13.4, 16.4 and 25.4% compared with N0, respectively. N addition also significantly increased easily extractable glomalin-related soil protein (EEG), soil organic carbon (SOC), total N, ammonium N and nitrate N contents. In addition, soil respiration was significantly and positively correlated with β-glucosidase activity, EEG, SOC, total N, ammonium N and nitrate N contents. The results indicated that high N fertilization improved soil chemical properties, but significantly increased soil respiration. PMID:26629695

Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations

PubMed Central

Wang, Junxiao; Wang, Xiaorui; Zhou, Shenglu; Wu, Shaohua; Zhu, Yan; Lu, Chunfeng

2016-01-01

With China’s rapid economic development, the reduction in arable land has emerged as one of the most prominent problems in the nation. The long-term dynamic monitoring of arable land quality is important for protecting arable land resources. An efficient practice is to select optimal sample points while obtaining accurate predictions. To this end, the selection of effective points from a dense set of soil sample points is an urgent problem. In this study, data were collected from Donghai County, Jiangsu Province, China. The number and layout of soil sample points are optimized by considering the spatial variations in soil properties and by using an improved simulated annealing (SA) algorithm. The conclusions are as follows: (1) Optimization results in the retention of more sample points in the moderate- and high-variation partitions of the study area; (2) The number of optimal sample points obtained with the improved SA algorithm is markedly reduced, while the accuracy of the predicted soil properties is improved by approximately 5% compared with the raw data; (3) With regard to the monitoring of arable land quality, a dense distribution of sample points is needed to monitor the granularity. PMID:27706051

Exploring functional relationships between post-fire soil water repellency, soil structure and physico-chemical properties

NASA Astrophysics Data System (ADS)

Quarfeld, Jamie; Brook, Anna; Keestra, Saskia; Wittenberg, Lea

2016-04-01

Soil water repellency (WR) and aggregate stability (AS) are two soil properties that are typically modified after burning and impose significant influence on subsequent hydrological and geomorphological dynamics. The response of AS and soil WR to fire depends upon how fire has influenced other key soil properties (e.g. soil OM, mineralogy). Meanwhile, routine thinning of trees and woody vegetation may alter soil properties (e.g. structure and porosity, wettability) by use of heavy machinery and species selection. The study area is situated along a north-facing slope of Mount Carmel national park (Israel). The selected sites are presented as a continuum of management intensity and fire histories. To date, the natural baseline of soil WR has yet to be thoroughly assessed and must be investigated alongside associated soil aggregating parameters in order to understand its overall impact. This study examines (i) the natural baseline of soil WR and physical properties compared to those of disturbed sites in the immediate (controlled burn) and long-term (10-years), and (ii) the interactions of soil properties with different control factors (management, surface cover, seasonal-temporal, burn temperature, soil organic carbon (OC) and mineralogy) in Mediterranean calcareous soils. Analysis of surface soil samples before and after destruction of WR by heating (200-600°C) was implemented using a combination of traditional methods and infrared (IR) spectroscopy. Management and surface cover type conditioned the wettability, soil structure and porosity of soils in the field, although this largely did not affect the heat-induced changes observed in the lab. A positive correlation was observed along an increasing temperature gradient, with relative maxima of MWD and BD reached by most soils at the threshold of 400-500°C. Preliminary analyses of soil OC (MIR) and mineralogical composition (VIS-NIR) support existing research regarding: (i) the importance of soil OC quality and composition in determining wettability rather than quantity, as evidenced both by the high variation observed in the field and the strong presence of aliphatic functional groups in the absence of WR; and (ii) commonly proposed mechanisms affecting soil aggregate properties - albeit with differing temperature thresholds and longer exposure times employed in this study. Namely, these mechanisms tend to involve: (i) soil OM and WR reduction at low to moderate temperatures, and (ii) thermal fusion of particles within moderate to high temperatures. Overall, results suggest a positive influence of management on soil properties as well as high soil resilience to moderate severity fire disturbance in the studied areas. However, the specific changes in soil OM and mineral composition that are responsible for destruction of WR and subsequent changes in AS remain poorly understood. Based on these results, a key next step within this study will entail a closer examination of OC ratios and their potential links with certain mineral species known to influence soil aggregation and soil WR. Noting the importance of soil OM-mineralogical interactions on run-off and erosion processes, results may contribute to better prediction of post-fire responses in the future and improve the ability to fine-tune site specific management approaches accordingly.

Time and temperature dependent adsorption-desorption behaviour of pretilachlor in soil.

PubMed

Kaur, Paawan; Kaur, Pervinder

2018-06-04

Understanding and quantifying the adsorption-desorption behaviour of herbicide in soil is imperative for predicting their fate and transport in the environment. In the present study, the effect of time and temperature on the adsorption-desorption behaviour of pretilachlor in soils was investigated using batch equilibration technique. The adsorption-desorption kinetics of pretilachlor in soils was two step process and was well described by pseudo-second-order kinetic model. Freundlich model accurately predicted the sorption behaviour of pretilachlor. The adsorption-desorption of pretilachlor varied significantly with the concentration, temperature and properties of soil viz. organic matter and clay content. All the studied soils had non-linear slopes (n < 1) and degree of nonlinearity increased with increase in clay, organic matter content and temperature (p < 0.05). Desorption of pretilachlor was hysteretic in studied soils and hysteresis coefficient varied from 0.023 to 0.275. Thermodynamic analysis showed that pretilachlor adsorption onto soils was a feasible, spontaneous and endothermic process which becomes more favourable at high temperature. It could be inferred that the adsorption of pretilachlor on soils was physical in nature. Copyright © 2018 Elsevier Inc. All rights reserved.

Estimation of the water retention curve from the soil hydraulic conductivity and sorptivity in an upward infiltration process

NASA Astrophysics Data System (ADS)

Moret-Fernández, David; Angulo, Marta; Latorre, Borja; González-Cebollada, César; López, María Victoria

2017-04-01

Determination of the saturated hydraulic conductivity, Ks, and the α and n parameters of the van Genuchten (1980) water retention curve, θ(h), are fundamental to fully understand and predict soil water distribution. This work presents a new procedure to estimate the soil hydraulic properties from the inverse analysis of a single cumulative upward infiltration curve followed by an overpressure step at the end of the wetting process. Firstly, Ks is calculated by the Darcy's law from the overpressure step. The soil sorptivity (S) is then estimated using the Haverkamp et al., (1994) equation. Next, a relationship between α and n, f(α,n), is calculated from the estimated Sand Ks. The α and n values are finally obtained by the inverse analysis of the experimental data after applying the f(α,n) relationship to the HYDRUS-1D model. The method was validated on theoretical synthetic curves for three different soils (sand, loam and clay), and subsequently tested on experimental sieved soils (sand, loam, clay loam and clay) of known hydraulic properties. A robust relationship was observed between the theoretical α and nvalues (R2 > 0.99) of the different synthetic soils and those estimated from inverse analysis of the upward infiltration curve. Consistent results were also obtained for the experimental soils (R2 > 0.85). These results demonstrated that this technique allowed accurate estimates of the soil hydraulic properties for a wide range of textures, including clay soils.

Impact of root growth and root hydraulic conductance on water availability of young walnut trees

NASA Astrophysics Data System (ADS)

Jerszurki, Daniela; Couvreur, Valentin; Hopmans, Jan W.; Silva, Lucas C. R.; Shackel, Kenneth A.; de Souza, Jorge L. M.

2015-04-01

Walnut (Juglans regia L.) is a tree species of high economic importance in the Central Valley of California. This crop has particularly high water requirements, which makes it highly dependent on irrigation. The context of decreasing water availability in the state calls for efficient water management practices, which requires improving our understanding of the relationship between water application and walnut water availability. In addition to the soil's hydraulic conductivity, two plant properties are thought to control the supply of water from the bulk soil to the canopy: (i) root distribution and (ii) plant hydraulic conductance. Even though these properties are clearly linked to crop water requirements, their quantitative relation remains unclear. The aim of this study is to quantitatively explain walnut water requirements under water deficit from continuous measurements of its water consumption, soil and stem water potential, root growth and root system hydraulic conductance. For that purpose, a greenhouse experiment was conducted for a two month period. Young walnut trees were planted in transparent cylindrical pots, equipped with: (i) rhizotron tubes, which allowed for non-invasive monitoring of root growth, (ii) pressure transducer tensiometers for soil water potential, (iii) psychrometers attached to non-transpiring leaves for stem water potential, and (iv) weighing scales for plant transpiration. Treatments consisted of different irrigation rates: 100%, 75% and 50% of potential crop evapotranspiration. Plant responses were compared to predictions from three simple process-based soil-plant-atmosphere models of water flow: (i) a hydraulic model of stomatal regulation based on stem water potential and vapor pressure deficit, (ii) a model of plant hydraulics predicting stem water potential from soil-root interfaces water potential, and (iii) a model of soil water depletion predicting the water potential drop between the bulk soil and soil-root interfaces. These models were combined to a global optimization algorithm to obtain parameters that best fit the observed soil-plant-atmosphere water dynamics. Eventually, relations between root system conductance and growth as well as water access strategies were quantitatively analyzed.

Disturbance, neutral theory, and patterns of beta diversity in soil communities.

PubMed

Maaß, Stefanie; Migliorini, Massimo; Rillig, Matthias C; Caruso, Tancredi

2014-12-01

Beta diversity describes how local communities within an area or region differ in species composition/abundance. There have been attempts to use changes in beta diversity as a biotic indicator of disturbance, but lack of theory and methodological caveats have hampered progress. We here propose that the neutral theory of biodiversity plus the definition of beta diversity as the total variance of a community matrix provide a suitable, novel, starting point for ecological applications. Observed levels of beta diversity (BD) can be compared to neutral predictions with three possible outcomes: Observed BD equals neutral prediction or is larger (divergence) or smaller (convergence) than the neutral prediction. Disturbance might lead to either divergence or convergence, depending on type and strength. We here apply these ideas to datasets collected on oribatid mites (a key, very diverse soil taxon) under several regimes of disturbances. When disturbance is expected to increase the heterogeneity of soil spatial properties or the sampling strategy encompassed a range of diverging environmental conditions, we observed diverging assemblages. On the contrary, we observed patterns consistent with neutrality when disturbance could determine homogenization of soil properties in space or the sampling strategy encompassed fairly homogeneous areas. With our method, spatial and temporal changes in beta diversity can be directly and easily monitored to detect significant changes in community dynamics, although the method itself cannot inform on underlying mechanisms. However, human-driven disturbances and the spatial scales at which they operate are usually known. In this case, our approach allows the formulation of testable predictions in terms of expected changes in beta diversity, thereby offering a promising monitoring tool.

Comparison of Predicted and Measured Soil Retention Curve in Lombardy Region Northern of Italy

NASA Astrophysics Data System (ADS)

Wassar, Fatma; Rienzner, Michele; Chiaradia, Enrico Antonio; Gandolfi, Claudio

2013-04-01

Water retention characteristics are crucial input parameters in any modeling study on water flow and solute transport. These properties are difficult to measure and therefore the use of both direct and indirect methods is required in order to adequately describe them with sufficient accuracy. Several field methods, laboratory methods and theoretical models for such determinations exist, each having their own limitations and advantages (Stephens, 1994). Therefore, extensive comparisons between estimated, field and laboratory results to determine it still requires their validity for a range of different soils and specific cases. This study attempts to make a contribution specifically in this connection. The soil water retention characteristics were determined in two representative sites (PMI-1 and PMI-5) located in Landriano field, in Lombardy region, northern Italy. In the laboratory, values of both volumetric water content (θ) and soil water matric potential (h) are measured in the same sample using the tensiometric box and pressure plate apparatus. Field determination of soil water retention involved measurements of soil water content with SENTEK probes, and matric potential with tensiometers. The retention curve characteristics were also determined using some of the most commonly cited and some recently developed PTFs that use soil properties such as particle-size distribution (sand, silt, and clay content), organic matter or organic Carbon content, and dry bulk density. Field methods are considered to be more representative than laboratory and estimation methods for determining water retention characteristics (Marion et al., 1996). Therefore, field retention curves were compared against retention curves obtained from laboratory measurements and PTFs estimations. The performances of laboratory and PTFs in predicting field measured data were evaluated using root mean square error (RMSE) and bias. The comparison showed that laboratory measurements were the most accurate. They had the highest ranking for the validation indices (RMSE ranging between 2.4 and 7.7% and bias between 0.1 and 6.4%). The second best technique was the PTF Rosetta (Schaap et al. 2001). They perform only slightly poorer than the laboratory measurements (RMSE ranging between 2.7 and 10% and bias between 0.3 and 7.7%). The lowest prediction accuracy is observed for the Rawls and Brakensiek (1985) PTF (RMSE ranging between 6.3 and 17% and bias between 5 and 10%) which is in contradiction with previous finding (Calzolari et al., 2001), showing that this function is well representing the retention characteristics of the area. We conclude that the Rosetta PTF developed by Schaap et al (2001) appears to be well suited to predict the soil moisture retention curve from easily available soil properties in the Lombardy area and further field investigations would be useful to reinforce this finding. Keywords: water retention curve; laboratory measurements; field measurements; pedotransfert functions; comparison.

Soil heating during wildfires and prescribed burns: a global evaluation based on existing and new data

NASA Astrophysics Data System (ADS)

Doerr, Stefan; Santin, Cristina; Reardon, James; Mataix-Solera, Jorge; Stoof, Cathelijne; Bryant, Rob; Miesel, Jessica; Badia, David

2017-04-01

Heat transfer from the combustion of ground fuels and soil organic matter during vegetation fires can cause substantial changes to the physical, chemical and biological characteristics of soils. Numerous studies have investigated the effects of wildfires and prescribed burns on soil properties based either on field samples or using laboratory experiments. Critical thresholds for changes in soil properties, however, have been determined largely based on laboratory heating experimentation. These experimental approaches have been criticized for being inadequate for reflecting the actual heating patterns soil experienced in vegetation fires, which remain poorly understood. To address this research gap, this study reviews existing and evaluates new field data on key soil heating parameters determined during wildfires and prescribed burns from a wide range of environments. The results highlight the high spatial and temporal variability in soil heating patters not only between, but also within fires. Most wildfires and prescribed burns are associated with heat pulses that are much shorter than those typically applied in laboratory studies, which can lead to erroneous conclusions when results from laboratory studies are used to predict fire impacts on soils in the field.

Main controlling factors and forecasting models of lead accumulation in earthworms based on low-level lead-contaminated soils.

PubMed

Tang, Ronggui; Ding, Changfeng; Ma, Yibing; Wan, Mengxue; Zhang, Taolin; Wang, Xingxiang

2018-06-02

To explore the main controlling factors in soil and build a predictive model between the lead concentrations in earthworms (Pb earthworm ) and the soil physicochemical parameters, 13 soils with low level of lead contamination were used to conduct toxicity experiments using earthworms. The results indicated that a relatively high bioaccumulation factor appeared in the soils with low pH values. The lead concentrations between earthworms and soils after log transformation had a significantly positive correlation (R 2  = 0.46, P < 0.0001, n = 39). Stepwise multiple linear regression analysis derived a fitting empirical model between Pb earthworm and the soil physicochemical properties: log(Pb earthworm ) = 0.96log(Pb soil ) - 0.74log(OC) - 0.22pH + 0.95, (R 2  = 0.66, n = 39). Furthermore, path analysis confirmed that the Pb concentrations in the soil (Pb soil ), soil pH, and soil organic carbon (OC) were the primary controlling factors of Pb earthworm with high pathway parameters (0.71, - 0.51, and - 0.49, respectively). The predictive model based on Pb earthworm in a nationwide range of soils with low-level lead contamination could provide a reference for the establishment of safety thresholds in Pb-contaminated soils from the perspective of soil-animal systems.

Assessment of organic matter resistance to biodegradation in volcanic ash soils assisted by automated interpretation of infrared spectra from humic acid and whole soil samples by using partial least squares

NASA Astrophysics Data System (ADS)

Hernández, Zulimar; Pérez Trujillo, Juan Pedro; Hernández-Hernández, Sergio Alexander; Almendros, Gonzalo; Sanz, Jesús

2014-05-01

From a practical viewpoint, the most interesting possibilities of applying infrared (IR) spectroscopy to soil studies lie on processing IR spectra of whole soil (WS) samples [1] in order to forecast functional descriptors at high organizational levels of the soil system, such as soil C resilience. Currently, there is a discussion on whether the resistance to biodegradation of soil organic matter (SOM) depends on its molecular composition or on environmental interactions between SOM and mineral components, such could be the case with physical encapsulation of particulate SOM or organo-mineral derivatives, e.g., those formed with amorphous oxides [2]. A set of about 200 dependent variables from WS and isolated, ash free, humic acids (HA) [3] was obtained in 30 volcanic ash soils from Tenerife Island (Spain). Soil biogeochemical properties such as SOM, allophane (Alo + 1 /2 Feo), total mineralization coefficient (TMC) or aggregate stability were determined in WS. In addition, structural information on SOM was obtained from the isolated HA fractions by visible spectroscopy and analytical pyrolysis (Py-GC/MS). Aiming to explore the potential of partial least squares regression (PLS) in forecasting soil dependent variables, exclusively using the information extracted from WS and HA IR spectral profiles, data were processed by using ParLeS [4] and Unscrambler programs. Data pre-treatments should be carefully chosen: the most significant PLS models from IR spectra of HA were obtained after second derivative pre-treatment, which prevented effects of intrinsically broadband spectral profiles typical in macromolecular heterogeneous material such as HA. Conversely, when using IR spectra of WS, the best forecasting models were obtained using linear baseline correction and maximum normalization pre-treatment. With WS spectra, the most successful prediction models were obtained for SOM, magnetite, allophane, aggregate stability, clay and total aromatic compounds, whereas the PLS-model for TMC was of little significance. On the other hand, the best successful prediction models using HA spectra were for SOM, TMC, allophane content and soil fungal pigments. In these particular volcanic ash soils, with large concentration of short-range minerals, the use of WS spectra, compared to the use of HA spectra, led to predict higher number of dependent variables. This is interpreted as the fact that the information of mineral constituents may help to explain soil emergent properties (e.g., SOM resilience or hydrophysical properties). The above results coincide with previous research [2] based on classification of soil properties by multidimensional scaling, where it was demonstrated that formation of stable organomineral complexes between HA and allophane coincide with large amounts of SOM and low TMC values. [1] Viscarra Rossel, R.A., Walvoort, D.J.J., McBratney, A.B., Janik, L.J. & Skjemstad, J.O. 2006. Geoderma 131, 59-75. [2] Hernández, Z., Almendros, G. 2012. Soil Biology & Biochemistry 44, 130-142. [3] Hernández, Z. 2009. Functional study of soil organic matter in vineyards from Tenerife Island (Spain). PhD. University of Alcalá, Alcalá de Henares, Madrid. [4] Viscarra-Rossel, R.A. 2008. Chemometrics & Intelligent Laboratory Systems 90, 72-83.

SoilGrids250m: Global gridded soil information based on machine learning

PubMed Central

Mendes de Jesus, Jorge; Heuvelink, Gerard B. M.; Ruiperez Gonzalez, Maria; Kilibarda, Milan; Blagotić, Aleksandar; Shangguan, Wei; Wright, Marvin N.; Geng, Xiaoyuan; Bauer-Marschallinger, Bernhard; Guevara, Mario Antonio; Vargas, Rodrigo; MacMillan, Robert A.; Batjes, Niels H.; Leenaars, Johan G. B.; Ribeiro, Eloi; Wheeler, Ichsani; Mantel, Stephan; Kempen, Bas

2017-01-01

This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods—random forest and gradient boosting and/or multinomial logistic regression—as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10–fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License. PMID:28207752

SoilGrids250m: Global gridded soil information based on machine learning.

PubMed

Hengl, Tomislav; Mendes de Jesus, Jorge; Heuvelink, Gerard B M; Ruiperez Gonzalez, Maria; Kilibarda, Milan; Blagotić, Aleksandar; Shangguan, Wei; Wright, Marvin N; Geng, Xiaoyuan; Bauer-Marschallinger, Bernhard; Guevara, Mario Antonio; Vargas, Rodrigo; MacMillan, Robert A; Batjes, Niels H; Leenaars, Johan G B; Ribeiro, Eloi; Wheeler, Ichsani; Mantel, Stephan; Kempen, Bas

2017-01-01

This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10-fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.

Using visible and near-infrared diffuse reflectance spectroscopy for predicting soil properties based on regression with peaks parameters as derived from continuum-removed spectra

NASA Astrophysics Data System (ADS)

Vasat, Radim; Klement, Ales; Jaksik, Ondrej; Kodesova, Radka; Drabek, Ondrej; Boruvka, Lubos

2014-05-01

Visible and near-infrared diffuse reflectance spectroscopy (VNIR-DRS) provides a rapid and inexpensive tool for simultaneous prediction of a variety of soil properties. Usually, some sophisticated multivariate mathematical or statistical methods are employed in order to extract the required information from the raw spectra measurement. For this purpose especially the Partial least squares regression (PLSR) and Support vector machines (SVM) are the most frequently used. These methods generally benefit from the complexity with which the soil spectra are treated. But it is interesting that also techniques that focus only on a single spectral feature, such as a simple linear regression with selected continuum-removed spectra (CRS) characteristic (e.g. peak depth), can often provide competitive results. Therefore, we decided to enhance the potential of CRS taking into account all possible CRS peak parameters (area, width and depth) and develop a comprehensive methodology based on multiple linear regression approach. The eight considered soil properties were oxidizable carbon content (Cox), exchangeable (pHex) and active soil pH (pHa), particle and bulk density, CaCO3 content, crystalline and amorphous (Fed) and amorphous Fe (Feox) forms. In four cases (pHa, bulk density, Fed and Feox), of which two (Fed and Feox) were predicted reliably accurately (0.50 < R2cv < 0.80) and the other two (pHa and bulk density) only poorly (R2cv < 0.50), we obtained slightly better results than with PLSR and SVM. In one case (pHex) we achieved a significantly higher, although just reliable, accuracy (R2cv = 0.601) than with PLSR and SVM (R2cv = 0.448 and 0.442, resp.). But most interestingly, in the case of particle density, the presented approach outperformed the PLSR and SVM dramatically offering a fairly accurate prediction (R2cv = 0.827) against two failures (R2cv = 0.034 and 0.121 for PLSR and SVM, resp.). In last two cases (Cox and CaCO3) a slightly worse results were achieved then with PLSR and SVM with overall fairly accurate prediction (R2cv > 0.80). Acknowledgment: Authors acknowledge the financial support of the Ministry of Agriculture of the Czech Republic (grant No. QJ1230319).

Estimating the spatial distribution of soil organic matter density and geochemical properties in a polygonal shaped Arctic Tundra using core sample analysis and X-ray computed tomography

NASA Astrophysics Data System (ADS)

Soom, F.; Ulrich, C.; Dafflon, B.; Wu, Y.; Kneafsey, T. J.; López, R. D.; Peterson, J.; Hubbard, S. S.

2016-12-01

The Arctic tundra with its permafrost dominated soils is one of the regions most affected by global climate change, and in turn, can also influence the changing climate through biogeochemical processes, including greenhouse gas release or storage. Characterization of shallow permafrost distribution and characteristics are required for predicting ecosystem feedbacks to a changing climate over decadal to century timescales, because they can drive active layer deepening and land surface deformation, which in turn can significantly affect hydrological and biogeochemical responses, including greenhouse gas dynamics. In this study, part of the Next-Generation Ecosystem Experiment (NGEE-Arctic), we use X-ray computed tomography (CT) to estimate wet bulk density of cores extracted from a field site near Barrow AK, which extend 2-3m through the active layer into the permafrost. We use multi-dimensional relationships inferred from destructive core sample analysis to infer organic matter density, dry bulk density and ice content, along with some geochemical properties from nondestructive CT-scans along the entire length of the cores, which was not obtained by the spatially limited destructive laboratory analysis. Multi-parameter cross-correlations showed good agreement between soil properties estimated from CT scans versus properties obtained through destructive sampling. Soil properties estimated from cores located in different types of polygons provide valuable information about the vertical distribution of soil and permafrost properties as a function of geomorphology.

Predicting changes of glass optical properties in polluted atmospheric environment by a neural network model

NASA Astrophysics Data System (ADS)

Verney-Carron, A.; Dutot, A. L.; Lombardo, T.; Chabas, A.

2012-07-01

Soiling results from the deposition of pollutants on materials. On glass, it leads to an alteration of its intrinsic optical properties. The nature and intensity of this phenomenon mirrors the pollution of an environment. This paper proposes a new statistical model in order to predict the evolution of haze (H) (i.e. diffuse/direct transmitted light ratio) as a function of time and major pollutant concentrations in the atmosphere (SO2, NO2, and PM10 (Particulate Matter < 10 μm)). The model was parameterized by using a large set of data collected in European cities (especially, Paris and its suburbs, Athens, Krakow, Prague, and Rome) during field exposure campaigns (French, European, and international programs). This statistical model, called NEUROPT-Glass, comes from an artificial neural network with two hidden layers and uses a non-linear parametric regression named Multilayer Perceptron (MLP). The results display a high determination coefficient (R2 = 0.88) between the measured and the predicted hazes and minimizes the dispersion of data compared to existing multilinear dose-response functions. Therefore, this model can be used with a great confidence in order to predict the soiling of glass as a function of time in world cities with different levels of pollution or to assess the effect of pollution reduction policies on glass soiling problems in urban environments.

Determination of macro-scale soil properties from pore-scale structures: model derivation.

PubMed

Daly, K R; Roose, T

2018-01-01

In this paper, we use homogenization to derive a set of macro-scale poro-elastic equations for soils composed of rigid solid particles, air-filled pore space and a poro-elastic mixed phase. We consider the derivation in the limit of large deformation and show that by solving representative problems on the micro-scale we can parametrize the macro-scale equations. To validate the homogenization procedure, we compare the predictions of the homogenized equations with those of the full equations for a range of different geometries and material properties. We show that the results differ by [Formula: see text] for all cases considered. The success of the homogenization scheme means that it can be used to determine the macro-scale poro-elastic properties of soils from the underlying structure. Hence, it will prove a valuable tool in both characterization and optimization.

A model framework to represent plant-physiology and rhizosphere processes in soil profile simulation models

NASA Astrophysics Data System (ADS)

Vanderborght, J.; Javaux, M.; Couvreur, V.; Schröder, N.; Huber, K.; Abesha, B.; Schnepf, A.; Vereecken, H.

2013-12-01

Plant roots play a crucial role in several key processes in soils. Besides their impact on biogeochemical cycles and processes, they also have an important influence on physical processes such as water flow and transport of dissolved substances in soils. Interaction between plant roots and soil processes takes place at different scales and ranges from the scale of an individual root and its directly surrounding soil or rhizosphere over the scale of a root system of an individual plant in a soil profile to the scale of vegetation patterns in landscapes. Simulation models that are used to predict water flow and solute transport in soil-plant systems mainly focus on the individual plant root system scale, parameterize single-root scale phenomena, and aggregate the root system scale to the vegetation scale. In this presentation, we will focus on the transition from the single root to the root system scale. Using high resolution non-invasive imaging techniques and methods, gradients in soil properties and states around roots and their difference from the bulk soil properties could be demonstrated. Recent developments in plant sciences provide new insights in the mechanisms that control water fluxes in plants and in the adaptation of root properties or root plasticity to changing soil conditions. However, since currently used approaches to simulate root water uptake neither resolve these small scale processes nor represent processes and controls within the root system, transferring this information to the whole soil-plant system scale is a challenge. Using a simulation model that describes flow and transport processes in the soil, resolves flow and transport towards individual roots, and describes flow and transport within the root system, such a transfer could be achieved. We present a few examples that illustrate: (i) the impact of changed rhizosphere hydraulic properties, (ii) the effect of root hydraulic properties and root system architecture, (iii) the regulation of plant transpiration by root-zone produced plant hormones, and (iv) the impact of salt accumulation at the soil-root interface on root water uptake. We further propose a framework how this process knowledge could be implemented in root zone simulation models that do not resolve small scale processes.

Predicting risk of rill initiation in a sub-catchment of Lake Balaton, Hungary

NASA Astrophysics Data System (ADS)

Hausner, C.; Sisák, I.

2009-04-01

Rill erosion is an accelerated form of soil degradation. It removes much more soil and nutrients from the agricultural land than sheet erosion. Soils in the southern sub-watershed of Lake Balaton are especially prone to rill erosion and they contribute to siltation of ditches, to muddy floods and to eutrofication of the lake. The parent material in this region is mainly (sandy) loess and the soils are already moderately or strongly eroded thus, the low tolerance of loess against erosion determines erodibility. Identification of soils with high risk of rill erosion is crucial to plan mitigation measures. Soil erodibility has been investigated in this study in the catchment of Tetves stream. The USLE soil erodibility factor and soil slaking are widely accepted indicators for soil erosion. Both of them are published for all soil texture classes in handbooks of soil mapping. We have found that erodibility derived from our physical model has a close linear correlation with the product of the USLE soil erodibility factor and soil slaking grade thus, USLE could be directly used to assess parameters for physical based models. Rill erosion is highly probable if the product of KUSLE X slaking grade is above 2. Digital maps were produced to delineate soils with high potential for rill erosion. The basic data for the soil properties were drawn from the 1:10,000 soil map. Soil texture classes were used to assign KUSLE and slaking grade to the soil units. Beyond soil properties, other factors also influence rill formation: slope, surface cover, rainfall intensity. However, identifying soil properties, which make soils prone to rill erosion, is an important initial step for the reduction of diffuse agricultural loads to Lake Balaton. It might be the objective of River Basin Management Plans in the Water Framework Directive to prevent rill erosion and our study provides scientific evidence for targeting this policy.

Climate-biomes, pedo-biomes and pyro-biomes: which world view explains the tropical forest - savanna boundary in South America?

NASA Astrophysics Data System (ADS)

Langan, Liam; Higgins, Steven; Scheiter, Simon

2015-04-01

Elucidating the drivers of broad vegetation formations improves our understanding of earth system functioning. The biome, defined primarily by the dominance of a particular growth strategy, is commonly employed to group vegetation into similar units. Predicting tropical forest and savanna biome boundaries in South America has proven difficult. Process based DGVMs (Dynamic global vegetation models) are our best tool to simulate vegetation patterns, make predictions for future changes and test theory, however, many DGVMs fail to accurately simulate the spatial distribution or indeed presence of the South American savanna biome which can result in large differences in modelled ecosystem structural properties. Evidence suggests fire plays a significant role in mediating these forest and savanna biome boundaries, however, fire alone does not appear to be sufficient to predict these boundaries in South America using DGVMs hinting at the presence of one or more missing environmental factors. We hypothesise that soil depth, which affects plant available water by determining maximum storage potential and influences temporal availability, may be one of these missing environmental factors. To test our hypothesis we use a novel vegetation model, the aDGVM2. This model has been specifically designed to allow plant trait strategies, constrained by trade-offs between traits, evolve based on the abiotic and biotic conditions where the resulting community trait suites are emergent properties of model dynamics. Furthermore it considers root biomass in multiple soil layers and therefore allows the consideration of alternative rooting strategies, which in turn allows us to explore in more detail the role of soil hydraulic factors in controlling biome boundary distributions. We find that changes in soil depth, interacting with fire, affect the relative dominance of tree and grass strategies and thus the presence and spatial distribution of forest and savanna biomes in South America. Using the ISRIC-WISE soil depth dataset we show that applying spatially variable soil depth, in contrast to globally fixed soil depth, improves the accuracy with which we predict the South American savanna biome distribution when compared to multiple contemporary biome maps and that the emergence of the savanna biome results in markedly different ecosystem structural properties such as tree height, tree cover and above ground biomass. Many of these areas are capable of supporting forest and savanna biome states and have been deemed bi-stable areas, we show that, in these bi-stable areas the emergent tree community trait suite differs markedly between forest and savanna biome states.

Predicting radiocaesium sorption characteristics with soil chemical properties for Japanese soils.

PubMed

Uematsu, Shinichiro; Smolders, Erik; Sweeck, Lieve; Wannijn, Jean; Van Hees, May; Vandenhove, Hildegarde

2015-08-15

The high variability of the soil-to-plant transfer factor of radiocaesium (RCs) compels a detailed analysis of the radiocaesium interception potential (RIP) of soil, which is one of the specific factors ruling the RCs transfer. The range of the RIP values for agricultural soils in the Fukushima accident affected area has not yet been fully surveyed. Here, the RIP and other major soil chemical properties were characterised for 51 representative topsoils collected in the vicinity of the Fukushima contaminated area. The RIP ranged a factor of 50 among the soils and RIP values were lower for Andosols compared to other soils, suggesting a role of soil mineralogy. Correlation analysis revealed that the RIP was most strongly and negatively correlated to soil organic matter content and oxalate extractable aluminium. The RIP correlated weakly but positively to soil clay content. The slope of the correlation between RIP and clay content showed that the RIP per unit clay was only 4.8 mmol g(-1) clay, about threefold lower than that for clays of European soils, suggesting more amorphous minerals and less micaceous minerals in the clay fraction of Japanese soils. The negative correlation between RIP and soil organic matter may indicate that organic matter can mask highly selective sorption sites to RCs. Multiple regression analysis with soil organic matter and cation exchange capacity explained the soil RIP (R(2)=0.64), allowing us to map soil RIP based on existing soil map information. Copyright © 2015 Elsevier B.V. All rights reserved.

Pragmatic hydraulic theory predicts stomatal responses to climatic water deficits.

PubMed

Sperry, John S; Wang, Yujie; Wolfe, Brett T; Mackay, D Scott; Anderegg, William R L; McDowell, Nate G; Pockman, William T

2016-11-01

Ecosystem models have difficulty predicting plant drought responses, partially from uncertainty in the stomatal response to water deficits in soil and atmosphere. We evaluate a 'supply-demand' theory for water-limited stomatal behavior that avoids the typical scaffold of empirical response functions. The premise is that canopy water demand is regulated in proportion to threat to supply posed by xylem cavitation and soil drying. The theory was implemented in a trait-based soil-plant-atmosphere model. The model predicted canopy transpiration (E), canopy diffusive conductance (G), and canopy xylem pressure (P canopy ) from soil water potential (P soil ) and vapor pressure deficit (D). Modeled responses to D and P soil were consistent with empirical response functions, but controlling parameters were hydraulic traits rather than coefficients. Maximum hydraulic and diffusive conductances and vulnerability to loss in hydraulic conductance dictated stomatal sensitivity and hence the iso- to anisohydric spectrum of regulation. The model matched wide fluctuations in G and P canopy across nine data sets from seasonally dry tropical forest and piñon-juniper woodland with < 26% mean error. Promising initial performance suggests the theory could be useful in improving ecosystem models. Better understanding of the variation in hydraulic properties along the root-stem-leaf continuum will simplify parameterization. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Predicting soil water content at - 33 kPa by pedotransfer functions in stoniness 1 soils in northeast Venezuela.

PubMed

Pineda, M C; Viloria, J; Martínez-Casasnovas, J A; Valera, A; Lobo, D; Timm, L C; Pires, L F; Gabriels, D

2018-02-22

Soil water content is a key property in the study of water available for plants, infiltration, drainage, hydraulic conductivity, irrigation, plant water stress and solute movement. However, its measurement consumes time and, in the case of stony soils, the presence of stones difficult to determinate the water content. An alternative is the use of pedotransfer functions (PTFs), as models to predict these properties from readily available data. The present work shows a comparison of different widely used PTFs to estimate water content at-33 kPa (WR -33kPa ) in high stoniness soils. The work was carried out in the Caramacate River, an area of high interest because the frequent landslides worsen the quality of drinking water. The performance of all evaluated PTFs was compared with a PTF generated for the study area. Results showed that the Urach's PTF presented the best performance in relation to the others and could be used to estimate WR -33kPa in soils of Caramacate River basin. The calculated PTFs had a R 2 of 0.65. This was slightly higher than the R 2 of the Urach's PTF. The inclusion of the rock fragment volume could have the better results. The weak performance of the other PTFs could be related to the fact that the mountain soils of the basin are rich in 2:1 clay and high stoniness, which were not used as independent variables for PTFs to estimate the WR -33kPa .

Aquifer sensitivity to pesticide leaching: Testing a soils and hydrogeologic index method

USGS Publications Warehouse

Mehnert, E.; Keefer, D.A.; Dey, W.S.; Wehrmann, H.A.; Wilson, S.D.; Ray, C.

2005-01-01

For years, researchers have sought index and other methods to predict aquifer sensitivity and vulnerability to nonpoint pesticide contamination. In 1995, an index method and map were developed to define aquifer sensitivity to pesticide leaching based on a combination of soil and hydrogeologic factors. The soil factor incorporated three soil properties: hydraulic conductivity, amount of organic matter within individual soil layers, and drainage class. These properties were obtained from a digital soil association map. The hydrogeologic factor was depth to uppermost aquifer material. To test this index method, a shallow ground water monitoring well network was designed, installed, and sampled in Illinois. The monitoring wells had a median depth of 7.6 m and were located adjacent to corn and soybean fields where the only known sources of pesticides were those used in normal agricultural production. From September 1998 through February 2001, 159 monitoring wells were sampled for 14 pesticides but no pesticide metabolites. Samples were collected and analyzed to assess the distribution of pesticide occurrence across three units of aquifer sensitivity. Pesticides were detected in 18% of all samples and nearly uniformly from samples from the three units of aquifer sensitivity. The new index method did not predict pesticide occurrence because occurrence was not dependent on the combined soil and hydrogeologic factors. However, pesticide occurrence was dependent on the tested hydrogeologic factor and was three times higher in areas where the depth to the uppermost aquifer was <6 m than in areas where the depth to the uppermost aquifer was 6 to <15 m. Copyright ?? 2005 National Ground Water Association.

Prediction of Ba, Co and Ni for tropical soils using diffuse reflectance spectroscopy and X-ray fluorescence spectroscopy

NASA Astrophysics Data System (ADS)

Arantes Camargo, Livia; Marques Júnior, José; Reynaldo Ferracciú Alleoni, Luís; Tadeu Pereira, Gener; De Bortoli Teixeira, Daniel; Santos Rabelo de Souza Bahia, Angélica

2017-04-01

Environmental impact assessments may be assisted by spatial characterization of potentially toxic elements (PTEs). Diffuse reflectance spectroscopy (DRS) and X-ray fluorescence spectroscopy (XRF) are rapid, non-destructive, low-cost, prediction tools for a simultaneous characterization of different soil attributes. Although low concentrations of PTEs might preclude the observation of spectral features, their contents can be predicted using spectroscopy by exploring the existing relationship between the PTEs and soil attributes with spectral features. This study aimed to evaluate, in three geomorphic surfaces of Oxisols, the capacity for predicting PTEs (Ba, Co, and Ni) and their spatial variability by means of diffuse reflectance spectroscopy (DRS) and X-ray fluorescence spectroscopy (XRF). For that, soil samples were collected from three geomorphic surfaces and analyzed for chemical, physical, and mineralogical properties, and then analyzed in DRS (visible + near infrared - VIS+NIR and medium infrared - MIR) and XRF equipment. PTE prediction models were calibrated using partial least squares regression (PLSR). PTE spatial distribution maps were built using the values calculated by the calibrated models that reached the best accuracy using geostatistics. PTE prediction models were satisfactorily calibrated using MIR DRS for Ba, and Co (residual prediction deviation - RPD > 3.0), Vis DRS for Ni (RPD > 2.0) and FRX for all the studied PTEs (RPD > 1.8). DRS- and XRF-predicted values allowed the characterization and the understanding of spatial variability of the studied PTEs.

Predicting arsenic bioavailability to hyperaccumulator Pteris vittata in arsenic-contaminated soils.

PubMed

Gonzaga, Maria Isidória Silva; Ma, Lena Q; Pacheco, Edson Patto; dos Santos, Wallace Melo

2012-12-01

Using chemical extraction to evaluate plant arsenic availability in contaminated soils is important to estimate the time frame for site cleanup during phytoremediation. It is also of great value to assess As mobility in soil and its risk in environmental contamination. In this study, four conventional chemical extraction methods (water, ammonium sulfate, ammonium phosphate, and Mehlich III) and a new root-exudate based method were used to evaluate As extractability and to correlate it with As accumulation in P. vittata growing in five As-contaminated soils under greenhouse condition. The relationship between different soil properties, and As extractability and plant As accumulation was also investigated. Arsenic extractability was 4.6%, 7.0%, 18%, 21%, and 46% for water, ammonium sulfate, organic acids, ammonium phosphate, and Mehlich III, respectively. Root exudate (organic acids) solution was suitable for assessing As bioavailability (81%) in the soils while Mehlich III (31%) overestimated the amount of As taken up by plants. Soil organic matter, P and Mg concentrations were positively correlated to plant As accumulation whereas Ca concentration was negatively correlated. Further investigation is needed on the effect of Ca and Mg on As uptake by P. vittata. Moreover, additional As contaminated soils with different properties should be tested.

The potential of UAS imagery for soil mapping at the agricultural plot scale

NASA Astrophysics Data System (ADS)

Gilliot, Jean-Marc; Michelin, Joël; Becu, Maxime; Cissé, Moustapha; Hadjar, Dalila; Vaudour, Emmanuelle

2017-04-01

Soil mapping is expensive and time consuming. Airborne and satellite remote sensing data have already been used to predict some soil properties but now Unmanned Aerial Systems (UAS) allow to do many images acquisitions in various field conditions in favour of developing methods for better prediction models construction. This study propose an operational method for spatial prediction of soil properties (organic carbon, clay) at the scale of the agricultural plot by using UAS imagery. An agricultural plot of 28 ha, located in the western region of Paris France, was studied from March to May 2016. An area of 3.6 ha was delimited within the plot and a total of 16 flights were completed. The UAS platforms used were the eBee fixed wing provided by Sensefly® flying at an altitude from 60m to 130m and the iris+ 3DR® Quadcopter (from 30m to 100m). Two multispectral visible near-infrared cameras were used: the AirInov® MultiSPEC 4C® and the Micasense® RedEdge®. 42 ground control points (GCP) were sampled within the 3.6 ha plot. A centimetric Trimble Geo 7x DGPS was used to determine precise GCP positions. On each GCP the soil horizons were described and the top soil were sampled for standard physico-chemical analysis. Ground spectral measurements with a Spectral Evolution® SR-3500 spectroradiometer were made synchronously with the drone flights. 22 additional GCP were placed around the 3.6 ha area in order to realize a precise georeferencing. The multispectral mosaics were calculated using the Agisoft Photoscan® software and all mapping processings were done with the ESRI ArcGIS® 10.3 software. The soil properties were estimated by partial least squares regression (PLSR) between the laboratory analyses and the multispectral information of the UAS images, with the PLS package of the R software. The objective was to establish a model that would achieve an acceptable prediction quality using minimum number of points. For this, we tested 5 models with a decreasing number of calibration points: 20, 15, 10, 5 and 3 points. The remaining points were used to validate the models. The point positions were determined on the basis of a soil brightness index map calculated from the UAS image, in order to distribute the points in areas of contrasted brightness. Root Mean Squared Error Prediction (RMSEP) obtained by cross-validation were 1.6 g.kg-1 and 28 g.kg-1 for organic carbon and clay respectively, with 20 points. Results showed ability to obtain acceptable precision (2 g.kg-1 and 48 g.kg-1) with only 3 points. This work was supported by the SolFIT research network of the BASC LabEx (Laboratory of Excellence) and by the TOSCA-PLEIADES-CO project of the French Space Agency (CNES).

Some engineering aspects of homoionized mixed clay minerals.

PubMed

Oren, Ali Hakan; Kaya, Abidin

2003-05-01

Many studies have been conducted to investigate the physicochemical behavior of pure clay minerals and predict their engineering performance in the field. In this study, the physicochemical properties of an artificial mixture of different clay minerals namely, 40-50% montmorillonite, 20-30% illite and 10-15% kaolin were investigated. The mixture was homoionized with sodium, Na+; calcium, Ca2+; and aluminum, Al3+. The engineering properties studied were consistency limits, sediment volume, compressibility behavior, and hydraulic conductivity. The results revealed that the liquid, plastic and shrinkage limits of soil increased with increasing cation valence. The hydraulic conductivity of the soil also increased with an increase in the valence of the cation at any given void ratio. Aluminum and sodium treated clays had the highest and the lowest modified compression index values, respectively. Furthermore, trivalent cation saturated clayey soil consolidates three times faster than that of monovalent and two times faster than that of divalent. These properties of the soils determined were, in general, similar to those of kaolinite rather than those of montmorillonite. The comparison of the results obtained with the published data in the literature revealed that the physicochemical behavior of the tested clay soil was, in general, similar to that of kaolinite.

Impact of Land Use Management and Soil Properties on Denitrifier Communities of Namibian Savannas.

PubMed

Braker, Gesche; Matthies, Diethart; Hannig, Michael; Brandt, Franziska Barbara; Brenzinger, Kristof; Gröngröft, Alexander

2015-11-01

We studied potential denitrification activity and the underlying denitrifier communities in soils from a semiarid savanna ecosystem of the Kavango region in NE Namibia to help in predicting future changes in N(2)O emissions due to continuing changes of land use in this region. Soil type and land use (pristine, fallow, and cultivated soils) influenced physicochemical characteristics of the soils that are relevant to denitrification activity and N(2)O fluxes from soils and affected potential denitrification activity. Potential denitrification activity was assessed by using the denitrifier enzyme activity (DEA) assay as a proxy for denitrification activity in the soil. Soil type and land use influenced C and N contents of the soils. Pristine soils that had never been cultivated had a particularly high C content. Cultivation reduced soil C content and the abundance of denitrifiers and changed the composition of the denitrifier communities. DEA was strongly and positively correlated with soil C content and was higher in pristine than in fallow or recently cultivated soils. Soil type and the composition of both the nirK- and nirS-type denitrifier communities also influenced DEA. In contrast, other soil characteristics like N content, C:N ratio, and pH did not predict DEA. These findings suggest that due to greater availability of soil organic matter, and hence a more effective N cycling, the natural semiarid grasslands emit more N(2)O than managed lands in Namibia.

Effect of pesticide fate parameters and their uncertainty on the selection of 'worst-case' scenarios of pesticide leaching to groundwater.

PubMed

Vanderborght, Jan; Tiktak, Aaldrik; Boesten, Jos J T I; Vereecken, Harry

2011-03-01

For the registration of pesticides in the European Union, model simulations for worst-case scenarios are used to demonstrate that leaching concentrations to groundwater do not exceed a critical threshold. A worst-case scenario is a combination of soil and climate properties for which predicted leaching concentrations are higher than a certain percentile of the spatial concentration distribution within a region. The derivation of scenarios is complicated by uncertainty about soil and pesticide fate parameters. As the ranking of climate and soil property combinations according to predicted leaching concentrations is different for different pesticides, the worst-case scenario for one pesticide may misrepresent the worst case for another pesticide, which leads to 'scenario uncertainty'. Pesticide fate parameter uncertainty led to higher concentrations in the higher percentiles of spatial concentration distributions, especially for distributions in smaller and more homogeneous regions. The effect of pesticide fate parameter uncertainty on the spatial concentration distribution was small when compared with the uncertainty of local concentration predictions and with the scenario uncertainty. Uncertainty in pesticide fate parameters and scenario uncertainty can be accounted for using higher percentiles of spatial concentration distributions and considering a range of pesticides for the scenario selection. Copyright © 2010 Society of Chemical Industry.

Characterization of soil spatial variability for site-specific management using soil electrical conductivity and other remotely sensed data

NASA Astrophysics Data System (ADS)

Bang, Jisu

Field-scale characterization of soil spatial variability using remote sensing technology has potential for achieving the successful implementation of site-specific management (SSM). The objectives of this study were to: (i) examine the spatial relationships between apparent soil electrical conductivity (EC a) and soil chemical and physical properties to determine if EC a could be useful to characterize soil properties related to crop productivity in the Coastal Plain and Piedmont of North Carolina; (ii) evaluate the effects of in-situ soil moisture variation on ECa mapping as a basis for characterization of soil spatial variability and as a data layer in cluster analysis as a means of delineating sampling zones; (iii) evaluate clustering approaches using different variable sets for management zone delineation to characterize spatial variability in soil nutrient levels and crop yields. Studies were conducted in two fields in the Piedmont and three fields in the Coastal Plain of North Carolina. Spatial measurements of ECa via electromagnetic induction (EMI) were compared with soil chemical parameters (extractable P, K, and micronutrients; pH, cation exchange capacity [CEC], humic matter or soil organic matter; and physical parameters (percentage sand, silt, and clay; and plant-available water [PAW] content; bulk density; cone index; saturated hydraulic conductivity [Ksat] in one of the coastal plain fields) using correlation analysis across fields. We also collected ECa measurements in one coastal plain field on four days with significantly different naturally occurring soil moisture conditions measured in five increments to 0.75 m using profiling time-domain reflectometry probes to evaluate the temporal variability of ECa associated with changes in in-situ soil moisture content. Nonhierarchical k-means cluster analysis using sensor-based field attributes including vertical ECa, near-infrared (NIR) radiance of bare-soil from an aerial color infrared (CIR) image, elevation, slope, and their combinations was performed to delineate management zones. The strengths and signs of the correlations between ECa and measured soil properties varied among fields. Few strong direct correlations were found between ECa and the soil chemical and physical properties studied (r2 < 0.50), but correlations improved considerably when zone mean ECa and zone means of selected soil properties among ECa zones were compared. The results suggested that field-scale ECa survey is not able to directly predict soil nutrient levels at any specific location, but could delimit distinct zones of soil condition among which soil nutrient levels differ, providing an effective basis for soil sampling on a zone basis. (Abstract shortened by UMI.)

ECOUL: an interactive computer tool to study hydraulic behavior of swelling and rigid soils

NASA Astrophysics Data System (ADS)

Perrier, Edith; Garnier, Patricia; Leclerc, Christian

2002-11-01

ECOUL is an interactive, didactic software package which simulates vertical water flow in unsaturated soils. End-users are given an easily-used tool to predict the evolution of the soil water profile, with a large range of possible boundary conditions, through a classical numerical solution scheme for the Richards equation. Soils must be characterized by water retention curves and hydraulic conductivity curves, the form of which can be chosen among different analytical expressions from the literature. When the parameters are unknown, an inverse method is provided to estimate them from available experimental flow data. A significant original feature of the software is to include recent algorithms extending the water flow model to deal with deforming porous media: widespread swelling soils, the volume of which varies as a function of water content, must be described by a third hydraulic characteristic property, the deformation curve. Again, estimation of the parameters by means of inverse procedures and visualization facilities enable exploration, understanding and then prediction of soil hydraulic behavior under various experimental conditions.

Geochemical variability of soils and biogeochemical variability of plants in the Piceance Basin, Colorado

USGS Publications Warehouse

Tuttle, M.L.; Severson, R.C.; Dean, W.E.; Klusman, R.W.

1986-01-01

Geochemical baselines for native soils and biogeochemical baselines for plants in the Piceance basin provide data that can be used to assess geochemical and biogeochemical effects of oil-shale development, monitor changes in the geochemical and biogeochemical environment during development, and assess the degree of success of rehabilitation of native materials after development. Baseline values for 52 properties in native soils, 15 properties in big sagebrush, and 13 properties in western wheatgrass were established. Our Study revealed statistically significant regional variations of the following properties across the basin: in soil&-aluminum, cobalt, copper, iron, manganese, sodium, nickel, phosphorus, lead, scandium, titanium, vanadium, zinc, organic and total carbon, pH, clay, dolomite, sodium feldspar, and DTPA-extractable calcium, cadmium, iron, potassium, manganese, nickel, phosphorus, yttrium, and zinc; in big sagebrush-barium, calcium, copper, magnesium, molybdenum, sodium, strontium, zinc, and ash; and in western wheatgrass-boron, barium, calcium, magnesium, manganese, molybdenum, strontium, zinc, and ash. These variations show up as north-south trends across the basin, or they reflect differences in elevation, hydrology, and soil parent material. Baseline values for properties that do not have statistically significant regional variations can be represented by geometric means and deviations calculated from all values within the basin. Chemical and mineralogical analyses of soil and chemical analyses of western wheatgrass samples from Colorado State University's experimental revegetation plot at Anvil Points provide data useful in assessing potential effects on soil and plant properties when largescale revegetation operations begin. The concentrations of certain properties are related to the presence of topsoil over spent shale in the lysimeters. In soils, calcium, fluorine, lithium, magnesium, sodium, phosphorus, strontium, carbonate and total carbon, and DTPA-extractable boron, copper, iron, magnesium, and nickel have lower concentrations in topsoil than in the spent oil shale; whereas, silicon, titanium, ytterbium, clay, quartz, and DTPA-extractable potassium have greater concentrations in the topsoil than in the spent oil shale. In western wheatgrass, molybdenum has a lower concentration in grasses growing on the topsoil than in grasses on the spent oil shale; whereas, barium, calcium, manganese, strontium, zinc, and ash have greater concentrations in grasses growing on the topsoil than on the spent oil shale. When compared to baseline values, soils in the revegetation plot are significantly higher in concentrations of lead, zinc, organic and total carbon, and DTP A-extractable cadmium, iron, manganese, nickel, phosphorus, and zinc. Whereas, western wheatgrass grown within the revegetation plot has concentrations which fall within the baseline values established in the regional study. The equations used in predicting concentrations of elements in plants from native and altered sites are cumbersome because of the large number of variables required to adequately predict expected concentrations and are of limited use because many explained only a small proportion of the total variation.

Biodiversity and agriculture in dynamic landscapes: Integrating ground and remotely-sensed baseline surveys.

PubMed

Gillison, Andrew N; Asner, Gregory P; Fernandes, Erick C M; Mafalacusser, Jacinto; Banze, Aurélio; Izidine, Samira; da Fonseca, Ambrósio R; Pacate, Hermenegildo

2016-07-15

Sustainable biodiversity and land management require a cost-effective means of forecasting landscape response to environmental change. Conventional species-based, regional biodiversity assessments are rarely adequate for policy planning and decision making. We show how new ground and remotely-sensed survey methods can be coordinated to help elucidate and predict relationships between biodiversity, land use and soil properties along complex biophysical gradients that typify many similar landscapes worldwide. In the lower Zambezi valley, Mozambique we used environmental, gradient-directed transects (gradsects) to sample vascular plant species, plant functional types, vegetation structure, soil properties and land-use characteristics. Soil fertility indices were derived using novel multidimensional scaling of soil properties. To facilitate spatial analysis, we applied a probabilistic remote sensing approach, analyzing Landsat 7 satellite imagery to map photosynthetically active and inactive vegetation and bare soil along each gradsect. Despite the relatively low sample number, we found highly significant correlations between single and combined sets of specific plant, soil and remotely sensed variables that permitted testable spatial projections of biodiversity and soil fertility across the regional land-use mosaic. This integrative and rapid approach provides a low-cost, high-return and readily transferable methodology that permits the ready identification of testable biodiversity indicators for adaptive management of biodiversity and potential agricultural productivity. Copyright © 2016 Elsevier Ltd. All rights reserved.

Spatiotemporal predictions of soil properties and states in variably saturated landscapes

NASA Astrophysics Data System (ADS)

Franz, Trenton E.; Loecke, Terrance D.; Burgin, Amy J.; Zhou, Yuzhen; Le, Tri; Moscicki, David

2017-07-01

Understanding greenhouse gas (GHG) fluxes from landscapes with variably saturated soil conditions is challenging given the highly dynamic nature of GHG fluxes in both space and time, dubbed hot spots, and hot moments. On one hand, our ability to directly monitor these processes is limited by sparse in situ and surface chamber observational networks. On the other hand, remote sensing approaches provide spatial data sets but are limited by infrequent imaging over time. We use a robust statistical framework to merge sparse sensor network observations with reconnaissance style hydrogeophysical mapping at a well-characterized site in Ohio. We find that combining time-lapse electromagnetic induction surveys with empirical orthogonal functions provides additional environmental covariates related to soil properties and states at high spatial resolutions ( 5 m). A cross-validation experiment using eight different spatial interpolation methods versus 120 in situ soil cores indicated an 30% reduction in root-mean-square error for soil properties (clay weight percent and total soil carbon weight percent) using hydrogeophysical derived environmental covariates with regression kriging. In addition, the hydrogeophysical derived environmental covariates were found to be good predictors of soil states (soil temperature, soil water content, and soil oxygen). The presented framework allows for temporal gap filling of individual sensor data sets as well as provides flexible geometric interpolation to complex areas/volumes. We anticipate that the framework, with its flexible temporal and spatial monitoring options, will be useful in designing future monitoring networks as well as support the next generation of hyper-resolution hydrologic and biogeochemical models.

Combining climatic and soil properties better predicts covers of Brazilian biomes.

PubMed

Arruda, Daniel M; Fernandes-Filho, Elpídio I; Solar, Ricardo R C; Schaefer, Carlos E G R

2017-04-01

Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km 2 for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

Combining climatic and soil properties better predicts covers of Brazilian biomes

NASA Astrophysics Data System (ADS)

Arruda, Daniel M.; Fernandes-Filho, Elpídio I.; Solar, Ricardo R. C.; Schaefer, Carlos E. G. R.

2017-04-01

Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km2 for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

Basic Aspects of Deep Soil Mixing Technology Control

NASA Astrophysics Data System (ADS)

Egorova, Alexandra A.; Rybak, Jarosław; Stefaniuk, Damian; Zajączkowski, Przemysław

2017-10-01

Improving a soil is a process of increasing its physical/mechanical properties without changing its natural structure. Improvement of soil subbase is reached by means of the knitted materials, or other methods when strong connection between soil particles is established. The method of DSM (Deep Soil Mixing) columns has been invented in Japan in 1970s. The main reason of designing cement-soil columns is to improve properties of local soils (such as strength and stiffness) by mixing them with various cementing materials. Cement and calcium are the most commonly used binders. However new research undertaken worldwide proves that apart from these materials, also gypsum or fly ashes can also be successfully implemented. As the Deep Soil Mixing is still being under development, anticipating mechanical properties of columns in particular soils and the usage of cementing materials in formed columns is very difficult and often inappropriate to predict. That is why a research is carried out in order to find out what binders and mixing technology should be used. The paper presents several remarks on the testing procedures related to quality and capacity control of Deep Soil Mixing columns. Soil improvement methods, their advantages and limitations are briefly described. The authors analyse the suitability of selected testing methods on subsequent stages of design and execution of special foundations works. Chosen examples from engineering practice form the basis for recommendations for the control procedures. Presented case studies concerning testing the on capacity field samples and laboratory procedures on various categories of soil-cement samples were picked from R&D and consulting works offered by Wroclaw University of Science and Technology. Special emphasis is paid to climate conditions which may affect the availability of performing and controlling of DSM techniques in polar zones, with a special regard to sample curing.

Using Remote Sensing Data to Evaluate Surface Soil Properties in Alabama Ultisols

NASA Technical Reports Server (NTRS)

Sullivan, Dana G.; Shaw, Joey N.; Rickman, Doug; Mask, Paul L.; Luvall, Jeff

2005-01-01

Evaluation of surface soil properties via remote sensing could facilitate soil survey mapping, erosion prediction and allocation of agrochemicals for precision management. The objective of this study was to evaluate the relationship between soil spectral signature and surface soil properties in conventionally managed row crop systems. High-resolution RS data were acquired over bare fields in the Coastal Plain, Appalachian Plateau, and Ridge and Valley provinces of Alabama using the Airborne Terrestrial Applications Sensor multispectral scanner. Soils ranged from sandy Kandiudults to fine textured Rhodudults. Surface soil samples (0-1 cm) were collected from 163 sampling points for soil organic carbon, particle size distribution, and citrate dithionite extractable iron content. Surface roughness, soil water content, and crusting were also measured during sampling. Two methods of analysis were evaluated: 1) multiple linear regression using common spectral band ratios, and 2) partial least squares regression. Our data show that thermal infrared spectra are highly, linearly related to soil organic carbon, sand and clay content. Soil organic carbon content was the most difficult to quantify in these highly weathered systems, where soil organic carbon was generally less than 1.2%. Estimates of sand and clay content were best using partial least squares regression at the Valley site, explaining 42-59% of the variability. In the Coastal Plain, sandy surfaces prone to crusting limited estimates of sand and clay content via partial least squares and regression with common band ratios. Estimates of iron oxide content were a function of mineralogy and best accomplished using specific band ratios, with regression explaining 36-65% of the variability at the Valley and Coastal Plain sites, respectively.

Application of RUSLE method to assess the intensity of erosion due to land use changes in a small Polish Carpathians catchment

NASA Astrophysics Data System (ADS)

Bucała-Hrabia, Anna; Kijowska-Strugała, Małgorzata; Demczuk, Piotr

2017-04-01

Intensity of soil erosion is mainly depends on land cover changes, soil properties, heavy rainfalls and slope gradients. This study compared the influence of land use changes on soil erosion in the Homerka catchment, an area of 19.3 km2 located in the West Polish Carpathians, using GIS techniques such the Revised Universal Soil Loss Equation (RUSLE) method and cartographic materials from 1977, 1987, 1996 and 2009. RUSLE is the most common method which allows to predict the average size of the soil erosion due to specific soil properties, relief as well as rainfall erosivity factor. The period between 1977 and 2009 covers the transformation of the Polish economy from a communist system to a free-market economy after 1989. The analysis indicates an increase in the forest area of the Homerka catchment by 18.14% and a decrease of cultivated land by 82.64%. The grasslands did not change significantly in their area, however, their spatial pattern was very dynamic related to their reduction due to forest expansion and enlargement due to cultivated land abandonment.

Major controlling factors and prediction models for arsenic uptake from soil to wheat plants.

PubMed

Dai, Yunchao; Lv, Jialong; Liu, Ke; Zhao, Xiaoyan; Cao, Yingfei

2016-08-01

The application of current Chinese agriculture soil quality standards fails to evaluate the land utilization functions appropriately due to the diversity of soil properties and plant species. Therefore, the standards should be amended. A greenhouse experiment was conducted to investigate arsenic (As) enrichment in various soils from 18 Chinese provinces in parallel with As transfer to 8 wheat varieties. The goal of the study was to build and calibrate soil-wheat threshold models to forecast the As threshold of wheat soils. In Shaanxi soils, Wanmai and Jimai were the most sensitive and insensitive wheat varieties, respectively; and in Jiangxi soils, Zhengmai and Xumai were the most sensitive and insensitive wheat varieties, respectively. Relationships between soil properties and the bioconcentration factor (BCF) were built based on stepwise multiple linear regressions. Soil pH was the best predictor of BCF, and after normalizing the regression equation (Log BCF=0.2054 pH- 3.2055, R(2)=0.8474, n=14, p<0.001), we obtained a calibrated model. Using the calibrated model, a continuous soil-wheat threshold equation (HC5=10((-0.2054 pH+2.9935))+9.2) was obtained for the species-sensitive distribution curve, which was built on Chinese food safety standards. The threshold equation is a helpful tool that can be applied to estimate As uptake from soil to wheat. Copyright © 2016 Elsevier Inc. All rights reserved.

Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings.

PubMed

Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P; Nair, Vimala D

2017-09-11

Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (K ex ) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.

Mapping specific soil functions based on digital soil property maps

NASA Astrophysics Data System (ADS)

Pásztor, László; Fodor, Nándor; Farkas-Iványi, Kinga; Szabó, József; Bakacsi, Zsófia; Koós, Sándor

2016-04-01

Quantification of soil functions and services is a great challenge in itself even if the spatial relevance is supposed to be identified and regionalized. Proxies and indicators are widely used in ecosystem service mapping. Soil services could also be approximated by elementary soil features. One solution is the association of soil types with services as basic principle. Soil property maps however provide quantified spatial information, which could be utilized more versatilely for the spatial inference of soil functions and services. In the frame of the activities referred as "Digital, Optimized, Soil Related Maps and Information in Hungary" (DOSoReMI.hu) numerous soil property maps have been compiled so far with proper DSM techniques partly according to GSM.net specifications, partly by slightly or more strictly changing some of its predefined parameters (depth intervals, pixel size, property etc.). The elaborated maps have been further utilized, since even DOSoReMI.hu was intended to take steps toward the regionalization of higher level soil information (secondary properties, functions, services). In the meantime the recently started AGRAGIS project requested spatial soil related information in order to estimate agri-environmental related impacts of climate change and support the associated vulnerability assessment. One of the most vulnerable services of soils in the context of climate change is their provisioning service. In our work it was approximated by productivity, which was estimated by a sequential scenario based crop modelling. It took into consideration long term (50 years) time series of both measured and predicted climatic parameters as well as accounted for the potential differences in agricultural practice and crop production. The flexible parametrization and multiple results of modelling was then applied for the spatial assessment of sensitivity, vulnerability, exposure and adaptive capacity of soils in the context of the forecasted changes in climatic conditions in the Carpathian Basin. In addition to soil fertility, degradation risk due to N-leaching was also assessed by the model runs by taking into account the movement of nitrate in the profile during the simulated periods. Our paper will present the resulted national maps and some conclusions drawn from the experiences. Acknowledgement: Our work was supported by Iceland, Liechtenstein and Norway through the EEA Grants and the REC (Project No: EEA C12-12) and the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Estimate Soil Erodibility Factors Distribution for Maioli Block

NASA Astrophysics Data System (ADS)

Lee, Wen-Ying

2014-05-01

The natural conditions in Taiwan are poor. Because of the steep slopes, rushing river and fragile geology, soil erosion turn into a serious problem. Not only undermine the sloping landscape, but also created sediment disaster like that reservoir sedimentation, river obstruction…etc. Therefore, predict and control the amount of soil erosion has become an important research topic. Soil erodibility factor (K) is a quantitative index of distinguish the ability of soil to resist the erosion separation and handling. Taiwan soil erodibility factors have been calculated 280 soil samples' erodibility factors by Wann and Huang (1989) use the Wischmeier and Smith nomorgraph. 221 samples were collected at the Maioli block in Miaoli. The coordinates of every sample point and the land use situations were recorded. The physical properties were analyzed for each sample. Three estimation methods, consist of Kriging, Inverse Distance Weighted (IDW) and Spline, were applied to estimate soil erodibility factors distribution for Maioli block by using 181 points data, and the remaining 40 points for the validation. Then, the SPSS regression analysis was used to comparison of the accuracy of the training data and validation data by three different methods. Then, the best method can be determined. In the future, we can used this method to predict the soil erodibility factors in other areas.

Aqueous and gaseous nitrogen losses induced by fertilizer application

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gu, C.; Maggi, F.; Riley, W.J.

2009-01-15

In recent years concern has grown over the contribution of nitrogen (N) fertilizer use to nitrate (NO{sub 3}{sup -}) water pollution and nitrous oxide (N{sub 2}O), nitric oxide (NO), and ammonia (NH{sub 3}) atmospheric pollution. Characterizing soil N effluxes is essential in developing a strategy to mitigate N leaching and emissions to the atmosphere. In this paper, a previously described and tested mechanistic N cycle model (TOUGHREACT-N) was successfully tested against additional observations of soil pH and N{sub 2}O emissions after fertilization and irrigation, and before plant emergence. We used TOUGHREACT-N to explain the significantly different N gas emissions andmore » nitrate leaching rates resulting from the different N fertilizer types, application methods, and soil properties. The N{sub 2}O emissions from NH{sub 4}{sup +}-N fertilizer were higher than from urea and NO{sub 3}{sup -}-N fertilizers in coarse-textured soils. This difference increased with decreases in fertilization application rate and increases in soil buffering capacity. In contrast to methods used to estimate global terrestrial gas emissions, we found strongly non-linear N{sub 2}O emissions as a function of fertilizer application rate and soil calcite content. Speciation of predicted gas N flux into N{sub 2}O and N{sub 2} depended on pH, fertilizer form, and soil properties. Our results highlighted the need to derive emission and leaching factors that account for fertilizer type, application method, and soil properties.« less

Soil properties drive a negative correlation between species diversity and genetic diversity in a tropical seasonal rainforest

PubMed Central

Xu, Wumei; Liu, Lu; He, Tianhua; Cao, Min; Sha, Liqing; Hu, Yuehua; Li, Qiaoming; Li, Jie

2016-01-01

A negative species-genetic diversity correlation (SGDC) could be predicted by the niche variation hypothesis, whereby an increase in species diversity within community reduces the genetic diversity of the co-occurring species because of the reduction in average niche breadth; alternatively, competition could reduce effective population size and therefore genetic diversity of the species within community. We tested these predictions within a 20 ha tropical forest dynamics plot (FDP) in the Xishuangbanna tropical seasonal rainforest. We established 15 plots within the FDP and investigated the soil properties, tree diversity, and genetic diversity of a common tree species Beilschmiedia roxburghiana within each plot. We observed a significant negative correlation between tree diversity and the genetic diversity of B. roxburghiana within the communities. Using structural equation modeling, we further determined that the inter-plot environmental characteristics (soil pH and phosphorus availability) directly affected tree diversity and that the tree diversity within the community determined the genetic diversity of B. roxburghiana. Increased soil pH and phosphorus availability might promote the coexistence of more tree species within community and reduce genetic diversity of B. roxburghiana for the reduced average niche breadth; alternatively, competition could reduce effective population size and therefore genetic diversity of B. roxburghiana within community. PMID:26860815

Soil properties drive a negative correlation between species diversity and genetic diversity in a tropical seasonal rainforest.

PubMed

Xu, Wumei; Liu, Lu; He, Tianhua; Cao, Min; Sha, Liqing; Hu, Yuehua; Li, Qiaoming; Li, Jie

2016-02-10

A negative species-genetic diversity correlation (SGDC) could be predicted by the niche variation hypothesis, whereby an increase in species diversity within community reduces the genetic diversity of the co-occurring species because of the reduction in average niche breadth; alternatively, competition could reduce effective population size and therefore genetic diversity of the species within community. We tested these predictions within a 20 ha tropical forest dynamics plot (FDP) in the Xishuangbanna tropical seasonal rainforest. We established 15 plots within the FDP and investigated the soil properties, tree diversity, and genetic diversity of a common tree species Beilschmiedia roxburghiana within each plot. We observed a significant negative correlation between tree diversity and the genetic diversity of B. roxburghiana within the communities. Using structural equation modeling, we further determined that the inter-plot environmental characteristics (soil pH and phosphorus availability) directly affected tree diversity and that the tree diversity within the community determined the genetic diversity of B. roxburghiana. Increased soil pH and phosphorus availability might promote the coexistence of more tree species within community and reduce genetic diversity of B. roxburghiana for the reduced average niche breadth; alternatively, competition could reduce effective population size and therefore genetic diversity of B. roxburghiana within community.

Statistical-physical model of the hydraulic conductivity

NASA Astrophysics Data System (ADS)

Usowicz, B.; Marczewski, W.; Usowicz, J. B.; Lukowski, M. I.

2012-04-01

The water content in unsaturated subsurface soil layer is determined by processes of exchanging mass and energy between media of soil and atmosphere, and particular members of layered media. Generally they are non-homogeneous on different scales, considering soil porosity, soil texture including presence of vegetation elements in the root zone, and canopy above the surface, and varying biomass density of plants above the surface in clusters. That heterogeneity determines statistically effective values of particular physical properties. This work considers mainly those properties which determine the hydraulic conductivity of soil. This property is necessary for characterizing physically water transfer in the root zone and access of nutrient matter for plants, but it also the water capacity on the field scale. The temporal variability of forcing conditions and evolutionarily changing vegetation causes substantial effects of impact on the water capacity in large scales, bringing the evolution of water conditions in the entire area, spanning a possible temporal state in the range between floods and droughts. The dynamic of this evolution of water conditions is highly determined by vegetation but is hardly predictable in evaluations. Hydrological models require feeding with input data determining hydraulic properties of the porous soil which are proposed in this paper by means of the statistical-physical model of the water hydraulic conductivity. The statistical-physical model was determined for soils being typical in Euroregion Bug, Eastern Poland. The model is calibrated on the base of direct measurements in the field scales, and enables determining typical characteristics of water retention by the retention curves bounding the hydraulic conductivity to the state of water saturation of the soil. The values of the hydraulic conductivity in two reference states are used for calibrating the model. One is close to full saturation, and another is for low water content far from saturation, in a particular case of the soil type. Effects of calibrating a soil depends on assumed ranges of soil properties engaged to recognizing the soil type. Among those properties, the key role is for the bulk density, the porosity and its dependence on the specific area of the soil. The aim of this work is to provide such variables of auxiliary data to SMOS, which would bring a relation of the soil moisture to the water capacity, under retrieving SM from SMOS L1C data. * The work was financially supported in part by the ESA Programme for European Cooperating States (PECS), No.98084 "SWEX-R, Soil Water and Energy Exchange/Research", AO3275.

Comparison of SWAT and GeoWEPP model in predicting the impact of stone bunds on runoff and erosion processes in the Northern Ethiopian Highlands

NASA Astrophysics Data System (ADS)

Demelash, Nigus; Flagler, Jared; Renschler, Chris; Strohmeier, Stefan; Holzmann, Hubert; Feras, Ziadat; Addis, Hailu; Zucca, Claudio; Bayu, Wondimu; Klik, Andreas

2017-04-01

Soil degradation is a major issue in the Ethiopian highlands which are most suitable for agriculture and, therefore, support a major part of human population and livestock. Heavy rainstorms during the rainy season in summer create soil erosion and runoff processes which affect soil fertility and food security. In the last years programs for soil conservation and afforestation were initiated by the Ethiopian government to reduce erosion risk, retain water in the landscape and improve crop yields. The study was done in two adjacent watersheds in the Northwestern highlands of Ethiopia. One of the watersheds is developed by soil and water conservation structures (stone bunds) in 2011 and the other one is without soil and water conservation structures. Spatial distribution of soil textures and other soil properties were determined in the field and in the laboratory and a soil map was derived. A land use map was evaluated based on satellite images and ground truth data. A Digital Elevation Model of the watershed was developed based on conventional terrestrial surveying using a total station. At the outlet of the watersheds weirs with cameras were installed to measure surface runoff. During each event runoff samples were collected and sediment concentration was analyzed. The objective of this study is 1) to assess the impact of stone bunds on runoff and erosion processes by using simulation models, and 2) to compare the performance of two soil erosion models in predicting the measurements. The selected erosion models were the Soil and Water Assessment Tool (SWAT) and the Geospatial Interface to the Water Erosion Prediction Project (GeoWEPP). The simulation models were calibrated/verified for the 2011-2013 periods and validated with 2014-2015 data. Results of this comparison will be presented.

Biochar particle size, shape, and porosity act together to influence soil water properties

PubMed Central

Dugan, Brandon; Masiello, Caroline A.; Gonnermann, Helge M.

2017-01-01

Many studies report that, under some circumstances, amending soil with biochar can improve field capacity and plant-available water. However, little is known about the mechanisms that control these improvements, making it challenging to predict when biochar will improve soil water properties. To develop a conceptual model explaining biochar’s effects on soil hydrologic processes, we conducted a series of well constrained laboratory experiments using a sand matrix to test the effects of biochar particle size and porosity on soil water retention curves. We showed that biochar particle size affects soil water storage through changing pore space between particles (interpores) and by adding pores that are part of the biochar (intrapores). We used these experimental results to better understand how biochar intrapores and biochar particle shape control the observed changes in water retention when capillary pressure is the main component of soil water potential. We propose that biochar’s intrapores increase water content of biochar-sand mixtures when soils are drier. When biochar-sand mixtures are wetter, biochar particles’ elongated shape disrupts the packing of grains in the sandy matrix, increasing the volume between grains (interpores) available for water storage. These results imply that biochars with a high intraporosity and irregular shapes will most effectively increase water storage in coarse soils. PMID:28598988

Biochar particle size, shape, and porosity act together to influence soil water properties.

PubMed

Liu, Zuolin; Dugan, Brandon; Masiello, Caroline A; Gonnermann, Helge M

2017-01-01

Many studies report that, under some circumstances, amending soil with biochar can improve field capacity and plant-available water. However, little is known about the mechanisms that control these improvements, making it challenging to predict when biochar will improve soil water properties. To develop a conceptual model explaining biochar's effects on soil hydrologic processes, we conducted a series of well constrained laboratory experiments using a sand matrix to test the effects of biochar particle size and porosity on soil water retention curves. We showed that biochar particle size affects soil water storage through changing pore space between particles (interpores) and by adding pores that are part of the biochar (intrapores). We used these experimental results to better understand how biochar intrapores and biochar particle shape control the observed changes in water retention when capillary pressure is the main component of soil water potential. We propose that biochar's intrapores increase water content of biochar-sand mixtures when soils are drier. When biochar-sand mixtures are wetter, biochar particles' elongated shape disrupts the packing of grains in the sandy matrix, increasing the volume between grains (interpores) available for water storage. These results imply that biochars with a high intraporosity and irregular shapes will most effectively increase water storage in coarse soils.

Influence of soil texture on hydraulic properties and water relations of a dominant warm-desert phreatophyte.

PubMed

Hultine, K R; Koepke, D F; Pockman, W T; Fravolini, A; Sperry, J S; Williams, D G

2006-03-01

We investigated hydraulic constraints on water uptake by velvet mesquite (Prosopis velutina Woot.) at a site with sandy-loam soil and at a site with loamy-clay soil in southeastern Arizona, USA. We predicted that trees on sandy-loam soil have less negative xylem and soil water potentials during drought and a lower resistance to xylem cavitation, and reach E(crit) (the maximum steady-state transpiration rate without hydraulic failure) at higher soil water potentials than trees on loamy-clay soil. However, minimum predawn leaf xylem water potentials measured during the height of summer drought were significantly lower at the sandy-loam site (-3.5 +/- 0.1 MPa; all errors are 95% confidence limits) than at the loamy-clay site (-2.9 +/- 0.1 MPa). Minimum midday xylem water potentials also were lower at the sandy-loam site (-4.5 +/- 0.1 MPa) than at the loamy-clay site (-4.0 +/- 0.1 MPa). Despite the differences in leaf water potentials, there were no significant differences in either root or stem xylem embolism, mean cavitation pressure or Psi(95) (xylem water potential causing 95% cavitation) between trees at the two sites. A soil-plant hydraulic model parameterized with the field data predicted that E(crit) approaches zero at a substantially higher bulk soil water potential (Psi(s)) on sandy-loam soil than on loamy-clay soil, because of limiting rhizosphere conductance. The model predicted that transpiration at the sandy-loam site is limited by E(crit) and is tightly coupled to Psi(s) over much of the growing season, suggesting that seasonal transpiration fluxes at the sandy-loam site are strongly linked to intra-annual precipitation pulses. Conversely, the model predicted that trees on loamy-clay soil operate below E(crit) throughout the growing season, suggesting that fluxes on fine-textured soils are closely coupled to inter-annual changes in precipitation. Information on the combined importance of xylem and rhizosphere constraints to leaf water supply across soil texture gradients provides insight into processes controlling plant water balance and larger scale hydrologic processes.

Application and validation of isotope dilution method (IDM) for predicting bioavailability of hydrophobic organic contaminants in soil.

PubMed

Wang, Jie; Taylor, Allison; Schlenk, Daniel; Gan, Jay

2018-05-01

Risk assessment of hydrophobic organic contaminants (HOCs) using the total chemical concentration following exhaustive extraction may overestimate the actual availability of HOCs to non-target organisms. Existing methods for estimating HOC bioavailability in soil have various operational limitations. In this study, we explored the application of isotope dilution method (IDM) to quantify the accessible fraction (E) of DDTs and PCBs in both historically-contaminated and freshly-spiked soils. After addition of 13 C or deuterated analogues to a soil sample, the phase distribution of isotope-labeled and native chemicals reached an apparent equilibrium within 48 h of mixing. The derived E values in the three soils ranged from 0.19 to 0.82, depending on the soil properties and also the contact time of HOCs (i.e., aging). The isotope dilution method consistently predicted greater accumulation into earthworm (Eisenia fetida) than that by polyethylene (PE) or solid phase microextraction (SPME) sampler, likely because desorption in the gut enhanced bioavailability of soil-borne HOCs. A highly significant linear regression (R 2  = 0.91) was found between IDM and 24-h Tenax desorption, with a slope statistically identical to 1. The IDM-derived accessible concentration (C e ) was further shown to accurately predict tissue residues in earthworm exposed in the same soils. Given the relatively short duration and simple steps, IDM has the potential to be readily adopted for measuring HOC bioaccessibility in soil and for improving risk assessment and evaluation of remediation efficiency. Copyright © 2018 Elsevier Ltd. All rights reserved.

Testing the Hole-in-the-Pipe Model of nitric and nitrous oxide emissions from soils using the TRAGNET Database

NASA Astrophysics Data System (ADS)

Davidson, Eric A.; Verchot, Louis V.

2000-12-01

Because several soil properties and processes affect emissions of nitric oxide (NO) and nitrous oxide (N2O) from soils, it has been difficult to develop effective and robust algorithms to predict emissions of these gases in biogeochemical models. The conceptual "hole-in-the-pipe" (HIP) model has been used effectively to interpret results of numerous studies, but the ranges of climatic conditions and soil properties are often relatively narrow for each individual study. The Trace Gas Network (TRAGNET) database offers a unique opportunity to test the validity of one manifestation of the HIP model across a broad range of sites, including temperate and tropical climates, grasslands and forests, and native vegetation and agricultural crops. The logarithm of the sum of NO + N2O emissions was positively and significantly correlated with the logarithm of the sum of extractable soil NH4+ + NO3-. The logarithm of the ratio of NO:N2O emissions was negatively and significantly correlated with water-filled pore space (WFPS). These analyses confirm the applicability of the HIP model concept, that indices of soil N availability correlate with the sum of NO+N2O emissions, while soil water content is a strong and robust controller of the ratio of NO:N2O emissions. However, these parameterizations have only broad-brush accuracy because of unaccounted variation among studies in the soil depths where gas production occurs, where soil N and water are measured, and other factors. Although accurate predictions at individual sites may still require site-specific parameterization of these empirical functions, the parameterizations presented here, particularly the one for WFPS, may be appropriate for global biogeochemical modeling. Moreover, this integration of data sets demonstrates the broad ranging applicability of the HIP conceptual approach for understanding soil emissions of NO and N2O.

Effects of the soil pore network architecture on the soil's physical functionalities

NASA Astrophysics Data System (ADS)

Smet, Sarah; Beckers, Eléonore; Léonard, Angélique; Degré, Aurore

2017-04-01

The soil fluid movement's prediction is of major interest within an agricultural or environmental scope because many processes depend ultimately on the soil fluids dynamic. It is common knowledge that the soil microscopic pore network structure governs the inner-soil convective fluids flow. There isn't, however, a general methodthat consider the pore network structure as a variable in the prediction of thecore scale soil's physical functionalities. There are various possible representations of the microscopic pore network: sample scale averaged structural parameters, extrapolation of theoretic pore network, or use of all the information available by modeling within the observed pore network. Different representations implydifferent analyzing methodologies. To our knowledge, few studies have compared the micro-and macroscopic soil's characteristics for the same soil core sample. The objective of our study is to explore the relationship between macroscopic physical properties and microscopic pore network structure. The saturated hydraulic conductivity, the air permeability, the retention curve, and others classical physical parameters were measured for ten soil samples from an agricultural field. The pore network characteristics were quantified through the analyses of X-ray micro-computed tomographic images(micro-CT system Skyscan-1172) with a voxel size of 22 µm3. Some of the first results confirmed what others studies had reported. Then, the comparison between macroscopic properties and microscopic parameters suggested that the air movements depended mostly on the pore connectivity and tortuosity than on the total porosity volume. We have also found that the fractal dimension calculated from the X-ray images and the fractal dimension calculated from the retention curve were significantly different. Our communication will detailthose results and discuss the methodology: would the results be similar with a different voxel size? What are the calculated and measured parameters uncertainties? Sarah Smet, as a research fellow, acknowledges the support of the National Fund for Scientific Research (Brussels, Belgium).

Infiltration and runoff generation processes in fire-affected soils

USGS Publications Warehouse

Moody, John A.; Ebel, Brian A.

2014-01-01

Post-wildfire runoff was investigated by combining field measurements and modelling of infiltration into fire-affected soils to predict time-to-start of runoff and peak runoff rate at the plot scale (1 m2). Time series of soil-water content, rainfall and runoff were measured on a hillslope burned by the 2010 Fourmile Canyon Fire west of Boulder, Colorado during cyclonic and convective rainstorms in the spring and summer of 2011. Some of the field measurements and measured soil physical properties were used to calibrate a one-dimensional post-wildfire numerical model, which was then used as a ‘virtual instrument’ to provide estimates of the saturated hydraulic conductivity and high-resolution (1 mm) estimates of the soil-water profile and water fluxes within the unsaturated zone.Field and model estimates of the wetting-front depth indicated that post-wildfire infiltration was on average confined to shallow depths less than 30 mm. Model estimates of the effective saturated hydraulic conductivity, Ks, near the soil surface ranged from 0.1 to 5.2 mm h−1. Because of the relatively small values of Ks, the time-to-start of runoff (measured from the start of rainfall),  tp, was found to depend only on the initial soil-water saturation deficit (predicted by the model) and a measured characteristic of the rainfall profile (referred to as the average rainfall acceleration, equal to the initial rate of change in rainfall intensity). An analytical model was developed from the combined results and explained 92–97% of the variance of  tp, and the numerical infiltration model explained 74–91% of the variance of the peak runoff rates. These results are from one burned site, but they strongly suggest that  tp in fire-affected soils (which often have low values of Ks) is probably controlled more by the storm profile and the initial soil-water saturation deficit than by soil hydraulic properties.

A Comparison of Land Surface Model Soil Hydraulic Properties Estimated by Inverse Modeling and Pedotransfer Functions

NASA Technical Reports Server (NTRS)

Gutmann, Ethan D.; Small, Eric E.

2007-01-01

Soil hydraulic properties (SHPs) regulate the movement of water in the soil. This in turn plays an important role in the water and energy cycles at the land surface. At present, SHPS are commonly defined by a simple pedotransfer function from soil texture class, but SHPs vary more within a texture class than between classes. To examine the impact of using soil texture class to predict SHPS, we run the Noah land surface model for a wide variety of measured SHPs. We find that across a range of vegetation cover (5 - 80% cover) and climates (250 - 900 mm mean annual precipitation), soil texture class only explains 5% of the variance expected from the real distribution of SHPs. We then show that modifying SHPs can drastically improve model performance. We compare two methods of estimating SHPs: (1) inverse method, and (2) soil texture class. Compared to texture class, inverse modeling reduces errors between measured and modeled latent heat flux from 88 to 28 w/m(exp 2). Additionally we find that with increasing vegetation cover the importance of SHPs decreases and that the van Genuchten m parameter becomes less important, while the saturated conductivity becomes more important.

Evaluation of Phytoavailability of Heavy Metals to Chinese Cabbage (Brassica chinensis L.) in Rural Soils

PubMed Central

Hseu, Zeng-Yei; Zehetner, Franz

2014-01-01

This study compared the extractability of Cd, Cu, Ni, Pb, and Zn by 8 extraction protocols for 22 representative rural soils in Taiwan and correlated the extractable amounts of the metals with their uptake by Chinese cabbage for developing an empirical model to predict metal phytoavailability based on soil properties. Chemical agents in these protocols included dilute acids, neutral salts, and chelating agents, in addition to water and the Rhizon soil solution sampler. The highest concentrations of extractable metals were observed in the HCl extraction and the lowest in the Rhizon sampling method. The linear correlation coefficients between extractable metals in soil pools and metals in shoots were higher than those in roots. Correlations between extractable metal concentrations and soil properties were variable; soil pH, clay content, total metal content, and extractable metal concentration were considered together to simulate their combined effects on crop uptake by an empirical model. This combination improved the correlations to different extents for different extraction methods, particularly for Pb, for which the extractable amounts with any extraction protocol did not correlate with crop uptake by simple correlation analysis. PMID:25295297

Controls on accumulation and soil solution partitioning of heavy metals across upland sites in United Kingdom (UK).

PubMed

Zia, Afia; van den Berg, Leon; Ahmad, Muhammad Nauman; Riaz, Muhammad; Zia, Dania; Ashmore, Mike

2018-05-31

A significant body of knowledge suggests that soil solution pH and dissolved organic carbon (DOC) strongly influence metal concentrations and speciation in porewater, however, these effects vary between different metals. This study investigated the factors influencing soil and soil solution concentrations of copper (Cu), lead (Pb), nickel (Ni) and zinc (Zn) under field conditions in upland soils from UK having a wide range of pH, DOC and organic matter contents. The study primarily focussed on predicting soil and soil solution metal concentrations from the data on total soil metal concentrations (HNO 3 extracts) and soil and soil solution properties (pH, DOC and organic matter content). We tested the multiple regression models proposed by Tipping et al. (2003) to predict heavy metal concentrations in soil solutions and the results indicated a better fit (higher R 2 values) in both studies for Pb compared to the Zn and Cu concentrations. Both studies observed consistent negative relationships of metals with pH and loss on ignition (LOI) suggesting an increase in soil solution metal concentrations with increasing acidity. The positive relationship between Pb concentrations in porewater and HNO 3 extracts was similar for both studies, however, similar relationships were not found for the Zn and Cu concentrations because of the negative coefficients for these metals in our study. The results of this study conclude that the predictive equations of Tipping et al. (2003) may not be applicable to the field sites where the range of DOC and metal concentrations is much lower than their study. Our study also suggests that the extent to which metals are partitioned into soil solution is lower in soils with a higher organic matter contents due to binding of these metals to soil organic matter. Copyright © 2018 Elsevier Ltd. All rights reserved.

Reactive transport modelling to infer changes in soil hydraulic properties induced by non-conventional water irrigation

NASA Astrophysics Data System (ADS)

Valdes-Abellan, Javier; Jiménez-Martínez, Joaquín; Candela, Lucila; Jacques, Diederik; Kohfahl, Claus; Tamoh, Karim

2017-06-01

The use of non-conventional water (e.g., treated wastewater, desalinated water) for different purposes is increasing in many water scarce regions of the world. Its use for irrigation may have potential drawbacks, because of mineral dissolution/precipitation processes, such as changes in soil physical and hydraulic properties (e.g., porosity, permeability), modifying infiltration and aquifer recharge processes or blocking root growth. Prediction of soil and groundwater impacts is essential for achieving sustainable agricultural practices. A numerical model to solve unsaturated water flow and non-isothermal multicomponent reactive transport has been modified implementing the spatio-temporal evolution of soil physical and hydraulic properties. A long-term process simulation (30 years) of agricultural irrigation with desalinated water, based on a calibrated/validated 1D numerical model in a semi-arid region, is presented. Different scenarios conditioning reactive transport (i.e., rainwater irrigation, lack of gypsum in the soil profile, and lower partial pressure of CO2 (pCO2)) have also been considered. Results show that although boundary conditions and mineral soil composition highly influence the reactive processes, dissolution/precipitation of carbonate species is triggered mainly by pCO2, closely related to plant roots. Calcite dissolution occurs in the root zone, precipitation takes place under it and at the soil surface, which will lead a root growth blockage and a direct soil evaporation decrease, respectively. For the studied soil, a gypsum dissolution up to 40 cm depth is expected at long-term, with a general increase of porosity and hydraulic conductivity.

Which key properties controls the preferential transport in the vadose zone under transient hydrological conditions

NASA Astrophysics Data System (ADS)

Groh, J.; Vanderborght, J.; Puetz, T.; Gerke, H. H.; Rupp, H.; Wollschlaeger, U.; Stumpp, C.; Priesack, E.; Vereecken, H.

2015-12-01

Understanding water flow and solute transport in the unsaturated zone is of great importance for an appropriate land use management strategy. The quantification and prediction of water and solute fluxes through the vadose zone can help to improve management practices in order to limit potential risk on our fresh water resources. Water related solute transport and residence time is strongly affected by preferential flow paths in the soil. Water flow in soils depends on soil properties and site factors (climate or experiment conditions, land use) and are therefore important factors to understand preferential solute transport in the unsaturated zone. However our understanding and knowledge of which on-site properties or conditions define and enhance preferential flow and transport is still poor and mostly limited onto laboratory experimental conditions (small column length and steady state boundary conditions). Within the TERENO SOILCan lysimeter network, which was designed to study the effects of climate change on soil functions, a bromide tracer was applied on 62 lysimeter at eight different test sites between Dec. 2013 and Jan. 2014. The TERENO SOILCan infrastructure offers the unique possibility to study the occurrence of preferential flow and transport of various soil types under different natural transient hydrological conditions and land use (crop, bare and grassland) at eight TERENO SOILCan observatories. Working with lysimeter replicates at each observatory allows defining the spatial variability of preferential transport and flow. Additionally lysimeters in the network were transferred within and between observatories in order to subject them to different rainfall and temperature regimes and enable us to relate the soil type susceptibility of preferential flow and transport not only to site specific physical and land use properties, but also to different transient boundary conditions. Comparison and statistical analysis between preferential flow indicators 5% arrival time and potential key soil properties, site factors and boundary conditions will be presented in order to identify key properties which control the preferential transport in the vadose zone under transient hydrological conditions.

Inferring changes in soil organic matter in post-wildfire soil burn severity levels in a temperate climate.

PubMed

Merino, Agustín; Fonturbel, María T; Fernández, Cristina; Chávez-Vergara, Bruno; García-Oliva, Felipe; Vega, Jose A

2018-06-15

Simple, rapid and reliable methods of assessing soil burn severity (SBS) are required in order to prioritize post-fire emergency stabilization actions. SBS proxies based on visual identification and changes in soil organic matter (SOM) content and quality can be related to other soil properties in order to determine the extent to which soil is perturbed following fire. This task is addressed in the present study by an approach involving the use of differential scanning calorimetry-thermogravimetric analysis (DSC-TGA) to determine changes in SOM generated in soils subjected to different levels of SBS. Intact topsoil monoliths comprising the organic horizons and the surface mineral soil (alumic-humic umbrisols) were collected from a representative P. pinaster stand in NW Spain. The monoliths were experimentally burned in a combustion wind tunnel to simulate different fire conditions (fuel bed comprising forest pine litter and wood; air flow, 0.6 m s -1 ). Changes in OM properties in the soil organic layer and mineral soils samples (0-2 cm) at the different temperatures and SBS levels were identified. For both duff and mineral soil, the data revealed a temperature-induced increase in aromatic compounds and a concomitant decrease of carbohydrates and alkyl products. However, for a given temperature, the degree of carbonization/aromatization was lower in the mineral soil than in the duff, possibly due to the different composition of the OM and to the different combustion conditions. The low degree of aromatization of the organic matter suggests that this soil component could undergo subsequent biological degradation. SOM content and thermal recalcitrance (measured as T50) discriminated the SBS levels. Use of visual identification of SBS levels in combination with DSC-TGA enables rapid evaluation of the spatial variability of the effects of fire on SOM properties. This information is useful to predict soil degradation process and implement emergency soil stabilization techniques. Copyright © 2018 Elsevier B.V. All rights reserved.

Thermal alteration of soil organic matter properties: a systematic study to infer response of Sierra Nevada climosequence soils to forest fires

NASA Astrophysics Data System (ADS)

Araya, Samuel N.; Fogel, Marilyn L.; Asefaw Berhe, Asmeret

2017-02-01

Fire is a major driver of soil organic matter (SOM) dynamics, and contemporary global climate change is changing global fire regimes. We conducted laboratory heating experiments on soils from five locations across the western Sierra Nevada climosequence to investigate thermal alteration of SOM properties and determine temperature thresholds for major shifts in SOM properties. Topsoils (0 to 5 cm depth) were exposed to a range of temperatures that are expected during prescribed and wild fires (150, 250, 350, 450, 550, and 650 °C). With increase in temperature, we found that the concentrations of carbon (C) and nitrogen (N) decreased in a similar pattern among all five soils that varied considerably in their original SOM concentrations and mineralogies. Soils were separated into discrete size classes by dry sieving. The C and N concentrations in the larger aggregate size fractions (2-0.25 mm) decreased with an increase in temperature, so that at 450 °C the remaining C and N were almost entirely associated with the smaller aggregate size fractions ( < 0.25 mm). We observed a general trend of 13C enrichment with temperature increase. There was also 15N enrichment with temperature increase, followed by 15N depletion when temperature increased beyond 350 °C. For all the measured variables, the largest physical, chemical, elemental, and isotopic changes occurred at the mid-intensity fire temperatures, i.e., 350 and 450 °C. The magnitude of the observed changes in SOM composition and distribution in three aggregate size classes, as well as the temperature thresholds for critical changes in physical and chemical properties of soils (such as specific surface area, pH, cation exchange capacity), suggest that transformation and loss of SOM are the principal responses in heated soils. Findings from this systematic investigation of soil and SOM response to heating are critical for predicting how soils are likely to be affected by future climate and fire regimes.

Prediction of erodibility in Oxisols using iron oxides, soil color and diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Arantes Camargo, Livia; Marques, José, Jr.

2015-04-01

The prediction of erodibility using indirect methods such as diffuse reflectance spectroscopy could facilitate the characterization of the spatial variability in large areas and optimize implementation of conservation practices. The aim of this study was to evaluate the prediction of interrill erodibility (Ki) and rill erodibility (Kr) by means of iron oxides content and soil color using multiple linear regression and diffuse reflectance spectroscopy (DRS) using regression analysis by least squares partial (PLSR). The soils were collected from three geomorphic surfaces and analyzed for chemical, physical and mineralogical properties, plus scanned in the spectral range from the visible and infrared. Maps of spatial distribution of Ki and Kr were built with the values calculated by the calibrated models that obtained the best accuracy using geostatistics. Interrill-rill erodibility presented negative correlation with iron extracted by dithionite-citrate-bicarbonate, hematite, and chroma, confirming the influence of iron oxides in soil structural stability. Hematite and hue were the attributes that most contributed in calibration models by multiple linear regression for the prediction of Ki (R2 = 0.55) and Kr (R2 = 0.53). The diffuse reflectance spectroscopy via PLSR allowed to predict Interrill-rill erodibility with high accuracy (R2adj = 0.76, 0.81 respectively and RPD> 2.0) in the range of the visible spectrum (380-800 nm) and the characterization of the spatial variability of these attributes by geostatistics.

A probabilistic approach to modeling erosion for spatially-varied conditions

Treesearch

William J. Elliot; Peter R. Robichaud; C. D. Pannkuk

2001-01-01

In the years following a major forest disturbance, such as fire, the erosion rate is greatly influenced by variability in weather, in soil properties, and in spatial distribution. This paper presents a method to incorporate these variabilities into the erosion rate predicted by the Water Erosion Prediction Project model. It appears that it is not necessary to describe...

Analytical model for vibration prediction of two parallel tunnels in a full-space

NASA Astrophysics Data System (ADS)

He, Chao; Zhou, Shunhua; Guo, Peijun; Di, Honggui; Zhang, Xiaohui

2018-06-01

This paper presents a three-dimensional analytical model for the prediction of ground vibrations from two parallel tunnels embedded in a full-space. The two tunnels are modelled as cylindrical shells of infinite length, and the surrounding soil is modelled as a full-space with two cylindrical cavities. A virtual interface is introduced to divide the soil into the right layer and the left layer. By transforming the cylindrical waves into the plane waves, the solution of wave propagation in the full-space with two cylindrical cavities is obtained. The transformations from the plane waves to cylindrical waves are then used to satisfy the boundary conditions on the tunnel-soil interfaces. The proposed model provides a highly efficient tool to predict the ground vibration induced by the underground railway, which accounts for the dynamic interaction between neighbouring tunnels. Analysis of the vibration fields produced over a range of frequencies and soil properties is conducted. When the distance between the two tunnels is smaller than three times the tunnel diameter, the interaction between neighbouring tunnels is highly significant, at times in the order of 20 dB. It is necessary to consider the interaction between neighbouring tunnels for the prediction of ground vibrations induced underground railways.

Soil fauna: key to new carbon models

NASA Astrophysics Data System (ADS)

Filser, Juliane; Faber, Jack H.; Tiunov, Alexei V.; Brussaard, Lijbert; Frouz, Jan; De Deyn, Gerlinde; Uvarov, Alexei V.; Berg, Matty P.; Lavelle, Patrick; Loreau, Michel; Wall, Diana H.; Querner, Pascal; Eijsackers, Herman; José Jiménez, Juan

2016-11-01

Soil organic matter (SOM) is key to maintaining soil fertility, mitigating climate change, combatting land degradation, and conserving above- and below-ground biodiversity and associated soil processes and ecosystem services. In order to derive management options for maintaining these essential services provided by soils, policy makers depend on robust, predictive models identifying key drivers of SOM dynamics. Existing SOM models and suggested guidelines for future SOM modelling are defined mostly in terms of plant residue quality and input and microbial decomposition, overlooking the significant regulation provided by soil fauna. The fauna controls almost any aspect of organic matter turnover, foremost by regulating the activity and functional composition of soil microorganisms and their physical-chemical connectivity with soil organic matter. We demonstrate a very strong impact of soil animals on carbon turnover, increasing or decreasing it by several dozen percent, sometimes even turning C sinks into C sources or vice versa. This is demonstrated not only for earthworms and other larger invertebrates but also for smaller fauna such as Collembola. We suggest that inclusion of soil animal activities (plant residue consumption and bioturbation altering the formation, depth, hydraulic properties and physical heterogeneity of soils) can fundamentally affect the predictive outcome of SOM models. Understanding direct and indirect impacts of soil fauna on nutrient availability, carbon sequestration, greenhouse gas emissions and plant growth is key to the understanding of SOM dynamics in the context of global carbon cycling models. We argue that explicit consideration of soil fauna is essential to make realistic modelling predictions on SOM dynamics and to detect expected non-linear responses of SOM dynamics to global change. We present a decision framework, to be further developed through the activities of KEYSOM, a European COST Action, for when mechanistic SOM models include soil fauna. The research activities of KEYSOM, such as field experiments and literature reviews, together with dialogue between empiricists and modellers, will inform how this is to be done.

Using scaling factors for evaluating spatial and temporal variability of soil hydraulic properties within one elevation transect

NASA Astrophysics Data System (ADS)

Nikodem, Antonín; Kodešová, Radka; Jakšík, Ondřej; Fér, Miroslav; Klement, Aleš

2016-04-01

This study was carried out in Southern Moravia, in the Czech Republic. The original soil unit in the wider area is a Haplic Chernozem developed on loess. The intensive agricultural exploitation in combination with terrain morphology has resulted in a highly diversified soil spatial pattern. Nowadays the original soil unit is preserved only on top of relatively flat parts, and is gradually transformed by water erosion up to Regosols on the steepest slopes, while colluvial soils are formed in terrain depressions and at toe slopes due to sedimentation of previously eroded material. Soils within this area has been intensively investigated during the last several years (e.g. Jakšík et al., 2015; Vašát et al., 2014, 2015a,b). Soil sampling (disturbed and undisturbed 100-cm3 soil samples) was performed at 5 points of one elevation transect in November 2010 (after wheat sowing) and August 2011 (after wheat harvest). Disturbed soil samples were used to determine basic soil properties (grain size distribution and organic carbon content etc.). Undisturbed soil samples were used to determine the soil water retention curves and the hydraulic conductivity functions using the multiple outflow tests in Tempe cells and a numerical inversion with HYDRUS 1-D. Scaling factors (alpha-h for pressure head, alpha-theta for soil water contents and alpha-k for hydraulic conductivities) were used here to express soil hydraulic properties variability. Evaluated scaling factors reflected position within the elevation transect as well as time of soil sampling. In general large values of alpha-h, lower values of alpha-k and similar values of alpha-theta were obtained in 2010 in comparison to values obtained in 2011, which indicates development of soil structure during the vegetation season. Jakšík, O., Kodešová, R., Kubiš, A., Stehlíková, I., Drábek, O., Kapička, A. (2015): Soil aggregate stability within morphologically diverse areas. Catena, 127, 287-299. Vašát, R., Kodešová, R., Borůvka, L., Jakšík, O., Klement, A., Drábek, O. (2015a): Absorption features in soil spectra assessment. Applied Spectroscopy, 69(12), 1425-1431. Vašát, R., Kodešová, R., Borůvka, L., Klement, A., Jakšík, O., Gholizadeh, A. (2014): Consideration of peak parameters derived from continuum-removed spectra to predict extractable nutrients in soils with visible and near-infrared diffuse reflectance spectroscopy (VNIR-DRS). Geoderma, 232-234, 208-218. Vašát, R., Kodešová, R., Klement, A., Jakšík, O. (2015b): Predicting oxidizable carbon content via visible- and near-infrared diffuse reflectance spectroscopy in soils heavily affected by water erosion. Soil and Water Research, 10 (2), 74-77.

Examining nitrogen dynamics in heterogeneous soils: preliminary work

NASA Astrophysics Data System (ADS)

Jolicoeur, J. L.; Salvage, K. M.

2004-05-01

A study is being conducted in the Catatonk Creek watershed, in the headwaters of the Susquehanna River, in order to determine the vulnerability of the valley-fill aquifers to nitrate contamination. The overall objective of this study is to evaluate the nitrogen retention mechanisms for a combination of different soil types and different agricultural land uses and is scheduled to last approximately 2 years with ongoing fieldwork starting the summer of 2003 to the spring of 2005. This project will investigate the residence time and the quantity of the nitrate leached below the root zone and due to enter eventually the groundwater, and the existence of subsurface flow draining the nitrate from the root zone to the adjacent streams. Finally, a numerical and an analytical model will be developed that can be used as a tool for predicting the long-term effect of fertilizer application as a source of nitrate loading to the underlying aquifer or to surface water. In order to address the objectives of this research, a field investigation of three experimental sites will be carried out. Data will be collected on land uses, agricultural practices, climatic factors, soil properties, nitrogen dynamics in the soil, and the flow pattern in the unsaturated soil zone. At each site soil physical and chemical properties will be determined for each layer of the root zone to a depth of 90 cm. The soil physical properties include soil moisture, saturated and unsaturated hydraulic conductivity, bulk density, soil temperature, particle size distribution and its water retention curve. Soil water content and matric potential will be monitored using conventional and geophysical techniques including matric potential blocks, water content reflectometer sensors, Time Domain Reflectometry (TDR) and Ground Penetrating Radar (GPR). The soil chemical properties include soil total organic carbon and total nitrogen, nitrate (NO3) and ammonium (NH4) and will be determined at the beginning and at the end of the field season. The soil water will be collected monthly at three depths at each site throughout the growing season and will be analyzed for nitrate and ammonium.

Can plant phloem properties affect the link between ecosystem assimilation and respiration?

NASA Astrophysics Data System (ADS)

Mencuccini, M.; Hölttä, T.; Sevanto, S.; Nikinmaa, E.

2012-04-01

Phloem transport of carbohydrates in plants under field conditions is currently not well understood. This is largely the result of the lack of techniques suitable for measuring phloem physiological properties continuously under field conditions. This lack of knowledge is currently hampering our efforts to link ecosystem-level processes of carbon fixation, allocation and use, especially belowground. On theoretical grounds, the properties of the transport pathway from canopy to roots must be important in affecting the link between carbon assimilation and respiration, but it is unclear whether their effect is partially or entirely masked by processes occurring in other parts of the ecosystem. One can also predict the characteristic time scales over which these effects should occur and, as consequence, predict whether the transfer of turgor and osmotic signals from the site of carbon assimilation to the sites of carbon use are likely to control respiration. We will present two sources of evidence suggesting that the properties of the phloem transport system may affect processes that are dependent on the supply of carbon substrate, such as root or soil respiration. Firstly, we will summarize the results of a literature survey on soil and ecosystem respiration where the speed of transfer of photosynthetic sugars from the plant canopy to the soil surface was determined. Estimates of the transfer speed could be grouped according to whether the study employed isotopic or canopy soil flux-based techniques. These two groups provided very different estimates of transfer times likely because transport of sucrose molecules, and pressure-concentration waves, in phloem differed. Secondly, we will argue that simultaneous measurements of bark and xylem diameters provide a novel tool to determine the continuous variations of phloem turgor in vivo in the field. We will present a model that interprets these changes in xylem and live bark diameters and present data testing the model predictions for mature trees in the field. At the diurnal scale, the calculated phloem turgor signal related to patterns of photosynthetic activity and inferred phloem loading. At the seasonal scale, phloem turgor showed rapid changes during two droughts and after two rainfall events consistent with physiological predictions of phloem transport. Daily cumulative totals of calculated phloem osmotic concentrations were strongly related to daily cumulative totals of canopy photosynthesis. We propose that this method has potential for continuous field monitoring of tree phloem function.

The Impact of Rhizosphere Processes on Water Flow and Root Water Uptake

NASA Astrophysics Data System (ADS)

Schwartz, Nimrod; Kroener, Eva; Carminati, Andrea; Javaux, Mathieu

2015-04-01

For many years, the rhizosphere, which is the zone of soil in the vicinity of the roots and which is influenced by the roots, is known as a unique soil environment with different physical, biological and chemical properties than those of the bulk soil. Indeed, in recent studies it has been shown that root exudate and especially mucilage alter the hydraulic properties of the soil, and that drying and wetting cycles of mucilage result in non-equilibrium water dynamics in the rhizosphere. While there are experimental evidences and simplified 1D model for those concepts, an integrated model that considers rhizosphere processes with a detailed model for water and roots flow is absent. Therefore, the objective of this work is to develop a 3D physical model of water flow in the soil-plant continuum that take in consideration root architecture and rhizosphere specific properties. Ultimately, this model will enhance our understanding on the impact of processes occurring in the rhizosphere on water flow and root water uptake. To achieve this objective, we coupled R-SWMS, a detailed 3D model for water flow in soil and root system (Javaux et al 2008), with the rhizosphere model developed by Kroener et al (2014). In the new Rhizo-RSWMS model the rhizosphere hydraulic properties differ from those of the bulk soil, and non-equilibrium dynamics between the rhizosphere water content and pressure head is also considered. We simulated a wetting scenario. The soil was initially dry and it was wetted from the top at a constant flow rate. The model predicts that, after infiltration the water content in the rhizosphere remained lower than in the bulk soil (non-equilibrium), but over time water infiltrated into the rhizosphere and eventually the water content in the rhizosphere became higher than in the bulk soil. These results are in qualitative agreement with the available experimental data on water dynamics in the rhizosphere. Additionally, the results show that rhizosphere processes affect the spatial distribution of root water uptake. This suggests that rhizosphere processes effect root water uptake at the plant scale. Overall, these preliminary results demonstrate the impact of rhizosphere on water flow and root water uptake, and the ability of the Rhizo-RSWMS to simulate these processes. References Javaux, M., Schröder, T., Vanderborght, J., & Vereecken, H. (2008). Use of a three-dimensional detailed modeling approach for predicting root water uptake. Vadose Zone Journal, 7(3), 1079-1088.‏ Kroener, E., Zarebanadkouki, M., Kaestner, A., & Carminati, A. (2014). Nonequilibrium water dynamics in the rhizosphere: How mucilage affects water flow in soils. Water Resources Research, 50(8), 6479-6495.‏

Predicting small mammal and flea abundance using landform and soil properties in a plague endemic area in Lushoto District, Tanzania.

PubMed

Meliyo, Joel L; Kimaro, Didas N; Msanya, Balthazar M; Mulungu, Loth S; Hieronimo, Proches; Kihupi, Nganga I; Gulinck, Hubert; Deckers, Jozef A

2014-07-01

Small mammals particularly rodents, are considered the primary natural hosts of plague. Literature suggests that plague persistence in natural foci has a root cause in soils. The objective of this study was to investigate the relationship between on the one hand landforms and associated soil properties, and on the other hand small mammals and fleas in West Usambara Mountains in Tanzania, a plague endemic area. Standard field survey methods coupled with Geographical Information System (GIS) technique were used to examine landform and soils characteristics. Soil samples were analysed in the laboratory for physico-chemical properties. Small mammals were trapped on pre-established landform positions and identified to genus/species level. Fleas were removed from the trapped small mammals and counted. Exploration of landform and soil data was done using ArcGIS Toolbox functions and descriptive statistical analysis. The relationships between landforms, soils, small mammals and fleas were established by generalised linear regression model (GLM) operated in R statistics software. Results show that landforms and soils influence the abundance of small mammals and fleas and their spatial distribution. The abundance of small mammals and fleas increased with increase in elevation. Small mammal species richness also increases with elevation. A landform-soil model shows that available phosphorus, slope aspect and elevation were statistically significant predictors explaining richness and abundance of small mammals. Fleas' abundance and spatial distribution were influenced by hill-shade, available phosphorus and base saturation. The study suggests that landforms and soils have a strong influence on the richness and evenness of small mammals and their fleas' abundance hence could be used to explain plague dynamics in the area.

Risk assessment of gas oil and kerosene contamination on some properties of silty clay soil.

PubMed

Fallah, M; Shabanpor, M; Zakerinia, M; Ebrahimi, S

2015-07-01

Soil and ground water resource pollution by petroleum compounds and chemical solvents has multiple negative environmental impacts. The aim of this research was to investigate the impacts of kerosene and gas oil pollutants on some physical and chemical properties, breakthrough curve (BTC), and water retention curve (SWRC) of silty clay soil during a 3-month period. Therefore, some water-saturated soils were artificially contaminated in the pulse condition inside some glassy cylinders by applying half and one pore volume of these pollutants, and then parametric investigations of the SWRC were performed using RETC software for Van Genukhten and Brooks-Corey equations in the various suctions and the soil properties were determined before and after pollution during 3 months. The results showed that gas oil and kerosene had a slight effect on soil pH and caused the cumulative enhancement in the soil respiration, increase in the bulk density and organic matter, and reduction in the soil porosity and electrical and saturated hydraulic conductivity. Furthermore, gas oil retention was significantly more than kerosene (almost 40%) in the soil. The survey of SWRC indicated that the contaminated soil samples had a little higher amount of moisture retention (just under 15% in most cases) compared to the unpolluted ones during this 3-month period. The parametric analysis of SWRC demonstrated an increase in the saturated water content, Θ s, from nearly 49% in the control sample to just under 53% in the polluted ones. Contaminants not only decreased the residual water content, Θ r, but also reduced the SWRC gradient, n, and amount of α parameter. The evaluation of both equations revealed more accurate prediction of SWRC's parameters by Van Genukhten compared to those of Brooks and Corey.

Phytotoxicity of trace metals in spiked and field-contaminated soils: Linking soil-extractable metals with toxicity.

PubMed

Hamels, Fanny; Malevé, Jasmina; Sonnet, Philippe; Kleja, Dan Berggren; Smolders, Erik

2014-11-01

Soil tests have been widely developed to predict trace metal uptake by plants. The prediction of metal toxicity, however, has rarely been tested. The present study was set up to compare 8 established soil tests for diagnosing phytotoxicity in contaminated soils. Nine soils contaminated with Zn or Cu by metal mining, smelting, or processing were collected. Uncontaminated reference soils with similar soil properties were sampled, and series of increasing contamination were created by mixing each with the corresponding soil. In addition, each reference soil was spiked with either ZnCl2 or CuCl2 at several concentrations. Total metal toxicity to barley seedling growth in the field-contaminated soils was up to 30 times lower than that in corresponding spiked soils. Total metal (aqua regia-soluble) toxicity thresholds of 50% effective concentrations (EC50) varied by factors up to 260 (Zn) or 6 (Cu) among soils. For Zn, variations in EC50 thresholds decreased as aqua regia > 0.43 M HNO3  > 0.05 M ethylenediamine tetraacetic acid (EDTA) > 1 M NH4 NO3  > cobaltihexamine > diffusive gradients in thin films (DGT) > 0.001 M CaCl2 , suggesting that the last extraction is the most robust phytotoxicity index for Zn. The EDTA extraction was the most robust for Cu-contaminated soils. The isotopically exchangeable fraction of the total soil metal in the field-contaminated soils markedly explained the lower toxicity compared with spiked soils. The isotope exchange method can be used to translate soil metal limits derived from soils spiked with metal salts to site-specific soil metal limits. © 2014 SETAC.

The effect of measured and estimated soil hydraulic properties on simulated water regime in the analysis of grapevine adaptability to future climate

NASA Astrophysics Data System (ADS)

Bonfante, Antonello; Alfieri, Silvia Maria; Agrillo, Antonietta; Dragonetti, Giovanna; Mileti, Antonio; Monaco, Eugenia; De Lorenzi, Francesca

2013-04-01

In the last years many research works have been addressed to evaluate the impact of future climate on crop productivity and plant water use at different spatial scales (global, regional, field) by means of simulation models of agricultural crop systems. Most of these approaches use estimated soil hydraulic properties, through pedotransfer functions (PTF). This choice is related to soil data availability: soil data bases lack measured soil hydraulic properties, but generally they contain information that allow the application of PTF . Although the reliability of the predicted future climate scenarios cannot be immediately validated, we address to evaluate the effects of a simplification of the soil system by using PTF. Thus we compare simulations performed with measured soil hydraulic properties versus simulations carried out with estimated properties. The water regimes resulting from the two procedures are evaluated with respect to crop adaptability to future climate. In particular we will examine if the two procedures bring about different seasonal and spatial variations in the soil water regime patterns, and if these patterns influence adaptation options. The present case study uses the agro-hydrological model SWAP (soil-water-atmosphere and plant) and studies future adaptability of grapevine. The study area is a viticultural area of Southern Italy (Valle Telesina, BN) devoted to the production of high quality wines (DOC and DOCG), and characterized by a complex geomorphology and pedology. The future climate scenario (2021-2050) was constructed applying statistical downscaling techniques to GCMs scenarios. The moisture regime for 25 soils of the selected study area was calculated by means of SWAP model, using both measured and estimated soil hydraulic properties. In the simulation, the upper boundary conditions were derived from the regional climate scenarios. Unit gradient in soil water potential was set as lower boundary condition. Crop-specific input data and model parameters were estimated on the basis of scientific literature and assumed to be generically representative of the species. From the output of the simulation runs, the relative evapotranspiration deficit (or Crop Water Stress Index - CWSI) of the soil units was calculated. Since CWSI is considered an important indicator of the qualitative grapevine responses, its pattern in both simulation procedures has been evaluated. The work was carried out within the Italian national project AGROSCENARI funded by the Ministry for Agricultural, Food and Forest Policies (MIPAAF, D.M. 8608/7303/2008)

Effect of spatial resolution of soil data on predictions of eggshell trace element levels in the Rook Corvus frugilegus.

PubMed

Orłowski, Grzegorz; Siebielec, Grzegorz; Kasprzykowski, Zbigniew; Dobicki, Wojciech; Pokorny, Przemysław; Wuczyński, Andrzej; Polechoński, Ryszard; Mazgajski, Tomasz D

2016-12-01

Although a considerable research effort has gone into studying the dietary pathways of metals to the bodies of laying female birds and their eggs in recent years, no detailed investigations have yet been carried out relating the properties of the biogeochemical environment at large spatial scales to eggshell trace element levels in typical soil-invertebrate feeding birds under natural conditions. We used data from a large-scale nationwide monitoring survey of soil quality in Poland (3724 sampling points from the 43 792 available) to predict levels of five trace elements (copper [Cu], cadmium [Cd], nickel [Ni], zinc [Zn] and lead [Pb]) in Rook Corvus frugilegus eggshells from 42 breeding colonies. Our major aim was to test whether differences exist in the explanatory power of soil data (acidity, content of elements and organic matter, and particle size) used as a correlate of concentrations of eggshell trace elements among four different distances (5, 10, 15 and 20 km) around rookeries. Over all four distances around the rookeries only the concentrations of Cu and Cd in eggshells were positively correlated with those in soil, while eggshell Pb was correlated with the soil Pb level at the two longest distances (15 and 20 km) around the rookeries. The physical properties of soil (primarily the increase in pH) adversely affected eggshell Cd and Pb concentrations. The patterns and factors governing metal bioaccumulation in soil invertebrates and eggshells appear to be coincident, which strongly suggests a general similarity in the biochemical pathways of elements at different levels of the food web. The increasing acidification of arable soil as a result of excessive fertilisation and over-nitrification can enhance the bioavailability of toxic elements to laying females and their eggs. Copyright © 2016 Elsevier Ltd. All rights reserved.

Influence of soil properties and soil leaching on the toxicity of ionic silver to plants.

PubMed

Langdon, Kate A; McLaughlin, Mike J; Kirby, Jason K; Merrington, Graham

2015-11-01

Silver (Ag) has been shown to exhibit antimicrobial properties; as a result, it is being used increasingly in a wide range of consumer products. With these uses, the likelihood that Ag may enter the environment has increased, predominately via land application of biosolids or irrigation with treated wastewater effluent. The aim of the present study was to investigate the toxicity of Ag to 2 plant species: barley (Hordeum vulgare L. CV Triumph) and tomato (Lycopersicum esculentum) in a range of soils under both leached and unleached conditions. The concentrations that resulted in a 50% reduction of plant growth (EC50) were found to vary up to 20-fold across the soils, indicating a large influence of soil type on Ag toxicity. Overall, barley root elongation was found to be the least sensitive to added Ag, with EC50 values ranging from 51 mg/kg to 1030 mg/kg, whereas the tomato plant height showed higher sensitivity with EC50 values ranging from 46 mg/kg to 486 mg/kg. The effect of leaching was more evident in the barley toxicity results, where higher concentrations of Ag were required to induce toxicity. Variations in soil organic carbon and pH were found to be primarily responsible for mitigating Ag toxicity; therefore, these properties may be used in future risk assessments for Ag to predict toxicity in a wide range of soil types. © 2015 SETAC.

Comparing simple and complex approaches to simulate the impacts of soil water repellency on runoff and erosion in burnt Mediterranean forest slopes

NASA Astrophysics Data System (ADS)

Nunes, João Pedro; Catarina Simões Vieira, Diana; Keizer, Jan Jacob

2017-04-01

Fires impact soil hydrological properties, enhancing soil water repellency and therefore increasing the potential for surface runoff generation and soil erosion. In consequence, the successful application of hydrological models to post-fire conditions requires the appropriate simulation of the effects of soil water repellency on soil hydrology. This work compared three approaches to model soil water repellency impacts on soil hydrology in burnt eucalypt and pine forest slopes in central Portugal: 1) Daily approach, simulating repellency as a function of soil moisture, and influencing the maximum soil available water holding capacity. It is based on the Thornthwaite-Mather soil water modelling approach, and is parameterized with the soil's wilting point and field capacity, and a parameter relating soil water repellency with water holding capacity. It was tested with soil moisture data from burnt and unburnt hillslopes. This approach was able to simulate post-fire soil moisture patterns, which the model without repellency was unable to do. However, model parameters were different between the burnt and unburnt slopes, indicating that more research is needed to derive standardized parameters from commonly measured soil and vegetation properties. 2) Seasonal approach, pre-determining repellency at the seasonal scale (3 months) in four classes (from none to extreme). It is based on the Morgan-Morgan-Finney (MMF) runoff and erosion model, applied at the seasonal scale and is parameterized with a parameter relating repellency class with field capacity. It was tested with runoff and erosion data from several experimental plots, and led to important improvements on runoff prediction over an approach with constant field capacity for all seasons (calibrated for repellency effects), but only slight improvements in erosion predictions. In contrast with the daily approach, the parameters could be reproduced between different sites 3) Constant approach, specifying values for soil water repellency for the three years after the fire, and keeping them constant throughout the year. It is based on a daily Curve Number (CN) approach, and was incorporated directly in the Soil and Water Assessment Tool (SWAT) model and tested with erosion data from a burnt hillslope. This approach was able to successfully reproduce soil erosion. The results indicate that simplified approaches can be used to adapt existing models for post-fire simulation, taking repellency into account. Taking into account the seasonality of repellency seems more important to simulate surface runoff than erosion, possibly since simulating the larger runoff rates correctly is sufficient for erosion simulation. The constant approach can be applied directly in the parameterization of existing runoff and erosion models for soil loss and sediment yield prediction, while the seasonal approach can readily be developed as a next step, with further work being needed to assess if the approach and associated parameters can be applied in multiple post-fire environments.

Estimating soil hydraulic properties from soil moisture time series by inversion of a dual-permeability model

NASA Astrophysics Data System (ADS)

Dalla Valle, Nicolas; Wutzler, Thomas; Meyer, Stefanie; Potthast, Karin; Michalzik, Beate

2017-04-01

Dual-permeability type models are widely used to simulate water fluxes and solute transport in structured soils. These models contain two spatially overlapping flow domains with different parameterizations or even entirely different conceptual descriptions of flow processes. They are usually able to capture preferential flow phenomena, but a large set of parameters is needed, which are very laborious to obtain or cannot be measured at all. Therefore, model inversions are often used to derive the necessary parameters. Although these require sufficient input data themselves, they can use measurements of state variables instead, which are often easier to obtain and can be monitored by automated measurement systems. In this work we show a method to estimate soil hydraulic parameters from high frequency soil moisture time series data gathered at two different measurement depths by inversion of a simple one dimensional dual-permeability model. The model uses an advection equation based on the kinematic wave theory to describe the flow in the fracture domain and a Richards equation for the flow in the matrix domain. The soil moisture time series data were measured in mesocosms during sprinkling experiments. The inversion consists of three consecutive steps: First, the parameters of the water retention function were assessed using vertical soil moisture profiles in hydraulic equilibrium. This was done using two different exponential retention functions and the Campbell function. Second, the soil sorptivity and diffusivity functions were estimated from Boltzmann-transformed soil moisture data, which allowed the calculation of the hydraulic conductivity function. Third, the parameters governing flow in the fracture domain were determined using the whole soil moisture time series. The resulting retention functions were within the range of values predicted by pedotransfer functions apart from very dry conditions, where all retention functions predicted lower matrix potentials. The diffusivity function predicted values of a similar range as shown in other studies. Overall, the model was able to emulate soil moisture time series for low measurement depths, but deviated increasingly at larger depths. This indicates that some of the model parameters are not constant throughout the profile. However, overall seepage fluxes were still predicted correctly. In the near future we will apply the inversion method to lower frequency soil moisture data from different sites to evaluate the model's ability to predict preferential flow seepage fluxes at the field scale.

Are variations in heterotrophic soil respiration related to changes in substrate availability and microbial biomass carbon in the subtropical forests?

PubMed

Wei, Hui; Chen, Xiaomei; Xiao, Guoliang; Guenet, Bertrand; Vicca, Sara; Shen, Weijun

2015-12-16

Soil temperature and moisture are widely-recognized controlling factors on heterotrophic soil respiration (Rh), although they often explain only a portion of Rh variability. How other soil physicochemical and microbial properties may contribute to Rh variability has been less studied. We conducted field measurements on Rh half-monthly and associated soil properties monthly for two years in four subtropical forests of southern China to assess influences of carbon availability and microbial properties on Rh. Rh in coniferous forest was significantly lower than that in the other three broadleaf species-dominated forests and exhibited obvious seasonal variations in the four forests (P < 0.05). Temperature was the primary factor influencing the seasonal variability of Rh while moisture was not in these humid subtropical forests. The quantity and decomposability of dissolved organic carbon (DOC) were significantly important to Rh variations, but the effect of DOC content on Rh was confounded with temperature, as revealed by partial mantel test. Microbial biomass carbon (MBC) was significantly related to Rh variations across forests during the warm season (P = 0.043). Our results suggest that DOC and MBC may be important when predicting Rh under some conditions, and highlight the complexity by mutual effects of them with environmental factors on Rh variations.

Stable isotopic constraints on global soil organic carbon turnover

NASA Astrophysics Data System (ADS)

Wang, Chao; Houlton, Benjamin Z.; Liu, Dongwei; Hou, Jianfeng; Cheng, Weixin; Bai, Edith

2018-02-01

Carbon dioxide release during soil organic carbon (SOC) turnover is a pivotal component of atmospheric CO2 concentrations and global climate change. However, reliably measuring SOC turnover rates on large spatial and temporal scales remains challenging. Here we use a natural carbon isotope approach, defined as beta (β), which was quantified from the δ13C of vegetation and soil reported in the literature (176 separate soil profiles), to examine large-scale controls of climate, soil physical properties and nutrients over patterns of SOC turnover across terrestrial biomes worldwide. We report a significant relationship between β and calculated soil C turnover rates (k), which were estimated by dividing soil heterotrophic respiration rates by SOC pools. ln( - β) exhibits a significant linear relationship with mean annual temperature, but a more complex polynomial relationship with mean annual precipitation, implying strong-feedbacks of SOC turnover to climate changes. Soil nitrogen (N) and clay content correlate strongly and positively with ln( - β), revealing the additional influence of nutrients and physical soil properties on SOC decomposition rates. Furthermore, a strong (R2 = 0.76; p < 0.001) linear relationship between ln( - β) and estimates of litter and root decomposition rates suggests similar controls over rates of organic matter decay among the generalized soil C stocks. Overall, these findings demonstrate the utility of soil δ13C for independently benchmarking global models of soil C turnover and thereby improving predictions of multiple global change influences over terrestrial C-climate feedback.

Sampling Soil for Characterization and Site Description

NASA Technical Reports Server (NTRS)

Levine, Elissa

1999-01-01

The sampling scheme for soil characterization within the GLOBE program is uniquely different from the sampling methods of the other protocols. The strategy is based on an understanding of the 5 soil forming factors (parent material, climate, biota, topography, and time) at each study site, and how each of these interact to produce a soil profile with unique characteristics and unique input and control into the atmospheric, biological, and hydrological systems. Soil profile characteristics, as opposed to soil moisture and temperature, vegetative growth, and atmospheric and hydrologic conditions, change very slowly, depending on the parameter being measured, ranging from seasonally to many thousands of years. Thus, soil information, including profile description and lab analysis, is collected only one time for each profile at a site. These data serve two purposes: 1) to supplement existing spatial information about soil profile characteristics across the landscape at local, regional, and global scales, and 2) to provide specific information within a given area about the basic substrate to which elements within the other protocols are linked. Because of the intimate link between soil properties and these other environmental elements, the static soil properties at a given site are needed to accurately interpret and understand the continually changing dynamics of soil moisture and temperature, vegetation growth and phenology, atmospheric conditions, and chemistry and turbidity in surface waters. Both the spatial and specific soil information can be used for modeling purposes to assess and make predictions about global change.

Small-scale spatial variability in phylogenetic community structure during early plant succession depends on soil properties.

PubMed

Ulrich, Werner; Piwczyński, Marcin; Zaplata, Markus Klemens; Winter, Susanne; Schaaf, Wolfgang; Fischer, Anton

2014-07-01

During early plant succession, the phylogenetic structure of a community changes in response to important environmental filters and emerging species interactions. We traced the development of temperate-zone plant communities during the first 7 years of primary succession on catchment soils to explore patterns of initial species assembly. We found pronounced small-scale differences in the phylogenetic composition of neighbouring plant assemblages and a large-scale trend towards phylogenetic evenness. This small-scale variability appears to be mediated by soil properties, particularly carbonate content. Therefore, abiotic environmental conditions might counteract or even supersede the effects of interspecific competition among closely related species, which are usually predicted to exhibit patterns of phylogenetic evenness. We conclude that theories on phylogenetic community composition need to incorporate effects of small-scale variability of environmental factors.

Prediction of soil urea conversion and quantification of the importance degrees of influencing factors through a new combinatorial model based on cluster method and artificial neural network.

PubMed

Lei, Tao; Guo, Xianghong; Sun, Xihuan; Ma, Juanjuan; Zhang, Shaowen; Zhang, Yong

2018-05-01

Quantitative prediction of soil urea conversion is crucial in determining the mechanism of nitrogen transformation and understanding the dynamics of soil nutrients. This study aimed to establish a combinatorial prediction model (MCA-F-ANN) for soil urea conversion and quantify the relative importance degrees (RIDs) of influencing factors with the MCA-F-ANN method. Data samples were obtained from laboratory culture experiments, and soil nitrogen content and physicochemical properties were measured every other day. Results showed that when MCA-F-ANN was used, the mean-absolute-percent error values of NH 4 + -N, NO 3 - -N, and NH 3 contents were 3.180%, 2.756%, and 3.656%, respectively. MCA-F-ANN predicted urea transformation under multi-factor coupling conditions more accurately than traditional models did. The RIDs of reaction time (RT), electrical conductivity (EC), temperature (T), pH, nitrogen application rate (F), and moisture content (W) were 32.2%-36.5%, 24.0%-28.9%, 12.8%-15.2%, 9.8%-12.5%, 7.8%-11.0%, and 3.5%-6.0%, respectively. The RIDs of the influencing factors in a descending order showed the pattern RT > EC > T > pH > F > W. RT and EC were the key factors in the urea conversion process. The prediction accuracy of urea transformation process was improved, and the RIDs of the influencing factors were quantified. Copyright © 2018 Elsevier Ltd. All rights reserved.

Improving Numerical Weather Predictions of Summertime Precipitation Over the Southeastern U.S. Through a High-Resolution Initialization of the Surface State

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Kumar, Sujay V.; Krikishen, Jayanthi; Jedlovec, Gary J.

2011-01-01

It is hypothesized that high-resolution, accurate representations of surface properties such as soil moisture and sea surface temperature are necessary to improve simulations of summertime pulse-type convective precipitation in high resolution models. This paper presents model verification results of a case study period from June-August 2008 over the Southeastern U.S. using the Weather Research and Forecasting numerical weather prediction model. Experimental simulations initialized with high-resolution land surface fields from the NASA Land Information System (LIS) and sea surface temperature (SST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared to a set of control simulations initialized with interpolated fields from the National Centers for Environmental Prediction 12-km North American Mesoscale model. The LIS land surface and MODIS SSTs provide a more detailed surface initialization at a resolution comparable to the 4-km model grid spacing. Soil moisture from the LIS spin-up run is shown to respond better to the extreme rainfall of Tropical Storm Fay in August 2008 over the Florida peninsula. The LIS has slightly lower errors and higher anomaly correlations in the top soil layer, but exhibits a stronger dry bias in the root zone. The model sensitivity to the alternative surface initial conditions is examined for a sample case, showing that the LIS/MODIS data substantially impact surface and boundary layer properties.

Transient hazard model using radar data for predicting debris flows in Madison County, Virginia

USGS Publications Warehouse

Morrissey, M.M.; Wieczorek, G.F.; Morgan, B.A.

2004-01-01

During the rainstorm of June 27, 1995, roughly 330-750 mm of rain fell within a 16-hour period, initiating floods and over 600 debris flows in a small area (130 km2) of Madison County, VA. We developed a distributed version of Iverson's transient response model for regional slope stability analysis for the Madison County debris flows. This version of the model evaluates pore-pressure head response and factor of safety on a regional scale in areas prone to rainfall-induced shallow (<2-3 m) landslides. These calculations used soil properties of shear strength and hydraulic conductivity from laboratory measurements of soil samples collected from field sites where debris flows initiated. Rainfall data collected by radar every 6 minutes provided a basis for calculating the temporal variation of slope stability during the storm. The results demonstrate that the spatial and temporal variation of the factor of safety correlates with the movement of the storm cell. When the rainstorm was treated as two separate rainfall events and a larger hydraulic conductivity and friction angle than the laboratory values were used, the timing and location of landslides predicted by the model were in closer agreement with eyewitness observations of debris flows. Application of spatially variable initial pre-storm water table depth and soil properties may improve both the spatial and temporal prediction of instability.

Revealing chemophoric sites in organophosphorus insecticides through the MIA-QSPR modeling of soil sorption data.

PubMed

Daré, Joyce K; Silva, Cristina F; Freitas, Matheus P

2017-10-01

Soil sorption of insecticides employed in agriculture is an important parameter to probe the environmental fate of organic chemicals. Therefore, methods for the prediction of soil sorption of new agrochemical candidates, as well as for the rationalization of the molecular characteristics responsible for a given sorption profile, are extremely beneficial for the environment. A quantitative structure-property relationship method based on chemical structure images as molecular descriptors provided a reliable model for the soil sorption prediction of 24 widely used organophosphorus insecticides. By means of contour maps obtained from the partial least squares regression coefficients and the variable importance in projection scores, key molecular moieties were targeted for possible structural modification, in order to obtain novel and more environmentally friendly insecticide candidates. The image-based descriptors applied encode molecular arrangement, atoms connectivity, groups size, and polarity; consequently, the findings in this work cannot be achieved by a simple relationship with hydrophobicity, usually described by the octanol-water partition coefficient. Copyright © 2017 Elsevier Inc. All rights reserved.

A Model for Hydraulic Properties Based on Angular Pores with Lognormal Size Distribution

NASA Astrophysics Data System (ADS)

Durner, W.; Diamantopoulos, E.

2014-12-01

Soil water retention and unsaturated hydraulic conductivity curves are mandatory for modeling water flow in soils. It is a common approach to measure few points of the water retention curve and to calculate the hydraulic conductivity curve by assuming that the soil can be represented as a bundle of capillary tubes. Both curves are then used to predict water flow at larger spatial scales. However, the predictive power of these curves is often very limited. This can be very easily illustrated if we measure the soil hydraulic properties (SHPs) for a drainage experiment and then use these properties to predict the water flow in the case of imbibition. Further complications arise from the incomplete wetting of water at the solid matrix which results in finite values of the contact angles between the solid-water-air interfaces. To address these problems we present a physically-based model for hysteretic SHPs. This model is based on bundles of angular pores. Hysteresis for individual pores is caused by (i) different snap-off pressures during filling and emptying of single angular pores and (ii) by different advancing and receding contact angles for fluids that are not perfectly wettable. We derive a model of hydraulic conductivity as a function of contact angle by assuming flow perpendicular to pore cross sections and present closed-form expressions for both the sample scale water retention and hydraulic conductivity function by assuming a log-normal statistical distribution of pore size. We tested the new model against drainage and imbibition experiments for various sandy materials which were conducted with various liquids of differing wettability. The model described both imbibition and drainage experiments very well by assuming a unique pore size distribution of the sample and a zero contact angle for the perfectly wetting liquid. Eventually, we see the possibility to relate the particle size distribution with a model which describes the SHPs.

Relations between soil hydraulic properties and burn severity

USGS Publications Warehouse

Moody, John A.; Ebel, Brian A.; Nyman, Petter; Martin, Deborah A.; Stoof, Cathelijne R.; McKinley, Randy

2015-01-01

Wildfire can affect soil hydraulic properties, often resulting in reduced infiltration. The magnitude of change in infiltration varies depending on the burn severity. Quantitative approaches to link burn severity with changes in infiltration are lacking. This study uses controlled laboratory measurements to determine relations between a remotely sensed burn severity metric (dNBR, change in normalised burn ratio) and soil hydraulic properties (SHPs). SHPs were measured on soil cores collected from an area burned by the 2013 Black Forest fire in Colorado, USA. Six sites with the same soil type were selected across a range of burn severities, and 10 random soil cores were collected from each site within a 30-m diameter circle. Cumulative infiltration measurements were made in the laboratory using a tension infiltrometer to determine field-saturated hydraulic conductivity, Kfs, and sorptivity, S. These measurements were correlated with dNBR for values ranging from 124 (low severity) to 886 (high severity). SHPs were related to dNBR by inverse functions for specific conditions of water repellency (at the time of sampling) and soil texture. Both functions had a threshold value for dNBR between 124 and 420, where Kfs and S were unchanged and equal to values for soil unaffected by fire. For dNBRs >~420, the Kfs was an exponentially decreasing function of dNBR and S was a linearly decreasing function of dNBR. These initial quantitative empirical relations provide a first step to link SHPs to burn severity, and can be used in quantitative infiltration models to predict post-wildfire infiltration and resulting runoff.

Soil water repellency: the knowledge base, advances and challenges

NASA Astrophysics Data System (ADS)

Doerr, S. H.

2012-04-01

The topic of soil water repellency (SWR or soil hydrophobicity) has moved from being perhaps a little known curiosity a few decades ago to a well established sub-discipline of soil physics and soil hydrology. In terms of the number of journal publications, SWR is comparable with other physical soil properties or processes such as crusting, aggregation or preferential flow. SWR refers to a condition when soil does not wet readily when in contact with water. This may be evident at the soil surface, when SWR leads to prolonged ponding on soils despite the presence of sufficient pore openings, or in the soil matrix, as manifest by enhanced uneven wetting and preferential flow that is not caused by structural in homogeneity. Amongst major milestones advancing the knowledge base of SWR have been the recognition that: (1) many, if not most, soils can exhibit SWR when the soil moisture content falls below a critical threshold, (2) it can be induced (and destroyed) during vegetation fires, but many soils exhibit SWR irrespective of burning, (3) it can be caused, in principle, by a large variety of naturally-abundant chemical compounds, (4) it is typically highly variable in space, time and its degree (severity and persistence), and (5) its impacts on, for example, soil hydrology, erosion and plant growth have the potential to be very substantial, but also that impacts are often minor for naturally vegetated and undisturbed soils. Amongst the key challenges that remain are: (a) predicting accurately the conditions when soils prone to SWR actually develop this property, (b) unravelling, for fire effected environments, to what degree any presence of absence of SWR is due to fire and post-fire recovery, (c) the exact nature and origin the material causing SWR at the molecular level in different environments, (d) understanding the implications of the spatial and temporal variability at different scales, (e) the capability to model and predict under which environmental conditions its impacts are of significance, and (f) what changes to SWR and its implications we can expect under future climatic and land management conditions. This presentation aims to provide a brief overview of the main milestones reached to date in SWR research and of some of the key challenges for future research in this rapidly growing field.

Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation

NASA Astrophysics Data System (ADS)

Akbar, Ruzbeh; Short Gianotti, Daniel; McColl, Kaighin A.; Haghighi, Erfan; Salvucci, Guido D.; Entekhabi, Dara

2018-03-01

The soil water content profile is often well correlated with the soil moisture state near the surface. They share mutual information such that analysis of surface-only soil moisture is, at times and in conjunction with precipitation information, reflective of deeper soil fluxes and dynamics. This study examines the characteristic length scale, or effective depth Δz, of a simple active hydrological control volume. The volume is described only by precipitation inputs and soil water dynamics evident in surface-only soil moisture observations. To proceed, first an observation-based technique is presented to estimate the soil moisture loss function based on analysis of soil moisture dry-downs and its successive negative increments. Then, the length scale Δz is obtained via an optimization process wherein the root-mean-squared (RMS) differences between surface soil moisture observations and its predictions based on water balance are minimized. The process is entirely observation-driven. The surface soil moisture estimates are obtained from the NASA Soil Moisture Active Passive (SMAP) mission and precipitation from the gauge-corrected Climate Prediction Center daily global precipitation product. The length scale Δz exhibits a clear east-west gradient across the contiguous United States (CONUS), such that large Δz depths (>200 mm) are estimated in wetter regions with larger mean precipitation. The median Δz across CONUS is 135 mm. The spatial variance of Δz is predominantly explained and influenced by precipitation characteristics. Soil properties, especially texture in the form of sand fraction, as well as the mean soil moisture state have a lesser influence on the length scale.

Associations between soil bacterial community structure and nutrient cycling functions in long-term organic farm soils following cover crop and organic fertilizer amendment.

PubMed

Fernandez, Adria L; Sheaffer, Craig C; Wyse, Donald L; Staley, Christopher; Gould, Trevor J; Sadowsky, Michael J

2016-10-01

Agricultural management practices can produce changes in soil microbial populations whose functions are crucial to crop production and may be detectable using high-throughput sequencing of bacterial 16S rRNA. To apply sequencing-derived bacterial community structure data to on-farm decision-making will require a better understanding of the complex associations between soil microbial community structure and soil function. Here 16S rRNA sequencing was used to profile soil bacterial communities following application of cover crops and organic fertilizer treatments in certified organic field cropping systems. Amendment treatments were hairy vetch (Vicia villosa), winter rye (Secale cereale), oilseed radish (Raphanus sativus), buckwheat (Fagopyrum esculentum), beef manure, pelleted poultry manure, Sustane(®) 8-2-4, and a no-amendment control. Enzyme activities, net N mineralization, soil respiration, and soil physicochemical properties including nutrient levels, organic matter (OM) and pH were measured. Relationships between these functional and physicochemical parameters and soil bacterial community structure were assessed using multivariate methods including redundancy analysis, discriminant analysis, and Bayesian inference. Several cover crops and fertilizers affected soil functions including N-acetyl-β-d-glucosaminidase and β-glucosidase activity. Effects, however, were not consistent across locations and sampling timepoints. Correlations were observed among functional parameters and relative abundances of individual bacterial families and phyla. Bayesian analysis inferred no directional relationships between functional activities, bacterial families, and physicochemical parameters. Soil functional profiles were more strongly predicted by location than by treatment, and differences were largely explained by soil physicochemical parameters. Composition of soil bacterial communities was predictive of soil functional profiles. Differences in soil function were better explained using both soil physicochemical test values and bacterial community structure data than using soil tests alone. Pursuing a better understanding of bacterial community composition and how it is affected by farming practices is a promising avenue for increasing our ability to predict the impact of management practices on important soil functions. Copyright © 2016. Published by Elsevier B.V.

Relation between Soil Order and Sorptive Capacity for Dissolved Organic Carbon

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heal, Katherine R; Brandt, Craig C; Mayes, Melanie

2012-01-01

Soils have historically been considered a temporary sink for organic C, but deeper soils may serve as longer term C sinks due to the sorption of dissolved organic C (DOC) onto Fe- and clay-rich mineral soil particles. This project provides an improved understanding and predictive capability of the physical and chemical properties of deep soils that control their sorptive capacities for DOC. Two hundred thirteen subsurface soil samples (72 series from five orders) were selected from the eastern and central United States. A characterized natural DOC source was added to the soils, and the Langmuir sorption equation was fitted tomore » the observed data by adjusting the maximum DOC sorption capacity (Q{sub max}) and the binding coefficient (k). Different isotherm shapes were observed for Ultisols, Alfisols, and Mollisols due to statistically significant differences in the magnitude of k, while Q{sub max} was statistically invariant among these three orders. Linear regressions were performed on the entire database and as a function of soil order to correlate Langmuir fitted parameters with measured soil properties, e.g., pH, clay content, total organic C (TOC), and total Fe oxide content. Together, textural clay and Fe oxide content accounted for 35% of the variation in Q{sub max} in the database, and clay was most important for Alfisols and Ultisols. The TOC content, however, accounted for 27% of the variation in Q{sub max} in Mollisols. Soil pH accounted for 45% of the variation in k for the entire database, 41% for Mollisols, and 22% for Alfisols. Our findings demonstrate that correlations between Langmuir parameters and soil properties are different for different soil orders and that k is a more sensitive parameter for DOC sorption than is Q{sub max} for temperate soils from the central and eastern United States.« less

Influence of management history and landscape variables on soil organic carbon and soil redistribution

USGS Publications Warehouse

Venteris, E.R.; McCarty, G.W.; Ritchie, J.C.; Gish, T.

2004-01-01

Controlled studies to investigate the interaction between crop growth, soil properties, hydrology, and management practices are common in agronomy. These sites (much as with real world farmland) often have complex management histories and topographic variability that must be considered. In 1993 an interdisiplinary study was started for a 20-ha site in Beltsville, MD. Soil cores (271) were collected in 1999 in a 30-m grid (with 5-m nesting) and analyzed as part of the site characterization. Soil organic carbon (SOC) and 137Cesium (137Cs) were measured. Analysis of aerial photography from 1992 and of farm management records revealed that part of the site had been maintained as a swine pasture and the other portion as cropped land. Soil properties, particularly soil redistribution and SOC, show large differences in mean values between the two areas. Mass C is 0.8 kg m -2 greater in the pasture area than in the cropped portion. The pasture area is primarily a deposition site, whereas the crop area is dominated by erosion. Management influence is suggested, but topographic variability confounds interpretation. Soil organic carbon is spatially structured, with a regionalized variable of 120 m. 137Cs activity lacks spatial structure, suggesting disturbance of the profile by animal activity and past structures such as swine shelters and roads. Neither SOC nor 137Cs were strongly correlated to terrain parameters, crop yields, or a seasonal soil moisture index predicted from crop yields. SOC and 137Cs were weakly correlated (r2 ???0.2, F-test P-value 0.001), suggesting that soil transport controls, in part, SOC distribution. The study illustrates the importance of past site history when interpreting the landscape distribution of soil properties, especially those strongly influenced by human activity. Confounding variables, complex soil hydrology, and incomplete documentation of land use history make definitive interpretations of the processes behind the spatial distributions difficult. Such complexity may limit the accuracy of scaling approaches to mapping SOC and soil redistribution.

Comparing the performance of various digital soil mapping approaches to map physical soil properties

NASA Astrophysics Data System (ADS)

Laborczi, Annamária; Takács, Katalin; Pásztor, László

2015-04-01

Spatial information on physical soil properties is intensely expected, in order to support environmental related and land use management decisions. One of the most widely used properties to characterize soils physically is particle size distribution (PSD), which determines soil water management and cultivability. According to their size, different particles can be categorized as clay, silt, or sand. The size intervals are defined by national or international textural classification systems. The relative percentage of sand, silt, and clay in the soil constitutes textural classes, which are also specified miscellaneously in various national and/or specialty systems. The most commonly used is the classification system of the United States Department of Agriculture (USDA). Soil texture information is essential input data in meteorological, hydrological and agricultural prediction modelling. Although Hungary has a great deal of legacy soil maps and other relevant soil information, it often occurs, that maps do not exist on a certain characteristic with the required thematic and/or spatial representation. The recent developments in digital soil mapping (DSM), however, provide wide opportunities for the elaboration of object specific soil maps (OSSM) with predefined parameters (resolution, accuracy, reliability etc.). Due to the simultaneous richness of available Hungarian legacy soil data, spatial inference methods and auxiliary environmental information, there is a high versatility of possible approaches for the compilation of a given soil map. This suggests the opportunity of optimization. For the creation of an OSSM one might intend to identify the optimum set of soil data, method and auxiliary co-variables optimized for the resources (data costs, computation requirements etc.). We started comprehensive analysis of the effects of the various DSM components on the accuracy of the output maps on pilot areas. The aim of this study is to compare and evaluate different digital soil mapping methods and sets of ancillary variables for producing the most accurate spatial prediction of texture classes in a given area of interest. Both legacy and recently collected data on PSD were used as reference information. The predictor variable data set consisted of digital elevation model and its derivatives, lithology, land use maps as well as various bands and indices of satellite images. Two conceptionally different approaches can be applied in the mapping process. Textural classification can be realized after particle size data were spatially extended by proper geostatistical method. Alternatively, the textural classification is carried out first, followed by the spatial extension through suitable data mining method. According to the first approach, maps of sand, silt and clay percentage have been computed through regression kriging (RK). Since the three maps are compositional (their sum must be 100%), we applied Additive Log-Ratio (alr) transformation, instead of kriging them independently. Finally, the texture class map has been compiled according to the USDA categories from the three maps. Different combinations of reference and training soil data and auxiliary covariables resulted several different maps. On the basis of the other way, the PSD were classified firstly into the USDA categories, then the texture class maps were compiled directly by data mining methods (classification trees and random forests). The various results were compared to each other as well as to the RK maps. The performance of the different methods and data sets has been examined by testing the accuracy of the geostatistically computed and the directly classified results to assess the most predictive and accurate method. Acknowledgement: Our work was supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Long-term settlement prediction at open dumping area using Hossein and Gabr method for new development

NASA Astrophysics Data System (ADS)

Pauzi, Nur Irfah Mohd; Shariffuddin, Ahmad Sulaimi; Omar, Husaini; Misran, Halina

2017-07-01

In Malaysia, the most common method of disposal is landfill/open dumping. The soil at the dumping area are mixed with waste and soil. Thus, it was called as waste soil. Due to its heterogeneity properties, waste soil has a different settlement rate because different types of waste tends to settle differently. The Hussein and Gabr model which used empirical model was proposed to compute the long-term settlement. This Hussein and Gabr model is one of the soil settlement model that can be used to predict the long-term settlement at the dumping area. The model relates between the compression index and the time factor. The time factor are t1, t2, t3 and t4. The compression index is Cα1=compression index and Cβ is biodegradation index. The duration for initial compression, the compression, the biological compression and time creep are included in the model. The sample of waste soil is taken from closed dumping area in Lukut, Negeri Sembilan with the height of waste approximately 1 to 3 meters. The sample is tested using consolidation test for determining the geotechnical parameters and compressibility index. Based on the Hossein and Gabr model, the predicted long-term settlement for 20 years (ΔH) for the waste height of 1 to 3 meters are 0.21m, 0.42m and 0.63m respectively and are below the percentages of proposed maximum settlement for waste soil which is acceptable for new development to takes place.. The types of deep or shallow foundation are proposed based on the predicted settlement. The abandoned open dumping area can now be reused for the new development after the long-term settlement are predicted and some of the precaution measures has been taken as a safety measures.

Potential role of soil properties in the spread of CWD in western Canada.

PubMed

Kuznetsova, Alsu; McKenzie, Debbie; Banser, Pamela; Siddique, Tariq; Aiken, Judd M

2014-01-01

Chronic wasting disease (CWD) is a horizontally transmissible prion disease of free ranging deer, elk and moose. Recent experimental transmission studies indicate caribou are also susceptible to the disease. CWD is present in southeast Alberta and southern Saskatchewan. This CWD-endemic region is expanding, threatening Manitoba and areas of northern Alberta and Saskatchewan, home to caribou. Soil can serve as a stable reservoir for infectious prion proteins; prions bound to soil particles remain infectious in the soils for many years. Soils of western Canada are very diverse and the ability of CWD prions to bind different soils and the impact of this interaction on infectivity is not known. In general, clay-rich soils may bind prions avidly and enhance their infectivity comparable to pure clay mineral montmorillonite. Organic components of soils are also diverse and not well characterized, yet can impact prion-soil interaction. Other important contributing factors include soil pH, composition of soil solution and amount of metals (metal oxides). In this review, properties of soils of the CWD-endemic region in western Canada with its surrounding terrestrial environment are described and used to predict bioavailability and, thus, potential spread of CWD. The major soils in the CWD-endemic region of Alberta and Saskatchewan are Chernozems, present in 60% of the total area; they are generally similar in texture, clay mineralogy and soil organic matter content, and can be characterized as clay loamy, montmorillonite (smectite) soils with 6-10% organic carbon. The greatest risk of CWD spread in western Canada relates to clay loamy, montmorillonite soils with humus horizon. Such soils are predominant in the southern region of Alberta, Saskatchewan and Manitoba, but are less common in northern regions of the provinces where quartz-illite sandy soils with low amount of humus prevail.

Prescribed Fire Effects on Runoff, Erosion, and Soil Water Repellency on Steeply-Sloped Sagebrush Rangeland over a Five Year Period

NASA Astrophysics Data System (ADS)

Williams, C. J.; Pierson, F. B.; Al-Hamdan, O. Z.

2014-12-01

Fire is an inherent component of sagebrush steppe rangelands in western North America and can dramatically affect runoff and erosion processes. Post-fire flooding and erosion events pose substantial threats to proximal resources, property, and human life. Yet, prescribed fire can serve as a tool to manage vegetation and fuels on sagebrush rangelands and to reduce the potential for large catastrophic fires and mass erosion events. The impact of burning on event hydrologic and erosion responses is strongly related to the degree to which burning alters vegetation, ground cover, and surface soils and the intensity and duration of precipitation. Fire impacts on hydrologic and erosion response may be intensified or reduced by inherent site characteristics such as topography and soil properties. Parameterization of these diverse conditions in predictive tools is often limited by a lack of data and/or understanding for the domain of interest. Furthermore, hydrologic and erosion functioning change as vegetation and ground cover recover in the years following burning and few studies track these changes over time. In this study, we evaluated the impacts of prescribed fire on vegetation, ground cover, soil water repellency, and hydrologic and erosion responses 1, 2, and 5 yr following burning of a mountain big sagebrush community on steep hillslopes with fine-textured soils. The study site is within the Reynolds Creek Experimental Watershed, southwestern Idaho, USA. Vegetation, ground cover, and soil properties were measured over plot scales of 0.5 m2 to 9 m2. Rainfall simulations (0.5 m2) were used to assess the impacts of fire on soil water repellency, infiltration, runoff generation, and splash-sheet erosion. Overland flow experiments (9 m2) were used to assess the effects of fire-reduced ground cover on concentrated-flow runoff and erosion processes. The study results provide insight regarding fire impacts on runoff, erosion, and soil water repellency in the immediate and short-term post-fire recovery years for steeply-sloped sagebrush sites with fine-textured soils. The study results also serve to inform development and enhancement of the Rangeland Hydrology and Erosion Model for predicting runoff and erosion responses from disturbed and undisturbed sagebrush rangelands.

Assessment of metal(loid)s phytoavailability in intensive agricultural soils by the application of single extractions to rhizosphere soil.

PubMed

Pinto, Edgar; Almeida, Agostinho A; Ferreira, Isabel M P L V O

2015-03-01

The influence of soil properties on the phytoavailability of metal(loid)s in a soil-plant system was evaluated. The content of extractable metal(loid)s obtained by using different extraction methods was also compared. To perform this study, a test plant (Lactuca sativa) and rhizosphere soil were sampled at 5 different time points (2, 4, 6, 8 and 10 weeks of plant growth). Four extraction methods (Mehlich 3, DTPA, NH4NO3 and CaCl2) were used. Significant positive correlations between the soil extractable content and lettuce shoot content were obtained for several metal(loid)s. The extraction with NH4NO3 showed the higher number of strong positive correlations indicating the suitability of this method to estimate metal(loid)s phytoavailability. The soil CEC, OM, pH, texture and oxides content significantly influenced the distribution of metal(loid)s between the phytoavailable and non-phytoavailable fractions. A reliable prediction model for Cr, V, Ni, As, Pb, Co, Cd, and Sb phytoavailability was obtained considering the amount of metal(loid) extracted by the NH4NO3 method and the main soil properties. This work shows that the analysis of rhizosphere soil by single extractions methods is a reliable approach to estimate metal(loid)s phytoavailability. Copyright © 2014 Elsevier Inc. All rights reserved.

Unsaturated flow processes in structurally-variable pathways in wildfire-affected soils and ash

NASA Astrophysics Data System (ADS)

Ebel, B. A.

2016-12-01

Prediction of flash flood and debris flow generation in wildfire-affected soils and ash hinges on understanding unsaturated flow processes. Water resources issues, such as groundwater recharge, also rely on our ability to quantify subsurface flow. Soil-hydraulic property data provide insight into unsaturated flow processes and timescales. A literature review and synthesis of existing data from the literature for wildfire-affected soils, including ash and unburned soils, facilitated calculating metrics and timescales of hydrologic response related to infiltration and surface runoff generation. Sorptivity (S) and the Green-Ampt wetting front parameter (Ψf) were significantly lower in burned soils compared to unburned soils, while field-saturated hydraulic conductivity (Kfs) was not significantly different. The magnitude and duration of the influence of capillarity was substantially reduced in burned soils, leading to faster ponding times in response to rainfall. Ash had large values of S and Kfs compared to unburned and burned soils but intermediate values of Ψf, suggesting that ash has long ponding times in response to rainfall. The ratio of S2/Kfs was nearly constant ( 100 mm) for unburned soils, but was more variable in burned soils. Post-wildfire changes in this ratio suggested that unburned soils had a balance between gravity and capillarity contributions to infiltration, which may depend on soil organic matter, while burning shifted infiltration more towards gravity contributions by reducing S. Taken together, the changes in post-wildfire soil-hydraulic properties increased the propensity for surface runoff generation and may have enhanced subsurface preferential flow through pathways altered by wildfire.

Understanding controls of hydrologic processes across two headwater monolithological catchments using model-data synthesis

NASA Astrophysics Data System (ADS)

Xiao, D.; Shi, Y.; Hoagland, B.; Del Vecchio, J.; Russo, T. A.; DiBiase, R. A.; Li, L.

2017-12-01

How do watershed hydrologic processes differ in catchments derived from different lithology? This study compares two first order, deciduous forest watersheds in Pennsylvania, a sandstone watershed, Garner Run (GR, 1.34 km2), and a shale-derived watershed, Shale Hills (SH, 0.08 km2). Both watersheds are simulated using a combination of national datasets and field measurements, and a physics-based land surface hydrologic model, Flux-PIHM. We aim to evaluate the effects of lithology on watershed hydrology and assess if we can simulate a new watershed without intensive measurements, i.e., directly use calibration information from one watershed (SH) to reproduce hydrologic dynamics of another watershed (GR). Without any calibration, the model at GR based on national datasets and calibration inforamtion from SH cannot capture some discharge peaks or the baseflow during dry periods. The model prediction agrees well with the GR field discharge and soil moisture after calibrating the soil hydraulic parameters using the uncertainty based Hornberger-Spear-Young algorithm and the Latin Hypercube Sampling method. Agreeing with the field observation and national datasets, the difference in parameter values shows that the sandstone watershed has a larger averaged soil pore diameter, greater water storage created by porosity, lower water retention ability, and greater preferential flow. The water budget calculation shows that the riparian zone and the colluvial valley serves as buffer zones that stores water at GR. Using the same procedure, we compared Flux-PIHM simulations with and without a field measured surface boulder map at GR. When the boulder map is used, the prediction of areal averaged soil moisture is improved, without performing extra calibration. When calibrated separately, the cases with or without boulder map yield different calibration values, but their hydrologic predictions are similar, showing equifinality. The calibrated soil hydraulic parameter values in the with boulder map case is more physically plausible than the without boulder map case. We switched the topography and soil properties between GR and SH, and results indicate that the hydrologic processes are more sensitive to changes in domain topography than to changes in the soil properties.

Beyond clay - using selective extractions to improve predictions of soil carbon content

NASA Astrophysics Data System (ADS)

Rasmussen, C.; Berhe, A. A.; Blankinship, J. C.; Crow, S. E.; Druhan, J. L.; Heckman, K. A.; Keiluweit, M.; Lawrence, C. R.; Marin-Spiotta, E.; Plante, A. F.; Schaedel, C.; Schimel, J.; Sierra, C. A.; Thompson, A.; Wagai, R.; Wieder, W. R.

2016-12-01

A central component of modern soil carbon (C) models is the use of clay content to scale the relative partitioning of decomposing plant material to respiration and mineral stabilized soil C. However, numerous pedon to plot scale studies indicate that other soil mineral parameters, such as Fe- or Al-oxyhydroxide content and specific surface area, may be more effective than clay alone for predicting soil C content and stabilization. Here we directly address the following question: Are there soil physicochemical parameters that represent mineral C association and soil C content that can replace or be used in conjunction with clay content as scalars in soil C models. We explored the relationship of soil C content to a number of soil physicochemical and physiographic parameters using the National Cooperative Soil Survey database that contains horizon level data for > 62,000 pedons spanning global ecoregions and geographic areas. The data indicated significant variation in the degree of correlation among soil C, clay and Fe-/Al-oxyhydroxides with increasing moisture variability. Specifically, dry, water-limited systems (PET/MAP > 1) presented strong positive correlations between clay and soil C, that decreased significantly to little or no correlation in wet, energy-limited systems (PET/MAP < 1). In contrast, the correlation of soil C to oxalate extractable Al+Fe increased significantly with increasing moisture availability. This pattern was particularly well expressed for subsurface B horizons. Multivariate analyses indicated similar patterns, with clear climate and ecosystem level variation in the degree of correlation among soil C and soil physicochemical properties. The results indicate a need to modify current soil C models to incorporate additional C partitioning parameters that better account for climate and ecoregion variability in C stabilization mechanisms.

Spatial assessment of soil organic carbon and physicochemical properties in a horticultural orchard at arid zone of India using geostatistical approaches.

PubMed

Singh, Akath; Santra, Priyabrata; Kumar, Mahesh; Panwar, Navraten; Meghwal, P R

2016-09-01

Soil organic carbon (SOC) is a major indicator of long-term sustenance of agricultural production system. Apart from sustaining productivity, SOC plays a crucial role in context of climate change. Keeping in mind these potentials, spatial variation of SOC contents of a fruit orchard comprising several arid fruit plantations located at arid region of India is assessed in this study through geostatistical approaches. For this purpose, surface and subsurface soil samples from 175 locations from a fruit orchard spreading over 14.33 ha area were collected along with geographical coordinates. SOC content and soil physicochemical properties of collected soil samples were determined followed by geostatistical analysis for mapping purposes. Average SOC stock density of the orchard was 14.48 Mg ha(-1) for 0- to 30-cm soil layer ranging from 9.01 Mg ha(-1) in Carissa carandas to 19.52 Mg ha(-1) in Prosopis cineraria block. Range of spatial variation of SOC content was found about 100 m, while two other soil physicochemical properties, e.g., pH and electrical conductivity (EC) also showed similar spatial trend. This indicated that minimum sampling distance for future SOC mapping programme may be kept lower than 100 m for better accuracy. Ordinary kriging technique satisfactorily predicted SOC contents (in percent) at unsampled locations with root-mean-squared residual (RMSR) of 0.35-0.37. Co-kriging approach was found slightly superior (RMSR = 0.26-0.28) than ordinary kriging for spatial prediction of SOC contents because of significant correlations of SOC contents with pH and EC. Uncertainty of SOC estimation was also presented in terms of 90 % confidence interval. Spatial estimates of SOC stock through ordinary kriging or co-kriging approach were also found with low uncertainty of estimation than non-spatial estimates, e.g., arithmetic averaging approach. Among different fruit block plantations of the orchard, the block with Prosopis cineraria ('khejri') has higher SOC stock density than others.

Evaluation of thermal X/5-detector Skylab S-192 data for estimating evapotranspiration and thermal properties of soils for irrigation management

NASA Technical Reports Server (NTRS)

Moore, D. G.; Horton, M. L.; Russell, M. J.; Myers, V. I.

1975-01-01

An energy budget approach to evaluating the SKYLAB X/5-detector S-192 data for prediction of soil moisture and evapotranspiration rate was pursued. A test site which included both irrigated and dryland agriculture in Southern Texas was selected for the SL-4 SKYLAB mission. Both vegetated and fallow fields were included. Data for a multistage analysis including ground, NC-130B aircraft, RB-57F aircraft, and SKYLAB altitudes were collected. The ground data included such measurements as gravimetric soil moisture, percent of the ground covered by green vegetation, soil texture, net radiation, soil temperature gradients, surface emittance, soil heat flux, air temperature and humidity gradients, and cultural practices. Ground data were used to characterize energy budgets and to evaluate the utility of an energy budget approach for determining soil moisture differences among twelve specific agricultural fields.

Prediction of Ba, Mn and Zn for tropical soils using iron oxides and magnetic susceptibility

NASA Astrophysics Data System (ADS)

Marques Júnior, José; Arantes Camargo, Livia; Reynaldo Ferracciú Alleoni, Luís; Tadeu Pereira, Gener; De Bortoli Teixeira, Daniel; Santos Rabelo de Souza Bahia, Angelica

2017-04-01

Agricultural activity is an important source of potentially toxic elements (PTEs) in soil worldwide but particularly in heavily farmed areas. Spatial distribution characterization of PTE contents in farming areas is crucial to assess further environmental impacts caused by soil contamination. Designing prediction models become quite useful to characterize the spatial variability of continuous variables, as it allows prediction of soil attributes that might be difficult to attain in a large number of samples through conventional methods. This study aimed to evaluate, in three geomorphic surfaces of Oxisols, the capacity for predicting PTEs (Ba, Mn, Zn) and their spatial variability using iron oxides and magnetic susceptibility (MS). Soil samples were collected from three geomorphic surfaces and analyzed for chemical, physical, mineralogical properties, as well as magnetic susceptibility (MS). PTE prediction models were calibrated by multiple linear regression (MLR). MLR calibration accuracy was evaluated using the coefficient of determination (R2). PTE spatial distribution maps were built using the values calculated by the calibrated models that reached the best accuracy by means of geostatistics. The high correlations between the attributes clay, MS, hematite (Hm), iron oxides extracted by sodium dithionite-citrate-bicarbonate (Fed), and iron oxides extracted using acid ammonium oxalate (Feo) with the elements Ba, Mn, and Zn enabled them to be selected as predictors for PTEs. Stepwise multiple linear regression showed that MS and Fed were the best PTE predictors individually, as they promoted no significant increase in R2 when two or more attributes were considered together. The MS-calibrated models for Ba, Mn, and Zn prediction exhibited R2 values of 0.88, 0.66, and 0.55, respectively. These are promising results since MS is a fast, cheap, and non-destructive tool, allowing the prediction of a large number of samples, which in turn enables detailed mapping of large areas. MS predicted values enabled the characterization and the understanding of spatial variability of the studied PTEs.

Effects of meteorological models on the solution of the surface energy balance and soil temperature variations in bare soils

NASA Astrophysics Data System (ADS)

Saito, Hirotaka; Šimůnek, Jiri

2009-07-01

SummaryA complete evaluation of the soil thermal regime can be obtained by evaluating the movement of liquid water, water vapor, and thermal energy in the subsurface. Such an evaluation requires the simultaneous solution of the system of equations for the surface water and energy balance, and subsurface heat transport and water flow. When only daily climatic data is available, one needs not only to estimate diurnal cycles of climatic data, but to calculate the continuous values of various components in the energy balance equation, using different parameterization methods. The objective of this study is to quantify the impact of the choice of different estimation and parameterization methods, referred together to as meteorological models in this paper, on soil temperature predictions in bare soils. A variety of widely accepted meteorological models were tested on the dataset collected at a proposed low-level radioactive-waste disposal site in the Chihuahua Desert in West Texas. As the soil surface was kept bare during the study, no vegetation effects were evaluated. A coupled liquid water, water vapor, and heat transport model, implemented in the HYDRUS-1D program, was used to simulate diurnal and seasonal soil temperature changes in the engineered cover installed at the site. The modified version of HYDRUS provides a flexible means for using various types of information and different models to evaluate surface mass and energy balance. Different meteorological models were compared in terms of their prediction errors for soil temperatures at seven observation depths. The results obtained indicate that although many available meteorological models can be used to solve the energy balance equation at the soil-atmosphere interface in coupled water, vapor, and heat transport models, their impact on overall simulation results varies. For example, using daily average climatic data led to greater prediction errors, while relatively simple meteorological models may significantly improve soil temperature predictions. On the other hand, while models for the albedo and soil emissivity had little impact on soil temperature predictions, the choice of the atmospheric emissivity models had a greater impact. A comparison of all the different models indicates that the error introduced at the soil atmosphere interface propagates to deeper layers. Therefore, attention needs to be paid not only to the precise determination of the soil hydraulic and thermal properties, but also to the selection of proper meteorological models for the components involved in the surface energy balance calculations.

Target Soil Impact Verification: Experimental Testing and Kayenta Constitutive Modeling.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Broome, Scott Thomas; Flint, Gregory Mark; Dewers, Thomas

2015-11-01

This report details experimental testing and constitutive modeling of sandy soil deformation under quasi - static conditions. This is driven by the need to understand constitutive response of soil to target/component behavior upon impact . An experimental and constitutive modeling program was followed to determine elastic - plastic properties and a compressional failure envelope of dry soil . One hydrostatic, one unconfined compressive stress (UCS), nine axisymmetric compression (ACS) , and one uniaxial strain (US) test were conducted at room temperature . Elastic moduli, assuming isotropy, are determined from unload/reload loops and final unloading for all tests pre - failuremore » and increase monotonically with mean stress. Very little modulus degradation was discernable from elastic results even when exposed to mean stresses above 200 MPa . The failure envelope and initial yield surface were determined from peak stresses and observed onset of plastic yielding from all test results. Soil elasto - plastic behavior is described using the Brannon et al. (2009) Kayenta constitutive model. As a validation exercise, the ACS - parameterized Kayenta model is used to predict response of the soil material under uniaxial strain loading. The resulting parameterized and validated Kayenta model is of high quality and suitable for modeling sandy soil deformation under a range of conditions, including that for impact prediction.« less

Simulating Soil C Stock with the Process-based Model CQESTR

NASA Astrophysics Data System (ADS)

Gollany, H.; Liang, Y.; Rickman, R.; Albrecht, S.; Follett, R.; Wilhelm, W.; Novak, J.; Douglas, C.

2009-04-01

The prospect of storing carbon (C) in soil, as soil organic matter (SOM), provides an opportunity for agriculture to contribute to the reduction of carbon dioxide in the atmosphere while enhancing soil properties. Soil C models are useful for examining the complex interactions between crop, soil management practices and climate and their effects on long-term carbon storage or loss. The process-based carbon model CQESTR, pronounced ‘sequester,' was developed by USDA-ARS scientists at the Columbia Plateau Conservation Research Center, Pendleton, Oregon, USA. It computes the rate of biological decomposition of crop residues or organic amendments as they convert to SOM. CQESTR uses readily available field-scale data to assess long-term effects of cropping systems or crop residue removal on SOM accretion/loss in agricultural soil. Data inputs include weather, above- ground and below-ground biomass additions, N content of residues and amendments, soil properties, and management factors such as tillage and crop rotation. The model was calibrated using information from six long-term experiments across North America (Florence, SC, 19 yrs; Lincoln, NE, 26 yrs; Hoytville, OH, 31 yrs; Breton, AB, 60 yrs; Pendleton, OR, 76 yrs; and Columbia, MO, >100 yrs) having a range of soil properties and climate. CQESTR was validated using data from several additional long-term experiments (8 - 106 yrs) across North America having a range of SOM (7.3 - 57.9 g SOM/kg). Regression analysis of 306 pairs of predicted and measured SOM data under diverse climate, soil texture and drainage classes, and agronomic practices at 13 agricultural sites resulted in a linear relationship with an r2 of 0.95 (P < 0.0001) and a 95% confidence interval of 4.3 g SOM/kg. Estimated SOC values from CQESTR and IPCC (the Intergovernmental Panel on Climate Change) were compared to observed values in three relatively long-term experiments (20 - 24 years). At one site, CQESTR and IPCC estimates of SOC stocks were within 5% of each other for three rotations. At a second site, decreasing tillage intensity increased SOC stocks for winter wheat-fallow rotation for both observed and estimated values by CQESTR and IPCC. At the third site, CQESTR simulated an increase in SOC stocks with increased fertility levels, while IPCC estimates of SOC stocks did not reflect an increase. The CQESTR model successfully predicts SOM dynamics from various management practices and offers the potential for C sequestration planning for C credits or to guide crop residue removal for bio-energy production without degrading the soil resource, environmental quality, or productivity.

Effect of suction-dependent soil deformability on landslide susceptibility maps

NASA Astrophysics Data System (ADS)

Lizarraga, Jose J.; Buscarnera, Giuseppe; Frattini, Paolo; Crosta, Giovanni B.

2016-04-01

This contribution presents a physically-based, spatially-distributed model for shallow landslides promoted by rainfall infiltration. The model features a set of Factor of Safety values aimed to capture different failure mechanisms, namely frictional slips with limited mobility and flowslide events associated with the liquefaction of the considered soils. Indices of failure associated with these two modes of instability have been derived from unsaturated soil stability principles. In particular, the propensity to wetting-induced collapse of unsaturated soils is quantified through the introduction of a rigid-plastic model with suction-dependent yielding and strength properties. The model is combined with an analytical approach (TRIGRS) to track the spatio-temporal evolution of soil suction in slopes subjected to transient infiltration. The model has been tested to reply the triggering of shallow landslides in pyroclastic deposits in Sarno (1998, Campania Region, Southern Italy). It is shown that suction-dependent mechanical properties, such as soil deformability, have important effects on the predicted landslide susceptibility scenarios, resulting on computed unstable zones that may encompass a wide range of slope inclinations, saturation levels, and depths. Such preliminary results suggest that the proposed methodology offers an alternative mechanistic interpretation to the variability in behavior of rainfall-induced landslides. Differently to standard methods the explanation to this variability is based on suction-dependent soil behavior characteristics.

Alterations of hydraulic soil properties influenced by land-use changes and agricultural management systems

NASA Astrophysics Data System (ADS)

Weninger, Thomas; Kreiselmeier, Janis; Chandrasekhar, Parvathy; Jülich, Stefan; Schwärzel, Kai; Schwen, Andreas

2016-04-01

Estimation and modeling of soil water movement and the hydrologic balance of soils requires sound knowledge about hydraulic soil properties (HSP). The soil water characteristics, the hydraulic conductivity function and the pore size distribution (PSD) are commonly used instruments for the mathematical representation of HSP. Recent research highlighted the temporal variability of these functions caused by meteorological or land-use influences. State of the art modeling software for the continuous simulation of soil water movement uses a stationary approach for the HSP which means that their time dependent alterations and the subsequent effects on soil water balance is not considered. Mathematical approaches to describe the evolution of PSD are nevertheless known, but there is a lack of sound data basis for parameter estimation. Based on extensive field and laboratory measurements at 5 locations along a climatic gradient across Austria and Germany, this study will quantify short-term changes in HSP, detect driving forces and introduce a method to predict the effects of soil and land management actions on the soil water balance. Amongst several soil properties, field-saturated and unsaturated hydraulic conductivities will be determined using a hood infiltration experiments in the field as well as by evaporation and dewpoint potentiometer method in the lab. All measurements will be carried out multiple times over a span of 2 years which will allow a detailed monitoring of changes in HSP. Experimental sites where we expect significant inter-seasonal changes will be equipped with sensors for soil moisture and matric potential. The choice of experimental field sites follows the intention to involve especially the effects of tillage operations, different cultivation strategies, microclimatically effective structures and land-use changes. The international project enables the coverage of a broad range of soil types as well as climate conditions and hence will have broad applicability of the implemented model modifications.

Visible and infrared spectroscopy to evaluate soil quality in degraded sites: an applicative study in southern Italy

NASA Astrophysics Data System (ADS)

Ancona, Valeria; Matarrese, Raffaella; Salvatori, Rosamaria; Salzano, Roberto; Regano, Simona; Calabrese, Angelantonio; Campanale, Claudia; Felice Uricchio, Vito

2014-05-01

Land degradation processes like organic matter impoverishment and contamination are growing increasingly all over the world due to a non-rational and often sustainable spread of human activities on the territory. Consequently the need to characterize and monitor degraded sites is becoming very important, with the aim to hinder such main threats, which could compromise drastically, soil quality. Visible and infrared spectroscopy is a well-known technique/tool to study soil properties. Vis-NIR spectral reflectance, in fact, can be used to characterize spatial and temporal variation in soil constituents (Brown et al., 2006; Viscarra Rossel et al., 2006), and potentially its surface structure (Chappell et al., 2006, 2007). It is a rapid, non-destructive, reproducible and cost-effective analytical method to analyse soil properties and therefore, it can be a useful method to study land degradation phenomena. In this work, we present the results of proximal sensing investigations of three degraded sites (one affected by organic and inorganic contamination and two affected by soil organic matter decline) situated southern Italy close to Taranto city (in Apulia Region). A portable spectroradiometer (ASD-FieldSpec) was used to measure the reflectance properties in the spectral range between 350-2500 nm of the soil, in the selected sites, before and after a recovery treatment by using compost (organic fertilizer). For each measurement point the soil was sampled in order to perform chemical analyses to evaluate soil quality status. Three in-situ campaigns have been carried out (September 2012, June 2013, and September 2013), collecting about 20 soil samples for each site and for each campaign. Chemical and spectral analyses have been focused on investigating soil organic carbon, carbonate content, texture and, in the case of polluted site, heavy metals and organic toxic compounds. Statistical analyses have been carried out to test a prediction model of different soil quality indicators based on the spectral signatures behaviour of each sample ranging.

Soil physical conditions as livestock treading effect in tropical Agroecosystem of dryland and strategies to mitigate desertification risk

NASA Astrophysics Data System (ADS)

Florentino, A.; Torres, D.; Ospina, A.; Contreras, J.; Palma, Z.; Silvera, J.

2012-04-01

Soil degradation in natural ecosystem of arid and semi-arid zones of Venezuela due to livestock treading (goats) it is an important problem that affect their environment functions; increase soil erodibility, bulk density, water losses and reduce porosity, water infiltration rate and soil structural stability. The presence of biological crust (BSC) in this type of soil it is very common. The objective of this study was to evaluate the soil surface physical quality through the use of selected indicators, mainly some of that related to structural stability, infiltrability and the prediction of soil erosion risk in two zones of Lara state: 1) Quíbor (QUI) and 2) Humocaro Bajo (HB). The study was conducted on two selected plots (30 m x 20 m) in each zone, with natural vegetation and BSC cover, with areas affected by different degree of compaction due to treading in the paths where the goats are moving. Five sites per plot (50 cm x 50 cm) under vegetation cover and five sites over the path with bare soil were sampled (0-7,5 and 7,5-15 cm depth). The results showed that soil macroaggregate stability (equivalent diameter of aggregates >0,25 mm) was significantly higher (p<0,05 %) in soil with vegetation cover and BSC compared with bare soil. Sealing index, as a measure of aggregate stability, determined in laboratory under simulated rain and expressed as hydraulic conductivity of soil surface sealing (Kse), decreased with decreasing soil vegetation cover and the presence of BSC. However, Ksei (i: inicial) and Ksef (f: final) were significantly greater in soil with more than 75 % of BSC in comparison to bare soils. The sealing index it is used to for to estimate changes in soil water losses. As the sealing index increases, the susceptibility of the soil to undergo surface sealing or slaking decrease. These results suggested that soil physical properties are potential indicators of soil quality with regard to soil erodibility and showed that soils under vegetation cover had higher quality level than bare soils. Some predictive regression equation had a high R2 value and was a useful tool for to evaluate the risk of extreme climatic changes and to mitigate their detrimental effects. We conclude that the global climatic change (CCG) will have a negative effect on these agroecosystems functions, mainly in soil and water conservation, carbon sequestration, and productivity. Natural recovery of soil physical properties from treading damage of pastoral soils will be possible in the future with the implementation of soil management strategies, mainly through re-vegetation and recuperation of the BSC. Key word: Soil structure; aggregate stability; soil sealing index; hydraulic conductivity of surface sealing.

Three-parameter modeling of the soil sorption of acetanilide and triazine herbicide derivatives.

PubMed

Freitas, Mirlaine R; Matias, Stella V B G; Macedo, Renato L G; Freitas, Matheus P; Venturin, Nelson

2014-02-01

Herbicides have widely variable toxicity and many of them are persistent soil contaminants. Acetanilide and triazine family of herbicides have widespread use, but increasing interest for the development of new herbicides has been rising to increase their effectiveness and to diminish environmental hazard. The environmental risk of new herbicides can be accessed by estimating their soil sorption (logKoc), which is usually correlated to the octanol/water partition coefficient (logKow). However, earlier findings have shown that this correlation is not valid for some acetanilide and triazine herbicides. Thus, easily accessible quantitative structure-property relationship models are required to predict logKoc of analogues of the these compounds. Octanol/water partition coefficient, molecular weight and volume were calculated and then regressed against logKoc for two series of acetanilide and triazine herbicides using multiple linear regression, resulting in predictive and validated models.

Pier and contraction scour prediction in cohesive soils at selected bridges in Illinois

USGS Publications Warehouse

Straub, Timothy D.; Over, Thomas M.

2010-01-01

This report presents the results of testing the Scour Rate In Cohesive Soils-Erosion Function Apparatus (SRICOS-EFA) method for estimating scour depth of cohesive soils at 15 bridges in Illinois. The SRICOS-EFA method for complex pier and contraction scour in cohesive soils has two primary components. The first component includes the calculation of the maximum contraction and pier scour (Zmax). The second component is an integrated approach that considers a time factor, soil properties, and continued interaction between the contraction and pier scour (SRICOS runs). The SRICOS-EFA results were compared to scour prediction results for non-cohesive soils based on Hydraulic Engineering Circular No. 18 (HEC-18). On average, the HEC-18 method predicted higher scour depths than the SRICOS-EFA method. A reduction factor was determined for each HEC-18 result to make it match the maximum of three types of SRICOS run results. The unconfined compressive strength (Qu) for the soil was then matched with the reduction factor and the results were ranked in order of increasing Qu. Reduction factors were then grouped by Qu and applied to each bridge site and soil. These results, and comparison with the SRICOS Zmax calculation, show that less than half of the reduction-factor method values were the lowest estimate of scour; whereas, the Zmax method values were the lowest estimate for over half. A tiered approach to predicting pier and contraction scour was developed. There are four levels to this approach numbered in order of complexity, with the fourth level being a full SRICOS-EFA analysis. Levels 1 and 2 involve the reduction factors and Zmax calculation, and can be completed without EFA data. Level 3 requires some surrogate EFA data. Levels 3 and 4 require streamflow for input into SRICOS. Estimation techniques for both EFA surrogate data and streamflow data were developed.

Forms of organic phosphorus in wetland soils

NASA Astrophysics Data System (ADS)

Cheesman, A. W.; Turner, B. L.; Reddy, K. R.

2014-12-01

Phosphorus (P) cycling in freshwater wetlands is dominated by biological mechanisms, yet there has been no comprehensive examination of the forms of biogenic P (i.e., forms derived from biological activity) in wetland soils. We used solution 31P NMR spectroscopy to identify and quantify P forms in surface soils of 28 palustrine wetlands spanning a range of climatic, hydrogeomorphic, and vegetation types. Total P concentrations ranged between 51 and 3516 μg P g-1, of which an average of 58% was extracted in a single-step NaOH-EDTA procedure. The extracts contained a broad range of P forms, including phosphomonoesters (averaging 24% of the total soil P), phosphodiesters (averaging 10% of total P), phosphonates (up to 4% of total P), and both pyrophosphate and long-chain polyphosphates (together averaging 6% of total P). Soil P composition was found to be dependant upon two key biogeochemical properties: organic matter content and pH. For example, stereoisomers of inositol hexakisphosphate were detected exclusively in acidic soils with high mineral content, while phosphonates were detected in soils from a broad range of vegetation and hydrogeomorphic types but only under acidic conditions. Conversely inorganic polyphosphates occurred in a broad range of wetland soils, and their abundance appears to reflect more broadly that of a "substantial" and presumably active microbial community with a significant relationship between total inorganic polyphosphates and microbial biomass P. We conclude that soil P composition varies markedly among freshwater wetlands but can be predicted by fundamental soil properties.

How far are rheological parameters from amplitude sweep tests predictable using common physicochemical soil properties?

NASA Astrophysics Data System (ADS)

Stoppe, N.; Horn, R.

2017-01-01

A basic understanding of soil behavior on the mesoscale resp. macroscale (i.e. soil aggregates resp. bulk soil) requires knowledge of the processes at the microscale (i.e. particle scale), therefore rheological investigations of natural soils receive growing attention. In the present research homogenized and sieved (< 2 mm) samples from Marshland soils of the riparian zone of the River Elbe (North Germany) were analyzed with a modular compact rheometer MCR 300 (Anton Paar, Ostfildern, Germany) with a profiled parallel-plate measuring system. Amplitude sweep tests (AST) with controlled shear deformation were conducted to investigate the viscoelastic properties of the studied soils under oszillatory stress. The gradual depletion of microstructural stiffness during AST cannot only be characterized by the well-known rheological parameters G, G″ and tan δ but also by the dimensionless area parameter integral z, which quantifies the elasticity of microstructure. To discover the physicochemical parameters, which influences the microstructural stiffness, statistical tests were used taking the combined effects of these parameters into account. Although the influence of the individual factors varies depending on soil texture, the physicochemical features significantly affecting soil micro structure were identified. Based on the determined statistical relationships between rheological and physicochemical parameters, pedotransfer functions (PTF) have been developed, which allow a mathematical estimation of the rheological target value integral z. Thus, stabilizing factors are: soil organic matter, concentration of Ca2+, content of CaCO3 and pedogenic iron oxides; whereas the concentration of Na+ and water content represent structurally unfavorable factors.

Impact of alpine meadow degradation on soil hydraulic properties over the Qinghai-Tibetan Plateau

NASA Astrophysics Data System (ADS)

Zeng, Chen; Zhang, Fan; Wang, Quanjiu; Chen, Yingying; Joswiak, Daniel R.

2013-01-01

SummaryAlpine meadow soil is an important ecosystem component of the Qinghai-Tibetan Plateau. However, the alpine meadow soil is undergoing serious degradation mainly due to global climate change, overgrazing, human activities and rodents. In this paper, spatial sequencing was chosen over time succession sequencing to study the changes of soil hydraulic properties under different degrees of alpine meadow degradation. Soil saturated hydraulic conductivity (Ks) and Gardner α both at the surface and at 40-50 cm depth were investigated in the field using tension infiltrometers. Soil physical and chemical properties, together with the root index at 0-10 cm and 40-50 cm soil layer depths were also analyzed. Pearson correlations were adopted to study the relationships among the investigated factors and principal component analysis was performed to identify the dominant factor. Results show that with increasing degree of degradation, soil sand content increased while soil Ks and Gardner α as well as soil clay content, soil porosity decreased in the 0-10 cm soil layers, and organic matter and root gravimetric density decreased in both the 0-10 cm and 40-50 cm soil layers. However, soil moisture showed no significant changes with increasing degradation. With decreasing pressure head, soil unsaturated hydraulic conductivity reduced more slowly under degraded conditions than non-degraded conditions. Soil Ks and Gardner α were significantly correlated (P = 0.01) with bulk density, soil porosity, soil organic matter and root gravimetric density. Among these, soil porosity is the dominant factor explaining about 90% of the variability in total infiltration flow. Under non-degraded conditions, the infiltration flow principally depended on the presence of macropores. With increasing degree of degradation, soil macropores quickly changed to mesopores or micropores. The proportion of total infiltration flow through macropores and mesopores significantly decreased with the most substantial decrease observed for the macropores in the 0-10 cm soil layer. The substantial decrease of macropores caused a cut in soil moisture and hydraulic conductivity. This study improves the understanding and prediction of alpine meadow soil and ecosystem changes and provides guidelines for improving water flow modeling under the background of global climate change over the Qinghai-Tibetan Plateau and similar regions.

A simple biosphere model (SiB) for use within general circulation models

NASA Technical Reports Server (NTRS)

Sellers, P. J.; Mintz, Y.; Sud, Y. C.; Dalcher, A.

1986-01-01

A simple realistic biosphere model for calculating the transfer of energy, mass and momentum between the atmosphere and the vegetated surface of the earth has been developed for use in atmospheric general circulation models. The vegetation in each terrestrial model grid is represented by an upper level, representing the perennial canopy of trees and shrubs, and a lower level, representing the annual cover of grasses and other heraceous species. The vegetation morphology and the physical and physiological properties of the vegetation layers determine such properties as: the reflection, transmission, absorption and emission of direct and diffuse radiation; the infiltration, drainage, and storage of the residual rainfall in the soil; and the control over the stomatal functioning. The model, with prescribed vegetation parameters and soil interactive soil moisture, can be used for prediction of the atmospheric circulation and precipitaion fields for short periods of up to a few weeks.

Soil process-oriented modelling of within-field variability based on high-resolution 3D soil type distribution maps.

NASA Astrophysics Data System (ADS)

Bönecke, Eric; Lück, Erika; Gründling, Ralf; Rühlmann, Jörg; Franko, Uwe

2016-04-01

Today, the knowledge of within-field variability is essential for numerous purposes, including practical issues, such as precision and sustainable soil management. Therefore, process-oriented soil models have been applied for a considerable time to answer question of spatial soil nutrient and water dynamics, although, they can only be as consistent as their variation and resolution of soil input data. Traditional approaches, describe distribution of soil types, soil texture or other soil properties for greater soil units through generalised point information, e.g. from classical soil survey maps. Those simplifications are known to be afflicted with large uncertainties. Varying soil, crop or yield conditions are detected even within such homogenised soil units. However, recent advances of non-invasive soil survey and on-the-go monitoring techniques, made it possible to obtain vertical and horizontal dense information (3D) about various soil properties, particularly soil texture distribution which serves as an essential soil key variable affecting various other soil properties. Thus, in this study we based our simulations on detailed 3D soil type distribution (STD) maps (4x4 m) to adjacently built-up sufficient informative soil profiles including various soil physical and chemical properties. Our estimates of spatial STD are based on high-resolution lateral and vertical changes of electrical resistivity (ER), detected by a relatively new multi-sensor on-the-go ER monitoring device. We performed an algorithm including fuzzy-c-mean (FCM) logic and traditional soil classification to estimate STD from those inverted and layer-wise available ER data. STD is then used as key input parameter for our carbon, nitrogen and water transport model. We identified Pedological horizon depths and inferred hydrological soil variables (field capacity, permanent wilting point) from pedotransferfunctions (PTF) for each horizon. Furthermore, the spatial distribution of soil organic carbon (SOC), as essential input variable, was predicted by measured soil samples and associated to STD of the upper 30 cm. The comprehensive and high-resolution (4x4 m) soil profile information (up to 2 m soil depth) were then used to initialise a soil process model (Carbon and Nitrogen Dynamics - CANDY) for soil functional modelling (daily steps of matter fluxes, soil temperature and water balances). Our study was conducted on a practical field (~32,000 m²) of an agricultural farm in Central Germany with Chernozem soils under arid conditions (average rainfall < 550 mm). This soil region is known to have differences in soil structure mainly occurring within the subsoil, since topsoil conditions are described as homogenous. The modelled soil functions considered local climate information and practical farming activities. Results show, as expected, distinguished functional variability, both on spatial and temporal resolution for subsoil evident structures, e.g. visible differences for available water capacity within 0-100 cm but homogenous conditions for the topsoil.

Highly spatially- and seasonally-resolved predictive contamination maps for persistent organic pollutants: development and validation.

PubMed

Ballabio, Cristiano; Guazzoni, Niccoló; Comolli, Roberto; Tremolada, Paolo

2013-08-01

A reliable spatial assessment of the POPs contamination in soils is essential for burden studies and flux evaluations. Soil characteristics and properties vary enormously even within small spatial scale and over time; therefore soil capacity of accumulating POPs varies greatly. In order to include this very high spatial and temporal variability, models can be used for assessing soil accumulation capacity in a specific time and space and, from it, the spatial distribution and temporal trends of POPs concentrations. In this work, predictive contamination maps of the accumulation capacity of soils were developed at a space resolution of 1×1m with a time frame of one day, in a study area located in the central Alps. Physical algorithms for temperature and organic carbon estimation along the soil profile and across the year were fitted to estimate the horizontal, vertical and seasonal distribution of the contamination potential for PCBs in soil (Ksa maps). The resulting maps were cross-validated with an independent set of PCB contamination data, showing very good agreement (e.g. for CB-153, R(2)=0.80, p-value≤2.2·10(-06)). Slopes of the regression between predicted Ksa and experimental concentrations were used to map the soil contamination for the whole area, taking into account soil characteristics and temperature conditions. These maps offer the opportunity to evaluate burden (concentration maps) and fluxes (emission maps) with highly resolved temporal and spatial detail. In addition, in order to explain the observed low autumn PCB concentrations in soil related to the high Ksa values of this period, a dynamic model of seasonal variation of soil concentrations was developed basing on rate parameters fitted on measured concentrations. The model was able to describe, at least partially, the observed different behavior between the quite rapid discharge phase in summer and the slow recharge phase in autumn. Copyright © 2013 Elsevier B.V. All rights reserved.

Spatial disaggregation of complex soil map units at regional scale based on soil-landscape relationships

NASA Astrophysics Data System (ADS)

Vincent, Sébastien; Lemercier, Blandine; Berthier, Lionel; Walter, Christian

2015-04-01

Accurate soil information over large extent is essential to manage agronomical and environmental issues. Where it exists, information on soil is often sparse or available at coarser resolution than required. Typically, the spatial distribution of soil at regional scale is represented as a set of polygons defining soil map units (SMU), each one describing several soil types not spatially delineated, and a semantic database describing these objects. Delineation of soil types within SMU, ie spatial disaggregation of SMU allows improved soil information's accuracy using legacy data. The aim of this study was to predict soil types by spatial disaggregation of SMU through a decision tree approach, considering expert knowledge on soil-landscape relationships embedded in soil databases. The DSMART (Disaggregation and Harmonization of Soil Map Units Through resampled Classification Trees) algorithm developed by Odgers et al. (2014) was used. It requires soil information, environmental covariates, and calibration samples, to build then extrapolate decision trees. To assign a soil type to a particular spatial position, a weighed random allocation approach is applied: each soil type in the SMU is weighted according to its assumed proportion of occurrence in the SMU. Thus soil-landscape relationships are not considered in the current version of DSMART. Expert rules on soil distribution considering the relief, parent material and wetlands location were proposed to drive the procedure of allocation of soil type to sampled positions, in order to integrate the soil-landscape relationships. Semantic information about spatial organization of soil types within SMU and exhaustive landscape descriptors were used. In the eastern part of Brittany (NW France), 171 soil types were described; their relative area in the SMU were estimated, geomorphological and geological contexts were recorded. The model predicted 144 soil types. An external validation was performed by comparing predicted with effectively observed soil types derived from available soil maps at scale of 1:25.000 or 1:50.000. Overall accuracies were 63.1% and 36.2%, respectively considering or not the adjacent pixels. The introduction of expert rules based on soil-landscape relationships to allocate soil types to calibration samples enhanced dramatically the results in comparison with a simple weighted random allocation procedure. It also enabled the production of a comprehensive soil map, retrieving expected spatial organization of soils. Estimation of soil properties for various depths is planned using disaggregated soil types, according to the GlobalSoilmap.net specifications. Odgers, N.P., Sun, W., McBratney, A.B., Minasny, B., Clifford, D., 2014. Disaggregating and harmonising soil map units through resampled classification trees. Geoderma 214, 91-100.

The effect of soil properties on the toxicity of silver to the soil nitrification process.

PubMed

Langdon, Kate A; McLaughlin, Mike J; Kirby, Jason K; Merrington, Graham

2014-05-01

Silver (Ag) is being increasingly used in a range of consumer products, predominantly as an antimicrobial agent, leading to a higher likelihood of its release into the environment. The present study investigated the toxicity of Ag to the nitrification process in European and Australian soils in both leached and unleached conditions. Overall, leaching of soils was found to have a minimal effect on the final toxicity data, with an average leaching factor of approximately 1. Across the soils, the toxicity was found to vary by several orders of magnitude, with concentrations of Ag causing a 50% reduction in nitrification relative to the controls (EC50) ranging from 0.43 mg Ag/kg to >640 mg Ag/kg. Interestingly, the dose-response relationships in most of the soils showed significant stimulation in nitrification at low Ag concentrations (i.e., hormesis), which in some cases produced responses up to double that observed in the controls. Soil pH and organic carbon were the properties found to have the greatest influence on the variations in toxicity thresholds across the soils, and significant relationships were developed that accounted for approximately 90% of the variability in the data. The toxicity relationships developed from the present study will assist in future assessment of potential Ag risks and enable the site-specific prediction of Ag toxicity. © 2014 SETAC.

Cracking up (and down): Linking multi-domain hydraulic properties with multi-scale hydrological processes in shrink-swell soils

NASA Astrophysics Data System (ADS)

Stewart, R. D.; Rupp, D. E.; Abou Najm, M. R.; Selker, J. S.

2017-12-01

Shrink-swell soils, often classified as Vertisols or vertic intergrades, are found on every continent except Antarctica and within many agricultural and urban regions. These soils are characterized by cyclical shrinking and swelling, in which bulk density and porosity distributions vary as functions of time and soil moisture. Crack networks that form in these soils act as dominant environmental controls on the movement of water, contaminants, and gases, making it important to develop fundamental understanding and tractable models of their hydrologic characteristics and behaviors. In this study, which took place primarily in the Secano Interior region of South-Central Chile, we quantified soil-water interactions across scales using a diverse and innovative dataset. These measurements were then utilized to develop a set of parsimonious multi-domain models for describing hydraulic properties and hydrological processes in shrink-swell soils. In a series of examples, we show how this model can predict porosity distributions, crack widths, saturated hydraulic conductivities, and surface runoff (i.e., overland flow) thresholds, by capturing the dominant mechanisms by which water moves through and interacts with clayey soils. Altogether, these models successfully link small-scale shrinkage/swelling behaviors with large-scale thresholds, and can be applied to describe important processes such as infiltration, overland flow development, and the preferential flow and transport of fluids and gases.

Prediction of zeolite-cement-sand unconfined compressive strength using polynomial neural network

NASA Astrophysics Data System (ADS)

MolaAbasi, H.; Shooshpasha, I.

2016-04-01

The improvement of local soils with cement and zeolite can provide great benefits, including strengthening slopes in slope stability problems, stabilizing problematic soils and preventing soil liquefaction. Recently, dosage methodologies are being developed for improved soils based on a rational criterion as it exists in concrete technology. There are numerous earlier studies showing the possibility of relating Unconfined Compressive Strength (UCS) and Cemented sand (CS) parameters (voids/cement ratio) as a power function fits. Taking into account the fact that the existing equations are incapable of estimating UCS for zeolite cemented sand mixture (ZCS) well, artificial intelligence methods are used for forecasting them. Polynomial-type neural network is applied to estimate the UCS from more simply determined index properties such as zeolite and cement content, porosity as well as curing time. In order to assess the merits of the proposed approach, a total number of 216 unconfined compressive tests have been done. A comparison is carried out between the experimentally measured UCS with the predictions in order to evaluate the performance of the current method. The results demonstrate that generalized polynomial-type neural network has a great ability for prediction of the UCS. At the end sensitivity analysis of the polynomial model is applied to study the influence of input parameters on model output. The sensitivity analysis reveals that cement and zeolite content have significant influence on predicting UCS.

Temperature Sensitivity as a Microbial Trait Using Parameters from Macromolecular Rate Theory

PubMed Central

Alster, Charlotte J.; Baas, Peter; Wallenstein, Matthew D.; Johnson, Nels G.; von Fischer, Joseph C.

2016-01-01

The activity of soil microbial extracellular enzymes is strongly controlled by temperature, yet the degree to which temperature sensitivity varies by microbe and enzyme type is unclear. Such information would allow soil microbial enzymes to be incorporated in a traits-based framework to improve prediction of ecosystem response to global change. If temperature sensitivity varies for specific soil enzymes, then determining the underlying causes of variation in temperature sensitivity of these enzymes will provide fundamental insights for predicting nutrient dynamics belowground. In this study, we characterized how both microbial taxonomic variation as well as substrate type affects temperature sensitivity. We measured β-glucosidase, leucine aminopeptidase, and phosphatase activities at six temperatures: 4, 11, 25, 35, 45, and 60°C, for seven different soil microbial isolates. To calculate temperature sensitivity, we employed two models, Arrhenius, which predicts an exponential increase in reaction rate with temperature, and Macromolecular Rate Theory (MMRT), which predicts rate to peak and then decline as temperature increases. We found MMRT provided a more accurate fit and allowed for more nuanced interpretation of temperature sensitivity in all of the enzyme × isolate combinations tested. Our results revealed that both the enzyme type and soil isolate type explain variation in parameters associated with temperature sensitivity. Because we found temperature sensitivity to be an inherent and variable property of an enzyme, we argue that it can be incorporated as a microbial functional trait, but only when using the MMRT definition of temperature sensitivity. We show that the Arrhenius metrics of temperature sensitivity are overly sensitive to test conditions, with activation energy changing depending on the temperature range it was calculated within. Thus, we propose the use of the MMRT definition of temperature sensitivity for accurate interpretation of temperature sensitivity of soil microbial enzymes. PMID:27909429

Sorption of organic carbon compounds to the fine fraction of surface and Subsurface Soils

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jagadamma, Sindhu; Mayes, Melanie; Zinn, Yuri

2014-01-01

Dissolved organic carbon (DOC) transported from the soil surface is stabilized in deeper soil profiles by physicochemical sorption processes. However, it is unclear how different forms of organic carbon (OC) compounds common in soil organic matter interact with soil minerals in the surface (A) and subsurface (B) horizons. We added four compounds (glucose, starch, cinnamic acid and stearic acid) to the silt- and clay-sized fraction (fine fraction) of A and B horizons of eight soils from varying climates (3 temperate, 3 tropical, 1 arctic and 1 sub-arctic). Equilibriumbatch experiments were conducted using 0 to 100 mg C L 1 ofmore » 14C-labeled compounds for 8 h. Sorption parameters (maximum sorption capacity, Qmax and binding coefficient, k) calculated by fitting sorption data to the Langmuir equation showed that Qmax of A and B horizons was very similar for all compounds. Both Qmax and k values were related to sorbate properties, with Qmax being lowest for glucose (20 500 mg kg 1), highest for stearic acid (20,000 200,000 mg kg 1), and intermediate for both cinnamic acid (200 4000 mg kg 1) and starch (400 6000 mg kg 1). Simple linear regression analysis revealed that physicochemical properties of the sorbents influenced the Qmax of cinnamic acid and stearic acid, but not glucose and starch. The sorbent properties did not show predictive ability for binding coefficient k. By using the fine fraction as sorbent, we found that the mineral fractions of A horizons are equally reactive as the B horizons irrespective of soil organic carbon content.« less

Accelerated decay rates drive soil organic matter persistence and storage in temperate forests via greater mineral stabilization of microbial residues.

NASA Astrophysics Data System (ADS)

Phillips, R.; Craig, M.; Turner, B. L.; Liang, C.

2017-12-01

Climate predicts soil organic matter (SOM) stocks at the global scale, yet controls on SOM stocks at finer spatial scales are still debated. A current hypothesis predicts that carbon (C) and nitrogen (N) storage in soils should be greater when decomposition is slow owing to microbial competition for nutrients or the recalcitrance of organic substrates (hereafter the `slow decay' hypothesis). An alternative hypothesis predicts that soil C and N storage should be greater in soils with rapid decomposition, owing to the accelerated production of microbial residues and their stabilization on soil minerals (hereafter the `stabilization hypothesis'). To test these alternative hypotheses, we quantified soil C and N to 1-m depth in temperate forests across the Eastern and Midwestern US that varied in their biotic, climatic, and edaphic properties. At each site, we sampled (1) soils dominated by arbuscular mycorrhizal (AM) tree species, which typically have fast decay rates and accelerated N cycling, (2) soils dominated by ectomycorrhizal (ECM) tree species, which generally have slow decay rates and slow N cycling, and (3) soils supporting both AM and ECM trees. To the extent that trees and theor associated microbes reflect and reinforce soil conditions, support for the slow decay hypothesis would be greater SOM storage in ECM soils, whereas support for the stabilization hypothesis would be greater SOM storage in AM soils. We found support for both hypotheses, as slow decomposition in ECM soils increased C and N storage in topsoil, whereas fast decomposition in AM soils increased C and N storage in subsoil. However, at all sites we found 57% greater total C and N storage in the entire profile in AM- soils (P < 0.0001), supporting the stabilization hypothesis. Amino sugar biomarkers (an indicator of microbial necromass) and particle size fractionation revealed that the greater SOM storage in AM soils was driven by an accumulation of microbial residues on clay minerals and metal oxides. Taken together, our results indicate that tree species influence soil C and N storage owing to how differences in decay rates affect mineral stabilization of organic matter. Further, our findings indicate that slow decay promotes soil C and N stocks at the soil surface, whereas fast decay promotes greater soil C and N stocks at depth.

Non-Invasive Methods to Characterize Soil-Plant Interactions at Different Scales

NASA Astrophysics Data System (ADS)

Javaux, M.; Kemna, A.; Muench, M.; Oberdoerster, C.; Pohlmeier, A.; Vanderborght, J.; Vereecken, H.

2006-05-01

Root water uptake is a dynamic and non-linear process, which interacts with the soil natural variability and boundary conditions to generate heterogeneous spatial distributions of soil water. Soil-root fluxes are spatially variable due to heterogeneous gradients and hydraulic connections between soil and roots. While 1-D effective representation of the root water uptake has been successfully applied to predict transpiration and average water content profiles, finer spatial characterization of the water distribution may be needed when dealing with solute transport. Indeed, root water uptake affects the water velocity field, which has an effect on solute velocity and dispersion. Although this variability originates from small-scale processes, these may still play an important role at larger scales. Therefore, in addition to investigate the variability of the soil hydraulic properties, experimental and numerical tools for characterizing root water uptake (and its effects on soil water distribution) from the pore to the field scales are needed to predict in a proper way the solute transport. Obviously, non-invasive and modeling techniques which are helpful to achieve this objective will evolve with the scale of interest. At the pore scale, soil structure and root-soil interface phenomena have to be investigated to understand the interactions between soil and roots. Magnetic resonance imaging may help to monitor water gradients and water content changes around roots while spectral induced polarization techniques may be used to characterize the structure of the pore space. At the column scale, complete root architecture of small plants and water content depletion around roots can be imaged by magnetic resonance. At that scale, models should explicitly take into account the three-dimensional gradient dependency of the root water uptake, to be able to predict solute transport. At larger scales however, simplified models, which implicitly take into account the heterogeneous root water uptake along roots, should be preferred given the complexity of the system. At such scales, electrical resistance tomography or ground-penetrating radar can be used to map the water content changes and derive effective parameters for predicting solute transport.

Black Carbon (Biochar) In Water/Soil Environments: Molecular Structure, Sorption, Stability, and Potential Risk.

PubMed

Lian, Fei; Xing, Baoshan

2017-12-05

Black carbon (BC) is ubiquitous in the environments and participates in various biogeochemical processes. Both positive and negative effects of BC (especially biochar) on the ecosystem have been identified, which are mainly derived from its diverse physicochemical properties. Nevertheless, few studies systematically examined the linkage between the evolution of BC molecular structure with the resulted BC properties, environmental functions as well as potential risk, which is critical for understanding the BC environmental behavior and utilization as a multifunctional product. Thus, this review highlights the molecular structure evolution of BC during pyrolysis and the impact of BC physicochemical properties on its sorption behavior, stability, and potential risk in terrestrial and aqueous ecosystems. Given the wide application of BC and its important role in biogeochemical processes, future research should focus on the following: (1) establishing methodology to more precisely predict and design BC properties on the basis of pyrolysis and phase transformation of biomass; (2) developing an assessment system to evaluate the long-term effect of BC on stabilization and bioavailability of contaminants, agrochemicals, and nutrient elements in soils; and (3) elucidating the interaction mechanisms of BC with plant roots, microorganisms, and soil components.

Soil hydraulic material properties and layered architecture from time-lapse GPR

NASA Astrophysics Data System (ADS)

Jaumann, Stefan; Roth, Kurt

2018-04-01

Quantitative knowledge of the subsurface material distribution and its effective soil hydraulic material properties is essential to predict soil water movement. Ground-penetrating radar (GPR) is a noninvasive and nondestructive geophysical measurement method that is suitable to monitor hydraulic processes. Previous studies showed that the GPR signal from a fluctuating groundwater table is sensitive to the soil water characteristic and the hydraulic conductivity function. In this work, we show that the GPR signal originating from both the subsurface architecture and the fluctuating groundwater table is suitable to estimate the position of layers within the subsurface architecture together with the associated effective soil hydraulic material properties with inversion methods. To that end, we parameterize the subsurface architecture, solve the Richards equation, convert the resulting water content to relative permittivity with the complex refractive index model (CRIM), and solve Maxwell's equations numerically. In order to analyze the GPR signal, we implemented a new heuristic algorithm that detects relevant signals in the radargram (events) and extracts the corresponding signal travel time and amplitude. This algorithm is applied to simulated as well as measured radargrams and the detected events are associated automatically. Using events instead of the full wave regularizes the inversion focussing on the relevant measurement signal. For optimization, we use a global-local approach with preconditioning. Starting from an ensemble of initial parameter sets drawn with a Latin hypercube algorithm, we sequentially couple a simulated annealing algorithm with a Levenberg-Marquardt algorithm. The method is applied to synthetic as well as measured data from the ASSESS test site. We show that the method yields reasonable estimates for the position of the layers as well as for the soil hydraulic material properties by comparing the results to references derived from ground truth data as well as from time domain reflectometry (TDR).

Increased ambient air temperature alters the severity of soil water repellency

NASA Astrophysics Data System (ADS)

van Keulen, Geertje; Sinclair, Kat; Hallin, Ingrid; Doerr, Stefan; Urbanek, Emilia; Quinn, Gerry; Matthews, Peter; Dudley, Ed; Francis, Lewis; Gazze, S. Andrea; Whalley, Richard

2017-04-01

Soil repellency, the inability of soils to wet readily, has detrimental environmental impacts such as increased runoff, erosion and flooding, reduced biomass production, inefficient use of irrigation water and preferential leaching of pollutants. Its impacts may exacerbate (summer) flood risks associated with more extreme drought and precipitation events. In this study we have tested the hypothesis that transitions between hydrophobic and hydrophilic soil particle surface characteristics, in conjunction with soil structural properties, strongly influence the hydrological behaviour of UK soils under current and predicted UK climatic conditions. We have addressed the hypothesis by applying different ambient air temperatures under controlled conditions to simulate the effect of predicted UK climatic conditions on the wettability of soils prone to develop repellency at different severities. Three UK silt-loam soils under permanent vegetation were selected for controlled soil perturbation studies. The soils were chosen based on the severity of hydrophobicity that can be achieved in the field: severe to extreme (Cefn Bryn, Gower, Wales), intermediate to severe (National Botanical Garden, Wales), and subcritical (Park Grass, Rothamsted Research near London). The latter is already highly characterised so was also used as a control. Soils were fully saturated with water and then allowed to dry out gradually upon exposure to controlled laboratory conditions. Soils were allowed to adapt for a few hours to a new temperature prior to initiation of the controlled experiments. Soil wettability was determined at highly regular intervals by measuring water droplet penetration times. Samples were collected at four time points: fully wettable, just prior to and after the critical soil moisture concentrations (CSC), and upon reaching air dryness (to constant weight), for further (ultra)metaproteomic and nanomechanical studies to allow integration of bulk soil characterisations with functional expression and nanoscale studies to generate deep mechanistic understanding of the roles of microbes in soil ecosystems. Our controlled soil pertubation studies have shown that an increase in ambient temperature has consistently affected the severity of soil water repellency. Surprisingly, a higher ambient air temperature impacts soils that in the field develop subcritical and extreme repellency, differently under controlled laboratory conditions. We will discuss the impact of these results in relation to predicted UK climatic conditions. Soil metaproteomics will provide mechanistic insight at the molecular level whether differential microbial adaptation is correlated with the apparent different response to a higher ambient air temperature.

Impacts of 120 years of fertilizer addition on a temperate grassland ecosystem

PubMed Central

Kidd, Jonathan; Manning, Peter; Simkin, Janet; Peacock, Simon; Stockdale, Elizabeth

2017-01-01

The widespread application of fertilizers has greatly influenced many processes and properties of agroecosystems, and agricultural fertilization is expected to increase even further in the future. To date, most research on fertilizer impacts has used short-term studies, which may be unrepresentative of long-term responses, thus hindering our capacity to predict long-term impacts. Here, we examined the effects of long-term fertilizer addition on key ecosystem properties in a long-term grassland experiment (Palace Leas Hay Meadow) in which farmyard manure (FYM) and inorganic fertilizer treatments have been applied consistently for 120 years in order to characterize the experimental site more fully and compare ecosystem responses with those observed at other long-term and short-term experiments. FYM inputs increased soil organic carbon (SOC) stocks, hay yield, nutrient availability and acted as a buffer against soil acidification (>pH 5). In contrast, N-containing inorganic fertilizers strongly acidified the soil (

Decision Tree Approach for Soil Liquefaction Assessment

PubMed Central

Gandomi, Amir H.; Fridline, Mark M.; Roke, David A.

2013-01-01

In the current study, the performances of some decision tree (DT) techniques are evaluated for postearthquake soil liquefaction assessment. A database containing 620 records of seismic parameters and soil properties is used in this study. Three decision tree techniques are used here in two different ways, considering statistical and engineering points of view, to develop decision rules. The DT results are compared to the logistic regression (LR) model. The results of this study indicate that the DTs not only successfully predict liquefaction but they can also outperform the LR model. The best DT models are interpreted and evaluated based on an engineering point of view. PMID:24489498

Decision tree approach for soil liquefaction assessment.

PubMed

Gandomi, Amir H; Fridline, Mark M; Roke, David A

2013-01-01

In the current study, the performances of some decision tree (DT) techniques are evaluated for postearthquake soil liquefaction assessment. A database containing 620 records of seismic parameters and soil properties is used in this study. Three decision tree techniques are used here in two different ways, considering statistical and engineering points of view, to develop decision rules. The DT results are compared to the logistic regression (LR) model. The results of this study indicate that the DTs not only successfully predict liquefaction but they can also outperform the LR model. The best DT models are interpreted and evaluated based on an engineering point of view.

Microbial and geochemical assessment of bauxitic un-mined and post-mined chronosequence soils from Mocho Mountains, Jamaica.

PubMed

Lewis, Dawn E; Chauhan, Ashvini; White, John R; Overholt, Will; Green, Stefan J; Jasrotia, Puja; Wafula, Denis; Jagoe, Charles

2012-10-01

Microorganisms are very sensitive to environmental change and can be used to gauge anthropogenic impacts and even predict restoration success of degraded environments. Here, we report assessment of bauxite mining activities on soil biogeochemistry and microbial community structure using un-mined and three post-mined sites in Jamaica. The post-mined soils represent a chronosequence, undergoing restoration since 1987, 1997, and 2007. Soils were collected during dry and wet seasons and analyzed for pH, organic matter (OM), total carbon (TC), nitrogen (TN), and phosphorus. The microbial community structure was assessed through quantitative PCR and massively parallel bacterial ribosomal RNA (rRNA) gene sequencing. Edaphic factors and microbial community composition were analyzed using multivariate statistical approaches and revealed a significant, negative impact of mining on soil that persisted even after greater than 20 years of restoration. Seasonal fluctuations contributed to variation in measured soil properties and community composition, but they were minor in comparison to long-term effects of mining. In both seasons, post-mined soils were higher in pH but OM, TC, and TN decreased. Bacterial rRNA gene analyses demonstrated a general decrease in diversity in post-mined soils and up to a 3-log decrease in rRNA gene abundance. Community composition analyses demonstrated that bacteria from the Proteobacteria (α, β, γ, δ), Acidobacteria, and Firmicutes were abundant in all soils. The abundance of Firmicutes was elevated in newer post-mined soils relative to the un-mined soil, and this contrasted a decrease, relative to un-mined soils, in proteobacterial and acidobacterial rRNA gene abundances. Our study indicates long-lasting impacts of mining activities to soil biogeochemical and microbial properties with impending loss in soil productivity.

Phenanthrene sorption with heterogeneous organic matter in a landfill aquifer material

USGS Publications Warehouse

Karapanagioti, H.K.; Sabatini, D.A.; Kleineidam, S.; Grathwohl, P.; Ligouis, B.

1999-01-01

Phenanthrene was used as a model chemical to study the sorption properties of Canadian River Alluvium aquifer material. Both equilibrium and kinetic sorption processes were evaluated through batch studies. The bulk sample was divided into subsamples with varying properties such as particle size, organic content, equilibration time, etc. in order to determine the effect of these properties on resulting sorption parameters. The data have been interpreted and the effect of experimental variables was quantified using the Freundlich isotherm model and a numerical solution of Fick's 2nd law in porous media. Microscopic organic matter characterization proved to be a valuable tool for explaining the results. Different organic matter properties and sorption mechanisms were observed for each soil subsample. Samples containing coal particles presented high Koc values. Samples with organic matter dominated by organic coatings on quartz grains presented low Koc values and contained a high percentage of fast sorption sites. The numerical solution of Fick's 2ndlaw requires the addition of two terms (fast and slow) in order to fit the kinetics of these heterogeneous samples properly. These results thus demonstrate the need for soil organic matter characterization in order to predict and explain the sorption properties of a soil sample containing heterogeneous organic matter and also the difficulty and complexity of modeling sorption in such samples.

Activation energy of extracellular enzymes in soils from different biomes.

PubMed

Steinweg, J Megan; Jagadamma, Sindhu; Frerichs, Joshua; Mayes, Melanie A

2013-01-01

Enzyme dynamics are being incorporated into soil carbon cycling models and accurate representation of enzyme kinetics is an important step in predicting belowground nutrient dynamics. A scarce number of studies have measured activation energy (Ea) in soils and fewer studies have measured Ea in arctic and tropical soils, or in subsurface soils. We determined the Ea for four typical lignocellulose degrading enzymes in the A and B horizons of seven soils covering six different soil orders. We also elucidated which soil properties predicted any measurable differences in Ea. β-glucosidase, cellobiohydrolase, phenol oxidase and peroxidase activities were measured at five temperatures, 4, 21, 30, 40, and 60°C. Ea was calculated using the Arrhenius equation. β-glucosidase and cellobiohydrolase Ea values for both A and B horizons in this study were similar to previously reported values, however we could not make a direct comparison for B horizon soils because of the lack of data. There was no consistent relationship between hydrolase enzyme Ea and the environmental variables we measured. Phenol oxidase was the only enzyme that had a consistent positive relationship between Ea and pH in both horizons. The Ea in the arctic and subarctic zones for peroxidase was lower than the hydrolases and phenol oxidase values, indicating peroxidase may be a rate limited enzyme in environments under warming conditions. By including these six soil types we have increased the number of soil oxidative enzyme Ea values reported in the literature by 50%. This study is a step towards better quantifying enzyme kinetics in different climate zones.

Application of a level IV fugacity model to simulate the long-term fate of hexachlorocyclohexane isomers in the lower reach of Yellow River basin, China.

PubMed

Ao, Jiangting; Chen, Jingwen; Tian, Fulin; Cai, Xiyun

2009-01-01

A level IV multimedia fugacity model was established to simulate the fate and transfer of hexachlorocyclohexane (HCH) isomers in the lower reach of the Yellow River basin, China, during 1952-2010. The predicted concentrations of HCHs are in good agreement with the observed ones, as indicated by the residual errors being generally lower than 0.5 logarithmic units. The effects of extensive agricultural application and subsequent prohibition of HCHs are reflected by the temporal variation of HCHs predicted by the model. It is predicted that only 1.8 tons of HCHs will be left in 2010, less than 0.06% of the highest contents (in 1983) in the study area, and about 99% of HCHs remain in soil. The proportions of HCH isomers in the environment also changed with time due to their different physicochemical properties. Although beta-HCH is not the main component of the technical HCHs, it has become the most abundant isomer in the environment because of its persistence. The dominant transfer processes between the adjacent compartments were deposition from air to soil, air diffusion through the air-water interface and runoff from soil to water. Sensitivity analysis showed that degradation rate in soil, parameters related to major sources, and thickness of soils had the strongest influence on the model result. Results of Monte Carlo simulation indicated the overall uncertainty of model predictions, and the coefficients of variation of the estimated concentrations of HCHs in all the compartments ranged from 0.5 to 5.8.

Archaeal dominated ammonia-oxidizing communities in Icelandic grassland soils are moderately affected by long-term N fertilization and geothermal heating

PubMed Central

Daebeler, Anne; Abell, Guy C. J.; Bodelier, Paul L. E.; Bodrossy, Levente; Frampton, Dion M. F.; Hefting, Mariet M.; Laanbroek, Hendrikus J.

2012-01-01

The contribution of ammonia-oxidizing bacteria and archaea (AOB and AOA, respectively) to the net oxidation of ammonia varies greatly between terrestrial environments. To better understand, predict and possibly manage terrestrial nitrogen turnover, we need to develop a conceptual understanding of ammonia oxidation as a function of environmental conditions including the ecophysiology of associated organisms. We examined the discrete and combined effects of mineral nitrogen deposition and geothermal heating on ammonia-oxidizing communities by sampling soils from a long-term fertilization site along a temperature gradient in Icelandic grasslands. Microarray, clone library and quantitative PCR analyses of the ammonia monooxygenase subunit A (amoA) gene accompanied by physico-chemical measurements of the soil properties were conducted. In contrast to most other terrestrial environments, the ammonia-oxidizing communities consisted almost exclusively of archaea. Their bacterial counterparts proved to be undetectable by quantitative polymerase chain reaction suggesting AOB are only of minor relevance for ammonia oxidation in these soils. Our results show that fertilization and local, geothermal warming affected detectable ammonia-oxidizing communities, but not soil chemistry: only a subset of the detected AOA phylotypes was present in higher temperature soils and AOA abundance was increased in the fertilized soils, while soil physio-chemical properties remained unchanged. Differences in distribution and structure of AOA communities were best explained by soil pH and clay content irrespective of temperature or fertilizer treatment in these grassland soils, suggesting that these factors have a greater potential for ecological niche-differentiation of AOA in soil than temperature and N fertilization. PMID:23060870

Archaeal dominated ammonia-oxidizing communities in Icelandic grassland soils are moderately affected by long-term N fertilization and geothermal heating.

PubMed

Daebeler, Anne; Abell, Guy C J; Bodelier, Paul L E; Bodrossy, Levente; Frampton, Dion M F; Hefting, Mariet M; Laanbroek, Hendrikus J

2012-01-01

The contribution of ammonia-oxidizing bacteria and archaea (AOB and AOA, respectively) to the net oxidation of ammonia varies greatly between terrestrial environments. To better understand, predict and possibly manage terrestrial nitrogen turnover, we need to develop a conceptual understanding of ammonia oxidation as a function of environmental conditions including the ecophysiology of associated organisms. We examined the discrete and combined effects of mineral nitrogen deposition and geothermal heating on ammonia-oxidizing communities by sampling soils from a long-term fertilization site along a temperature gradient in Icelandic grasslands. Microarray, clone library and quantitative PCR analyses of the ammonia monooxygenase subunit A (amoA) gene accompanied by physico-chemical measurements of the soil properties were conducted. In contrast to most other terrestrial environments, the ammonia-oxidizing communities consisted almost exclusively of archaea. Their bacterial counterparts proved to be undetectable by quantitative polymerase chain reaction suggesting AOB are only of minor relevance for ammonia oxidation in these soils. Our results show that fertilization and local, geothermal warming affected detectable ammonia-oxidizing communities, but not soil chemistry: only a subset of the detected AOA phylotypes was present in higher temperature soils and AOA abundance was increased in the fertilized soils, while soil physio-chemical properties remained unchanged. Differences in distribution and structure of AOA communities were best explained by soil pH and clay content irrespective of temperature or fertilizer treatment in these grassland soils, suggesting that these factors have a greater potential for ecological niche-differentiation of AOA in soil than temperature and N fertilization.

Ensemble learning for spatial interpolation of soil potassium content based on environmental information.

PubMed

Liu, Wei; Du, Peijun; Wang, Dongchen

2015-01-01

One important method to obtain the continuous surfaces of soil properties from point samples is spatial interpolation. In this paper, we propose a method that combines ensemble learning with ancillary environmental information for improved interpolation of soil properties (hereafter, EL-SP). First, we calculated the trend value for soil potassium contents at the Qinghai Lake region in China based on measured values. Then, based on soil types, geology types, land use types, and slope data, the remaining residual was simulated with the ensemble learning model. Next, the EL-SP method was applied to interpolate soil potassium contents at the study site. To evaluate the utility of the EL-SP method, we compared its performance with other interpolation methods including universal kriging, inverse distance weighting, ordinary kriging, and ordinary kriging combined geographic information. Results show that EL-SP had a lower mean absolute error and root mean square error than the data produced by the other models tested in this paper. Notably, the EL-SP maps can describe more locally detailed information and more accurate spatial patterns for soil potassium content than the other methods because of the combined use of different types of environmental information; these maps are capable of showing abrupt boundary information for soil potassium content. Furthermore, the EL-SP method not only reduces prediction errors, but it also compliments other environmental information, which makes the spatial interpolation of soil potassium content more reasonable and useful.

Development of a model to predict ash transport and water pollution risk in fire-affected environments

NASA Astrophysics Data System (ADS)

Neris, Jonay; Elliot, William J.; Doerr, Stefan H.; Robichaud, Peter R.

2017-04-01

An estimated that 15% of the world's population lives in volcanic areas. Recent catastrophic erosion events following wildfires in volcanic terrain have highlighted the geomorphological instability of this soil type under disturbed conditions and steep slopes. Predicting the hydrological and erosional response of this soils in the post-fire period is the first step to design and develop adequate actions to minimize risks in the post-fire period. In this work we apply, for the first time, the Water Erosion Prediction Project model for predicting erosion and runoff events in fire-affected volcanic soils in Europe. Two areas affected by wildfires in 2015 were selected in Tenerife (Spain) representative of different fire behaviour (downhill surface fire with long residence time vs uphill crown fire with short residence time), severity (moderate soil burn severity vs light soil burn severity) and climatic conditions (average annual precipitation of 750 and 210 mm respectively). The actual erosion processes were monitored in the field using silt fences. Rainfall and rill simulations were conducted to determine hydrologic, interrill and rill erosion parameters. The soils were sampled and key properties used as model input, evaluated. During the first 18 months after the fire 7 storms produced runoff and erosion in the selected areas. Sediment delivery reached 5.4 and 2.5 Mg ha-1 respectively in the first rainfall event monitored after the fire, figures comparable to those reported for fire-affected areas of the western USA with similar climatic conditions but lower than those showed by wetter environments. The validation of the WEPP model using field data showed reasonable estimates of hillslope sediment delivery in the post-fire period and, therefore, it is suggested that this model can support land managers in volcanic areas in Europe in predicting post-fire hydrological and erosional risks and designing suitable mitigation treatments.

First application of the WEPP model to predict runoff and erosion risk in fire-affected volcanic areas in Europe

NASA Astrophysics Data System (ADS)

Neris, Jonay; Robichaud, Peter R.; Elliot, William J.; Doerr, Stefan H.; Notario del Pino, Jesús S.; Lado, Marcos

2017-04-01

An estimated that 15% of the world's population lives in volcanic areas. Recent catastrophic erosion events following wildfires in volcanic terrain have highlighted the geomorphological instability of this soil type under disturbed conditions and steep slopes. Predicting the hydrological and erosional response of this soils in the post-fire period is the first step to design and develop adequate actions to minimize risks in the post-fire period. In this work we apply, for the first time, the Water Erosion Prediction Project model for predicting erosion and runoff events in fire-affected volcanic soils in Europe. Two areas affected by wildfires in 2015 were selected in Tenerife (Spain) representative of different fire behaviour (downhill surface fire with long residence time vs uphill crown fire with short residence time), severity (moderate soil burn severity vs light soil burn severity) and climatic conditions (average annual precipitation of 750 and 210 mm respectively). The actual erosion processes were monitored in the field using silt fences. Rainfall and rill simulations were conducted to determine hydrologic, interrill and rill erosion parameters. The soils were sampled and key properties used as model input, evaluated. During the first 18 months after the fire 7 storms produced runoff and erosion in the selected areas. Sediment delivery reached 5.4 and 2.5 Mg ha-1 respectively in the first rainfall event monitored after the fire, figures comparable to those reported for fire-affected areas of the western USA with similar climatic conditions but lower than those showed by wetter environments. The validation of the WEPP model using field data showed reasonable estimates of hillslope sediment delivery in the post-fire period and, therefore, it is suggested that this model can support land managers in volcanic areas in Europe in predicting post-fire hydrological and erosional risks and designing suitable mitigation treatments.

Measurement of soil lead bioavailability and influence of soil types and properties: A review.

PubMed

Yan, Kaihong; Dong, Zhaomin; Wijayawardena, M A Ayanka; Liu, Yanju; Naidu, Ravi; Semple, Kirk

2017-10-01

Lead (Pb) is a widespread heavy metal which is harmful to human health, especially to young children. To provide a human health risk assessment that is more relevant to real conditions, Pb bioavailability in soils is increasingly employed in the assessment procedure. Both in vivo and in vitro measurements for lead bioavailability are available. In vivo models are time- consuming and expensive, while in vitro models are rapid, economic, reproducible, and reliable while involving more uncertainties. Uncertainties in various measurements create difficulties in accurately predicting Pb bioavailability, resulting in the unnecessary remediation of sites. In this critical review, we utilised available data from in vivo and in vitro studies to identify the key parameters influencing the in vitro measurements, and presented uncertainties existing in Pb bioavailability measurements. Soil type, properties and metal content are reported to influence lead bioavailability; however, the differences in methods for assessing bioavailability and the differences in Pb source limit one's ability to conduct statistical analyses on influences of soil factors on Pb bioavailability. The information provided in the review is fundamentally useful for the measurement of bioavailability and risk assessment practices. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting potentially plant-available lead in contaminated residential sites.

PubMed

Andra, Syam S; Sarkar, Dibyendu; Saminathan, Sumathi K M; Datta, Rupali

2011-04-01

Lead (Pb)-based paints pose a serious health problem to people living in residential settings constructed prior to 1978. Children are at a greater risk to Pb exposure resulting from hand-to-mouth activity in Pb-contaminated residential soils. For soil Pb, the most environmentally friendly, potentially cheap, and visually unobtrusive in situ technology is phytoremediation. However, the limiting factor in a successful phytoremediation strategy is the availability of Pb for plant uptake. The purpose of this study was to establish a relationship between soil properties and the plant-available/exchangeable Pb fraction in the selected Pb-based paint-contaminated residential sites. We selected 20 such sites from two different locations (San Antonio, Texas and Baltimore, Maryland) with varying soil properties and total soil Pb concentrations ranging between 256 and 4,182 mg kg(-1). Despite higher Pb levels in these soils that exceeds US EPA permissible limit of 400 mg kg(-1), it is known that the plant-available Pb pools are significantly lower because of their sorption to soil components such as organic matter, Fe-Mn oxides, and clays, and their precipitation in the form of carbonates, hydroxides, and phosphates. Principal component analysis and hierarchical clustering showed that the potentially plant-available Pb fraction is controlled by soil pH in the case of acidic Baltimore soils, while soil organic matter plays a major role in alkaline San Antonio soils. Statistical models developed suggest that Pb is likely to be more available for plant uptake in Baltimore soils and a chelant-assisted phytoextraction strategy will be potentially necessary for San Antonio soils in mobilizing Pb from complexed pool to the plant-available pool. A thorough knowledge of site-specific factors is therefore essential in developing a suitable and successful phytoremediation model.

Linking the climatic and geochemical controls on global soil carbon cycling

NASA Astrophysics Data System (ADS)

Doetterl, Sebastian; Stevens, Antoine; Six, Johan; Merckx, Roel; Van Oost, Kristof; Casanova Pinto, Manuel; Casanova-Katny, Angélica; Muñoz, Cristina; Boudin, Mathieu; Zagal Venegas, Erick; Boeckx, Pascal

2015-04-01

Climatic and geochemical parameters are regarded as the primary controls for soil organic carbon (SOC) storage and turnover. However, due to the difference in scale between climate and geochemical-related soil research, the interaction of these key factors for SOC dynamics have rarely been assessed. Across a large geochemical and climatic transect in similar biomes in Chile and the Antarctic Peninsula we show how abiotic geochemical soil features describing soil mineralogy and weathering pose a direct control on SOC stocks, concentration and turnover and are central to explaining soil C dynamics at larger scales. Precipitation and temperature had an only indirect control by regulating geochemistry. Soils with high SOC content have low specific potential CO2 respiration rates, but a large fraction of SOC that is stabilized via organo-mineral interactions. The opposite was observed for soils with low SOC content. The observed differences for topsoil SOC stocks along this transect of similar biomes but differing geo-climatic site conditions are of the same magnitude as differences observed for topsoil SOC stocks across all major global biomes. Using precipitation and a set of abiotic geochemical parameters describing soil mineralogy and weathering status led to predictions of high accuracy (R2 0.53-0.94) for different C response variables. Partial correlation analyses revealed that the strength of the correlation between climatic predictors and SOC response variables decreased by 51 - 83% when controlling for geochemical predictors. In contrast, controlling for climatic variables did not result in a strong decrease in the strength of the correlations of between most geochemical variables and SOC response variables. In summary, geochemical parameters describing soil mineralogy and weathering were found to be essential for accurate predictions of SOC stocks and potential CO2 respiration, while climatic factors were of minor importance as a direct control, but are important through governing soil weathering and geochemistry. In conclusion, we pledge for a stronger implementation of geochemical soil properties to predict SOC stocks on a global scale. Understanding the effects of climate (temperature and precipitation) change on SOC dynamics also requires good understanding of the relationship between climate and soil geochemistry.

Integrating effects of species composition and soil properties to predict shifts in montane forest carbon-water relations.

PubMed

Maxwell, Toby M; Silva, Lucas C R; Horwath, William R

2018-05-01

This study was designed to address a major source of uncertainty pertaining to coupled carbon-water cycles in montane forest ecosystems. The Sierra Nevada of California was used as a model system to investigate connections between the physiological performance of trees and landscape patterns of forest carbon and water use. The intrinsic water-use efficiency (iWUE)-an index of CO 2 fixed per unit of potential water lost via transpiration-of nine dominant species was determined in replicated transects along an ∼1,500-m elevation gradient, spanning a broad range of climatic conditions and soils derived from three different parent materials. Stable isotope ratios of carbon and oxygen measured at the leaf level were combined with field-based and remotely sensed metrics of stand productivity, revealing that variation in iWUE depends primarily on leaf traits (∼24% of the variability), followed by stand productivity (∼16% of the variability), climatic regime (∼13% of the variability), and soil development (∼12% of the variability). Significant interactions between species composition and soil properties proved useful to predict changes in forest carbon-water relations. On the basis of observed shifts in tree species composition, ongoing since the 1950s and intensified in recent years, an increase in water loss through transpiration (ranging from 10 to 60% depending on parent material) is now expected in mixed conifer forests throughout the region. Copyright © 2018 the Author(s). Published by PNAS.

Average variograms to guide soil sampling

NASA Astrophysics Data System (ADS)

Kerry, R.; Oliver, M. A.

2004-10-01

To manage land in a site-specific way for agriculture requires detailed maps of the variation in the soil properties of interest. To predict accurately for mapping, the interval at which the soil is sampled should relate to the scale of spatial variation. A variogram can be used to guide sampling in two ways. A sampling interval of less than half the range of spatial dependence can be used, or the variogram can be used with the kriging equations to determine an optimal sampling interval to achieve a given tolerable error. A variogram might not be available for the site, but if the variograms of several soil properties were available on a similar parent material and or particular topographic positions an average variogram could be calculated from these. Averages of the variogram ranges and standardized average variograms from four different parent materials in southern England were used to suggest suitable sampling intervals for future surveys in similar pedological settings based on half the variogram range. The standardized average variograms were also used to determine optimal sampling intervals using the kriging equations. Similar sampling intervals were suggested by each method and the maps of predictions based on data at different grid spacings were evaluated for the different parent materials. Variograms of loss on ignition (LOI) taken from the literature for other sites in southern England with similar parent materials had ranges close to the average for a given parent material showing the possible wider application of such averages to guide sampling.

Integrating auxiliary data and geophysical techniques for the estimation of soil clay content using CHAID algorithm

NASA Astrophysics Data System (ADS)

Abbaszadeh Afshar, Farideh; Ayoubi, Shamsollah; Besalatpour, Ali Asghar; Khademi, Hossein; Castrignano, Annamaria

2016-03-01

This study was conducted to estimate soil clay content in two depths using geophysical techniques (Ground Penetration Radar-GPR and Electromagnetic Induction-EMI) and ancillary variables (remote sensing and topographic data) in an arid region of the southeastern Iran. GPR measurements were performed throughout ten transects of 100 m length with the line spacing of 10 m, and the EMI measurements were done every 10 m on the same transect in six sites. Ten soil cores were sampled randomly in each site and soil samples were taken from the depth of 0-20 and 20-40 cm, and then the clay fraction of each of sixty soil samples was measured in the laboratory. Clay content was predicted using three different sets of properties including geophysical data, ancillary data, and a combination of both as inputs to multiple linear regressions (MLR) and decision tree-based algorithm of Chi-Squared Automatic Interaction Detection (CHAID) models. The results of the CHAID and MLR models with all combined data showed that geophysical data were the most important variables for the prediction of clay content in two depths in the study area. The proposed MLR model, using the combined data, could explain only 0.44 and 0.31% of the total variability of clay content in 0-20 and 20-40 cm depths, respectively. Also, the coefficient of determination (R2) values for the clay content prediction, using the constructed CHAID model with the combined data, was 0.82 and 0.76 in 0-20 and 20-40 cm depths, respectively. CHAID models, therefore, showed a greater potential in predicting soil clay content from geophysical and ancillary data, while traditional regression methods (i.e. the MLR models) did not perform as well. Overall, the results may encourage researchers in using georeferenced GPR and EMI data as ancillary variables and CHAID algorithm to improve the estimation of soil clay content.

Silver(I) Binding Properties of Organic Soil Materials Are Different from Those of Isolated Humic Substances.

PubMed

B Kleja, Dan; Nakata, Satomi; Persson, Ingmar; Gustafsson, Jon Petter

2016-07-19

The solubility of silver(I) in many soils is controlled by complexation reactions with organic matter. In this work we have compared the ability of isolated humic and fulvic acids to bind silver(I) with that of mor and peat materials. One new data set for Suwannee River Fulvic Acid was produced, which was consistent with published data sets for isolated fulvic and humic acids. The ability of soil materials to bind silver(I) was studied as a function of pH in the range 2.5-5.0, at a wide range of silver(I)-to-soil ratios (10(-4.2) - 10(-1.9) mol kg(-1)). By calibrating the Stockholm Humic Model on the humic and fulvic acids data sets, we showed that binding of silver(I) to both types of soil materials was much stronger (up to 2 orders of magnitude) than predicted from the silver(I) binding properties of the isolated humic materials. Thus, the approach taken for many other metals, that is, to model solubility in soils by using metal and proton binding parameters derived from isolated humic and fulvic acids, cannot be used for silver(I). One possible explanation for the discrepancy could be that silver(I) predominately interacted with various biomolecules in the soil samples, instead of humic- and fulvic-acid type materials.

Strategies for soil-based precision agriculture in cotton

NASA Astrophysics Data System (ADS)

Neely, Haly L.; Morgan, Cristine L. S.; Stanislav, Scott; Rouze, Gregory; Shi, Yeyin; Thomasson, J. Alex; Valasek, John; Olsenholler, Jeff

2016-05-01

The goal of precision agriculture is to increase crop yield while maximizing the use efficiency of farm resources. In this application, UAV-based systems are presenting agricultural researchers with an opportunity to study crop response to environmental and management factors in real-time without disturbing the crop. The spatial variability soil properties, which drive crop yield and quality, cannot be changed and thus keen agronomic choices with soil variability in mind have the potential to increase profits. Additionally, measuring crop stress over time and in response to management and environmental conditions may enable agronomists and plant breeders to make more informed decisions about variety selection than the traditional end-of-season yield and quality measurements. In a previous study, seed-cotton yield was measured over 4 years and compared with soil variability as mapped by a proximal soil sensor. It was found that soil properties had a significant effect on seed-cotton yield and the effect was not consistent across years due to different precipitation conditions. However, when seed-cotton yield was compared to the normalized difference vegetation index (NDVI), as measured using a multispectral camera from a UAV, predictions improved. Further improvement was seen when soil-only pixels were removed from the analysis. On-going studies are using UAV-based data to uncover the thresholds for stress and yield potential. Long-term goals of this research include detecting stress before yield is reduced and selecting better adapted varieties.

Future trends in soil cadmium concentration under current cadmium fluxes to European agricultural soils.

PubMed

Six, L; Smolders, E

2014-07-01

The gradual increase of soil cadmium concentrations in European soils during the 20th century has prompted environmental legislation to limit soil cadmium (Cd) accumulation. Mass balances (input-output) reflecting the period 1980-1995 predicted larger Cd inputs via phosphate (P) fertilizers and atmospheric deposition than outputs via crop uptake and leaching. This study updates the Cd mass balance for the agricultural top soils of EU-27+Norway (EU-27+1). Over the past 15 years, the use of P fertilizers in the EU-27+1 has decreased by 40%. The current mean atmospheric deposition of Cd in EU is 0.35 g Cd ha(-1) yr(-1), this is strikingly smaller than values used in the previous EU mass balances (~3 g Cd ha(-1) yr(-1)). Leaching of Cd was estimated with most recent data of soil solution Cd concentrations in 151 soils, which cover the range of European soil properties. No significant time trends were found in the data of net applications of Cd via manure, compost, sludge and lime, all being small sources of Cd at a large scale. Modelling of the future long-term changes in soil Cd concentrations in agricultural top soils under cereal or potato culture predicts soil Cd concentrations to decrease by 15% over the next 100 years in an average scenario, with decreasing trends in some scenarios being more prevalent than increasing trends in other scenarios. These Cd balances have reverted from the general positive balances estimated 10 or more years ago. Uncertainty analysis suggests that leaching is the most uncertain relative to other fluxes. Copyright © 2014 Elsevier B.V. All rights reserved.

Effects of soil type, prepercolation, and ageing on bioaccumulation and toxicity of zinc for the springtail Folsomia candida

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smit, C.E.; Van Gestel, C.A.M.

1998-06-01

Soil properties are a major influence on the bioavailability and toxicity of metals and represent one of the important factors that complicate the extrapolation of results from laboratory tests to field situations. The influence of soil characteristics and way of contamination on the bioaccumulation and toxicity of zinc was investigated for the springtail Folsomia candida, and the applicability of chemical extraction techniques for the prediction of zinc uptake and toxicity was evaluated. Bioaccumulation of zinc in F. candida was related to water-soluble zinc concentrations, and uptake was dependent on the test soil used. Effects of zinc for F. candida couldmore » not be fully explained by bioaccumulation. This indicates that the existence of a fixed internal threshold concentration of zinc above which physiological functions are impaired is not likely for F. candida. In freshly contaminated soils, zinc toxicity was related to organic matter and clay content of the soil; however, the use of these soils overestimated the effects of zinc for F. candida by a factor of 5 to 8 compared to a test soil that was subjected to ageing under field conditions for 1.5 years. Equilibration of the zinc contamination by percolating the soils with water before use in the toxicity experiment strongly reduced the difference in zinc toxicity between laboratory-treated and aged soils. Water-soluble concentrations are most appropriate to predict effects of zinc on reproduction of F. candida in soils with unknown contamination histories. For laboratory toxicity tests, it is recommended to percolate soils with water after contamination and to include an equilibration period prior to use to achieve a more realistic exposure situation.« less

Use of a flux-based field capacity criterion to identify effective hydraulic parameters of layered soil profiles subjected to synthetic drainage experiments

NASA Astrophysics Data System (ADS)

Nasta, Paolo; Romano, Nunzio

2016-01-01

This study explores the feasibility of identifying the effective soil hydraulic parameterization of a layered soil profile by using a conventional unsteady drainage experiment leading to field capacity. The flux-based field capacity criterion is attained by subjecting the soil profile to a synthetic drainage process implemented numerically in the Soil-Water-Atmosphere-Plant (SWAP) model. The effective hydraulic parameterization is associated to either aggregated or equivalent parameters, the former being determined by the geometrical scaling theory while the latter is obtained through the inverse modeling approach. Outcomes from both these methods depend on information that is sometimes difficult to retrieve at local scale and rather challenging or virtually impossible at larger scales. The only knowledge of topsoil hydraulic properties, for example, as retrieved by a near-surface field campaign or a data assimilation technique, is often exploited as a proxy to determine effective soil hydraulic parameterization at the largest spatial scales. Comparisons of the effective soil hydraulic characterization provided by these three methods are conducted by discussing the implications for their use and accounting for the trade-offs between required input information and model output reliability. To better highlight the epistemic errors associated to the different effective soil hydraulic properties and to provide some more practical guidance, the layered soil profiles are then grouped by using the FAO textural classes. For the moderately heterogeneous soil profiles available, all three approaches guarantee a general good predictability of the actual field capacity values and provide adequate identification of the effective hydraulic parameters. Conversely, worse performances are encountered for the highly variable vertical heterogeneity, especially when resorting to the "topsoil-only" information. In general, the best performances are guaranteed by the equivalent parameters, which might be considered a reference for comparisons with other techniques. As might be expected, the information content of the soil hydraulic properties pertaining only to the uppermost soil horizon is rather inefficient and also not capable to map out the hydrologic behavior of the real vertical soil heterogeneity since the drainage process is significantly affected by profile layering in almost all cases.

Evaluating the importance of characterizing soil structure and horizons in parameterizing a hydrologic process model

USGS Publications Warehouse

Mirus, Benjamin B.

2015-01-01

Incorporating the influence of soil structure and horizons into parameterizations of distributed surface water/groundwater models remains a challenge. Often, only a single soil unit is employed, and soil-hydraulic properties are assigned based on textural classification, without evaluating the potential impact of these simplifications. This study uses a distributed physics-based model to assess the influence of soil horizons and structure on effective parameterization. This paper tests the viability of two established and widely used hydrogeologic methods for simulating runoff and variably saturated flow through layered soils: (1) accounting for vertical heterogeneity by combining hydrostratigraphic units with contrasting hydraulic properties into homogeneous, anisotropic units and (2) use of established pedotransfer functions based on soil texture alone to estimate water retention and conductivity, without accounting for the influence of pedon structures and hysteresis. The viability of this latter method for capturing the seasonal transition from runoff-dominated to evapotranspiration-dominated regimes is also tested here. For cases tested here, event-based simulations using simplified vertical heterogeneity did not capture the state-dependent anisotropy and complex combinations of runoff generation mechanisms resulting from permeability contrasts in layered hillslopes with complex topography. Continuous simulations using pedotransfer functions that do not account for the influence of soil structure and hysteresis generally over-predicted runoff, leading to propagation of substantial water balance errors. Analysis suggests that identifying a dominant hydropedological unit provides the most acceptable simplification of subsurface layering and that modified pedotransfer functions with steeper soil-water retention curves might adequately capture the influence of soil structure and hysteresis on hydrologic response in headwater catchments.

Combining an experimental study and ANFIS modeling to predict landfill leachate transport in underlying soil-a case study in north of Iran.

PubMed

Yousefi Kebria, D; Ghavami, M; Javadi, S; Goharimanesh, M

2017-12-16

In the contemporary world, urbanization and progressive industrial activities increase the rate of waste material generated in many developed countries. Municipal solid waste landfills (MSWs) are designed to dispose the waste from urban areas. However, discharged landfill leachate, the soluble water mixture that filters through solid waste landfills, can potentially migrate into the soil and affect living organisms by making harmful biological changes in the ecosystem. Due to well-documented landfill problems involving contamination, it is necessary to investigate the long-term influence of discharged leachate on the consistency of the soil beds beneath MSW landfills. To do so, the current study collected vertical deep core samples from different locations in the same unlined landfill. The impacts of effluent leachate on physical and chemical properties of the soil and its propagation depth were studied, and the leachate-transport pattern between successive boreholes was predicted by a developed mathematical model using an adaptive neuro-fuzzy inference system (ANFIS). The decomposition of organic leachate admixtures in the landfill yield is to produce organic acids as well as carbon dioxide, which diminishes the pH level of the landfill soil. The chemical analysis of discharged leachate in the soil samples showed that the concentrations of heavy metals are much lower than those of chloride, COD, BOD 5 , and bicarbonate. Using linear regression and mean square errors between the measured and predicted data, the accuracy of the proposed ANFIS model has been validated. Results show a high correlation between observed and predicated data.

Evaluating oxidation-reduction properties of dissolved organic matter from Chinese milk vetch (Astragalus sinicus L.): a comprehensive multi-parametric study.

PubMed

Liu, Yong; Lou, Jun; Li, Fang-Bai; Xu, Jian-Ming; Yu, Xiong-Sheng; Zhu, Li-An; Wang, Feng

2014-08-01

Green manuring is a common practice in replenishment of soil organic matter and nutrients in rice paddy field. Owing to the complex interplay of multiple factors, the oxidation--reduction (redox) properties of dissolved organic matter (DOM) from green manure crops are presently not fully understood. In this study, a variety of surrogate parameters were used to evaluate the redox capacity and redox state of DOM derived from Chinese milk vetch (CMV, Astragalus sinicus L.) via microbial decomposition under continuously flooded (CF) and non-flooded (NF) conditions. Additionally, the correlation between the surrogate parameters of CMV-DOM and the kinetic parameters of relevant redox reactions was evaluated in a soil-water system containing CMV-DOM. Results showed that the redox properties of CMV-DOM were substantially different between the fresh and decomposed CMV-DOM treatments. Determination of the surrogate parameters via ultraviolet-visible/Fourier transform infrared absorption spectroscopy and gel permeation chromatography generally provided high-quality data for predicting the redox capacity of CMV-DOM, while the surrogate parameters determined by elemental analysis were suitable for predicting the redox state of CMV-DOM. Depending on the redox capacity and redox state of various moieties/components, NF-decomposed CMV-DOM could easily accelerate soil reduction by shuttling electrons to iron oxides, because it contained more reversible redox-active functional groups (e.g. quinone and hydroquinone pairs) than CF-decomposed CMV-DOM. This work demonstrates that a single index cannot interpret complex changes in multiple factors that jointly determine the redox reactivity of CMV-DOM. Thus, a multi-parametric study is needed for providing comprehensive information on the redox properties of green manure DOM.

Spatio-temporal patterns of soil water storage under dryland agriculture at the watershed scale

NASA Astrophysics Data System (ADS)

Ibrahim, Hesham M.; Huggins, David R.

2011-07-01

SummarySpatio-temporal patterns of soil water are major determinants of crop yield potential in dryland agriculture and can serve as the basis for delineating precision management zones. Soil water patterns can vary significantly due to differences in seasonal precipitation, soil properties and topographic features. In this study we used empirical orthogonal function (EOF) analysis to characterize the spatial variability of soil water at the Washington State University Cook Agronomy Farm (CAF) near Pullman, WA. During the period 1999-2006, the CAF was divided into three roughly equal blocks (A, B, and C), and soil water at 0.3 m intervals to a depth of 1.5 m measured gravimetrically at approximately one third of the 369 geo-referenced points on the 37-ha watershed. These data were combined with terrain attributes, soil bulk density and apparent soil conductivity (EC a). The first EOF generated from the three blocks explained 73-76% of the soil water variability. Field patterns of soil water based on EOF interpolation varied between wet and dry conditions during spring and fall seasons. Under wet conditions, elevation and wetness index were the dominant factors regulating the spatial patterns of soil water. As soil dries out during summer and fall, soil properties (EC a and bulk density) become more important in explaining the spatial patterns of soil water. The EOFs generated from block B, which represents average topographic and soil properties, provided better estimates of soil water over the entire watershed with larger Nash-Sutcliffe Coefficient of Efficiency (NSCE) values, especially when the first two EOFs were retained. Including more than the first two EOFs did not significantly increase the NSCE of soil water estimate. The EOF interpolation method to estimate soil water variability worked slightly better during spring than during fall, with average NSCE values of 0.23 and 0.20, respectively. The predictable patterns of stored soil water in the spring could serve as the basis for delineating precision management zones as yield potential is largely driven by water availability. The EOF-based method has the advantage of estimating the soil water variability based on soil water data from several measurement times, whereas in regression methods only soil water measurement at a single time are used. The EOF-based method can also be used to estimate soil water at any time other than measurement times, assuming the average soil water of the watershed is known at that time.

Soil moisture and properties estimation by assimilating soil temperatures using particle batch smoother: A new perspective for DTS

NASA Astrophysics Data System (ADS)

Dong, J.; Steele-Dunne, S. C.; Ochsner, T. E.; Van De Giesen, N.

2015-12-01

Soil moisture, hydraulic and thermal properties are critical for understanding the soil surface energy balance and hydrological processes. Here, we will discuss the potential of using soil temperature observations from Distributed Temperature Sensing (DTS) to investigate the spatial variability of soil moisture and soil properties. With DTS soil temperature can be measured with high resolution (spatial <1m, and temporal < 1min) in cables up to kilometers in length. Soil temperature evolution is primarily controlled by the soil thermal properties, and the energy balance at the soil surface. Hence, soil moisture, which affects both soil thermal properties and the energy that participates the evaporation process, is strongly correlated to the soil temperatures. In addition, the dynamics of the soil moisture is determined by the soil hydraulic properties.Here we will demonstrate that soil moisture, hydraulic and thermal properties can be estimated by assimilating observed soil temperature at shallow depths using the Particle Batch Smoother (PBS). The PBS can be considered as an extension of the particle filter, which allows us to infer soil moisture and soil properties using the dynamics of soil temperature within a batch window. Both synthetic and real field data will be used to demonstrate the robustness of this approach. We will show that the proposed method is shown to be able to handle different sources of uncertainties, which may provide a new view of using DTS observations to estimate sub-meter resolution soil moisture and properties for remote sensing product validation.

Sub-parcel terroir mapping supported by UAV-based hyperspectral imagery

NASA Astrophysics Data System (ADS)

Takács, Katalin; Árvai, Mátyás; Koós, Sándor; Deák, Márton; Bakacsi, Zsófia; László, Péter; Pásztor, László

2017-04-01

There is a greater need to better understand the regional-to-parcel variations in viticultural potential. The differentiation and mapping of the variability of grape and wine quality require comprehensive spatial modelling of climatic, topographic and soil properties and a "terroir-based approach". Using remote and proximal sensing sensors and instruments are the most effective way for surveying vineyard status, such as geomorphologic and soil conditions, plant water and nutrient availability, plant health. UAV (Unmanned Aerial Vechicle) platforms are ideal for the remote monitoring of small and medium size vineyards, because flight planning is flexible and very high spatial ground resolution (even centimeters) can be achieved. Using hyperspectral remote sensing techniques the spectral response of the vegetation and the bare soil surface can be analyzed in very high spectral resolution, which can support terroir mapping on a sub-parcel level. Our study area is located in Hungary, in the Tokaj Wine Region, which is a historical region for botrityzed dessert wine making. The area of Tokaj Wine Region was formed mostly by Miocene volcanic activity, where andesite, rhyolite lavas and tuffs are characteristic and loess cover also occurs in some regions. The various geology and morphology of this area result diversity in soil types and soil properties as well. The study site was surveyed by a Cubert UHD-185 hyperspectral camera set on a Cortex Octocopter platform. The hyperspectral images were acquired in VIS-NIR (visible and near-infrared; 450-950 nm), with 4 nm sampling interval. The image acquisition was carried out at bare soil conditions, therefore the most important soil properties, which has dominant role by the delineation of terroir, can be predicted. In our paper we will present the first results of the hyperspectral survey.

Part V--Sorption of pharmaceuticals and personal care products.

PubMed

Pan, Bo; Ning, Ping; Xing, Baoshan

2009-01-01

Pharmaceuticals and personal care products (PPCPs) including antibiotics, endocrine-disrupting chemicals, and veterinary pharmaceuticals are emerging pollutants, and their environmental risk was not emphasized until a decade ago. These compounds have been reported to cause adverse impacts on wildlife and human. However, compared to the studies on hydrophobic organic contaminants (HOCs) whose sorption characteristics is reviewed in Part IV of this review series, information on PPCPs is very limited. Thus, a summary of recent research progress on PPCP sorption in soils or sediments is necessary to clarify research requirements and directions. We reviewed the research progress on PPCP sorption in soils or sediments highlighting PPCP sorption different from that of HOCs. Special function of humic substances (HSs) on PPCP behavior is summarized according to several features of PPCP-soil or sediment interaction. In addition, we discussed the behavior of xenobiotic chemicals in a three-phase system (dissolved organic matter (DOM)-mineral-water). The complexity of three-phase systems was also discussed. Nonideal sorption of PPCPs in soils or sediments is generally reported, and PPCP sorption behavior is relatively a more complicated process compared to HOC sorption, such as the contribution of inorganic fractions, fast degradation and metabolite sorption, and species-specific sorption mechanism. Thus, mechanistic studies are urgently needed for a better understanding of their environmental risk and for pollution control. Recent research progress on nonideal sorption has not been incorporated into fate modeling of xenobiotic chemicals. A major reason is the complexity of the three-phase system. First of all, lack of knowledge in describing DOM fractionation after adsorption by mineral particles is one of the major restrictions for an accurate prediction of xenobiotic chemical behavior in the presence of DOM. Secondly, no explicit mathematical relationship between HS chemical-physical properties, and their sorption characteristics has been proposed. Last but not least, nonlinear interactions could exponentially increase the complexity and uncertainties of environmental fate models for xenobiotics. Discussion on proper simplification of fate modeling in the framework of nonlinear interactions is still unavailable. Although the methodologies and concepts for studying HOC environmental fate could be adopted for PPCP study, their differences should be highly understood. Prediction of PPCP environmental behavior needs to combine contributions from various fractions of soils or sediments and the sorption of their metabolites and different species. More detailed studies on PPCP sorption in separated soil or sediment fractions are needed in order to propose a model predicting PPCP sorption in soils or sediments based on soil or sediment properties. The information on sorption of PPCP metabolites and species and the competition between them is still not enough to be incorporated into any predictive models.

Selenium(IV) and (VI) sorption by soils surrounding fly ash management facilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hyun, S.; Burns, P.E.; Murarka, I.

2006-11-15

Leachate derived from unlined coal ash disposal facilities is one of the most significant anthropogenic sources of selenium to the environment. To establish a practical framework for predicting transport of selenium in ash leachate, sorption of Se(IV) and Se(VI) from 1 mM CaSO{sub 4} was measured for 18 soils obtained down-gradient from three ash landfill sites and evaluated with respect to several soil properties. Furthermore, soil attenuation from lab-generated ash leachate and the effect of Ca{sup 2+} and SO{sub 4}{sup 2-} concentrations as well as pH on both Se(IV) and Se(VI) was quantified for a subset of soils. For bothmore » Se(IV) and Se(VI), pH combined with either percentage clay or dithionite-citrate-bicarbonate (DCB)-extractable Fe described {gt} 80% of the differences in sorption across all soils, yielding an easy approach for making initial predictions regarding site-specific selenium transport to sensitive water bodies. Se(IV) consistently exhibited an order of magnitude greater sorption than Se(VI). Selenium sorption was highest at lower pH values, with Se(IV) sorption decreasing at pH values above 6, whereas Se(VI) decreased over the entire pH range (2.5-10). Using these pH adsorption envelopes, the likely effect of ash leachate-induced changes in soil pore water pH with time on selenium attenuation by down gradient soils can be predicted. Selenium sorption increased with increasing Ca{sup 2+} concentrations while SO{sub 4}2- suppressed sorption well above enhancements by Ca{sup 2+}. Soil attenuation of selenium from ash leachates agreed well with sorption measured from 1 mM CaSO{sub 4}, indicating that 1 mM CaSO{sub 4} is a reasonable synthetic leachate for assessing selenium behavior at ash landfill sites.« less

Soil nutrient-landscape relationships in a lowland tropical rainforest in Panama

USGS Publications Warehouse

Barthold, F.K.; Stallard, R.F.; Elsenbeer, H.

2008-01-01

Soils play a crucial role in biogeochemical cycles as spatially distributed sources and sinks of nutrients. Any spatial patterns depend on soil forming processes, our understanding of which is still limited, especially in regards to tropical rainforests. The objective of our study was to investigate the effects of landscape properties, with an emphasis on the geometry of the land surface, on the spatial heterogeneity of soil chemical properties, and to test the suitability of soil-landscape modeling as an appropriate technique to predict the spatial variability of exchangeable K and Mg in a humid tropical forest in Panama. We used a design-based, stratified sampling scheme to collect soil samples at 108 sites on Barro Colorado Island, Panama. Stratifying variables are lithology, vegetation and topography. Topographic variables were generated from high-resolution digital elevation models with a grid size of 5 m. We took samples from five depths down to 1 m, and analyzed for total and exchangeable K and Mg. We used simple explorative data analysis techniques to elucidate the importance of lithology for soil total and exchangeable K and Mg. Classification and Regression Trees (CART) were adopted to investigate importance of topography, lithology and vegetation for the spatial distribution of exchangeable K and Mg and with the intention to develop models that regionalize the point observations using digital terrain data as explanatory variables. Our results suggest that topography and vegetation do not control the spatial distribution of the selected soil chemical properties at a landscape scale and lithology is important to some degree. Exchangeable K is distributed equally across the study area indicating that other than landscape processes, e.g. biogeochemical processes, are responsible for its spatial distribution. Lithology contributes to the spatial variation of exchangeable Mg but controlling variables could not be detected. The spatial variation of soil total K and Mg is mainly influenced by lithology. ?? 2007 Elsevier B.V. All rights reserved.

Rock Content Influence on Soil Hydraulic Properties

NASA Astrophysics Data System (ADS)

Parajuli, K.; Sadeghi, M.; Jones, S. B.

2015-12-01

Soil hydraulic properties including the soil water retention curve (SWRC) and hydraulic conductivity function are important characteristics of soil affecting a variety of soil properties and processes. The hydraulic properties are commonly measured for seived soils (i.e. particles < 2 mm), but many natural soils include rock fragments of varying size that alter bulk hydraulic properties. Relatively few studies have addressed this important problem using physically-based concepts. Motivated by this knowledge gap, we set out to describe soil hydraulic properties using binary mixtures (i.e. rock fragment inclusions in a soil matrix) based on individual properties of the rock and soil. As a first step of this study, special attention was devoted to the SWRC, where the impact of rock content on the SWRC was quantified using laboratory experiments for six different mixing ratios of soil matrix and rock. The SWRC for each mixture was obtained from water mass and water potential measurements. The resulting data for the studied mixtures yielded a family of SWRC indicating how the SWRC of the mixture is related to that of the individual media, i.e., soil and rock. A consistent model was also developed to describe the hydraulic properties of the mixture as a function of the individual properties of the rock and soil matrix. Key words: Soil hydraulic properties, rock content, binary mixture, experimental data.

The Soil Model Development and Intercomparison Panel (SoilMIP) of the International Soil Modeling Consortium (ISMC)

NASA Astrophysics Data System (ADS)

Vanderborght, Jan; Priesack, Eckart

2017-04-01

The Soil Model Development and Intercomparison Panel (SoilMIP) is an initiative of the International Soil Modeling Consortium. Its mission is to foster the further development of soil models that can predict soil functions and their changes (i) due to soil use and land management and (ii) due to external impacts of climate change and pollution. Since soil functions and soil threats are diverse but linked with each other, the overall aim is to develop holistic models that represent the key functions of the soil system and the links between them. These models should be scaled up and integrated in terrestrial system models that describe the feedbacks between processes in the soil and the other terrestrial compartments. We propose and illustrate a few steps that could be taken to achieve these goals. A first step is the development of scenarios that compare simulations by models that predict the same or different soil services. Scenarios can be considered at three different levels of comparisons: scenarios that compare the numerics (accuracy but also speed) of models, scenarios that compare the effect of differences in process descriptions, and scenarios that compare simulations with experimental data. A second step involves the derivation of metrics or summary statistics that effectively compare model simulations and disentangle parameterization from model concept differences. These metrics can be used to evaluate how more complex model simulations can be represented by simpler models using an appropriate parameterization. A third step relates to the parameterization of models. Application of simulation models implies that appropriate model parameters have to be defined for a range of environmental conditions and locations. Spatial modelling approaches are used to derive parameter distributions. Considering that soils and their properties emerge from the interaction between physical, chemical and biological processes, the combination of spatial models with process models would lead to consistent parameter distributions correlations and could potentially represent self-organizing processes in soils and landscapes.

Soil Electromagnetic Properties and Metal Detector Performance: Theory and Measurement

DTIC Science & Technology

2008-11-17

configuration on 51 a uniform conductive half-space are predicted by the following relation reported by Oldenburg (1978) S(r, z) = s(r1, z)− s(r2, z)− s(r3...where γQ denotes an instrument-specific scaling factor. In complete analogy with sensitivity functions described for quadrapole electrode ar- rays ...www.bartington.com/media/aedd335c/om0408%20MS2%20v46.pdf Billings, S.D., Pasion, L.R. and Oldenburg , D.W., 2003, Characterizing magnetic soils: state of the art and

Evaluation of Deterministic Models for Near Surface Soil Moisture Prediction

DTIC Science & Technology

1988-05-01

soil hydrological properties (max of 3) ’ . 30. mean length of segment (hWen) 31. cmax of each layer ( cmax I (k,j), k= kno, j=1,jno) 32. porosity of...kelvin Variable name: tac Subroutines: ’bevapor’ Description: air temperature in celsius • * Variable name: tak Subroutines: ’bevapor’ Description: air...3 C - 1 *’ C GET-TABLE-VALUES assign 9865 to i9930 goto 9930 9865 cloud-yn takc-ta tac-( tak -273.15) ea-6. 108*rh*exp( (ac*tac)/ ( tak -bc)) alphi

Chemodynamics of heavy metals in long-term contaminated soils: metal speciation in soil solution.

PubMed

Kim, Kwon-Rae; Owens, Gary

2009-01-01

The concentration and speciation of heavy metals in soil solution isolated from long-term contaminated soils were investigated. The soil solution was extracted at 70% maximum water holding capacity (MWHC) after equilibration for 24 h. The free metal concentrations (Cd2+, CU2+, Pb2+, and Zn2+) in soil solution were determined using the Donnan membrane technique (DMT). Initially the DMT was validated using artificial solutions where the percentage of free metal ions were significantly correlated with the percentages predicted using MINTEQA2. However, there was a significant difference between the absolute free ion concentrations predicted by MINTEQA2 and the values determined by the DMT. This was due to the significant metal adsorption onto the cation exchange membrane used in the DMT with 20%, 28%, 44%, and 8% mass loss of the initial total concentration of Cd, Cu, Pb, and Zn in solution, respectively. This could result in a significant error in the determination of free metal ions when using DMT if no allowance for membrane cation adsorption was made. Relative to the total soluble metal concentrations the amounts of free Cd2+ (3%-52%) and Zn2+ (11%-72%) in soil solutions were generally higher than those of Cu2+ (0.2%-30%) and Pb2+ (0.6%-10%). Among the key soil solution properties, dissolved heavy metal concentrations were the most significant factor governing free metal ion concentrations. Soil solution pH showed only a weak relationship with free metal ion partitioning coefficients (K(p)) and dissolved organic carbon did not show any significant influence on K(p).

Effects of long-term soil and crop management on soil hydraulic properties for claypan soils

USDA-ARS?s Scientific Manuscript database

Regional and national soil maps have been developed along with associated soil property databases to assist users in making land management decisions based on soil characteristics. These soil properties include average values from soil characterization for each soil series. In reality, these propert...

Soil geochemical factors regulate Cd accumulation by metal hyperaccumulating Noccaea caerulescens (J. Presl & C. Presl) F.K. Mey in field-contaminated soils.

PubMed

Rosenfeld, Carla E; Chaney, Rufus L; Martínez, Carmen E

2018-03-01

Cadmium contamination in soil is a substantial global problem, and of significant concern due to high food-chain transfer. Cadmium hyperaccumulators are of particular interest because of their ability to tolerate and take up significant amounts of heavy metal pollution from soils. One particular plant, Noccaea caerulescens (formerly, Thlaspi caerulescens), has been extensively studied in terms of its capacity to accumulate heavy metals (specifically Zn and Cd), though these studies have primarily utilized hydroponic and metal-spiked model soil systems. We studied Cd and nutrient uptake by two N. caerulescens ecotypes, Prayon (Zn-only hyperaccumulator) and Ganges (Zn- and Cd-hyperaccumulator) in four long-term field-contaminated soils. Our data suggest that individual soil properties such as total soil Cd, Zn:Cd molar ratio, or soil pH do not accurately predict Cd uptake by hyperaccumulating plants. Additionally, total Cd uptake by the hyperaccumulating Ganges ecotype was substantially less than its physiological capacity, which is likely due to Cd-containing solid phases (primarily iron oxides) and pH that play an important role in regulating and limiting Cd solubility. Increased P accumulation in the Ganges leaves, and greater plant Fe accumulation from Cd-containing soils suggests that rhizosphere alterations via proton, and potentially organic acid, secretion may also play a role in nutrient and Cd acquisition by the plant roots. The current study highlights the role that soil geochemical factors play in influencing Cd uptake by hyperaccumulating plants. While these plants may have high physiological potential to accumulate metals from contaminated soils, individual soil geochemical factors and the plant-soil interactions in that soil will dictate the actual amount of phytoextractable metal. This underlines the need for site-specific understanding of metal-containing solid phases and geochemical properties of soils before undertaking phytoextraction efforts. Copyright © 2017 Elsevier B.V. All rights reserved.

Biophysical processes supporting the diversity of microbial life in soil

PubMed Central

Tecon, Robin

2017-01-01

Abstract Soil, the living terrestrial skin of the Earth, plays a central role in supporting life and is home to an unimaginable diversity of microorganisms. This review explores key drivers for microbial life in soils under different climates and land-use practices at scales ranging from soil pores to landscapes. We delineate special features of soil as a microbial habitat (focusing on bacteria) and the consequences for microbial communities. This review covers recent modeling advances that link soil physical processes with microbial life (termed biophysical processes). Readers are introduced to concepts governing water organization in soil pores and associated transport properties and microbial dispersion ranges often determined by the spatial organization of a highly dynamic soil aqueous phase. The narrow hydrological windows of wetting and aqueous phase connectedness are crucial for resource distribution and longer range transport of microorganisms. Feedbacks between microbial activity and their immediate environment are responsible for emergence and stabilization of soil structure—the scaffolding for soil ecological functioning. We synthesize insights from historical and contemporary studies to provide an outlook for the challenges and opportunities for developing a quantitative ecological framework to delineate and predict the microbial component of soil functioning. PMID:28961933

A soil map of a large watershed in China: applying digital soil mapping in a data sparse region

NASA Astrophysics Data System (ADS)

Barthold, F.; Blank, B.; Wiesmeier, M.; Breuer, L.; Frede, H.-G.

2009-04-01

Prediction of soil classes in data sparse regions is a major research challenge. With the advent of machine learning the possibilities to spatially predict soil classes have increased tremendously and given birth to new possibilities in soil mapping. Digital soil mapping is a research field that has been established during the last decades and has been accepted widely. We now need to develop tools to reduce the uncertainty in soil predictions. This is especially challenging in data sparse regions. One approach to do this is to implement soil taxonomic distance as a classification error criterion in classification and regression trees (CART) as suggested by Minasny et al. (Geoderma 142 (2007) 285-293). This approach assumes that the classification error should be larger between soils that are more dissimilar, i.e. differ in a larger number of soil properties, and smaller between more similar soils. Our study area is the Xilin River Basin, which is located in central Inner Mongolia in China. It is characterized by semi arid climate conditions and is representative for the natural occurring steppe ecosystem. The study area comprises 3600 km2. We applied a random, stratified sampling design after McKenzie and Ryan (Geoderma 89 (1999) 67-94) with landuse and topography as stratifying variables. We defined 10 sampling classes, from each class 14 replicates were randomly drawn and sampled. The dataset was split into 100 soil profiles for training and 40 soil profiles for validation. We then applied classification and regression trees (CART) to quantify the relationships between soil classes and environmental covariates. The classification tree explained 75.5% of the variance with land use and geology as most important predictor variables. Among the 8 soil classes that we predicted, the Kastanozems cover most of the area. They are predominantly found in steppe areas. However, even some of the soils at sand dune sites, which were thought to show only little soil formation, can be classified as Kastanozems. Besides the Kastanozems, Regosols are most common at the sand dune sites as well as at sites that are defined as bare soil which are characterized by little or no vegetation. Gleysols are mostly found at sites in the vicinity of the Xilin river, which are connected to the groundwater. They can also be found in small valleys or depressions where sub-surface waters from neighboring areas collect. The richest soils are found in mountain meadow areas. Pedogenetic conditions here are most favorable and lead to the formation of Chernozems with deep humic Ah horizons. Other soil types that occur in the study area are Arenosols, Calcisols, Cambisol and Phaeozems. In addition, soil taxonomic distance is implemented into the decision tree procedure as a measure of classification error. The results of incorporating taxonomic distance as a loss function in the decision tree will be compared with the standard application of the decision tree.

Influence of Elevation Data Resolution on Spatial Prediction of Colluvial Soils in a Luvisol Region

PubMed Central

Penížek, Vít; Zádorová, Tereza; Kodešová, Radka; Vaněk, Aleš

2016-01-01

The development of a soil cover is a dynamic process. Soil cover can be altered within a few decades, which requires updating of the legacy soil maps. Soil erosion is one of the most important processes quickly altering soil cover on agriculture land. Colluvial soils develop in concave parts of the landscape as a consequence of sedimentation of eroded material. Colluvial soils are recognised as important soil units because they are a vast sink of soil organic carbon. Terrain derivatives became an important tool in digital soil mapping and are among the most popular auxiliary data used for quantitative spatial prediction. Prediction success rates are often directly dependent on raster resolution. In our study, we tested how raster resolution (1, 2, 3, 5, 10, 20 and 30 meters) influences spatial prediction of colluvial soils. Terrain derivatives (altitude, slope, plane curvature, topographic position index, LS factor and convergence index) were calculated for the given raster resolutions. Four models were applied (boosted tree, neural network, random forest and Classification/Regression Tree) to spatially predict the soil cover over a 77 ha large study plot. Models training and validation was based on 111 soil profiles surveyed on a regular sampling grid. Moreover, the predicted real extent and shape of the colluvial soil area was examined. In general, no clear trend in the accuracy prediction was found without the given raster resolution range. Higher maximum prediction accuracy for colluvial soil, compared to prediction accuracy of total soil cover of the study plot, can be explained by the choice of terrain derivatives that were best for Colluvial soils differentiation from other soil units. Regarding the character of the predicted Colluvial soils area, maps of 2 to 10 m resolution provided reasonable delineation of the colluvial soil as part of the cover over the study area. PMID:27846230

Non-climatic constraints on upper elevational plant range expansion under climate change

PubMed Central

Brown, Carissa D.; Vellend, Mark

2014-01-01

We are limited in our ability to predict climate-change-induced range shifts by our inadequate understanding of how non-climatic factors contribute to determining range limits along putatively climatic gradients. Here, we present a unique combination of observations and experiments demonstrating that seed predation and soil properties strongly limit regeneration beyond the upper elevational range limit of sugar maple, a tree species of major economic importance. Most strikingly, regeneration beyond the range limit occurred almost exclusively when seeds were experimentally protected from predators. Regeneration from seed was depressed on soil from beyond the range edge when this soil was transplanted to sites within the range, with indirect evidence suggesting that fungal pathogens play a role. Non-climatic factors are clearly in need of careful attention when attempting to predict the biotic consequences of climate change. At minimum, we can expect non-climatic factors to create substantial time lags between the creation of more favourable climatic conditions and range expansion. PMID:25253462

Estimating Grass-Soil Bioconcentration of Munitions Compounds from Molecular Structure.

PubMed

Torralba Sanchez, Tifany L; Liang, Yuzhen; Di Toro, Dominic M

2017-10-03

A partitioning-based model is presented to estimate the bioconcentration of five munitions compounds and two munition-like compounds in grasses. The model uses polyparameter linear free energy relationships (pp-LFERs) to estimate the partition coefficients between soil organic carbon and interstitial water and between interstitial water and the plant cuticle, a lipid-like plant component. Inputs for the pp-LFERs are a set of numerical descriptors computed from molecular structure only that characterize the molecular properties that determine the interaction with soil organic carbon, interstitial water, and plant cuticle. The model is validated by predicting concentrations measured in the whole plant during independent uptake experiments with a root-mean-square error (log predicted plant concentration-log observed plant concentration) of 0.429. This highlights the dominant role of partitioning between the exposure medium and the plant cuticle in the bioconcentration of these compounds. The pp-LFERs can be used to assess the environmental risk of munitions compounds and munition-like compounds using only their molecular structure as input.

Assessment the effect of homogenized soil on soil hydraulic properties and soil water transport

NASA Astrophysics Data System (ADS)

Mohawesh, O.; Janssen, M.; Maaitah, O.; Lennartz, B.

2017-09-01

Soil hydraulic properties play a crucial role in simulating water flow and contaminant transport. Soil hydraulic properties are commonly measured using homogenized soil samples. However, soil structure has a significant effect on the soil ability to retain and to conduct water, particularly in aggregated soils. In order to determine the effect of soil homogenization on soil hydraulic properties and soil water transport, undisturbed soil samples were carefully collected. Five different soil structures were identified: Angular-blocky, Crumble, Angular-blocky (different soil texture), Granular, and subangular-blocky. The soil hydraulic properties were determined for undisturbed and homogenized soil samples for each soil structure. The soil hydraulic properties were used to model soil water transport using HYDRUS-1D.The homogenized soil samples showed a significant increase in wide pores (wCP) and a decrease in narrow pores (nCP). The wCP increased by 95.6, 141.2, 391.6, 3.9, 261.3%, and nCP decreased by 69.5, 10.5, 33.8, 72.7, and 39.3% for homogenized soil samples compared to undisturbed soil samples. The soil water retention curves exhibited a significant decrease in water holding capacity for homogenized soil samples compared with the undisturbed soil samples. The homogenized soil samples showed also a decrease in soil hydraulic conductivity. The simulated results showed that water movement and distribution were affected by soil homogenizing. Moreover, soil homogenizing affected soil hydraulic properties and soil water transport. However, field studies are being needed to find the effect of these differences on water, chemical, and pollutant transport under several scenarios.

Three-dimensional mapping of soil chemical characteristics at micrometric scale: Statistical prediction by combining 2D SEM-EDX data and 3D X-ray computed micro-tomographic images

NASA Astrophysics Data System (ADS)

Hapca, Simona

2015-04-01

Many soil properties and functions emerge from interactions of physical, chemical and biological processes at microscopic scales, which can be understood only by integrating techniques that traditionally are developed within separate disciplines. While recent advances in imaging techniques, such as X-ray computed tomography (X-ray CT), offer the possibility to reconstruct the 3D physical structure at fine resolutions, for the distribution of chemicals in soil, existing methods, based on scanning electron microscope (SEM) and energy dispersive X-ray detection (EDX), allow for characterization of the chemical composition only on 2D surfaces. At present, direct 3D measurement techniques are still lacking, sequential sectioning of soils, followed by 2D mapping of chemical elements and interpolation to 3D, being an alternative which is explored in this study. Specifically, we develop an integrated experimental and theoretical framework which combines 3D X-ray CT imaging technique with 2D SEM-EDX and use spatial statistics methods to map the chemical composition of soil in 3D. The procedure involves three stages 1) scanning a resin impregnated soil cube by X-ray CT, followed by precision cutting to produce parallel thin slices, the surfaces of which are scanned by SEM-EDX, 2) alignment of the 2D chemical maps within the internal 3D structure of the soil cube, and 3) development, of spatial statistics methods to predict the chemical composition of 3D soil based on the observed 2D chemical and 3D physical data. Specifically, three statistical models consisting of a regression tree, a regression tree kriging and cokriging model were used to predict the 3D spatial distribution of carbon, silicon, iron and oxygen in soil, these chemical elements showing a good spatial agreement between the X-ray grayscale intensities and the corresponding 2D SEM-EDX data. Due to the spatial correlation between the physical and chemical data, the regression-tree model showed a great potential in predicting chemical composition in particular for iron, which is generally sparsely distributed in soil. For carbon, silicon and oxygen, which are more densely distributed, the additional kriging of the regression tree residuals improved significantly the prediction, whereas prediction based on co-kriging was less consistent across replicates, underperforming regression-tree kriging. The present study shows a great potential in integrating geo-statistical methods with imaging techniques to unveil the 3D chemical structure of soil at very fine scales, the framework being suitable to be further applied to other types of imaging data such as images of biological thin sections for characterization of microbial distribution. Key words: X-ray CT, SEM-EDX, segmentation techniques, spatial correlation, 3D soil images, 2D chemical maps.

Selection of a Geostatistical Method to Interpolate Soil Properties of the State Crop Testing Fields using Attributes of a Digital Terrain Model

NASA Astrophysics Data System (ADS)

Sahabiev, I. A.; Ryazanov, S. S.; Kolcova, T. G.; Grigoryan, B. R.

2018-03-01

The three most common techniques to interpolate soil properties at a field scale—ordinary kriging (OK), regression kriging with multiple linear regression drift model (RK + MLR), and regression kriging with principal component regression drift model (RK + PCR)—were examined. The results of the performed study were compiled into an algorithm of choosing the most appropriate soil mapping technique. Relief attributes were used as the auxiliary variables. When spatial dependence of a target variable was strong, the OK method showed more accurate interpolation results, and the inclusion of the auxiliary data resulted in an insignificant improvement in prediction accuracy. According to the algorithm, the RK + PCR method effectively eliminates multicollinearity of explanatory variables. However, if the number of predictors is less than ten, the probability of multicollinearity is reduced, and application of the PCR becomes irrational. In that case, the multiple linear regression should be used instead.

Edaphic controls on soil organic carbon stocks in restored grasslands

DOE Office of Scientific and Technical Information (OSTI.GOV)

O'Brien, Sarah L.; Jastrow, Julie D.; Grimley, David A.

Cultivation of undisturbed soils dramatically depletes organic carbon stocks at shallow depths, releasing a substantial quantity of stored carbon to the atmosphere. Restoration of native ecosystems can help degraded soils rebuild a portion of the depleted soil organic matter. However, the rate and magnitude of soil carbon accrual can be highly variable from site to site. Thus, a better understanding of the mechanisms controlling soil organic carbon stocks is necessary to improve predictions of soil carbon recovery. We measured soil organic carbon stocks and a suite of edaphic factors in the upper 10 cm of a series of restored tallgrassmore » prairies representing a range of drainage conditions. Our findings suggest that factors related to soil organic matter stabilization mechanisms (texture, polyvalent cations) were key predictors of soil organic carbon, along with variables that influence plant and microbial biomass (available phosphorus, pH) and soil moisture. Exchangeable soil calcium was the strongest single predictor, explaining 74% of the variation in soil organic carbon, followed by clay content,which explained 52% of the variation. Our results demonstrate that the cumulative effects of even relatively small differences in these edaphic properties can have a large impact on soil carbon stocks when integrated over several decades.« less

Computational scheme for the prediction of metal ion binding by a soil fulvic acid

USGS Publications Warehouse

Marinsky, J.A.; Reddy, M.M.; Ephraim, J.H.; Mathuthu, A.S.

1995-01-01

The dissociation and metal ion binding properties of a soil fulvic acid have been characterized. Information thus gained was used to compensate for salt and site heterogeneity effects in metal ion complexation by the fulvic acid. An earlier computational scheme has been modified by incorporating an additional step which improves the accuracy of metal ion speciation estimates. An algorithm is employed for the prediction of metal ion binding by organic acid constituents of natural waters (once the organic acid is characterized in terms of functional group identity and abundance). The approach discussed here, currently used with a spreadsheet program on a personal computer, is conceptually envisaged to be compatible with computer programs available for ion binding by inorganic ligands in natural waters.

The Temporal Dynamics of Spatial Patterns of Observed Soil Moisture Interpreted Using the Hydrus 1-D Model

NASA Astrophysics Data System (ADS)

Chen, M.; Willgoose, G. R.; Saco, P. M.

2009-12-01

This paper investigates the soil moisture dynamics over two subcatchments (Stanley and Krui) in the Goulburn River in NSW during a three year period (2005-2007) using the Hydrus 1-D unsaturated soil water flow model. The model was calibrated to the seven Stanley microcatchment sites (1 sqkm site) using continuous time surface 30cm and full profile soil moisture measurements. Soil type, leaf area index and soil depth were found to be the key parameters changing model fit to the soil moisture time series. They either shifted the time series up or down, changed the steepness of dry-down recessions or determined the lowest point of soil moisture dry-down respectively. Good correlations were obtained between observed and simulated soil water storage (R=0.8-0.9) when calibrated parameters for one site were applied to the other sites. Soil type was also found to be the main determinant (after rainfall) of the mean of modelled soil moisture time series. Simulations of top 30cm were better than those of the whole soil profile. Within the Stanley microcatchment excellent soil moisture matches could be generated simply by adjusting the mean of soil moisture up or down slightly. Only minor modification of soil properties from site to site enable good fits for all of the Stanley sites. We extended the predictions of soil moisture to a larger spatial scale of the Krui catchment (sites up to 30km distant from Stanley) using soil and vegetation parameters from Stanley but the locally recorded rainfall at the soil moisture measurement site. The results were encouraging (R=0.7~0.8). These results show that it is possible to use a calibrated soil moisture model to extrapolate the soil moisture to other sites for a catchment with an area of up to 1000km2. This paper demonstrates the potential usefulness of continuous time, point scale soil moisture (typical of that measured by permanently installed TDR probes) in predicting the soil wetness status over a catchment of significant size.

7 CFR 610.12 - Equations for predicting soil loss due to water erosion.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 6 2012-01-01 2012-01-01 false Equations for predicting soil loss due to water... ASSISTANCE Soil Erosion Prediction Equations § 610.12 Equations for predicting soil loss due to water erosion. (a) The equation for predicting soil loss due to erosion for both the USLE and the RUSLE is A = R × K...

7 CFR 610.12 - Equations for predicting soil loss due to water erosion.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 6 2014-01-01 2014-01-01 false Equations for predicting soil loss due to water... ASSISTANCE Soil Erosion Prediction Equations § 610.12 Equations for predicting soil loss due to water erosion. (a) The equation for predicting soil loss due to erosion for both the USLE and the RUSLE is A = R × K...

7 CFR 610.12 - Equations for predicting soil loss due to water erosion.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 6 2011-01-01 2011-01-01 false Equations for predicting soil loss due to water... ASSISTANCE Soil Erosion Prediction Equations § 610.12 Equations for predicting soil loss due to water erosion. (a) The equation for predicting soil loss due to erosion for both the USLE and the RUSLE is A = R × K...

7 CFR 610.12 - Equations for predicting soil loss due to water erosion.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 6 2013-01-01 2013-01-01 false Equations for predicting soil loss due to water... ASSISTANCE Soil Erosion Prediction Equations § 610.12 Equations for predicting soil loss due to water erosion. (a) The equation for predicting soil loss due to erosion for both the USLE and the RUSLE is A = R × K...

7 CFR 610.12 - Equations for predicting soil loss due to water erosion.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 6 2010-01-01 2010-01-01 false Equations for predicting soil loss due to water... ASSISTANCE Soil Erosion Prediction Equations § 610.12 Equations for predicting soil loss due to water erosion. (a) The equation for predicting soil loss due to erosion for both the USLE and the RUSLE is A = R × K...

Effects of Long-term Soil and Crop Management on Soil Hydraulic Properties for Claypan Soils

USDA-ARS?s Scientific Manuscript database

Regional and national soil maps and associated databases of soil properties have been developed to help land managers make decisions based on soil characteristics. Hydrologic modelers also utilize soil hydraulic properties provided in these databases, in which soil characterization is based on avera...

Effects of slash-and-burn land management on soil spectral properties estimated with VIS-NIR-SWIR spectroscopy

NASA Astrophysics Data System (ADS)

Rosero-Vlasova, Olga Alexandra; Vlassova, Lidia; Rosero Tufiño, Pedro; Pérez-Cabello, Fernando; Montorio Llovería, Raquel

2017-04-01

Slash-and-burn land management is typical for low-income tropical countries, such as Ecuador. It involves conversion of forest into areas used for agriculture. At first trees are cut and the wood debris is burnt. After initial clearing, biomass burning is performed after each production cycle. Usually, cultivation cycles are followed by the fallow period. In the medium and long term, these practices have negative effect on soil fertility and there is the need for clearing more forest for agricultural use. This is one of the reasons for continuing deforestation with the consequent loss of biodiversity. Changes in physico-chemical properties due to periodic burning are accompanied by changes in soil spectral properties and can be determined using VIS-NIR-SWIR spectroscopy, which can be a cost-effective alternative for traditional methods of soil analysis. The purpose of the study is to assess the viability of VIS-NIR-SWIR spectroscopy for characterization of soils from land areas under slash-and-burn management system. Eighteen samples from soil surface layer were collected from two corn fields in the province of Los Rios, Ecuador, in September 2015. One of the areas has experienced six slash-and-burn cycles, while in the other the samples were collected at the end of the first corn cultivation cycle. Spectral measurements of sieved and air-dried samples were performed in the laboratory of the University of Zaragoza using ASD Fieldspec®4 spectroradiometer (350-2500nm spectral range) and ASD Illuminator Lamp as a light source. Statistically significant differences were observed between soil spectra of the samples from two soil groups. Reflectance of repeatedly burnt soils was 20% higher (mean value for the entire spectrum) for 65% of the samples, being especially important in VIS (>45%) and NIR ( 35%), probably due to the lower organic matter (OM) content. OM models built using Partial least Squares Regression demonstrated high predictive capacity (R2>0.8). Thus, the study confirms VIS-NIR-SWIR soil spectroscopy can be used as a tool for monitoring changes in soils in areas of slash-and-burn land management systems.

Biochar alters the resistance and resilience to drought in a tropical soil

NASA Astrophysics Data System (ADS)

Liang, Chenfei; Zhu, Xiaolin; Fu, Shenglei; Méndez, Ana; Gascó, Gabriel; Paz-Ferreiro, Jorge

2014-05-01

Soil microbes play a key role in nutrient cycling and carbon sequestration. Global change can alter soil microbial population composition and behavior. Biochar addition has been explored in the last years as a way to mitigate global warming. However, responses of microbial communities to biochar addition in particular in relation to abiotic disturbances are seldom documented. An example of these disturbances, which is predicted to be exacerbated with global warming, is regional drought. It has been known that fungal-based food webs are more resistant to drought than their bacterial counterparts. Our study found that biochar addition can increase the resistance of both the bacterial and fungal networks to drought. Contrary to expected, this result was not related to a change in the dominance of fungal or bacteria. In general, soil amended with biochar was characterized by a faster recovery of soil microbial properties to its basal values. Biochar addition to the soil also suppressed the Birch effect, a result that has not been previously reported.

Charcoal disrupts cell-cell communication through multiple mechanisms

NASA Astrophysics Data System (ADS)

Gao, X.; Cheng, H. Y.; Liu, S.; Masiello, C. A.; Silberg, J. J.; Del Valle, I.

2016-12-01

Microbial cell-cell communication through the release and detection of small signaling molecules is employed by soil microbes to manage many biogeochemically relevant processes including production of biofilms, priming effects on native SOM, and management of methanogenesis and denitrification. Charcoal is a ubiquitous component of soil, entering soil either from natural fire or intentionally amended as biochar. Charcoal's presence in soil introduces spatial and temporal heterogeneity in nutrients and habitats for soil microbes and may trigger a range of biological effects not yet predictable, in part because it interferes with microbial cell-cell communication. We hypothesized that charcoal's alkalinity and large active surface area could affect the lifetime of some chemical compounds that microbes use for cell-cell signaling on times scales relevant to growth and communication. To test this idea, we examined the extent and rate of charcoal quenching of cell-cell communication caused by ten charcoals with a wide range of physicochemical properties. Our measurements focused on signaling mediated by an acyl-homoserine lactone (AHL), N-3-oxo-dodecanoyl-L-homoserine lactone, which is used by many gram-negative bacteria for quorum sensing. Our results from a bioassay and chemical sorption experiments revealed that charcoal can decrease the bioavailable level of AHL through a combination of sorption and pH-dependent hydrolysis of the lactone ring. We found that the kinetics of hydrolysis can exceed those of sorption. These findings implicate charcoal surface area and alkalinity as properties that could be tuned to regulate the degradation rates of cell-cell signaling molecules in soils. We then built a quantitative model that predicts the half-lives of different microbial signaling compounds in the presence of charcoals varying in pH and surface area. Our model results suggest that the effects of charcoal on pH-sensitive bacterial AHL signals will be fundamentally distinct from effects on pH-insensitive fungal signals, potentially leading to shifts in microbial community structures.

Dependence of samarium-soil interaction on samarium concentration: Implications for environmental risk assessment.

PubMed

Ramírez-Guinart, Oriol; Salaberria, Aitor; Vidal, Miquel; Rigol, Anna

2018-03-01

The sorption and desorption behaviour of samarium (Sm), an emerging contaminant, was examined in soil samples at varying Sm concentrations. The obtained sorption and desorption parameters revealed that soil possessed a high Sm retention capacity (sorption was higher than 99% and desorption lower than 2%) at low Sm concentrations, whereas at high Sm concentrations, the sorption-desorption behaviour varied among the soil samples tested. The fractionation of the Sm sorbed in soils, obtained by sequential extractions, allowed to suggest the soil properties (pH and organic matter solubility) and phases (organic matter, carbonates and clay minerals) governing the Sm-soil interaction. The sorption models constructed in the present work along with the sorption behaviour of Sm explained in terms of soil main characteristics will allow properly assessing the Sm-soil interaction depending on the contamination scenario under study. Moreover, the sorption and desorption K d values of radiosamarium in soils were strongly correlated with those of stable Sm at low concentrations (r = 0.98); indicating that the mobility of Sm radioisotopes and, thus, the risk of radioactive Sm contamination can be predicted using data from low concentrations of stable Sm. Copyright © 2017 Elsevier Ltd. All rights reserved.

The relative abundance of predicted genes associated with ammonia-oxidation, nitrate reduction, and biomass decomposition in mineral soil are altered by intensive timber harvest.

NASA Astrophysics Data System (ADS)

Mushinski, R. M.; Zhou, Y.; Gentry, T. J.; Boutton, T. W.

2017-12-01

Forest ecosystems in the southern United States are substantially altered by anthropogenic disturbances such as timber harvest and land conversion, with effects being observed in carbon and nutrient pools as well as biogeochemical processes. Furthermore, the desire to develop renewable energy sources in the form of biomass extraction from logging residues may result in alterations in soil community structure and function. While the impact of forest management on soil physicochemical properties of the region has been studied, its' long-term effect on soil bacterial community composition and metagenomic potential is relatively unknown, especially at deeper soil depths. This study investigates how intensive organic matter removal intensities associated with timber harvest influence decadal-scale alterations in bacterial community structure and functional potential in the upper 1-m of the soil profile, 18 years post-harvest in a Pinus taeda L. forest of eastern Texas. Amplicon sequencing of the 16S rRNA gene was used in conjunction with soil chemical analyses to evaluate treatment-induced differences in community composition and potential environmental drivers of associated change. Furthermore, functional potential was assessed by using amplicon data to make metagenomic predictions. Results indicate that increasing organic matter removal intensity leads to altered community composition and the relative abundance of dominant OTUs annotated to Burkholderia and Aciditerrimonas. The relative abundance of predicted genes associated with dissimilatory nitrate reduction and denitrification were highest in the most intensively harvested treatment while genes involved in nitrification were significantly lower in the most intensively harvested treatment. Furthermore, genes associated with glycosyltransferases were significantly reduced with increasing harvest intensity while polysaccharide lyases increased. These results imply that intensive organic matter removal may create long-term alterations in bacterial community structure with concurrent alterations to mineral soil carbon and nutrient cycling which may have future consequences on forest regeneration and subsequent stand productivity.

Influence of spatial and temporal variability of subsurface soil moisture and temperature on vapour intrusion

NASA Astrophysics Data System (ADS)

Bekele, Dawit N.; Naidu, Ravi; Chadalavada, Sreenivasulu

2014-05-01

A comprehensive field study was conducted at a site contaminated with chlorinated solvents, mainly trichloroethylene (TCE), to investigate the influence of subsurface soil moisture and temperature on vapour intrusion (VI) into built structures. Existing approaches to predict the risk of VI intrusion into buildings assume homogeneous or discrete layers in the vadose zone through which TCE migrates from an underlying source zone. In reality, the subsurface of the majority of contaminated sites will be subject to significant variations in moisture and temperature. Detailed site-specific data were measured contemporaneously to evaluate the impact of spatial and temporal variability of subsurface soil properties on VI exposure assessment. The results revealed that indoor air vapour concentrations would be affected by spatial and temporal variability of subsurface soil moisture and temperature. The monthly monitoring of soil-gas concentrations over a period of one year at a depth of 3 m across the study site demonstrated significant variation in TCE vapour concentrations, which ranged from 480 to 629,308 μg/m3. Soil-gas wells at 1 m depth exhibited high seasonal variability in TCE vapour concentrations with a coefficient of variation 1.02 in comparison with values of 0.88 and 0.74 in 2 m and 3 m wells, respectively. Contour plots of the soil-gas TCE plume during wet and dry seasons showed that the plume moved across the site, hence locations of soil-gas monitoring wells for human risk assessment is a site specific decision. Subsurface soil-gas vapour plume characterisation at the study site demonstrates that assessment for VI is greatly influenced by subsurface soil properties such as temperature and moisture that fluctuate with the seasons of the year.

Characterization of soil organic matter by FT-IR spectroscopy and its relationship with chlorpyrifos sorption.

PubMed

Parolo, María Eugenia; Savini, Mónica Claudia; Loewy, Ruth Miriam

2017-07-01

Sorption of non-ionic organic compounds to soil is usually expressed as the carbon-normalized partition coefficient (K OC ) assuming that the main factor that influences the amount sorbed is the organic carbon content (OC) of the soil. However, K OC can vary across a range of soils. The influence of certain soil characteristics on the chlorpyrifos K OC values variation for 12 representative soils of the Northpatagonian Argentinian region with different physicochemical properties was investigated for this study. The chlorpyrifos sorption coefficients normalized by the OC content were experimentally obtained using the batch equilibrium method; the K OC values ranged between 9000-20,000 L kg -1 . The soil characteristics assessed were pH, clay content and spectral data indicative of soil organic matter (SOM) quality measured by FT-IR on the whole soil. The bands considered in the spectroscopic analyses were those corresponding to the aliphatic components, 2947-2858 cm -1 (band A) and the hydrophilic components, 1647-1633 cm -1 (band B). A significant relationship was found (R 2  = 0.66) between chlorpyrifos sorption (K OC ) and the variables pH and A/B height band ratio. The correlation between the values predicted by the derived model and the experimental data was significant (r = 0.89 p < 0.05). Thus, this methodology could be used to estimate chlorpyrifos sorption coefficient through the use of a simple, rapid, and environmentally-friendly measurement. K OC analysis in relation to soil properties represents a valuable contribution to the understanding of the attenuation phenomena of the organic contaminants off-site migration in the environment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Methane Emissions from Natural Gas Vehicles in Beijing, Baoding, and Shijiazhuang, China during CAREBEIJING Field Campaign

NASA Astrophysics Data System (ADS)

Pérez García-Pando, C.; Miller, R. L.; Perlwitz, J. P.; Kok, J. F.; Scanza, R.; Mahowald, N. M.

2014-12-01

Mineral dust created by wind erosion of soil particles is the dominant aerosol by mass in the atmosphere. It exerts significant effects on radiative fluxes, clouds, ocean biogeochemistry, and human health. Models that predict the lifecycle of mineral dust aerosols generally assume a globally uniform mineral composition. However, this simplification limits our understanding of the role of dust in the Earth system, since the effects of dust strongly depend on the particles' physical and chemical properties, which vary with their mineral composition. Hence, not only a detailed understanding of the processes determining the dust emission flux is needed, but also information about its size dependent mineral composition. Determining the mineral composition of dust aerosols is complicated. The largest uncertainty derives from the current atlases of soil mineral composition. These atlases provide global estimates of soil mineral fractions, but they are based upon massive extrapolation of a limited number of soil samples assuming that mineral composition is related to soil type. This disregards the potentially large variability of soil properties within each defined soil type. In addition, the analysis of these soil samples is based on wet sieving, a technique that breaks the aggregates found in the undisturbed parent soil. During wind erosion, these aggregates are subject to partial fragmentation, which generates differences on the size distribution and composition between the undisturbed parent soil and the emitted dust aerosols. We review recent progress on the representation of the mineral and chemical composition of dust in climate models. We discuss extensions of brittle fragmentation theory to prescribe the emitted size-resolved dust composition, and we identify key processes and uncertainties based upon model simulations and an unprecedented compilation of observations.

Dust Composition in Climate Models: Current Status and Prospects

NASA Astrophysics Data System (ADS)

Pérez García-Pando, C.; Miller, R. L.; Perlwitz, J. P.; Kok, J. F.; Scanza, R.; Mahowald, N. M.

2015-12-01

Mineral dust created by wind erosion of soil particles is the dominant aerosol by mass in the atmosphere. It exerts significant effects on radiative fluxes, clouds, ocean biogeochemistry, and human health. Models that predict the lifecycle of mineral dust aerosols generally assume a globally uniform mineral composition. However, this simplification limits our understanding of the role of dust in the Earth system, since the effects of dust strongly depend on the particles' physical and chemical properties, which vary with their mineral composition. Hence, not only a detailed understanding of the processes determining the dust emission flux is needed, but also information about its size dependent mineral composition. Determining the mineral composition of dust aerosols is complicated. The largest uncertainty derives from the current atlases of soil mineral composition. These atlases provide global estimates of soil mineral fractions, but they are based upon massive extrapolation of a limited number of soil samples assuming that mineral composition is related to soil type. This disregards the potentially large variability of soil properties within each defined soil type. In addition, the analysis of these soil samples is based on wet sieving, a technique that breaks the aggregates found in the undisturbed parent soil. During wind erosion, these aggregates are subject to partial fragmentation, which generates differences on the size distribution and composition between the undisturbed parent soil and the emitted dust aerosols. We review recent progress on the representation of the mineral and chemical composition of dust in climate models. We discuss extensions of brittle fragmentation theory to prescribe the emitted size-resolved dust composition, and we identify key processes and uncertainties based upon model simulations and an unprecedented compilation of observations.

Evaluation of digital soil mapping approaches with large sets of environmental covariates

NASA Astrophysics Data System (ADS)

Nussbaum, Madlene; Spiess, Kay; Baltensweiler, Andri; Grob, Urs; Keller, Armin; Greiner, Lucie; Schaepman, Michael E.; Papritz, Andreas

2018-01-01

The spatial assessment of soil functions requires maps of basic soil properties. Unfortunately, these are either missing for many regions or are not available at the desired spatial resolution or down to the required soil depth. The field-based generation of large soil datasets and conventional soil maps remains costly. Meanwhile, legacy soil data and comprehensive sets of spatial environmental data are available for many regions. Digital soil mapping (DSM) approaches relating soil data (responses) to environmental data (covariates) face the challenge of building statistical models from large sets of covariates originating, for example, from airborne imaging spectroscopy or multi-scale terrain analysis. We evaluated six approaches for DSM in three study regions in Switzerland (Berne, Greifensee, ZH forest) by mapping the effective soil depth available to plants (SD), pH, soil organic matter (SOM), effective cation exchange capacity (ECEC), clay, silt, gravel content and fine fraction bulk density for four soil depths (totalling 48 responses). Models were built from 300-500 environmental covariates by selecting linear models through (1) grouped lasso and (2) an ad hoc stepwise procedure for robust external-drift kriging (georob). For (3) geoadditive models we selected penalized smoothing spline terms by component-wise gradient boosting (geoGAM). We further used two tree-based methods: (4) boosted regression trees (BRTs) and (5) random forest (RF). Lastly, we computed (6) weighted model averages (MAs) from the predictions obtained from methods 1-5. Lasso, georob and geoGAM successfully selected strongly reduced sets of covariates (subsets of 3-6 % of all covariates). Differences in predictive performance, tested on independent validation data, were mostly small and did not reveal a single best method for 48 responses. Nevertheless, RF was often the best among methods 1-5 (28 of 48 responses), but was outcompeted by MA for 14 of these 28 responses. RF tended to over-fit the data. The performance of BRT was slightly worse than RF. GeoGAM performed poorly on some responses and was the best only for 7 of 48 responses. The prediction accuracy of lasso was intermediate. All models generally had small bias. Only the computationally very efficient lasso had slightly larger bias because it tended to under-fit the data. Summarizing, although differences were small, the frequencies of the best and worst performance clearly favoured RF if a single method is applied and MA if multiple prediction models can be developed.

Spatio-temporal interpolation of soil moisture in 3D+T using automated sensor network data

NASA Astrophysics Data System (ADS)

Gasch, C.; Hengl, T.; Magney, T. S.; Brown, D. J.; Gräler, B.

2014-12-01

Soil sensor networks provide frequent in situ measurements of dynamic soil properties at fixed locations, producing data in 2- or 3-dimensions and through time (2D+T and 3D+T). Spatio-temporal interpolation of 3D+T point data produces continuous estimates that can then be used for prediction at unsampled times and locations, as input for process models, and can simply aid in visualization of properties through space and time. Regression-kriging with 3D and 2D+T data has successfully been implemented, but currently the field of geostatistics lacks an analytical framework for modeling 3D+T data. Our objective is to develop robust 3D+T models for mapping dynamic soil data that has been collected with high spatial and temporal resolution. For this analysis, we use data collected from a sensor network installed on the R.J. Cook Agronomy Farm (CAF), a 37-ha Long-Term Agro-Ecosystem Research (LTAR) site in Pullman, WA. For five years, the sensors have collected hourly measurements of soil volumetric water content at 42 locations and five depths. The CAF dataset also includes a digital elevation model and derivatives, a soil unit description map, crop rotations, electromagnetic induction surveys, daily meteorological data, and seasonal satellite imagery. The soil-water sensor data, combined with the spatial and temporal covariates, provide an ideal dataset for developing 3D+T models. The presentation will include preliminary results and address main implementation strategies.

Mapping spatial patterns of denitrifiers at large scales (Invited)

NASA Astrophysics Data System (ADS)

Philippot, L.; Ramette, A.; Saby, N.; Bru, D.; Dequiedt, S.; Ranjard, L.; Jolivet, C.; Arrouays, D.

2010-12-01

Little information is available regarding the landscape-scale distribution of microbial communities and its environmental determinants. Here we combined molecular approaches and geostatistical modeling to explore spatial patterns of the denitrifying community at large scales. The distribution of denitrifrying community was investigated over 107 sites in Burgundy, a 31 500 km2 region of France, using a 16 X 16 km sampling grid. At each sampling site, the abundances of denitrifiers and 42 soil physico-chemical properties were measured. The relative contributions of land use, spatial distance, climatic conditions, time and soil physico-chemical properties to the denitrifier spatial distribution were analyzed by canonical variation partitioning. Our results indicate that 43% to 85% of the spatial variation in community abundances could be explained by the measured environmental parameters, with soil chemical properties (mostly pH) being the main driver. We found spatial autocorrelation up to 739 km and used geostatistical modelling to generate predictive maps of the distribution of denitrifiers at the landscape scale. Studying the distribution of the denitrifiers at large scale can help closing the artificial gap between the investigation of microbial processes and microbial community ecology, therefore facilitating our understanding of the relationships between the ecology of denitrifiers and N-fluxes by denitrification.

Effect of Soil Washing for Lead and Zinc Removal on Soil Hydraulic Properties

NASA Astrophysics Data System (ADS)

Kammerer, Gerhard; Zupanc, Vesna; Gluhar, Simon; Lestan, Domen

2017-04-01

Soil washing as a metal pollution remediation process, especially part with intensive mixing of the soil slurry and soil compression after de-watering, significantly deteriorates physical properties of soil compared to those of non-remediated soil. Furthermore, changed physical characteristics of remediated soil influence interaction of plant roots with soil system and affect soil water regime. Remediated soils showed significant differences to their original state in water retention properties and changed structure due to the influence of artificial structure created during remediation process. Disturbed and undisturbed soil samples of remediated and original soils were analyzed. We evaluated soil hydraulic properties as a possible constraint for re-establishing soil structure and soil fertility after the remediation procedure.

Unstable infiltration fronts in porous media on laboratory scale

NASA Astrophysics Data System (ADS)

Schuetz, Cindi; Neuweiler, Insa

2014-05-01

Water flow and transport of substances in the unsaturated zone are important processes for the quality and quantity of water in the hydrologic cycle. The water movement through preferential paths is often much faster than standard models (e. g. Richards equation in homogeneous porous media) predict. One type/phenomenon of preferential flow can occur during water infiltration into coarse and/or dry porous media: the so-called gravity-driven fingering flow. To upscale the water content and to describe the averaged water fluxes in order to couple models of different spheres it is necessary to understand and to quantify the behavior of flow instabilities. We present different experiments of unstable infiltration in homogeneous and heterogeneous structures to analyze development and morphology of gravity-driven fingering flow on the laboratory scale. Experiments were carried out in two-dimensional and three-dimensional sand tanks as well as in larger two-dimensional sand tanks with homogeneous and heterogeneous filling of sand and glass beads. In the small systems, water content in the medium was measured at different times. We compare the experiments to prediction of theoretical approaches (e.g. Saffman and Taylor, 1958; Chuoke et al., 1959; Philip 1975a; White et al., 1976; Parlange and Hill, 1976a; Glass et al., 1989a; Glass et al., 1991; Wang et al., 1998c) that quantify properties of the gravity-driven fingers. We use hydraulic parameters needed for the theoretical predictions (the water-entry value (hwe), van Genuchten parameter (Wang et al., 1997, Wang et al., 2000) and saturated conductivity (Ks), van Genuchten parameter (Guarracino, 2007) to simplify the prediction of the finger properties and if necessary to identify a constant correction factor. We find in general that the finger properties correspond well to theoretical predictions. In heterogeneous settings, where fine inclusions are embedded into a coarse material, the finger properties do not change much, while the inclusions act as a storage that is filled during the infiltration process. References: Chouke, R.L., van Meurs, P., and van der Poel, C., 1959. The instability of slow immiscible, viscous liquid-liquid displacements in permeable media, Trans. AIME. 216:188-194. Glass, R.J., Steenhuis, T.S., and Parlange J.-Y., 1989a. Mechanism for finger persistence in homogeneous, unsaturated, porous media: Theory and verification, Soil Sci. 148:60-70. Glass R.J., Parlange, J.-Y., and Steenhuis, T.S., 1991. Immiscible displacement in porous media: Stability analysis of three-dimensional, axisymmetric disturbances with application to gravity-driven wetting front instability, Water Resour. Res., 27, 1947-1956. Guarracino, L., 2007. Estimation of saturated hydraulic conductivity Ks from the van Genuchten shape parameter , Water Resour. Res., 43, W11502. Parlange, J.-Y. and Hill, D.E., 1976a. Theoretical analysis of wetting front instability in soils, Soil Sci. 122:236-239. Philip, J. 1975a. Stability analysis of infiltration, Soil Sci. Soc. Am. Proc. 39:1042-1049. Saffman, P.G. and Taylor, G., 1958. The penetration of a fluid into a porous medium or hele-shaw cell containing a more viscous liquid, Proc. R. Soc. London, 245:312-329. Wang Z., Feyen, J., Nielsen, D.R., and van Genuchten, M.T., 1997. Two-phase flow infiltration equations accounting for air entrapment effects, Water Resour. Res., 33:2759-2767. Wang, Z., Feyen, J., and Elrick, D.E., 1998c. Prediction of fingering in porous media, Water Resour. Res. 34(9):2183-2190. Wang Z., Wu, L., and Wu, Q.J., 2000. Water-entry value as an alternative indicator of soil water-repellency and wettability, Journal of Hydrology., 231-232, 76-83. White, I., Colombera, P.M., and Philip, J.R., 1976. Experimental studies of wetting front instability induced by sudden changes of pressure gradient, Soil Sci. Soc. Am. Proc., 40:824-829.

Critical Zone Co-dynamics: Quantifying Interactions between Subsurface, Land Surface, and Vegetation Properties Using UAV and Geophysical Approaches

NASA Astrophysics Data System (ADS)

Dafflon, B.; Leger, E.; Peterson, J.; Falco, N.; Wainwright, H. M.; Wu, Y.; Tran, A. P.; Brodie, E.; Williams, K. H.; Versteeg, R.; Hubbard, S. S.

2017-12-01

Improving understanding and modelling of terrestrial systems requires advances in measuring and quantifying interactions among subsurface, land surface and vegetation processes over relevant spatiotemporal scales. Such advances are important to quantify natural and managed ecosystem behaviors, as well as to predict how watershed systems respond to increasingly frequent hydrological perturbations, such as droughts, floods and early snowmelt. Our study focuses on the joint use of UAV-based multi-spectral aerial imaging, ground-based geophysical tomographic monitoring (incl., electrical and electromagnetic imaging) and point-scale sensing (soil moisture sensors and soil sampling) to quantify interactions between above and below ground compartments of the East River Watershed in the Upper Colorado River Basin. We evaluate linkages between physical properties (incl. soil composition, soil electrical conductivity, soil water content), metrics extracted from digital surface and terrain elevation models (incl., slope, wetness index) and vegetation properties (incl., greenness, plant type) in a 500 x 500 m hillslope-floodplain subsystem of the watershed. Data integration and analysis is supported by numerical approaches that simulate the control of soil and geomorphic characteristic on hydrological processes. Results provide an unprecedented window into critical zone interactions, revealing significant below- and above-ground co-dynamics. Baseline geophysical datasets provide lithological structure along the hillslope, which includes a surface soil horizon, underlain by a saprolite layer and the fractured Mancos shale. Time-lapse geophysical data show very different moisture dynamics in various compartments and locations during the winter and growing season. Integration with aerial imaging reveals a significant linkage between plant growth and the subsurface wetness, soil characteristics and the topographic gradient. The obtained information about the organization and connectivity of the landscape is being transferred to larger regions using aerial imaging and will be used to constrain multi-scale, multi-physics hydro-biogeochemical simulations of the East River watershed response to hydrological perturbations.

On the prediction of threshold friction velocity of wind erosion using soil reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Li, Junran; Flagg, Cody; Okin, Gregory S.; Painter, Thomas H.; Dintwe, Kebonye; Belnap, Jayne

2015-12-01

Current approaches to estimate threshold friction velocity (TFV) of soil particle movement, including both experimental and empirical methods, suffer from various disadvantages, and they are particularly not effective to estimate TFVs at regional to global scales. Reflectance spectroscopy has been widely used to obtain TFV-related soil properties (e.g., moisture, texture, crust, etc.), however, no studies have attempted to directly relate soil TFV to their spectral reflectance. The objective of this study was to investigate the relationship between soil TFV and soil reflectance in the visible and near infrared (VIS-NIR, 350-2500 nm) spectral region, and to identify the best range of wavelengths or combinations of wavelengths to predict TFV. Threshold friction velocity of 31 soils, along with their reflectance spectra and texture were measured in the Mojave Desert, California and Moab, Utah. A correlation analysis between TFV and soil reflectance identified a number of isolated, narrow spectral domains that largely fell into two spectral regions, the VIS area (400-700 nm) and the short-wavelength infrared (SWIR) area (1100-2500 nm). A partial least squares regression analysis (PLSR) confirmed the significant bands that were identified by correlation analysis. The PLSR further identified the strong relationship between the first-difference transformation and TFV at several narrow regions around 1400, 1900, and 2200 nm. The use of PLSR allowed us to identify a total of 17 key wavelengths in the investigated spectrum range, which may be used as the optimal spectral settings for estimating TFV in the laboratory and field, or mapping of TFV using airborne/satellite sensors.

Temperature and aridity regulate spatial variability of soil multifunctionality in drylands across the globe.

PubMed

Durán, Jorge; Delgado-Baquerizo, Manuel; Dougill, Andrew J; Guuroh, Reginald T; Linstädter, Anja; Thomas, Andrew D; Maestre, Fernando T

2018-05-01

The relationship between the spatial variability of soil multifunctionality (i.e., the capacity of soils to conduct multiple functions; SVM) and major climatic drivers, such as temperature and aridity, has never been assessed globally in terrestrial ecosystems. We surveyed 236 dryland ecosystems from six continents to evaluate the relative importance of aridity and mean annual temperature, and of other abiotic (e.g., texture) and biotic (e.g., plant cover) variables as drivers of SVM, calculated as the averaged coefficient of variation for multiple soil variables linked to nutrient stocks and cycling. We found that increases in temperature and aridity were globally correlated to increases in SVM. Some of these climatic effects on SVM were direct, but others were indirectly driven through reductions in the number of vegetation patches and increases in soil sand content. The predictive capacity of our structural equation modelling was clearly higher for the spatial variability of N- than for C- and P-related soil variables. In the case of N cycling, the effects of temperature and aridity were both direct and indirect via changes in soil properties. For C and P, the effect of climate was mainly indirect via changes in plant attributes. These results suggest that future changes in climate may decouple the spatial availability of these elements for plants and microbes in dryland soils. Our findings significantly advance our understanding of the patterns and mechanisms driving SVM in drylands across the globe, which is critical for predicting changes in ecosystem functioning in response to climate change. © 2018 by the Ecological Society of America.

EFFECT OF SOIL PROPERTIES ON LEAD BIOAVAILABILITY AND TOXCITY TO EARTHWORMS

EPA Science Inventory

Soil properties are important factors modifying metal bioavailability to ecological receptors. Twenty-one soils with a wide range of soil properties were amended with a single concentration of Pb (2000 mg/kg) to determine the effects of soil properties on Pb bioavailability and ...

Fen ecohydrologic trajectories in response to groundwater drawdown with edaphic, floristic, and hydrologic feedbacks

NASA Astrophysics Data System (ADS)

Booth, E.; Steven, L. I.; Bart, D.

2017-12-01

Calcareous fens are unique and often isolated ecosystems of high conservation value worldwide because they provide habitat for many rare plant and animal species. Their identity is inextricably linked to an absolute dependence on a consistent discharge of groundwater that saturates the near surface for most of the growing season leading to the accumulation of carbon as peat or tufa and sequestration of nutrients. The stresses resulting from consistent saturation and low-nutrient availability result in high native plant diversity including very high rare species richness compared to other ecosystems. Decreases in the saturation stress by reduced groundwater inputs (e.g., from nearby pumping) can result in losses of native diversity, decreases in rare-species abundance, and increased invasion by non-native species. As such, fen ecosystems are particularly susceptible to changes in groundwater conditions including reduction in water levels due to nearby groundwater pumping. Trajectories of degradation are complex due to feedbacks between loss of soil organic carbon, changes in soil properties, and plant water use. We present a model of an archetype fen that couples a hydrological niche model with a variably-saturated groundwater flow model to predict changes in vegetation composition in response to different groundwater drawdown scenarios (step change, declining trend, and periodic drawdown during dry periods). The model also includes feedbacks among vegetation composition, plant water use, and soil properties. The hydrological niche models (using surface soil moisture as predictor) and relationships between vegetation composition, plant water use (via stomatal conductance and leaf-area index), and soil hydraulic properties (van Genuchten parameters) were determined based on data collected from six fens in Wisconsin under various states of degradation. Results reveal a complex response to drawdown and provide insight into other ecosystems with linkages between the hydrologic regime, plants, water use, and soil properties.

Assessment of different methods to estimate heavy metal bioavailability in 30 contrasting Spanish and New Zealand soils

NASA Astrophysics Data System (ADS)

Soriano-Disla, J. M.; Speir, T. W.; Gómez, I.; Clucas, L. M.; McLaren, R. G.; Navarro-Pedreño, J.

2009-04-01

The accumulation of heavy metals in soil from different sources (atmospheric deposition, agricultural practices, urban-industrial activities, etc.) is of a great environmental concern because of metal persistence and toxicity. In this sense, there is a consensus in the literature that the estimation of the bioavailable heavy metals in soil is a preferable tool to determine potential risks from soil contamination than the total contents. However, controversy exists around the definition of an accurate and universal bioavailability estimator that is useful for soils with different properties, since many factors control this parameter. Thus, the main objective of this work was to compare the effectiveness of different methods to predict heavy metals plant uptake from soils with different properties and heavy metal contents. For the development of the present work, 30 contrasting soils from New Zealand and Spain were selected. Apart from the analysis of the basic soil properties, different methods to estimate heavy metal bioavailability were performed: total heavy metals, DTPA-extractable soil metals, diffusive gradient technique (DGT), and total heavy metals in soil solution. In these soils, a bioassay using wheat (Triticum aestivum) was carried out in a constant environment room for 25 days (12 hours photoperiod, day and night temperature of 20°C and 15°C respectively). After this time, the plants were divided in roots and shoots and heavy metal content was analysed in each part. Simple correlations were performed comparing the phytoavailable contents with the bioavailability estimated by the different methods. As expected, higher heavy metal concentrations were found in roots compared with shoots. Comparing the theoretical available heavy metals estimated by the different methods with the root and shoot uptake, better correlations were found with the root contents, thus, the discussion is based in the comparisons with the uptake by this part of the plant. According to the results, DTPA seemed to be the extractant that best estimated plant uptake (except for Cd, not estimated by any of the methods used). Similar good results were found using the total heavy metal contents, except for Ni and Zn. DGT also worked well, but its use for Pb is not advisable, since many values were below the detection level. The heavy metals in soil solution were less successful for predicting plant uptake. In general, the good results obtained for Cr and Zn seemed to be influenced by a few high values found in some soils. Taking this point into account, the soils with very high levels of these heavy metals were removed from the analysis and simple correlations were done again with the remaining soils having a lower range of these metals. For the case of Cr, four soils were removed (soils with ten times or more total Cr than the average of the others 26 samples) and three for the case of Zn (soils with two times or more total Zn than the average of the others 27 samples). After this, the correlations with total heavy metals and DTPA became very weak, being the heavy metals in soil solution for Cr, and DGT for Zn, the methods that best estimated the plant uptake of these metals. This work has proved the importance of careful revision of the data distribution, since good results can be influenced by just few samples with high values. In this sense and as a conclusion, DTPA and total heavy metals followed similar patterns and were good predictors of Cu and Pb uptake, and useful to distinguish between low and high values for Cr and Zn. On the other hand, DGT and heavy metals in soil solution showed a similar effectiveness to estimate Cu, Ni, Pb, Zn and Cr, but DGT presented, in general, higher correlation levels (except for Cr). Taking all of the results together, it seems that the most robust and efficient estimator for all metals studied (except Cd, impossible to predict with any of the methods used) was the DGT. Acknowledgements: Jose. M. Soriano-Disla gratefully acknowledges the Spanish Ministry of Innovation and Culture for a research fellowship (AP2005-0320).

Field Identification of Andic Soil Properties for Soils of North-central Idaho

Treesearch

Brian Gardner

2007-01-01

Currently, laboratory measurements are definitive for identifying andic soil properties in both the USDA Soil Taxonomy (Soil Survey Staff 1999) and the World Reference Base for Soil Resources (FAO/ISRIC/ISSS 1998). Andic soil properties, as described in Soil Taxonomy, result mainly from the presence of significant amounts of allophone, imogolite, ferrihydrite or...

Hydrologic control on redox and nitrogen dynamics in a peatland soil.

PubMed

Rubol, Simonetta; Silver, Whendee L; Bellin, Alberto

2012-08-15

Soils are a dominant source of nitrous oxide (N(2)O), a potent greenhouse gas. However, the complexity of the drivers of N(2)O production and emissions has hindered our ability to predict the magnitude and spatial dynamics of N(2)O fluxes. Soil moisture can be considered a key driver because it influences oxygen (O(2)) supply, which feeds back on N(2)O sources (nitrification versus denitrification) and sinks (reduction to dinitrogen). Soil water content is directly linked to O(2) and redox potential, which regulate microbial metabolism and chemical transformations in the environment. Despite its importance, only a few laboratory studies have addressed the effects of hydrological transient dynamics on nitrogen (N) cycling in the vadose zone. To further investigate these aspects, we performed a long term experiment in a 1.5 m depth soil column supplemented by chamber experiments. With this experiment, we aimed to investigate how soil moisture dynamics influence redox sensitive N cycling in a peatland soil. As expected, increased soil moisture lowered O(2) concentrations and redox potential in the soil. The decline was more severe for prolonged saturated conditions than for short events and at deep than at the soil surface. Gaseous and dissolved N(2)O, dissolved nitrate (NO(3)(-)) and ammonium (NH(4)(+)) changed considerably along the soil column profile following trends in soil O(2) and redox potential. Hot spots of N(2)O concentrations corresponded to high variability in soil O(2) in the upper and lower parts of the column. Results from chamber experiments confirmed high NO(3)(-) reduction potential in soils, particularly from the bottom of the column. Under our experimental conditions, we identified a close coupling of soil O(2) and N(2)O dynamics, both of which lagged behind soil moisture changes. These results highlight the relationship among soil hydrologic properties, redox potential and N cycling, and suggest that models working at a daily scale need to consider soil O(2) dynamics in addition to soil moisture dynamics to accurately predict patterns in N(2)O fluxes. Copyright © 2012 Elsevier B.V. All rights reserved.

An investigation of soil-structure interaction effects observed at the MIT Green Building

USGS Publications Warehouse

Taciroglu, Ertugrul; Çelebi, Mehmet; Ghahari, S. Farid; Abazarsa, Fariba

2016-01-01

The soil-foundation impedance function of the MIT Green Building is identified from its response signals recorded during an earthquake. Estimation of foundation impedance functions from seismic response signals is a challenging task, because: (1) the foundation input motions (FIMs) are not directly measurable, (2) the as-built properties of the super-structure are only approximately known, and (3) the soil-foundation impedance functions are inherently frequency-dependent. In the present study, aforementioned difficulties are circumvented by using, in succession, a blind modal identification (BMID) method, a simplified Timoshenko beam model (TBM), and a parametric updating of transfer functions (TFs). First, the flexible-base modal properties of the building are identified from response signals using the BMID method. Then, a flexible-base TBM is updated using the identified modal data. Finally, the frequency-dependent soil-foundation impedance function is estimated by minimizing the discrepancy between TFs (of pairs instrumented floors) that are (1) obtained experimentally from earthquake data and (2) analytically from the updated TBM. Using the fully identified flexible-base TBM, the FIMs as well as building responses at locations without instruments can be predicted, as demonstrated in the present study.

Soil types and forest canopy structures in southern Missouri: A first look with AIS data

NASA Technical Reports Server (NTRS)

Green, G. M.; Arvidson, R. E.

1986-01-01

Spectral reflectance properties of deciduous oak-hickory forests covering the eastern half of the Rolla Quadrangle were examined using Thematic Mapper (TM) data acquired in August and December, 1982 and Airborne Imaging Spectrometer (AIS) data acquired in August, 1985. For the TM data distinctly high relative reflectance values (greater than 0.3) in the near infrared (Band 4, 0.73 to 0.94 micrometers) correspond to regions characterized by xeric (dry) forests that overlie soils with low water retention capacities. These soils are derived primarily from rhyolites. More mesic forests characterized by lower TM band 4 relative reflectances are associated with soils of higher retention capacities derived predominately from non-cherty carbonates. The major factors affecting canopy reflectance appear to be the leaf area index (LAI) and leaf optical properties. The Suits canopy reflectance model predicts the relative reflectance values for the xeric canopies. The mesic canopy reflectance is less well matched and incorporation of canopy shadowing caused by the irregular nature of the mesic canopy may be necessary. Preliminary examination of high spectral resolution AIS data acquired in August of 1985 reveals no more information than found in the broad band TM data.

Physical properties of forest soils

Treesearch

Charles H. Perry; Michael C. Amacher

2007-01-01

Why Are Physical Properties of the Soil Important? The soil quality indicator, when combined with other data collected by the FIA program, can indicate the current rates of soil erosion, the extent and intensity of soil compaction, and some basic physical properties of the forest floor and the top 20 cm of soil. In this report, two particular physical properties of the...

Impact of surface coal mining on soil hydraulic properties

Treesearch

X. Liu; J. Q. Wu; P. W. Conrad; S. Dun; C. S. Todd; R. L. McNearny; William Elliot; H. Rhee; P. Clark

2016-01-01

Soil erosion is strongly related to soil hydraulic properties. Understanding how surface coal mining affects these properties is therefore important in developing effective management practices to control erosion during reclamation. To determine the impact of mining activities on soil hydraulic properties, soils from undisturbed areas, areas of roughly graded mine...

Comparison of the Physical and Chemical Properties of Laboratory and Field-Aged Biochars.

PubMed

Bakshi, Santanu; Aller, Deborah M; Laird, David A; Chintala, Rajesh

2016-09-01

The long-term impact of biochar on soil properties and agronomic outcomes is influenced by changes in the physical and chemical properties of biochars that occur with time (aging) in soil environments. Fresh biochars, however, are often used in studies because aged biochars are generally unavailable. Therefore, a need exists to develop a method for rapid aging of biochars in the laboratory. The objectives of this study were to compare the physicochemical properties of fresh, laboratory-aged (LA), and field-aged (FA) (≥3 yr) biochars and to assess the appropriateness of a laboratory aging procedure that combines acidification, oxidation, and incubations as a mimic to field aging in neutral or acidic soil environments. Twenty-two biochars produced by fast and slow pyrolysis, and gasification techniques from five different biomass feedstocks (hardwood, corn stover, soybean stover, macadamia nut shells, and switchgrass) were studied. In general, both laboratory and field aging caused similar increases in ash-free volatile matter (% w/w), cation and anion exchange capacities, specific surface area, and modifications in oxygen-containing surface functional groups of the biochars. However, ash content increased for FA (18-195%) and decreased for LA (22-74%) biochars, and pH decreased to a greater extent for LA (2.8-6.7 units) than for FA (1.6-3.8 units) biochars. The results demonstrate that the proposed laboratory aging procedure is effective for predicting the direction of changes in biochar properties on field aging. However, in the future we recommend using a less aggressive acid treatment. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.