Large Area Crop Inventory Experiment (LACIE). Phase 2 evaluation report

NASA Technical Reports Server (NTRS)

1977-01-01

Documentation of the activities of the Large Area Crop Inventory Experiment during the 1976 Northern Hemisphere crop year is presented. A brief overview of the experiment is included as well as phase two area, yield, and production estimates for the United States Great Plains, Canada, and the Union of Soviet Socialist Republics spring winter wheat regions. The accuracies of these estimates are compared with independent government estimates. Accuracy assessment of the United States Great Plains yardstick region based on a through blind sight analysis is given, and reasons for variations in estimating performance are discussed. Other phase two technical activities including operations, exploratory analysis, reporting, methods of assessment, phase three and advanced system design, technical issues, and developmental activities are also included.

Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

USGS Publications Warehouse

Husak, G.J.; Marshall, M. T.; Michaelsen, J.; Pedreros, Diego; Funk, Christopher C.; Galu, G.

2008-01-01

Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.

Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

NASA Astrophysics Data System (ADS)

Husak, G. J.; Marshall, M. T.; Michaelsen, J.; Pedreros, D.; Funk, C.; Galu, G.

2008-07-01

Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.

Research in the application of spectral data to crop identification and assessment, volume 2

NASA Technical Reports Server (NTRS)

Daughtry, C. S. T. (Principal Investigator); Hixson, M. M.; Bauer, M. E.

1980-01-01

The development of spectrometry crop development stage models is discussed with emphasis on models for corn and soybeans. One photothermal and four thermal meteorological models are evaluated. Spectral data were investigated as a source of information for crop yield models. Intercepted solar radiation and soil productivity are identified as factors related to yield which can be estimated from spectral data. Several techniques for machine classification of remotely sensed data for crop inventory were evaluated. Early season estimation, training procedures, the relationship of scene characteristics to classification performance, and full frame classification methods were studied. The optimal level for combining area and yield estimates of corn and soybeans is assessed utilizing current technology: digital analysis of LANDSAT MSS data on sample segments to provide area estimates and regression models to provide yield estimates.

Accuracy assessment in the Large Area Crop Inventory Experiment

NASA Technical Reports Server (NTRS)

Houston, A. G.; Pitts, D. E.; Feiveson, A. H.; Badhwar, G.; Ferguson, M.; Hsu, E.; Potter, J.; Chhikara, R.; Rader, M.; Ahlers, C.

1979-01-01

The Accuracy Assessment System (AAS) of the Large Area Crop Inventory Experiment (LACIE) was responsible for determining the accuracy and reliability of LACIE estimates of wheat production, area, and yield, made at regular intervals throughout the crop season, and for investigating the various LACIE error sources, quantifying these errors, and relating them to their causes. Some results of using the AAS during the three years of LACIE are reviewed. As the program culminated, AAS was able not only to meet the goal of obtaining accurate statistical estimates of sampling and classification accuracy, but also the goal of evaluating component labeling errors. Furthermore, the ground-truth data processing matured from collecting data for one crop (small grains) to collecting, quality-checking, and archiving data for all crops in a LACIE small segment.

Use of Satellite-based Remote Sensing to inform Evapotranspiration parameters in Cropping System Models

NASA Astrophysics Data System (ADS)

Dhungel, S.; Barber, M. E.

2016-12-01

The objectives of this paper are to use an automated satellite-based remote sensing evapotranspiration (ET) model to assist in parameterization of a cropping system model (CropSyst) and to examine the variability of consumptive water use of various crops across the watershed. The remote sensing model is a modified version of the Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC™) energy balance model. We present the application of an automated python-based implementation of METRIC to estimate ET as consumptive water use for agricultural areas in three watersheds in Eastern Washington - Walla Walla, Lower Yakima and Okanogan. We used these ET maps with USDA crop data to identify the variability of crop growth and water use for the major crops in these three watersheds. Some crops, such as grapes and alfalfa, showed high variability in water use in the watershed while others, such as corn, had comparatively less variability. The results helped us to estimate the range and variability of various crop parameters that are used in CropSyst. The paper also presents a systematic approach to estimate parameters of CropSyst for a crop in a watershed using METRIC results. Our initial application of this approach was used to estimate irrigation application rate for CropSyst for a selected farm in Walla Walla and was validated by comparing crop growth (as Leaf Area Index - LAI) and consumptive water use (ET) from METRIC and CropSyst. This coupling of METRIC with CropSyst will allow for more robust parameters in CropSyst and will enable accurate predictions of changes in irrigation practices and crop rotation, which are a challenge in many cropping system models.

Classifying unresolved objects from simulated space data.

NASA Technical Reports Server (NTRS)

Nalepka, R. F.; Hyde, P. D.

1973-01-01

A multispectral scanner data set gathered at a flight altitude of 10,000 ft. over an agricultural area was modified to simulate the spatial resolution of the spacecraft scanners. Signatures were obtained for several major crops and their proportions were estimated over a large area. For each crop, a map was generated to show its approximate proportion in each resolution element, and hence its distribution over the area of interest. A statistical criterion was developed to identify data points that may not represent a mixture of the specified crops. This allows for great reduction in the effect of unknown or alien objects on the estimated proportions. This criterion can be used to locate special features, such as roads or farm houses. Preliminary analysis indicates a high level of consistency between estimated proportions and available ground truth. Large concentrations of major crops show up especially well on the maps.

Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation

USDA-ARS?s Scientific Manuscript database

The scale mismatch between remotely sensed observations and crop growth models simulated state variables decreases the reliability of crop yield estimates. To overcome this problem, we used a two-step data assimilation phases: first we generated a complete leaf area index (LAI) time series by combin...

Crop area estimation based on remotely-sensed data with an accurate but costly subsample

NASA Technical Reports Server (NTRS)

Gunst, R. F.

1983-01-01

Alternatives to sampling-theory stratified and regression estimators of crop production and timber biomass were examined. An alternative estimator which is viewed as especially promising is the errors-in-variable regression estimator. Investigations established the need for caution with this estimator when the ratio of two error variances is not precisely known.

Development of estimation method for crop yield using MODIS satellite imagery data and process-based model for corn and soybean in US Corn-Belt region

NASA Astrophysics Data System (ADS)

Lee, J.; Kang, S.; Jang, K.; Ko, J.; Hong, S.

2012-12-01

Crop productivity is associated with the food security and hence, several models have been developed to estimate crop yield by combining remote sensing data with carbon cycle processes. In present study, we attempted to estimate crop GPP and NPP using algorithm based on the LUE model and a simplified respiration model. The state of Iowa and Illinois was chosen as the study site for estimating the crop yield for a period covering the 5 years (2006-2010), as it is the main Corn-Belt area in US. Present study focuses on developing crop-specific parameters for corn and soybean to estimate crop productivity and yield mapping using satellite remote sensing data. We utilized a 10 km spatial resolution daily meteorological data from WRF to provide cloudy-day meteorological variables but in clear-say days, MODIS-based meteorological data were utilized to estimate daily GPP, NPP, and biomass. County-level statistics on yield, area harvested, and productions were used to test model predicted crop yield. The estimated input meteorological variables from MODIS and WRF showed with good agreements with the ground observations from 6 Ameriflux tower sites in 2006. For examples, correlation coefficients ranged from 0.93 to 0.98 for Tmin and Tavg ; from 0.68 to 0.85 for daytime mean VPD; from 0.85 to 0.96 for daily shortwave radiation, respectively. We developed county-specific crop conversion coefficient, i.e. ratio of yield to biomass on 260 DOY and then, validated the estimated county-level crop yield with the statistical yield data. The estimated corn and soybean yields at the county level ranged from 671 gm-2 y-1 to 1393 gm-2 y-1 and from 213 gm-2 y-1 to 421 gm-2 y-1, respectively. The county-specific yield estimation mostly showed errors less than 10%. Furthermore, we estimated crop yields at the state level which were validated against the statistics data and showed errors less than 1%. Further analysis for crop conversion coefficient was conducted for 200 DOY and 280 DOY. For the case of 280 DOY, Crop yield estimation showed better accuracy for soybean at county level. Though the case of 200 DOY resulted in less accuracy (i.e. 20% mean bias), it provides a useful tool for early forecasting of crop yield. We improved the spatial accuracy of estimated crop yield at county level by developing county-specific crop conversion coefficient. Our results indicate that the aboveground crop biomass can be estimated successfully with the simple LUE and respiration models combined with MODIS data and then, county-specific conversion coefficient can be different with each other across different counties. Hence, applying region-specific conversion coefficient is necessary to estimate crop yield with better accuracy.

Research in satellite-aided crop inventory and monitoring

NASA Technical Reports Server (NTRS)

Erickson, J. D.; Dragg, J. L.; Bizzell, R. M.; Trichel, M. C. (Principal Investigator)

1982-01-01

Automated information extraction procedures for analysis of multitemporal LANDSAT data in non-U.S. crop inventory and monitoring are reviewed. Experiments to develope and evaluate crop area estimation technologies for spring small grains, summer crops, corn, and soybeans are discussed.

Comparison Between the Use of SAR and Optical Data for Wheat Yield Estimations Using Crop Model Assimilation

NASA Astrophysics Data System (ADS)

Silvestro, Paolo Cosmo; Yang, Hao; Jin, X. L.; Yang, Guijun; Casa, Raffaele; Pignatti, Stefano

2016-08-01

The ultimate aim of this work is to develop methods for the assimilation of the biophysical variables estimated by remote sensing in a suitable crop growth model. Two strategies were followed, one based on the use of Leaf Area Index (LAI) estimated by optical data, and the other based on the use of biomass estimated by SAR. The first one estimates LAI from the reflectance measured by the optical sensors on board of HJ1A, HJ1B and Landsat, using a method based on the training of artificial neural networks (ANN) with PROSAIL model simulations. The retrieved LAI is used to improve wheat yield estimation, using assimilation methods based on the Ensemble Kalman Filter, which assimilate the biophysical variables into growth crop model. The second strategy estimates biomass from SAR imagery. Polarimetric decomposition methods were used based on multi-temporal fully polarimetric Radarsat-2 data during the entire growing season. The estimated biomass was assimilating to FAO Aqua crop model for improving the winter wheat yield estimation, with the Particle Swarm Optimization (PSO) method. These procedures were used in a spatial application with data collected in the rural area of Yangling (Shaanxi Province) in 2014 and were validated for a number of wheat fields for which ground yield data had been recorded and according to statistical yield data for the area.

Irrigated rice area estimation using remote sensing techniques: Project's proposal and preliminary results. [Rio Grande do Sul, Brazil

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Deassuncao, G. V.; Moreira, M. A.; Novaes, R. A.

1984-01-01

The development of a methodology for annual estimates of irrigated rice crop in the State of Rio Grande do Sul, Brazil, using remote sensing techniques is proposed. The project involves interpretation, digital analysis, and sampling techniques of LANDSAT imagery. Results are discussed from a preliminary phase for identifying and evaluating irrigated rice crop areas in four counties of the State, for the crop year 1982/1983. This first phase involved just visual interpretation techniques of MSS/LANDSAT images.

National-scale crop type mapping and area estimation using multi-resolution remote sensing and field survey

NASA Astrophysics Data System (ADS)

Song, X. P.; Potapov, P.; Adusei, B.; King, L.; Khan, A.; Krylov, A.; Di Bella, C. M.; Pickens, A. H.; Stehman, S. V.; Hansen, M.

2016-12-01

Reliable and timely information on agricultural production is essential for ensuring world food security. Freely available medium-resolution satellite data (e.g. Landsat, Sentinel) offer the possibility of improved global agriculture monitoring. Here we develop and test a method for estimating in-season crop acreage using a probability sample of field visits and producing wall-to-wall crop type maps at national scales. The method is first illustrated for soybean cultivated area in the US for 2015. A stratified, two-stage cluster sampling design was used to collect field data to estimate national soybean area. The field-based estimate employed historical soybean extent maps from the U.S. Department of Agriculture (USDA) Cropland Data Layer to delineate and stratify U.S. soybean growing regions. The estimated 2015 U.S. soybean cultivated area based on the field sample was 341,000 km2 with a standard error of 23,000 km2. This result is 1.0% lower than USDA's 2015 June survey estimate and 1.9% higher than USDA's 2016 January estimate. Our area estimate was derived in early September, about 2 months ahead of harvest. To map soybean cover, the Landsat image archive for the year 2015 growing season was processed using an active learning approach. Overall accuracy of the soybean map was 84%. The field-based sample estimated area was then used to calibrate the map such that the soybean acreage of the map derived through pixel counting matched the sample-based area estimate. The strength of the sample-based area estimation lies in the stratified design that takes advantage of the spatially explicit cropland layers to construct the strata. The success of the mapping was built upon an automated system which transforms Landsat images into standardized time-series metrics. The developed method produces reliable and timely information on soybean area in a cost-effective way and could be implemented in an operational mode. The approach has also been applied for other crops in other regions, such as winter wheat in Pakistan, soybean in Argentina and soybean in the entire South America. Similar levels of accuracy and timeliness were achieved as in the US.

Rabi cropped area forecasting of parts of Banaskatha District,Gujarat using MRS RISAT-1 SAR data

NASA Astrophysics Data System (ADS)

Parekh, R. A.; Mehta, R. L.; Vyas, A.

2016-10-01

Radar sensors can be used for large-scale vegetation mapping and monitoring using backscatter coefficients in different polarisations and wavelength bands. Due to cloud and haze interference, optical images are not always available at all phonological stages important for crop discrimination. Moreover, in cloud prone areas, exclusively SAR approach would provide operational solution. This paper presents the results of classifying the cropped and non cropped areas using multi-temporal SAR images. Dual polarised C- band RISAT MRS (Medium Resolution ScanSAR mode) data were acquired on 9thDec. 2012, 28thJan. 2013 and 22nd Feb. 2013 at 18m spatial resolution. Intensity images of two polarisations (HH, HV) were extracted and converted into backscattering coefficient images. Cross polarisation ratio (CPR) images and Radar fractional vegetation density index (RFDI) were created from the temporal data and integrated with the multi-temporal images. Signatures of cropped and un-cropped areas were used for maximum likelihood supervised classification. Separability in cropped and umcropped classes using different polarisation combinations and classification accuracy analysis was carried out. FCC (False Color Composite) prepared using best three SAR polarisations in the data set was compared with LISS-III (Linear Imaging Self-Scanning System-III) image. The acreage under rabi crops was estimated. The methodology developed was for rabi cropped area, due to availability of SAR data of rabi season. Though, the approach is more relevant for acreage estimation of kharif crops when frequent cloud cover condition prevails during monsoon season and optical sensors fail to deliver good quality images.

Evaluation of Different Phenological Information to Map Crop Rotation in Complex Irrigated Indus Basin

NASA Astrophysics Data System (ADS)

Ismaeel, A.; Zhou, Q.

2018-04-01

Accurate information of crop rotation in large basin is essential for policy decisions on land, water and nutrient resources around the world. Crop area estimation using low spatial resolution remote sensing data is challenging in a large heterogeneous basin having more than one cropping seasons. This study aims to evaluate the accuracy of two phenological datasets individually and in combined form to map crop rotations in complex irrigated Indus basin without image segmentation. Phenology information derived from Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, having 8-day temporal and 1000 m spatial resolution, was used in the analysis. An unsupervised (temporal space clustering) to supervised (area knowledge and phenology behavior) classification approach was adopted to identify 13 crop rotations. Estimated crop area was compared with reported area collected by field census. Results reveal that combined dataset (NDVI*LAI) performs better in mapping wheat-rice, wheat-cotton and wheat-fodder rotation by attaining root mean square error (RMSE) of 34.55, 16.84, 20.58 and mean absolute percentage error (MAPE) of 24.56 %, 36.82 %, 30.21 % for wheat, rice and cotton crop respectively. For sugarcane crop mapping, LAI produce good results by achieving RMSE of 8.60 and MAPE of 34.58 %, as compared to NDVI (10.08, 40.53 %) and NDVI*LAI (10.83, 39.45 %). The availability of major crop rotation statistics provides insight to develop better strategies for land, water and nutrient accounting frameworks to improve agriculture productivity.

Crop area estimation based on remotely-sensed data with an accurate but costly subsample

NASA Technical Reports Server (NTRS)

Gunst, R. F.

1985-01-01

Research activities conducted under the auspices of National Aeronautics and Space Administration Cooperative Agreement NCC 9-9 are discussed. During this contract period research efforts are concentrated in two primary areas. The first are is an investigation of the use of measurement error models as alternatives to least squares regression estimators of crop production or timber biomass. The secondary primary area of investigation is on the estimation of the mixing proportion of two-component mixture models. This report lists publications, technical reports, submitted manuscripts, and oral presentation generated by these research efforts. Possible areas of future research are mentioned.

Large Area Crop Inventory Experiment (LACIE). Feasibility of assessing crop condition and yield from LANDSAT data

NASA Technical Reports Server (NTRS)

1978-01-01

The author has identified the following significant results. Yield modelling for crop production estimation derived a means of predicting the within-a-year yield and the year-to-year variability of yield over some fixed or randomly located unit of area. Preliminary studies indicated that the requirements for interpreting LANDSAT data for yield may be sufficiently similar to those of signature extension that it is feasible to investigate the automated estimation of production. The concept of an advanced yield model consisting of both spectral and meteorological components was endorsed. Rationale for using meteorological parameters originated from known between season and near harvest dynamics in crop environmental-condition-yield relationships.

Banana orchard inventory using IRS LISS sensors

NASA Astrophysics Data System (ADS)

Nishant, Nilay; Upadhayay, Gargi; Vyas, S. P.; Manjunath, K. R.

2016-04-01

Banana is one of the major crops of India with increasing export potential. It is important to estimate the production and acreage of the crop. Thus, the present study was carried out to evolve a suitable methodology for estimating banana acreage. Area estimation methodology was devised around the fact that unlike other crops, the time of plantation of banana is different for different farmers as per their local practices or conditions. Thus in order to capture the peak signatures, biowindow of 6 months was considered, its NDVI pattern studied and the optimum two months were considered when banana could be distinguished from other competing crops. The final area of banana for the particular growing cycle was computed by integrating the areas of these two months using LISS III data with spatial resolution of 23m. Estimated banana acreage in the three districts were 11857Ha, 15202ha and 11373Ha for Bharuch, Anand and Vadodara respectively with corresponding accuracy of 91.8%, 90% and 88.16%. Study further compared the use of LISS IV data of 5.8m spatial resolution for estimation of banana using object based as well as per-pixel classification and the results were compared with statistical reports for both the approaches. In the current paper we depict the various methodologies to accurately estimate the banana acreage.

Analysis of scanner data for crop inventories

NASA Technical Reports Server (NTRS)

Horvath, R. (Principal Investigator); Cicone, R. C.; Kauth, R. J.; Malila, W. A.; Pont, W.; Thelen, B.; Sellman, A.

1981-01-01

Accomplishments for a machine-oriented small grains labeler T&E, and for Argentina ground data collection are reported. Features of the small grains labeler include temporal-spectral profiles, which characterize continuous patterns of crop spectral development, and crop calendar shift estimation, which adjusts for planting date differences of fields within a crop type. Corn and soybean classification technology development for area estimation for foreign commodity production forecasting is reported. Presentations supporting quarterly project management reviews and a quarterly technical interchange meeting are also included.

Can Canals Effectively Replace Groundwater Irrigation in Over-exploited Regions in India?

NASA Astrophysics Data System (ADS)

Jain, M.; Fishman, R.; Mondal, P.; Galford, G. L.; Bhattarai, N.; Naeem, S.; DeFries, R. S.

2017-12-01

We use high-resolution data on irrigation and cropping intensity across India to empirically estimate the impacts of losing access to groundwater irrigation in regions with critically exploited aquifers. India is the largest consumer of groundwater globally and is facing severe groundwater depletion. Canals are being promoted as an alternate irrigation source, yet few studies have quantified the effects that this transition may have on agricultural production. Our results suggest that farmers will be 50% less likely to plant a winter crop, have 20% less cropped area, and have cropped areas that are increasingly sensitive to rainfall variability when switching to canal irrigation. We estimate that national winter cropped area will decrease by approximately 13% if farmers lose access to groundwater irrigation in critically over-exploited regions, and 6% if farmers in these regions switch to canal irrigation. These results suggest that groundwater and canal irrigation are not substitutable, and farmers may have to switch to less water intensive crops or improve water use efficiency to maintain current levels of production in the future.

Identifying the Impact of Natural Hazards on Food Security in Africa: Crop Monitoring Using MODIS NDVI Time-Series

NASA Astrophysics Data System (ADS)

Freund, J. T.; Husak, G.; Funk, C.; Brown, M. E.; Galu, G.

2005-12-01

Most developing countries rely primarily on the successful cultivation of staple crops to ensure food security. Climatic hazards like drought and flooding often negatively impact economically vulnerable economies such as those in Eastern Africa. Effective tracking of food production is required in this area. Production is typically quantified as the simple product of a planted area and its corresponding crop yield. To date, crop yields have been estimated with reasonable accuracy using grid-cell techniques and a Water Requirement Satisfaction Index (WRSI), which draw from remotely sensed data. However, planted area and hence production estimation remains an arduous manual technique fraught with inevitable inaccuracies. In this study we present ongoing efforts to use MODIS NDVI time-series data as a surrogate for greenness, exploiting phenological contrast between cropland and other land cover types. In regions with small field sizes, variations in land cover can impose uncertainty in food production figures, resulting in a lack of consensus in the donor community as to the amount and type of food aid required during an emergency. To concentrate on this issue, statistical methods were employed to produce sub-pixel estimation, addressing the challenges in a monitoring system for use in subsistence-farmed areas. We will discuss two key results. Firstly, we established an inter-annual evaluation of crop health in primary agricultural areas in Kenya. These estimates will greatly improve our ability to anticipate and prevent famine in risk-prone regions through the FEWS NET early warning system. A primary goal is to build capacity in high-risk areas through the transfer of these results to local entities in the form of an operational tool. The low cost and accessibility of MODIS data lends itself well to this objective. Monitoring of crop health will be instituted for use on a yearly basis, and will draw on MODIS data analysis, ground sampling and valuable local expertise. Secondly, a baseline map of cropped areas was established, utilizing MODIS time-series data, Landsat ETM+ data and a custom dot-grid sampling method. This product aids in disaggregating crop location and density, and establishes a nominal quantitative assessment of farming practices. The techniques used to generate these results for Kenya can be expanded for use throughout developing Africa and beyond.

A root zone modelling approach to estimating groundwater recharge from irrigated areas

NASA Astrophysics Data System (ADS)

Jiménez-Martínez, J.; Skaggs, T. H.; van Genuchten, M. Th.; Candela, L.

2009-03-01

SummaryIn irrigated semi-arid and arid regions, accurate knowledge of groundwater recharge is important for the sustainable management of scarce water resources. The Campo de Cartagena area of southeast Spain is a semi-arid region where irrigation return flow accounts for a substantial portion of recharge. In this study we estimated irrigation return flow using a root zone modelling approach in which irrigation, evapotranspiration, and soil moisture dynamics for specific crops and irrigation regimes were simulated with the HYDRUS-1D software package. The model was calibrated using field data collected in an experimental plot. Good agreement was achieved between the HYDRUS-1D simulations and field measurements made under melon and lettuce crops. The simulations indicated that water use by the crops was below potential levels despite regular irrigation. The fraction of applied water (irrigation plus precipitation) going to recharge ranged from 22% for a summer melon crop to 68% for a fall lettuce crop. In total, we estimate that irrigation of annual fruits and vegetables produces 26 hm 3 y -1 of groundwater recharge to the top unconfined aquifer. This estimate does not include important irrigated perennial crops in the region, such as artichoke and citrus. Overall, the results suggest a greater amount of irrigation return flow in the Campo de Cartagena region than was previously estimated.

Use of remote sensing for land use policy formulation

NASA Technical Reports Server (NTRS)

1981-01-01

Progress in studies for using remotely sensed data for assessing crop stress and in crop estimation is reported. The estimation of acreage of small forested areas in the southern lower peninsula of Michigan using LANDSAT data is evaluated. Damage to small grains caused by the cereal leaf beetle was assessed through remote sensing. The remote detection of X-disease of peach and cherry trees and of fire blight of pear and apple trees was investigated. The reliability of improving on standard methods of crop production estimation was demonstrated. Areas of virus infestation in vineyards and blueberry fields in western and southwestern Michigan were identified. The installation and systems integration of a microcomputer system for processing and making available remotely sensed data are described.

Assessing gaps in irrigated agricultural productivity through satellite earth observations-A case study of the Fergana Valley, Central Asia

NASA Astrophysics Data System (ADS)

Löw, Fabian; Biradar, Chandrashekhar; Fliemann, Elisabeth; Lamers, John P. A.; Conrad, Christopher

2017-07-01

Improving crop area and/or crop yields in agricultural regions is one of the foremost scientific challenges for the next decades. This is especially true in irrigated areas because sustainable intensification of irrigated crop production is virtually the sole means to enhance food supply and contribute to meeting food demands of a growing population. Yet, irrigated crop production worldwide is suffering from soil degradation and salinity, reduced soil fertility, and water scarcity rendering the performance of irrigation schemes often below potential. On the other hand, the scope for improving irrigated agricultural productivity remains obscure also due to the lack of spatial data on agricultural production (e.g. crop acreage and yield). To fill this gap, satellite earth observations and a replicable methodology were used to estimate crop yields at the field level for the period 2010/2014 in the Fergana Valley, Central Asia, to understand the response of agricultural productivity to factors related to the irrigation and drainage infrastructure and environment. The results showed that cropping pattern, i.e. the presence or absence of multi-annual crop rotations, and spatial diversity of crops had the most persistent effects on crop yields across observation years suggesting the need for introducing sustainable cropping systems. On the other hand, areas with a lower crop diversity or abundance of crop rotation tended to have lower crop yields, with differences of partly more than one t/ha yield. It is argued that factors related to the infrastructure, for example, the distance of farms to the next settlement or the density of roads, had a persistent effect on crop yield dynamics over time. The improvement potential of cotton and wheat yields were estimated at 5%, compared to crop yields of farms in the direct vicinity of settlements or roads. In this study it is highlighted how remotely sensed estimates of crop production in combination with geospatial technologies provide a unique perspective that, when combined with field surveys, can support planners to identify management priorities for improving regional production and/or reducing environmental impacts.

Spatial and Temporal Uncertainty of Crop Yield Aggregations

NASA Technical Reports Server (NTRS)

Porwollik, Vera; Mueller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Iizumi, Toshichika; Ray, Deepak K.; Ruane, Alex C.; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe;

Airborne and ground-based remote sensing for the estimation of evapotranspiration and yield of bean, potato, and sugar beet crops

NASA Astrophysics Data System (ADS)

Jayanthi, Harikishan

The focus of this research was two-fold: (1) extend the reflectance-based crop coefficient approach to non-grain (potato and sugar beet), and vegetable crops (bean), and (2) develop vegetation index (VI)-yield statistical models for potato and sugar beet crops using high-resolution aerial multispectral imagery. Extensive crop biophysical sampling (leaf area index and aboveground dry biomass sampling) and canopy reflectance measurements formed the backbone of developing of canopy reflectance-based crop coefficients for bean, potato, and sugar beet crops in this study. Reflectance-based crop coefficient equations were developed for the study crops cultivated in Kimberly, Idaho, and subsequently used in water availability simulations in the plant root zone during 1998 and 1999 seasons. The simulated soil water profiles were compared with independent measurements of actual soil water profiles in the crop root zone in selected fields. It is concluded that the canopy reflectance-based crop coefficient technique can be successfully extended to non-grain crops as well. While the traditional basal crop coefficients generally expect uniform growth in a region the reflectance-based crop coefficients represent the actual crop growth pattern (in less than ideal water availability conditions) in individual fields. Literature on crop canopy interactions with sunlight states that there is a definite correspondence between leaf area index progression in the season and the final yield. In case of crops like potato and sugar beet, the yield is influenced not only on how early and how quickly the crop establishes its canopy but also on how long the plant stands on the ground in a healthy state. The integrated area under the crop growth curve has shown excellent correlations with hand-dug samples of potato and sugar beet crops in this research. Soil adjusted vegetation index-yield models were developed, and validated using multispectral aerial imagery. Estimated yield images were compared with the actual yields extracted from the ground. The remote sensing-derived yields compared well with the actual yields sampled on the ground. This research has highlighted the importance of the date of spectral emergence, the need to know the duration for which the crops stand on the ground, and the need to identify critical periods of time when multispectral coverages are essential for reliable tuber yield estimation.

Estimating irrigation water use in the humid eastern United States

USGS Publications Warehouse

Levin, Sara B.; Zarriello, Phillip J.

2013-01-01

Accurate accounting of irrigation water use is an important part of the U.S. Geological Survey National Water-Use Information Program and the WaterSMART initiative to help maintain sustainable water resources in the Nation. Irrigation water use in the humid eastern United States is not well characterized because of inadequate reporting and wide variability associated with climate, soils, crops, and farming practices. To better understand irrigation water use in the eastern United States, two types of predictive models were developed and compared by using metered irrigation water-use data for corn, cotton, peanut, and soybean crops in Georgia and turf farms in Rhode Island. Reliable metered irrigation data were limited to these areas. The first predictive model that was developed uses logistic regression to predict the occurrence of irrigation on the basis of antecedent climate conditions. Logistic regression equations were developed for corn, cotton, peanut, and soybean crops by using weekly irrigation water-use data from 36 metered sites in Georgia in 2009 and 2010 and turf farms in Rhode Island from 2000 to 2004. For the weeks when irrigation was predicted to take place, the irrigation water-use volume was estimated by multiplying the average metered irrigation application rate by the irrigated acreage for a given crop. The second predictive model that was developed is a crop-water-demand model that uses a daily soil water balance to estimate the water needs of a crop on a given day based on climate, soil, and plant properties. Crop-water-demand models were developed independently of reported irrigation water-use practices and relied on knowledge of plant properties that are available in the literature. Both modeling approaches require accurate accounting of irrigated area and crop type to estimate total irrigation water use. Water-use estimates from both modeling methods were compared to the metered irrigation data from Rhode Island and Georgia that were used to develop the models as well as two independent validation datasets from Georgia and Virginia that were not used in model development. Irrigation water-use estimates from the logistic regression method more closely matched mean reported irrigation rates than estimates from the crop-water-demand model when compared to the irrigation data used to develop the equations. The root mean squared errors (RMSEs) for the logistic regression estimates of mean annual irrigation ranged from 0.3 to 2.0 inches (in.) for the five crop types; RMSEs for the crop-water-demand models ranged from 1.4 to 3.9 in. However, when the models were applied and compared to the independent validation datasets from southwest Georgia from 2010, and from Virginia from 1999 to 2007, the crop-water-demand model estimates were as good as or better at predicting the mean irrigation volume than the logistic regression models for most crop types. RMSEs for logistic regression estimates of mean annual irrigation ranged from 1.0 to 7.0 in. for validation data from Georgia and from 1.8 to 4.9 in. for validation data from Virginia; RMSEs for crop-water-demand model estimates ranged from 2.1 to 5.8 in. for Georgia data and from 2.0 to 3.9 in. for Virginia data. In general, regression-based models performed better in areas that had quality daily or weekly irrigation data from which the regression equations were developed; however, the regression models were less reliable than the crop-water-demand models when applied outside the area for which they were developed. In most eastern coastal states that do not have quality irrigation data, the crop-water-demand model can be used more reliably. The development of predictive models of irrigation water use in this study was hindered by a lack of quality irrigation data. Many mid-Atlantic and New England states do not require irrigation water use to be reported. A survey of irrigation data from 14 eastern coastal states from Maine to Georgia indicated that, with the exception of the data in Georgia, irrigation data in the states that do require reporting commonly did not contain requisite ancillary information such as irrigated area or crop type, lacked precision, or were at an aggregated temporal scale making them unsuitable for use in the development of predictive models. Confidence in the reliability of either modeling method is affected by uncertainty in the reported data from which the models were developed or validated. Only through additional collection of quality data and further study can the accuracy and uncertainty of irrigation water-use estimates be improved in the humid eastern United States.

Large Area Crop Inventory Experiment (LACIE). Development of procedure M for multicrop inventory, with tests of a spring-wheat configuration

NASA Technical Reports Server (NTRS)

Horvath, R. (Principal Investigator); Cicone, R.; Crist, E.; Kauth, R. J.; Lambeck, P.; Malila, W. A.; Richardson, W.

1979-01-01

The author has identified the following significant results. An outgrowth of research and development activities in support of LACIE was a multicrop area estimation procedure, Procedure M. This procedure was a flexible, modular system that could be operated within the LACIE framework. Its distinctive features were refined preprocessing (including spatially varying correction for atmospheric haze), definition of field like spatial features for labeling, spectral stratification, and unbiased selection of samples to label and crop area estimation without conventional maximum likelihood classification.

Growth and reflectance characteristics of winter wheat canopies

NASA Technical Reports Server (NTRS)

Hinzman, L. D.; Bauer, M. E.; Daughtry, C. S. T.

1984-01-01

A valuable input to crop growth and yield models would be estimates of current crop condition. If multispectral reflectance indicates crop condition, then remote sensing may provide an additional tool for crop assessment. The effects of nitrogen fertilization on the spectral reflectance and agronomic characteristics of winter wheat (Triticum aestivum L.) were determined through field experiments. Spectral reflectance was measured during the 1979 and 1980 growing seasons with a spectroradiometer. Agronomic data included total leaf N concentration, leaf chlorophyll concentration, stage of development, leaf area index (LAI), plant moisture, and fresh and dry phytomass. Nitrogen deficiency caused increased visible, reduced near infrared, and increased middle infrared reflectance. These changes were related to lower levels of chlorophyll and reduced leaf area in the N-deficient plots. Green LAI, an important descriptor of wheat canopies, could be reliably estimated with multispectral data. The potential of remote sensing in distinguishing stressed from healthy crops is demonstrated. Evidence suggests multispectral imagery may be useful for monitoring crop condition.

Influence of precipitation and crop germination on resource selection by mule deer (Odocoileus hemionus) in southwest Colorado

USGS Publications Warehouse

Carrollo, Emily M.; Johnson, Heather E.; Fischer, Justin W.; Hammond, Matthew; Dorsey, Patricia D.; Anderson, Charles; Vercauteren, Kurt C.; Walter, W. David

2017-01-01

Mule deer (Odocoileus hemionus) populations in the western United States provide many benefits to local economies but can also cause considerable damage to agriculture, particularly damage to lucrative crops. Limited information exists to understand resource selection of mule deer in response to annual variation in crop rotation and climatic conditions. We tested the hypothesis that mule deer select certain crops, and in particular sunflower, based on annual climatic variability. Our objective was to use movements, estimates of home range, and resource selection analysis to identify resources selected by mule deer. We used annually-derived crop-specific datasets along with Global Positioning System collars to monitor 14 mule deer in an agricultural area near public lands in southwestern Colorado, USA. We estimated home ranges for two winter seasons that ranged between 7.68 and 9.88 km2, and for two summer seasons that ranged between 5.51 and 6.24 km2. Mule deer selected areas closer to forest and alfalfa for most periods during 2012, but selected areas closer to sunflower in a majority of periods during 2013. Considerable annual variation in climate patterns and precipitation levels appeared to influence selection by mule deer because of variability in crop rotation and success of germination of specific crops.

Influence of Precipitation and Crop Germination on Resource Selection by Mule Deer (Odocoileus hemionus) in Southwest Colorado.

PubMed

Carrollo, Emily M; Johnson, Heather E; Fischer, Justin W; Hammond, Matthew; Dorsey, Patricia D; Anderson, Charles; Vercauteren, Kurt C; Walter, W David

2017-11-09

Mule deer (Odocoileus hemionus) populations in the western United States provide many benefits to local economies but can also cause considerable damage to agriculture, particularly damage to lucrative crops. Limited information exists to understand resource selection of mule deer in response to annual variation in crop rotation and climatic conditions. We tested the hypothesis that mule deer select certain crops, and in particular sunflower, based on annual climatic variability. Our objective was to use movements, estimates of home range, and resource selection analysis to identify resources selected by mule deer. We used annually-derived crop-specific datasets along with Global Positioning System collars to monitor 14 mule deer in an agricultural area near public lands in southwestern Colorado, USA. We estimated home ranges for two winter seasons that ranged between 7.68 and 9.88 km 2 , and for two summer seasons that ranged between 5.51 and 6.24 km 2 . Mule deer selected areas closer to forest and alfalfa for most periods during 2012, but selected areas closer to sunflower in a majority of periods during 2013. Considerable annual variation in climate patterns and precipitation levels appeared to influence selection by mule deer because of variability in crop rotation and success of germination of specific crops.

Comparing LAI estimates of corn and soybean from vegetation indices of multi-resolution satellite images

USDA-ARS?s Scientific Manuscript database

Leaf area index (LAI) is important in explaining the ability of the crop to intercept solar energy for biomass production and in understanding the impact of crop management practices. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal ...

Area estimation using multiyear designs and partial crop identification

NASA Technical Reports Server (NTRS)

Sielken, R. L., Jr.

1984-01-01

Statistical procedures were developed for large area assessments using both satellite and conventional data. Crop acreages, other ground cover indices, and measures of change were the principal characteristics of interest. These characteristics are capable of being estimated from samples collected possibly from several sources at varying times, with different levels of identification. Multiyear analysis techniques were extended to include partially identified samples; the best current year sampling design corresponding to a given sampling history was determined; weights reflecting the precision or confidence in each observation were identified and utilized, and the variation in estimates incorporating partially identified samples were quantified.

An Empirical Bayes Approach to Spatial Analysis

NASA Technical Reports Server (NTRS)

Morris, C. N.; Kostal, H.

1983-01-01

Multi-channel LANDSAT data are collected in several passes over agricultural areas during the growing season. How empirical Bayes modeling can be used to develop crop identification and discrimination techniques that account for spatial correlation in such data is considered. The approach models the unobservable parameters and the data separately, hoping to take advantage of the fact that the bulk of spatial correlation lies in the parameter process. The problem is then framed in terms of estimating posterior probabilities of crop types for each spatial area. Some empirical Bayes spatial estimation methods are used to estimate the logits of these probabilities.

Large Area Crop Inventory Experiment (LACIE). Review of LACIE methodology, a project evaluation of technical acceptability

NASA Technical Reports Server (NTRS)

1976-01-01

The author has identified the following significant results. Results indicated that the LANDSAT data and the classification technology can estimate the small grains area within a sample segment accurately and reliably enough to meet the LACIE goals. Overall, the LACIE estimates in a 9 x 11 kilometer segment agree well with ground and aircraft determined area within these segments. The estimated c.v. of the random classification error was acceptably small. These analyses confirmed that bias introduced by various factors, such as LANDSAT spatial resolution, lack of spectral resolution, classifier bias, and repeatability, was not excessive in terms of the required performance criterion. Results of these tests did indicate a difficulty in differentiating wheat from other closely related small grains. However, satisfactory wheat area estimates were obtained through the reduction of the small grain area estimates in accordance with relative amounts of these crops as determined from historic data; these procedures are being further refined.

Large Area Crop Inventory Experiment (LACIE). Evaluation of the LACIE transition year crop calendar model. [Wheat growth in the Great Plains Corridor, North America

NASA Technical Reports Server (NTRS)

Cheffin, R. E.; Woolley, S. K. (Principal Investigator)

1979-01-01

The author has identified the following significant results. The estimates of developmental stage dates from the LACIE adjustable crop calendar (ACC) winter wheat model was somewhat more accurate than the historical crop calendar after jointing. The ACC winter wheat model was not so accurate for the Texas Panhandle as it was for the other areas of the USPG-7 because dry soil conditions delayed fall planting in the Panhandle. Since the LACIE ACC winter wheat model does not contain a moisture term and it was started with historical planting dates, lengthy delays in planting mean that the ACC model will probably be started early and will estimate the developmental growth stages to occur too early in the season. The LACIE ACC spring wheat model was also started early in most areas because of late planting due to fields wet from melting snow and rain. The starter model used to estimate spring planting dates was not accurate under these wet soil conditions and tended to predict the developmental stages to occur earlier than the dates observed in the fields.

Comparison of estimates of evapotranspiration and consumptive use in Palo Verde Valley, California

USGS Publications Warehouse

Raymond, Lee H.; Owen-Joyce, Sandra J.

1987-01-01

Estimates of evapotranspiration and consumptive use by vegetation in Palo Verde Valley, California, were compared for calendar years 1981 to 1984. Vegetation types were classified, and the areas covered by each type were computed from Landsat satellite digital-image analysis. Evapotranspiration was calculated by multiplying the area of each vegetation type by a corresponding water use rate adjusted for year-to-year variations in climate. The vegetation classification slightly underestimates the total vegetated area when compared to crop reports, because not all multiple cropping could be identified. The accuracy of evapotranspiration calculated from vegetation classification depends primarily on the correct classification of alfalfa and cotton because alfalfa and cotton have larger acreages and use more water/acre than the other crops in the valley. Consumptive use was calculated using a water budget for each of the 4 years. Estimates of evapotranspiration and consumptive use by vegetation, respectively, were: (1) 439,400 and 483,500 acre-ft in 1981, (2) 430,700 and 452,700 acre-ft in 1982, (3) 402,000 and 364,400 acre-ft in 1983, and (4) 406,700 and 373,800 acre-ft in 1984. Evapotranspiration estimates were lower than consumptive use estimates in 1981 and 1982 and higher in 1983 and 1984. Both estimates were lower in 1983 and 1984 than in 1981 and 1982. Yearly differences in estimates correspond most closely to significant changes in stage of the lower Colorado River caused by flood control releases in 1983 and 1984 and to changes in cropping practices. (Author 's abstract)

Crop Acreage Estimation: Landsat TM and Resourcesat-1 AWiFS Sensor Assessment of the Mississippi River Delta, 2005

NASA Technical Reports Server (NTRS)

Boryan, Claire; Johnson, Dave; Craig, Mike; Seffrin, Bob; Mueller, RIck

2007-01-01

AWiFs data are appropriate for crop acreage estimation over large, spectrally homogenous, crop areas such as the Mid-West, the Delta and the Northern Great Plains. Regression and Kappa statistics for soybean, corn, cotton, rice and sorghum produced using both the Landsat TM and AWiFS data are very similar. AWiFS data appear to be a suitable alternative or supplement to Landsat TM data for production of NASS'Cropland Data Layer product.

Evaporation from irrigated crops: Its measurement, modeling and estimation from remotely sensed data

NASA Astrophysics Data System (ADS)

Garatuza-Payan, Jaime

The research described in this dissertation is predicated on the hypothesis that remotely sensed information from climatological satellites can be used to estimate the actual evapotranspiration from agricultural crops to improve irrigation scheduling and water use efficiency. The goal of the enabling research program described here was to facilitate and demonstrate the potential use of satellite data for the rapid and routine estimation of water use by irrigated crops in the Yaqui Valley irrigation scheme, an extensive irrigated area in Sonora, Mexico. The approach taken was first, to measure and model the evapotranspiration and crop factors for wheat and cotton, the most common irrigated crops in the Yaqui Valley scheme. Second, to develop and test a high-resolution (4 km x 4 km) method for determining cloud cover and solar radiation from GOES satellite data. Then third, to demonstrate the application of satellite data to calculate the actual evaporation for sample crops in the Yaqui Valley scheme by combining estimates of potential rate with relevant crop factors and information on crop management. Results show that it is feasible to provide routine estimates of evaporation for the most common crops in the Yaqui Valley irrigation scheme from satellite data. Accordingly, a system to provide such estimates has been established and the Water Users Association, the entity responsible for water distribution in Yaqui Valley, can now use them to decide whether specific fields need irrigation. A Web site (teka-pucem.itson.mx) is also being created which will allow individual farmers to have direct access to the evaporation estimates via the Internet.

Remote sensing of agricultural crops and soils

NASA Technical Reports Server (NTRS)

Bauer, M. E. (Principal Investigator)

1983-01-01

Research in the correlative and noncorrelative approaches to image registration and the spectral estimation of corn canopy phytomass and water content is reported. Scene radiation research results discussed include: corn and soybean LANDSAT MSS classification performance as a function of scene characteristics; estimating crop development stages from MSS data; the interception of photosynthetically active radiation in corn and soybean canopies; costs of measuring leaf area index of corn; LANDSAT spectral inputs to crop models including the use of the greenness index to assess crop stress and the evaluation of MSS data for estimating corn and soybean development stages; field research experiment design data acquisition and preprocessing; and Sun-view angles studies of corn and soybean canopies in support of vegetation canopy reflection modeling.

Method for Estimating Annual Atrazine Use for Counties in the Conterminous United States, 1992-2007

USGS Publications Warehouse

Thelin, Gail P.; Stone, Wesley W.

2010-01-01

A method was developed to estimate annual atrazine use during 1992 to 2007 on sixteen crops and four agricultural land uses. For each year, atrazine use was estimated for all counties in the conterminous United States (except California) by combining (1) proprietary data from the Doane Marketing Research-Kynetec (DMRK) AgroTrak database on the mass of atrazine applied to agricultural crops, (2) county harvested crop acreage, by county, from the 1992, 1997, 2002, and 2007 Censuses of Agriculture, and (3) annual harvested crop acreage from National Agriculture Statistics Service (NASS) for non-Census years. DMRK estimates of pesticide use on individual crops were derived from surveys of major field crops and selected specialty crops in multicounty areas referred to as Crop Reporting Districts (CRD). The CRD-level atrazine-use estimates were disaggregated to obtain county-level application rates by dividing the mass (pounds) of pesticides applied to a crop by the acreage of that crop in the CRD to yield a rate per harvested acre. When atrazine-use estimates were not available for a CRD, crop, or year, an estimated rate was developed following a hierarchy of decision rules that checked first for the availability of a crop application rate from surveyed atrazine application rate(s) for adjacent CRDs for a specific year, and second, the rates from surveyed CRDs within for U.S. Department of Agriculture Farm Production Regions for a specific year or multiple years. The estimation method applied linear interpolation to estimate crop acreage for years when harvested acres for a crop and county were not reported in either the Census of Agriculture or the NASS database, but were reported by these data sources for other years for that crop and county. Data for atrazine use for the counties in California was obtained from farmers' reports of pesticide use collected and published by the California Department of Pesticide Regulation-Pesticide Use Reporting (DPR-PUR) because these data are more complete than DMRK survey data. National and state annual atrazine-use totals derived by this method were compared with other published pesticide-use estimates and were highly correlated. The method developed is designed to be applicable to other pesticides for which there are similar data; however, for some pesticides that are applied to specialty crops, fewer surveys are usually available to estimate application rates and there are a greater number of years with unreported crop acreage, potentially resulting in greater uncertainty in use

Assimilating Leaf Area Index Estimates from Remote Sensing into the Simulations of a Cropping Systems Model

USDA-ARS?s Scientific Manuscript database

Spatial extrapolation of cropping systems models for regional crop growth and water use assessment and farm-level precision management has been limited by the vast model input requirements and the model sensitivity to parameter uncertainty. Remote sensing has been proposed as a viable source of spat...

Determination of the optimal level for combining area and yield estimates

NASA Technical Reports Server (NTRS)

Bauer, M. E. (Principal Investigator); Hixson, M. M.; Jobusch, C. D.

1981-01-01

Several levels of obtaining both area and yield estimates of corn and soybeans in Iowa were considered: county, refined strata, refined/split strata, crop reporting district, and state. Using the CCEA model form and smoothed weather data, regression coefficients at each level were derived to compute yield and its variance. Variances were also computed with stratum level. The variance of the yield estimates was largest at the state and smallest at the county level for both crops. The refined strata had somewhat larger variances than those associated with the refined/split strata and CRD. For production estimates, the difference in standard deviations among levels was not large for corn, but for soybeans the standard deviation at the state level was more than 50% greater than for the other levels. The refined strata had the smallest standard deviations. The county level was not considered in evaluation of production estimates due to lack of county area variances.

Coupling AVHRR imagery with biogeochemical models of methane emission from rice crops

NASA Astrophysics Data System (ADS)

Paliouras, Eleni Joyce

2000-10-01

Rice is a staple food source for much of the world and most of it is grown in paddies which remain flooded for a large part of the growing season. This anaerobic environment is ideal for the activities of methanogenic bacteria, that are responsible for the production of methane gas, some of which is released into the atmosphere. In order to better understand the role that rice cropping plays in the levels of atmospheric methane, several models have been developed to predict the methane flux from the paddies. These models generally utilize some type of nominal plant growth curve based on one or two pieces of ground truth data. Ideally, satellite data could be used instead to provide these models with an estimate of biomass change over the growing season, eliminating the need for related ground truth. A technique proposed to accomplish this is presented here, and results that demonstrate its success when applied to rice cropping areas of Texas are discussed. Also presented is a method for utilizing satellite data to map rice cropping areas that could eventually aid in a scheme for populating a GIS-type database with information on exact rice cropping areas. Such a database could then be directly tied to the methane emission models to obtain flux estimates for extensive regional areas.

New developments in sampling and aggregation for remotely sensed surveys

NASA Technical Reports Server (NTRS)

Feiveson, A. H. (Principal Investigator)

1979-01-01

Sampling techniques used to construct large area crop estimates are briefly reviewed. Problem areas in sampling and aggregation are covered. The natural sampling strategy, two phase sampling, weighted aggregation, and multiyear estimation are among the topics discussed.

Large Area Crop Inventory Experiment (LACIE). Executive summary

NASA Technical Reports Server (NTRS)

1978-01-01

The author has identified the following significant results. The Large Area Crop Inventory Experiment (LACIE), completed June 30, 1978, has met the USDA at-harvest goals (90% accuracy with a 90% confidence level) in the US Great Plains and U.S.S.R. for two consecutive years. In addition, in the U.S.S.R., LACIE indicated a shortfall in the '76-'77 wheat crop about two months prior to harvest, thus demonstrating the capability of LACIE to make accurate preharvest estimates.

Identification and estimation of the area planted with irrigated rice based on the visual interpretation of LANDSAT MSS data

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Moreira, M. A.; Assuncao, G. V.; Novaes, R. A.; Mendoza, A. A. B.; Bauer, C. A.; Ritter, I. T.; Barros, J. A. I.; Perez, J. E.; Thedy, J. L. O.

1983-01-01

The objective was to test the feasibility of the application of MSS-LANDSAT data to irrigated rice crop identification and area evaluation, within four rice growing regions of the Rio Grande do Sul state, in order to extend the methodology for the whole state. The applied methodology was visual interpretation of the following LANDSAT products: channels 5 and 7 black and white imageries and color infrared composite imageries all at the scale of 1:250.000. For crop identification and evaluation, the multispectral criterion and the seasonal variation were utilized. Based on the results it was possible to conclude that: (1) the satellite data were efficient for crop area identification and evaluation; (2) the utilization of the multispectral criterion, allied to the seasonal variation of the rice crop areas from the other crops and, (3) the large cloud cover percentage found in the satellite data made it impossible to realize a rice crop spectral monitoring and, therefore, to define the best dates for such data acquisition for rice crop assessment.

Economic Benefits of Improved Information on Worldwide Crop Production: An Optimal Decision Model of Production and Distribution with Application to Wheat, Corn, and Soybeans

NASA Technical Reports Server (NTRS)

Andrews, J.

1977-01-01

An optimal decision model of crop production, trade, and storage was developed for use in estimating the economic consequences of improved forecasts and estimates of worldwide crop production. The model extends earlier distribution benefits models to include production effects as well. Application to improved information systems meeting the goals set in the large area crop inventory experiment (LACIE) indicates annual benefits to the United States of $200 to $250 million for wheat, $50 to $100 million for corn, and $6 to $11 million for soybeans, using conservative assumptions on expected LANDSAT system performance.

The limits of crop productivity: validating theoretical estimates and determining the factors that limit crop yields in optimal environments

NASA Technical Reports Server (NTRS)

Bugbee, B.; Monje, O.

1992-01-01

Plant scientists have sought to maximize the yield of food crops since the beginning of agriculture. There are numerous reports of record food and biomass yields (per unit area) in all major crop plants, but many of the record yield reports are in error because they exceed the maximal theoretical rates of the component processes. In this article, we review the component processes that govern yield limits and describe how each process can be individually measured. This procedure has helped us validate theoretical estimates and determine what factors limit yields in optimal environments.

Results from the Crop Identification Technology Assessment for Remote Sensing (CITARS) project

NASA Technical Reports Server (NTRS)

Bauer, M. E. (Principal Investigator); Davis, B. J.; Bizzell, R. M.; Hall, F. G.; Feiveson, A. H.; Malila, W. A.; Rice, D. P.

1976-01-01

The author has identified the following significant results. It was found that several factors had a significant effect on crop identification performance: (1) crop maturity and site characteristics, (2) which of several different single date automatic data processing procedures was used for local recognition, (3) nonlocal recognition, both with and without preprocessing for the extension of recognition signatures, and (4) use of multidate data. It also was found that classification accuracy for field center pixels was not a reliable indicator of proportion estimation performance for whole areas, that bias was present in proportion estimates, and that training data and procedures strongly influenced crop identification performance.

Preliminary validation of leaf area index sensor in Huailai

NASA Astrophysics Data System (ADS)

Cai, Erli; Li, Xiuhong; Liu, Qiang; Dou, Baocheng; Chang, Chongyan; Niu, Hailin; Lin, Xingwen; Zhang, Jialin

2015-12-01

Leaf area index (LAI) is a key variable in many land surface models that involve energy and mass exchange between vegetation and the environment. In recent years, extracting vegetation structure parameters from digital photography becomes a widely used indirect method to estimate LAI for its simplicity and ease of use. A Leaf Area Index Sensor (LAIS) system was developed to continuously monitor the growth of crops in several sampling points in Huailai, China. The system applies 3G/WIFI communication technology to remotely collect crop photos in real-time. Then the crop photos are automatically processed and LAI is estimated based on the improved leaf area index of Lang and Xiang (LAILX) algorithm in LAIS. The objective of this study is to primarily verify the LAI estimated from LAIS (Lphoto) through comparing them with the destructive green LAI (Ldest). Ldest was measured across the growing season ntil maximum canopy development while plants are still green. The preliminary verification shows that Lphoto corresponds well with the Ldest (R2=0.975). In general, LAI could be accurately estimated with LAIS and its LAI shows high consistency compared with the destructive green LAI. The continuous LAI measurement obtained from LAIS could be used for the validation of remote sensing LAI products.

The 1980 US/Canada wheat and barley exploratory experiment. Volume 2: Addenda

NASA Technical Reports Server (NTRS)

Bizzell, R. M.; Prior, H. L.; Payne, R. W.; Disler, J. M.

1983-01-01

Three study areas supporting the U.S./Canada Wheat and Barley Exploratory Experiment are discussed including an evaluation of the experiment shakedown test analyst labeling results, an evaluation of the crop proportion estimate procedure 1A component, and the evaluation of spring wheat and barley crop calendar models for the 1979 crop year.

The California Biomass Crop Adoption Model estimates biofuel feedstock crop production across diverse agro-ecological zones within the state, under different future climates

NASA Astrophysics Data System (ADS)

Kaffka, S.; Jenner, M.; Bucaram, S.; George, N.

2012-12-01

Both regulators and businesses need realistic estimates for the potential production of biomass feedstocks for biofuels and bioproducts. This includes the need to understand how climate change will affect mid-tem and longer-term crop performance and relative advantage. The California Biomass Crop Adoption Model is a partial mathematical programming optimization model that estimates the profit level needed for new crop adoption, and the crop(s) displaced when a biomass feedstock crop is added to the state's diverse set of cropping systems, in diverse regions of the state. Both yield and crop price, as elements of profit, can be varied. Crop adoption is tested against current farmer preferences derived from analysis of 10 years crop production data for all crops produced in California, collected by the California Department of Pesticide Regulation. Analysis of this extensive data set resulted in 45 distinctive, representative farming systems distributed across the state's diverse agro-ecological regions. Estimated yields and water use are derived from field trials combined with crop simulation, reported elsewhere. Crop simulation is carried out under different weather and climate assumptions. Besides crop adoption and displacement, crop resource use is also accounted, derived from partial budgets used for each crop's cost of production. Systematically increasing biofuel crop price identified areas of the state where different types of crops were most likely to be adopted. Oilseed crops like canola that can be used for biodiesel production had the greatest potential to be grown in the Sacramento Valley and other northern regions, while sugar beets (for ethanol) had the greatest potential in the northern San Joaquin Valley region, and sweet sorghum in the southern San Joaquin Valley. Up to approximately 10% of existing annual cropland in California was available for new crop adoption. New crops are adopted if the entire cropping system becomes more profitable. In particular, canola production resulted in less overall water use but increased farm profits. Most crop substitutions were resource neutral. If future climate is drier, more winter annual crops like canola are likely to be adopted. Crop displacement is also important for determining market-mediated effects of biomass crop production. Correctly estimating crop displacement at the local scale greatly improves upon estimates for indirect land use change derived from the macro-scale PE and CGE models currently used by US EPA and the California Air Resources Board.

Analysis of scanner data for crop inventories

NASA Technical Reports Server (NTRS)

Horvath, R. (Principal Investigator); Cicone, R.; Crist, E.; Kauth, R. J.; Pont, W.

1980-01-01

Classification and technology development for area estimation of corn, soybeans, wheat, barley, and sunflowers are outlined. Supporting research for corn and soybean foreign commodity production forecasting is highlighted. Graphs profiling the greenness and brightness of the crops are presented.

Evaluating soil moisture and yield of winter wheat in the Great Plains using Landsat data

NASA Technical Reports Server (NTRS)

Heilman, J. L.; Kanemasu, E. T.; Bagley, J. O.; Rasmussen, V. P.

1977-01-01

Locating areas where soil moisture is limiting to crop growth is important for estimating winter-wheat yields on a regional basis. In the 1975-76 growing season, we evaluated soil-moisture conditions and winter-wheat yields for a five-state region of the Great Plains using Landsat estimates of leaf area index (LAI) and an evapotranspiration (ET) model described by Kanemasu et al (1977). Because LAI was used as an input, the ET model responded to changes in crop growth. Estimated soil-water depletions were high for the Nebraska Panhandle, southwestern Kansas, southeastern Colorado, and the Texas Panhandle. Estimated yields in five-state region ranged from 1.0 to 2.9 metric ton/ha.

Space Data for Crop Management

NASA Technical Reports Server (NTRS)

1990-01-01

CROPIX, Inc., formed in 1984 by Frank Lamb, president of the Eastern Oregon Farming Company, monitors primarily potato crops in a 20,000 square mile area of northern Oregon and central Washington. Potatoes are a high value specialty crop that can be more profitable to the farmer if he has advance knowledge of market conditions, knows when to harvest, and when to take it to market. By processing and collecting data collected by the NASA-developed Landsat Earth Resources survey satellites, Lamb is able to provide accurate information on crop acreage and conditions on a more timely basis than the routine estimates by the USDA. CROPIX uses Landsat data to make acreage estimates of crops, and to calculate a field-by-field vegetative index number. CROPIX then distributes to its customers a booklet containing color-coded maps, an inventory of crops, plus data and graphs on crop conditions and other valuable information.

Generating an agricultural risk map based on limited ecological information: A case study using Sicyos angulatus.

PubMed

Osawa, Takeshi; Okawa, Shigenori; Kurokawa, Shunji; Ando, Shinichiro

2016-12-01

In this study, we propose a method for estimating the risk of agricultural damage caused by an invasive species when species-specific information is lacking. We defined the "risk" as the product of the invasion probability and the area of potentially damaged crop for production. As a case study, we estimated the risk imposed by an invasive weed, Sicyos angulatus, based on simple cellular simulations and governmental data on the area of crop that could potentially be damaged in Miyagi Prefecture, Japan. Simulation results revealed that the current distribution range was sufficiently accurate for practical purposes. Using these results and records of crop areas, we present risk maps for S. angulatus in agricultural fields. Managers will be able to use these maps to rapidly establish a management plan with minimal cost. Our approach will be valuable for establishing a management plan before or during the early stages of invasion.

Linking field observations, Landsat and MODIS data to estimate agricultural change in European Russia.

NASA Astrophysics Data System (ADS)

de Beurs, K. M.; Ioffe, G.

2011-12-01

Agricultural reform has been one of the most important anthropogenic change processes in European Russia that has been unfolding since the formal collapse of the Soviet Union at the end of 1991. Widespread land abandonment is perhaps the most vivid side effect of the reform, even visible in synoptic imagery. Currently, Russia is transitioning into a country with an internal "archipelago" of islands of productive agriculture around cities embedded in a matrix of unproductive, abandoned lands. This heterogeneous spatial pattern is mainly driven by depopulation of the least favorable parts of the countryside, where "least favorable" is a function of fertility, remoteness, and their interaction. In this work we provide a satellite, GIS and field based overview of the current agricultural developments in Russia and look beyond the unstable period immediately following the collapse of the Soviet Union. We apply Landsat images in one of Russia's oblasts to create a detailed land cover map. We then use a logistic model to link the Landsat land cover map with the inter-annual variability in key phenological parameters calculated from MODIS to derive the percent of cropland per 500m MODIS pixel. By evaluating the phenological characteristics of the MODIS curves for each year we determine whether a pixel was actually cropped or left fallow. A comparison of satellite-estimated cropped areas with regional statistics (by rayon) revealed that the satellite estimates are highly correlated with the regional statistics for both arable lands and successfully cropped areas. We use the crop maps to determine the number of times a particular area was cropped between 2002 and 2009 by summing all the years with crops per pixel. This variable provides a good indication about the intensification and de-intensification of the Russian croplands over the last decade. We have visited several rural areas in Russia and we link the satellite data with information acquired through field interviews and photographs. Russian farmers employ a variety of crop-rotation schemes. In the Russian grain belt, the farmers used to be on a seven-year rotation, which typically included only one year of fallow and a variety of grain crops in the remaining six years. Through field interviews and satellite observations we learned that the crop rotation schedules are changing from a seven year crop cycle focused on grain production to a three year crop cycle focused on the production of sunflower which is currently most profitable. In addition, a switch is underway from the dominant growth of spring wheat to stronger reliance on winter wheat which has better growth potential in the area. The number of cropped years, or the complementary number of fallow years, gives an indication of the type of crop cycle that is applied. In addition, drier areas are predicted to reveal more fallow years due to decisions by farm administrators. We will discuss how the ongoing changes represent adaptations to changing climatological and social circumstances.

Estimation of annual agricultural pesticide use for counties of the conterminous United States, 1992-2009

USGS Publications Warehouse

Thelin, Gail P.; Stone, Wesley W.

2013-01-01

A method was developed to calculate annual county level pesticide use for selected herbicides, insecticides, and fungicides applied to agricultural crops grown in the conterminous United States from 1992 through 2009. Pesticide-use data compiled by proprietary surveys of farm operations located within Crop Reporting Districts were used in conjunction with annual harvested-crop acreage reported by the U.S. Department of Agriculture National Agricultural Statistics Service (NASS) to calculate use rates per harvested crop acre, or an 'estimated pesticide use' (EPest) rate, for each crop by year. Pesticide-use data were not available for all Crop Reporting Districts and years. When data were unavailable for a Crop Reporting District in a particular year, EPest extrapolated rates were calculated from adjoining or nearby Crop Reporting Districts to ensure that pesticide use was estimated for all counties that reported harvested-crop acreage. EPest rates were applied to county harvested-crop acreage differently to obtain EPest-low and EPest-high estimates of pesticide-use for counties and states, with the exception of use estimates for California, which were taken from annual Department of Pesticide Regulation Pesticide Use Reports. Annual EPest-low and EPest-high use totals were compared with other published pesticide-use reports for selected pesticides, crops, and years. EPest-low and EPest-high national totals for five of seven herbicides were in close agreement with U.S. Environmental Protection Agency and National Pesticide Use Data estimates, but greater than most NASS national totals. A second set of analyses compared EPest and NASS annual state totals and state-by-crop totals for selected crops. Overall, EPest and NASS use totals were not significantly different for the majority of crop-stateyear combinations evaluated. Furthermore, comparisons of EPest and NASS use estimates for most pesticides had rank correlation coefficients greater than 0.75 and median relative errors of less than 15 percent. Of the 48 pesticide-by-crop combinations with 10 or more state-year combinations, 12 of the EPest-low and 17 of the EPest-high totals showed significant differences (p < 0.05) from NASS use estimates. The differences between EPest and NASS estimates did not follow consistent patterns related to particular crops, years, or states, and most correlation coefficients were greater than 0.75. EPest values from this study are suitable for making national, regional, and watershed assessments of annual pesticide use from 1992 to 2009. Although estimates are provided by county to facilitate estimation of watershed pesticide use for a wide variety of watersheds, there is a greater degree of uncertainty in individual county-level estimates when compared to Crop Reporting District or state-level estimates because (1) EPest crop-use rates were developed on the basis of pesticide use on harvested acres in multi-county areas (Crop Reporting Districts) and then allocated to county harvested cropland; (2) pesticide-by-crop use rates were not available for all Crop Reporting Districts in the conterminous United States, and extrapolation methods were used to estimate pesticide use for some counties; and (3) it is possible that surveyed pesticide-by-crop use rates do not reflect all agricultural use on all crops grown. The methods developed in this study also are applicable to other agricultural pesticides and years.

A survey of automated remote sensing for agriculture

NASA Technical Reports Server (NTRS)

Hall, F. G.; Macdonald, R. B.

1983-01-01

The state-of-the-art of the technology available to make remote sensing crop production estimates is reviewed with reference to several past and present research projects. In particular, attention is given to Landsat data acquisition, registration and preprocessing, data transformation, data modeling, proportion estimation, and labeling. Development stage models and crop condition models are briefly characterized, and areas where further research is needed are identified.

Estimating inter-annual diversity of seasonal agricultural area using multi-temporal resourcesat data.

PubMed

Sreenivas, K; Sekhar, N Seshadri; Saxena, Manoj; Paliwal, R; Pathak, S; Porwal, M C; Fyzee, M A; Rao, S V C Kameswara; Wadodkar, M; Anasuya, T; Murthy, M S R; Ravisankar, T; Dadhwal, V K

2015-09-15

The present study aims at analysis of spatial and temporal variability in agricultural land cover during 2005-6 and 2011-12 from an ongoing program of annual land use mapping using multidate Advanced Wide Field Sensor (AWiFS) data aboard Resourcesat-1 and 2. About 640-690 multi-temporal AWiFS quadrant data products per year (depending on cloud cover) were co-registered and radiometrically normalized to prepare state (administrative unit) mosaics. An 18-fold classification was adopted in this project. Rule-based techniques along with maximum-likelihood algorithm were employed to deriving land cover information as well as changes within agricultural land cover classes. The agricultural land cover classes include - kharif (June-October), rabi (November-April), zaid (April-June), area sown more than once, fallow lands and plantation crops. Mean kappa accuracy of these estimates varied from 0.87 to 0.96 for various classes. Standard error of estimate has been computed for each class annually and the area estimates were corrected using standard error of estimate. The corrected estimates range between 99 and 116 Mha for kharif and 77-91 Mha for rabi. The kharif, rabi and net sown area were aggregated at 10 km × 10 km grid on annual basis for entire India and CV was computed at each grid cell using temporal spatially-aggregated area as input. This spatial variability of agricultural land cover classes was analyzed across meteorological zones, irrigated command areas and administrative boundaries. The results indicate that out of various states/meteorological zones, Punjab was consistently cropped during kharif as well as rabi seasons. Out of all irrigated commands, Tawa irrigated command was consistently cropped during rabi season. Copyright © 2014 Elsevier Ltd. All rights reserved.

Agricultural irrigated land-use inventory for Osceola County, Florida, October 2013-April 2014

USGS Publications Warehouse

Marella, Richard L.; Dixon, Joann F.

2014-01-01

A detailed inventory of irrigated crop acreage is not available at the level of resolution needed to increase the accuracy of current water-use estimates or to project future water demands in many Florida counties. This report provides a detailed digital map and summary of irrigated areas within Osceola County for the agricultural growing period October 2013–April 2014. The irrigated areas were first delineated using land-use data and satellite imagery and then field verified between February and April 2014. Selected attribute data were collected for the irrigated areas, including crop type, primary water source, and type of irrigation system. Results indicate that an estimated 27,450 acres were irrigated during the study period. This includes 4,370 acres of vegetables, 10,970 acres of orchard crops, 1,620 acres of field crops, and 10,490 acres of ornamentals and grasses. Specifically, irrigated acreage included citrus (10,860 acres), sod (5,640 acres), pasture (4,580 acres), and potatoes (3,320 acres). Overall, groundwater was used to irrigate 18,350 acres (67 percent of the total acreage), and surface water was used to irrigate the remaining 9,100 acres (33 percent). Microirrigation systems accounted for 45 percent of the total acreage irrigated, flood systems 30 percent, and sprinkler systems the remaining 25 percent. An accurate, detailed, spatially referenced, and field-verified inventory of irrigated crop acreage can be used to assist resource managers making current and future county-level water-use estimates in Osceola County.

Global assessment of urban and peri-urban agriculture: irrigated and rainfed croplands

NASA Astrophysics Data System (ADS)

Thebo, A. L.; Drechsel, P.; Lambin, E. F.

2014-11-01

The role of urban agriculture in global food security is a topic of increasing discussion. Existing research on urban and peri-urban agriculture consists largely of case studies that frequently use disparate definitions of urban and peri-urban agriculture depending on the local context and study objectives. This lack of consistency makes quantification of the extent of this practice at the global scale difficult. This study instead integrates global data on croplands and urban extents using spatial overlay analysis to estimate the global area of urban and peri-urban irrigated and rainfed croplands. The global area of urban irrigated croplands was estimated at about 24 Mha (11.0 percent of all irrigated croplands) with a cropping intensity of 1.48. The global area of urban rainfed croplands found was approximately 44 Mha (4.7 percent of all rainfed croplands) with a cropping intensity of 1.03. These values were derived from the MIRCA2000 Maximum Monthly Cropped Area Grids for irrigated and rainfed crops and therefore their sum does not necessarily represent the total urban cropland area when the maximum extent of irrigated and rainfed croplands occurs in different months. Further analysis of croplands within 20 km of urban extents show that 60 and 35 percent of, respectively, all irrigated and rainfed croplands fall within this distance range.

Assessment of actual transpiration rate in olive tree field combining sap-flow, leaf area index and scintillometer measurements

NASA Astrophysics Data System (ADS)

Agnese, C.; Cammalleri, C.; Ciraolo, G.; Minacapilli, M.; Provenzano, G.; Rallo, G.; de Bruin, H. A. R.

2009-09-01

Models to estimate the actual evapotranspiration (ET) in sparse vegetation area can be fundamental for agricultural water managements, especially when water availability is a limiting factor. Models validation must be carried out by considering in situ measurements referred to the field scale, which is the relevant scale of the modelled variables. Moreover, a particular relevance assumes to consider separately the components of plant transpiration (T) and soil evaporation (E), because only the first is actually related to the crop stress conditions. Objective of the paper was to assess a procedure aimed to estimate olive trees actual transpiration by combining sap flow measurements with the scintillometer technique at field scale. The study area, located in Western Sicily (Italy), is mainly cultivated with olive crop and is characterized by typical Mediterranean semi-arid climate. Measurements of sap flow and crop actual evapotranspiration rate were carried out during 2008 irrigation season. Crop transpiration fluxes, measured on some plants by means of sap flow sensors, were upscaled considering the leaf area index (LAI). The comparison between evapotranspiration values, derived by displaced-beam small-aperture scintillometer (DBSAS-SLS20, Scintec AG), with the transpiration fluxes obtained by the sap flow sensors, also allowed to evaluate the contribute of soil evaporation in an area characterized by low vegetation coverage.

Development of the crop residue and rangeland burning in the 2014 National Emissions Inventory using information from multiple sources.

PubMed

Pouliot, George; Rao, Venkatesh; McCarty, Jessica L; Soja, Amber

2017-05-01

Biomass burning has been identified as an important contributor to the degradation of air quality because of its impact on ozone and particulate matter. One component of the biomass burning inventory, crop residue burning, has been poorly characterized in the National Emissions Inventory (NEI). In the 2011 NEI, wildland fires, prescribed fires, and crop residue burning collectively were the largest source of PM 2.5 . This paper summarizes our 2014 NEI method to estimate crop residue burning emissions and grass/pasture burning emissions using remote sensing data and field information and literature-based, crop-specific emission factors. We focus on both the postharvest and pre-harvest burning that takes place with bluegrass, corn, cotton, rice, soybeans, sugarcane and wheat. Estimates for 2014 indicate that over the continental United States (CONUS), crop residue burning excluding all areas identified as Pasture/Grass, Grassland Herbaceous, and Pasture/Hay occurred over approximately 1.5 million acres of land and produced 19,600 short tons of PM 2.5 . For areas identified as Pasture/Grass, Grassland Herbaceous, and Pasture/Hay, biomass burning emissions occurred over approximately 1.6 million acres of land and produced 30,000 short tons of PM 2.5 . This estimate compares with the 2011 NEI and 2008 NEI as follows: 2008: 49,650 short tons and 2011: 141,180 short tons. Note that in the previous two NEIs rangeland burning was not well defined and so the comparison is not exact. The remote sensing data also provided verification of our existing diurnal profile for crop residue burning emissions used in chemical transport modeling. In addition, the entire database used to estimate this sector of emissions is available on EPA's Clearinghouse for Inventories and Emission Factors (CHIEF, http://www3.epa.gov/ttn/chief/index.html ). Estimates of crop residue burning and rangeland burning emissions can be improved by using satellite detections. Local information is helpful in distinguishing crop residue and rangeland burning from all other types of fires.

Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe

USGS Publications Warehouse

Funk, Chris; Budde, Michael E.

2009-01-01

For thirty years, simple crop water balance models have been used by the early warning community to monitor agricultural drought. These models estimate and accumulate actual crop evapotranspiration, evaluating environmental conditions based on crop water requirements. Unlike seasonal rainfall totals, these models take into account the phenology of the crop, emphasizing conditions during the peak grain filling phase of crop growth. In this paper we describe an analogous metric of crop performance based on time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery. A special temporal filter is used to screen for cloud contamination. Regional NDVI time series are then composited for cultivated areas, and adjusted temporally according to the timing of the rainy season. This adjustment standardizes the NDVI response vis-??-vis the expected phenological response of maize. A national time series index is then created by taking the cropped-area weighted average of the regional series. This national time series provides an effective summary of vegetation response in agricultural areas, and allows for the identification of NDVI green-up during grain filling. Onset-adjusted NDVI values following the grain filling period are well correlated with U.S. Department of Agriculture production figures, possess desirable linear characteristics, and perform better than more common indices such as maximum seasonal NDVI or seasonally averaged NDVI. Thus, just as appropriately calibrated crop water balance models can provide more information than seasonal rainfall totals, the appropriate agro-phenological filtering of NDVI can improve the utility and accuracy of space-based agricultural monitoring.

Estimating plant area index for monitoring crop growth dynamics using Landsat-8 and RapidEye images

NASA Astrophysics Data System (ADS)

Shang, Jiali; Liu, Jiangui; Huffman, Ted; Qian, Budong; Pattey, Elizabeth; Wang, Jinfei; Zhao, Ting; Geng, Xiaoyuan; Kroetsch, David; Dong, Taifeng; Lantz, Nicholas

2014-01-01

This study investigates the use of two different optical sensors, the multispectral imager (MSI) onboard the RapidEye satellites and the operational land imager (OLI) onboard the Landsat-8 for mapping within-field variability of crop growth conditions and tracking the seasonal growth dynamics. The study was carried out in southern Ontario, Canada, during the 2013 growing season for three annual crops, corn, soybeans, and winter wheat. Plant area index (PAI) was measured at different growth stages using digital hemispherical photography at two corn fields, two winter wheat fields, and two soybean fields. Comparison between several conventional vegetation indices derived from concurrently acquired image data by the two sensors showed a good agreement. The two-band enhanced vegetation index (EVI2) and the normalized difference vegetation index (NDVI) were derived from the surface reflectance of the two sensors. The study showed that EVI2 was more resistant to saturation at high biomass range than NDVI. A linear relationship could be used for crop green effective PAI estimation from EVI2, with a coefficient of determination (R2) of 0.85 and root-mean-square error of 0.53. The estimated multitemporal product of green PAI was found to be able to capture the seasonal dynamics of the three crops.

Phenological features for winter rapeseed identification in Ukraine using satellite data

NASA Astrophysics Data System (ADS)

Kravchenko, Oleksiy

2014-05-01

Winter rapeseed is one of the major oilseed crops in Ukraine that is characterized by high profitability and often grown with violations of the crop rotation requirements leading to soil degradation. Therefore, rapeseed identification using satellite data is a promising direction for operational estimation of the crop acreage and rotation control. Crop acreage of rapeseed is about 0.5-3% of total area of Ukraine, which poses a major problem for identification using satellite data [1]. While winter rapeseed could be classified using biomass features observed during autumn vegetation, these features are quite unstable due to field to field differences in planting dates as well as spatial and temporal heterogeneity in soil moisture availability. Due to this fact autumn biomass level features could be used only locally (at NUTS-3 level) and are not suitable for large-scale country wide crop identification. We propose to use crop parameters at flowering phenological stage for crop identification and present a method for parameters estimation using time-series of moderate resolution data. Rapeseed flowering could be observed as a bell-shaped peak in red reflectance time series. However the duration of the flowering period that is observable by satellite data is about only two weeks, which is quite short period taking into account inevitable cloud coverage issues. Thus we need daily time series to resolve the flowering peak and due to this we are limited to moderate resolution data. We used daily atmospherically corrected MODIS data coming from Terra and Aqua satellites within 90-160 DOY period to perform features calculations. Empirical BRDF correction is used to minimize angular effects. We used Gaussian Processes Regression (GPR) for temporal interpolation to minimize errors due to residual could coverage, atmospheric correction and a mixed pixel problems. We estimate 12 parameters for each time series. They are red and near-infrared (NIR) reflectance and the timing at four stages: before and after the flowering, at the peak flowering and at the maximum NIR level. We used Support Vector Machine for data classification. The most relevant feature for classification is flowering peak timing followed by flowering peak magnitude. The dependency of the peak time on the latitude as a sole feature could be used to reject 90% of non-rapeseed pixels that is greatly reduces the imbalance of the classification problem. To assess the accuracy of our approach we performed a stratified area frame sampling survey in Odessa region (NUTS-2 level) in 2013. The omission error is about 12.6% while commission error is higher at the level of 22%. This fact is explained by high viewing angle composition criterion that is used in our approach to mitigate high cloud coverage problem. However the errors are quite stable spatially and could be easily corrected by regression technique. To do this we performed area estimation for Odessa region using regression estimator and obtained good area estimation accuracy with 4.6% error (1σ). [1] Gallego, F.J., et al., Efficiency assessment of using satellite data for crop area estimation in Ukraine. Int. J. Appl. Earth Observ. Geoinf. (2014), http://dx.doi.org/10.1016/j.jag.2013.12.013

LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status

USDA-ARS?s Scientific Manuscript database

Optical remote sensing of crop nitrogen (N) status is developing into a powerful diagnostic tool that can improve N management decisions. Crop N status is a function of dry mass per unit area (W) and N concentration (%Na), which can be used to calculate N nutrition index (NNI),where NNI is %Na/%Nc (...

Yield Model Development (YMD) implementation plan for fiscal years 1981 and 1982

NASA Technical Reports Server (NTRS)

Ambroziak, R. A. (Principal Investigator)

1981-01-01

A plan is described for supporting USDA crop production forecasting and estimation by (1) testing, evaluating, and selecting crop yield models for application testing; (2) identifying areas of feasible research for improvement of models; and (3) conducting research to modify existing models and to develop new crop yield assessment methods. Tasks to be performed for each of these efforts are described as well as for project management and support. The responsibilities of USDA, USDC, USDI, and NASA are delineated as well as problem areas to be addressed.

Estimating Hydrologic Fluxes, Crop Water Use, and Agricultural Land Area in China using Data Assimilation

NASA Astrophysics Data System (ADS)

Smith, Tiziana; McLaughlin, Dennis B.; Hoisungwan, Piyatida

2016-04-01

Crop production has significantly altered the terrestrial environment by changing land use and by altering the water cycle through both co-opted rainfall and surface water withdrawals. As the world's population continues to grow and individual diets become more resource-intensive, the demand for food - and the land and water necessary to produce it - will continue to increase. High-resolution quantitative data about water availability, water use, and agricultural land use are needed to develop sustainable water and agricultural planning and policies. However, existing data covering large areas with high resolution are susceptible to errors and can be physically inconsistent. China is an example of a large area where food demand is expected to increase and a lack of data clouds the resource management dialogue. Some assert that China will have insufficient land and water resources to feed itself, posing a threat to global food security if they seek to increase food imports. Others believe resources are plentiful. Without quantitative data, it is difficult to discern if these concerns are realistic or overly dramatized. This research presents a quantitative approach using data assimilation techniques to characterize hydrologic fluxes, crop water use (defined as crop evapotranspiration), and agricultural land use at 0.5 by 0.5 degree resolution and applies the methodology in China using data from around the year 2000. The approach uses the principles of water balance and of crop water requirements to assimilate existing data with a least-squares estimation technique, producing new estimates of water and land use variables that are physically consistent while minimizing differences from measured data. We argue that this technique for estimating water fluxes and agricultural land use can provide a useful basis for resource management modeling and policy, both in China and around the world.

Fiscal year 1981 US corn and soybeans pilot preliminary experiment plan, phase 1

NASA Technical Reports Server (NTRS)

Livingston, G. P.; Nedelman, K. S.; Norwood, D. F.; Smith, J. H. (Principal Investigator)

1981-01-01

A draft of the preliminary experiment plan for the foreign commodity production forecasting project fiscal year 1981 is presented. This draft plan includes: definition of the phase 1 and 2 U.S. pilot objectives; the proposed experiment design to evaluate crop calendar, area estimation, and area aggregation components for corn and soybean technologies using 1978/1979 crop-year data; a description of individual sensitivity evaluations of the baseline corn and soybean segment classification procedure; and technology and data assessment in support of the corn and soybean estimation technology for use in the U.S. central corn belt.

Response of double cropping suitability to climate change in the United States

NASA Astrophysics Data System (ADS)

Seifert, Christopher A.; Lobell, David B.

2015-02-01

In adapting US agriculture to the climate of the 21st century, a key unknown is whether cropping frequency may increase, helping to offset projected negative yield impacts in major production regions. Combining daily weather data and crop phenology models, we find that cultivated area in the US suited to dryland winter wheat-soybeans, the most common double crop (DC) system, increased by up to 28% from 1988 to 2012. Changes in the observed distribution of DC area over the same period agree well with this suitability increase, evidence consistent with climate change playing a role in recent DC expansion in phenologically constrained states. We then apply the model to projections of future climate under the RCP45 and RCP85 scenarios and estimate an additional 126-239% increase, respectively, in DC area. Sensitivity tests reveal that in most instances, increases in mean temperature are more important than delays in fall freeze in driving increased DC suitability. The results suggest that climate change will relieve phenological constraints on wheat-soy DC systems over much of the United States, though it should be recognized that impacts on corn and soybean yields in this region are expected to be negative and larger in magnitude than the 0.4-0.75% per decade benefits we estimate here for double cropping.

Crop biomass and evapotranspiration estimation using SPOT and Formosat-2 Data

NASA Astrophysics Data System (ADS)

Veloso, Amanda; Demarez, Valérie; Ceschia, Eric; Claverie, Martin

2013-04-01

The use of crop models allows simulating plant development, growth and yield under different environmental and management conditions. When combined with high spatial and temporal resolution remote sensing data, these models provide new perspectives for crop monitoring at regional scale. We propose here an approach to estimate time courses of dry aboveground biomass, yield and evapotranspiration (ETR) for summer (maize, sunflower) and winter crops (wheat) by assimilating Green Area Index (GAI) data, obtained from satellite observations, into a simple crop model. Only high spatial resolution and gap-free satellite time series can provide enough information for efficient crop monitoring applications. The potential of remote sensing data is often limited by cloud cover and/or gaps in observation. Data from different sensor systems need then to be combined. For this work, we employed a unique set of Formosat-2 and SPOT images (164 images) and in-situ measurements, acquired from 2006 to 2010 in southwest France. Among the several land surface biophysical variables accessible from satellite observations, the GAI is the one that has a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Many methods have been developed to relate GAI to optical remote sensing signal. Here, seasonal dynamics of remotely sensed GAI were estimated by applying a method based on the inversion of a radiative transfer model using artificial neural networks. The modelling approach is based on the Simple Algorithm for Yield and Evapotranspiration estimate (SAFYE) model, which couples the FAO-56 model with an agro-meteorological model, based on Monteith's light-use efficiency theory. The SAFYE model is a daily time step crop model that simulates time series of GAI, dry aboveground biomass, grain yield and ETR. Crop and soil model parameters were determined using both in-situ measurements and values found in the literature. Phenological parameters were calibrated by the assimilation of the remotely sensed GAI time series. The calibration process led to accurate spatial estimates of GAI, ETR as well as of biomass and yield over the study area (24 km x 24 km window). The results highlight the interest of using a combined approach (crop model coupled with high spatial and temporal resolution remote sensing data) for the estimation of agronomical variables. At local scale, the model reproduced correctly the biomass production and ETR for summer crops (with relative RMSE of 29% and 35%, respectively). At regional scale, estimated yield and water requirement for irrigation were compared to regional statistics of yield and irrigation inventories provided by the local water agency. Results showed good agreements for inter-annual dynamics of yield estimates. Differences between water requirement for irrigation and actual supply were lower than 10% and inter-annual variability was well represented as well. The work, initially focused on summer crops, is being adapted to winter crops.

Monitoring Crop Yield in USA Using a Satellite-Based Climate-Variability Impact Index

NASA Technical Reports Server (NTRS)

Zhang, Ping; Anderson, Bruce; Tan, Bin; Barlow, Mathew; Myneni, Ranga

2011-01-01

A quantitative index is applied to monitor crop growth and predict agricultural yield in continental USA. The Climate-Variability Impact Index (CVII), defined as the monthly contribution to overall anomalies in growth during a given year, is derived from 1-km MODIS Leaf Area Index. The growing-season integrated CVII can provide an estimate of the fractional change in overall growth during a given year. In turn these estimates can provide fine-scale and aggregated information on yield for various crops. Trained from historical records of crop production, a statistical model is used to produce crop yield during the growing season based upon the strong positive relationship between crop yield and the CVII. By examining the model prediction as a function of time, it is possible to determine when the in-season predictive capability plateaus and which months provide the greatest predictive capacity.

Economic damages of ozone air pollution to crops using combined air quality and GIS modelling

NASA Astrophysics Data System (ADS)

Vlachokostas, Ch.; Nastis, S. A.; Achillas, Ch.; Kalogeropoulos, K.; Karmiris, I.; Moussiopoulos, N.; Chourdakis, E.; Banias, G.; Limperi, N.

2010-09-01

This study aims at presenting a combined air quality and GIS modelling methodological approach in order to estimate crop damages from photochemical air pollution, depict their spatial resolution and assess the order of magnitude regarding the corresponding economic damages. The analysis is conducted within the Greater Thessaloniki Area, Greece, a Mediterranean territory which is characterised by high levels of photochemical air pollution and considerable agricultural activity. Ozone concentration fields for 2002 and for specific emission reduction scenarios for the year 2010 were estimated with the Ozone Fine Structure model in the area under consideration. Total economic damage to crops turns out to be significant and estimated to be approximately 43 M€ for the reference year. Production of cotton presents the highest economic loss, which is over 16 M€, followed by table tomato (9 M€), rice (4.2 M€), wheat (4 M€) and oilseed rape (2.8 M€) cultivations. Losses are not spread uniformly among farmers and the major losses occur in areas with valuable ozone-sensitive crops. The results are very useful for highlighting the magnitude of the total economic impacts of photochemical air pollution to the area's agricultural sector and can potentially be used for comparison with studies worldwide. Furthermore, spatial analysis of the economic damage could be of importance for governmental authorities and decision makers since it provides an indicative insight, especially if the economic instruments such as financial incentives or state subsidies to farmers are considered.

Crop identification and acreage measurement utilizing ERTS imagery. [Missouri, Kansa, Idaho, and South Dakota

NASA Technical Reports Server (NTRS)

Wigton, W. H.; Vonsteen, D. H.

1974-01-01

The Statistical Reporting Service of the U.S. Department of Agriculture is evaluating ERTS-1 imagery as a potential tool for estimating crop acreage. A main data source for the estimates is obtained by enumerating small land parcels that have been randomly selected from the total U.S. land area. These small parcels are being used as ground observations in this investigation. The test sites are located in Missouri, Kansas, Idaho, and South Dakota. The major crops of interest are wheat, cotton, corn, soybeans, sugar beets, potatoes, oats, alfalfa, and grain sorghum. Some of the crops are unique to a given site while others are common in two or three states. This provides an opportunity to observe crops grown under different conditions. Results for the Missouri test site are presented. Results of temporal overlays, unequal prior probabilities, and sample classifiers are discussed. The amount of improvement that each technique contributes is shown in terms of overall performance. The results show that useful information for making crop acreage estimates can be obtained from ERTS-1 data.

Wheat productivity estimates using LANDSAT data

NASA Technical Reports Server (NTRS)

Nalepka, R. F.; Colwell, J. E. (Principal Investigator); Rice, D. P.; Bresnahan, P. A.

1977-01-01

The author has identified the following significant results. Large area LANDSAT yield estimates were generated. These results were compared with estimates computed using a meteorological yield model (CCEA). Both of these estimates were compared with Kansas Crop and Livestock Reporting Service (KCLRS) estimates of yield, in an attempt to assess the relative and absolute accuracy of the LANDSAT and CCEA estimates. Results were inconclusive. A large area direct wheat prediction procedure was implemented. Initial results have produced a wheat production estimate comparable with the KCLRS estimate.

Advancing Methods for Estimating Cropland Area

NASA Astrophysics Data System (ADS)

King, L.; Hansen, M.; Stehman, S. V.; Adusei, B.; Potapov, P.; Krylov, A.

2014-12-01

Measurement and monitoring of complex and dynamic agricultural land systems is essential with increasing demands on food, feed, fuel and fiber production from growing human populations, rising consumption per capita, the expansion of crops oils in industrial products, and the encouraged emphasis on crop biofuels as an alternative energy source. Soybean is an important global commodity crop, and the area of land cultivated for soybean has risen dramatically over the past 60 years, occupying more than 5% of all global croplands (Monfreda et al 2008). Escalating demands for soy over the next twenty years are anticipated to be met by an increase of 1.5 times the current global production, resulting in expansion of soybean cultivated land area by nearly the same amount (Masuda and Goldsmith 2009). Soybean cropland area is estimated with the use of a sampling strategy and supervised non-linear hierarchical decision tree classification for the United States, Argentina and Brazil as the prototype in development of a new methodology for crop specific agricultural area estimation. Comparison of our 30 m2 Landsat soy classification with the National Agricultural Statistical Services Cropland Data Layer (CDL) soy map shows a strong agreement in the United States for 2011, 2012, and 2013. RapidEye 5m2 imagery was also classified for soy presence and absence and used at the field scale for validation and accuracy assessment of the Landsat soy maps, describing a nearly 1 to 1 relationship in the United States, Argentina and Brazil. The strong correlation found between all products suggests high accuracy and precision of the prototype and has proven to be a successful and efficient way to assess soybean cultivated area at the sub-national and national scale for the United States with great potential for application elsewhere.

A field and statistical modeling study to estimate irrigation water use at Benchmark Farms study sites in southwestern Georgia, 1995-96

USGS Publications Warehouse

Fanning, Julia L.; Schwarz, Gregory E.; Lewis, William C.

2001-01-01

A benchmark irrigation monitoring network of farms located in a 32-county area in southwestern Georgia was established in 1995 to improve estimates of irrigation water use. A stratified random sample of 500 permitted irrigators was selected from a data base--maintained by the Georgia Department of Natural Resources, Georgia Environmental Protection Division, Water Resources Management Branch--to obtain 180 voluntary participants in the study area. Site-specific irrigation data were collected at each farm using running-time totalizers and noninvasive flowmeters. Data were collected and compiled for 50 farms for 1995 and 130 additional farms for the 1996 growing season--a total of 180 farms. Irrigation data collected during the 1996 growing season were compiled for 180 benchmark farms and used to develop a statistical model to estimate irrigation water use in 32 counties in southwestern Georgia. The estimates derived were developed from using a statistical approach know as "bootstrap analysis" that allows for the estimation of precision. Five model components--whether-to-irrigate, acres irrigated, crop selected, seasonal-irrigation scheduling, and the amount of irrigation applied--compose the irrigation model and were developed to reflect patterns in the data collected at Benchmark Farms Study area sites. The model estimated that peak irrigation for all counties in the study area occurred during July with significant irrigation also occurring during May, June, and August. Irwin and Tift were the most irrigated and Schley and Houston were the least irrigated counties in the study area. High irrigation intensity primarily was located along the eastern border of the study area; whereas, low irrigation intensity was located in the southwestern quadrant where ground water was the dominant irrigation source. Crop-level estimates showed sizable variations across crops and considerable uncertainty for all crops other than peanuts and pecans. Counties having the most irrigated acres showed higher variations in annual irrigation than counties having the least irrigated acres. The Benchmark Farms Study model estimates were higher than previous irrigation estimates, with 20 percent of the bias a result of underestimating irrigation acreage in earlier studies. Model estimates showed evidence of an upward bias of about 15 percent with the likely cause being a misrepresented inches-applied model. A better understanding of the causes of bias in the model could be determined with a larger irrigation sample size and increased substantially by automating the reporting of monthly totalizer amounts.

Farmland Drought Evaluation Based on the Assimilation of Multi-Temporal Multi-Source Remote Sensing Data into AquaCrop Model

NASA Astrophysics Data System (ADS)

Yang, Guijun; Yang, Hao; Jin, Xiuliang; Pignatti, Stefano; Casa, Faffaele; Silverstro, Paolo Cosmo

2016-08-01

Drought is the most costly natural disasters in China and all over the world. It is very important to evaluate the drought-induced crop yield losses and further improve water use efficiency at regional scale. Firstly, crop biomass was estimated by the combined use of Synthetic Aperture Radar (SAR) and optical remote sensing data. Then the estimated biophysical variable was assimilated into crop growth model (FAO AquaCrop) by the Particle Swarm Optimization (PSO) method from farmland scale to regional scale.At farmland scale, the most important crop parameters of AquaCrop model were determined to reduce the used parameters in assimilation procedure. The Extended Fourier Amplitude Sensitivity Test (EFAST) method was used for assessing the contribution of different crop parameters to model output. Moreover, the AquaCrop model was calibrated using the experiment data in Xiaotangshan, Beijing.At regional scale, spatial application of our methods were carried out and validated in the rural area of Yangling, Shaanxi Province, in 2014. This study will provide guideline to make irrigation decision of balancing of water consumption and yield loss.

Multi-temporal UAV based data for mapping crop type and structure in smallholder dominated Tanzanian agricultural landscape

NASA Astrophysics Data System (ADS)

Nagol, J. R.; Chung, C.; Dempewolf, J.; Maurice, S.; Mbungu, W.; Tumbo, S.

2015-12-01

Timely mapping and monitoring of crops like Maize, an important food security crop in Tanzania, can facilitate timely response by government and non-government organizations to food shortage or surplus conditions. Small UAVs can play an important role in linking the spaceborne remote sensing data and ground based measurement to improve the calibration and validation of satellite based estimates of in-season crop metrics. In Tanzania most of the growing season is often obscured by clouds. UAV data, if collected within a stratified statistical sampling framework, can also be used to directly in lieu of spaceborne data to infer mid-season yield estimates at regional scales.Here we present an object based approach to estimate crop metrics like crop type, area, and height using multi-temporal UAV based imagery. The methods were tested at three 1km2 plots in Kilosa, Njombe, and Same districts in Tanzania. At these sites both ground based and UAV based data were collected on a monthly time-step during the year 2015 growing season. SenseFly eBee drone with RGB and NIR-R-G camera was used to collect data. Crop type classification accuracies of above 85% were easily achieved.

Evaluation of Multispectral Based Radiative Transfer Model Inversion to Estimate Leaf Area Index in Wheat

USDA-ARS?s Scientific Manuscript database

Leaf area index (LAI) is a critical variable for predicting the growth and productivity of crops. Remote sensing estimates of LAI have relied upon empirical relationships between spectral vegetation indices and ground measurements that are costly to obtain. Radiative transfer model inversion based o...

Development of the crop residue and rangeland burning in the ...

EPA Pesticide Factsheets

Biomass burning has been identified as an important contributor to the degradation of air quality because of its impact on ozone and particulate matter. One component of the biomass burning inventory, crop residue burning, has been poorly characterized in the National Emissions Inventory (NEI). In the 2011 NEI, wildland fires, prescribed fires, and crop residue burning collectively were the largest source of PM2.5. This paper summarizes our 2014 NEI method to estimate crop residue burning emissions and grass/pasture burning emissions using remote sensing data and field information and literature-based, crop-specific emission factors. We focus on both the postharvest and pre-harvest burning that takes place with bluegrass, corn, cotton, rice, soybeans, sugarcane and wheat. Estimates for 2014 indicate that over the continental United States (CONUS), crop residue burning excluding all areas identified as Pasture/Grass, Grassland Herbaceous, and Pasture/Hay occurred over approximately 1.5 million acres of land and produced 19,600 short tons of PM2.5. For areas identified as Pasture/Grass, Grassland Herbaceous, and Pasture/Hay, biomass burning emissions occurred over approximately 1.6 million acres of land and produced 30,000 short tons of PM2.5. This estimate compares with the 2011 NEI and 2008 NEI as follows: 2008: 49,650 short tons and 2011: 141,180 short tons. Note that in the previous two NEIs rangeland burning was not well defined and so the comparison is not e

Retrospective Analog Year Analyses Using NASA Satellite Data to Improve USDA's World Agricultural Supply and Demand Estimates

NASA Astrophysics Data System (ADS)

Teng, W. L.; Shannon, H. D.

2011-12-01

The USDA World Agricultural Outlook Board (WAOB) is responsible for monitoring weather and climate impacts on domestic and foreign crop development. One of WAOB's primary goals is to determine the net cumulative effect of weather and climate anomalies on final crop yields. To this end, a broad array of information is consulted, including maps, charts, and time series of recent weather, climate, and crop observations; numerical output from weather and crop models; and reports from the press, USDA attachés, and foreign governments. The resulting agricultural weather assessments are published in the Weekly Weather and Crop Bulletin, to keep farmers, policy makers, and commercial agricultural interests informed of weather and climate impacts on agriculture. Because both the amount and timing of precipitation significantly impact crop yields, WAOB often uses precipitation time series to identify growing seasons with similar weather patterns and help estimate crop yields for the current growing season, based on observed yields in analog years. Although, historically, these analog years are identified through visual inspection, the qualitative nature of this methodology sometimes precludes the definitive identification of the best analog year. One goal of this study is to introduce a more rigorous, statistical approach for identifying analog years. This approach is based on a modified coefficient of determination, termed the analog index (AI). The derivation of AI will be described. Another goal of this study is to compare the performance of AI for time series derived from surface-based observations vs. satellite-based measurements (NASA TRMM and other data). Five study areas and six growing seasons of data were analyzed (2003-2007 as potential analog years and 2008 as the target year). Results thus far show that, for all five areas, crop yield estimates derived from satellite-based precipitation data are closer to measured yields than are estimates derived from surface-based precipitation measurements. Work is continuing to include satellite-based surface soil moisture data and model-assimilated root zone soil moisture. This study is part of a larger effort to improve WAOB estimates by integrating NASA remote sensing observations and research results into WAOB's decision-making environment.

The Maximum Likelihood Estimation of Signature Transformation /MLEST/ algorithm. [for affine transformation of crop inventory data

NASA Technical Reports Server (NTRS)

Thadani, S. G.

1977-01-01

The Maximum Likelihood Estimation of Signature Transformation (MLEST) algorithm is used to obtain maximum likelihood estimates (MLE) of affine transformation. The algorithm has been evaluated for three sets of data: simulated (training and recognition segment pairs), consecutive-day (data gathered from Landsat images), and geographical-extension (large-area crop inventory experiment) data sets. For each set, MLEST signature extension runs were made to determine MLE values and the affine-transformed training segment signatures were used to classify the recognition segments. The classification results were used to estimate wheat proportions at 0 and 1% threshold values.

Use of Unmanned Aerial Vehicles for Improving Farm Scale Agricultural Water Management in Agriculture at a Farm Scale. A case study for field crops in the California's Central Valley

NASA Astrophysics Data System (ADS)

Medellin-Azuara, J.; Morande, J. A.; Jin, Y.; Chen, Y.; Paw U, K. T.; Viers, J. H.

2016-12-01

Traditional methods for estimating consumptive water use as evapotranspiration (ET) for agriculture in areas with water limitations such as California have always been a challenge for farmers, water managers, researchers and government agencies. Direct measurement of evapotranspiration (ET) and crop water stress in agriculture can be a cumbersome and costly task. Furthermore, spatial variability of applied water and irrigation and stress level in crops, due to inherent heterogeneity in soil conditions, topography, management practices, and lack of uniformity in water applications may affect estimates water use efficiency and water balances. This situation difficult long-term management of agroecosystems. This paper presents a case study for various areas in California's Central Valley using Unmanned Aerial Vehicles (UAVs) for a late portion of the 2016 irrigation season These estimates are compared those obtained by direct measurement (from previously deployed stations), and energy balance approaches with remotely sensed data in a selection of field crop parcels. This research improves information on water use and site conditions in agriculture by enhancing remote sensing-based estimations through the use of higher resolution multi-spectral and thermal imagery captured by UAV. We assess whether more frequent information at higher spatial resolution from UAVs can improve estimations of overall ET through energy balance and imagery. Stress levels and ET are characterized spatially to examine irrigation practices and their performance to improve water use in the agroecosystem. Ground based data such as air and crop temperature and stem water potential is collected to validate UAV aerial measurements. Preliminary results show the potential of UAV technology to improve timing, resolution and accuracy in the ET estimation and assessment of crop stress at a farm scales. Side to side comparison with ground level stations employing surface renewal, eddy covariance and energy balance provides a testbed to improve understanding of consumptive use and crop water management in water scarce irrigated agriculture regions. Keywords. California Central Valley, Agricultural Water Use, Remote Sensing, Energy Balance, Evapotranspiration, Water management,

Time Series Analysis of Remote Sensing Observations for Citrus Crop Growth Stage and Evapotranspiration Estimation

NASA Astrophysics Data System (ADS)

Sawant, S. A.; Chakraborty, M.; Suradhaniwar, S.; Adinarayana, J.; Durbha, S. S.

2016-06-01

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (http://earthexplorer.usgs.gov/). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.

OVoG Inversion for the Retrieval of Agricultural Crop Structure by Means of Multi-Baseline Polarimetric SAR Interferometry

NASA Astrophysics Data System (ADS)

Pichierri, Manuele; Hajnsek, Irena

2015-04-01

In this work, the potential of multi-baseline Pol-InSAR for crop parameter estimation (e.g. crop height and extinction coefficients) is explored. For this reason, a novel Oriented Volume over Ground (OVoG) inversion scheme is developed, which makes use of multi-baseline observables to estimate the whole stack of model parameters. The proposed algorithm has been initially validated on a set of randomly-generated OVoG scenarios, to assess its stability over crop structure changes and its robustness against volume decorrelation and other decorrelation sources. Then, it has been applied to a collection of multi-baseline repeat-pass SAR data, acquired over a rural area in Germany by DLR's F-SAR.

Assessing patterns of human-wildlife conflicts and compensation around a Central Indian protected area.

PubMed

Karanth, Krithi K; Gopalaswamy, Arjun M; DeFries, Ruth; Ballal, Natasha

2012-01-01

Mitigating crop and livestock loss to wildlife and improving compensation distribution are important for conservation efforts in landscapes where people and wildlife co-occur outside protected areas. The lack of rigorously collected spatial data poses a challenge to management efforts to minimize loss and mitigate conflicts. We surveyed 735 households from 347 villages in a 5154 km(2) area surrounding Kanha Tiger Reserve in India. We modeled self-reported household crop and livestock loss as a function of agricultural, demographic and environmental factors, and mitigation measures. We also modeled self-reported compensation received by households as a function of demographic factors, conflict type, reporting to authorities, and wildlife species involved. Seventy-three percent of households reported crop loss and 33% livestock loss in the previous year, but less than 8% reported human injury or death. Crop loss was associated with greater number of cropping months per year and proximity to the park. Livestock loss was associated with grazing animals inside the park and proximity to the park. Among mitigation measures only use of protective physical structures were associated with reduced livestock loss. Compensation distribution was more likely for tiger related incidents, and households reporting loss and located in the buffer. Average estimated probability of crop loss was 0.93 and livestock loss was 0.60 for surveyed households. Estimated crop and livestock loss and compensation distribution were higher for households located inside the buffer. Our approach modeled conflict data to aid managers in identifying potential conflict hotspots, influential factors, and spatially maps risk probability of crop and livestock loss. This approach could help focus allocation of conservation efforts and funds directed at conflict prevention and mitigation where high densities of people and wildlife co-occur.

Assessing Patterns of Human-Wildlife Conflicts and Compensation around a Central Indian Protected Area

PubMed Central

Karanth, Krithi K.; Gopalaswamy, Arjun M.; DeFries, Ruth; Ballal, Natasha

2012-01-01

Mitigating crop and livestock loss to wildlife and improving compensation distribution are important for conservation efforts in landscapes where people and wildlife co-occur outside protected areas. The lack of rigorously collected spatial data poses a challenge to management efforts to minimize loss and mitigate conflicts. We surveyed 735 households from 347 villages in a 5154 km2 area surrounding Kanha Tiger Reserve in India. We modeled self-reported household crop and livestock loss as a function of agricultural, demographic and environmental factors, and mitigation measures. We also modeled self-reported compensation received by households as a function of demographic factors, conflict type, reporting to authorities, and wildlife species involved. Seventy-three percent of households reported crop loss and 33% livestock loss in the previous year, but less than 8% reported human injury or death. Crop loss was associated with greater number of cropping months per year and proximity to the park. Livestock loss was associated with grazing animals inside the park and proximity to the park. Among mitigation measures only use of protective physical structures were associated with reduced livestock loss. Compensation distribution was more likely for tiger related incidents, and households reporting loss and located in the buffer. Average estimated probability of crop loss was 0.93 and livestock loss was 0.60 for surveyed households. Estimated crop and livestock loss and compensation distribution were higher for households located inside the buffer. Our approach modeled conflict data to aid managers in identifying potential conflict hotspots, influential factors, and spatially maps risk probability of crop and livestock loss. This approach could help focus allocation of conservation efforts and funds directed at conflict prevention and mitigation where high densities of people and wildlife co-occur. PMID:23227173

Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq.

PubMed

Qader, Sarchil Hama; Dash, Jadunandan; Atkinson, Peter M

2018-02-01

Crop production and yield estimation using remotely sensed data have been studied widely, but such information is generally scarce in arid and semi-arid regions. In these regions, inter-annual variation in climatic factors (such as rainfall) combined with anthropogenic factors (such as civil war) pose major risks to food security. Thus, an operational crop production estimation and forecasting system is required to help decision-makers to make early estimates of potential food availability. Data from NASA's MODIS with official crop statistics were combined to develop an empirical regression-based model to forecast winter wheat and barley production in Iraq. The study explores remotely sensed indices representing crop productivity over the crop growing season to find the optimal correlation with crop production. The potential of three different remotely sensed indices, and information related to the phenology of crops, for forecasting crop production at the governorate level was tested and their results were validated using the leave-one-year-out approach. Despite testing several methodological approaches, and extensive spatio-temporal analysis, this paper depicts the difficulty in estimating crop yield on an annual base using current satellite low-resolution data. However, more precise estimates of crop production were possible. The result of the current research implies that the date of the maximum vegetation index (VI) offered the most accurate forecast of crop production with an average R 2 =0.70 compared to the date of MODIS EVI (Avg R 2 =0.68) and a NPP (Avg R 2 =0.66). When winter wheat and barley production were forecasted using NDVI, EVI and NPP and compared to official statistics, the relative error ranged from -20 to 20%, -45 to 28% and -48 to 22%, respectively. The research indicated that remotely sensed indices could characterize and forecast crop production more accurately than simple cropping area, which was treated as a null model against which to evaluate the proposed approach. Copyright © 2017 Elsevier B.V. All rights reserved.

Winter Crop Mapping for Improving Crop Production Estimates in Argentina Using Moderation Resolution Satellite Imagery

NASA Astrophysics Data System (ADS)

Humber, M. L.; Copati, E.; Sanchez, A.; Sahajpal, R.; Puricelli, E.; Becker-Reshef, I.

2017-12-01

Accurate crop production data is fundamental for reducing uncertainly and volatility in the domestic and international agricultural markets. The Agricultural Estimates Department of the Buenos Aires Grain Exchange has worked since 2000 on the estimation of different crop production data. With this information, the Grain Exchange helps different actors of the agricultural chain, such as producers, traders, seed companies, market analyst, policy makers, into their day to day decision making. Since 2015/16 season, the Grain Exchange has worked on the development of a new earth observations-based method to identify winter crop planted area at a regional scale with the aim of improving crop production estimates. The objective of this new methodology is to create a reliable winter crop mask at moderate spatial resolution using Landsat-8 imagery by exploiting bi-temporal differences in the phenological stages of winter crops as compared to other landcover types. In collaboration with the University of Maryland, the map has been validated by photointerpretation of a stratified statistically random sample of independent ground truth data in the four largest producing provinces of Argentina: Buenos Aires, Cordoba, La Pampa, and Santa Fe. In situ measurements were also used to further investigate conditions in the Buenos Aires province. Preliminary results indicate that while there are some avenues for improvement, overall the classification accuracy of the cropland and non-cropland classes are sufficient to improve downstream production estimates. Continuing research will focus on improving the methodology for winter crop mapping exercises on a yearly basis as well as improving the sampling methodology to optimize collection of validation data in the future.

Identification of agricultural crops by computer processing of ERTS MSS data

NASA Technical Reports Server (NTRS)

Bauer, M. E.; Cipra, J. E.

1973-01-01

Quantitative evaluation of computer-processed ERTS MSS data classifications has shown that major crop species (corn and soybeans) can be accurately identified. The classifications of satellite data over a 2000 square mile area not only covered more than 100 times the area previously covered using aircraft, but also yielded improved results through the use of temporal and spatial data in addition to the spectral information. Furthermore, training sets could be extended over far larger areas than was ever possible with aircraft scanner data. And, preliminary comparisons of acreage estimates from ERTS data and ground-based systems agreed well. The results demonstrate the potential utility of this technology for obtaining crop production information.

A summary of the history of the development of automated remote sensing for agricultural applications

NASA Technical Reports Server (NTRS)

Macdonald, R. B.

1984-01-01

An historical account is given of the development of technology for the processing of satellite-acquired multispectral data aimed at the identification of the type, condition, and ontogenic stages of agricultural areas. During 1972 and 1973, research established the feasibility of automating digital classification for the processing of large volumes of Landsat MSS data. This capability was successfully demonstrated during the Large Area Crop Inventory Experiment, which estimated wheat crop production on a global basis. This achievement in turn led to the Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing, which investigated other portions of the electromagnetic spectrum and expanded the study of key commercial crops in important agricultural areas.

Benefits of seasonal forecasts of crop yields

NASA Astrophysics Data System (ADS)

Sakurai, G.; Okada, M.; Nishimori, M.; Yokozawa, M.

2017-12-01

Major factors behind recent fluctuations in food prices include increased biofuel production and oil price fluctuations. In addition, several extreme climate events that reduced worldwide food production coincided with upward spikes in food prices. The stabilization of crop yields is one of the most important tasks to stabilize food prices and thereby enhance food security. Recent development of technologies related to crop modeling and seasonal weather forecasting has made it possible to forecast future crop yields for maize and soybean. However, the effective use of these technologies remains limited. Here we present the potential benefits of seasonal crop-yield forecasts on a global scale for choice of planting day. For this purpose, we used a model (PRYSBI-2) that can well replicate past crop yields both for maize and soybean. This model system uses a Bayesian statistical approach to estimate the parameters of a basic process-based model of crop growth. The spatial variability of model parameters was considered by estimating the posterior distribution of the parameters from historical yield data by using the Markov-chain Monte Carlo (MCMC) method with a resolution of 1.125° × 1.125°. The posterior distributions of model parameters were estimated for each spatial grid with 30 000 MCMC steps of 10 chains each. By using this model and the estimated parameter distributions, we were able to estimate not only crop yield but also levels of associated uncertainty. We found that the global average crop yield increased about 30% as the result of the optimal selection of planting day and that the seasonal forecast of crop yield had a large benefit in and near the eastern part of Brazil and India for maize and the northern area of China for soybean. In these countries, the effects of El Niño and Indian Ocean dipole are large. The results highlight the importance of developing a system to forecast global crop yields.

Proceedings of Technical Sessions, Volumes 1 and 2: the LACIE Symposium

NASA Technical Reports Server (NTRS)

1979-01-01

The technical design of the Large Area Crop Inventory Experiment is examined and data acquired over 3 global crop years is analyzed with respect to (1) sampling and aggregation; (2) growth size estimation; (3) classification and mensuration; (4) yield estimation; and (5) accuracy assessment. Seventy-nine papers delivered at conference sessions cover system implementation and operation; data processing systems; experiment results and accuracy; supporting research and technology; and the USDA application test system.

WSR-88D doppler radar detection of corn earworm moth migration.

PubMed

Westbrook, J K; Eyster, R S; Wolf, W W

2014-07-01

Corn earworm (Lepidoptera: Noctuidae) (CEW) populations infesting one crop production area may rapidly migrate and infest distant crop production areas. Although entomological radars have detected corn earworm moth migrations, the spatial extent of the radar coverage has been limited to a small horizontal view above crop production areas. The Weather Service Radar (version 88D) (WSR-88D) continuously monitors the radar-transmitted energy reflected by, and radial speed of, biota as well as by precipitation over areas that may encompass crop production areas. We analyzed data from the WSR-88D radar (S-band) at Brownsville, Texas, and related these data to aerial concentrations of CEW estimated by a scanning entomological radar (X-band) and wind velocity measurements from rawinsonde and pilot balloon ascents. The WSR-88D radar reflectivity was positively correlated (r2=0.21) with the aerial concentration of corn earworm-size insects measured by a scanning X-band radar. WSR-88D radar constant altitude plan position indicator estimates of wind velocity were positively correlated with wind speed (r2=0.56) and wind direction (r2=0.63) measured by pilot balloons and rawinsondes. The results reveal that WSR-88D radar measurements of insect concentration and displacement speed and direction can be used to estimate the migratory flux of corn earworms and other nocturnal insects, information that could benefit areawide pest management programs. In turn, identification of the effects of spatiotemporal patterns of migratory flights of corn earworm-size insects on WSR-88D radar measurements may lead to the development of algorithms that increase the accuracy of WSR-88D radar measurements of reflectivity and wind velocity for operational meteorology.

WSR-88D doppler radar detection of corn earworm moth migration

NASA Astrophysics Data System (ADS)

Westbrook, J. K.; Eyster, R. S.; Wolf, W. W.

2014-07-01

Corn earworm (Lepidoptera: Noctuidae) (CEW) populations infesting one crop production area may rapidly migrate and infest distant crop production areas. Although entomological radars have detected corn earworm moth migrations, the spatial extent of the radar coverage has been limited to a small horizontal view above crop production areas. The Weather Service Radar (version 88D) (WSR-88D) continuously monitors the radar-transmitted energy reflected by, and radial speed of, biota as well as by precipitation over areas that may encompass crop production areas. We analyzed data from the WSR-88D radar (S-band) at Brownsville, Texas, and related these data to aerial concentrations of CEW estimated by a scanning entomological radar (X-band) and wind velocity measurements from rawinsonde and pilot balloon ascents. The WSR-88D radar reflectivity was positively correlated ( r 2 = 0.21) with the aerial concentration of corn earworm-size insects measured by a scanning X-band radar. WSR-88D radar constant altitude plan position indicator estimates of wind velocity were positively correlated with wind speed ( r 2 = 0.56) and wind direction ( r 2 = 0.63) measured by pilot balloons and rawinsondes. The results reveal that WSR-88D radar measurements of insect concentration and displacement speed and direction can be used to estimate the migratory flux of corn earworms and other nocturnal insects, information that could benefit areawide pest management programs. In turn, identification of the effects of spatiotemporal patterns of migratory flights of corn earworm-size insects on WSR-88D radar measurements may lead to the development of algorithms that increase the accuracy of WSR-88D radar measurements of reflectivity and wind velocity for operational meteorology.

Large Area Crop Inventory Experiment (LACIE). An overview of the Large Area Crop Inventory Experiment and the outlook for a satellite crop inventory. [Great Plains Corridor (North America), Canada, U.S.S.R., Brazil, China, India, and Australia

NASA Technical Reports Server (NTRS)

Erb, R. B. (Principal Investigator)

1979-01-01

The author has identified the following significant results. The most important LACIE finding was that the technology worked very well in estimating wheat production in important geographic locations. Based on working through the many successes and shortcomings of LACIE, it can be stated with confidence that: (1) the current technology can successfully monitor what production in regions having similar characteristics to those of the U.S.S.R. wheat areas and the U.S. hard red winter wheat areas; (2) with additional applied research, significant improvements in capabilities to monitor wheat in these and other important production regions can be expected in the near future; (3) the remote sensing and weather effects modeling technology approached used by LACIE is generally applicable to other major crops and crop-producing regions of the world; and (4) with suitable effort, this technology can now advance rapidly and could be widespread use in the late 1980's.

Climate change and drought effects on rural income distribution in the Mediterranean: a case study for Spain

NASA Astrophysics Data System (ADS)

Quiroga, Sonia; Suárez, Cristina

2016-06-01

This paper examines the effects of climate change and drought on agricultural incomes in Spanish rural areas. Present research has focused on the effects of these extreme climatological events through response functions, considering effects on crop productivity and average incomes. Among the impacts of droughts, we focused on potential effects on income distribution. The study of the effects on abnormally dry periods is therefore needed in order to perform an analysis of diverse social aspects in the long term. We estimate crop production functions for a range of Mediterranean crops in Spain and we use a measure of the decomposition of inequality to estimate the impact of climate change and drought on yield disparities. Certain adaptation measures may require a better understanding of risks by the public to achieve general acceptance. We provide empirical estimations for the marginal effects of the two impacts considered: farms' average income and income distribution. Our estimates consider crop production response to both biophysical and socio-economic aspects to analyse long-term implications on competitiveness and disparities. As for the results, we find disparities in the adaptation priorities depending on the crop and the region analysed.

Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops

NASA Astrophysics Data System (ADS)

Kross, Angela; McNairn, Heather; Lapen, David; Sunohara, Mark; Champagne, Catherine

2015-02-01

Leaf area index (LAI) and biomass are important indicators of crop development and the availability of this information during the growing season can support farmer decision making processes. This study demonstrates the applicability of RapidEye multi-spectral data for estimation of LAI and biomass of two crop types (corn and soybean) with different canopy structure, leaf structure and photosynthetic pathways. The advantages of Rapid Eye in terms of increased temporal resolution (∼daily), high spatial resolution (∼5 m) and enhanced spectral information (includes red-edge band) are explored as an individual sensor and as part of a multi-sensor constellation. Seven vegetation indices based on combinations of reflectance in green, red, red-edge and near infrared bands were derived from RapidEye imagery between 2011 and 2013. LAI and biomass data were collected during the same period for calibration and validation of the relationships between vegetation indices and LAI and dry above-ground biomass. Most indices showed sensitivity to LAI from emergence to 8 m2/m2. The normalized difference vegetation index (NDVI), the red-edge NDVI and the green NDVI were insensitive to crop type and had coefficients of variations (CV) ranging between 19 and 27%; and coefficients of determination ranging between 86 and 88%. The NDVI performed best for the estimation of dry leaf biomass (CV = 27% and r2 = 090) and was also insensitive to crop type. The red-edge indices did not show any significant improvement in LAI and biomass estimation over traditional multispectral indices. Cumulative vegetation indices showed strong performance for estimation of total dry above-ground biomass, especially for corn (CV ≤ 20%). This study demonstrated that continuous crop LAI monitoring over time and space at the field level can be achieved using a combination of RapidEye, Landsat and SPOT data and sensor-dependant best-fit functions. This approach eliminates/reduces the need for reflectance resampling, VIs inter-calibration and spatial resampling.

What is the potential of cropland albedo management in the fight against global warming? A case study based on the use of cover crops

NASA Astrophysics Data System (ADS)

Carrer, Dominique; Pique, Gaétan; Ferlicoq, Morgan; Ceamanos, Xavier; Ceschia, Eric

2018-04-01

Land cover management in agricultural areas is a powerful tool that could play a role in the mitigation of climate change and the counterbalance of global warming. First, we attempted to quantify the radiative forcing that would increase the surface albedo of croplands in Europe following the inclusion of cover crops during the fallow period. This is possible since the albedo of bare soil in many areas of Europe is lower than the albedo of vegetation. By using satellite data, we demonstrated that the introduction of cover crops into the crop rotation during the fallow period would increase the albedo over 4.17% of Europe’s surface. According to our study, the effect resulting from this increase in the albedo of the croplands would be equivalent to a mitigation of 3.16 MtCO2-eq.year‑1 over a 100 year time horizon. This is equivalent to a mitigation potential per surface unit (m2) of introduced cover crop over Europe of 15.91 gCO2-eq.year‑1.m‑2. This value, obtained at the European scale, is consistent with previous estimates. We show that this mitigation potential could be increased by 27% if the cover crop is maintained for a longer period than 3 months and reduced by 28% in the case of no irrigation. In the second part of this work, based on recent studies estimating the impact of cover crops on soil carbon sequestration and the use of fertilizer, we added the albedo effect to those estimates, and we argued that, by considering areas favourable to their introduction, cover crops in Europe could mitigate human-induced agricultural greenhouse gas emissions by up to 7% per year, using 2011 as a reference. The impact of the albedo change per year would be between 10% and 13% of this total impact. The countries showing the greatest mitigation potentials are France, Bulgaria, Romania, and Germany.

Rice crop growth monitoring using ENVISAT-1/ASAR AP mode

NASA Astrophysics Data System (ADS)

Konishi, Tomohisa; Suga, Yuzo; Omatu, Shigeru; Takeuchi, Shoji; Asonuma, Kazuyoshi

2007-10-01

Hiroshima Institute of Technology (HIT) is operating the direct down-links of microwave and optical earth observation satellite data in Japan. This study focuses on the validation for rice crop monitoring using microwave remotely sensed image data acquired by ENIVISAT-1 referring to ground truth data such as height of rice crop, vegetation cover rate and leaf area index in the test sites of Hiroshima district, the western part of Japan. ENVISAT-1/ASAR data has the capabilities for the monitoring of the rice crop growing cycle by using alternating cross polarization mode images. However, ASAR data is influenced by several parameters such as land cover structure, direction and alignment of rice crop fields in the test sites. In this study, the validation was carried out to be combined with microwave image data and ground truth data regarding rice crop fields to investigate the above parameters. Multi-temporal, multi-direction (descending and ascending) and multi-angle ASAR alternating cross polarization mode images were used to investigate during the rice crop growing cycle. On the other hand, LANDSAT-7/ETM+ data were used to detect land cover structure, direction and alignment of rice crop fields corresponding to the backscatter of ASAR. Finally, the extraction of rice planted area was attempted by using multi-temporal ASAR AP mode data such as VV/VH and HH/HV. As the result of this study, it is clear that the estimated rice planted area coincides with the existing statistical data for area of the rice crop field. In addition, HH/HV is more effective than VV/VH in the rice planted area extraction.

Satellite Based Cropland Carbon Monitoring System

NASA Astrophysics Data System (ADS)

Bandaru, V.; Jones, C. D.; Sedano, F.; Sahajpal, R.; Jin, H.; Skakun, S.; Pnvr, K.; Kommareddy, A.; Reddy, A.; Hurtt, G. C.; Izaurralde, R. C.

2017-12-01

Agricultural croplands act as both sources and sinks of atmospheric carbon dioxide (CO2); absorbing CO2 through photosynthesis, releasing CO2 through autotrophic and heterotrophic respiration, and sequestering CO2 in vegetation and soils. Part of the carbon captured in vegetation can be transported and utilized elsewhere through the activities of food, fiber, and energy production. As well, a portion of carbon in soils can be exported somewhere else by wind, water, and tillage erosion. Thus, it is important to quantify how land use and land management practices affect the net carbon balance of croplands. To monitor the impacts of various agricultural activities on carbon balance and to develop management strategies to make croplands to behave as net carbon sinks, it is of paramount importance to develop consistent and high resolution cropland carbon flux estimates. Croplands are typically characterized by fine scale heterogeneity; therefore, for accurate carbon flux estimates, it is necessary to account for the contribution of each crop type and their spatial distribution. As part of NASA CMS funded project, a satellite based Cropland Carbon Monitoring System (CCMS) was developed to estimate spatially resolved crop specific carbon fluxes over large regions. This modeling framework uses remote sensing version of Environmental Policy Integrated Climate Model and satellite derived crop parameters (e.g. leaf area index (LAI)) to determine vertical and lateral carbon fluxes. The crop type LAI product was developed based on the inversion of PRO-SAIL radiative transfer model and downscaled MODIS reflectance. The crop emergence and harvesting dates were estimated based on MODIS NDVI and crop growing degree days. To evaluate the performance of CCMS framework, it was implemented over croplands of Nebraska, and estimated carbon fluxes for major crops (i.e. corn, soybean, winter wheat, grain sorghum, alfalfa) grown in 2015. Key findings of the CCMS framework will be presented and discussed some of which include 1) comparison of remote sensing based crop type LAI and crop phenology estimates with observed field scale data 2) comparison of carbon flux estimates from CCMS framework with measured fluxes at flux tower sites 3) regional scale differences in carbon fluxes among various crops in Nebraska.

Crop monitoring & yield forecasting system based on Synthetic Aperture Radar (SAR) and process-based crop growth model: Development and validation in South and South East Asian Countries

NASA Astrophysics Data System (ADS)

Setiyono, T. D.

2014-12-01

Accurate and timely information on rice crop growth and yield helps governments and other stakeholders adapting their economic policies and enables relief organizations to better anticipate and coordinate relief efforts in the wake of a natural catastrophe. Such delivery of rice growth and yield information is made possible by regular earth observation using space-born Synthetic Aperture Radar (SAR) technology combined with crop modeling approach to estimate yield. Radar-based remote sensing is capable of observing rice vegetation growth irrespective of cloud coverage, an important feature given that in incidences of flooding the sky is often cloud-covered. The system allows rapid damage assessment over the area of interest. Rice yield monitoring is based on a crop growth simulation and SAR-derived key information, particularly start of season and leaf growth rate. Results from pilot study sites in South and South East Asian countries suggest that incorporation of SAR data into crop model improves yield estimation for actual yields. Remote-sensing data assimilation into crop model effectively capture responses of rice crops to environmental conditions over large spatial coverage, which otherwise is practically impossible to achieve. Such improvement of actual yield estimates offers practical application such as in a crop insurance program. Process-based crop simulation model is used in the system to ensure climate information is adequately captured and to enable mid-season yield forecast.

Linking Field and Satellite Observations to Reveal Differences in Single vs. Double-Cropped Soybean Yields in Central Brazil

NASA Astrophysics Data System (ADS)

Jeffries, G. R.; Cohn, A.

2016-12-01

Soy-corn double cropping (DC) has been widely adopted in Central Brazil alongside single cropped (SC) soybean production. DC involves different cropping calendars, soy varieties, and may be associated with different crop yield patterns and volatility than SC. Study of the performance of the region's agriculture in a changing climate depends on tracking differences in the productivity of SC vs. DC, but has been limited by crop yield data that conflate the two systems. We predicted SC and DC yields across Central Brazil, drawing on field observations and remotely sensed data. We first modeled field yield estimates as a function of remotely sensed DC status and vegetation index (VI) metrics, and other management and biophysical factors. We then used the statistical model estimated to predict SC and DC soybean yields at each 500 m2 grid cell of Central Brazil for harvest years 2001 - 2015. The yield estimation model was constructed using 1) a repeated cross-sectional survey of soybean yields and management factors for years 2007-2015, 2) a custom agricultural land cover classification dataset which assimilates earlier datasets for the region, and 3) 500m 8-day MODIS image composites used to calculate the wide dynamic range vegetation index (WDRVI) and derivative metrics such as area under the curve for WDRVI values in critical crop development periods. A statistical yield estimation model which primarily entails WDRVI metrics, DC status, and spatial fixed effects was developed on a subset of the yield dataset. Model validation was conducted by predicting previously withheld yield records, and then assessing error and goodness-of-fit for predicted values with metrics including root mean squared error (RMSE), mean squared error (MSE), and R2. We found a statistical yield estimation model which incorporates WDRVI and DC status to be way to estimate crop yields over the region. Statistical properties of the resulting gridded yield dataset may be valuable for understanding linkages between crop yields, farm management factors, and climate.

Flexible Strategies for Coping with Rainfall Variability: Seasonal Adjustments in Cropped Area in the Ganges Basin

PubMed Central

Siderius, Christian; Biemans, Hester; van Walsum, Paul E. V.; van Ierland, Ekko C.; Kabat, Pavel; Hellegers, Petra J. G. J.

2016-01-01

One of the main manifestations of climate change will be increased rainfall variability. How to deal with this in agriculture will be a major societal challenge. In this paper we explore flexibility in land use, through deliberate seasonal adjustments in cropped area, as a specific strategy for coping with rainfall variability. Such adjustments are not incorporated in hydro-meteorological crop models commonly used for food security analyses. Our paper contributes to the literature by making a comprehensive model assessment of inter-annual variability in crop production, including both variations in crop yield and cropped area. The Ganges basin is used as a case study. First, we assessed the contribution of cropped area variability to overall variability in rice and wheat production by applying hierarchical partitioning on time-series of agricultural statistics. We then introduced cropped area as an endogenous decision variable in a hydro-economic optimization model (WaterWise), coupled to a hydrology-vegetation model (LPJmL), and analyzed to what extent its performance in the estimation of inter-annual variability in crop production improved. From the statistics, we found that in the period 1999–2009 seasonal adjustment in cropped area can explain almost 50% of variability in wheat production and 40% of variability in rice production in the Indian part of the Ganges basin. Our improved model was well capable of mimicking existing variability at different spatial aggregation levels, especially for wheat. The value of flexibility, i.e. the foregone costs of choosing not to crop in years when water is scarce, was quantified at 4% of gross margin of wheat in the Indian part of the Ganges basin and as high as 34% of gross margin of wheat in the drought-prone state of Rajasthan. We argue that flexibility in land use is an important coping strategy to rainfall variability in water stressed regions. PMID:26934389

Biodiversity Hotspots, Climate Change, and Agricultural Development: Global Limits of Adaptation

NASA Astrophysics Data System (ADS)

Schneider, U. A.; Rasche, L.; Schmid, E.; Habel, J. C.

2017-12-01

Terrestrial ecosystems are threatened by climate and land management change. These changes result from complex and heterogeneous interactions of human activities and natural processes. Here, we study the potential change in pristine area in 33 global biodiversity hotspots within this century under four climate projections (representative concentration pathways) and associated population and income developments (shared socio-economic pathways). A coupled modelling framework computes the regional net expansion of crop and pasture lands as result of changes in food production and consumption. We use a biophysical crop simulation model to quantify climate change impacts on agricultural productivity, water, and nutrient emissions for alternative crop management systems in more than 100 thousand agricultural land polygons (homogeneous response units) and for each climate projection. The crop simulation model depicts detailed soil, weather, and management information and operates with a daily time step. We use time series of livestock statistics to link livestock production to feed and pasture requirements. On the food consumption side, we estimate national demand shifts in all countries by processing population and income growth projections through econometrically estimated Engel curves. Finally, we use a global agricultural sector optimization model to quantify the net change in pristine area in all biodiversity hotspots under different adaptation options. These options include full-scale global implementation of i) crop yield maximizing management without additional irrigation, ii) crop yield maximizing management with additional irrigation, iii) food yield maximizing crop mix adjustments, iv) food supply maximizing trade flow adjustments, v) healthy diets, and vi) combinations of the individual options above. Results quantify the regional potentials and limits of major agricultural producer and consumer adaptation options for the preservation of pristine areas in biodiversity hotspots. Results also quantify the conflicts between food and water security, biodiversity protection, and climate change mitigation.

Dynamic drought risk assessment using crop model and remote sensing techniques

NASA Astrophysics Data System (ADS)

Sun, H.; Su, Z.; Lv, J.; Li, L.; Wang, Y.

2017-02-01

Drought risk assessment is of great significance to reduce the loss of agricultural drought and ensure food security. The normally drought risk assessment method is to evaluate its exposure to the hazard and the vulnerability to extended periods of water shortage for a specific region, which is a static evaluation method. The Dynamic Drought Risk Assessment (DDRA) is to estimate the drought risk according to the crop growth and water stress conditions in real time. In this study, a DDRA method using crop model and remote sensing techniques was proposed. The crop model we employed is DeNitrification and DeComposition (DNDC) model. The drought risk was quantified by the yield losses predicted by the crop model in a scenario-based method. The crop model was re-calibrated to improve the performance by the Leaf Area Index (LAI) retrieved from MODerate Resolution Imaging Spectroradiometer (MODIS) data. And the in-situ station-based crop model was extended to assess the regional drought risk by integrating crop planted mapping. The crop planted area was extracted with extended CPPI method from MODIS data. This study was implemented and validated on maize crop in Liaoning province, China.

Agricultural Industry Advanced Vehicle Technology: Benchmark Study for Reduction in Petroleum Use

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

Roger Hoy

2014-09-01

Diesel use on farms in the United States has remained relatively constant since 1985, decreasing slightly in 2009, which may be attributed to price increases and the economic recession. During this time, the United States’ harvested area also has remained relatively constant at roughly 300 million acres. In 2010, farm diesel use was 5.4% of the total United States diesel use. Crops accounting for an estimated 65% of United States farm diesel use include corn, soybean, wheat, hay, and alfalfa, respectively, based on harvested crop area and a recent analysis of estimated fuel use by crop. Diesel use in thesemore » cropping systems primarily is from tillage, harvest, and various other operations (e.g., planting and spraying) (Figure 3). Diesel efficiency is markedly variable due to machinery types, conditions of operation (e.g., soil type and moisture), and operator variability. Farm diesel use per acre has slightly decreased in the last two decades and diesel is now estimated to be less than 5% of farm costs per acre. This report will explore current trends in increasing diesel efficiency in the farm sector. The report combines a survey of industry representatives, a review of literature, and data analysis to identify nascent technologies for increasing diesel efficiency« less

Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment

USDA-ARS?s Scientific Manuscript database

We develop a robust understanding of the effects of assimilating remote sensing observations of leaf area index and soil moisture (in the top 5 cm) on DSSAT-CSM CropSim-Ceres wheat yield estimates. Synthetic observing system simulation experiments compare the abilities of the Ensemble Kalman Filter...

Identifying populations potentially exposed to agricultural pesticides using remote sensing and a Geographic Information System

USGS Publications Warehouse

Ward, M.H.; Nuckols, J.R.; Weigel, S. J.; Cantor, K.P.; Miller, Roger S.

2000-01-01

Pesticides used in agriculture may cause adverse health effects among the population living near agricultural areas. However, identifying the populations most likely to be exposed is difficult. We conducted a feasibility study to determine whether satellite imagery could be used to reconstruct historical crop patterns. We used historical Farm Service Agency records as a source of ground reference data to classify a late summer 1984 satellite image into crop species in a three-county area in south central Nebraska. Residences from a population-based epidemiologic study of non-Hodgkin lymphoma were located on the crop maps using a geographic information system (GIS). Corn, soybeans, sorghum, and alfalfa were the major crops grown in the study area. Eighty-five percent of residences could be located, and of these 22% had one of the four major crops within 500 m of the residence, an intermediate distance for the range of drift effects from pesticides applied in agriculture. We determined the proximity of residences to specific crop species and calculated crop-specific probabilities of pesticide use based on available data. This feasibility study demonstrated that remote sensing data and historical records on crop location can be used to create historical crop maps. The crop pesticides that were likely to have been applied can be estimated when information about crop-specific pesticide use is available. Using a GIS, zones of potential exposure to agricultural pesticides and proximity measures can be determined for residences in a study.

Enhancing USDA's Retrospective Analog Year Analyses Using NASA Satellite Precipitation and Soil Moisture Data

NASA Astrophysics Data System (ADS)

Teng, W. L.; Shannon, H. D.

2013-12-01

The USDA World Agricultural Outlook Board (WAOB) is responsible for monitoring weather and climate impacts on domestic and foreign crop development. One of WAOB's primary goals is to determine the net cumulative effect of weather and climate anomalies on final crop yields. To this end, a broad array of information is consulted, including maps, charts, and time series of recent weather, climate, and crop observations; numerical output from weather and crop models; and reports from the press, USDA attachés, and foreign governments. The resulting agricultural weather assessments are published in the Weekly Weather and Crop Bulletin, to keep farmers, policy makers, and commercial agricultural interests informed of weather and climate impacts on agriculture. Because both the amount and timing of precipitation significantly affect crop yields, WAOB has often, as part of its operational process, used historical time series of surface-based precipitation observations to visually identify growing seasons with similar (analog) weather patterns as, and help estimate crop yields for, the current growing season. As part of a larger effort to improve WAOB estimates by integrating NASA remote sensing observations and research results into WAOB's decision-making environment, a more rigorous, statistical method for identifying analog years was developed. This method, termed the analog index (AI), is based on the Nash-Sutcliffe model efficiency coefficient. The AI was computed for five study areas and six growing seasons of data analyzed (2003-2007 as potential analog years and 2008 as the target year). Previously reported results compared the performance of AI for time series derived from surface-based observations vs. satellite-retrieved precipitation data. Those results showed that, for all five areas, crop yield estimates derived from satellite-retrieved precipitation data are closer to measured yields than are estimates derived from surface-based precipitation observations. Subsequent work has compared the relative performance of AI for time series derived from satellite-retrieved surface soil moisture data and from root zone soil moisture derived from the assimilation of surface soil moisture data into a land surface model. These results, which also showed the potential benefits of satellite data for analog year analyses, will be presented.

Crop-specific seasonal estimates of irrigation-water demand in South Asia

NASA Astrophysics Data System (ADS)

Biemans, Hester; Siderius, Christian; Mishra, Ashok; Ahmad, Bashir

2016-05-01

Especially in the Himalayan headwaters of the main rivers in South Asia, shifts in runoff are expected as a result of a rapidly changing climate. In recent years, our insight into these shifts and their impact on water availability has increased. However, a similar detailed understanding of the seasonal pattern in water demand is surprisingly absent. This hampers a proper assessment of water stress and ways to cope and adapt. In this study, the seasonal pattern of irrigation-water demand resulting from the typical practice of multiple cropping in South Asia was accounted for by introducing double cropping with monsoon-dependent planting dates in a hydrology and vegetation model. Crop yields were calibrated to the latest state-level statistics of India, Pakistan, Bangladesh and Nepal. The improvements in seasonal land use and cropping periods lead to lower estimates of irrigation-water demand compared to previous model-based studies, despite the net irrigated area being higher. Crop irrigation-water demand differs sharply between seasons and regions; in Pakistan, winter (rabi) and monsoon summer (kharif) irrigation demands are almost equal, whereas in Bangladesh the rabi demand is ~ 100 times higher. Moreover, the relative importance of irrigation supply versus rain decreases sharply from west to east. Given the size and importance of South Asia improved regional estimates of food production and its irrigation-water demand will also affect global estimates. In models used for global water resources and food-security assessments, processes like multiple cropping and monsoon-dependent planting dates should not be ignored.

Comparison of Irrigation Water Use Estimates Calculated from Remotely Sensed Irrigated Acres and State Reported Irrigated Acres in the Lake Altus Drainage Basin, Oklahoma and Texas, 2000 Growing Season

USGS Publications Warehouse

Masoner, J.R.; Mladinich, C.S.; Konduris, A.M.; Smith, S. Jerrod

2003-01-01

Increased demand for water in the Lake Altus drainage basin requires more accurate estimates of water use for irrigation. The U.S. Geological Survey, in cooperation with the U.S. Bureau of Reclamation, is investigating new techniques to improve water-use estimates for irrigation purposes in the Lake Altus drainage basin. Empirical estimates of reference evapotranspiration, crop evapotranspiration, and crop irrigation water requirements for nine major crops were calculated from September 1999 to October 2000 using a solar radiation-based evapotranspiration model. Estimates of irrigation water use were calculated using remotely sensed irrigated crop acres derived from Landsat 7 Enhanced Thematic Mapper Plus imagery and were compared with irrigation water-use estimates calculated from irrigated crop acres reported by the Oklahoma Water Resources Board and the Texas Water Development Board for the 2000 growing season. The techniques presented will help manage water resources in the Lake Altus drainage basin and may be transferable to other areas with similar water management needs. Irrigation water use calculated from the remotely sensed irrigated acres was estimated at 154,920 acre-feet; whereas, irrigation water use calculated from state reported irrigated crop acres was 196,026 acre-feet, a 23 percent difference. The greatest difference in irrigation water use was in Carson County, Texas. Irrigation water use for Carson County, Texas, calculated from the remotely sensed irrigated acres was 58,555 acrefeet; whereas, irrigation water use calculated from state reported irrigated acres was 138,180 acre-feet, an 81 percent difference. The second greatest difference in irrigation water use occurred in Beckham County, Oklahoma. Differences between the two irrigation water use estimates are due to the differences of irrigated crop acres derived from the mapping process and those reported by the Oklahoma Water Resources Board and Texas Water Development Board.

Estimation of Rice Crop Yields Using Random Forests in Taiwan

NASA Astrophysics Data System (ADS)

Chen, C. F.; Lin, H. S.; Nguyen, S. T.; Chen, C. R.

2017-12-01

Rice is globally one of the most important food crops, directly feeding more people than any other crops. Rice is not only the most important commodity, but also plays a critical role in the economy of Taiwan because it provides employment and income for large rural populations. The rice harvested area and production are thus monitored yearly due to the government's initiatives. Agronomic planners need such information for more precise assessment of food production to tackle issues of national food security and policymaking. This study aimed to develop a machine-learning approach using physical parameters to estimate rice crop yields in Taiwan. We processed the data for 2014 cropping seasons, following three main steps: (1) data pre-processing to construct input layers, including soil types and weather parameters (e.g., maxima and minima air temperature, precipitation, and solar radiation) obtained from meteorological stations across the country; (2) crop yield estimation using the random forests owing to its merits as it can process thousands of variables, estimate missing data, maintain the accuracy level when a large proportion of the data is missing, overcome most of over-fitting problems, and run fast and efficiently when handling large datasets; and (3) error verification. To execute the model, we separated the datasets into two groups of pixels: group-1 (70% of pixels) for training the model and group-2 (30% of pixels) for testing the model. Once the model is trained to produce small and stable out-of-bag error (i.e., the mean squared error between predicted and actual values), it can be used for estimating rice yields of cropping seasons. The results obtained from the random forests-based regression were compared with the actual yield statistics indicated the values of root mean square error (RMSE) and mean absolute error (MAE) achieved for the first rice crop were respectively 6.2% and 2.7%, while those for the second rice crop were 5.3% and 2.9%, respectively. Although there are several uncertainties attributed to the data quality of input layers, our study demonstrates the promising application of random forests for estimating rice crop yields at the national level in Taiwan. This approach could be transferable to other regions of the world for improving large-scale estimation of rice crop yields.

Estimation of crop water requirements using remote sensing for operational water resources management

NASA Astrophysics Data System (ADS)

Vasiliades, Lampros; Spiliotopoulos, Marios; Tzabiras, John; Loukas, Athanasios; Mylopoulos, Nikitas

2015-06-01

An integrated modeling system, developed in the framework of "Hydromentor" research project, is applied to evaluate crop water requirements for operational water resources management at Lake Karla watershed, Greece. The framework includes coupled components for operation of hydrotechnical projects (reservoir operation and irrigation works) and estimation of agricultural water demands at several spatial scales using remote sensing. The study area was sub-divided into irrigation zones based on land use maps derived from Landsat 5 TM images for the year 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) was used to derive actual evapotranspiration (ET) and crop coefficient (ETrF) values from Landsat TM imagery. Agricultural water needs were estimated using the FAO method for each zone and each control node of the system for a number of water resources management strategies. Two operational strategies of hydro-technical project development (present situation without operation of the reservoir and future situation with the operation of the reservoir) are coupled with three water demand strategies. In total, eight (8) water management strategies are evaluated and compared. The results show that, under the existing operational water resources management strategies, the crop water requirements are quite large. However, the operation of the proposed hydro-technical projects in Lake Karla watershed coupled with water demand management measures, like improvement of existing water distribution systems, change of irrigation methods, and changes of crop cultivation could alleviate the problem and lead to sustainable and ecological use of water resources in the study area.

Asia Rice Crop Estimation and Monitoring (Asia-RiCE) for GEOGLAM

NASA Astrophysics Data System (ADS)

Oyoshi, K.; Tomiyama, N.; Okumura, T.; Sobue, S.

2013-12-01

Food security is a critical issue for the international community because of rapid population and economic growth, and climate change. In June 2011, the meeting of G20 agriculture ministers was held to discuss food security and food price volatility, and they agreed on an 'Action Plan on Food Price Volatility and Agriculture'. This plan includes a GEO Global Agricultural Monitoring (GEOGLAM) initiative. The aim of GEOGLAM is to reinforce the international community's ability to produce and disseminate relevant, timely, and accurate forecasts of agricultural production on regional, national, and global scales by utilizing remote sensing technology. GEOGLAM focused on four major grain crops, wheat, maize, soybeans and rice. In particular, Asian countries are responsible for approximately 90% of the world rice production and consumption, rice is the most significant cereal crop in Asian region. Hence, Asian space and agricultural agencies with an interest in the development of rice crop monitoring technology launched an Asia-Rice Crop Estimation & Monitoring (Asia-RiCE) component for the GEOGLAM initiative. In Asian region, rice is mainly cultivated in rainy season, and a large amount of cloud limits rice crop monitoring with optical sensors. But, Synthetic Aperture RADAR (SAR) is all-weather sensor and can observe land surface even if the area is covered by cloud. Therefore, SAR technology would be powerful tool to monitor rice crop in Asian region. Asia-RiCE team required mainly SAR observation data including ALOS-2, RISAT-1, Sentinel-1 and RADARSAT, TerraSAR-X, COSMO-SkyMed for Asia-RiCE GEOGLAM Phase 1 implementation (2013-2015) to the Committee on Earth Observations (CEOS) in the GEOGLAM-CEOS Global Agricultural Monitoring Co-community Meeting held in June 2013. And also, rice crop has complicated cropping systems such as rein-fed or irrigated cultivation, single, double or sometimes triple cropping. In addition, each agricultural field is smaller than that of other regions. The methodology for rice crop monitoring is different from that for other crops, and these characteristics make rice crop monitoring by Earth observation data more difficult and complicated. Now, Asian-RiCE team has selected four technical demonstration sites, Indonesia, Thailand, Vietnam (North and South) for Phase1A implementation (June 2013 to November 2014) to verify methodologies that estimate multi-season crop calendar, rice planted area, yield and production by the blending of Earth observation data including satellite data from SAR or optical sensor and in-situ data. We already developed some prototype systems for rice planed area mapping by SAR and agro-weather monitoring including soil moisture or drought index by microwave and optical data. These technologies would be contribute to the development of rice crop monitoring framework for Asia-RiCE. In this presentation, we introduce the framework and ongoing activities of Asia-RiCE component for GEOGLAM and developed systems for rice crop and agro-weather monitoring.

Combining Remote Sensing imagery of both fine and coarse spatial resolution to Estimate Crop Evapotranspiration and quantifying its Influence on Crop Growth Monitoring.

NASA Astrophysics Data System (ADS)

Sepulcre-Cantó, Guadalupe; Gellens-Meulenberghs, Françoise; Arboleda, Alirio; Duveiller, Gregory; Piccard, Isabelle; de Wit, Allard; Tychon, Bernard; Bakary, Djaby; Defourny, Pierre

2010-05-01

This study has been carried out in the framework of the GLOBAM -Global Agricultural Monitoring system by integration of earth observation and modeling techniques- project whose objective is to fill the methodological gap between the state of the art of local crop monitoring and the operational requirements of the global monitoring system programs. To achieve this goal, the research aims to develop an integrated approach using remote sensing and crop growth modeling. Evapotranspiration (ET) is a valuable parameter in the crop monitoring context since it provides information on the plant water stress status, which strongly influences crop development and, by extension, crop yield. To assess crop evapotranspiration over the GLOBAM study areas (300x300 km sites in Northern Europe and Central Ethiopia), a Soil-Vegetation-Atmosphere Transfer (SVAT) model forced with remote sensing and numerical weather prediction data has been used. This model runs at pre-operational level in the framework of the EUMETSAT LSA-SAF (Land Surface Analysis Satellite Application Facility) using SEVIRI and ECMWF data, as well as the ECOCLIMAP database to characterize the vegetation. The model generates ET images at the Meteosat Second Generation (MSG) spatial resolution (3 km at subsatellite point),with a temporal resolution of 30 min and monitors the entire MSG disk which covers Europe, Africa and part of Sud America . The SVAT model was run for 2007 using two approaches. The first approach is at the standard pre-operational mode. The second incorporates remote sensing information at various spatial resolutions going from LANDSAT (30m) to SEVIRI (3-5 km) passing by AWIFS (56m) and MODIS (250m). Fine spatial resolution data consists of crop type classification which enable to identify areas where pure crop specific MODIS time series can be compiled and used to derive Leaf Area Index estimations for the most important crops (wheat and maize). The use of this information allowed to characterize the type of vegetation and its state of development in a more accurate way than using the ECOCLIMAP database. Finally, the CASA method was applied using the evapotranspiration images with FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) images from LSA-SAF to obtain Dry Matter Productivity (DMP) and crop yield. The potential of using evapotranspiration obtained from remote sensing in crop growth modeling is studied and discussed. Results of comparing the evapotranspiration obtained with ground truth data are shown as well as the influence of using high resolution information to characterize the vegetation in the evapotranspiration estimation. The values of DMP and yield obtained with the CASA method are compared with those obtained using crop growth modeling and field data, showing the potential of using this simplified remote sensing method for crop monitoring and yield forecasting. This methodology could be applied in an operative way to the entire MSG disk, allowing the continuous crop growth monitoring.

Linking groundwater use and stress to specific crops using the groundwater footprint in the Central Valley and High Plains aquifer systems, U.S.

NASA Astrophysics Data System (ADS)

Esnault, Laurent; Gleeson, Tom; Wada, Yoshihide; Heinke, Jens; Gerten, Dieter; Flanary, Elizabeth; Bierkens, Marc F. P.; van Beek, Ludovicus P. H.

2014-06-01

A number of aquifers worldwide are being depleted, mainly by agricultural activities, yet groundwater stress has not been explicitly linked to specific agricultural crops. Using the newly developed concept of the groundwater footprint (the area required to sustain groundwater use and groundwater-dependent ecosystem services), we develop a methodology to derive crop-specific groundwater footprints. We illustrate this method by calculating high-resolution groundwater footprint estimates of crops in two heavily used aquifer systems: the Central Valley and High Plains, U.S. In both aquifer systems, hay and haylage, corn, and cotton have the largest groundwater footprints, which highlights that most of the groundwater stress is induced by crops meant for cattle feed. Our results are coherent with other studies in the High Plains but suggest lower groundwater stress in the Central Valley, likely due to artificial recharge from surface water diversions which were not taken into account in previous estimates. Uncertainties of recharge and irrigation application efficiency contribute the most to the total relative uncertainty of the groundwater footprint to aquifer area ratios. Our results and methodology will be useful for hydrologists, water resource managers, and policy makers concerned with which crops are causing the well-documented groundwater stress in semiarid to arid agricultural regions around the world.

Linking Groundwater Use and Stress to Specific Crops Using the Groundwater Footprint in the Central Valley and High Plains Aquifer Systems, U.S.

NASA Astrophysics Data System (ADS)

Wada, Y.; Esnault, L.; Gleeson, T.; Heinke, J.; Gerten, D.; Flanary, E.; Bierkens, M. F.; Van Beek, L. P.

2014-12-01

A number of aquifers worldwide are being depleted, mainly by agricultural activities, yet groundwater stress has not been explicitly linked to specific agricultural crops. Using the newly-developed concept of the groundwater footprint (the area required to sustain groundwater use and groundwater-dependent ecosystem services), we develop a methodology to derive crop-specific groundwater footprints. We illustrate this method by calculating high resolution groundwater footprint estimates of crops in two heavily used aquifer systems: the Central Valley and High Plains, U.S. In both aquifer systems, hay and haylage, corn and cotton have the largest groundwater footprints, which highlights that most of the groundwater stress is induced by crops meant for cattle feed. Our results are coherent with other studies in the High Plains but suggest lower groundwater stress in the Central Valley, likely due to artificial recharge from surface water diversions which were not taken into account in previous estimates. Uncertainties of recharge and irrigation application efficiency contribute the most to the total relative uncertainty of the groundwater footprint to aquifer area ratios. Our results and methodology will be useful for hydrologists, water resource managers, and policy makers concerned with which crops are causing the well-documented groundwater stress in semiarid to arid agricultural regions around the world.

Remote sensing in Iowa agriculture. [cropland inventory, soils, forestland, and crop diseases

NASA Technical Reports Server (NTRS)

Mahlstede, J. P. (Principal Investigator); Carlson, R. E.

1973-01-01

The author has identified the following significant results. Results include the estimation of forested and crop vegetation acreages using the ERTS-1 imagery. The methods used to achieve these estimates still require refinement, but the results appear promising. Practical applications would be directed toward achieving current land use inventories of these natural resources. This data is presently collected by sampling type surveys. If ERTS-1 can observe this and area estimates can be determined accurately, then a step forward has been achieved. Cost benefit relationship will have to be favorable. Problems still exist in these estimation techniques due to the diversity of the scene observed in the ERTS-1 imagery covering other part of Iowa. This is due to influence of topography and soils upon the adaptability of the vegetation to specific areas of the state. The state mosaic produced from ERTS-1 imagery shows these patterns very well. Research directed to acreage estimates is continuing.

Comparative Performance Analysis of a Hyper-Temporal Ndvi Analysis Approach and a Landscape-Ecological Mapping Approach

NASA Astrophysics Data System (ADS)

Ali, A.; de Bie, C. A. J. M.; Scarrott, R. G.; Ha, N. T. T.; Skidmore, A. K.

2012-07-01

Both agricultural area expansion and intensification are necessary to cope with the growing demand for food, and the growing threat of food insecurity which is rapidly engulfing poor and under-privileged sections of the global population. Therefore, it is of paramount importance to have the ability to accurately estimate crop area and spatial distribution. Remote sensing has become a valuable tool for estimating and mapping cropland areas, useful in food security monitoring. This work contributes to addressing this broad issue, focusing on the comparative performance analysis of two mapping approaches (i) a hyper-temporal Normalized Difference Vegetation Index (NDVI) analysis approach and (ii) a Landscape-ecological approach. The hyper-temporal NDVI analysis approach utilized SPOT 10-day NDVI imagery from April 1998-December 2008, whilst the Landscape-ecological approach used multitemporal Landsat-7 ETM+ imagery acquired intermittently between 1992 and 2002. Pixels in the time-series NDVI dataset were clustered using an ISODATA clustering algorithm adapted to determine the optimal number of pixel clusters to successfully generalize hyper-temporal datasets. Clusters were then characterized with crop cycle information, and flooding information to produce an NDVI unit map of rice classes with flood regime and NDVI profile information. A Landscape-ecological map was generated using a combination of digitized homogenous map units in the Landsat-7 ETM+ imagery, a Land use map 2005 of the Mekong delta, and supplementary datasets on the regions terrain, geo-morphology and flooding depths. The output maps were validated using reported crop statistics, and regression analyses were used to ascertain the relationship between land use area estimated from maps, and those reported in district crop statistics. The regression analysis showed that the hyper-temporal NDVI analysis approach explained 74% and 76% of the variability in reported crop statistics in two rice crop and three rice crop land use systems respectively. In contrast, 64% and 63% of the variability was explained respectively by the Landscape-ecological map. Overall, the results indicate the hyper-temporal NDVI analysis approach is more accurate and more useful in exploring when, why and how agricultural land use manifests itself in space and time. Furthermore, the NDVI analysis approach was found to be easier to implement, was more cost effective, and involved less subjective user intervention than the landscape-ecological approach.

Crop evapotranspiration estimation using remote sensing and the existing network of meteorological stations in Cyprus

NASA Astrophysics Data System (ADS)

Papadavid, G.; Hadjimitsis, D.; Michaelides, S.; Nisantzi, A.

2011-05-01

Cyprus is frequently confronted with severe droughts and the need for accurate and systematic data on crop evapotranspiration (ETc) is essential for decision making, regarding water irrigation management and scheduling. The aim of this paper is to highlight how data from meteorological stations in Cyprus can be used for monitoring and determining the country's irrigation demands. This paper shows how daily ETc can be estimated using FAO Penman-Monteith method adapted to satellite data and auxiliary meteorological parameters. This method is widely used in many countries for estimating crop evapotranspiration using auxiliary meteorological data (maximum and minimum temperatures, relative humidity, wind speed) as inputs. Two case studies were selected in order to determine evapotranspiration using meteorological and low resolution satellite data (MODIS - TERRA) and to compare it with the results of the reference method (FAO-56) which estimates the reference evapotranspiration (ETo) by using only meteorological data. The first approach corresponds to the FAO Penman-Monteith method adapted for using both meteorological and remotely sensed data. Furthermore, main automatic meteorological stations in Cyprus were mapped using Geographical Information System (GIS). All the agricultural areas of the island were categorized according to the nearest meteorological station which is considered as "representative" of the area. Thiessen polygons methodology was used for this purpose. The intended goal was to illustrate what can happen to a crop, in terms of water requirements, if meteorological data are retrieved from other than the representative stations. The use of inaccurate data can result in low yields or excessive irrigation which both lead to profit reduction. The results have shown that if inappropriate meteorological data are utilized, then deviations from correct ETc might be obtained, leading to water losses or crop water stress.

A simple method to estimate vegetation indices and crop canopy factors using field spectroscopy for solanum tuberosum during the whole phenological cycle

NASA Astrophysics Data System (ADS)

Perdikou, S.; Papadavid, G.; Hadjimitsis, M.; Hadjimitsis, D.; Neofytou, N.

2013-08-01

Field spectroscopy is a part of the remote sensing techniques and very important for studies in agriculture. A GER-1500 field spectro-radiometer was used in this study in order to retrieve the necessary spectrum data of the spring potatoes for estimating spectral vegetation indices (SVI's). A field campaign was undertaken from September to the end of November 2012 for the collection of spectro-radiometric measurements. The study area was in the Mandria Village in Paphos district in Cyprus. This paper demonstrates how crop canopy factors can be statistically related to remotely sensed data, namely vegetation indices. The paper is a part of an EU cofounded project regarding estimating crop water requirements using remote sensing techniques and informing the farmers through 3G smart telephony.

An innovative approach for Predicting Farmers' Adaptive Behavior at the Large Watershed Scale: Implications for Water Quality and Crop Yields

NASA Astrophysics Data System (ADS)

Valcu-Lisman, A. M.; Gassman, P. W.; Arritt, R. W.; Kling, C.; Arbuckle, J. G.; Roesch-McNally, G. E.; Panagopoulos, Y.

2017-12-01

Projected changes in the climatic patterns (higher temperatures, changes in extreme precipitation events, and higher levels of humidity) will affect agricultural cropping and management systems in major agricultural production areas. The concept of adaption to new climatic or economic conditions is an important aspect of the agricultural decision-making process. Adopting cover crops, reduced tillage, extending the drainage systems and adjusting crop management are only a few examples of adaptive actions. These actions can be easily implemented as long as they have private benefits (increased profits, reduced risk). However, each adaptive action has a different impact on water quality. Cover crops and no till usually have a positive impact on water quality, but increased tile drainage typically results in more degraded water quality due primarily to increased export of soluble nitrogen and phosphorus. The goal of this research is to determine the changes in water quality as well in crop yields as farmers undertake these adaptive measures. To answer this research question, we need to estimate the likelihood that these actions will occur, identify the agricultural areas where these actions are most likely to be implemented, and simulate the water quality impacts associated with each of these scenarios. We apply our modeling efforts to the whole Upper-Mississippi River Basin Basin (UMRB) and the Ohio-Tennessee River Basin (OTRB). These two areas are critical source regions for the re-occurring hypoxic zone in the gulf of Mexico. The likelihood of each adaptive agricultural action is estimated using data from a survey conducted in 2012. A large, representative sample of farmers in the Corn Belt was used in the survey to elicit behavioral intentions regarding three of the most important agricultural adaptation strategies (no-till, cover crops and tile drainage). We use these data to study the relationship between intent to adapt, farmer characteristics, farm characteristics, and weather characteristics, and to predict the probability of adoption for each action. Next, we use these estimated probabilities to create different scenarios for the two large scale-watersheds. Finally, we simulate the impact of these scenarios on water quality using calibrated UMRB and OTRB SWAT water quality models.

Climate change and drought effects on rural income distribution in the Mediterranean: a case study for Spain

NASA Astrophysics Data System (ADS)

Quiroga, S.; Suárez, C.

2015-07-01

This paper examines the effects of climate change and drought on agricultural outputs in Spanish rural areas. By now the effects of drought as a response to climate change or policy restrictions have been analyzed through response functions considering direct effects on crop productivity and incomes. These changes also affect incomes distribution in the region and therefore modify the social structure. Here we consider this complementary indirect effect on social distribution of incomes which is essential in the long term. We estimate crop production functions for a range of Mediterranean crops in Spain and we use a decomposition of inequalities measure to estimate the impact of climate change and drought on yield disparities. This social aspect is important for climate change policies since it can be determinant for the public acceptance of certain adaptation measures in a context of drought. We provide the empirical estimations for the marginal effects of the two considered impacts: farms' income average and social income distribution. In our estimates we consider crop productivity response to both bio-physical and socio-economic aspects to analyze long term implications on both competitiveness and social disparities. We find disparities in the adaptation priorities depending on the crop and the region analyzed.

Multiyear high-resolution carbon exchange over European croplands from the integration of observed crop yields into CarbonTracker Europe

NASA Astrophysics Data System (ADS)

Combe, Marie; Vilà-Guerau de Arellano, Jordi; de Wit, Allard; Peters, Wouter

2016-04-01

Carbon exchange over croplands plays an important role in the European carbon cycle over daily-to-seasonal time scales. Not only do crops occupy one fourth of the European land area, but their photosynthesis and respiration are large and affect CO2 mole fractions at nearly every atmospheric CO2 monitoring site. A better description of this crop carbon exchange in our CarbonTracker Europe data assimilation system - which currently treats crops as unmanaged grasslands - could strongly improve its ability to constrain terrestrial carbon fluxes. Available long-term observations of crop yield, harvest, and cultivated area allow such improvements, when combined with the new crop-modeling framework we present. This framework can model the carbon fluxes of 10 major European crops at high spatial and temporal resolution, on a 12x12 km grid and 3-hourly time-step. The development of this framework is threefold: firstly, we optimize crop growth using the process-based WOrld FOod STudies (WOFOST) agricultural crop growth model. Simulated yields are downscaled to match regional crop yield observations from the Statistical Office of the European Union (EUROSTAT) by estimating a yearly regional parameter for each crop species: the yield gap factor. This step allows us to better represent crop phenology, to reproduce the observed multiannual European crop yields, and to construct realistic time series of the crop carbon fluxes (gross primary production, GPP, and autotrophic respiration, Raut) on a fine spatial and temporal resolution. Secondly, we combine these GPP and Raut fluxes with a simple soil respiration model to obtain the total ecosystem respiration (TER) and net ecosystem exchange (NEE). And thirdly, we represent the horizontal transport of carbon that follows crop harvest and its back-respiration into the atmosphere during harvest consumption. We distribute this carbon using observations of the density of human and ruminant populations from EUROSTAT. We assess the model's ability to represent the seasonal GPP, TER and NEE fluxes using observations at 6 European FluxNet winter wheat and grain maize sites and compare it with the fluxes of the current terrestrial carbon cycle model of CarbonTracker Europe: the Simple Biosphere - Carnegie-Ames-Stanford Approach (SiBCASA) model. We find that the new model framework provides a detailed, realistic, and strongly observation-driven estimate of carbon exchange over European croplands. Its products will be made available to the scientific community through the ICOS Carbon Portal, and serve as a new cropland component in CarbonTracker Europe flux estimates.

LACIE--An Application of Meteorology for United States and Foreign Wheat Assessment.

NASA Astrophysics Data System (ADS)

Hill, Jerry D.; Strommen, Norton D.; Sakamoto, Clarence M.; Leduc, Sharon K.

1980-01-01

The development of a critical world food situation during the early 1970's was the background leading to the Large Area Crop Inventory Experiment (LACIE). The need was to develop a capability for timely monitoring of crops on a global scale. Three U.S. Government agencies, NASA, NOAA and USDA, undertook the task of developing technology to extract the crop-related information available from the global weather-reporting network and the Landsat satellite. This paper describes the overall LACIE technical approach to make a quasi-operational application of existing research results and the accomplishments of this cooperative experiment in utilizing the weather information.Using available agrometeorological data, techniques were implemented to estimate crop development, assess relative crop vigor and estimate yield for wheat, the crop of principal interest to the experiment. Global weather data were utilized in preparing timely yield estimates for selected areas of the U.S. Great Plains, the U.S.S.R. and Canada. Additionally, wheat yield models were developed and pilot tested for Brazil, Australia, India and Argentina. The results of the work show that heading dates for wheat in North America can be predicted with an average absolute error of about 5 days for winter wheat and 4 days for spring wheat. Independent tests of wheat yield models over a 10-year period for the U.S. Great Plains produced a root-mean-square error of 1.12 quintals per hectare (q ha1) while similar tests in the U.S.S.R. produced an error of 1.31 q ha1. Research designed to improve the initial capability is described as is the rationale for further evolution of a capability to monitor global climate and assess its impact on world food supplies.

An experimental case study to estimate Pre-harvest Wheat Acreage/Production in Hilly and Plain region of Uttarakhand state: Challenges and solutions of problems by using satellite data

NASA Astrophysics Data System (ADS)

Uniyal, D.; Kimothi, M. M.; Bhagya, N.; Ram, R. D.; Patel, N. K.; Dhaundiya, V. K.

2014-11-01

Wheat is an economically important Rabi crop for the state, which is grown on around 26 % of total available agriculture area in the state. There is a variation in productivity of wheat crop in hilly and tarai region. The agricultural productivity is less in hilly region in comparison of tarai region due to terrace cultivation, traditional system of agriculture, small land holdings, variation in physiography, top soil erosion, lack of proper irrigation system etc. Pre-harvest acreage/yield/production estimation of major crops is being done with the help of conventional crop cutting method, which is biased, inaccurate and time consuming. Remote Sensing data with multi-temporal and multi-spectral capabilities has shown new dimension in crop discrimination analysis and acreage/yield/production estimation in recent years. In view of this, Uttarakhand Space Applications Centre (USAC), Dehradun with the collaboration of Space Applications Centre (SAC), ISRO, Ahmedabad and Uttarakhand State Agriculture Department, have developed different techniques for the discrimination of crops and estimation of pre-harvest wheat acreage/yield/production. In the 1st phase, five districts (Dehradun, Almora, Udham Singh Nagar, Pauri Garhwal and Haridwar) with distinct physiography i.e. hilly and plain regions, have been selected for testing and verification of techniques using IRS (Indian Remote Sensing Satellites), LISS-III, LISS-IV satellite data of Rabi season for the year 2008-09 and whole 13 districts of the Uttarakhand state from 2009-14 along with ground data were used for detailed analysis. Five methods have been developed i.e. NDVI (Normalized Differential Vegetation Index), Supervised classification, Spatial modeling, Masking out method and Programming on visual basics methods using multitemporal satellite data of Rabi season along with the collateral and ground data. These methods were used for wheat discriminations and preharvest acreage estimations and subsequently results were compared with Bureau of Estimation Statistics (BES). Out of these five different methods, wheat area that was estimated by spatial modeling and programming on visual basics has been found quite near to Bureau of Estimation Statistics (BES). But for hilly region, maximum fields were going in shadow region, so it was difficult to estimate accurate result, so frequency distribution curve method has been used and frequency range has been decided to discriminate wheat pixels from other pixels in hilly region, digitized those regions and result shows good result. For yield estimation, an algorithm has been developed by using soil characteristics i.e. texture, depth, drainage, temperature, rainfall and historical yield data. To get the production estimation, estimated yield multiplied by acreage of crop per hectare. Result shows deviation for acreage estimation from BES is around 3.28 %, 2.46 %, 3.45 %, 1.56 %, 1.2 % and 1.6 % (estimation not declared till now by state Agriculture dept. For the year 2013-14) estimation and deviation for production estimation is around 4.98 %, 3.66 % 3.21 % , 3.1 % NA and 2.9 % for the consecutive above mentioned years i.e. 2008-09, 2009-10, 2010-11, 2011-12, 2012-13 and 2013-14. The estimated data has been provided to State Agriculture department for their use. To forecast production before harvest facilitate the formulation of workable marketing strategies leading to better export/import of crop in the state, which will help to lead better economic condition of the state. Yield estimation would help agriculture department in assessment of productivity of land for specific crop. Pre-harvest wheat acreage/production estimation, is useful to facilitate the reliable and timely estimates and enable the administrators and planners to take strategic decisions on import-export policy matters and trade negotiations.

Remote sensing in agriculture. [using Earth Resources Technology Satellite photography

NASA Technical Reports Server (NTRS)

Downs, S. W., Jr.

1974-01-01

Some examples are presented of the use of remote sensing in cultivated crops, forestry, and range management. Areas of concern include: the determination of crop areas and types, prediction of yield, and detection of disease; the determination of forest areas and types, timber volume estimation, detection of insect and disease attack, and forest fires; and the determination of range conditions and inventory, and livestock inventory. Articles in the literature are summarized and specific examples of work being performed at the Marshall Space Flight Center are given. Primarily, aerial photographs and photo-like ERTS images are considered.

Changing Pattern of Crop Fraction in Late Blight Induced Potato Crops in Potato Bowl of West Bengal by using Multi-temporal Time Series AWiFs Data

NASA Astrophysics Data System (ADS)

Chakrabarty, Abhisek

2016-07-01

Crop fraction is the ratio of crop occupying a unit area in ground pixel, is very important for monitoring crop growth. One of the most important variables in crop growth monitoring is the fraction of available solar radiation intercepted by foliage. Late blight of potato (Solanum tuberosum), caused by the oomycete pathogen Phytophthora infestans, is considered to be the most destructive crop diseases of potato worldwide. Under favourable climatic conditions, and without intervention (i.e. fungicide sprays), the disease can destroy potato crop within few weeks. Therefore it is important to evaluate the crop fraction for monitoring the healthy and late blight affected potato crops. This study was conducted in potato bowl of West Bengal, which consists of districts of Hooghly, Howrah, Burdwan, Bankuara, and Paschim Medinipur. In this study different crop fraction estimation method like linear spectral un-mixing, Normalized difference vegetation index (NDVI) based DPM model (Zhang et al. 2013), Ratio vegetation index based DPM model, improved Pixel Dichotomy Model (Li et al. 2014) ware evaluated using multi-temporal IRS AWiFs data in two successive potato growing season of 2012-13 and 2013-14 over the study area and compared with measured crop fraction. The comparative study based on measured healthy and late blight affected potato crop fraction showed that improved Pixel Dichotomy Model maintain the high coefficient of determination (R2= 0.835) with low root mean square error (RMSE=0.21) whereas the correlation values of NDVI based DPM model and RVI based DPM model is 0.763 and 0.694 respectively. The changing pattern of crop fraction profile of late blight affected potato crop was studied in respect of healthy potato crop fraction which was extracted from the 269 GPS points of potato field. It showed that the healthy potato crop fraction profile maintained the normal phenological trend whereas the late blight affected potato crop fraction profile suddenly fallen after late blight disease affected in potato crops. Therefore, it can be concluded that based on the result of this study the improved Pixel Dichotomy Model is the most convenient method for crop fraction estimation for this region with satisfactory accuracy.

Spatial and spectral simulation of LANDSAT images of agricultural areas

NASA Technical Reports Server (NTRS)

Pont, W. F., Jr. (Principal Investigator)

1982-01-01

A LANDSAT scene simulation capability was developed to study the effects of small fields and misregistration on LANDSAT-based crop proportion estimation procedures. The simulation employs a pattern of ground polygons each with a crop ID, planting date, and scale factor. Historical greenness/brightness crop development profiles generate the mean signal values for each polygon. Historical within-field covariances add texture to pixels in each polygon. The planting dates and scale factors create between-field/within-crop variation. Between field and crop variation is achieved by the above and crop profile differences. The LANDSAT point spread function is used to add correlation between nearby pixels. The next effect of the point spread function is to blur the image. Mixed pixels and misregistration are also simulated.

Production cost analysis of Euphorbia lathyris. Final report

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

Mendel, D.A.

1979-08-01

The purpose of this study is to estimate costs of production for Euphorbia lathyris (hereafter referred to as Euphorbia) in commercial-scale quantities. Selection of five US locations for analysis was based on assumed climatic and cultivation requirements. The five areas are: nonirrigated areas (Southeast Kansas and Central Oklahoma, Northeast Louisiana and Central Mississippi, Southern Illinois), and irrigated areas: (San Joaquin Valley and the Imperial Valley, California and Yuma, Arizona). Cost estimates are tailored to reflect each region's requirements and capabilities. Variable costs for inputs such as cultivation, planting, fertilization, pesticide application, and harvesting include material costs, equipment ownership, operating costs,more » and labor. Fixed costs include land, management, and transportation of the plant material to a conversion facility. Euphorbia crop production costs, on the average, range between $215 per acre in nonirrigated areas to $500 per acre in irrigated areas. Extraction costs for conversion of Euphorbia plant material to oil are estimated at $33.76 per barrel of oil, assuming a plant capacity of 3000 dry ST/D. Estimated Euphorbia crop production costs are competitive with those of corn. Alfalfa production costs per acre are less than those of Euphorbia in the Kansas/Oklahoma and Southern Illinois site, but greater in the irrigated regions. This disparity is accounted for largely by differences in productivity and irrigation requirements.« less

The economic impact of climate change on Kenyan crop agriculture: A Ricardian approach

NASA Astrophysics Data System (ADS)

Kabubo-Mariara, Jane; Karanja, Fredrick K.

2007-06-01

This paper measures the economic impact of climate on crops in Kenya. We use cross-sectional data on climate, hydrological, soil and household level data for a sample of 816 households. We estimate a seasonal Ricardian model to assess the impact of climate on net crop revenue per acre. The results show that climate affects crop productivity. There is a non-linear relationship between temperature and revenue on one hand and between precipitation and revenue on the other. Estimated marginal impacts suggest that global warming is harmful for crop productivity. Predictions from global circulation models confirm that global warming will have a substantial impact on net crop revenue in Kenya. The results also show that the temperature component of global warming is much more important than precipitation. Findings call for monitoring of climate change and dissemination of information to farmers to encourage adaptations to climate change. Improved management and conservation of available water resources, water harvesting and recycling of wastewater could generate water for irrigation purposes especially in the arid and semi-arid areas.

Predicting Nitrogen in Streams: A Comparison of Two Estimates of Fertilizer Application

NASA Astrophysics Data System (ADS)

Mehaffey, M.; Neale, A.

2011-12-01

Decision makers frequently rely on water and air quality models to develop nutrient management strategies. Obviously, the results of these models (e.g., SWAT, SPARROW, CMAQ) are only as good as the nutrient source input data and recently the Nutrient Innovations Task Group has called for a better accounting of nonpoint nutrient sources. Currently, modelers frequently rely on county level fertilizer sales records combined with acreage of crops to estimate nitrogen sources from fertilizer for counties or watersheds. However, since fertilizer sales data are based on reported amounts they do not necessarily reflect actual use on the fields. In addition the reported sales data quality varies by state resulting in differing accuracy between states. In this study we examine an alternative method potentially providing a more uniform, spatially explicit, estimate of fertilizer use. Our nitrogen application data is estimated at a 30m pixel resolution which allows for scalable inputs for use in water and air quality models. To develop this dataset we combined raster data from the National Cropland data layer (CDL) data with the National Land Cover Data (NLCD). This process expanded the NLCD's 'cultivated crops' classes to included major grains, cover crops, and vegetable and fruits. The Agriculture Resource Management Survey chemical fertilizer application rate data were summarized by crop type and year for each state, encompassing the corn, soybean, spring wheat, and winter wheat crop types (ARMS, 2002-2005). The chemical fertilizer application rate data were then used to estimate annual application parameters for nitrogen, phosphate, potash, herbicide, pesticide, and total pesticide, all expressed on a mass-per-unit-crop-area basis for each state for each crop type. By linking crop types to nitrogen application rates, we can better estimate where applied fertilizer would likely be in excess of the amounts used by crops or where conservation practices may improve retention and uptake helping offset the impacts to water. To test the accuracy of our finer resolution nitrogen application data, we compare its ability to predict nitrogen concentrations in streams with the ability of the county sales data to do the same.

Reevaluating Suitability Estimates Based on Dynamics of Cropland Expansion in the Brazilian Amazon

NASA Technical Reports Server (NTRS)

Morton, Douglas C.; Noojipady, Praveen; Macedo, Marcia M.; Victoria, Daniel C.; Bolfe, Edson L.

2016-01-01

Agricultural suitability maps are a key input for land use zoning and projections of cropland expansion. Suitability assessments typically consider edaphic conditions, climate, crop characteristics, and sometimes incorporate accessibility to transportation and market infrastructure. However, correct weighting among these disparate factors is challenging, given rapid development of new crop varieties, irrigation, and road networks, as well as changing global demand for agricultural commodities. Here, we compared three independent assessments of cropland suitability to spatial and temporal dynamics of agricultural expansion in the Brazilian state of Mato Grosso during 2001 2012. We found that areas of recent cropland expansion identified using satellite data were generally designated as low to moderate suitability for rainfed crop production. Our analysis highlighted the abrupt nature of suitability boundaries, rather than smooth gradients of agricultural potential, with little additional cropland expansion beyond the extent of the flattest areas (0-2% slope). Satellite-based estimates of the interannual variability in the use of existing crop areas also provided an alternate means to assess suitability. On average, cropland areas in the Cerrado biome had higher utilization (84%) than croplands in the Amazon region of northern Mato Grosso (74%). Areas of more recent expansion had lower utilization than croplands established before 2002, providing empirical evidence for lower suitability or alternative management strategies (e.g., pasture soya rotations) for lands undergoing more recent land use transitions. This unplanted reserve constitutes a large area of potentially available cropland (PAC)without further expansion, within the management limits imposed for pest management and fallow cycles. Using two key constraints on future cropland expansion, slope and restrictions on further deforestation of Amazon or Cerrado vegetation, we found little available flat land for further legal expansion of crop production in Mato Grosso. Dynamics of cropland expansion from more than a decade of satellite observations indicated narrow ranges of suitability criteria, restricting PAC under current policy conditions, and emphasizing the advantages of field-scale information to assess suitability and utilization.

Retrieval of Winter Wheat Leaf Area Index from Chinese GF-1 Satellite Data Using the PROSAIL Model.

PubMed

Li, He; Liu, Gaohuan; Liu, Qingsheng; Chen, Zhongxin; Huang, Chong

2018-04-06

Leaf area index (LAI) is one of the key biophysical parameters in crop structure. The accurate quantitative estimation of crop LAI is essential to verify crop growth and health. The PROSAIL radiative transfer model (RTM) is one of the most established methods for estimating crop LAI. In this study, a look-up table (LUT) based on the PROSAIL RTM was first used to estimate winter wheat LAI from GF-1 data, which accounted for some available prior knowledge relating to the distribution of winter wheat characteristics. Next, the effects of 15 LAI-LUT strategies with reflectance bands and 10 LAI-LUT strategies with vegetation indexes on the accuracy of the winter wheat LAI retrieval with different phenological stages were evaluated against in situ LAI measurements. The results showed that the LUT strategies of LAI-GNDVI were optimal and had the highest accuracy with a root mean squared error (RMSE) value of 0.34, and a coefficient of determination (R²) of 0.61 during the elongation stages, and the LUT strategies of LAI-Green were optimal with a RMSE of 0.74, and R² of 0.20 during the grain-filling stages. The results demonstrated that the PROSAIL RTM had great potential in winter wheat LAI inversion with GF-1 satellite data and the performance could be improved by selecting the appropriate LUT inversion strategies in different growth periods.

Estimation of available water capacity components of two-layered soils using crop model inversion: Effect of crop type and water regime

NASA Astrophysics Data System (ADS)

Sreelash, K.; Buis, Samuel; Sekhar, M.; Ruiz, Laurent; Kumar Tomer, Sat; Guérif, Martine

2017-03-01

Characterization of the soil water reservoir is critical for understanding the interactions between crops and their environment and the impacts of land use and environmental changes on the hydrology of agricultural catchments especially in tropical context. Recent studies have shown that inversion of crop models is a powerful tool for retrieving information on root zone properties. Increasing availability of remotely sensed soil and vegetation observations makes it well suited for large scale applications. The potential of this methodology has however never been properly evaluated on extensive experimental datasets and previous studies suggested that the quality of estimation of soil hydraulic properties may vary depending on agro-environmental situations. The objective of this study was to evaluate this approach on an extensive field experiment. The dataset covered four crops (sunflower, sorghum, turmeric, maize) grown on different soils and several years in South India. The components of AWC (available water capacity) namely soil water content at field capacity and wilting point, and soil depth of two-layered soils were estimated by inversion of the crop model STICS with the GLUE (generalized likelihood uncertainty estimation) approach using observations of surface soil moisture (SSM; typically from 0 to 10 cm deep) and leaf area index (LAI), which are attainable from radar remote sensing in tropical regions with frequent cloudy conditions. The results showed that the quality of parameter estimation largely depends on the hydric regime and its interaction with crop type. A mean relative absolute error of 5% for field capacity of surface layer, 10% for field capacity of root zone, 15% for wilting point of surface layer and root zone, and 20% for soil depth can be obtained in favorable conditions. A few observations of SSM (during wet and dry soil moisture periods) and LAI (within water stress periods) were sufficient to significantly improve the estimation of AWC components. These results show the potential of crop model inversion for estimating the AWC components of two-layered soils and may guide the sampling of representative years and fields to use this technique for mapping soil properties that are relevant for distributed hydrological modelling.

Added-values of high spatiotemporal remote sensing data in crop yield estimation

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.

2017-12-01

Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing derived parameters have been used for estimating crop yield by using either empirical or crop growth models. The uses of remote sensing vegetation index (VI) in crop yield modeling have been typically evaluated at regional and country scales using coarse spatial resolution (a few hundred to kilo-meters) data or assessed over a small region at field level using moderate resolution spatial resolution data (10-100m). Both data sources have shown great potential in capturing spatial and temporal variability in crop yield. However, the added value of data with both high spatial and temporal resolution data has not been evaluated due to the lack of such data source with routine, global coverage. In recent years, more moderate resolution data have become freely available and data fusion approaches that combine data acquired from different spatial and temporal resolutions have been developed. These make the monitoring crop condition and estimating crop yield at field scale become possible. Here we investigate the added value of the high spatial and temporal VI for describing variability of crop yield. The explanatory ability of crop yield based on high spatial and temporal resolution remote sensing data was evaluated in a rain-fed agricultural area in the U.S. Corn Belt. Results show that the fused Landsat-MODIS (high spatial and temporal) VI explains yield variability better than single data source (Landsat or MODIS alone), with EVI2 performing slightly better than NDVI. The maximum VI describes yield variability better than cumulative VI. Even though VI is effective in explaining yield variability within season, the inter-annual variability is more complex and need additional information (e.g. weather, water use and management). Our findings augment the importance of high spatiotemporal remote sensing data and supports new moderate resolution satellite missions for agricultural applications.

Temperature Increase Reduces Global Yields of Major Crops in Four Independent Estimates

NASA Technical Reports Server (NTRS)

Zhao, Chuang; Liu, Bing; Piao, Shilong; Wang, Xuhui; Lobell, David B.; Huang, Yao; Huang, Mengtian; Yao, Yitong; Bassu, Simona; Ciais, Philippe;

Temperature increase reduces global yields of major crops in four independent estimates

PubMed Central

Zhao, Chuang; Piao, Shilong; Wang, Xuhui; Lobell, David B.; Huang, Yao; Huang, Mengtian; Yao, Yitong; Bassu, Simona; Ciais, Philippe; Durand, Jean-Louis; Elliott, Joshua; Ewert, Frank; Janssens, Ivan A.; Li, Tao; Lin, Erda; Liu, Qiang; Martre, Pierre; Peng, Shushi; Wallach, Daniel; Wang, Tao; Wu, Donghai; Liu, Zhuo; Zhu, Yan; Zhu, Zaichun; Asseng, Senthold

2017-01-01

Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population. PMID:28811375

Temperature increase reduces global yields of major crops in four independent estimates.

PubMed

Zhao, Chuang; Liu, Bing; Piao, Shilong; Wang, Xuhui; Lobell, David B; Huang, Yao; Huang, Mengtian; Yao, Yitong; Bassu, Simona; Ciais, Philippe; Durand, Jean-Louis; Elliott, Joshua; Ewert, Frank; Janssens, Ivan A; Li, Tao; Lin, Erda; Liu, Qiang; Martre, Pierre; Müller, Christoph; Peng, Shushi; Peñuelas, Josep; Ruane, Alex C; Wallach, Daniel; Wang, Tao; Wu, Donghai; Liu, Zhuo; Zhu, Yan; Zhu, Zaichun; Asseng, Senthold

2017-08-29

Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO 2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.

Spectrally-Based Assessment of Crop Seasonal Performance and Yield

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana; Borisova, Denitsa; Georgiev, Georgy

The rapid advances of space technologies concern almost all scientific areas from aeronautics to medicine, and a wide range of application fields from communications to crop yield predictions. Agricultural monitoring is among the priorities of remote sensing observations for getting timely information on crop development. Monitoring agricultural fields during the growing season plays an important role in crop health assessment and stress detection provided that reliable data is obtained. Successfully spreading is the implementation of hyperspectral data to precision farming associated with plant growth and phenology monitoring, physiological state assessment, and yield prediction. In this paper, we investigated various spectral-biophysical relationships derived from in-situ reflectance measurements. The performance of spectral data for the assessment of agricultural crops condition and yield prediction was examined. The approach comprisesd development of regression models between plant spectral and state-indicative variables such as biomass, vegetation cover fraction, leaf area index, etc., and development of yield forecasting models from single-date (growth stage) and multitemporal (seasonal) reflectance data. Verification of spectral predictions was performed through comparison with estimations from biophysical relationships between crop growth variables. The study was carried out for spring barley and winter wheat. Visible and near-infrared reflectance data was acquired through the whole growing season accompanied by detailed datasets on plant phenology and canopy structural and biochemical attributes. Empirical relationships were derived relating crop agronomic variables and yield to various spectral predictors. The study findings were tested using airborne remote sensing inputs. A good correspondence was found between predicted and actual (ground-truth) estimates

A Remote Sensing-Derived Corn Yield Assessment Model

NASA Astrophysics Data System (ADS)

Shrestha, Ranjay Man

Agricultural studies and food security have become critical research topics due to continuous growth in human population and simultaneous shrinkage in agricultural land. In spite of modern technological advancements to improve agricultural productivity, more studies on crop yield assessments and food productivities are still necessary to fulfill the constantly increasing food demands. Besides human activities, natural disasters such as flood and drought, along with rapid climate changes, also inflect an adverse effect on food productivities. Understanding the impact of these disasters on crop yield and making early impact estimations could help planning for any national or international food crisis. Similarly, the United States Department of Agriculture (USDA) Risk Management Agency (RMA) insurance management utilizes appropriately estimated crop yield and damage assessment information to sustain farmers' practice through timely and proper compensations. Through County Agricultural Production Survey (CAPS), the USDA National Agricultural Statistical Service (NASS) uses traditional methods of field interviews and farmer-reported survey data to perform annual crop condition monitoring and production estimations at the regional and state levels. As these manual approaches of yield estimations are highly inefficient and produce very limited samples to represent the entire area, NASS requires supplemental spatial data that provides continuous and timely information on crop production and annual yield. Compared to traditional methods, remote sensing data and products offer wider spatial extent, more accurate location information, higher temporal resolution and data distribution, and lower data cost--thus providing a complementary option for estimation of crop yield information. Remote sensing derived vegetation indices such as Normalized Difference Vegetation Index (NDVI) provide measurable statistics of potential crop growth based on the spectral reflectance and could be further associated with the actual yield. Utilizing satellite remote sensing products, such as daily NDVI derived from Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m pixel size, the crop yield estimation can be performed at a very fine spatial resolution. Therefore, this study examined the potential of these daily NDVI products within agricultural studies and crop yield assessments. In this study, a regression-based approach was proposed to estimate the annual corn yield through changes in MODIS daily NDVI time series. The relationship between daily NDVI and corn yield was well defined and established, and as changes in corn phenology and yield were directly reflected by the changes in NDVI within the growing season, these two entities were combined to develop a relational model. The model was trained using 15 years (2000-2014) of historical NDVI and county-level corn yield data for four major corn producing states: Kansas, Nebraska, Iowa, and Indiana, representing four climatic regions as South, West North Central, East North Central, and Central, respectively, within the U.S. Corn Belt area. The model's goodness of fit was well defined with a high coefficient of determination (R2>0.81). Similarly, using 2015 yield data for validation, 92% of average accuracy signified the performance of the model in estimating corn yield at county level. Besides providing the county-level corn yield estimations, the derived model was also accurate enough to estimate the yield at finer spatial resolution (field level). The model's assessment accuracy was evaluated using the randomly selected field level corn yield within the study area for 2014, 2015, and 2016. A total of over 120 plot level corn yield were used for validation, and the overall average accuracy was 87%, which statistically justified the model's capability to estimate plot-level corn yield. Additionally, the proposed model was applied to the impact estimation by examining the changes in corn yield due to flood events during the growing season. Using a 2011 Missouri River flood event as a case study, field-level flood impact map on corn yield throughout the flooded regions was produced and an overall agreement of over 82.2% was achieved when compared with the reference impact map. The future research direction of this dissertation research would be to examine other major crops outside the Corn Belt region of the U.S.

The limit of irrigation adaption due to the inter-crop conflict of water use under changing climate and landuse

NASA Astrophysics Data System (ADS)

Okada, M.; Iizumi, T.; Sakamoto, T.; Kotoku, M.; Sakurai, G.; Nishimori, M.

2017-12-01

Replacing rainfed cropping system by irrigated one is assumed to be an effective measure for climate change adaptation in agriculture. However, in many agricultural impact assessments, future irrigation scenarios are externally given and do not consider variations in the availability of irrigation water under changing climate and land use. Therefore, we assess the potential effects of adaption measure expanding irrigated area under climate change by using a large-scale crop-river coupled model, CROVER [Okada et al. 2015, JAMES]. The CROVER model simulates the large-scale terrestrial hydrological cycle and crop growth depending on climate, soil properties, landuse, crop cultivation management, socio-economic water demand, and reservoir operation management. The bias-corrected GCMs outputs under the RCP 8.5 scenario were used. The future expansion of irrigation area was estimated by using the extrapolation method based on the historical change in irrigated and rainfed areas. As the results, the irrigation adaptation has only a limited effect on the rice production in East Asia due to the conflict of water use for irrigation with the other crops, whose farmlands require unsustainable water extraction with the excessively expanding irrigated area. In contrast, the irrigation adaptation benefits maize production in Europe due to the little conflict of water use for irrigation. Our findings suggest the importance of simulating the river water availability and crop production in a single model for the more realistic assessment in the irrigation adaptation potential effects of crop production under changing climate and land use.

Analysis of scanner data for crop inventories

NASA Technical Reports Server (NTRS)

Horvath, R. (Principal Investigator); Cicone, R. C.; Kauth, R. J.; Malila, W. A.

1981-01-01

Progress and technical issues are reported in the development of corn/soybeans area estimation procedures for use on data from South America, with particular emphasis on Argentina. Aspects related to the supporting research section of the AgRISTARS Project discussed include: (1) multisegment corn/soybean estimation; (2) through the season separability of corn and soybeans within the U.S. corn belt; (3) TTS estimation; (4) insights derived from the baseline corn and soybean procedure; (5) small fields research; and (6) simulating the spectral appearance of wheat as a function of its growth and development. To assist the foreign commodity production forecasting, the performance of the baseline corn/soybean procedure was analyzed and the procedure modified. Fundamental limitations were found in the existing guidelines for discriminating these two crops. The temporal and spectral characteristics of corn and soybeans must be determined because other crops grow with them in Argentina. The state of software technology is assessed and the use of profile techniques for estimation is considered.

Evaluation of hydrologic conditions and nitrate concentrations in the Rio Nigua de Salinas alluvial fan aquifer, Salinas, Puerto Rico, 2002-03

USGS Publications Warehouse

Rodriguez, Jose M.

2006-01-01

A ground-water quality study to define the potential sources and concentration of nitrate in the Rio Nigua de Salinas alluvial fan aquifer was conducted between January 2002 and March 2003. The study area covers about 3,600 hectares of the coastal plain within the municipality of Salinas in southern Puerto Rico, extending from the foothills to the Caribbean Sea. Agriculture is the principal land use and includes cultivation of diverse crops, turf grass, bioengineered crops for seed production, and commercial poultry farms. Ground-water withdrawal in the alluvial fan was estimated to be about 43,500 cubic meters per day, of which 49 percent was withdrawn for agriculture, 42 percent for public supply, and 9 percent for industrial use. Ground-water flow in the study area was primarily to the south and toward a cone of depression within the south-central part of the alluvial fan. The presence of that cone of depression and a smaller one located in the northeastern quadrant of the study area may contribute to the increase in nitrate concentration within a total area of about 545 hectares by 'recycling' ground water used for irrigation of cultivated lands. In an area that covers about 405 hectares near the center of the Salinas alluvial fan, nitrate concentrations increased from 0.9 to 6.7 milligrams per liter as nitrogen in 1986 to 8 to 12 milligrams per liter as nitrogen in 2002. Principal sources of nitrate in the study area are fertilizers (used in the cultivated farmlands) and poultry farm wastes. The highest nitrogen concentrations were found at poultry farms in the foothills area. In the area of disposed poultry farm wastes, nitrate concentrations in ground water ranged from 25 to 77 milligrams per liter as nitrogen. Analyses for the stable isotope ratios of nitrogen-15/nitrogen-14 in nitrate were used to distinguish the source of nitrate in the coastal plain alluvial fan aquifer. Potential nitrate loads from areas under cultivation were estimated for the principal crops in the area. The load estimates ranged from 18 kilograms per hectare per year as nitrogen for sorghum crops to 430 kilograms per hectare per year as nitrogen for turf-grass farms. Potential nitrate load from poultry farm wastes and from communities with septic tanks were estimated at about 580 and 47 kilograms per hectare per year as nitrogen, respectively. Results obtained from the analyses of the stable isotope ratios of nitrogen-15/nitrogen-14 in nitrate samples indicated that the high nitrate concentrations are from poultry wastes near the foothills, whereas artificial fertilizers were estimated to contribute between 39 to 97 percent of the total nitrate in the central part of the alluvial fan.

Using satellite vegetation and compound topographic indices to map highly erodible cropland buffers for cellulosic biofuel crop developments in eastern Nebraska, USA

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.

2015-01-01

Cultivating annual row crops in high topographic relief waterway buffers has negative environmental effects and can be environmentally unsustainable. Growing perennial grasses such as switchgrass (Panicum virgatum L.) for biomass (e.g., cellulosic biofuel feedstocks) instead of annual row crops in these high relief waterway buffers can improve local environmental conditions (e.g., reduce soil erosion and improve water quality through lower use of fertilizers and pesticides) and ecosystem services (e.g., minimize drought and flood impacts on production; improve wildlife habitat, plant vigor, and nitrogen retention due to post-senescence harvest for cellulosic biofuels; and serve as carbon sinks). The main objectives of this study are to: (1) identify cropland areas with high topographic relief (high runoff potentials) and high switchgrass productivity potential in eastern Nebraska that may be suitable for growing switchgrass, and (2) estimate the total switchgrass production gain from the potential biofuel areas. Results indicate that about 140,000 hectares of waterway buffers in eastern Nebraska are suitable for switchgrass development and the total annual estimated switchgrass biomass production for these suitable areas is approximately 1.2 million metric tons. The resulting map delineates high topographic relief croplands and provides useful information to land managers and biofuel plant investors to make optimal land use decisions regarding biofuel crop development and ecosystem service optimization in eastern Nebraska.

Allometric method to estimate leaf area index for row crops

USDA-ARS?s Scientific Manuscript database

Leaf area index (LAI) is critical for predicting plant metabolism, biomass production, evapotranspiration, and greenhouse gas sequestration, but direct LAI measurements are difficult and labor intensive. Several methods are available to measure LAI indirectly or calculate LAI using allometric method...

Sampling Simulations for Assessing the Accuracy of U.S. Agricultural Crop Mapping from Remotely Sensed Imagery

NASA Astrophysics Data System (ADS)

Dwyer, Linnea; Yadav, Kamini; Congalton, Russell G.

2017-04-01

Providing adequate food and water for a growing, global population continues to be a major challenge. Mapping and monitoring crops are useful tools for estimating the extent of crop productivity. GFSAD30 (Global Food Security Analysis Data at 30m) is a program, funded by NASA, that is producing global cropland maps by using field measurements and remote sensing images. This program studies 8 major crop types, and includes information on cropland area/extent, if crops are irrigated or rainfed, and the cropping intensities. Using results from the US and the extensive reference data available, CDL (USDA Crop Data Layer), we will experiment with various sampling simulations to determine optimal sampling for thematic map accuracy assessment. These simulations will include varying the sampling unit, the sampling strategy, and the sample number. Results of these simulations will allow us to recommend assessment approaches to handle different cropping scenarios.

Assessment of Crop Damage by Protected Wild Mammalian Herbivores on the Western Boundary of Tadoba-Andhari Tiger Reserve (TATR), Central India

PubMed Central

Bayani, Abhijeet; Tiwade, Dilip; Dongre, Ashok; Dongre, Aravind P.; Phatak, Rasika; Watve, Milind

2016-01-01

Crop raiding by wild herbivores close to an area of protected wildlife is a serious problem that can potentially undermine conservation efforts. Since there is orders of magnitude difference between farmers’ perception of damage and the compensation given by the government, an objective and realistic estimate of damage was found essential. We employed four different approaches to estimate the extent of and patterns in crop damage by wild herbivores along the western boundary of Tadoba-Andhari Tiger Reserve in the state of Maharashtra, central India. These approaches highlight different aspects of the problem but converge on an estimated damage of over 50% for the fields adjacent to the forest, gradually reducing in intensity with distance. We found that the visual damage assessment method currently employed by the government for paying compensation to farmers was uncorrelated to and grossly underestimated actual damage. The findings necessitate a radical rethinking of policies to assess, mitigate as well as compensate for crop damage caused by protected wildlife species. PMID:27093293

The 2014 National Emission Inventory for Rangeland Fires ...

EPA Pesticide Factsheets

Biomass burning has been identified as an important contributor to the degradation of air quality because of its impact on ozone and particulate matter. One component of the biomass burning inventory, crop residue burning, has been poorly characterized in the National Emissions Inventory. In the 2011 NEI, Wildland fires, prescribed fires, and crop residue burning collectively were the largest source of PM2.5 This paper summarizes our 2014 NEI method to estimate crop residue burning emissions and grass/pasture burning emissions using remote sensing data and field information and literature-based, crop-specific emission factors. We will focus on both the post-harvest and pre-harvest burning that takes place with bluegrass, corn, cotton, rice, soybeans, sugarcane and wheat. Estimates for 2014 indicate that over the continental United States (CONUS), crop residue burning including all areas identified as Pasture/Grass, Grassland Herbaceous, and Pasture/Hay produced 64,994 short tons of PM2.5. This estimate compares with the 2011 NEI and 2008 NEI as follows: 2008: 49,653 short tons and 2011: 141,184 short tons. Note that in the previous two NEI’s rangeland burning was not well-defined and so the comparison is not exact. In addition, the entire database used to estimate this sector of emissions is available on EPA’s Clearinghouse for Inventories and Emission Factors (CHIEF http://www3.epa.gov/ttn/chief/index.html The National Emissions Inventory is developed on

Taxonomic classification of world map units in crop producing areas of Argentina and Brazil with representative US soil series and major land resource areas in which they occur

NASA Technical Reports Server (NTRS)

Huckle, H. F. (Principal Investigator)

1980-01-01

The most probable current U.S. taxonomic classification of the soils estimated to dominate world soil map units (WSM)) in selected crop producing states of Argentina and Brazil are presented. Representative U.S. soil series the units are given. The map units occurring in each state are listed with areal extent and major U.S. land resource areas in which similar soils most probably occur. Soil series sampled in LARS Technical Report 111579 and major land resource areas in which they occur with corresponding similar WSM units at the taxonomic subgroup levels are given.

Forage-based dairying in a water-limited future: use of models to investigate farming system adaptation in southern Australia.

PubMed

Chapman, D F; Dassanayake, K; Hill, J O; Cullen, B R; Lane, N

2012-07-01

The irrigated dairy industry in southern Australia has experienced significant restrictions in irrigation water allocations since 2005, consistent with climate change impact predictions for the region. Simulation models of pasture growth (DairyMod), crop yield (Agricultural Production Systems Simulator, APSIM), and dairy system management and production (UDDER) were used in combination to investigate a range of forage options that may be capable of sustaining dairy business profitability under restricted water-allocation scenarios in northern Victoria, Australia. A total of 23 scenarios were simulated and compared with a base farm system (100% of historical water allocations, grazed perennial ryegrass pasture with supplements; estimated operating surplus $A2,615/ha at a milk price of $A4.14/kg of milk solids). Nine simulations explored the response of the base farm to changes in stocking rate or the implementation of a double cropping rotation on 30% of farm area, or both. Five simulations explored the extreme scenario of dairying without any irrigation water. Two general responses to water restrictions were investigated in a further 9 simulations. Annual ryegrass grazed pasture, complemented by a double cropping rotation (maize grown in summer for silage, followed by either brassica forage crop and annual ryegrass for silage in winter and spring) on 30% of farm area, led to an estimated operating surplus of $A1746/ha at the same stocking rate as the base farm when calving was moved to autumn (instead of late winter, as in the base system). Estimated total irrigation water use was 2.7ML/ha compared with 5.4ML/ha for the base system. Summer-dormant perennial grass plus double cropping (30% of farm area) lifted operating surplus by a further $A100/ha if associated with autumn calving (estimated total irrigation water use 3.1ML/ha). Large shifts in the forage base of dairy farms could sustain profitability in the face of lower, and fluctuating, water allocations. However, changes in other strategic management policies, notably calving date and stocking rate, would be required, and these systems would be more complex to manage. The adaptation scenarios that resulted in the highest estimated operating surplus were those where at least 10 t of pasture or crop DM was grazed directly by cows per hectare per year, resulting in grazed pasture intake of at least 2 t of DM/cow, and at least 60% of all homegrown feed that was consumed was grazed directly. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Quantifying the contribution of groundwater on water consumption in arid crop land with shallow groundwater

NASA Astrophysics Data System (ADS)

Huo, Z.; Liu, Z.; Wang, X.; Qu, Z.

2016-12-01

Groundwater from the shallow aquifers can supply substantial water for evapotranspiration of crops. However, it is difficult to quantify to the contribution of groundwater on crop's water consumption. In present study, regional scale evapotranspiration and the groundwater contribution to evapotranspiration were estimated by the soil water balance equation in Hetao irrigation distric with shallow aquifers, China. Estimates used an 8-year (2006-2013) hydrological dataset including soil moisture, the depth to water table, irrigation amounts, rainfall data, and drainage water flow. The 8-year mean evapotranspiration was estimated to be 664 mm. The mean groundwater supported evapotranspiration (ETg) was estimated to be 228 mm, with variation from 145 mm to 412 mm during the crop growth period. Analysis of the positive correlation between evapotranspiration and the sum of irrigation and rainfall, and the analysis of the negative correlation between ETg/ET and the sum of irrigation and rainfall, reflect the need of groundwater to meet the evapotranspiration demand. Approximately 20% to 40% of the evapotranspiration is from the shallow aquifers in the study area. Furthermore, a new method estimating daily ETg during the crop growing season was developed. The effects of crop growth stage, climate condition, groundwater depth and soil moisture are considered in the model. The method was tested with controlled lysimeter experiments of winter wheat including five controlled water table depths and four soil profiles of different textures. The simulated ETg is a good agreement with the measured data for four soil profiles and different depths to groundwater table. These results could be useful for the government to understand the significant role of the groundwater and make reasonable water use policy in the semiarid agricultural regions.

A study of the utilization of ERTS-1 data from the Wabash River Basin

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator)

1974-01-01

The author has identified the following significant results. The identification and area estimation of crops experiment tested the usefulness of ERTS data for crop survey and produced results indicating that crop statistics could be obtained from ERTS imagery. Soil association mapping results showed that strong relationships exist between ERTS data derived maps and conventional soil maps. Urban land use analysis experiment results indicate potential for accurate gross land use mapping. Water resources mapping demonstrated the feasibility of mapping water bodies using ERTS imagery.

GTAP model and database modification to better handle cropping changes on the intensive margin.

DOT National Transportation Integrated Search

2017-07-25

Previously, induced land use change estimates considered only the extensive margin; that is, adding to harvested area by converting forest or pasture to cropland. However, recent data suggests that some of the increase in global harvested area comes ...

A data-oriented semi-process model for evaluating the yields of major crops at global scale (PRYSBI-2)

NASA Astrophysics Data System (ADS)

Sakurai, G.; Iizumi, T.; Yokozawa, M.

2013-12-01

Demand for major cereal crops will double by 2050 compared to the amount in 2005 due to the population growth, dietary change, and increase in biofuel use. This requires substantial efforts to increase crop yields under changing climate, water resources, and land use. In order to explore possible paths to meet the supply target, global crop modeling is a useful approach. To that end, we developed a process-based large-area crop model (called PRYSBIE-2) for major crops, including soybean. This model consisted of the enzyme kinetics model for photosynthetic carbon assimilation and soil water balance model from SWAT. The parameter values on water stress, nitrogen stress were calibrated over global croplands from one grid cell to another (1.125° in latitude and longitude) using Markov Chain Monte Carlo (MCMC) methods. The historical yield data collected from major crop-producing countries on a state, county, or prefecture scale were used as the calibration data. Then we obtained the model parameter sets that can give high correlation coefficients between the historical and estimated yield time series for the period 1980-2006. We analyzed the impacts on soybean yields in the three top soybean-producing countries (the USA, China, and Brazil) associated with the changes in climate and CO2 during the period 1980-2006, using the model. We found that, given the simulated yields and reported harvested areas, the estimated average net benefit from the CO2 fertilization effect (with one standard deviation) in the USA, Brazil, and China in the years was 42.70×32.52 Mt, 35.30×28.55 Mt, and 12.52×15.11 Mt, respectively. Results suggest that the CO2-induced increases in soybean yields in the USA and China likely offset a part of the negative impacts on yields due to the historical temperature rise. In contrast, the net effect of the past change in climate and CO2 in Brazil appeared to be positive. This study demonstrates a quantitative estimation of the impacts of the changes in climate and CO2 during the past few decades using a new global crop model.

Climate change impact and potential adaptation strategies under alternate realizations of climate scenarios for three major crops in Europe

NASA Astrophysics Data System (ADS)

Donatelli, Marcello; Srivastava, Amit Kumar; Duveiller, Gregory; Niemeyer, Stefan; Fumagalli, Davide

2015-07-01

This study presents an estimate of the effects of climate variables and CO2 on three major crops, namely wheat, rapeseed and sunflower, in EU27 Member States. We also investigated some technical adaptation options which could offset climate change impacts. The time-slices 2000, 2020 and 2030 were chosen to represent the baseline and future climate, respectively. Furthermore, two realizations within the A1B emission scenario proposed by the Special Report on Emissions Scenarios (SRES), from the ECHAM5 and HadCM3 GCM, were selected. A time series of 30 years for each GCM and time slice were used as input weather data for simulation. The time series were generated with a stochastic weather generator trained over GCM-RCM time series (downscaled simulations from the ENSEMBLES project which were statistically bias-corrected prior to the use of the weather generator). GCM-RCM simulations differed primarily for rainfall patterns across Europe, whereas the temperature increase was similar in the time horizons considered. Simulations based on the model CropSyst v. 3 were used to estimate crop responses; CropSyst was re-implemented in the modelling framework BioMA. The results presented in this paper refer to abstraction of crop growth with respect to its production system, and consider growth as limited by weather and soil water. How crop growth responds to CO2 concentrations; pests, diseases, and nutrients limitations were not accounted for in simulations. The results show primarily that different realization of the emission scenario lead to noticeably different crop performance projections in the same time slice. Simple adaptation techniques such as changing sowing dates and the use of different varieties, the latter in terms of duration of the crop cycle, may be effective in alleviating the adverse effects of climate change in most areas, although response to best adaptation (within the techniques tested) differed across crops. Although a negative impact of climate scenarios is evident in most areas, the combination of rainfall patterns and increased photosynthesis efficiency due to CO2 concentrations showed possible improvements of production patterns in some areas, including Southern Europe. The uncertainty deriving from GCM realizations with respect to rainfall suggests that articulated and detailed testing of adaptation techniques would be redundant. Using ensemble simulations would allow for the identification of areas where adaptation, like those simulated, may be run autonomously by farmers, hence not requiring specific intervention in terms of support policies.

Mapping Croplands in Morocco and Tunisia using MAGE

NASA Astrophysics Data System (ADS)

McGaughey, K.; Purcell, B.; Tetrault, R. L.; Wasko, C.

2017-12-01

Morocco and Tunisia are both net-wheat importing countries, which experience fluctuations in production due to drought. Knowledge of food supply is critical for the local governments' understanding of food security and to re-assure the population regarding food availability. Tunisia was the epicenter for the event known as the "Arab Spring." Although the Arab Spring had several societal components, chronic uncertainty in the governments' reliability raised concerns about food supply and food availability despite the government's policy responses. Due to its importance for geopolitical as well as market opportunity reasons, in March and April of 2017, analysts from the USDA Foreign Agricultural Service (FAS) traveled to Morocco and Tunisia to conduct crop assessment. Fieldwork data collection is necessary for us as crop analysts to use our convergence of evidence approach, which includes ground information, satellite imagery, meteorological information and reports from our offices abroad. During this trip, analysts used the new mobile application from the National Geospatial Intelligence Agency (NGA) called MAGE to collect fieldwork data for crop classification. Using the several thousand training data collected through the mobile application and by leveraging satellite data within Google Earth Engine, an in-season crop mask was created. The final product was delivered to the team just a few days after returning to USDA Washington and was used in the first wheat production estimate of the season released May 10, 2017. The final product helps analysts determine an experimental in-season area estimation and to focus our other remote sensing tools and products on our areas of interest. The US Department of Agriculture's International Production Assessment Division is responsible for publishing monthly production, area and yield estimates for 17 commodities in over 150 countries.

Low-cost Assessment for Early Vigor and Canopy Cover Estimation in Durum Wheat Using RGB Images.

NASA Astrophysics Data System (ADS)

Fernandez-Gallego, J. A.; Kefauver, S. C.; Aparicio Gutiérrez, N.; Nieto-Taladriz, M. T.; Araus, J. L.

2017-12-01

Early vigor and canopy cover is an important agronomical component for determining grain yield in wheat. Estimates of the canopy cover area at early stages of the crop cycle may contribute to efficiency of crop management practices and breeding programs. Canopy-image segmentation is complicated in field conditions by numerous factors, including soil, shadows and unexpected objects, such as rocks, weeds, plant remains, or even part of the photographer's boots (many times it appears in the scene); and the algorithms must be robust to accommodate these conditions. Field trials were carried out in two sites (Aranjuez and Valladolid, Spain) during the 2016/2017 crop season. A set of 24 varieties of durum wheat in two growing conditions (rainfed and support irrigation) per site were used to create the image database. This work uses zenithal RGB images taken from above the crop in natural light conditions. The images were taken with Canon IXUS 320HS camera in Aranjuez, holding the camera by hand, and with a Nikon D300 camera in Valladolid, using a monopod. The algorithm for early vigor and canopy cover area estimation uses three main steps: (i) Image decorrelation (ii) Colour space transformation and (iii) Canopy cover segmentation using an automatic threshold based on the image histogram. The first step was chosen to enhance the visual interpretation and separate the pixel colors into the scene; the colour space transformation contributes to further separate the colours. Finally an automatic threshold using a minimum method allows for correct segmentation and quantification of the canopy pixels. The percent of area covered by the canopy was calculated using a simple algorithm for counting pixels in the final binary segmented image. The comparative results demonstrate the algorithm's effectiveness through significant correlations between early vigor and canopy cover estimation compared to NDVI (Normalized difference vegetation index) and grain yield.

Quantifying Crop Specific Blue and Green Water Footprints and the Spatial Allocation of Virtual Water in China

NASA Astrophysics Data System (ADS)

Pan, J.; Smith, T.; McLaughlin, D.

2016-12-01

China, which had a population of 1.38 billion in 2013, is expected to peak at about 1.45 billion around 2030, with per capita food demand likely to increase significantly. The population growth and diet change make prospects of future available water and food worrisome for China. Quantitative estimates of crop specific blue and green water footprints provide useful insight about the roles of different water sources and give guidance for agricultural and water resource planning. This study uses reanalysis methods to merge diverse datasets, including information on water fluxes and land use, to estimate crop-specific green and blue water consumption at 0.5 degree spatial resolution. The estimates incorporate, through constraints in the reanalysis procedure, important physical connections between the water and land resources that support agriculture. These connections are important since land use affects evapotranspiration and runoff while water availability and crop area affect crop production and virtual water content. The results show that green water accounts for 86% and blue water accounts for 14% of the total national agricultural water footprint, respectively. The water footprints of cereals (wheat, maize and rice) and soybeans account for 51% of the total agricultural water footprint. Cereals and soybeans together account for 85% of the total blue water footprint.

Crop damage and livestock depredation by wildlife: a case study from Nanda Devi Biosphere Reserve, India.

PubMed

Rao, K S; Maikhuri, R K; Nautiyal, S; Saxena, K G

2002-11-01

The success of conserving biological resources in any Biosphere Reserve or protected area depends on the extent of support and positive attitudes and perceptions of local people have towards such establishments. Ignoring the dependence of the local people for their subsistence needs on resources of such areas leads to conflicts between protected area managers and the local inhabitants. Crop yield losses and livestock depredation were serious problems observed in most buffer zone villages of Nanda Devi Biosphere Reserve. In the present study 10 villages situated in the buffer zone of Nanada Devi Biosphere Reserve (1612 km2 area) in Chamoli district of Uttaranchal, India were studied during 1996-97 using a questionnaire survey of each household (419 = households; 2253 = total population in 1991; 273 ha = cultivated area). Estimates of crop yield losses were made using paired plots technique in four representative villages for each crop species. The magnitude of crop yield losses varied significantly with the distance of agricultural field from forest boundary. The total crop yield losses were high for wheat and potato in all the villages. The spatial distribution of total crop yield losses in any village indicated that they were highest in the area near to forest and least in the area near to village for all crops. Losses from areas near to forest contributed to more than 50% of total losses for each crop in all villages. However, in Lata, Peng and Tolma villages, the losses are high for kidney bean and chemmi (local variety of kidney bean) which varied between 18.5% to 30% of total losses in those villages. Potato alone represents 43.6% of total crop yield loss due to wildlife in Dronagiri village in monetary terms. Among the crops, the monetary value of yield losses are least for amaranth and highest for kidney bean. The projected total value of crop yield losses due to wildlife damage for buffer zone villages located in Garhwal Himalaya is about Rs. 538,620 (US$ 15,389). Besides food grains, horticultural crops i.e. apple, also suffered maximum damage. Major wildlife agents responsible for crop damage were wild boar, bear, porcupine, monkey, musk deer and partridge (chokor). Monkey and wild boar alone accounted for about 50% to 60% of total crop damage in the study villages. Goat and sheep are the major livestock killed by leopard. The total value of livestock losses at prevailing market rates is about Rs. 1,024,520 (US$ 29,272) in the study villages. Due to existing conservation policies and laxity in implementation of preventive measures, the problems for local inhabitants are increasing. Potential solutions discussed emphasize the need to undertake suitable and appropriate protective measures to minimize the crop losses. Change in cropping and crop composition, particularly cultivation of medicinal plants (high value low volume crops), were also suggested. Besides, fair and quick disbursement of compensation for crop loss and livestock killing need to be adopted. Local people of the buffer zone area already have a negative attitude towards park/reserve establishment due to socio-political changes inducing major economic losses and this attitude may lead to clashes and confrontations if proper ameliorative measures are not taken immediately.

Retrieval of Winter Wheat Leaf Area Index from Chinese GF-1 Satellite Data Using the PROSAIL Model

PubMed Central

Li, He; Liu, Gaohuan; Liu, Qingsheng; Chen, Zhongxin; Huang, Chong

2018-01-01

Leaf area index (LAI) is one of the key biophysical parameters in crop structure. The accurate quantitative estimation of crop LAI is essential to verify crop growth and health. The PROSAIL radiative transfer model (RTM) is one of the most established methods for estimating crop LAI. In this study, a look-up table (LUT) based on the PROSAIL RTM was first used to estimate winter wheat LAI from GF-1 data, which accounted for some available prior knowledge relating to the distribution of winter wheat characteristics. Next, the effects of 15 LAI-LUT strategies with reflectance bands and 10 LAI-LUT strategies with vegetation indexes on the accuracy of the winter wheat LAI retrieval with different phenological stages were evaluated against in situ LAI measurements. The results showed that the LUT strategies of LAI-GNDVI were optimal and had the highest accuracy with a root mean squared error (RMSE) value of 0.34, and a coefficient of determination (R2) of 0.61 during the elongation stages, and the LUT strategies of LAI-Green were optimal with a RMSE of 0.74, and R2 of 0.20 during the grain-filling stages. The results demonstrated that the PROSAIL RTM had great potential in winter wheat LAI inversion with GF-1 satellite data and the performance could be improved by selecting the appropriate LUT inversion strategies in different growth periods. PMID:29642395

Projected Irrigation Requirement Under Climate Change in Korean Peninsula by Apply Global Hydrologic Model to Local Scale.

NASA Astrophysics Data System (ADS)

Yang, B.; Lee, D. K.

2016-12-01

Understanding spatial distribution of irrigation requirement is critically important for agricultural water management. However, many studies considering future agricultural water management in Korea assessed irrigation requirement on watershed or administrative district scale, but have not accounted the spatial distribution. Lumped hydrologic model has typically used in Korea for simulating watershed scale irrigation requirement, while distribution hydrologic model can simulate the spatial distribution grid by grid. To overcome this shortcoming, here we applied a grid base global hydrologic model (H08) into local scale to estimate spatial distribution under future irrigation requirement of Korean Peninsula. Korea is one of the world's most densely populated countries, with also high produce and demand of rice which requires higher soil moisture than other crops. Although, most of the precipitation concentrate in particular season and disagree with crop growth season. This precipitation character makes management of agricultural water which is approximately 60% of total water usage critical issue in Korea. Furthermore, under future climate change, the precipitation predicted to be more concentrated and necessary need change of future water management plan. In order to apply global hydrological model into local scale, we selected appropriate major crops under social and local climate condition in Korea to estimate cropping area and yield, and revised the cropping area map more accurately. As a result, future irrigation requirement estimation varies under each projection, however, slightly decreased in most case. The simulation reveals, evapotranspiration increase slightly while effective precipitation also increase to balance the irrigation requirement. This finding suggest practical guideline to decision makers for further agricultural water management plan including future development of water supply plan to resolve water scarcity.

Satellite-based monitoring of cotton evapotranspiration

NASA Astrophysics Data System (ADS)

Dalezios, Nicolas; Dercas, Nicholas; Tarquis, Ana Maria

2016-04-01

Water for agricultural use represents the largest share among all water uses. Vulnerability in agriculture is influenced, among others, by extended periods of water shortage in regions exposed to droughts. Advanced technological approaches and methodologies, including remote sensing, are increasingly incorporated for the assessment of irrigation water requirements. In this paper, remote sensing techniques are integrated for the estimation and monitoring of crop evapotranspiration ETc. The study area is Thessaly central Greece, which is a drought-prone agricultural region. Cotton fields in a small agricultural sub-catchment in Thessaly are used as an experimental site. Daily meteorological data and weekly field data are recorded throughout seven (2004-2010) growing seasons for the computation of reference evapotranspiration ETo, crop coefficient Kc and cotton crop ETc based on conventional data. Satellite data (Landsat TM) for the corresponding period are processed to estimate cotton crop coefficient Kc and cotton crop ETc and delineate its spatiotemporal variability. The methodology is applied for monitoring Kc and ETc during the growing season in the selected sub-catchment. Several error statistics are used showing very good agreement with ground-truth observations.

Elevation of a cane-growing area of the state of Sao Paulo using LANDSAT data

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Mendonca, F. J.; Lee, D. C. L.; Tardin, A. T.; Shimabukuro, Y. E.; Chen, S. C.; Lucht, L. A. M.; Moreira, M. A.; Delima, A. M.; Maia, F. C. S.

1981-01-01

Images at a scale of 1:250.000 were visually interpreted for identification and area estimates of sugar cane plantations in Sao Paulo. The basic criteria for crop identification were the spectral characteristics of channels 5 and 7 and their temporal variations observed from different LANDSAT passes. Using this technique, it was possible to map the sugar cane areas as well as the sugar cane already harvested. An area of 801,950 hectares was estimated within the study area. The confidence interval of correct classification ranged from 87.11% to 94.71%.

The maximum economic depth of groundwater abstraction for irrigation

NASA Astrophysics Data System (ADS)

Bierkens, M. F.; Van Beek, L. P.; de Graaf, I. E. M.; Gleeson, T. P.

2017-12-01

Over recent decades, groundwater has become increasingly important for agriculture. Irrigation accounts for 40% of the global food production and its importance is expected to grow further in the near future. Already, about 70% of the globally abstracted water is used for irrigation, and nearly half of that is pumped groundwater. In many irrigated areas where groundwater is the primary source of irrigation water, groundwater abstraction is larger than recharge and we see massive groundwater head decline in these areas. An important question then is: to what maximum depth can groundwater be pumped for it to be still economically recoverable? The objective of this study is therefore to create a global map of the maximum depth of economically recoverable groundwater when used for irrigation. The maximum economic depth is the maximum depth at which revenues are still larger than pumping costs or the maximum depth at which initial investments become too large compared to yearly revenues. To this end we set up a simple economic model where costs of well drilling and the energy costs of pumping, which are a function of well depth and static head depth respectively, are compared with the revenues obtained for the irrigated crops. Parameters for the cost sub-model are obtained from several US-based studies and applied to other countries based on GDP/capita as an index of labour costs. The revenue sub-model is based on gross irrigation water demand calculated with a global hydrological and water resources model, areal coverage of crop types from MIRCA2000 and FAO-based statistics on crop yield and market price. We applied our method to irrigated areas in the world overlying productive aquifers. Estimated maximum economic depths range between 50 and 500 m. Most important factors explaining the maximum economic depth are the dominant crop type in the area and whether or not initial investments in well infrastructure are limiting. In subsequent research, our estimates of maximum economic depth will be combined with estimates of groundwater depth and storage coefficients to estimate economically attainable groundwater volumes worldwide.

Comparison of three NDVI time-series fitting methods in crop phenology detection in Northeast China

NASA Astrophysics Data System (ADS)

Wang, Meng; Tao, Fulu

2014-03-01

Phenological changes of cropland are the pivotal basis for farm management, agricultural production, and climate change research. Over the past decades, a range of methods have been used to extract phenological events based on satellite derived continuous vegetation index time series, however, large uncertainties still exist. In this study, three smoothing methods were compared to reduce the potential uncertainty and to quantify crop green-up dates over Northeast China. The results indicated that the crop spring onset dates estimated by three methods show variance in the dates, but with similar spatial pattern. In 60% of the study area, the standard deviation (SD) of the estimated starting date from different method is less than 10 days, while 39.5% of total pixels have SDs between 10days and 30 days. Through comparative analysis against the observation phenological data, we concluded that Asymmetric Gaussians produced the most approximative results of all, followed by Double Logistic algorithm, and Savizky-Glolay algorithm performed worst. The starting dates of crops occur mostly between May and June in this region. The Savitzky-Golay has the earliest estimates, while the Asymmetric Gaussians and Double logistic fitting method show similar and later estimates, which are more consistent with the observed data.

Method for estimating pesticide use for county areas of the conterminous United States

USGS Publications Warehouse

Thelin, Gail P.; Gianessi, Leonard P.

2000-01-01

Information on the amount and distribution of pesticide compounds used throughout the United States is essential to evaluate the relation between water quality and pesticide use. This information is the basis of the U.S. Geological Survey?s National Water-Quality Assessment (NAWQA) Program studies of the effects of pesticides on water quality in 57 major hydrologic systems, or study units, located throughout the conterminous United States. To support these studies, a method was devised to estimate county pesticide use for the conterminous United States by combining (1) state-level information on pesticide use rates available from the National Center for Food and Agricultural Policy, and (2) county-level information on harvested crop acreage from the Census of Agriculture. The average annual pesticide use, the total amount of pesticides applied (in pounds), and the corresponding area treated (in acres) were compiled for the 208 pesticide compounds that are applied to crops in the conterminous United States. Pesticide use was ranked by compound and crop on the basis of the amount of each compound applied to 86 selected crops. Tabular summaries of pesticide use for NAWQA study units and for the Nation were prepared, along with maps that show the distribution of selected pesticides to agricultural land.

Green Revolution research saved an estimated 18 to 27 million hectares from being brought into agricultural production

PubMed Central

Stevenson, James R.; Villoria, Nelson; Byerlee, Derek; Kelley, Timothy; Maredia, Mywish

2013-01-01

New estimates of the impacts of germplasm improvement in the major staple crops between 1965 and 2004 on global land-cover change are presented, based on simulations carried out using a global economic model (Global Trade Analysis Project Agro-Ecological Zone), a multicommodity, multiregional computable general equilibrium model linked to a global spatially explicit database on land use. We estimate the impact of removing the gains in cereal productivity attributed to the widespread adoption of improved varieties in developing countries. Here, several different effects—higher yields, lower prices, higher land rents, and trade effects—have been incorporated in a single model of the impact of Green Revolution research (and subsequent advances in yields from crop germplasm improvement) on land-cover change. Our results generally support the Borlaug hypothesis that increases in cereal yields as a result of widespread adoption of improved crop germplasm have saved natural ecosystems from being converted to agriculture. However, this relationship is complex, and the net effect is of a much smaller magnitude than Borlaug proposed. We estimate that the total crop area in 2004 would have been between 17.9 and 26.7 million hectares larger in a world that had not benefited from crop germplasm improvement since 1965. Of these hectares, 12.0–17.7 million would have been in developing countries, displacing pastures and resulting in an estimated 2 million hectares of additional deforestation. However, the negative impacts of higher food prices on poverty and hunger under this scenario would likely have dwarfed the welfare effects of agricultural expansion. PMID:23671086

Green Revolution research saved an estimated 18 to 27 million hectares from being brought into agricultural production.

PubMed

Stevenson, James R; Villoria, Nelson; Byerlee, Derek; Kelley, Timothy; Maredia, Mywish

2013-05-21

New estimates of the impacts of germplasm improvement in the major staple crops between 1965 and 2004 on global land-cover change are presented, based on simulations carried out using a global economic model (Global Trade Analysis Project Agro-Ecological Zone), a multicommodity, multiregional computable general equilibrium model linked to a global spatially explicit database on land use. We estimate the impact of removing the gains in cereal productivity attributed to the widespread adoption of improved varieties in developing countries. Here, several different effects--higher yields, lower prices, higher land rents, and trade effects--have been incorporated in a single model of the impact of Green Revolution research (and subsequent advances in yields from crop germplasm improvement) on land-cover change. Our results generally support the Borlaug hypothesis that increases in cereal yields as a result of widespread adoption of improved crop germplasm have saved natural ecosystems from being converted to agriculture. However, this relationship is complex, and the net effect is of a much smaller magnitude than Borlaug proposed. We estimate that the total crop area in 2004 would have been between 17.9 and 26.7 million hectares larger in a world that had not benefited from crop germplasm improvement since 1965. Of these hectares, 12.0-17.7 million would have been in developing countries, displacing pastures and resulting in an estimated 2 million hectares of additional deforestation. However, the negative impacts of higher food prices on poverty and hunger under this scenario would likely have dwarfed the welfare effects of agricultural expansion.

Migrant and Seasonal Agricultural Areas. Methodology for Designating High Impact.

ERIC Educational Resources Information Center

HCR, Washington, DC.

This report describes a method to estimate the number of migrant and seasonal farmworkers present in a prescribed area during crop harvest, and to pinpoint areas of high need for health and social services. The collection of health clinic and federal program data on migrant and seasonal farmworkers in Florida, northwestern Ohio, and Maryland's…

Estimating 20-year land-use change and derived CO2 emissions associated with crops, pasture and forestry in Brazil and each of its 27 states.

PubMed

Novaes, Renan M L; Pazianotto, Ricardo A A; Brandão, Miguel; Alves, Bruno J R; May, André; Folegatti-Matsuura, Marília I S

2017-09-01

Land-use change (LUC) in Brazil has important implications on global climate change, ecosystem services and biodiversity, and agricultural expansion plays a critical role in this process. Concerns over these issues have led to the need for estimating the magnitude and impacts associated with that, which are increasingly reported in the environmental assessment of products. Currently, there is an extensive debate on which methods are more appropriate for estimating LUC and related emissions and regionalized estimates are lacking for Brazil, which is a world leader in agricultural production (e.g. food, fibres and bioenergy). We developed a method for estimating scenarios of past 20-year LUC and derived CO 2 emission rates associated with 64 crops, pasture and forestry in Brazil as whole and in each of its 27 states, based on time-series statistics and in accordance with most used carbon-footprinting standards. The scenarios adopted provide a range between minimum and maximum rates of CO 2 emissions from LUC according to different possibilities of land-use transitions, which can have large impacts in the results. Specificities of Brazil, like multiple cropping and highly heterogeneous carbon stocks, are also addressed. The highest CO 2 emission rates are observed in the Amazon biome states and crops with the highest rates are those that have undergone expansion in this region. Some states and crops showing large agricultural areas have low emissions associated, especially in southern and eastern Brazil. Native carbon stocks and time of agricultural expansion are the most decisive factors to the patterns of emissions. Some implications on LUC estimation methods and standards and on agri-environmental policies are discussed. © 2017 John Wiley & Sons Ltd.

EnviroAtlas - Manure application to agricultural lands from confined animal feeding operations by 12-digit HUC for the Conterminous United States, 2006

EPA Pesticide Factsheets

This EnviroAtlas dataset contains data on the mean livestock manure application to cultivated crop and hay/pasture lands by 12-digit Hydrologic Unit (HUC) in 2006. Livestock manure inputs to cultivated crop and hay/pasture lands were estimated using county-level estimates of recoverable animal manure from confined feeding operations compiled for 2007. Recoverable manure is defined as manure that is collected, stored, and available for land application from confined feeding operations. County-scale data on livestock populations -- needed to calculate manure inputs -- were only available for the year 2007 from the USDA Census of Agriculture (http://www.agcensus.usda.gov/index.php). We acquired county-level data describing total farm-level inputs (kg N/yr) of recoverable manure to individual counties in 2007 from the International Plant Nutrition Institute (IPNI) Nutrient Geographic Information System (NuGIS; http://www.ipni.net/nugis). These data were converted to per area rates (kg N/ha/yr) of manure N inputs by dividing the total N input by the land area (ha) of combined cultivated crop and hay/pasture (agricultural) lands within a county as determined from county-level summarization of the 2006 NLCD. We distributed county-specific, per area N inputs rates to cultivated crop and hay/pasture lands (30 x 30 m pixels) within the corresponding county. Manure data described here represent an average input to a typical agricultural land type within a county, i.e., the

Evaluation of spatial filtering on the accuracy of wheat area estimate

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Moreira, M. A.; Chen, S. C.; Delima, A. M.

1982-01-01

A 3 x 3 pixel spatial filter for postclassification was used for wheat classification to evaluate the effects of this procedure on the accuracy of area estimation using LANDSAT digital data obtained from a single pass. Quantitative analyses were carried out in five test sites (approx 40 sq km each) and t tests showed that filtering with threshold values significantly decreased errors of commission and omission. In area estimation filtering improved the overestimate of 4.5% to 2.7% and the root-mean-square error decreased from 126.18 ha to 107.02 ha. Extrapolating the same procedure of automatic classification using spatial filtering for postclassification to the whole study area, the accuracy in area estimate was improved from the overestimate of 10.9% to 9.7%. It is concluded that when single pass LANDSAT data is used for crop identification and area estimation the postclassification procedure using a spatial filter provides a more accurate area estimate by reducing classification errors.

Ghg and Aerosol Emission from Fire Pixel during Crop Residue Burning Under Rice and Wheat Cropping Systems in North-West India

NASA Astrophysics Data System (ADS)

Acharya, Prasenjit; Sreekesh, S.; Kulshrestha, Umesh

2016-10-01

Emission of smoke and aerosol from open field burning of crop residue is a long-standing subject matter of atmospheric pollution. In this study, we proposed a new approach of estimating fuel load in the fire pixels and corresponding emissions of selected GHGs and aerosols i.e. CO2, CO, NO2, SO2, and total particulate matter (TPM) due to burning of crop residue under rice and wheat cropping systems in Punjab in north-west India from 2002 to 2012. In contrasts to the conventional method that uses RPR ratio to estimate the biomass, fuel load in the fire pixels was estimated as a function of enhanced vegetation index (EVI). MODIS fire products were used to detect the fire pixels during harvesting seasons of rice and wheat. Based on the field measurements, fuel load in the fire pixels were modelled as a function of average EVI using second order polynomial regression. Average EVI for rice and wheat crops that were extracted through Fourier transformation were computed from MODIS time series 16 day EVI composites. About 23 % of net shown area (NSA) during rice and 11 % during wheat harvesting seasons are affected by field burning. The computed average fuel loads are 11.32 t/ha (±17.4) during rice and 10.89 t/ha (±8.7) during wheat harvesting seasons. Calculated average total emissions of CO2, CO, NO2, SO2 and TPM were 8108.41, 657.85, 8.10, 4.10, and 133.21 Gg during rice straw burning and 6896.85, 625.09, 1.42, 1.77, and 57.55 Gg during wheat burning. Comparison of estimated values shows better agreement with the previous concurrent estimations. The method, however, shows its efficiency parallel to the conventional method of estimation of fuel load and related pollutant emissions.

Groundwater recharge in irrigated semi-arid areas: quantitative hydrological modelling and sensitivity analysis

NASA Astrophysics Data System (ADS)

Jiménez-Martínez, Joaquín; Candela, Lucila; Molinero, Jorge; Tamoh, Karim

2010-12-01

For semi-arid regions, methods of assessing aquifer recharge usually consider the potential evapotranspiration. Actual evapotranspiration rates can be below potential rates for long periods of time, even in irrigated systems. Accurate estimations of aquifer recharge in semi-arid areas under irrigated agriculture are essential for sustainable water-resources management. A method to estimate aquifer recharge from irrigated farmland has been tested. The water-balance-modelling approach was based on VisualBALAN v. 2.0, a computer code that simulates water balance in the soil, vadose zone and aquifer. The study was carried out in the Campo de Cartagena (SE Spain) in the period 1999-2008 for three different groups of crops: annual row crops (lettuce and melon), perennial vegetables (artichoke) and fruit trees (citrus). Computed mean-annual-recharge values (from irrigation+precipitation) during the study period were 397 mm for annual row crops, 201 mm for perennial vegetables and 194 mm for fruit trees: 31.4, 20.7 and 20.5% of the total applied water, respectively. The effects of rainfall events on the final recharge were clearly observed, due to the continuously high water content in soil which facilitated the infiltration process. A sensitivity analysis to assess the reliability and uncertainty of recharge estimations was carried out.

Assessing winter cover crop nutrient uptake efficiency using a water quality simulation model

NASA Astrophysics Data System (ADS)

Yeo, I.-Y.; Lee, S.; Sadeghi, A. M.; Beeson, P. C.; Hively, W. D.; McCarty, G. W.; Lang, M. W.

2013-11-01

Winter cover crops are an effective conservation management practice with potential to improve water quality. Throughout the Chesapeake Bay Watershed (CBW), which is located in the Mid-Atlantic US, winter cover crop use has been emphasized and federal and state cost-share programs are available to farmers to subsidize the cost of winter cover crop establishment. The objective of this study was to assess the long-term effect of planting winter cover crops at the watershed scale and to identify critical source areas of high nitrate export. A physically-based watershed simulation model, Soil and Water Assessment Tool (SWAT), was calibrated and validated using water quality monitoring data and satellite-based estimates of winter cover crop species performance to simulate hydrological processes and nutrient cycling over the period of 1991-2000. Multiple scenarios were developed to obtain baseline information on nitrate loading without winter cover crops planted and to investigate how nitrate loading could change with different winter cover crop planting scenarios, including different species, planting times, and implementation areas. The results indicate that winter cover crops had a negligible impact on water budget, but significantly reduced nitrate leaching to groundwater and delivery to the waterways. Without winter cover crops, annual nitrate loading was approximately 14 kg ha-1, but it decreased to 4.6-10.1 kg ha-1 with winter cover crops resulting in a reduction rate of 27-67% at the watershed scale. Rye was most effective, with a potential to reduce nitrate leaching by up to 93% with early planting at the field scale. Early planting of winter cover crops (~30 days of additional growing days) was crucial, as it lowered nitrate export by an additional ~2 kg ha-1 when compared to late planting scenarios. The effectiveness of cover cropping increased with increasing extent of winter cover crop implementation. Agricultural fields with well-drained soils and those that were more frequently used to grow corn had a higher potential for nitrate leaching and export to the waterways. This study supports the effective implement of winter cover crop programs, in part by helping to target critical pollution source areas for winter cover crop implementation.

Prediction of seasonal climate-induced variations in global food production

NASA Astrophysics Data System (ADS)

Iizumi, Toshichika; Sakuma, Hirofumi; Yokozawa, Masayuki; Luo, Jing-Jia; Challinor, Andrew J.; Brown, Molly E.; Sakurai, Gen; Yamagata, Toshio

2013-10-01

Consumers, including the poor in many countries, are increasingly dependent on food imports and are thus exposed to variations in yields, production and export prices in the major food-producing regions of the world. National governments and commercial entities are therefore paying increased attention to the cropping forecasts of important food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We found that moderate-to-marked yield loss over a substantial percentage (26-33%) of the harvested area of these crops is reliably predictable if climatic forecasts are near perfect. However, only rice and wheat production are reliably predictable at three months before the harvest using within-season hindcasts. The reliabilities of estimates varied substantially by crop--rice and wheat yields were the most predictable, followed by soybean and maize. The reasons for variation in the reliability of the estimates included the differences in crop sensitivity to the climate and the technology used by the crop-producing regions. Our findings reveal that the use of seasonal climatic forecasts to predict crop failures will be useful for monitoring global food production and will encourage the adaptation of food systems toclimatic extremes.

Matching the best viewing angle in depth cameras for biomass estimation based on poplar seedling geometry.

PubMed

Andújar, Dionisio; Fernández-Quintanilla, César; Dorado, José

2015-06-04

In energy crops for biomass production a proper plant structure is important to optimize wood yields. A precise crop characterization in early stages may contribute to the choice of proper cropping techniques. This study assesses the potential of the Microsoft Kinect for Windows v.1 sensor to determine the best viewing angle of the sensor to estimate the plant biomass based on poplar seedling geometry. Kinect Fusion algorithms were used to generate a 3D point cloud from the depth video stream. The sensor was mounted in different positions facing the tree in order to obtain depth (RGB-D) images from different angles. Individuals of two different ages, e.g., one month and one year old, were scanned. Four different viewing angles were compared: top view (0°), 45° downwards view, front view (90°) and ground upwards view (-45°). The ground-truth used to validate the sensor readings consisted of a destructive sampling in which the height, leaf area and biomass (dry weight basis) were measured in each individual plant. The depth image models agreed well with 45°, 90° and -45° measurements in one-year poplar trees. Good correlations (0.88 to 0.92) between dry biomass and the area measured with the Kinect were found. In addition, plant height was accurately estimated with a few centimeters error. The comparison between different viewing angles revealed that top views showed poorer results due to the fact the top leaves occluded the rest of the tree. However, the other views led to good results. Conversely, small poplars showed better correlations with actual parameters from the top view (0°). Therefore, although the Microsoft Kinect for Windows v.1 sensor provides good opportunities for biomass estimation, the viewing angle must be chosen taking into account the developmental stage of the crop and the desired parameters. The results of this study indicate that Kinect is a promising tool for a rapid canopy characterization, i.e., for estimating crop biomass production, with several important advantages: low cost, low power needs and a high frame rate (frames per second) when dynamic measurements are required.

Wild bees visiting cucumber on midwestern U.S. organic farms benefit from near-farm semi-natural areas.

PubMed

Smith, A A; Bentley, M; Reynolds, H L

2013-02-01

Wild bees that provide pollination services to vegetable crops depend on forage resources, nesting sites, and overwintering sites in the agricultural landscape. The scale at which crop-visiting bees use resources in the landscape can vary regionally, and has not been characterized in the Midwestern United States. We investigated the effects of seminatural land cover on wild bee visitation frequency to cucumber (Cucumis sativus L.) and on wild bee species richness on 10 organic farms in Indiana. We estimated the spatial scale at which the effects of land cover were strongest, and also examined the effects of nonlandscape factors on wild bees. The visitation frequency of wild bees to cucumber was positively related to the proportion of seminatural land in the surrounding landscape, and this relationship was strongest within 250 m of the cucumber patch. The species richness of wild cucumber visitors was not affected by land cover at any spatial scale, nor by any of the nonlandscape factors we considered. Our results indicate that wild, crop visiting bees benefit from seminatural areas in the agricultural landscape, and benefit most strongly from seminatural areas within 250 m of the crop field. This suggests that setting aside natural areas in the near vicinity of vegetable fields may be an effective way to support wild, crop-visiting bees and secure their pollination services.

Estimating national crop yield potential and the relevance of weather data sources

NASA Astrophysics Data System (ADS)

Van Wart, Justin

2011-12-01

To determine where, when, and how to increase yields, researchers often analyze the yield gap (Yg), the difference between actual current farm yields and crop yield potential. Crop yield potential (Yp) is the yield of a crop cultivar grown under specific management limited only by temperature and solar radiation and also by precipitation for water limited yield potential (Yw). Yp and Yw are critical components of Yg estimations, but are very difficult to quantify, especially at larger scales because management data and especially daily weather data are scarce. A protocol was developed to estimate Yp and Yw at national scales using site-specific weather, soils and management data. Protocol procedures and inputs were evaluated to determine how to improve accuracy of Yp, Yw and Yg estimates. The protocol was also used to evaluate raw, site-specific and gridded weather database sources for use in simulations of Yp or Yw. The protocol was applied to estimate crop Yp in US irrigated maize and Chinese irrigated rice and Yw in US rainfed maize and German rainfed wheat. These crops and countries account for >20% of global cereal production. The results have significant implications for past and future studies of Yp, Yw and Yg. Accuracy of national long-term average Yp and Yw estimates was significantly improved if (i) > 7 years of simulations were performed for irrigated and > 15 years for rainfed sites, (ii) > 40% of nationally harvested area was within 100 km of all simulation sites, (iii) observed weather data coupled with satellite derived solar radiation data were used in simulations, and (iv) planting and harvesting dates were specified within +/- 7 days of farmers actual practices. These are much higher standards than have been applied in national estimates of Yp and Yw and this protocol is a substantial step in making such estimates more transparent, robust, and straightforward. Finally, this protocol may be a useful tool for understanding yield trends and directing research and development efforts aimed at providing for a secure and stable future food supply.

The auxiliary use of LANDSAT data in estimating crop acreages: Results of the 1975 Illinois crop-acreage experiment

NASA Technical Reports Server (NTRS)

Gleason, C. (Principal Investigator); Starbuck, R. R.; Sigman, R. S.; Hanuschak, G. A.; Craig, M. E.; Cook, P. W.; Allen, R. D.

1977-01-01

The author has identified the following significant results. It was found that classifier performance was influenced by a number of temporal, methodological, and geographical factors. Best results were obtained when corn was tasselled and near the dough stage of development. Dates earlier or later in the growing season produced poor results. Atmospheric effects on results cannot be independently measured or completely separated from the effects due to the maturity stage of the crops. Poor classifier performance was observed in areas where considerable spectral confusion was present.

Application of future remote sensing systems to irrigation

NASA Technical Reports Server (NTRS)

Miller, L. D.

1982-01-01

Area estimates of irrigated crops and knowledge of crop type are required for modeling water consumption to assist farmers, rangers, and agricultural consultants in scheduling irrigation for distributed management of crop yields. Information on canopy physiology and soil moisture status on a spatial basis is potentially available from remote sensors, so the questions to be addressed relate to: (1) timing (data frequency, instantaneous and integrated measurement); and scheduling (widely distributed spatial demands); (2) spatial resolution; (3) radiometric and geometric accuracy and geoencoding; and (4) information/data distribution. This latter should be overnight, with no central storage, onsite capture, and low cost.

Estimating landscape-scale impacts of agricultural management on soil carbon using measurements and models

NASA Astrophysics Data System (ADS)

Schipanski, M.; Rosenzweig, S. T.; Robertson, A. D.; Sherrod, L. A.; Ghimire, R.; McMaster, G. S.

2017-12-01

Agriculture covers 40% of Earth's ice-free land area and has broad impacts on global biogeochemical cycles. While some agricultural management changes are small in scale or impact, others have the potential to shift biogeochemical cycles at landscape and larger scales if widely adopted. Understanding which management practices have the potential to contribute to climate change adaptation and mitigation while maintaining productivity requires scaling up estimates spatially and temporally. We used on-farm, long-term, and landscape scale datasets to estimate how crop rotations impact soil organic carbon (SOC) accumulation rates under current and future climate scenarios across the semi-arid Central and Southern Great Plains. We used a stratified, landscape-scale soil sampling approach across 96 farm fields to evaluate crop rotation intensity effects on SOC pools and pesticide inputs. Replacing traditional wheat-fallow rotations with more diverse, continuously cropped rotations increased SOC by 17% and 12% in 0-10 cm and 0-20 cm depths, respectively, and reduced herbicide use by 50%. Using USDA Cropland Data Layer, we estimated soil C accumulation and pesticide reduction potentials of shifting to more intensive rotations. We also used a 30-year cropping systems experiment to calibrate and validate the Daycent model to evaluate rotation intensify effects under future climate change scenarios. The model estimated greater SOC accumulation rates under continuously cropped rotations, but SOC stocks peaked and then declined for all cropping systems beyond 2050 under future climate scenarios. Perennial grasslands were the only system estimated to maintain SOC levels in the future. In the Southern High Plains, soil C declined despite increasing input intensity under current weather while modest gains were simulated under future climate for sorghum-based cropping systems. Our findings highlight the potential vulnerability of semi-arid regions to climate change, which will be compounded by declining groundwater levels along the western edge of the High Plains Aquifer that increase reliance on dryland farming systems. Understanding these challenges provides opportunities to develop future transition and adaptation strategies in partnership with producers, policy makers, and rural communities.

Mapping marginal croplands suitable for cellulosic feedstock crops in the Great Plains, United States

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.

2016-01-01

Growing cellulosic feedstock crops (e.g., switchgrass) for biofuel is more environmentally sustainable than corn-based ethanol. Specifically, this practice can reduce soil erosion and water quality impairment from pesticides and fertilizer, improve ecosystem services and sustainability (e.g., serve as carbon sinks), and minimize impacts on global food supplies. The main goal of this study was to identify high-risk marginal croplands that are potentially suitable for growing cellulosic feedstock crops (e.g., switchgrass) in the US Great Plains (GP). Satellite-derived growing season Normalized Difference Vegetation Index, a switchgrass biomass productivity map obtained from a previous study, US Geological Survey (USGS) irrigation and crop masks, and US Department of Agriculture (USDA) crop indemnity maps for the GP were used in this study. Our hypothesis was that croplands with relatively low crop yield but high productivity potential for switchgrass may be suitable for converting to switchgrass. Areas with relatively low crop indemnity (crop indemnity <$2 157 068) were excluded from the suitable areas based on low probability of crop failures. Results show that approximately 650 000 ha of marginal croplands in the GP are potentially suitable for switchgrass development. The total estimated switchgrass biomass productivity gain from these suitable areas is about 5.9 million metric tons. Switchgrass can be cultivated in either lowland or upland regions in the GP depending on the local soil and environmental conditions. This study improves our understanding of ecosystem services and the sustainability of cropland systems in the GP. Results from this study provide useful information to land managers for making informed decisions regarding switchgrass development in the GP.

An investigation of spectral change as influenced by irrigation and evapotranspiration volume estimation in western Nebraska

USGS Publications Warehouse

Seevers, P.M.; Sadowski, F.C.; Lauer, D.T.

1990-01-01

Retrospective satellite image data were evaluated for their ability to demonstrate the influence of center-pivot irrigation development in western Nebraska on spectral change and climate-related factors for the region. Periodic images of an albedo index and a normalized difference vegetation index (NDVI) were generated from calibrated Landsat multispectral scanner (MSS) data and used to monitor spectral changes associated with irrigation development from 1972 through 1986. The albedo index was not useful for monitoring irrigation development. For the NDVI, it was found that proportions of counties in irrigated agriculture, as discriminated by a threshold, were more highly correlated with reported ground estimates of irrigated agriculture than were county mean greenness values. A similar result was achieved when using coarse resolution Advanced Very High Resolution Radiometer (AVHRR) image data for estimating irrigated agriculture. The NDVI images were used to evaluate a procedure for making areal estimates of actual evapotranspiration (ET) volumes. Estimates of ET volumes for test counties, using reported ground acreages and corresponding standard crop coefficients, were correlated with the estimates of ET volume using crop coefficients scaled to NDVI values and pixel counts of crop areas. These county estimates were made under the assumption that soil water availability was unlimited. For nonirrigated vegetation, this may result in over-estimation of ET volumes. Ground information regarding crop types and acreages are required to derive the NDVI scaling factor. Potential ET, estimated with the Jensen-Haise model, is common to both methods. These results, achieved with both MSS and AVHRR data, show promise for providing climatologically important land surface information for regional and global climate models. ?? 1990 Kluwer Academic Publishers.

Remote Sensing-Based Quantification of the Impact of Flash Flooding on the Rice Production: A Case Study over Northeastern Bangladesh

PubMed Central

Rahaman, Khan Rubayet; Kok, Aaron; Hassan, Quazi K.

2017-01-01

The northeastern region of Bangladesh often experiences flash flooding during the pre-harvesting period of the boro rice crop, which is the major cereal crop in the country. In this study, our objective was to delineate the impact of the 2017 flash flood (that initiated on 27 March 2017) on boro rice using multi-temporal Landsat-8 OLI and MODIS data. Initially, we opted to use Landsat-8 OLI data for mapping the damages; however, during and after the flooding event the acquisition of cloud free images were challenging. Thus, we used this data to map the cultivated boro rice acreage considering the planting to mature stages of the crop. Also, in order to map the extent of the damaged boro area, we utilized MODIS data as their 16-day composites provided cloud free information. Our results indicated that both the cultivated and damaged boro area estimates based on satellite data had strong relationships while compared to the ground-based estimates (i.e., r2 values approximately 0.92 for both cases, and RMSE of 18,374 and 9380 ha for cultivated and damaged areas, respectively). Finally, we believe that our study would be critical for planning and ensuring food security for the country. PMID:29036896

Remote Sensing-Based Quantification of the Impact of Flash Flooding on the Rice Production: A Case Study over Northeastern Bangladesh.

PubMed

Ahmed, M Razu; Rahaman, Khan Rubayet; Kok, Aaron; Hassan, Quazi K

2017-10-14

The northeastern region of Bangladesh often experiences flash flooding during the pre-harvesting period of the boro rice crop, which is the major cereal crop in the country. In this study, our objective was to delineate the impact of the 2017 flash flood (that initiated on 27 March 2017) on boro rice using multi-temporal Landsat-8 OLI and MODIS data. Initially, we opted to use Landsat-8 OLI data for mapping the damages; however, during and after the flooding event the acquisition of cloud free images were challenging. Thus, we used this data to map the cultivated boro rice acreage considering the planting to mature stages of the crop. Also, in order to map the extent of the damaged boro area, we utilized MODIS data as their 16-day composites provided cloud free information. Our results indicated that both the cultivated and damaged boro area estimates based on satellite data had strong relationships while compared to the ground-based estimates (i.e., r ² values approximately 0.92 for both cases, and RMSE of 18,374 and 9380 ha for cultivated and damaged areas, respectively). Finally, we believe that our study would be critical for planning and ensuring food security for the country.

Evaluation of results of US corn and soybeans exploratory experiment: Classification procedures verification test. [Missouri, Iowa, Indiana, and Illinois

NASA Technical Reports Server (NTRS)

Carnes, J. G.; Baird, J. E. (Principal Investigator)

1980-01-01

The classification procedure utilized in making crop proportion estimates for corn and soybeans using remotely sensed data was evaluated. The procedure was derived during the transition year of the Large Area Crop Inventory Experiment. Analysis of variance techniques were applied to classifications performed by 3 groups of analysts who processed 25 segments selected from 4 agrophysical units (APU's). Group and APU effects were assessed to determine factors which affected the quality of the classifications. The classification results were studied to determine the effectiveness of the procedure in producing corn and soybeans proportion estimates.

Coupling of pollination services and coffee suitability under climate change.

PubMed

Imbach, Pablo; Fung, Emily; Hannah, Lee; Navarro-Racines, Carlos E; Roubik, David W; Ricketts, Taylor H; Harvey, Celia A; Donatti, Camila I; Läderach, Peter; Locatelli, Bruno; Roehrdanz, Patrick R

2017-09-26

Climate change will cause geographic range shifts for pollinators and major crops, with global implications for food security and rural livelihoods. However, little is known about the potential for coupled impacts of climate change on pollinators and crops. Coffee production exemplifies this issue, because large losses in areas suitable for coffee production have been projected due to climate change and because coffee production is dependent on bee pollination. We modeled the potential distributions of coffee and coffee pollinators under current and future climates in Latin America to understand whether future coffee-suitable areas will also be suitable for pollinators. Our results suggest that coffee-suitable areas will be reduced 73-88% by 2050 across warming scenarios, a decline 46-76% greater than estimated by global assessments. Mean bee richness will decline 8-18% within future coffee-suitable areas, but all are predicted to contain at least 5 bee species, and 46-59% of future coffee-suitable areas will contain 10 or more species. In our models, coffee suitability and bee richness each increase (i.e., positive coupling) in 10-22% of future coffee-suitable areas. Diminished coffee suitability and bee richness (i.e., negative coupling), however, occur in 34-51% of other areas. Finally, in 31-33% of the future coffee distribution areas, bee richness decreases and coffee suitability increases. Assessing coupled effects of climate change on crop suitability and pollination can help target appropriate management practices, including forest conservation, shade adjustment, crop rotation, or status quo, in different regions.

Exploring the Usefulness of MISR-HR Products to Estimate Maize Crop Extent and Using Field Evidence to Evaluate the Results in South Africa's Free State Province

NASA Astrophysics Data System (ADS)

Verstraete, M. M.; Knox, N. M.; Hunt, L. A.; Kleyn, L.

2014-12-01

The MISR instrument on NASA's Terra platform has been operating for almost 15 years. Standard products are generated at a spatial resolution of 1.1 km or coarser, but a recently developed method to re-analyze the Level-1B2 data allows the retrieval of biogeophysical products at the native spatial resolution of the instrument (275 m). This development opens new opportunities to better address issues such as the management of agricultural production and food security. South African maize production is of great economic and social importance, not only nationally, but on the global market too, being one of the top ten maize producing countries. Seasonal maize production statistics are currently based on a combination of field measurements and estimates derived from manually digitizing high resolution imagery from the SPOT satellite. The field measurements are collected using the Producer Independent Crop Estimate System (PICES) developed by Crop Estimates Committee of the Department of Agriculture, Forestry and Fisheries. There is a strong desire to improve the quality of these statistics, to generate those earlier, and to automate the process to encompass larger areas. This paper will explore the feasibility of using the MISR-HR spectral and directional products, combined with the finer spatial resolution and the relatively frequent coverage afforded by that instrument, to address these needs. The study area is based in the Free State, South Africa, one of the primary maize growing areas in the country, and took place during the 2012-2013 summer growing season. The significance of the outcomes will be evaluated in the context of the 14+ years of available MISR data.

Linkages among climate change, crop yields and Mexico–US cross-border migration

PubMed Central

Feng, Shuaizhang; Krueger, Alan B.; Oppenheimer, Michael

2010-01-01

Climate change is expected to cause mass human migration, including immigration across international borders. This study quantitatively examines the linkages among variations in climate, agricultural yields, and people's migration responses by using an instrumental variables approach. Our method allows us to identify the relationship between crop yields and migration without explicitly controlling for all other confounding factors. Using state-level data from Mexico, we find a significant effect of climate-driven changes in crop yields on the rate of emigration to the United States. The estimated semielasticity of emigration with respect to crop yields is approximately −0.2, i.e., a 10% reduction in crop yields would lead an additional 2% of the population to emigrate. We then use the estimated semielasticity to explore the potential magnitude of future emigration. Depending on the warming scenarios used and adaptation levels assumed, with other factors held constant, by approximately the year 2080, climate change is estimated to induce 1.4 to 6.7 million adult Mexicans (or 2% to 10% of the current population aged 15–65 y) to emigrate as a result of declines in agricultural productivity alone. Although the results cannot be mechanically extrapolated to other areas and time periods, our findings are significant from a global perspective given that many regions, especially developing countries, are expected to experience significant declines in agricultural yields as a result of projected warming. PMID:20660749

Linkages among climate change, crop yields and Mexico-US cross-border migration.

PubMed

Feng, Shuaizhang; Krueger, Alan B; Oppenheimer, Michael

2010-08-10

Climate change is expected to cause mass human migration, including immigration across international borders. This study quantitatively examines the linkages among variations in climate, agricultural yields, and people's migration responses by using an instrumental variables approach. Our method allows us to identify the relationship between crop yields and migration without explicitly controlling for all other confounding factors. Using state-level data from Mexico, we find a significant effect of climate-driven changes in crop yields on the rate of emigration to the United States. The estimated semielasticity of emigration with respect to crop yields is approximately -0.2, i.e., a 10% reduction in crop yields would lead an additional 2% of the population to emigrate. We then use the estimated semielasticity to explore the potential magnitude of future emigration. Depending on the warming scenarios used and adaptation levels assumed, with other factors held constant, by approximately the year 2080, climate change is estimated to induce 1.4 to 6.7 million adult Mexicans (or 2% to 10% of the current population aged 15-65 y) to emigrate as a result of declines in agricultural productivity alone. Although the results cannot be mechanically extrapolated to other areas and time periods, our findings are significant from a global perspective given that many regions, especially developing countries, are expected to experience significant declines in agricultural yields as a result of projected warming.

Modelling and estimating pollen movement in oilseed rape (Brassica napus) at the landscape scale using genetic markers.

PubMed

Devaux, C; Lavigne, C; Austerlitz, F; Klein, E K

2007-02-01

Understanding patterns of pollen movement at the landscape scale is important for establishing management rules following the release of genetically modified (GM) crops. We use here a mating model adapted to cultivated species to estimate dispersal kernels from the genotypes of the progenies of male-sterile plants positioned at different sampling sites within a 10 x 10-km oilseed rape production area. Half of the pollen clouds sampled by the male-sterile plants originated from uncharacterized pollen sources that could consist of both large volunteer and feral populations, and fields within and outside the study area. The geometric dispersal kernel was the most appropriate to predict pollen movement in the study area. It predicted a much larger proportion of long-distance pollination than previously fitted dispersal kernels. This best-fitting mating model underestimated the level of differentiation among pollen clouds but could predict its spatial structure. The estimation method was validated on simulated genotypic data, and proved to provide good estimates of both the shape of the dispersal kernel and the rate and composition of pollen issued from uncharacterized pollen sources. The best dispersal kernel fitted here, the geometric kernel, should now be integrated into models that aim at predicting gene flow at the landscape level, in particular between GM and non-GM crops.

Decision Support Systems To Manage Water Resources At Irrigation District Level In Southern Italy Using Remote Sensing Information. An Integrated Project (AQUATER)

NASA Astrophysics Data System (ADS)

Rinaldi, M.; Castrignanò, A.; Mastrorilli, M.; Rana, G.; Ventrella, D.; Acutis, M.; D'Urso, G.; Mattia, F.

2006-08-01

An efficient management of water resources is crucial point for Italy and in particular for southern areas characterized by Mediterranean climate in order to improve the economical and environmental sustainability of the agricultural activity. A three-year Project (2005-2008) has been funded by the Italian Ministry of Agriculture and Forestry Policies; it involves four Italian research institutions: the Agricultural Research Council (ISA, Bari), the National Research Council (ISSIA, Bari) and two Universities (Federico II-Naples and Milan). It is focused on the remote sensing, the plant and the climate and, for interdisciplinary relationships, the project working group consists of agronomists, engineers and physicists. The aims of the Project are: a) to produce a Decision Support System (DSS) combining remote sensing information, spatial data and simulation models to manage water resources in irrigation districts; b) to simulate irrigation scenarios to evaluate the effects of water stress on crop yield using agro-ecological indicators; c) to identify the most sensitive areas to drought risk in Southern Italy. The tools used in this Project will be: 1. Remote sensing images, topographic maps, soil and land use maps; 2. Geographic Information Systems; 3. Geostatistic methodologies; 4. Ground truth measurements (land use, canopy and soil temperatures, soil and plant water status, Normalized Difference Vegetation Index, Crop Water Stress Index, Leaf Area Index, actual evapotranspiration, crop coefficients, crop yield, agro-ecological indicators); 5. Crop simulation models. The Project is structured in four work packages with specific objectives, high degree of interaction and information exchange: 1) Remote Sensing and Image Analysis; 2) Cropping Systems; 3) Modelling and Softwares Development; 4) Stakeholders. The final product will be a DSS with the purpose of integrating remote sensing images, to estimate crop and soil variables related to drought, to assimilate these variables into a simulation model at district scale and, finally, to estimate evapotranspiration, plant water status and drought indicators. A project Web home page, a technical course about DSS for the employers of irrigation authorities and dissemination of results (meetings, publications, reports), are also planned.

Light- and water-use efficiency model synergy: a revised look at crop yield estimation for agricultural decision-making

NASA Astrophysics Data System (ADS)

Marshall, M.; Tu, K. P.

2015-12-01

Large-area crop yield models (LACMs) are commonly employed to address climate-driven changes in crop yield and inform policy makers concerned with climate change adaptation. Production efficiency models (PEMs), a class of LACMs that rely on the conservative response of carbon assimilation to incoming solar radiation absorbed by a crop contingent on environmental conditions, have increasingly been used over large areas with remote sensing spectral information to improve the spatial resolution of crop yield estimates and address important data gaps. Here, we present a new PEM that combines model principles from the remote sensing-based crop yield and evapotranspiration (ET) model literature. One of the major limitations of PEMs is that they are evaluated using data restricted in both space and time. To overcome this obstacle, we first validated the model using 2009-2014 eddy covariance flux tower Gross Primary Production data in a rice field in the Central Valley of California- a critical agro-ecosystem of the United States. This evaluation yielded a Willmot's D and mean absolute error of 0.81 and 5.24 g CO2/d, respectively, using CO2, leaf area, temperature, and moisture constraints from the MOD16 ET model, Priestley-Taylor ET model, and the Global Production Efficiency Model (GLOPEM). A Monte Carlo simulation revealed that the model was most sensitive to the Enhanced Vegetation Index (EVI) input, followed by Photosynthetically Active Radiation, vapor pressure deficit, and air temperature. The model will now be evaluated using 30 x 30m (Landsat resolution) biomass transects developed in 2011 and 2012 from spectroradiometric and other non-destructive in situ metrics for several cotton, maize, and rice fields across the Central Valley. Finally, the model will be driven by Daymet and MODIS data over the entire State of California and compared with county-level crop yield statistics. It is anticipated that the new model will facilitate agro-climatic decision-making in various regions across the globe and with different remote sensing inputs, given its interpretability, low data requirement, flexibility, and high correlation with in situ data.

Spatio-temporal evaluation of plant height in corn via unmanned aerial systems

NASA Astrophysics Data System (ADS)

Varela, Sebastian; Assefa, Yared; Vara Prasad, P. V.; Peralta, Nahuel R.; Griffin, Terry W.; Sharda, Ajay; Ferguson, Allison; Ciampitti, Ignacio A.

2017-07-01

Detailed spatial and temporal data on plant growth are critical to guide crop management. Conventional methods to determine field plant traits are intensive, time-consuming, expensive, and limited to small areas. The objective of this study was to examine the integration of data collected via unmanned aerial systems (UAS) at critical corn (Zea mays L.) developmental stages for plant height and its relation to plant biomass. The main steps followed in this research were (1) workflow development for an ultrahigh resolution crop surface model (CSM) with the goal of determining plant height (CSM-estimated plant height) using data gathered from the UAS missions; (2) validation of CSM-estimated plant height with ground-truthing plant height (measured plant height); and (3) final estimation of plant biomass via integration of CSM-estimated plant height with ground-truthing stem diameter data. Results indicated a correlation between CSM-estimated plant height and ground-truthing plant height data at two weeks prior to flowering and at flowering stage, but high predictability at the later growth stage. Log-log analysis on the temporal data confirmed that these relationships are stable, presenting equal slopes for both crop stages evaluated. Concluding, data collected from low-altitude and with a low-cost sensor could be useful in estimating plant height.

A Phenology-based Method For Identifying the Planting Fraction of Winter Wheat Using Moderate-resolution Satellite Data

NASA Astrophysics Data System (ADS)

Dong, J.; Liu, W.; Han, W.; Lei, T.; Xia, J.; Yuan, W.

2017-12-01

Winter wheat is a staple food crop for most of the world's population, and the area and spatial distribution of winter wheat are key elements in estimating crop production and ensuring food security. However, winter wheat planting areas contain substantial spatial heterogeneity with mixed pixels for coarse- and moderate-resolution satellite data, leading to significant errors in crop acreage estimation. This study has developed a phenology-based approach using moderate-resolution satellite data to estimate sub-pixel planting fractions of winter wheat. Based on unmanned aerial vehicle (UAV) observations, the unique characteristics of winter wheat with high vegetation index values at the heading stage (May) and low values at the harvest stage (June) were investigated. The differences in vegetation index between heading and harvest stages increased with the planting fraction of winter wheat, and therefore the planting fractions were estimated by comparing the NDVI differences of a given pixel with those of predetermined pure winter wheat and non-winter wheat pixels. This approach was evaluated using aerial images and agricultural statistical data in an intensive agricultural region, Shandong Province in North China. The method explained 60% and 85% of the spatial variation in county- and municipal-level statistical data, respectively. More importantly, the predetermined pure winter wheat and non-winter wheat pixels can be automatically identified using MODIS data according to their NDVI differences, which strengthens the potential to use this method at regional and global scales without any field observations as references.

A generic model for estimating biomass accumulation and greenhouse gas emissions from perennial crops

NASA Astrophysics Data System (ADS)

Ledo, Alicia; Heathcote, Richard; Hastings, Astley; Smith, Pete; Hillier, Jonathan

2017-04-01

Agriculture is essential to maintain humankind but is, at the same time, a substantial emitter of greenhouse gas (GHG) emissions. With a rising global population, the need for agriculture to provide secure food and energy supply is one of the main human challenges. At the same time, it is the only sector which has significant potential for negative emissions through the sequestration of carbon and offsetting via supply of feedstock for energy production. Perennial crops accumulate carbon during their lifetime and enhance organic soil carbon increase via root senescence and decomposition. However, inconsistency in accounting for this stored biomass undermines efforts to assess the benefits of such cropping systems when applied at scale. A consequence of this exclusion is that efforts to manage this important carbon stock are neglected. Detailed information on carbon balance is crucial to identify the main processes responsible for greenhouse gas emissions in order to develop strategic mitigation programs. Perennial crops systems represent 30% in area of total global crop systems, a considerable amount to be ignored. Furthermore, they have a major standing both in the bioenergy and global food industries. In this study, we first present a generic model to calculate the carbon balance and GHGs emissions from perennial crops, covering both food and bioenergy crops. The model is composed of two simple process-based sub-models, to cover perennial grasses and other perennial woody plants. The first is a generic individual based sub-model (IBM) covering crops in which the yield is the fruit and the plant biomass is an unharvested residue. Trees, shrubs and climbers fall into this category. The second model is a generic area based sub-model (ABM) covering perennial grasses, in which the harvested part includes some of the plant parts in which the carbon storage is accounted. Most second generation perennial bioenergy crops fall into this category. Both generic sub-models presented in this paper can be parametrized for different crops. Quantifying CO2 capture by plants and biomass accumulation and changes in soil carbon, are key in evaluating the impacts of perennial crops in life cycle analysis. We then use this model to illustrate the importance of biomass in the overall GHG estimation from four important perennial crops - sugarcane, Miscanthus, coffee, and apples - which were chosen to cover tropical and temperate regions, trees and grasses, and energy and food supply.

How does spatial and temporal resolution of vegetation index impact crop yield estimation?

USDA-ARS?s Scientific Manuscript database

Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing data have long been used in crop yield estimation for decades. The process-based approach uses light use efficiency model to estimate crop yield. Vegetation index (VI) ...

Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages

PubMed Central

Din, Mairaj; Zheng, Wen; Rashid, Muhammad; Wang, Shanqin; Shi, Zhihua

2017-01-01

Hyperspectral reflectance derived vegetation indices (VIs) are used for non-destructive leaf area index (LAI) monitoring for precise and efficient N nutrition management. This study tested the hypothesis that there is potential for using various hyperspectral VIs for estimating LAI at different growth stages of rice under varying N rates. Hyperspectral reflectance and crop canopy LAI measurements were carried out over 2 years (2015 and 2016) in Meichuan, Hubei, China. Different N fertilization, 0, 45, 82, 127, 165, 210, 247, and 292 kg ha-1, were applied to generate various scales of VIs and LAI values. Regression models were used to perform quantitative analyses between spectral VIs and LAI measured under different phenological stages. In addition, the coefficient of determination and RMSE were employed to evaluate these models. Among the nine VIs, the ratio vegetation index, normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), modified triangular vegetation index (MTVI2) and exhibited strong and significant relationships with the LAI estimation at different phenological stages. The enhanced vegetation index performed moderately. However, the green normalized vegetation index and blue normalized vegetation index confirmed that there is potential for crop LAI estimation at early phenological stages; the soil-adjusted vegetation index and optimized soil-adjusted vegetation index were more related to the soil optical properties, which were predicted to be the least accurate for LAI estimation. The noise equivalent accounted for the sensitivity of the VIs and MSAVI, MTVI2, and NDVI for the LAI estimation at phenological stages. The results note that LAI at different crop phenological stages has a significant influence on the potential of hyperspectral derived VIs under different N management practices. PMID:28588596

Contribution of multitemporal polarimetric synthetic aperture radar data for monitoring winter wheat and rapeseed crops

NASA Astrophysics Data System (ADS)

Betbeder, Julie; Fieuzal, Remy; Philippets, Yannick; Ferro-Famil, Laurent; Baup, Frederic

2016-04-01

This paper aims to evaluate the contribution of multitemporal polarimetric synthetic aperture radar (SAR) data for winter wheat and rapeseed crops parameters [height, leaf area index, and dry biomass (DB)] estimation, during their whole vegetation cycles in comparison to backscattering coefficients and optical data. Angular sensitivities and dynamics of polarimetric indicators were also analyzed following the growth stages of these two common crop types using, in total, 14 radar images (Radarsat-2), 16 optical images (Formosat-2, Spot-4/5), and numerous ground data. The results of this study show the importance of correcting the angular effect on SAR signals especially for copolarized signals and polarimetric indicators associated to single-bounce scattering mechanisms. The analysis of the temporal dynamic of polarimetric indicators has shown their high potential to detect crop growth changes. Moreover, this study shows the high interest of using SAR parameters (backscattering coefficients and polarimetric indicators) for crop parameters estimation during the whole vegetation cycle instead of optical vegetation index. They particularly revealed their high potential for rapeseed height and DB monitoring [i.e., Shannon entropy polarimetry (r2=0.70) and radar vegetation index (r2=0.80), respectively].

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)

Estimating maize production in Kenya using NDVI: Some statistical considerations

USGS Publications Warehouse

Lewis, J.E.; Rowland, James; Nadeau , A.

1998-01-01

A regression model approach using a normalized difference vegetation index (NDVI) has the potential for estimating crop production in East Africa. However, before production estimation can become a reality, the underlying model assumptions and statistical nature of the sample data (NDVI and crop production) must be examined rigorously. Annual maize production statistics from 1982-90 for 36 agricultural districts within Kenya were used as the dependent variable; median area NDVI (independent variable) values from each agricultural district and year were extracted from the annual maximum NDVI data set. The input data and the statistical association of NDVI with maize production for Kenya were tested systematically for the following items: (1) homogeneity of the data when pooling the sample, (2) gross data errors and influence points, (3) serial (time) correlation, (4) spatial autocorrelation and (5) stability of the regression coefficients. The results of using a simple regression model with NDVI as the only independent variable are encouraging (r 0.75, p 0.05) and illustrate that NDVI can be a responsive indicator of maize production, especially in areas of high NDVI spatial variability, which coincide with areas of production variability in Kenya.

EnviroAtlas - Synthetic N fertilizer application to agricultural lands by 12-digit HUC in the Conterminous United States, 2006

EPA Pesticide Factsheets

This EnviroAtlas dataset contains data on the mean synthetic nitrogen (N) fertilizer application to cultivated crop and hay/pasture lands per 12-digit Hydrologic Unit (HUC) in 2006. Synthetic N fertilizer inputs in 2006 were estimated using county-level estimates of farm N fertilizer inputs. We acquired county-level data describing total farm-level inputs (kg N/yr) of synthetic N fertilizer to individual counties in 2006 from the United States Geological Survey (USGS) (http://pubs.usgs.gov/sir/2012/5207/). These data were converted to per area rates (kg N/ha/yr) of synthetic N fertilizer application by dividing the total N input by the land area (ha) of combined cultivated crop and hay/pasture lands within a county as determined from county-level (http://cta.ornl.gov/transnet/Boundaries.html) summarization of the 2006 National Land Cover Database (NLCD; http://www.mrlc.gov/nlcd06_data.php). We distributed county-specific, annual per area N inputs rates (kg N/ha/yr) to cultivated crop and hay/pasture lands (30 x 30 m pixels) within the corresponding county using the raster calculator tool in ArcMap 10.0 (ESRI, Inc., Redlands, CA). Fertilizer data described here represent an average input to a typical agricultural land type within a county, i.e., they are not specific to individual crop types. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the us

Validation of Leaf Area Index measurements based on the Wireless Sensor Network platform

NASA Astrophysics Data System (ADS)

Song, Q.; Li, X.; Liu, Q.

2017-12-01

The leaf area index (LAI) is one of the important parameters for estimating plant canopy function, which has significance for agricultural analysis such as crop yield estimation and disease evaluation. The quick and accurate access to acquire crop LAI is particularly vital. In the study, LAI measurement of corn crops is mainly through three kinds of methods: the leaf length and width method (LAILLW), the instruments indirect measurement method (LAII) and the leaf area index sensor method(LAIS). Among them, LAI value obtained from LAILLW can be regarded as approximate true value. LAI-2200,the current widespread LAI canopy analyzer,is used in LAII. LAIS based on wireless sensor network can realize the automatic acquisition of crop images,simplifying the data collection work,while the other two methods need person to carry out field measurements.Through the comparison of LAIS and other two methods, the validity and reliability of LAIS observation system is verified. It is found that LAI trend changes are similar in three methods, and the rate of change of LAI has an increase with time in the first two months of corn growth when LAIS costs less manpower, energy and time. LAI derived from LAIS is more accurate than LAII in the early growth stage,due to the small blade especially under the strong light. Besides, LAI processed from a false color image with near infrared information is much closer to the true value than true color picture after the corn growth period up to one and half months.

Estimating variability in grain legume yields across Europe and the Americas

NASA Astrophysics Data System (ADS)

Cernay, Charles; Ben-Ari, Tamara; Pelzer, Elise; Meynard, Jean-Marc; Makowski, David

2015-06-01

Grain legume production in Europe has recently come under scrutiny. Although legume crops are often promoted to provide environmental services, European farmers tend to turn to non-legume crops. It is assumed that high variability in legume yields explains this aversion, but so far this hypothesis has not been tested. Here, we estimate the variability of major grain legume and non-legume yields in Europe and the Americas from yield time series over 1961-2013. Results show that grain legume yields are significantly more variable than non-legume yields in Europe. These differences are smaller in the Americas. Our results are robust at the level of the statistical methods. In all regions, crops with high yield variability are allocated to less than 1% of cultivated areas. Although the expansion of grain legumes in Europe may be hindered by high yield variability, some species display risk levels compatible with the development of specialized supply chains.

Intercepted photosynthetically active radiation in wheat canopies estimated by spectral reflectance. [Phoenix, Arizona

NASA Technical Reports Server (NTRS)

Hatfield, J. L.; Asrar, G.; Kanemasu, E. T.

1982-01-01

The interception of photosynthetically active radiation (PAR) was evaluated relative to greenness and normalized difference (MSS 7-5/7+5) for five planting dates of wheat for 1978-79 and 1979-80 in Phoenix. Intercepted PAR was calculated from a model driven by leaf area index and stage of growth. Linear relationships were found between greenness and normalized difference with a separate model representing growth and senescence of the crop. Normalized difference was a significantly better model and would be easier to apply than the empirically derived greenness parameter. For the leaf area growth portion of the season the model between PAR interception and normalized difference was the same over years, however, for the leaf senescence the models showed more variability due to the lack of data on measured interception in sparse canopies. Normalized difference could be used to estimate PAR interception directly for crop growth models.

Efficient crop type mapping based on remote sensing in the Central Valley, California

NASA Astrophysics Data System (ADS)

Zhong, Liheng

Most agricultural systems in California's Central Valley are purposely flexible and intentionally designed to meet the demands of dynamic markets. Agricultural land use is also impacted by climate change and urban development. As a result, crops change annually and semiannually, which makes estimating agricultural water use difficult, especially given the existing method by which agricultural land use is identified and mapped. A minor portion of agricultural land is surveyed annually for land-use type, and every 5 to 8 years the entire valley is completely evaluated. So far no effort has been made to effectively and efficiently identify specific crop types on an annual basis in this area. The potential of satellite imagery to map agricultural land cover and estimate water usage in the Central Valley is explored. Efforts are made to minimize the cost and reduce the time of production during the mapping process. The land use change analysis shows that a remote sensing based mapping method is the only means to map the frequent change of major crop types. The traditional maximum likelihood classification approach is first utilized to map crop types to test the classification capacity of existing algorithms. High accuracy is achieved with sufficient ground truth data for training, and crop maps of moderate quality can be timely produced to facilitate a near-real-time water use estimate. However, the large set of ground truth data required by this method results in high costs in data collection. It is difficult to reduce the cost because a trained classification algorithm is not transferable between different years or different regions. A phenology based classification (PBC) approach is developed which extracts phenological metrics from annual vegetation index profiles and identifies crop types based on these metrics using decision trees. According to the comparison with traditional maximum likelihood classification, this phenology-based approach shows great advantages when the size of the training set is limited by ground truth availability. Once developed, the classifier is able to be applied to different years and a vast area with only a few adjustments according to local agricultural and annual weather conditions. 250 m MODIS imagery is utilized as the main input to the PBC algorithm and displays promising capacity in crop identification in several counties in the Central Valley. A time series of Landsat TM/ETM+ images at a 30 m resolution is necessary in the crop mapping of counties with smaller land parcels, although the processing time is longer. Spectral characteristics are also employed to identify crops in PBC. Spectral signatures are associated with phenological stages instead of imaging dates, which highly increases the stability of the classifier performance and overcomes the problem of over-fitting. Moderate accuracies are achieved by PBC, with confusions mostly within the same crop categories. Based on a quantitative analysis, misclassification in PBC has very trivial impacts on the accuracy of agricultural water use estimate. The cost of the entire PBC procedure is controlled to a very low level, which will enable its usage in routine annual crop mapping in the Central Valley.

Policy issues and data communications for NASA earth observation missions until 1985

NASA Technical Reports Server (NTRS)

Corte, A. B.; Warren, C. J.

1975-01-01

The series of LANDSAT sensors with the highest potential data rates of the missions were examined. An examination of LANDSAT imagery uses shows that relatively few require transmission of the full resolution data on a repetitive quasi real time basis. Accuracy of global crop size forecasting can possibly be improved through information derived from LANDSAT imagery. A current forecasting experiment uses the imagery for crop area estimation only, yield being derived from other data sources.

General multiyear aggregation technology: Methodology and software documentation. [estimating seasonal crop acreage proportions

NASA Technical Reports Server (NTRS)

Baker, T. C. (Principal Investigator)

1982-01-01

A general methodology is presented for estimating a stratum's at-harvest crop acreage proportion for a given crop year (target year) from the crop's estimated acreage proportion for sample segments from within the stratum. Sample segments from crop years other than the target year are (usually) required for use in conjunction with those from the target year. In addition, the stratum's (identifiable) crop acreage proportion may be estimated for times other than at-harvest in some situations. A by-product of the procedure is a methodology for estimating the change in the stratum's at-harvest crop acreage proportion from crop year to crop year. An implementation of the proposed procedure as a statistical analysis system routine using the system's matrix language module, PROC MATRIX, is described and documented. Three examples illustrating use of the methodology and algorithm are provided.

Integrating future scenario‐based crop expansion and crop conditions to map switchgrass biofuel potential in eastern Nebraska, USA

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.

2018-01-01

Switchgrass (Panicum virgatum) has been evaluated as one potential source for cellulosic biofuel feedstocks. Planting switchgrass in marginal croplands and waterway buffers can reduce soil erosion, improve water quality, and improve regional ecosystem services (i.e. it serves as a potential carbon sink). In previous studies, we mapped high risk marginal croplands and highly erodible cropland buffers that are potentially suitable for switchgrass development, which would improve ecosystem services and minimally impact food production. In this study, we advance our previous study results and integrate future crop expansion information to develop a switchgrass biofuel potential ensemble map for current and future croplands in eastern Nebraska. The switchgrass biomass productivity and carbon benefits (i.e. NEP: net ecosystem production) for the identified biofuel potential ensemble areas were quantified. The future scenario‐based (‘A1B’) land use and land cover map for 2050, the US Geological Survey crop type and Compound Topographic Index (CTI) maps, and long‐term (1981–2010) averaged annual precipitation data were used to identify future crop expansion regions that are suitable for switchgrass development. Results show that 2528 km2 of future crop expansion regions (~3.6% of the study area) are potentially suitable for switchgrass development. The total estimated biofuel potential ensemble area (including cropland buffers, marginal croplands, and future crop expansion regions) is 4232 km2 (~6% of the study area), potentially producing 3.52 million metric tons of switchgrass biomass per year. Converting biofuel ensemble regions to switchgrass leads to potential carbon sinks (the total NEP for biofuel potential areas is 0.45 million metric tons C) and is environmentally sustainable. Results from this study improve our understanding of environmental conditions and ecosystem services of current and future cropland systems in eastern Nebraska and provide useful information to land managers to make land use decisions regarding switchgrass development.

Living with wildlife and mitigating conflicts around three Indian protected areas.

PubMed

Karanth, Krithi K; Naughton-Treves, Lisa; Defries, Ruth; Gopalaswamy, Arjun M

2013-12-01

Crop and livestock losses to wildlife are a concern for people neighboring many protected areas (PAs) and can generate opposition to conservation. Examining patterns of conflict and associated tolerance is important to devise policies to reduce conflict impacts on people and wildlife. We surveyed 398 households from 178 villages within 10 km of Ranthambore, Kanha, and Nagarahole parks in India. We compared different attitudes toward wildlife, and presented hypothetical response scenarios, including killing the problem animal(s). Eighty percent of households reported crop losses to wildlife and 13 % livestock losses. Higher crop loss was associated with more cropping months per year, greater crop variety, and more harvest seasons per year but did not vary with proximity to the PA, suggesting that PAs are not necessarily "sources" for crop raiders. By contrast, complaints of "depredating carnivores" were associated with people-grazing animals and collecting resources from PAs. Many households (83 %) engaged in mitigation efforts. We found that only fencing and guard animals reduce crop losses, and no efforts to lower livestock losses. Contrary to our expectations, carnivores were not viewed with more hostility than crop-raiding wildlife. Households reported greater inclination to kill herbivores destroying crops or carnivores harming people, but not carnivores preying on livestock.Our model estimated probability of [corrected] crop loss was 82 % across surveyed households (highest in Kanha),while the livestock loss experienced was 27 % (highest in Ranthambore). Our comparative study provides insights into factors associated with conflict loss and tolerance, and aids in improving ongoing conservation and compensation efforts.

Modeling the status, trends, and impacts of wild bee abundance in the United States

PubMed Central

Koh, Insu; Lonsdorf, Eric V.; Williams, Neal M.; Brittain, Claire; Isaacs, Rufus; Gibbs, Jason; Ricketts, Taylor H.

2016-01-01

Wild bees are highly valuable pollinators. Along with managed honey bees, they provide a critical ecosystem service by ensuring stable pollination to agriculture and wild plant communities. Increasing concern about the welfare of both wild and managed pollinators, however, has prompted recent calls for national evaluation and action. Here, for the first time to our knowledge, we assess the status and trends of wild bees and their potential impacts on pollination services across the coterminous United States. We use a spatial habitat model, national land-cover data, and carefully quantified expert knowledge to estimate wild bee abundance and associated uncertainty. Between 2008 and 2013, modeled bee abundance declined across 23% of US land area. This decline was generally associated with conversion of natural habitats to row crops. We identify 139 counties where low bee abundances correspond to large areas of pollinator-dependent crops. These areas of mismatch between supply (wild bee abundance) and demand (cultivated area) for pollination comprise 39% of the pollinator-dependent crop area in the United States. Further, we find that the crops most highly dependent on pollinators tend to experience more severe mismatches between declining supply and increasing demand. These trends, should they continue, may increase costs for US farmers and may even destabilize crop production over time. National assessments such as this can help focus both scientific and political efforts to understand and sustain wild bees. As new information becomes available, repeated assessments can update findings, revise priorities, and track progress toward sustainable management of our nation’s pollinators. PMID:26699460

Modeling the status, trends, and impacts of wild bee abundance in the United States.

PubMed

Koh, Insu; Lonsdorf, Eric V; Williams, Neal M; Brittain, Claire; Isaacs, Rufus; Gibbs, Jason; Ricketts, Taylor H

2016-01-05

Wild bees are highly valuable pollinators. Along with managed honey bees, they provide a critical ecosystem service by ensuring stable pollination to agriculture and wild plant communities. Increasing concern about the welfare of both wild and managed pollinators, however, has prompted recent calls for national evaluation and action. Here, for the first time to our knowledge, we assess the status and trends of wild bees and their potential impacts on pollination services across the coterminous United States. We use a spatial habitat model, national land-cover data, and carefully quantified expert knowledge to estimate wild bee abundance and associated uncertainty. Between 2008 and 2013, modeled bee abundance declined across 23% of US land area. This decline was generally associated with conversion of natural habitats to row crops. We identify 139 counties where low bee abundances correspond to large areas of pollinator-dependent crops. These areas of mismatch between supply (wild bee abundance) and demand (cultivated area) for pollination comprise 39% of the pollinator-dependent crop area in the United States. Further, we find that the crops most highly dependent on pollinators tend to experience more severe mismatches between declining supply and increasing demand. These trends, should they continue, may increase costs for US farmers and may even destabilize crop production over time. National assessments such as this can help focus both scientific and political efforts to understand and sustain wild bees. As new information becomes available, repeated assessments can update findings, revise priorities, and track progress toward sustainable management of our nation's pollinators.

Use of thermal and visible imagery for estimating crop water status of irrigated grapevine.

PubMed

Möller, M; Alchanatis, V; Cohen, Y; Meron, M; Tsipris, J; Naor, A; Ostrovsky, V; Sprintsin, M; Cohen, S

2007-01-01

Achieving high quality wine grapes depends on the ability to maintain mild to moderate levels of water stress in the crop during the growing season. This study investigates the use of thermal imaging for monitoring water stress. Experiments were conducted on a wine-grape (Vitis vinifera cv. Merlot) vineyard in northern Israel. Irrigation treatments included mild, moderate, and severe stress. Thermal and visible (RGB) images of the crop were taken on four days at midday with a FLIR thermal imaging system and a digital camera, respectively, both mounted on a truck-crane 15 m above the canopy. Aluminium crosses were used to match visible and thermal images in post-processing and an artificial wet surface was used to estimate the reference wet temperature (T(wet)). Monitored crop parameters included stem water potential (Psi(stem)), leaf conductance (g(L)), and leaf area index (LAI). Meteorological parameters were measured at 2 m height. CWSI was highly correlated with g(L) and moderately correlated with Psi(stem). The CWSI-g(L) relationship was very stable throughout the season, but for that of CWSI-Psi(stem) both intercept and slope varied considerably. The latter presumably reflects the non-direct nature of the physiological relationship between CWSI and Psi(stem). The highest R(2) for the CWSI to g(L) relationship, 0.91 (n=12), was obtained when CWSI was computed using temperatures from the centre of the canopy, T(wet) from the artificial wet surface, and reference dry temperature from air temperature plus 5 degrees C. Using T(wet) calculated from the inverted Penman-Monteith equation and estimated from an artificially wetted part of the canopy also yielded crop water-stress estimates highly correlated with g(L) (R(2)=0.89 and 0.82, respectively), while a crop water-stress index using 'theoretical' reference temperatures computed from climate data showed significant deviations in the late season. Parameter variability and robustness of the different CWSI estimates are discussed. Future research should aim at developing thermal imaging into an irrigation scheduling tool applicable to different crops.

Simulated Near-term Climate Change Impacts on Major Crops across Latin America and the Caribbean

NASA Astrophysics Data System (ADS)

Gourdji, S.; Mesa-Diez, J.; Obando-Bonilla, D.; Navarro-Racines, C.; Moreno, P.; Fisher, M.; Prager, S.; Ramirez-Villegas, J.

2016-12-01

Robust estimates of climate change impacts on agricultural production can help to direct investments in adaptation in the coming decades. In this study commissioned by the Inter-American Development Bank, near-term climate change impacts (2020-2049) are simulated relative to a historical baseline period (1971-2000) for five major crops (maize, rice, wheat, soybean and dry bean) across Latin America and the Caribbean (LAC) using the DSSAT crop model. No adaptation or technological change is assumed, thereby providing an analysis of existing climatic stresses on yields in the region and a worst-case scenario in the coming decades. DSSAT is run across irrigated and rain-fed growing areas in the region at a 0.5° spatial resolution for each crop. Crop model inputs for soils, planting dates, crop varieties and fertilizer applications are taken from previously-published datasets, and also optimized for this study. Results show that maize and dry bean are the crops most affected by climate change, followed by wheat, with only minimal changes for rice and soybean. Generally, rain-fed production sees more severe yield declines than irrigated production, although large increases in irrigation water are needed to maintain yields, reducing the yield-irrigation productivity in most areas and potentially exacerbating existing supply limitations in watersheds. This is especially true for rice and soybean, the two crops showing the most neutral yield changes. Rain-fed yields for maize and bean are projected to decline most severely in the sub-tropical Caribbean, Central America and northern South America, where climate models show a consistent drying trend. Crop failures are also projected to increase in these areas, necessitating switches to other crops or investment in adaptation measures. Generally, investment in agricultural adaptation to climate change (such as improved seed and irrigation infrastructure) will be needed throughout the LAC region in the 21st century.

Sampling for area estimation: A comparison of full-frame sampling with the sample segment approach. [Kansas

NASA Technical Reports Server (NTRS)

Hixson, M. M.; Bauer, M. E.; Davis, B. J.

1979-01-01

The effect of sampling on the accuracy (precision and bias) of crop area estimates made from classifications of LANDSAT MSS data was investigated. Full-frame classifications of wheat and non-wheat for eighty counties in Kansas were repetitively sampled to simulate alternative sampling plants. Four sampling schemes involving different numbers of samples and different size sampling units were evaluated. The precision of the wheat area estimates increased as the segment size decreased and the number of segments was increased. Although the average bias associated with the various sampling schemes was not significantly different, the maximum absolute bias was directly related to sampling unit size.

Kansas environmental and resource study: A Great Plains model. Extraction of agricultural statistics from ERTS-1 data of Kansas. [wheat inventory and agriculture land use

NASA Technical Reports Server (NTRS)

Morain, S. A. (Principal Investigator); Williams, D. L.

1974-01-01

The author has identified the following significant results. Wheat area, yield, and production statistics as derived from satellite image analysis, combined with a weather model, are presented for a ten county area in southwest Kansas. The data (representing the 1972-73 crop year) are compared for accuracy against both the USDA August estimate and its final (official) tabulation. The area estimates from imagery for both dryland and irrigated winter wheat were within 5% of the official figures for the same area, and predated them by almost one year. Yield on dryland wheat was estimated by the Thompson weather model to within 0.1% of the observed yield. A combined irrigated and dryland wheat production estimate for the ten county area was completed in July, 1973 and was within 1% of the production reported by USDA in February, 1974.

Look-up-table approach for leaf area index retrieval from remotely sensed data based on scale information

NASA Astrophysics Data System (ADS)

Zhu, Xiaohua; Li, Chuanrong; Tang, Lingli

2018-03-01

Leaf area index (LAI) is a key structural characteristic of vegetation and plays a significant role in global change research. Several methods and remotely sensed data have been evaluated for LAI estimation. This study aimed to evaluate the suitability of the look-up-table (LUT) approach for crop LAI retrieval from Satellite Pour l'Observation de la Terre (SPOT)-5 data and establish an LUT approach for LAI inversion based on scale information. The LAI inversion result was validated by in situ LAI measurements, indicating that the LUT generated based on the PROSAIL (PROSPECT+SAIL: properties spectra + scattering by arbitrarily inclined leaves) model was suitable for crop LAI estimation, with a root mean square error (RMSE) of ˜0.31m2 / m2 and determination coefficient (R2) of 0.65. The scale effect of crop LAI was analyzed based on Taylor expansion theory, indicating that when the SPOT data aggregated by 200 × 200 pixel, the relative error is significant with 13.7%. Finally, an LUT method integrated with scale information was proposed in this article, improving the inversion accuracy with RMSE of 0.20 m2 / m2 and R2 of 0.83.

Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data

NASA Astrophysics Data System (ADS)

Kouadio, Louis; Duveiller, Grégory; Djaby, Bakary; El Jarroudi, Moussa; Defourny, Pierre; Tychon, Bernard

2012-08-01

Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for crop monitoring at different levels. At the field level, the close correlation between green leaf area (GLA) during maturation and grain yield in wheat revealed that the onset and rate of senescence appeared to be important factors for determining wheat grain yield. Our study sought to explore a simple approach for wheat yield forecasting at the regional level, based on metrics derived from the senescence phase of the green area index (GAI) retrieved from remote sensing data. This study took advantage of recent methodological improvements in which imagery with high revisit frequency but coarse spatial resolution can be exploited to derive crop-specific GAI time series by selecting pixels whose ground-projected instantaneous field of view is dominated by the target crop: winter wheat. A logistic function was used to characterize the GAI senescence phase and derive the metrics of this phase. Four regression-based models involving these metrics (i.e., the maximum GAI value, the senescence rate and the thermal time taken to reach 50% of the green surface in the senescent phase) were related to official wheat yield data. The performances of such models at this regional scale showed that final yield could be estimated with an RMSE of 0.57 ton ha-1, representing about 7% as relative RMSE. Such an approach may be considered as a first yield estimate that could be performed in order to provide better integrated yield assessments in operational systems.

Large Area Crop Inventory Experiment (LACIE). LACIE phase 1 and phase 2 accuracy assessment. [Kansas, Texas, Minnesota, Montana, and North Dakota

NASA Technical Reports Server (NTRS)

1978-01-01

The author has identified the following significant results. The initial CAS estimates, which were made for each month from April through August, were considerably higher than the USDA/SRS estimates. This was attributed to: (1) the practice of considering bare ground as potential wheat and counting it as wheat; (2) overestimation of the wheat proportions in segments having only a small amount of wheat; and (3) the classification of confusion crops as wheat. At the end of the season most of the segments were reworked using improved methods based on experience gained during the season. In particular, new procedures were developed to solve the three problems listed above. These and other improvements used in the rework experiment resulted in at-harvest estimates that were much closer to the USDA/SRS estimates than those obtained during the regular season.

Climate change, variability and extreme events : risk assessment and management strategies in a Peach cultivated area in Italy.

NASA Astrophysics Data System (ADS)

Alfieri, Silvia Maria; De Lorenzi, Francesca; Basile, Angelo; Bonfante, Antonello; Missere, Daniele; Menenti, Massimo

2014-05-01

Climate change in Mediterranean area is likely to reduce precipitation amounts and to increase temperature thus affecting the timing of development stages and the productivity of crops. Further, extreme weather events are expected to increase in the future leading to significant increase in agricultural risk. Some strategies for effectively managing risks and adapting to climate change involve adjustments to irrigation management and use of different varieties. We quantified the risk on Peach production in an irrigated area of "Emilia Romagna" region ( Italy) taking into account the impact on crop yield due to climate change and variability and to extreme weather events as well as the ability of the agricultural system to modulate this impact (adaptive capacity) through changes in water and crop management. We have focused on climatic events causing insufficient water supply to crops, while taking into account the effect of climate on the duration and timing of phenological stages. Further, extreme maximum and minimum temperature events causing significant reduction of crop yield have been considered using phase-specific critical temperatures. In our study risk was assessed as the product of the probability of a damaging event (hazard), such as drought or extreme temperatures, and the estimated impact of such an event (vulnerability). To estimate vulnerability we took into account the possible options to reduce risk, by combining estimates of the sensitivity of the system (negative impact on crop yield) and its adaptive capacity. The latter was evaluated as the relative improvement due to alternate management options: the use of alternate varieties or the changes in irrigation management. Vulnerability was quantified using cultivar-specific thermal and hydrologic requirements of a set of cultivars determined by experimental data and from scientific literature. Critical temperatures determining a certain reduction of crop yield have been estimated and used to assess thermal hazard and vulnerability in sensitive phenological stages. Cultivar-specific yield response functions to water availability were used to assess the reduction of yield for a determinate management option. Downscaled climate scenarios have been used to calculate indicators of soil water availability and thermal times and to evaluate the variability of crop phenology in combination with critical temperatures. Two climate scenarios were considered: reference (1961-90) and future (2021-2050) climate, the former from climatic statistics on observed variables, and the latter from statistical downscaling of general circulation models (AOGCM). Management options were defined by combinations of irrigation strategies (optimal, rainfed and deficit) with use of alternate varieties. As regards hydrologic conditions, risk assessment has been done at landscape scale in all soil units within each study area. The mechanistic model SWAP (Soil-Water-Atmosphere-Plant model) of water flow in the soil-plant-atmosphere system was used to describe the hydrological conditions in response to climate and irrigation. Different farm management options were evaluated. In a moderate water shortage scenario, deficit irrigation was an effective strategy to cope with climate change risks. In a severe water shortage scenario, the study showed the potentiality of intra-specific biodiversity to reduce risk of yield losses, although costs should be evaluated against the benefits of each specific management option. 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)

Observation and Modelling of Soil Water Content Towards Improved Performance Indicators of Large Irrigation Schemes

NASA Astrophysics Data System (ADS)

Labbassi, Kamal; Akdim, Nadia; Alfieri, Silvia Maria; Menenti, Massimo

2014-05-01

Irrigation performance may be evaluated for different objectives such as equity, adequacy, or effectiveness. We are using two performance indicators: IP2 measures the consistency of the allocation of the irrigation water with gross Crop Water requirements, while IP3 measures the effectiveness of irrigation by evaluating the increase in crop transpiration between the case of no irrigation and the case of different levels of irrigation. To evaluate IP3 we need to calculate the soil water balance for the two cases. We have developed a system based on the hydrological model SWAP (Soil Water atmosphere Plant) to calculate spatial and temporal patterns of crop transpiration T(x, y, t) and of the vertical distribution of soil water content θ(x, y, z, t). On one hand, in the absence of ground measurement of soil water content to validate and evaluate the precision of the estimated one, a possibility would be to use satellite retrievals of top soil water content, such as the data to be provided by SMAP. On the other hand, to calculate IP3 we need root zone rather than top soil water content. In principle, we could use the model SWAP to establish a relationship between the top soil and root zone water content. Such relationship could be a simple empirical one or a data assimilation procedure. In our study area (Doukkala- Morocco) we have assessed the consistency of the water allocation with the actual irrigated area and crop water requirements (CWR) by using a combination of multispectral satellite image time series (i,e RapidEye (REIS), SPOT4 (HRVIR1) and Landsat 8 (OLI) images acquired during the 2012/2013 agricultural season). To obtain IP2 (x, y, t) we need to determine ETc (x, y, t). We have applied two (semi)empirical approaches: the first one is the Kc-NDVI method, based on the correlation between the Near Difference Vegetation Index (NDVI) and the value of crop coefficient (kc); the second one is the analytical approach based on the direct application of Penman-Monteith equation with reflectance-based estimates of canopy biophysical variables, such as surface albedo (r), leaf area index (LAI) and crop height (hc). The validation of spatial results using the dual crop coefficient approach (kcb) showed that the satellite-based estimates of ETc corresponded well with ground-based ETc i.e, R²=0.75 and RMSE=0.79 versus R²=0.73 and RMSE=0.89 for respectively kc-NDVI and analytical approach. To monitor IP3 (x, y, t) with the SWAP model we mapped soil hydrological properties combining soil maps with grain size analysis of a number of samples, and agricultural crops using multi-temporal classification of NDVI time series. The assessment of irrigation performance in term of adequacy between requirement and allocation showed that CWR are much larger than water supply for entire area, this mismatch is improved in the beginning of the growing season by means of Irrigation water requirement (IWR) and even more using the net irrigation water requirement (NIWR) estimated using SWAP model. We expect that the availability of SMAP data products will significantly improve the reliability and temporal sampling of our indicators.

Detecting spatio-temporal changes in agricultural land use in Heilongjiang province, China using MODIS time-series data and a random forest regression model

NASA Astrophysics Data System (ADS)

Hu, Q.; Friedl, M. A.; Wu, W.

2017-12-01

Accurate and timely information regarding the spatial distribution of crop types and their changes is essential for acreage surveys, yield estimation, water management, and agricultural production decision-making. In recent years, increasing population, dietary shifts and climate change have driven drastic changes in China's agricultural land use. However, no maps are currently available that document the spatial and temporal patterns of these agricultural land use changes. Because of its short revisit period, rich spectral bands and global coverage, MODIS time series data has been shown to have great potential for detecting the seasonal dynamics of different crop types. However, its inherently coarse spatial resolution limits the accuracy with which crops can be identified from MODIS in regions with small fields or complex agricultural landscapes. To evaluate this more carefully and specifically understand the strengths and weaknesses of MODIS data for crop-type mapping, we used MODIS time-series imagery to map the sub-pixel fractional crop area for four major crop types (rice, corn, soybean and wheat) at 500-m spatial resolution for Heilongjiang province, one of the most important grain-production regions in China where recent agricultural land use change has been rapid and pronounced. To do this, a random forest regression (RF-g) model was constructed to estimate the percentage of each sub-pixel crop type in 2006, 2011 and 2016. Crop type maps generated through expert visual interpretation of high spatial resolution images (i.e., Landsat and SPOT data) were used to calibrate the regression model. Five different time series of vegetation indices (155 features) derived from different spectral channels of MODIS land surface reflectance (MOD09A1) data were used as candidate features for the RF-g model. An out-of-bag strategy and backward elimination approach was applied to select the optimal spectra-temporal feature subset for each crop type. The resulting crop maps were assessed in two ways: (1) wall-to-wall pixel comparison with corresponding high spatial resolution reference maps; and (2) county-level comparison with census data. Based on these derived maps, changes in crop type, total area, and spatial patterns of change in Heilongjiang province during 2006-2016 were analyzed.

A GIS-based method for household recruitment in a prospective pesticide exposure study.

PubMed

Allpress, Justine L E; Curry, Ross J; Hanchette, Carol L; Phillips, Michael J; Wilcosky, Timothy C

2008-04-30

Recent advances in GIS technology and remote sensing have provided new opportunities to collect ecologic data on agricultural pesticide exposure. Many pesticide studies have used historical or records-based data on crops and their associated pesticide applications to estimate exposure by measuring residential proximity to agricultural fields. Very few of these studies collected environmental and biological samples from study participants. One of the reasons for this is the cost of identifying participants who reside near study fields and analyzing samples obtained from them. In this paper, we present a cost-effective, GIS-based method for crop field selection and household recruitment in a prospective pesticide exposure study in a remote location. For the most part, our multi-phased approach was carried out in a research facility, but involved two brief episodes of fieldwork for ground truthing purposes. This method was developed for a larger study designed to examine the validity of indirect pesticide exposure estimates by comparing measured exposures in household dust, water and urine with records-based estimates that use crop location, residential proximity and pesticide application data. The study focused on the pesticide atrazine, a broadleaf herbicide used in corn production and one of the most widely-used pesticides in the U.S. We successfully used a combination of remotely-sensed data, GIS-based methods and fieldwork to select study fields and recruit participants in Illinois, a state with high corn production and heavy atrazine use. Our several-step process consisted of the identification of potential study fields and residential areas using aerial photography; verification of crop patterns and land use via site visits; development of a GIS-based algorithm to define recruitment areas around crop fields; acquisition of geocoded household-level data within each recruitment area from a commercial vendor; and confirmation of final participant household locations via ground truthing. The use of these procedures resulted in a sufficient sample of participants from 14 recruitment areas in seven Illinois counties. One of the challenges in pesticide research is the identification and recruitment of study participants, which is time consuming and costly, especially when the study site is in a remote location. We have demonstrated how GIS-based processes can be used to recruit participants, increase efficiency and enhance accuracy. The method that we used ultimately made it possible to collect biological samples from a specific demographic group within strictly defined exposure areas, with little advance knowledge of the location or population.

Monitoring irrigation water consumption using high resolution NDVI image time series (Sentinel-2 like). Calibration and validation in the Kairouan plain (Tunisia)

NASA Astrophysics Data System (ADS)

Saadi, Sameh; Simonneaux, Vincent; Boulet, Gilles; Mougenot, Bernard; Zribi, Mehrez; Lili Chabaane, Zohra

2015-04-01

Water scarcity is one of the main factors limiting agricultural development in semi-arid areas. It is thus of major importance to design tools allowing a better management of this resource. Remote sensing has long been used for computing evapotranspiration estimates, which is an input for crop water balance monitoring. Up to now, only medium and low resolution data (e.g. MODIS) are available on regular basis to monitor cultivated areas. However, the increasing availability of high resolution high repetitivity VIS-NIR remote sensing, like the forthcoming Sentinel-2 mission to be lunched in 2015, offers unprecedented opportunity to improve this monitoring. In this study, regional crops water consumption was estimated with the SAMIR software (Satellite of Monitoring Irrigation) using the FAO-56 dual crop coefficient water balance model fed with high resolution NDVI image time series providing estimates of both the actual basal crop coefficient (Kcb) and the vegetation fraction cover. The model includes a soil water model, requiring the knowledge of soil water holding capacity, maximum rooting depth, and water inputs. As irrigations are usually not known on large areas, they are simulated based on rules reproducing the farmer practices. The main objective of this work is to assess the operationality and accuracy of SAMIR at plot and perimeter scales, when several land use types (winter cereals, summer vegetables…), irrigation and agricultural practices are intertwined in a given landscape, including complex canopies such as sparse orchards. Meteorological ground stations were used to compute the reference evapotranspiration and get the rainfall depths. Two time series of ten and fourteen high-resolution SPOT5 have been acquired for the 2008-2009 and 2012-2013 hydrological years over an irrigated area in central Tunisia. They span the various successive crop seasons. The images were radiometrically corrected, first, using the SMAC6s Algorithm, second, using invariant objects located on the scene, based on visual observation of the images. From these time series, a Normalized Difference Vegetation Index (NDVI) profile was generated for each pixel. SAMIR was first calibrated based on ground measurements of evapotranspiration achieved using eddy-correlation devices installed on irrigated wheat and barley plots. After calibration, the model was run to spatialize irrigation over the whole area and a validation was done using cumulated seasonal water volumes obtained from ground survey at both plot and perimeter scales. The results show that although determination of model parameters was successful at plot scale, irrigation rules required an additional calibration which was achieved at perimeter scale.

Estimated flows of gases and carbon within CEEF ecosystem composed of human, crops and goats

NASA Astrophysics Data System (ADS)

Tako, Y.; Komatsubara, O.; Honda, G.; Arai, R.; Nitta, K.

The Closed Ecology Experiment Facilities (CEEF) can be used as a test bed for Controlled Ecological Life Support Systems (CELSS), because technologies developed for the CEEF system facilitate self-sufficient material circulation necessary for long term missions such as Lunar and Mars exploration. In the experiment conducted under closed condition in FY2003, rice and soybeans were cultivated sequentially in two chambers and a chamber, each having a cultivation area of 30 m2 and floor area of 43 m2, inside the Plantation Module with artificial lighting of the CEEF. In the chamber having a cultivation area of 60 m2 and floor area of 65 m2, inside the Plantation Module with natural and artificial lighting, peanuts and safflowers were also cultivated. Stable transplant (or seeding) and harvest of each crop were maintained during a month. Flows of CO2, O2 and carbon to and from the crops were analyzed during the stable cultivation period. Simulated works and stay in the CEEF lasting five days were conducted two times under ventilating condition in FY2003. Gas exchange of human was estimated using heart rate data collected during the experiments and correlation between gas exchange rate and heart rate. Gas exchange rate and carbon balance of female goats were determined using an open-flow measurement system with a gastight chamber. From these results, flows of gases and carbon in the CEEF were discussed.

Trading carbon for food: global comparison of carbon stocks vs. crop yields on agricultural land.

PubMed

West, Paul C; Gibbs, Holly K; Monfreda, Chad; Wagner, John; Barford, Carol C; Carpenter, Stephen R; Foley, Jonathan A

2010-11-16

Expanding croplands to meet the needs of a growing population, changing diets, and biofuel production comes at the cost of reduced carbon stocks in natural vegetation and soils. Here, we present a spatially explicit global analysis of tradeoffs between carbon stocks and current crop yields. The difference among regions is striking. For example, for each unit of land cleared, the tropics lose nearly two times as much carbon (∼120 tons·ha(-1) vs. ∼63 tons·ha(-1)) and produce less than one-half the annual crop yield compared with temperate regions (1.71 tons·ha(-1)·y(-1) vs. 3.84 tons·ha(-1)·y(-1)). Therefore, newly cleared land in the tropics releases nearly 3 tons of carbon for every 1 ton of annual crop yield compared with a similar area cleared in the temperate zone. By factoring crop yield into the analysis, we specify the tradeoff between carbon stocks and crops for all areas where crops are currently grown and thereby, substantially enhance the spatial resolution relative to previous regional estimates. Particularly in the tropics, emphasis should be placed on increasing yields on existing croplands rather than clearing new lands. Our high-resolution approach can be used to determine the net effect of local land use decisions.

The Asia-RiCE activity with data cube

NASA Astrophysics Data System (ADS)

Oyoshi, K.; Sobue, S.; LE Toan, T.; Lam, N. D.

2017-12-01

The Asia-RiCE initiative (http://www.asia-rice.org) has been organized to enhance rice production estimates through the use of Earth observation satellites data, and seeks to ensure that Asian rice crops are appropriately represented within GEO Global Agriculture Monitoring (GEO-GLAM) to support FAO Agriculture Market Information System (FAO-AMIS). Asia-RiCE is composed of national teams that are actively contributing to the Crop Monitor for AMIS and developing technical demonstrations of rice crop monitoring activities using both Synthetic Aperture Radar (SAR) data (Radarsat-2 from 2013; Sentinel-1 and ALOS-2 from 2015.From 2016 after the successful rice crop area and growing estimation using SAR in a technical demonstration site (provincial level), wall-to-wall (national scale) excurse as phase 2 has been implemented in Vietnam and Indonesia in cooperation with ministry of agriculture and space agencies. This paper reports this year activity of 2017 accomplishment and way forward, especially for analysis ready data (ARD) definition of SAR to ingest to CEOS data cube to provide national scale service in Vietnam and Indonesia.

Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble

NASA Astrophysics Data System (ADS)

Löw, Fabian; Schorcht, Gunther; Michel, Ulrich; Dech, Stefan; Conrad, Christopher

2012-10-01

Accurate crop identification and crop area estimation are important for studies on irrigated agricultural systems, yield and water demand modeling, and agrarian policy development. In this study a novel combination of Random Forest (RF) and Support Vector Machine (SVM) classifiers is presented that (i) enhances crop classification accuracy and (ii) provides spatial information on map uncertainty. The methodology was implemented over four distinct irrigated sites in Middle Asia using RapidEye time series data. The RF feature importance statistics was used as feature-selection strategy for the SVM to assess possible negative effects on classification accuracy caused by an oversized feature space. The results of the individual RF and SVM classifications were combined with rules based on posterior classification probability and estimates of classification probability entropy. SVM classification performance was increased by feature selection through RF. Further experimental results indicate that the hybrid classifier improves overall classification accuracy in comparison to the single classifiers as well as useŕs and produceŕs accuracy.

Assessing winter cover crop nutrient uptake efficiency using a water quality simulation model

USGS Publications Warehouse

Yeo, In-Young; Lee, Sangchui; Sadeghi, Ali M.; Beeson, Peter C.; Hively, W. Dean; McCarty, Greg W.; Lang, Megan W.

2013-01-01

Winter cover crops are an effective conservation management practice with potential to improve water quality. Throughout the Chesapeake Bay Watershed (CBW), which is located in the Mid-Atlantic US, winter cover crop use has been emphasized and federal and state cost-share programs are available to farmers to subsidize the cost of winter cover crop establishment. The objective of this study was to assess the long-term effect of planting winter cover crops at the watershed scale and to identify critical source areas of high nitrate export. A physically-based watershed simulation model, Soil and Water Assessment Tool (SWAT), was calibrated and validated using water quality monitoring data and satellite-based estimates of winter cover crop species performance to simulate hydrological processes and nutrient cycling over the period of 1991–2000. Multiple scenarios were developed to obtain baseline information on nitrate loading without winter cover crops planted and to investigate how nitrate loading could change with different winter cover crop planting scenarios, including different species, planting times, and implementation areas. The results indicate that winter cover crops had a negligible impact on water budget, but significantly reduced nitrate leaching to groundwater and delivery to the waterways. Without winter cover crops, annual nitrate loading was approximately 14 kg ha−1, but it decreased to 4.6–10.1 kg ha−1 with winter cover crops resulting in a reduction rate of 27–67% at the watershed scale. Rye was most effective, with a potential to reduce nitrate leaching by up to 93% with early planting at the field scale. Early planting of winter cover crops (~30 days of additional growing days) was crucial, as it lowered nitrate export by an additional ~2 kg ha−1 when compared to late planting scenarios. The effectiveness of cover cropping increased with increasing extent of winter cover crop implementation. Agricultural fields with well-drained soils and those that were more frequently used to grow corn had a higher potential for nitrate leaching and export to the waterways. This study supports the effective implement of winter cover crop programs, in part by helping to target critical pollution source areas for winter cover crop implementation.

Biophysical and spectral modeling for crop identification and assessment

NASA Technical Reports Server (NTRS)

Goel, N. S. (Principal Investigator)

1984-01-01

The development of a technique for estimating all canopy parameters occurring in a canopy reflectance model from the measured canopy reflectance data is summarized. The Suits and the SAIL model for a uniform and homogeneous crop canopy were used to determine if the leaf area index and the leaf angle distribution could be estimated. Optimal solar/view angles for measuring CR were also investigated. The use of CR in many wavelengths or spectral bands and of linear and nonlinear transforms of CRs for various solar/view angles and various spectral bands is discussed as well as the inversion of rediance data inside the canopy, angle transforms for filtering out terrain slope effects, and modification of one dimensional models.

Hierarchical Satellite-based Approach to Global Monitoring of Crop Condition and Food Production

NASA Astrophysics Data System (ADS)

Zheng, Y.; Wu, B.; Gommes, R.; Zhang, M.; Zhang, N.; Zeng, H.; Zou, W.; Yan, N.

2014-12-01

The assessment of global food security goes beyond the mere estimate of crop production: It needs to take into account the spatial and temporal patterns of food availability, as well as physical and economic access. Accurate and timely information is essential to both food producers and consumers. Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, such as FY-2/3A, HJ-1 CCD, CropWatch has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The new monitoring approach adopts a hierarchical system covering four spatial levels of detail: global (sixty-five Monitoring and Reporting Units, MRU), seven major production zones (MPZ), thirty-one key countries (including China) and "sub- countries." The thirty-one countries encompass more that 80% of both global exports and production of four major crops (maize, rice, soybean and wheat). The methodology resorts to climatic and remote sensing indicators at different scales, using the integrated information to assess global, regional, and national (as well as sub-national) crop environmental condition, crop condition, drought, production, and agricultural trends. The climatic indicators for rainfall, temperature, photosynthetically active radiation (PAR) as well as potential biomass are first analysed at global scale to describe overall crop growing conditions. At MPZ scale, the key indicators pay more attention to crops and include Vegetation health index (VHI), Vegetation condition index (VCI), Cropped arable land fraction (CALF) as well as Cropping intensity (CI). Together, they characterise agricultural patterns, farming intensity and stress. CropWatch carries out detailed crop condition analyses for thirty one individual countries at the national scale with a comprehensive array of variables and indicators. The Normalized difference vegetation index (NDVI), cropped areas and crop condition are associated to derive food production estimates. Based on trends analysis, CropWatch also issues crop production supply outlooks, covering both long-term variations and short-term dynamic changes in key food exporters and importers. CropWatch bulletin can be downloaded from the CropWatch website at http://www.cropwatch.com.cn.

Spatially-Explicit Estimates of Greenhouse Gas Emissions from Fire and Land-Use Change in the Brazilian Cerrado

NASA Astrophysics Data System (ADS)

Galford, G. L.; Spera, S. A.; Coe, M. T.; Costa, C., Jr.

2014-12-01

Understanding the multiple types of land-use changes that can occur within an ecosystem provides a comprehensive picture of the human's impact on natural systems. We use the Cerrado (savanna) of Brazil to examine the primary and secondary impacts of land-use change on greenhouse gas emissions. The primary land-use changes include fires for land-clearing, conversions to pasture and row-crop agriculture, and shifting management practices of agricultural lands. Secondary land-use changes include savanna degradation due to fires that escape from intended burn areas. These escape fires typically have a lower combustion completion coefficient than clearing fires, so it is important to distinguish them to correctly estimate the regional greenhouse gas budget. We have created a first-order spatio-temporal model of greenhouse gas emissions that can be easily modified for other savanna regions using globally available data products as inputs. Our data inputs are derived from publically available remote sensing imagery. Initial biomass is estimated by Baccini et al. 2012, which is derived from LiDAR and MODIS imagery. All other input data sets give annual estimates. Clearing of the savanna is documented by LAPIG of Universidade Federal de Goias using MODIS (MOD13Q1), LANDSAT and CBERS images. MODIS burned area products delineate annual fires; in combination with the savanna clearing database we determine primary and escape fires. Pastures and row-crop agriculture are documented by LAPIG and Spera et al. 2014, respectively. The row-crop agriculture dataset enables us to estimate greenhouse gas emissions associated with specific crops (e.g., soy or maize) and management (e.g., fertilizer use). Recent contributions to the literature have provided many in situ measurements from the land-use changes of interest needed to estimate a regional greenhouse gas budget, including combustion coefficients of savanna sub-types, carbon emission soil stocks, nitrogen emissions from fertilizer, and carbon storage in pastures. With this wealth of information, we present a complete greenhouse gas portfolio including a sensitivity analysis for this dynamic region with an eye to applications for other savanna regions.

Irrigated Acreage Within the Basin and Range Carbonate-Rock Aquifer System, White Pine County, Nevada, and Adjacent Areas in Nevada and Utah

USGS Publications Warehouse

Welborn, Toby L.; Moreo, Michael T.

2007-01-01

Accurate delineations of irrigated acreage are needed for the development of water-use estimates and in determining water-budget calculations for the Basin and Range carbonate-rock aquifer system (BARCAS) study. Irrigated acreage is estimated routinely for only a few basins in the study area. Satellite imagery from the Landsat Thematic Mapper and Enhanced Thematic Mapper platforms were used to delineate irrigated acreage on a field-by-field basis for the entire study area. Six hundred and forty-three fields were delineated. The water source, irrigation system, crop type, and field activity for 2005 were identified and verified through field reconnaissance. These data were integrated in a geodatabase and analyzed to develop estimates of irrigated acreage for the 2000, 2002, and 2005 growing seasons by hydrographic area and subbasin. Estimated average annual potential evapotranspiration and average annual precipitation also were estimated for each field.The geodatabase was analyzed to determine the spatial distribution of field locations, the total amount of irrigated acreage by potential irrigation water source, by irrigation system, and by crop type. Irrigated acreage in 2005 totaled nearly 32,000 acres ranging from less than 200 acres in Butte, Cave, Jakes, Long, and Tippett Valleys to 9,300 acres in Snake Valley. Irrigated acreage increased about 20 percent between 2000 and 2005 and increased the most in Snake and White River Valleys. Ground-water supplies as much as 80 percent of irrigation water during dry years. Almost 90 percent of the irrigated acreage was planted with alfalfa.

Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index

NASA Astrophysics Data System (ADS)

Pasqualotto, Nieves; Delegido, Jesús; Van Wittenberghe, Shari; Verrelst, Jochem; Rivera, Juan Pablo; Moreno, José

2018-05-01

Crop canopy water content (CWC) is an essential indicator of the crop's physiological state. While a diverse range of vegetation indices have earlier been developed for the remote estimation of CWC, most of them are defined for specific crop types and areas, making them less universally applicable. We propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain). This dataset consists of water content and other biophysical variables for five common crop types (lucerne, corn, potato, sugar beet and onion) and corresponding top-of-canopy (TOC) reflectance spectra acquired by the hyperspectral HyMap airborne sensor. First, commonly used water content index formulations were analysed and validated for the variety of crops, overall resulting in a R2 lower than 0.6. In an attempt to move towards more generically applicable indices, the two new CWC indices exploit the principal water absorption features in the near-infrared by using multiple bands sensitive to water content. We propose the Water Absorption Area Index (WAAI) as the difference between the area under the null water content of TOC reflectance (reference line) simulated with PROSAIL and the area under measured TOC reflectance between 911 and 1271 nm. We also propose the Depth Water Index (DWI), a simplified four-band index based on the spectral depths produced by the water absorption at 970 and 1200 nm and two reference bands. Both the WAAI and DWI outperform established indices in predicting CWC when applied to heterogeneous croplands, with a R2 of 0.8 and 0.7, respectively, using an exponential fit. However, these indices did not perform well for species with a low fractional vegetation cover (<30%). HyMap CWC maps calculated with both indices are shown for the Barrax region. The results confirmed the potential of using generically applicable indices for calculating CWC over a great variety of crops.

Analysis of thematic mapper simulator data collected over eastern North Dakota

NASA Technical Reports Server (NTRS)

Anderson, J. E. (Principal Investigator)

1982-01-01

The results of the analysis of aircraft-acquired thematic mapper simulator (TMS) data, collected to investigate the utility of thematic mapper data in crop area and land cover estimates, are discussed. Results of the analysis indicate that the seven-channel TMS data are capable of delineating the 13 crop types included in the study to an overall pixel classification accuracy of 80.97% correct, with relative efficiencies for four crop types examined between 1.62 and 26.61. Both supervised and unsupervised spectral signature development techniques were evaluated. The unsupervised methods proved to be inferior (based on analysis of variance) for the majority of crop types considered. Given the ground truth data set used for spectral signature development as well as evaluation of performance, it is possible to demonstrate which signature development technique would produce the highest percent correct classification for each crop type.

Large Scale Crop Mapping in Ukraine Using Google Earth Engine

NASA Astrophysics Data System (ADS)

Shelestov, A.; Lavreniuk, M. S.; Kussul, N.

2016-12-01

There are no globally available high resolution satellite-derived crop specific maps at present. Only coarse-resolution imagery (> 250 m spatial resolution) has been utilized to derive global cropland extent. In 2016 we are going to carry out a country level demonstration of Sentinel-2 use for crop classification in Ukraine within the ESA Sen2-Agri project. But optical imagery can be contaminated by cloud cover that makes it difficult to acquire imagery in an optimal time range to discriminate certain crops. Due to the Copernicus program since 2015, a lot of Sentinel-1 SAR data at high spatial resolution is available for free for Ukraine. It allows us to use the time series of SAR data for crop classification. Our experiment for one administrative region in 2015 showed much higher crop classification accuracy with SAR data than with optical only time series [1, 2]. Therefore, in 2016 within the Google Earth Engine Research Award we use SAR data together with optical ones for large area crop mapping (entire territory of Ukraine) using cloud computing capabilities available at Google Earth Engine (GEE). This study compares different classification methods for crop mapping for the whole territory of Ukraine using data and algorithms from GEE. Classification performance assessed using overall classification accuracy, Kappa coefficients, and user's and producer's accuracies. Also, crop areas from derived classification maps compared to the official statistics [3]. S. Skakun et al., "Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine," IEEE Journal of Selected Topics in Applied Earth Observ. and Rem. Sens., 2015, DOI: 10.1109/JSTARS.2015.2454297. N. Kussul, S. Skakun, A. Shelestov, O. Kussul, "The use of satellite SAR imagery to crop classification in Ukraine within JECAM project," IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp.1497-1500, 13-18 July 2014, Quebec City, Canada. F.J. Gallego, N. Kussul, S. Skakun, O. Kravchenko, A. Shelestov, O. Kussul, "Efficiency assessment of using satellite data for crop area estimation in Ukraine," International Journal of Applied Earth Observation and Geoinformation vol. 29, pp. 22-30, 2014.

Spectral estimators of absorbed photosynthetically active radiation in corn canopies

NASA Technical Reports Server (NTRS)

Gallo, K. P.; Daughtry, C. S. T.; Bauer, M. E.

1985-01-01

Most models of crop growth and yield require an estimate of canopy leaf area index (LAI) or absorption of radiation. Relationships between photosynthetically active radiation (PAR) absorbed by corn canopies and the spectral reflectance of the canopies were investigated. Reflectance factor data were acquired with a Landsat MSS band radiometer. From planting to silking, the three spectrally predicted vegetation indices examined were associated with more than 95 percent of the variability in absorbed PAR. The relationships developed between absorbed PAR and the three indices were evaluated with reflectance factor data acquired from corn canopies planted in 1979 through 1982. Seasonal cumulations of measured LAI and each of the three indices were associated with greater than 50 percent of the variation in final grain yields from the test years. Seasonal cumulations of daily absorbed PAR were associated with up to 73 percent of the variation in final grain yields. Absorbed PAR, cumulated through the growing season, is a better indicator of yield than cumulated leaf area index. Absorbed PAR may be estimated reliably from spectral reflectance data of crop canopies.

Spectral estimators of absorbed photosynthetically active radiation in corn canopies

NASA Technical Reports Server (NTRS)

Gallo, K. P.; Daughtry, C. S. T.; Bauer, M. E.

1984-01-01

Most models of crop growth and yield require an estimate of canopy leaf area index (LAI) or absorption of radiation. Relationships between photosynthetically active radiation (PAR) absorbed by corn canopies and the spectral reflectance of the canopies were investigated. Reflectance factor data were acquired with a LANDSAT MSS band radiometer. From planting to silking, the three spectrally predicted vegetation indices examined were associated with more than 95% of the variability in absorbed PAR. The relationships developed between absorbed PAR and the three indices were evaluated with reflectance factor data acquired from corn canopies planted in 1979 through 1982. Seasonal cumulations of measured LAI and each of the three indices were associated with greater than 50% of the variation in final grain yields from the test years. Seasonal cumulations of daily absorbed PAR were associated with up to 73% of the variation in final grain yields. Absorbed PAR, cumulated through the growing season, is a better indicator of yield than cumulated leaf area index. Absorbed PAR may be estimated reliably from spectral reflectance data of crop canopies.

Recent decline in crop water productivity in the United States: a call to grow "more crop per drop"

NASA Astrophysics Data System (ADS)

Marshall, M. T.; Tu, K. P.; Thenkabail, P.; Brown, J. F.

2016-12-01

Irrigation for agriculture accounts for approximately 80 to 90% of U.S. consumptive water use. Recent declines in freshwater supply for irrigated agriculture in the western U.S. is particularly alarming, because climate change, water withdrawals from growing and competing sectors, and water pollution, are projected to put further strain on this vital sector. Innovative water management strategies are being proposed to combat this eminent water crisis and include: developing water markets, improving crop water productivity (CWP: "more crop per drop"), and coordinating the use of surface and groundwater supplies. The increase in CWP through crop type or variety selection is particularly lucrative, because it aims to increase the marketable yield of a crop, while reducing the cost of consumptive water use. Here we estimated CWP from 2000-2015 for the Contiguous United States over the primary growing season (mid May - late October) using a recently developed and validated light-use efficiency model for estimating crop yield and the transpiration component of the Priestley-Taylor Jet Propulsion Laboratory evapotranspiration model. The models were parameterized with daily DAYMET 1 km meteorological and 7-day EROS Moderate Resolution Imaging Spectroradiometer 250 m vegetation data. An analysis will be performed on CWP and its components to characterize the magnitude, direction, and persistence of trends. CWP estimates and trends will be overlaid with the U.S. Department of Agriculture's Cropland Data Layer to rank major crops by water use versus marketable yield and to characterize intervention hotspots, respectively. County-level data on surface and ground water withdrawals for irrigated agriculture available through the U.S. Geological Survey will be used to further scrutinize emerging patterns. It is anticipated that over much of the irrigated areas of the western U.S. that persistent and decreasing trends in CWP for major water users (e.g. alfalfa) due to temperature-driven increases in atmospheric moisture demand or potential evapotranspiration will correspond to a decrease (increase) in surface (ground) water use for irrigation.

Potato Production as Affected by Crop Parameters and Meteoro Logical Elements

NASA Astrophysics Data System (ADS)

Pereira, André B.; Villa Nova, Nilson A.; Pereira, Antonio R.

Meteorological elements directly influence crop potential productivity, regulating its transpiration, photosynthesis, and respiration processes in such a way as to control the growth and development of the plants throughout their physiological mechanisms at a given site. The interaction of the meteorological factors with crop responses is complex and has been the target of attention of many researchers from all over the world. There is currently a great deal of interest in estimating crop productivity as a function of climate by means of different crop weather models in order to help growers choose planting locations and timing to produce high yields with good tuber quality under site-specific atmospheric conditions. In this manuscript an agrometeorological model based on maximum carbon dioxide assimilation rates for C3 plants, fraction of photosynthetically active radiation, air temperature, photoperiod duration, and crop parameters is assessed as to its performance under tropical conditions. Crop parameters include leaf areaand harvest indexes, dry matter content of potato tubers, and crop cycles to estimate potato potential yields. Productivity obtained with the cultivar Itararé, grown with adequate soil water supply conditions at four different sites in the State of São Paulo (Itararé, Piracicaba, TatuÍ, and São Manuel), Brazil, were used to test the model. The results showed thatthe agrometeorological model tested under the climatic conditions of the State of São Paulo in general underestimated irrigated potato yield by less than 10%.This justifies the recommendation to test the performance of the model in study in other climaticregions for different crops and genotypes under optimal irrigationconditions in further scientific investigations. We reached the conclusion that the agrometeorological model taking into account information on leaf area index, photoperiod duration, photosynthetically active radiation and air temperature is feasible to estimate potential tuber yield at a commercial scale. The performance test shows that it can then be used to forecast harvest time, and also as an effective tool to predict the suitability of potential regions to the cultivation of potato crop, cultivar Itararé, at the State of São Paulo, Brazil.

Gaussian process models for reference ET estimation from alternative meteorological data sources

USDA-ARS?s Scientific Manuscript database

Accurate estimates of daily crop evapotranspiration (ET) are needed for efficient irrigation management, especially in arid and semi-arid regions where crop water demand exceeds rainfall. Daily grass or alfalfa reference ET values and crop coefficients are widely used to estimate crop water demand. ...

AgRISTARS: Foreign commodity production forecasting. The 1980 US/Canada wheat and barley exploratory experiment

NASA Technical Reports Server (NTRS)

Payne, R. W. (Principal Investigator)

1981-01-01

The crop identification procedures used performed were for spring small grains and are conducive to automation. The performance of the machine processing techniques shows a significant improvement over previously evaluated technology; however, the crop calendars require additional development and refinements prior to integration into automated area estimation technology. The integrated technology is capable of producing accurate and consistent spring small grains proportion estimates. Barley proportion estimation technology was not satisfactorily evaluated because LANDSAT sample segment data was not available for high density barley of primary importance in foreign regions and the low density segments examined were not judged to give indicative or unequvocal results. Generally, the spring small grains technology is ready for evaluation in a pilot experiment focusing on sensitivity analysis to a variety of agricultural and meteorological conditions representative of the global environment.

Rice crop growth and outlook monitoring using SAR in Asia

NASA Astrophysics Data System (ADS)

Hamamoto, K.; Sobue, S.; Oyoshi, K.; Ikehata, Y.

2016-12-01

The Asia-RiCE initiative (http://www.asia-rice.org) has been organized to enhance rice production estimates through the use of Earth observation satellites data, and seeks to ensure that Asian rice crops are appropriately represented within GEO Global Agriculture Monitoring (GEO-GLAM) to support FAO Agriculture Market Information System (FAO-AMIS). Asia-RiCE is composed of national teams that are actively contributing to the Crop Monitor for AMIS and developing technical demonstrations of rice crop monitoring activities using both Synthetic Aperture Radar (SAR) data (Radarsat-2 from 2013; Sentinel-1 and ALOS-2 from 2015; TerraSAR-X, Cosmo-SkyMed, RISAT, and others) and optical imagery (such as from MODIS, SPOT-5, Landsat, and Sentinel-2) for 100x100km Technical Demonstration Sites (TDS) as a phase 1 (2013-2015) in Asia. with satellite -based cultivated area and growing stage map. The Asia-RiCE teams are also developing satellite-based agro-met information for rice crop outlook, crop calendars and damage assessment in cooperation with ASEAN food security information system (AFSIS) for selected countries (currently Indonesia, Thailand, Vietnam, Philippine, and Japan; http://www.afsisnc.org/blog), using JAXA's Satellite-based MonItoring Network system as a contribution to the FAO AMIS outlook (JASMIN) with University of Tokyo (http://suzaku.eorc.jaxa.jp/cgi-bin/gcomw/jasm/jasm_top.cgi). Because of continous El Nino in South East Asia, there are less precipitation and rain fall pattern change in South East Asia, crop pattern has been changed and production may be decreased, especially for dry season crop. JAXA provides drought index (KBDI) and accumulated precipitation of Tak province, Thailand where main reservior is located, to AFSIS and national experts to assess rice crop outlook and NDVI time seriese to Ang Tong province where is main rice production area in downstream area of that reservior.From 2016 as a phase 2, Asia-RiCE initiative deploy up-scaling activity from a province (100x100km) to major crop areas or entire country to implement operational use for rice crop production information in low Mekong, Vietnam and top 10 provinces in Indonesia using space based technology. This paper reports this year activity of 2016 accomplishment and way forward.

Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data

USGS Publications Warehouse

Gumma, Murali Krishna; Thenkabail, Prasad S.; Teluguntla, Pardhasaradhi G.; Rao, Mahesh N.; Mohammed, Irshad A.; Whitbread, Anthony M.

2016-01-01

The goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia, using MODIS 250 m time-series data and identify where the farming system may be intensified by the inclusion of a short-season crop during the fallow period. Rice-fallow cropland areas are those areas where rice is grown during the kharif growing season (June–October), followed by a fallow during the rabi season (November–February). These cropland areas are not suitable for growing rabi-season rice due to their high water needs, but are suitable for a short -season (≤3 months), low water-consuming grain legumes such as chickpea (Cicer arietinum L.), black gram, green gram, and lentils. Intensification (double-cropping) in this manner can improve smallholder farmer’s incomes and soil health via rich nitrogen-fixation legume crops as well as address food security challenges of ballooning populations without having to expand croplands. Several grain legumes, primarily chickpea, are increasingly grown across Asia as a source of income for smallholder farmers and at the same time providing rich and cheap source of protein that can improve the nutritional quality of diets in the region. The suitability of rainfed and irrigated rice-fallow croplands for grain legume cultivation across South Asia were defined by these identifiers: (a) rice crop is grown during the primary (kharif) crop growing season or during the north-west monsoon season (June–October); (b) same croplands are left fallow during the second (rabi) season or during the south-east monsoon season (November–February); and (c) ability to support low water-consuming, short-growing season (≤3 months) grain legumes (chickpea, black gram, green gram, and lentils) during rabi season. Existing irrigated or rainfed crops such as rice or wheat that were grown during kharif were not considered suitable for growing during the rabi season, because the moisture/water demand of these crops is too high. The study established cropland classes based on the every 16-day 250 m normalized difference vegetation index (NDVI) time series for one year (June 2010–May 2011) of Moderate Resolution Imaging Spectroradiometer (MODIS) data, using spectral matching techniques (SMTs), and extensive field knowledge. Map accuracy was evaluated based on independent ground survey data as well as compared with available sub-national level statistics. The producers’ and users’ accuracies of the cropland fallow classes were between 75% and 82%. The overall accuracy and the kappa coefficient estimated for rice classes were 82% and 0.79, respectively. The analysis estimated approximately 22.3 Mha of suitable rice-fallow areas in South Asia, with 88.3% in India, 0.5% in Pakistan, 1.1% in Sri Lanka, 8.7% in Bangladesh, 1.4% in Nepal, and 0.02% in Bhutan. Decision-makers can target these areas for sustainable intensification of short-duration grain legumes.

Nutrients discharged to the Mississippi River from eastern Iowa watersheds, 1996-1997

USGS Publications Warehouse

Becher, Kent D.; Schnoebelen, Douglas J.; Akers, Kimberlee K.

2000-01-01

The introduction of nutrients from chemical fertilizer, animal manure, wastewater, and atmospheric deposition to the eastern Iowa environment creates a large potential for nutrient transport in watersheds. Agriculture constitutes 93 percent of all land use in eastern Iowa. As part of the U.S. Geological Survey National Water Quality Assessment Program, water samples were collected (typically monthly) from six small and six large watersheds in eastern Iowa between March 1996 and September 1997. A Geographic Information System (GIS) was used to determine land use and quantify inputs of nitrogen and phosphorus within the study area. Streamliow from the watersheds is to the Mississippi River. Chemical fertilizer and animal manure account for 92 percent of the estimated total nitrogen and 99.9 percent of the estimated total phosphorus input in the study area. Total nitrogen and total phosphorus loads for 1996 were estimated for nine of the 12 rivers and creeks using a minimum variance unbiased estimator model. A seasonal pattern of concentrations and loads was observed. The greatest concentrations and loads occur in the late spring to early summer in conjunction with row-crop fertilizer applications and spring nmoff and again in the late fall to early winter as vegetation goes into dormancy and additional fertilizer is applied to row-crop fields. The three largest rivers in eastern Iowa transported an estimated total of 79,000 metric tons of total nitrogen and 6,800 metric tons of total phosphorus to the Mississippi River in 1996. The estimated mass of total nitrogen and total phosphorus transported to the Mississippi River represents about 19 percent of all estimated nitrogen and 9 percent of all estimated phosphorus input to the study area.

78 FR 38483 - Area Risk Protection Insurance Regulations and Area Risk Protection Insurance Crop Provisions

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-26

...The Federal Crop Insurance Corporation (FCIC) finalizes the Area Risk Protection Insurance (ARPI) Basic Provisions, ARPI Barley Crop Insurance Provisions, ARPI Corn Crop Insurance Provisions, ARPI Cotton Crop Insurance Provisions, ARPI Forage Crop Insurance Provisions, ARPI Grain Sorghum Crop Insurance Provisions, ARPI Peanut Crop Insurance Provisions, ARPI Soybean Crop Insurance Provisions, and ARPI Wheat Crop Insurance Provisions to provide area yield protection and area revenue protection. These provisions will replace the Group Risk Plan (GRP) provisions in 7 CFR part 407, which includes the: GRP Basic Provisions, GRP Barley Crop Provisions, GRP Corn Crop Provisions, GRP Cotton Crop Provisions, GRP Forage Crop Provisions, GRP Peanut Crop Provisions, GRP Sorghum Crop Provisions, GRP Soybean Crop Provisions, and GRP Wheat Crop Provisions. The ARPI provisions will also replace the Group Risk Income Protection (GRIP) Basic Provisions, the GRIP Crop Provisions, and the GRIP-Harvest Revenue Option (GRIP-HRO). The GRP and GRIP plans of insurance will no longer be available. The intended effect of this action is to offer producers a choice of Area Revenue Protection, Area Revenue Protection with the Harvest Price Exclusion, or Area Yield Protection, all within one Basic Provision and the applicable Crop Provisions. This will reduce the amount of information producers must read to determine the best risk management tool for their operation and will improve the provisions to better meet the needs of insureds. The changes will apply for the 2014 and succeeding crop years.

A spatial modeling framework to evaluate domestic biofuel-induced potential land use changed and emissions

USGS Publications Warehouse

Elliot, Joshua; Sharma, Bhavna; Best, Neil; Glotter, Michael; Dunn, Jennifer B.; Foster, Ian; Miguez, Fernando; Mueller, Steffen; Wang, Michael

2014-01-01

We present a novel bottom-up approach to estimate biofuel-induced land-use change (LUC) and resulting CO2 emissions in the U.S. from 2010 to 2022, based on a consistent methodology across four essential components: land availability, land suitability, LUC decision-making, and induced CO2 emissions. Using highresolution geospatial data and modeling, we construct probabilistic assessments of county-, state-, and national-level LUC and emissions for macroeconomic scenarios. We use the Cropland Data Layer and the Protected Areas Database to characterize availability of land for biofuel crop cultivation, and the CERES-Maize and BioCro biophysical crop growth models to estimate the suitability (yield potential) of available lands for biofuel crops. For LUC decisionmaking, we use a county-level stochastic partial-equilibrium modeling framework and consider five scenarios involving annual ethanol production scaling to 15, 22, and 29 BG, respectively, in 2022, with corn providing feedstock for the first 15 BG and the remainder coming from one of two dedicated energy crops. Finally, we derive high-resolution above-ground carbon factors from the National Biomass and Carbon Data set to estimate emissions from each LUC pathway. Based on these inputs, we obtain estimates for average total LUC emissions of 6.1, 2.2, 1.0, 2.2, and 2.4 gCO2e/MJ for Corn-15 Billion gallons (BG), Miscanthus × giganteus (MxG)-7 BG, Switchgrass (SG)-7 BG, MxG-14 BG, and SG-14 BG scenarios, respectively.

Differences in CH4 and N2O emissions between rice nurseries in Chinese major rice cropping areas

NASA Astrophysics Data System (ADS)

Zhang, Yi; Li, Zhijie; Feng, Jinfei; Zhang, Xin; Jiang, Yu; Chen, Jin; Zhang, Mingqian; Deng, Aixing; Zhang, Weijian

2014-10-01

Studies on greenhouse gas (GHG) emissions from paddy field have primarily focused on the post-transplanting period, however, recent researches raise new concerns about GHGs emission from rice nursery. In this study, CH4 and N2O fluxes were determined from different nurseries under major rice cropping systems in China. The tested nurseries included flooded nursery (FN), moist nursery (MN) and dry nursery (DN). Methane emissions from FN were significantly higher than those from MN and DN under all the rice cropping systems. When comparing with FN, MN decreased total CH4 emissions by 74.2%, 72.1% and 49.6% under the rice-upland rotation cropping system (RUR), and the double rice cropping system for the early rice (EDR) and the late rice (LDR), respectively. DN decreased CH4 emissions by 99.2%, 92.0%, 99.0% and 78.6% compared to FN under the single rice cropping system (SR), RUR, EDR and LDR, respectively. When comparing with FN, MN and DN increased N2O emissions by 58.1-134.1% and 28.2-332.7%, respectively. Ultimately, compared with FN across the cropping systems, MN and DN decreased net global warming potentials (GWPs) of CH4 and N2O by 33-68% and 43-86%, respectively. The mitigating effect of MN and DN on total GWPs varied greatly across the systems, ranging from 30.8% in the LDR to 86.5% in the SR. Chinese actual emission from rice nurseries was reduced to 956.66 × 103 t CO2 eq from the theoretical estimate of 2242.59 × 103 t CO2 eq if under the flooded nursery scenario in 2012. Taking into account the large rice nursery area (2032.52 × 103 ha) in China, the results of this study clearly indicate the importance to estimate and mitigate GHGs emission from flooded rice nursery. Being effective to reduce GHG emissions and increase rice yield, dry nursery technique is a promising candidate for climate smart rice cropping.

Shifts in comparative advantages for maize, oat and wheat cropping under climate change in Europe.

PubMed

Elsgaard, L; Børgesen, C D; Olesen, J E; Siebert, S; Ewert, F; Peltonen-Sainio, P; Rötter, R P; Skjelvåg, A O

2012-01-01

Climate change is anticipated to affect European agriculture, including the risk of emerging or re-emerging feed and food hazards. Indirectly, climate change may influence such hazards (e.g. the occurrence of mycotoxins) due to geographic shifts in the distribution of major cereal cropping systems and the consequences this may have for crop rotations. This paper analyses the impact of climate on cropping shares of maize, oat and wheat on a 50-km square grid across Europe (45-65°N) and provides model-based estimates of the changes in cropping shares in response to changes in temperature and precipitation as projected for the time period around 2040 by two regional climate models (RCM) with a moderate and a strong climate change signal, respectively. The projected cropping shares are based on the output from the two RCMs and on algorithms derived for the relation between meteorological data and observed cropping shares of maize, oat and wheat. The observed cropping shares show a south-to-north gradient, where maize had its maximum at 45-55°N, oat had its maximum at 55-65°N, and wheat was more evenly distributed along the latitudes in Europe. Under the projected climate changes, there was a general increase in maize cropping shares, whereas for oat no areas showed distinct increases. For wheat, the projected changes indicated a tendency towards higher cropping shares in the northern parts and lower cropping shares in the southern parts of the study area. The present modelling approach represents a simplification of factors determining the distribution of cereal crops, and also some uncertainties in the data basis were apparent. A promising way of future model improvement could be through a systematic analysis and inclusion of other variables, such as key soil properties and socio-economic conditions, influencing the comparative advantages of specific crops.

A double-hurdle model estimation of cocoa farmers' willingness to pay for crop insurance in Ghana.

PubMed

Okoffo, Elvis Dartey; Denkyirah, Elisha Kwaku; Adu, Derick Taylor; Fosu-Mensah, Benedicta Yayra

2016-01-01

Agriculture is an important sector in Ghana's economy, however, with high risk due to natural factors like climate change, pests and diseases and bush fires among others. Farmers in the Brong-Ahafo region of Ghana which is known as one of the major cocoa producing regions, face these risks which sometimes results in crop failure. The need for farmers to therefore insure their farms against crop loss is crucial. Insurance has been a measure to guard against risk. The aim of this study was to assess cocoa farmers' willingness to access crop insurance, the factors affecting willingness to pay (WTP) for crop insurance scheme and insurance companies' willingness to provide crop insurance to cocoa farmers. Multi-stage sampling technique was used to sample 240 farmers from four communities in the Dormaa West District in Brong-Ahafo Region. The double-hurdle model shows that age, marital status and education significantly and positively influenced cocoa farmer's willingness to insure their farms whiles household size and cropped area negatively influenced farmers' willingness to insure their farms. Similarly, age, household size and cropped area significantly and positively influenced the premium cocoa farmers were willing to pay whiles marital status and cocoa income negatively influenced the premium farmers were willing to pay. The contingent valuation method shows that the maximum, minimum and average amounts cocoa farmers are willing to pay for crop insurance per production cost per acre was GH¢128.40, GH¢32.10 and GH¢49.32 respectively. Insurance companies do not have crop insurance policy but willing to provide crop insurance policy to cocoa farmers on a condition that farmers adopt modern cultivation practices to reduce the level of risk. The study recommends that cocoa farmers should be well educated on crop insurance and should be involved in planning the crop insurance scheme in order to conclude on the premium to be paid by them.

LACIE performance predictor final operational capability program description, volume 3

NASA Technical Reports Server (NTRS)

1976-01-01

The requirements and processing logic for the LACIE Error Model program (LEM) are described. This program is an integral part of the Large Area Crop Inventory Experiment (LACIE) system. LEM is that portion of the LPP (LACIE Performance Predictor) which simulates the sample segment classification, strata yield estimation, and production aggregation. LEM controls repetitive Monte Carlo trials based on input error distributions to obtain statistical estimates of the wheat area, yield, and production at different levels of aggregation. LEM interfaces with the rest of the LPP through a set of data files.

Reflectance of vegetation, soil, and water

NASA Technical Reports Server (NTRS)

Wiegand, C. L. (Principal Investigator); Gausman, H. W.; Leamer, R. W.; Richardson, A. J.; Gerbermann, A. H.; Torline, R. J.; Gautreaux, M. R.; Everitt, J. H.; Guellar, J. A.; Rodriguez, R. R.

1974-01-01

The author has identified the following significant results. Bands 4, 5, and 7 and 5, 6, and 7 were best for distinguishing among crop and soil categories in ERTS-1 SCENES 1182-16322 (1-21-73) and 1308-16323 (5-21-73) respectively. Chlorotic sorghum areas 2.8 acres or larger in size were identified on a computer printout of band 5 data. Reflectance of crop residues was more often different from bare soil in band 4 than in bands 5, 6, and 7. Simultaneously acquired aircraft and spacecraft MSS data indicated that spacecraft surveys are as reliable as aircraft surveys. ERTS-1 data were successfully used to estimate acreage of citrus, cotton, and sorghum as well as idle crop land.

Environmental technologies of woody crop production systems

USDA-ARS?s Scientific Manuscript database

Land degradation is a threat to global sustainability with an estimated 25% of the world’s land area already degraded. Soil erosion, loss of productivity potential, biodiversity loss, water shortage, and soil pollution are ongoing processes that decrease or degrade ecosystem services. Degradation ra...

Estimation of Carbon Budgets for Croplands by Combining High Resolution Remote Sensing Data with a Crop Model and Validation Ground Data

NASA Astrophysics Data System (ADS)

Mangiarotti, S.; Veloso, A.; Ceschia, E.; Tallec, T.; Dejoux, J. F.

2015-12-01

Croplands occupy large areas of Earth's land surface playing a key role in the terrestrial carbon cycle. Hence, it is essential to quantify and analyze the carbon fluxes from those agro-ecosystems, since they contribute to climate change and are impacted by the environmental conditions. In this study we propose a regional modeling approach that combines high spatial and temporal resolutions (HSTR) optical remote sensing data with a crop model and a large set of in-situ measurements for model calibration and validation. The study area is located in southwest France and the model that we evaluate, called SAFY-CO2, is a semi-empirical one based on the Monteith's light-use efficiency theory and adapted for simulating the components of the net ecosystem CO2 fluxes (NEE) and of the annual net ecosystem carbon budgets (NECB) at a daily time step. The approach is based on the assimilation of satellite-derived green area index (GAI) maps for calibrating a number of the SAFY-CO2 parameters linked to crop phenology. HSTR data from the Formosat-2 and SPOT satellites were used to produce the GAI maps. The experimental data set includes eddy covariance measurements of net CO2 fluxes from two experimental sites and partitioned into gross primary production (GPP) and ecosystem respiration (Reco). It also includes measurements of GAI, biomass and yield between 2005 and 2011, focusing on the winter wheat crop. The results showed that the SAFY-CO2 model correctly reproduced the biomass production, its dynamic and the yield (relative errors about 24%) in contrasted climatic, environmental and management conditions. The net CO2 flux components estimated with the model were overall in agreement with the ground data, presenting good correlations (R² about 0.93 for GPP, 0.77 for Reco and 0.86 for NEE). The evaluation of the modelled NECB for the different site-years highlighted the importance of having accurate estimates of each component of the NECB. Future works aim at considering systematically post-harvest events (such as re-growths, weeds and intercrops) on NEE assessment and at assimilating radar remote sensing data for estimating GAI and biomass more accurately. This approach is currently being extended to summer crops and it could be applied to larger scales thanks to the recent satellite missions (Landsat-8, Sentinel-1 and 2…).

Biocrude crop production in arid lands. [Calotropis procera, Chrysothamus paniculatus, Euphorbia lathyris, Grindelia camporum

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

McLaughlin, S.P.; Kingsolver, B.E.; Hoffmann, J.J.

1983-01-01

Published and unpublished estimates of land and water requirements and energy yield were used to prepare energy budgets for 4 potential biocrude (liquid fuel) crops in the SW USA: the perennials Calotropis procera and Chrysothamnus paniculatus and the annuals Euphorbia lathyris and Grindelia camporum. Estimated annual costs are examined and discussed for an operation processing 300,000 t/yr. The cheapest energy was produced by C. paniculatus, although it required the largest land area. The paper emphasizes that selecting for biocrude content (biomass quality) of plants may be at the expense of productivity (quantity) since the 2 have been shown to bemore » inversely related in many cases. 8 references.« less

Low tortoise abundances in pine forest plantations in forest-shrubland transition areas

PubMed Central

Rodríguez-Caro, Roberto C.; Oedekoven, Cornelia S.; Graciá, Eva; Anadón, José D.; Buckland, Stephen T.; Esteve-Selma, Miguel A.; Martinez, Julia; Giménez, Andrés

2017-01-01

In the transition between Mediterranean forest and the arid subtropical shrublands of the southeastern Iberian Peninsula, humans have transformed habitat since ancient times. Understanding the role of the original mosaic landscapes in wildlife species and the effects of the current changes as pine forest plantations, performed even outside the forest ecological boundaries, are important conservation issues. We studied variation in the density of the endangered spur-thighed tortoise (Testudo graeca) in three areas that include the four most common land types within the species’ range (pine forests, natural shrubs, dryland crop fields, and abandoned crop fields). Tortoise densities were estimated using a two-stage modeling approach with line transect distance sampling. Densities in dryland crop fields, abandoned crop fields and natural shrubs were higher (>6 individuals/ha) than in pine forests (1.25 individuals/ha). We also found large variation in density in the pine forests. Recent pine plantations showed higher densities than mature pine forests where shrub and herbaceous cover was taller and thicker. We hypothesize that mature pine forest might constrain tortoise activity by acting as partial barriers to movements. This issue is relevant for management purposes given that large areas in the tortoise’s range have recently been converted to pine plantations. PMID:28273135

Low tortoise abundances in pine forest plantations in forest-shrubland transition areas.

PubMed

Rodríguez-Caro, Roberto C; Oedekoven, Cornelia S; Graciá, Eva; Anadón, José D; Buckland, Stephen T; Esteve-Selma, Miguel A; Martinez, Julia; Giménez, Andrés

2017-01-01

In the transition between Mediterranean forest and the arid subtropical shrublands of the southeastern Iberian Peninsula, humans have transformed habitat since ancient times. Understanding the role of the original mosaic landscapes in wildlife species and the effects of the current changes as pine forest plantations, performed even outside the forest ecological boundaries, are important conservation issues. We studied variation in the density of the endangered spur-thighed tortoise (Testudo graeca) in three areas that include the four most common land types within the species' range (pine forests, natural shrubs, dryland crop fields, and abandoned crop fields). Tortoise densities were estimated using a two-stage modeling approach with line transect distance sampling. Densities in dryland crop fields, abandoned crop fields and natural shrubs were higher (>6 individuals/ha) than in pine forests (1.25 individuals/ha). We also found large variation in density in the pine forests. Recent pine plantations showed higher densities than mature pine forests where shrub and herbaceous cover was taller and thicker. We hypothesize that mature pine forest might constrain tortoise activity by acting as partial barriers to movements. This issue is relevant for management purposes given that large areas in the tortoise's range have recently been converted to pine plantations.

Validation of Global EO Biophysical Products at JECAM Test Site in Ukraine

NASA Astrophysics Data System (ADS)

Skakun, Sergii; Kussul, Nataliia; Kravchenko, Oleksiy; Basarab, Ruslan; Ostapenko, Vadym; Yailymov, Bohdan; Shelestov, Andrii; Kolotii, Andrii; Mironov, Andrii

Efficient global agriculture monitoring requires appropriate validation of Earth observation (EO) products for different regions and cropping system. This problem is addressed within the Joint Experiment of Crop Assessment and Monitoring (JECAM) initiative which aims to develop monitoring and reporting protocols and best practices for a variety of global agricultural systems. Ukraine is actively involved into JECAM, and a JECAM Ukraine test site was officially established in 2011. The following problems are being solved within JECAM Ukraine: (i) crop identification and crop area estimation [1]; (ii) crop yield forecasting [2]; (iii) EO products validation. The following case study regions were selected for these purposes: (i) the whole Kyiv oblast (28,000 sq. km) indented for crop mapping and acreage estimation; (ii) intensive observation sub-site in Pshenichne which is a research farm from the National University of Life and Environmental Sciences of Ukraine and indented for crop biophysical parameters estimation; (iii) Lviv region for rape-seed identification and crop rotation control; (iv) Crimea region for crop damage assessment due to droughts, and illegial field detection. In 2013, Ukrainian JECAM test site was selected as one of the “Champion User” for the ESA Sentinel-2 for Agriculture project. The test site was observed with SPOT-4 and RapidEye satellites every 5 days. The collected images are then used to simulate Sentinel-2 images for agriculture purposes. JECAM Ukraine is responsible for collecting ground observation data for validation purposes, and is involved in providing user requirements for Sentinel-2 agriculture related products. In particular, three field campaigns to characterize the vegetation biophysical parameters at the Pshenichne test site were carried out: First campaign - 14th to 17th of May 2013; second campaign - 12th to 15th of June 2013; third campaign - 14th to 17th of July 2013. Digital Hemispheric Photographs (DHP) images were acquired with a NIKON D70 camera. The images acquired during the field campaign are processed with the CAN-EYE software to derive LAI, FAPAR and FCOVER. The in situ biophysical values were used for producing LAI, FCOVER and FAPAR maps from optical satellite images, and provide cross-validation, and validation of global remote sensing products. The following satellite data were used: SPOT-4, RapidEye and Landsat-8. Inter-comparison of the derived products is performed. The paper presents an insight on the general methodology used within JECAM test site, the results achieved so far and challenges, and future planned activities. 1. Gallego, F.J., Kussul, N., Skakun, S., Kravchenko, O., Shelestov, A., Kussul, O. “Efficiency assessment of using satellite data for crop area estimation in Ukraine,” International Journal of Applied Earth Observation and Geoinformation, vol. 29, pp. 22-30, 2014. 2. Kogan, F., Kussul, N., Adamenko, T., Skakun, S., Kravchenko, O., Kryvobok, O., Shelestov, A., Kolotii, A., Kussul, O., Lavrenyuk, A., “Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models,” International Journal of Applied Earth Observation and Geoinformation, vol. 23, pp. 192-203, 2013.

Derivation of Optimal Cropping Pattern in Part of Hirakud Command using Cuckoo Search

NASA Astrophysics Data System (ADS)

Rath, Ashutosh; Biswal, Sudarsan; Samantaray, Sandeep; Swain, Prakash Chandra, PROF.

2017-08-01

The economicgrowth of a Nation depends on agriculture which relies on the obtainable water resources, available land and crops. The contribution of water in an appropriate quantity at appropriate time plays avitalrole to increase the agricultural production. Optimal utilization of available resources can be achieved by proper planning and management of water resources projects and adoption of appropriate technology. In the present work, the command area of Sambalpur distribrutary System is taken up for investigation. Further, adoption of a fixed cropping pattern causes the reduction of yield. The present study aims at developing different crop planning strategies to increase the net benefit from the command area with minimum investment. Optimization models are developed for Kharif season using LINDO and Cuckoo Search (CS) algorithm for maximization of the net benefits. In process of development of Optimization model the factors such as cultivable land, seeds, fertilizers, man power, water cost, etc. are taken as constraints. The irrigation water needs of major crops and the total available water through canals in the command of Sambalpur Distributary are estimated. LINDO and Cuckoo Search models are formulated and used to derive the optimal cropping pattern yielding maximum net benefits. The net benefits of Rs.585.0 lakhs in Kharif Season are obtained by adopting LINGO and 596.07 lakhs from Cuckoo Search, respectively, whereas the net benefits of 447.0 lakhs is received by the farmers of the locality with the adopting present cropping pattern.

Use of the Budyko Framework to Estimate the Virtual Water Content in Shijiazhuang Plain, North China

NASA Astrophysics Data System (ADS)

Zhang, E.; Yin, X.

2017-12-01

One of the most challenging steps in implementing analysis of virtual water content (VWC) of agricultural crops is how to properly assess the volume of consumptive water use (CWU) for crop production. In practice, CWU is considered equivalent to the crop evapotranspiration (ETc). Following the crop coefficient method, ETc can be calculated under standard or non-standard conditions by multiplying the reference evapotranspiration (ET0) by one or a few coefficients. However, when current crop growing conditions deviate from standard conditions, accurately determining the coefficients under non-standard conditions remains to be a complicated process and requires lots of field experimental data. Based on regional surface water-energy balance, this research integrates the Budyko framework into the traditional crop coefficient approach to simplify the coefficients determination. This new method enables us to assess the volume of agricultural VWC only based on some hydrometeorological data and agricultural statistic data in regional scale. To demonstrate the new method, we apply it to the Shijiazhuang Plain, which is an agricultural irrigation area in the North China Plain. The VWC of winter wheat and summer maize is calculated and we further subdivide VWC into blue and green water components. Compared with previous studies in this study area, VWC calculated by the Budyko-based crop coefficient approach uses less data and agrees well with some of the previous research. It shows that this new method may serve as a more convenient tool for assessing VWC.

Large Area Crop Inventory Experiment (LACIE). First interim phase 3 evaluation report. [Great Plains and U.S.S.R.

NASA Technical Reports Server (NTRS)

1978-01-01

The author has identified the following significant results. LACIE acreage estimates were in close agreement with SRS estimates, and an operational system with a 14 day LANDSAT data turnaround could have produced an accurate acreage estimate (one which satisfied the 90/90 criterion) 1 1/2 to 2 months before harvest. Low yield estimates, resulting from agromet conditions not taken into account in the yield models, caused production estimates to be correspondingly low. However, both yield and production estimates satisfied the LACIE 90/90 criterion for winter wheat in the yardstick region.

Biophysical parameters in a wheat producer region in southern Brazil

NASA Astrophysics Data System (ADS)

Leivas, Janice F.; de C. Teixeira, Antonio Heriberto; Andrade, Ricardo G.; de C. Victoria, Daniel; Bolfe, Edson L.; Cruz, Caroline R.

2014-10-01

Wheat (Triticum aestivum) is the second most produced cereal in the world, and has major importance in the global agricultural economy. Brazil is a large producer of wheat, especially the Rio Grande do Sul state, located in the south of the country. The purpose of this study was to analyze the estimation of biophysical parameters - evapotranspiration (ET), biomass (BIO) and water productivity (WP) - from satellite images of the municipalities with large areas planted with wheat in Rio Grande do Sul (RS). The evapotranspiration rate was obtained using the SAFER Model (Simple Algorithm for Retrieving Evapotranspiration) on MODIS (Moderate Resolution Imaging Spectroradiometer) images taken in the agricultural year 2012. In order to obtain biomass and water productivity rates we applied the Monteith model and the ratio between BIO and ET. In the beginning of the cycle (the planting period) we observed low values for ET, BIO and WP. During the development period, we observed an increase in the values of the parameters and decline at the end of the cycle, for the period of the wheat harvest. The SAFER model proved effective for estimating the biophysical parameters evapotranspiration, biomass production and water productivity in areas planted with wheat in Brazilian Southern. The methodology can be used for monitoring the crops' water conditions and biomass using satellite images, assisting in estimates of productivity and crop yield. The results may assist the understanding of biophysical properties of important agro-ecosystems, like wheat crop, and are important to improve the rational use of water resources.

Site specific toxicological risk from fluoride exposure through ingestion of vegetables and cereal crops in Unnao district, Uttar Pradesh, India.

PubMed

Jha, S K; Nayak, A K; Sharma, Y K

2011-05-01

A study was carried out to assess toxicological risk from the fluoride (F) exposure due to ingestion of vegetables and cereal crops such as rice and wheat grown in potentially fluoridated area (brick kiln and sodic areas), of different age groups in Unnao district, Uttar Pradesh, India. Fluoride contents in vegetables and cereal were found to be in the order brick kiln sites>sodic sites>normal sites. Among vegetables maximum F concentration was found in spinach and mint, whereas in cereal crops, wheat accumulated more F than rice. The exposure dose of F was determined using estimated daily intake (EDI) and bio-concentration factor (BCF) of F. The children of age group 3-14 years in the potentially fluoridated area were found to be at the risk of fluorosis. The mean BCF value of F was the highest in mint (36.6 mg/kg(dwt) plant/mg/kg(dwt) soil), followed by spinach (33.99 mg/kg(dwt) plant.mg/kg(dwt) soil). Copyright © 2011 Elsevier Inc. All rights reserved.

A Global-Scale Estimate of Ecosystem Services from Urban Agriculture: Understanding Incentives for Natural Capital in Cities

NASA Astrophysics Data System (ADS)

Clinton, N.; Stuhlmacher, M.; Miles, A.; Uludere, N.; Wagner, M.; Georgescu, M.; Herwig, C.; Gong, P.

2017-12-01

Despite substantial interest in urban agriculture, little is known about the aggregate benefits conferred by natural capital for growing food in cities. Here we perform a scenario-based analysis to quantify ecosystem services from adoption of urban agriculture at varying intensity. To drive the scenarios, we created global-scale estimates of vacant land, rooftop and building surface area, at one kilometer resolution, from remotely sensed and modeled geospatial data. We used national scale agricultural reports, climate and other geospatial data at global scale to estimate agricultural production and economic returns, storm-water avoidance, energy savings from avoided heating and cooling costs, and ecosystem services provided by nitrogen sequestration, pollination and biocontrol of pests. The results indicate that vacant lands, followed by rooftops, represent the largest opportunities for natural capital put to agricultural use in urban areas. Ecosystem services from putting such spaces to productive use are dominated by agricultural returns, but energy savings conferred by insulative characteristics of growth substrate also provide economic incentives. Storm water avoidance was estimated to be substantial, but no economic value was estimated. Relatively low economic returns were estimated from the other ecosystem services examined. In aggregate, approximately $10-100 billion in economic incentives, before costs, were estimated. The results showed that relatively developed, high-income countries stand the most to gain from urban agricultural adoption due to the unique combination of climate, crop mixture and crop prices. While the results indicate that urban agriculture is not a panacea for urban food security issues, there is potential to simultaneously ameliorate multiple issues around food, energy and water in urbanized areas.

Regression model estimation of early season crop proportions: North Dakota, some preliminary results

NASA Technical Reports Server (NTRS)

Lin, K. K. (Principal Investigator)

1982-01-01

To estimate crop proportions early in the season, an approach is proposed based on: use of a regression-based prediction equation to obtain an a priori estimate for specific major crop groups; modification of this estimate using current-year LANDSAT and weather data; and a breakdown of the major crop groups into specific crops by regression models. Results from the development and evaluation of appropriate regression models for the first portion of the proposed approach are presented. The results show that the model predicts 1980 crop proportions very well at both county and crop reporting district levels. In terms of planted acreage, the model underpredicted 9.1 percent of the 1980 published data on planted acreage at the county level. It predicted almost exactly the 1980 published data on planted acreage at the crop reporting district level and overpredicted the planted acreage by just 0.92 percent.

Remote sensing inputs to water demand modeling

NASA Technical Reports Server (NTRS)

Estes, J. E.; Jensen, J. R.; Tinney, L. R.; Rector, M.

1975-01-01

In an attempt to determine the ability of remote sensing techniques to economically generate data required by water demand models, the Geography Remote Sensing Unit, in conjunction with the Kern County Water Agency of California, developed an analysis model. As a result it was determined that agricultural cropland inventories utilizing both high altitude photography and LANDSAT imagery can be conducted cost effectively. In addition, by using average irrigation application rates in conjunction with cropland data, estimates of agricultural water demand can be generated. However, more accurate estimates are possible if crop type, acreage, and crop specific application rates are employed. An analysis of the effect of saline-alkali soils on water demand in the study area is also examined. Finally, reference is made to the detection and delineation of water tables that are perched near the surface by semi-permeable clay layers. Soil salinity prediction, automated crop identification on a by-field basis, and a potential input to the determination of zones of equal benefit taxation are briefly touched upon.

Modeling crop residue burning experiments to evaluate smoke emissions and plume transport.

PubMed

Zhou, Luxi; Baker, Kirk R; Napelenok, Sergey L; Pouliot, George; Elleman, Robert; O'Neill, Susan M; Urbanski, Shawn P; Wong, David C

2018-06-15

Crop residue burning is a common land management practice that results in emissions of a variety of pollutants with negative health impacts. Modeling systems are used to estimate air quality impacts of crop residue burning to support retrospective regulatory assessments and also for forecasting purposes. Ground and airborne measurements from a recent field experiment in the Pacific Northwest focused on cropland residue burning was used to evaluate model performance in capturing surface and aloft impacts from the burning events. The Community Multiscale Air Quality (CMAQ) model was used to simulate multiple crop residue burns with 2 km grid spacing using field-specific information and also more general assumptions traditionally used to support National Emission Inventory based assessments. Field study specific information, which includes area burned, fuel consumption, and combustion completeness, resulted in increased biomass consumption by 123 tons (60% increase) on average compared to consumption estimated with default methods in the National Emission Inventory (NEI) process. Buoyancy heat flux, a key parameter for model predicted fire plume rise, estimated from fuel loading obtained from field measurements can be 30% to 200% more than when estimated using default field information. The increased buoyancy heat flux resulted in higher plume rise by 30% to 80%. This evaluation indicates that the regulatory air quality modeling system can replicate intensity and transport (horizontal and vertical) features for crop residue burning in this region when region-specific information is used to inform emissions and plume rise calculations. Further, previous vertical emissions allocation treatment of putting all cropland residue burning in the surface layer does not compare well with measured plume structure and these types of burns should be modeled more similarly to prescribed fires such that plume rise is based on an estimate of buoyancy. Copyright © 2018 Elsevier B.V. All rights reserved.

Hydrological and sedimentation implications of landscape changes in a Himalayan catchment due to bioenergy cropping

NASA Astrophysics Data System (ADS)

Remesan, Renji; Holman, Ian; Janes, Victoria

2015-04-01

There is a global effort to focus on the development of bioenergy and energy cropping, due to the generally increasing demand for crude oil, high oil price volatility and climate change mitigation challenges. Second generation energy cropping is expected to increase greatly in India as the Government of India has recently approved a national policy of 20 % biofuel blending by 2017; furthermore, the country's biomass based power generation potential is estimated as around ~24GW and large investments are expected in coming years to increase installed capacity. In this study, we have modelled the environmental influences (e.g.: hydrology and sediment) of scenarios of increased biodiesel cropping (Jatropha curcas) using the Soil and Water Assessment Tool (SWAT) in a northern Indian river basin. SWAT has been applied to the River Beas basin, using daily Tropical Rainfall Measuring Mission (TRMM) precipitation and NCEP Climate Forecast System Reanalysis (CFSR) meteorological data to simulate the river regime and crop yields. We have applied Sequential Uncertainty Fitting Ver. 2 (SUFI-2) to quantify the parameter uncertainty of the stream flow modelling. The model evaluation statistics for daily river flows at the Jwalamukhi and Pong gauges show good agreement with measured flows (Nash Sutcliffe efficiency of 0.70 and PBIAS of 7.54 %). The study has applied two land use change scenarios of (1) increased bioenergy cropping in marginal (grazing) lands in the lower and middle regions of catchment (2) increased bioenergy cropping in low yielding areas of row crops in the lower and middle regions of the catchment. The presentation will describe the improved understanding of the hydrological, erosion and sediment delivery and food production impacts arising from the introduction of a new cropping variety to a marginal area; and illustrate the potential prospects of bioenergy production in Himalayan valleys.

Rice crop risk map in Babahoyo canton (Ecuador)

NASA Astrophysics Data System (ADS)

Valverde Arias, Omar; Tarquis, Ana; Garrido, Alberto

2016-04-01

It is widely known that extreme climatic phenomena occur with more intensity and frequency. This fact has put more pressure over farming, making agricultural and livestock production riskier. In order to reduce hazards and economic loses that could jeopardize farmer's incomes and even its business continuity, it is very important to implement agriculture risk management plans by governments and institutions. One of the main strategies is transfer risk by agriculture insurance. Agriculture insurance based in indexes has a significant growth in the last decade. And consist in a comparison between measured index values with a defined threshold that triggers damage losses. However, based index insurance could not be based on an isolated measurement. It is necessary to be integrated in a complete monitoring system that uses many sources of information and tools. For example, index influence areas, crop production risk maps, crop yields, claim statistics, and so on. Crop production risk is related with yield variation of crops and livestock, due to weather, pests, diseases, and other factors that affect both the quantity and quality of commodities produced. This is the risk which farmers invest more time managing, and it is completely under their control. The aim of this study is generate a crop risk map of rice that can provide risk manager important information about the status of crop facing production risks. Then, based on this information, it will be possible to make best decisions to deal with production risk. The rice crop risk map was generated qualifying a 1:25000 scale soil and climatic map of Babahoyo canton, which is located in coast region of Ecuador, where rice is one of the main crops. The methodology to obtain crop risk map starts by establishing rice crop requirements and indentifying the risks associated with this crop. A second step is to evaluate soil and climatic conditions of the study area related to optimal crop requirements. Based on it, we can determinate which level of rice crop requirement is met. Finally we have established rice crop zones classified as: suitable, moderate suitable, marginal suitable and unsuitable. Several methods have been used to estimate the degree with which crop requirements are satisfied, pondering weights of limiting factors to adequate crop conditions. Better conditions for cropping in a specific area imply less risk in production. In this case, crop will be less affected by pests and disease, although this closely depends on crop management. Farmers have to invest less money to produce and could increase their benefit. Results are showed and discussed with the aim to study the efficiency and potential of this risk map.

Ammonia emissions from the agriculture sector in Argentina; 2000-2012

NASA Astrophysics Data System (ADS)

Castesana, Paula S.; Dawidowski, Laura E.; Finster, Laura; Gómez, Darío R.; Taboada, Miguel A.

2018-04-01

Agriculture is one of the key economic sectors in Argentina and, in the last decades, the increase in prices and competitiveness of some grains has imposed important changes. In this process, crop cultivation occupied significant extensions of land areas previously dedicated to livestock farming, which in turn have experienced intensification in terms of production through an increasing share of feedlot systems. The agriculture sector is the main NH3 emitter in Argentina, however no inventory developed locally has been thus far available. We estimated the time series 2000-2012 of NH3 emissions, both at national and spatially disaggregated levels. National NH3 emissions in 2012 amounted to 0.31 ± 0.08 Tg, with the use of mineral fertilizers accounting for 43.0%, manure in pasture 32.5%, manure management 23.0% and agricultural waste burning 1.5%. Urea use was the major source of NH3 emissions and its application on wheat and corn crops dominated the trend. Emissions from open biomass burning were estimated but not included in the national totals because of the difficulties in differentiating between agricultural (i.e., prescribed burning of savannas) and non-agricultural emission sources. Compared to this work, NH3 emissions reported by EDGAR were 83% higher than our estimates. The time series of spatially distributed NH3 emission estimates clearly showed the effect of the expansion of cropland, the displacement of planted areas of N-fertilizes crops by competing soybean cultivation and the relocation and intensification of beef cattle production. This new inventory constitutes a tool for policies concerning the impact of agricultural activities on air quality and contributes with more accurate and updated information useful for atmospheric chemical transport modeling. The accuracy and applicability of the inventory may be improved by local studies aimed at refining the spatial disaggregation by focusing in specific areas of fertilizer application, reflecting seasonal and monthly patterns in agricultural practices and climate conditions and addressing likely changes in diets, productivity and excretion rates over time.

Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue, France

NASA Astrophysics Data System (ADS)

Manfron, Giacinto; Delmotte, Sylvestre; Busetto, Lorenzo; Hossard, Laure; Ranghetti, Luigi; Brivio, Pietro Alessandro; Boschetti, Mirco

2017-05-01

Crop simulation models are commonly used to forecast the performance of cropping systems under different hypotheses of change. Their use on a regional scale is generally constrained, however, by a lack of information on the spatial and temporal variability of environment-related input variables (e.g., soil) and agricultural practices (e.g., sowing dates) that influence crop yields. Satellite remote sensing data can shed light on such variability by providing timely information on crop dynamics and conditions over large areas. This paper proposes a method for analyzing time series of MODIS satellite data in order to estimate the inter-annual variability of winter wheat sowing dates. A rule-based method was developed to automatically identify a reliable sample of winter wheat field time series, and to infer the corresponding sowing dates. The method was designed for a case study in the Camargue region (France), where winter wheat is characterized by vernalization, as in other temperate regions. The detection criteria were chosen on the grounds of agronomic expertise and by analyzing high-confidence time-series vegetation index profiles for winter wheat. This automatic method identified the target crop on more than 56% (four-year average) of the cultivated areas, with low commission errors (11%). It also captured the seasonal variability in sowing dates with errors of ±8 and ±16 days in 46% and 66% of cases, respectively. Extending the analysis to the years 2002-2012 showed that sowing in the Camargue was usually done on or around November 1st (±4 days). Comparing inter-annual sowing date variability with the main local agro-climatic drivers showed that the type of preceding crop and the weather conditions during the summer season before the wheat sowing had a prominent role in influencing winter wheat sowing dates.

From LACIE to GEOGLAM: Integrating Earth Observations into Operational Agricultural Monitoring Systems

NASA Astrophysics Data System (ADS)

Becker-Reshef, I.; Justice, C. O.

2012-12-01

Earth observation data, owing to their synoptic, timely and repetitive coverage, have long been recognized as an indispensible tool for agricultural monitoring at local to global scales. Research and development over the past several decades in the field of agricultural remote sensing has led to considerable capacity for crop monitoring within the current operational monitoring systems. These systems are relied upon nationally and internationally to provide crop outlooks and production forecasts as the growing season progresses. This talk will discuss the legacy and current state of operational agricultural monitoring using earth observations. In the US, the National Aeronautics and Space Administration (NASA) and the US Department of Agriculture (USDA) have been collaborating to monitor global agriculture from space since the 1970s. In 1974, the USDA, NASA and National Oceanic and Atmospheric Administration (NOAA) initiated the Large Area Crop Inventory Experiment (LACIE) which demonstrated that earth observations could provide vital information on crop production, with unprecedented accuracy and timeliness, prior to harvest. This experiment spurred many agencies and researchers around the world to further develop and evaluate remote sensing technologies for timely, large area, crop monitoring. The USDA and NASA continue to closely collaborate. More recently they jointly initiated the Global Agricultural Monitoring Project (GLAM) to enhance the agricultural monitoring and the crop-production estimation capabilities of the USDA Foreign Agricultural Service by using the new generation of NASA satellite observations including from MODIS and the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments. Internationally, in response to the growing calls for improved agricultural information, the Group on Earth Observations (partnership of governments and international organizations) developed the Global Agricultural Monitoring (GEOGLAM) initiative which was adopted by the G20 as part of the action plan on food price volatility and agriculture. The goal of GEOGLAM is to enhance agricultural production estimates through leveraging advances in the research domain and in satellite technologies, and integrating these into the existing operational monitoring systems.

An indirect approach to assess the pests on sorghum by remote sensing

NASA Astrophysics Data System (ADS)

Singh, D.; Sao, R.

In today's world of advanced technology various techniques are being used to study ecological parameter and gathering data for agricultural benefits. The major aspects of remote sensing are timely estimates of agriculture crop yield, prediction of pest etc. The damage caused by the pest to crop is well known. Therefore, in this paper, an attempt has to be made to estimate the number of pests on sorghum by remote sensing technique. The studies were made on crop Sorghum (Meethi Sudan) that is a forage variety and the pest observed is a species of grasshopper. The beds of crop sorghum were specially prepared for pests as well as microwave scattering measurements. In first phase of study, dependence of number of pests on sorghum plant parameters (i.e., crop covered moist soil (SM), plant height (PH), leaf area index (LAI), percentage Biomass (BIO), Total chlorophyll (TC)) have been observed by the regression analyses and it was found that pests were more dependent on sorghum chlorophyll than other plant parameters, while climatic conditions were taken as constant. A linear relationship has been obtained between number of pests and TC with quite significant values of coefficient of determination (r^2=0.86). These crop parameters are easily assessable through microwave remote sensing so they can form the basis for prediction of pest remotely. In second phase of study, several observations were carried out for various growth stages of sorghum using bistatic scatterometer for both like polarizations (i.e., HH- and VV-) and different incidence angles at X-band (9.5 GHz). Linear, and multiple regression analysis were carried out to check dependence of scattering coefficient on these crop parameters and it was noticed that scattering coefficient was more dependent on sorghum TC than other plant parameters at X-band. A negative correlation has been obtained between TC and scattering coefficient with quite good values of r^2 (0.82). VV-pol gives better results than HH-pol and incidence angle should be more than 40 degree for both like pols for assessing the sorghum TC at X-band. The TC assessed by the microwave measurements was helpful to estimate the number of pests on sorghum. Combining both phase of study, number of pests was estimated and a quite good agreement (r^2=0.76) was found between observed and estimated pests.

Automated mapping of soybean and corn using phenology

NASA Astrophysics Data System (ADS)

Zhong, Liheng; Hu, Lina; Yu, Le; Gong, Peng; Biging, Gregory S.

2016-09-01

For the two of the most important agricultural commodities, soybean and corn, remote sensing plays a substantial role in delivering timely information on the crop area for economic, environmental and policy studies. Traditional long-term mapping of soybean and corn is challenging as a result of the high cost of repeated training data collection, the inconsistency in image process and interpretation, and the difficulty of handling the inter-annual variability of weather and crop progress. In this study, we developed an automated approach to map soybean and corn in the state of Paraná, Brazil for crop years 2010-2015. The core of the approach is a decision tree classifier with rules manually built based on expert interaction for repeated use. The automated approach is advantageous for its capacity of multi-year mapping without the need to re-train or re-calibrate the classifier. Time series MODerate-resolution Imaging Spectroradiometer (MODIS) reflectance product (MCD43A4) were employed to derive vegetation phenology to identify soybean and corn based on crop calendar. To deal with the phenological similarity between soybean and corn, the surface reflectance of the shortwave infrared band scaled to a phenological stage was used to fully separate the two crops. Results suggested that the mapped areas of soybean and corn agreed with official statistics at the municipal level. The resultant map in the crop year 2012 was evaluated using an independent reference data set, and the overall accuracy and Kappa coefficient were 87.2% and 0.804 respectively. As a result of mixed pixel effect at the 500 m resolution, classification results were biased depending on topography. In the flat, broad and highly-cropped areas, uncultivated lands were likely to be identified as soybean or corn, causing over-estimation of cropland area. By contrast, scattered crop fields in mountainous regions with dense natural vegetation tend to be overlooked. For future mapping efforts, it has great potential to apply the automated mapping algorithm to other image series at various scales especially high-resolution images.

Crop Characteristics Research: Growth and Reflectance Analysis

NASA Technical Reports Server (NTRS)

Badhwar, G. D. (Principal Investigator)

1985-01-01

Much of the early research in remote sensing follows along developing spectral signatures of cover types. It was found, however, that a signature from an unknown cover class could not always be matched to a catalog value of known cover class. This approach was abandoned and supervised classification schemes followed. These were not efficient and required extensive training. It was obvious that data acquired at a single time could not separate cover types. A large portion of the proposed research has concentrated on modeling the temporal behavior of agricultural crops and on removing the need for any training data in remote sensing surveys; the key to which is the solution of the so-called 'signature extension' problem. A clear need to develop spectral estimaters of crop ontogenic stages and yield has existed even though various correlations have been developed. Considerable effort in developing techniques to estimate these variables was devoted to this work. The need to accurately evaluate existing canopy reflectance model(s), improve these models, use them to understand the crop signatures, and estimate leaf area index was the third objective of the proposed work. A synopsis of this research effort is discussed.

Mapping Multi-Cropped Land Use to Estimate Water Demand Using the California Pesticide Reporting Database

NASA Astrophysics Data System (ADS)

Henson, W.; Baillie, M. N.; Martin, D.

2017-12-01

Detailed and dynamic land-use data is one of the biggest data deficiencies facing food and water security issues. Better land-use data results in improved integrated hydrologic models that are needed to look at the feedback between land and water use, specifically for adequately representing changes and dynamics in rainfall-runoff, urban and agricultural water demands, and surface fluxes of water (e.g., evapotranspiration, runoff, and infiltration). Currently, land-use data typically are compiled from annual (e.g., Crop Scape) or multi-year composites if mapped at all. While this approach provides information about interannual land-use practices, it does not capture the dynamic changes in highly developed agricultural lands prevalent in California agriculture such as (1) dynamic land-use changes from high frequency multi-crop rotations and (2) uncertainty in sub-annual crop distribution, planting times, and cropped areas. California has collected spatially distributed data for agricultural pesticide use since 1974 through the California Pesticide Information Portal (CalPIP). A method leveraging the CalPIP database has been developed to provide vital information about dynamic agricultural land use (e.g., crop distribution and planting times) and water demand issues in Salinas Valley, California, along the central coast. This 7 billion dollar/year agricultural area produces up to 50% of U.S. lettuce and broccoli. Therefore, effective and sustainable water resource development in the area must balance the needs of this essential industry, other beneficial uses, and the environment. This new tool provides a way to provide more dynamic crop data in hydrologic models. While the current application focuses on the Salinas Valley, the methods are extensible to all of California and other states with similar pesticide reporting. The improvements in representing variability in crop patterns and associated water demands increase our understanding of land-use change and precision of hydrologic decision models. Ultimately, further refinement to the parcel level will completely capture the changing topology of agricultural land use.

Improving Snow Process Modeling with Satellite-Based Estimation of Near-Surface-Air-Temperature Lapse Rate

NASA Astrophysics Data System (ADS)

Wahome, A.; Ndungu, L. W.; Ndubi, A. O.; Ellenburg, W. L.; Flores Cordova, A. I.

2016-12-01

Climate variability coupled with over-reliance on rain-fed agricultural production on already strained land that is facing degradation and declining soil fertility; highly impacts food security in Africa. In Kenya, dependence on the approximately 20% of land viable for agricultural production under climate stressors such as variations in amount and frequency of rainfall within the main growing season in March-April-May(MAM) and changing temperatures influence production. With time, cropping zones have changed with the changing climatic conditions. In response, the needs of decision makers to effectively assess the current cropped areas and the changes in cropping patterns, SERVIR East and Southern Africa developed updated crop maps and change maps. Specifically, the change maps depict the change in cropping patterns between 2000 and 2015 with a further assessment done on important food crops such as maize. Between 2001 and 2015 a total of 5394km2 of land was converted to cropland with 3370km2 being conversion to maize production. However, 318 sq km were converted from maize to other crops or conversion to other land use types. To assess the changes in climatic conditions, climate parameters such as precipitation trends, variation and averages over time were derived from CHIRPs (Climate Hazards Infra-red Precipitation with stations) which is a quasi-global blended precipitation dataset available at a resolution of approximately 5km. Water Requirements Satisfaction Index (WRSI) water balance model was used to assess long term trends in crop performance as a proxy for maize yields. From the results, areas experiencing declining and varying precipitation with a declining WRSI index during the long rains displayed agricultural expansion with new areas being converted to cropland. In response to climate variability, farmers have converted more land to cropland instead of adopting better farming methods such as adopting drought resistant cultivars and using better farm inputs.

Understanding the climate-included variations in the seasonal water demands of irrigated crops in Northern India

NASA Astrophysics Data System (ADS)

Bhattarai, N.; Jain, M.

2016-12-01

Expected changes in temperature and precipitation patterns in the rice-wheat belt of Northern India have implications for balancing crop water demand and available water resources. Because the impacts of water scarcity and reduced crop production are realized at a local scale, water-saving interventions are most effective when implemented locally. However, a paucity of fine-scale studies on the relationship between variations in climate and crop water demand has limited our ability to effectively implement such interventions. In an effort to better understand the responses of irrigated crops to changing climate in Northern India at finer-scales, we propose a remote sensing based semi-empirical approach. First, we employ a multi-model surface energy balance (SEB) approach to map seasonal evapotranspiration (ET)/water use (1995-2015) at 30 to 100 m resolution from space and investigate how seasonal and inter-annual variations in temperature and precipitation are associated with regional surface-energy budgets. Second, using remote estimates of ET and other biophysical variables, such as vegetation indices, land surface temperature, and albedo, we will explain the possible relationships between climate change and seasonal water demands of crops. Our estimates of high/moderate resolution (30 to 100 m) seasonal ET maps can make clear distinctions between impacts of climate variations on crop water demand at field, plot, and regional scales in Northern India. Finally, by improving our ability to identify targeted area for water-saving interventions, this study supports agricultural resiliency of Northern India in the face of climate change.

Marginal cost curves for water footprint reduction in irrigated agriculture: a policy and decision making guide for efficient water use in crop production

NASA Astrophysics Data System (ADS)

Chukalla, Abebe; Krol, Maarten; Hoekstra, Arjen

2016-04-01

Reducing water footprints (WF) in irrigated crop production is an essential element in water management, particularly in water-scarce areas. To achieve this, policy and decision making need to be supported with information on marginal cost curves that rank measures to reduce the WF according to their cost-effectiveness and enable the estimation of the cost associated with a certain WF reduction target, e.g. towards a certain reasonable WF benchmark. This paper aims to develop marginal cost curves (MCC) for WF reduction. The AquaCrop model is used to explore the effect of different measures on evapotranspiration and crop yield and thus WF that is used as input in the MCC. Measures relate to three dimensions of management practices: irrigation techniques (furrow, sprinkler, drip and subsurface drip); irrigation strategies (full and deficit irrigation); and mulching practices (no mulching, organic and synthetic mulching). A WF benchmark per crop is calculated as resulting from the best-available production technology. The marginal cost curve is plotted using the ratios of the marginal cost to WF reduction of the measures as ordinate, ranking with marginal costs rise with the increase of the reduction effort. For each measure, the marginal cost to reduce WF is estimated by comparing the associated WF and net present value (NPV) to the reference case (furrow irrigation, full irrigation, no mulching). The NPV for each measure is based on its capital costs, operation and maintenances costs (O&M) and revenues. A range of cases is considered, including: different crops, soil types and different environments. Key words: marginal cost curve, water footprint benchmark, soil water balance, crop growth, AquaCrop

East Europe Report, Economic and Industrial Affairs, No. 2394

DTIC Science & Technology

1983-04-28

been designated to replenish the sowing stocks of private farmers. The requirement for the fodder plant seeds is estimated at 4,600 tons. The Plant ...Interview Lublin Area January Inventory Results, by Stanislaw Ozonek Winter Crops, Spring Planting Olsztyn Area Spring Preparations YUGOSLAVIA...country. The Seventh Plenum of the CPCZ Central Committee in November 1982 designated foreign economic relations as one of the priorities of party

Landscape Effects on Reproduction of Euschistus servus (Hemiptera: Pentatomidae), a Mobile, Polyphagous, Multivoltine Arthropod Herbivore.

PubMed

Olson, Dawn M; Prescott, Kristina K; Zeilinger, Adam R; Hou, Suqin; Coffin, Alisa W; Smith, Coby M; Ruberson, John R; Andow, David A

2018-06-06

Landscape factors can significantly influence arthropod populations. The economically important brown stink bug, Euschistus servus (Say) (Hemiptera: Pentatomidae), is a native mobile, polyphagous and multivoltine pest of many crops in southeastern United States and understanding the relative influence of local and landscape factors on their reproduction may facilitate population management. Finite rate of population increase (λ) was estimated in four major crop hosts-maize, peanut, cotton, and soybean-over 3 yr in 16 landscapes of southern Georgia. A geographic information system (GIS) was used to characterize the surrounding landscape structure. LASSO regression was used to identify the subset of local and landscape characteristics and predator densities that account for variation in λ. The percentage area of maize, peanut and woodland and pasture in the landscape and the connectivity of cropland had no influence on E. servus λ. The best model for explaining variation in λ included only four predictor variables: whether or not the sampled field was a soybean field, mean natural enemy density in the field, percentage area of cotton in the landscape and the percentage area of soybean in the landscape. Soybean was the single most important variable for determining E. servus λ, with much greater reproduction in soybean fields than in other crop species. Penalized regression and post-selection inference provide conservative estimates of the landscape-scale determinants of E. servus reproduction and indicate that a relatively simple set of in-field and landscape variables influences reproduction in this species.

Estimation of yield and water requirements of maize crops combining high spatial and temporal resolution images with a simple crop model, in the perspective of the Sentinel-2 mission

NASA Astrophysics Data System (ADS)

Battude, Marjorie; Bitar, Ahmad Al; Brut, Aurore; Cros, Jérôme; Dejoux, Jean-François; Huc, Mireille; Marais Sicre, Claire; Tallec, Tiphaine; Demarez, Valérie

2016-04-01

Water resources are under increasing pressure as a result of global change and of a raising competition among the different users (agriculture, industry, urban). It is therefore important to develop tools able to estimate accurately crop water requirements in order to optimize irrigation while maintaining acceptable production. In this context, remote sensing is a valuable tool to monitor vegetation development and water demand. This work aims at developing a robust and generic methodology mainly based on high resolution remote sensing data to provide accurate estimates of maize yield and water needs at the watershed scale. Evapotranspiration (ETR) and dry aboveground biomass (DAM) of maize crops were modeled using time series of GAI images used to drive a simple agro-meteorological crop model (SAFYE, Duchemin et al., 2005). This model is based on a leaf partitioning function (Maas, 1993) for the simulation of crop biomass and on the FAO-56 methodology for the ETR simulation. The model also contains a module to simulate irrigation. This study takes advantage of the SPOT4 and SPOT5 Take5 experiments initiated by CNES (http://www.cesbio.ups-tlse.fr/multitemp/). They provide optical images over the watershed from February to May 2013 and from April to August 2015 respectively, with a temporal and spatial resolution similar to future images from the Sentinel-2 and VENμS missions. This dataset was completed with LandSat8 and Deimos1 images in order to cover the whole growing season while reducing the gaps in remote sensing time series. Radiometric, geometric and atmospheric corrections were achieved by the THEIA land data center, and the KALIDEOS processing chain. The temporal dynamics of the green area index (GAI) plays a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Consistent seasonal dynamics of the remotely sensed GAI was estimated by applying a radiative transfer model based on artificial neural networks (BVNET, Baret,Weiss et al.). This tool allows using multiple sensors at different view angles while removing sensor and acquisition artifacts. Simultaneously, in situ data such as GAI, DAM, final grain yield, soil humidity and irrigation rates were collected over a set of plots allowing to sample the heterogeneity of the entire watershed. ETR fluxes were also measured continuously over maize crops in the Lamasquère (CESBIO) experimental site (http://fluxnet.ornl.gov/site/477). Preliminary results show that the model reproduced correctly the final yield at both local and regional scale and for different years. It was also tested in a predictive mode with quite good results. The model is also able to provide good estimates of ETR. The results highlighted the capacity to take into account the effect of water stress and irrigation on DAM. This approach combined with Sentinel-2 mission can offer a great opportunity for operational applications such as optimization of crop water management over large areas.

A coupled remote sensing and the Surface Energy Balance based algorithms to estimate actual evapotranspiration over the western and southern regions of Saudi Arabia

NASA Astrophysics Data System (ADS)

Mahmoud, Shereif H.; Alazba, A. A.

2016-07-01

In countries with absolute water scarcity such as the Kingdom of Saudi Arabia (KSA), large-scale actual evapotranspiration estimation is of great concern in water use practices. Herein, spatial and temporal distribution of actual evapotranspiration (AET) in the western and southern regions of KSA during 1992-2014 was estimated using the SEBAL model with field observations. Zonal statistics for each land use-cover type were also identified, in order to understand their effects on water consumption. In addition, daily and seasonal water consumption for major crops was computed. Results revealed a gradual increase in monthly AET values from January to April and subsequent decline from May to December. The maximum monthly AET values were observed for irrigated cropland in southwestern, central, and southeastern regions of Asir Province, central and southwestern regions of Al-Baha Province, central and the plains region of Jazan Province, southern portion of Makkah Province, and limited areas in the northern regions of Madinah Province. The annual AET ranged from 418.8 to 3442.3 mm yr-1. The normal distribution of mean annual AET values ranged from 717 to 1020 mm yr-1. Forty-two percent of the study area had an annual AET that ranged from 717 to 1020 mm yr-1. The second highest range of frequencies was concentrated around 1020-1322 mm yr-1, representing the majority of agricultural land. The consumptive water use of the different land cover types in study area indicated that irrigated cropland which occupied 14.6% of the study area had AET rates much higher than other land uses. Water bodies are the next highest, with forest and shrubland and sparse vegetation slightly lower, and very low AET rates from bare soil. Daily and seasonal water consumption of major cropping systems varied spatially depending on cropping practices and climatic conditions.

Synergetic use of Sentinel-1 and 2 to improve agro-hydrological modeling. Results of groundwater pumping estimates in south-India and nitrogen excess in south-west of France

NASA Astrophysics Data System (ADS)

Ferrant, S.; Le Page, M.; Kerr, Y. H.; Selles, A.; Mermoz, S.; Al-Bitar, A.; Muddu, S.; Gascoin, S.; Marechal, J. C.; Durand, P.; Salmon-Monviola, J.; Ceschia, E.; Bustillo, V.

2016-12-01

Nitrogen transfers at agricultural catchment level are intricately linked to water transfers. Agro-hydrological modeling approaches aim at integrating spatial heterogeneity of catchment physical properties together with agricultural practices to spatially estimate the water and nitrogen cycles. As in hydrology, the calibration schemes are designed to optimize the performance of the temporal dynamics and biases in model simulations, while ignoring the simulated spatial pattern. Yet, crop uses, i.e. transpiration and nitrogen exported by harvest, are the main fluxes at the catchment scale, highly variable in space and time. Geo-information time-series of vegetation and water index with multi-spectral optical detection S2 together with surface roughness time series with C-band radar detection S1 are used to reset soil water holding capacity parameters (depth, porosity) and agricultural practices (sowing date, irrigated area extent) of a crop model coupled with a hydrological model. This study takes two agro-hydrological contexts as demonstrators: 1-spatial nitrogen excess estimation in south-west of France, and 2-groundwater extraction for rice irrigation in south-India. Spatio-temporal patterns are involved in respectively surface water contamination due to over-fertilization and local groundwater shortages due to over-pumping for above rice inundation. Optimized Leaf Area Index profiles are simulated at the satellite images pixel level using an agro-hydrological model to reproduce spatial and temporal crop growth dynamics in south-west of France, improving the in-stream nitrogen fluxes by 12%. Accurate detection of irrigated area extents are obtained with the thresholding method based on optical indices, with a kappa of 0.81 for the dry season 2016. The actual monsoon season is monitored and will be presented. These extents drive the groundwater pumping and are highly variable in time (from 2 to 8% of the total area).

The estimation of rice paddy yield with GRAMI crop model and Geostationary Ocean Color Imager (GOCI) image over South Korea

NASA Astrophysics Data System (ADS)

Yeom, J. M.; Kim, H. O.

2014-12-01

In this study, we estimated the rice paddy yield with moderate geostationary satellite based vegetation products and GRAMI model over South Korea. Rice is the most popular staple food for Asian people. In addition, the effects of climate change are getting stronger especially in Asian region, where the most of rice are cultivated. Therefore, accurate and timely prediction of rice yield is one of the most important to accomplish food security and to prepare natural disasters such as crop defoliation, drought, and pest infestation. In the present study, GOCI, which is world first Geostationary Ocean Color Image, was used for estimating temporal vegetation indices of the rice paddy by adopting atmospheric correction BRDF modeling. For the atmospheric correction with LUT method based on Second Simulation of the Satellite Signal in the Solar Spectrum (6S), MODIS atmospheric products such as MOD04, MOD05, MOD07 from NASA's Earth Observing System Data and Information System (EOSDIS) were used. In order to correct the surface anisotropy effect, Ross-Thick Li-Sparse Reciprocal (RTLSR) BRDF model was performed at daily basis with 16day composite period. The estimated multi-temporal vegetation images was used for crop classification by using high resolution satellite images such as Rapideye, KOMPSAT-2 and KOMPSAT-3 to extract the proportional rice paddy area in corresponding a pixel of GOCI. In the case of GRAMI crop model, initial conditions are determined by performing every 2 weeks field works at Chonnam National University, Gwangju, Korea. The corrected GOCI vegetation products were incorporated with GRAMI model to predict rice yield estimation. The predicted rice yield was compared with field measurement of rice yield.

Quantifying the Limitation to World Cereal Production Due To Soil Phosphorus Status

NASA Astrophysics Data System (ADS)

Kvakić, Marko; Pellerin, Sylvain; Ciais, Philippe; Achat, David L.; Augusto, Laurent; Denoroy, Pascal; Gerber, James S.; Goll, Daniel; Mollier, Alain; Mueller, Nathaniel D.; Wang, Xuhui; Ringeval, Bruno

2018-01-01

Phosphorus (P) is an essential element for plant growth. Low P availability in soils is likely to limit crop yields in many parts of the world, but this effect has never been quantified at the global scale by process-based models. Here we attempt to estimate P limitation in three major cereals worldwide for the year 2000 by combining information on soil P distribution in croplands and a generic crop model, while accounting for the nature of soil-plant P transport. As a global average, the diffusion-limited soil P supply meets the crop's P demand corresponding to the climatic yield potential, due to the legacy soil P in highly fertilized areas. However, when focusing on the spatial distribution of P supply versus demand, we found strong limitation in regions like North and South America, Africa, and Eastern Europe. Averaged over grid cells where P supply is lower than demand, the global yield gap due to soil P is estimated at 22, 55, and 26% in winter wheat, maize, and rice. Assuming that a fraction (20%) of the annual P applied in fertilizers is directly available to the plant, the global P yield gap lowers by only 5-10%, underlying the importance of the existing soil P supply in sustaining crop yields. The study offers a base for exploring P limitation in crops worldwide but with certain limitations remaining. These could be better accounted for by describing the agricultural P cycle with a fully coupled and mechanistic soil-crop model.

Producing remote sensing-based emission estimates of prescribed burning in the contiguous United States for the U.S. Environmental Protection Agency 2011 National Emissions Inventory

NASA Astrophysics Data System (ADS)

McCarty, J. L.; Pouliot, G. A.; Soja, A. J.; Miller, M. E.; Rao, T.

2013-12-01

Prescribed fires in agricultural landscapes generally produce smaller burned areas than wildland fires but are important contributors to emissions impacting air quality and human health. Currently, there are a variety of available satellite-based estimates of crop residue burning, including the NOAA/NESDIS Hazard Mapping System (HMS) the Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation (SMARTFIRE 2), the Moderate Resolution Imaging Spectroradiometer (MODIS) Official Burned Area Product (MCD45A1)), the MODIS Direct Broadcast Burned Area Product (MCD64A1) the MODIS Active Fire Product (MCD14ML), and a regionally-tuned 8-day cropland differenced Normalized Burn Ratio product for the contiguous U.S. The purpose of this NASA-funded research was to refine the regionally-tuned product utilizing higher spatial resolution crop type data from the USDA NASS Cropland Data Layer and burned area training data from field work and high resolution commercial satellite data to improve the U.S. Environmental Protection Agency's (EPA) National Emissions Inventory (NEI). The final product delivered to the EPA included a detailed database of 25 different atmospheric emissions at the county level, emission distributions by crop type and seasonality, and GIS data. The resulting emission databases were shared with the U.S. EPA and regional offices, the National Wildfire Coordinating Group (NWGC) Smoke Committee, and all 48 states in the contiguous U.S., with detailed error estimations for Wyoming and Indiana and detailed analyses of results for Florida, Minnesota, North Dakota, Oklahoma, and Oregon. This work also provided opportunities in discovering the different needs of federal and state partners, including the various geospatial abilities and platforms across the many users and how to incorporate expert air quality, policy, and land management knowledge into quantitative earth observation-based estimations of prescribed fire emissions. Finally, this work created direct communication paths between federal and state partners to the scientists creating the remote sensing-based products, further improving the geospatial products and understanding of air quality impacts of prescribed burning at the state, regional, and national scales.

Estimating Agricultural Water Use using the Operational Simplified Surface Energy Balance Evapotranspiration Estimation Method

NASA Astrophysics Data System (ADS)

Forbes, B. T.

2015-12-01

Due to the predominantly arid climate in Arizona, access to adequate water supply is vital to the economic development and livelihood of the State. Water supply has become increasingly important during periods of prolonged drought, which has strained reservoir water levels in the Desert Southwest over past years. Arizona's water use is dominated by agriculture, consuming about seventy-five percent of the total annual water demand. Tracking current agricultural water use is important for managers and policy makers so that current water demand can be assessed and current information can be used to forecast future demands. However, many croplands in Arizona are irrigated outside of areas where water use reporting is mandatory. To estimate irrigation withdrawals on these lands, we use a combination of field verification, evapotranspiration (ET) estimation, and irrigation system qualification. ET is typically estimated in Arizona using the Modified Blaney-Criddle method which uses meteorological data to estimate annual crop water requirements. The Modified Blaney-Criddle method assumes crops are irrigated to their full potential over the entire growing season, which may or may not be realistic. We now use the Operational Simplified Surface Energy Balance (SSEBop) ET data in a remote-sensing and energy-balance framework to estimate cropland ET. SSEBop data are of sufficient resolution (30m by 30m) for estimation of field-scale cropland water use. We evaluate our SSEBop-based estimates using ground-truth information and irrigation system qualification obtained in the field. Our approach gives the end user an estimate of crop consumptive use as well as inefficiencies in irrigation system performance—both of which are needed by water managers for tracking irrigated water use in Arizona.

Ammonia Emissions from Agriculture in China

NASA Astrophysics Data System (ADS)

Chen, Y.; Zhang, L.; Zhao, Y.; Huang, B.

2016-12-01

Ammonia (NH3) is an important alkaline pollutant in the atmosphere and it has various environmental and climatic effects. We will present an improved bottom-up estimate of ammonia emissions from agriculture in China at 0.5°×0.5° horizontal resolution and monthly variability. Ammonia emissions from fertilizer use are derived using data of crop planting area, fertilizer application time and rate for 18 main crops. Ammonia emission factors from fertilizer use are estimated as a function of soil properties such as soil pH, cation exchange capacity (CEC), and agricultural activity information such as crop type, fertilizer type, and application mode. We further consider ambient temperature and wind speed to account for the meteorological influences on ammonia emission factors of fertilizer use. We also estimate the ammonia emission from livestock over China using the mass-flow methodology. The derived ammonia emissions in China for the year 2005 are 4.55 Tg NH3 from fertilizer use and 6.96 Tg from livestock. Henan and Jiangsu provinces are the two largest emitting areas for ammonia from fertilizer use (470 Gg NH3 and 365 Gg NH3). Henan (621 Gg NH3) and Shandong (533 Gg NH3) have the largest ammonia emissions from livestock. Both ammonia emissions from fertilizer use and livestock have distinct seasonal variations; peaking in June for fertilizer use (822 Gg NH3) and in July for livestock (1244 Gg NH3), and are both lowest in January (80 Gg and 241 Gg, respectively). Combining with other ammonia source (eg. human waste and transport) estimates from the REAS v2.1 emission inventory, we show that total ammonia emissions in China for the year 2005 are 14.0 Tg NH3 a-1. Comparisons with satellite measurements of ammonia columns will also be presented.

Evaluation of Bayesian Sequential Proportion Estimation Using Analyst Labels

NASA Technical Reports Server (NTRS)

Lennington, R. K.; Abotteen, K. M. (Principal Investigator)

1980-01-01

The author has identified the following significant results. A total of ten Large Area Crop Inventory Experiment Phase 3 blind sites and analyst-interpreter labels were used in a study to compare proportional estimates obtained by the Bayes sequential procedure with estimates obtained from simple random sampling and from Procedure 1. The analyst error rate using the Bayes technique was shown to be no greater than that for the simple random sampling. Also, the segment proportion estimates produced using this technique had smaller bias and mean squared errors than the estimates produced using either simple random sampling or Procedure 1.

Information Technology Supports Integration of Satellite Imagery with Irrigation Management in California's Central Valley

USDA-ARS?s Scientific Manuscript database

Remotely sensed data can potentially be used to develop crop coefficient estimates over large areas and make irrigation scheduling more practical, convenient, and accurate. A demonstration system is being developed under NASA's Terrestrial Observation and Prediction System (TOPS) to automatically r...

Combined Analysis of SENTINEL-1 and Rapideye Data for Improved Crop Type Classification: AN Early Season Approach for Rapeseed and Cereals

NASA Astrophysics Data System (ADS)

Lussem, U.; Hütt, C.; Waldhoff, G.

2016-06-01

Timely availability of crop acreage estimation is crucial for maintaining economic and ecological sustainability or modelling purposes. Remote sensing data has proven to be a reliable source for crop mapping and acreage estimation on parcel-level. However, when relying on a single source of remote sensing data, e.g. multispectral sensors like RapidEye or Landsat, several obstacles can hamper the desired outcome, for example cloud cover or haze. Another limitation may be a similarity in optical reflectance patterns of crops, especially in an early season approach by the end of March, early April. Usually, a reliable crop type map for winter-crops (winter wheat/rye, winter barley and rapeseed) in Central Europe can be obtained by using optical remote sensing data from late April to early May, given a full coverage of the study area and cloudless conditions. These prerequisites can often not be met. By integrating dual-polarimetric SAR-sensors with high temporal and spatial resolution, these limitations can be overcome. SAR-sensors are not influenced by clouds or haze and provide an additional source of information due to the signal-interaction with plant-architecture. The overall goal of this study is to investigate the contribution of Sentinel-1 SAR-data to regional crop type mapping for an early season map of disaggregated winter-crops for a subset of the Rur-Catchment in North Rhine-Westphalia (Germany). For this reason, RapidEye data and Sentinel-1 data are combined and the performance of Support Vector Machine and Maximum Likelihood classifiers are compared. Our results show that a combination of Sentinel-1 and RapidEye is a promising approach for most crops, but consideration of phenology for data selection can improve results. Thus the combination of optical and radar remote sensing data indicates advances for crop-type classification, especially when optical data availability is limited.

A dense camera network for cropland (CropInsight) - developing high spatiotemporal resolution crop Leaf Area Index (LAI) maps through network images and novel satellite data

NASA Astrophysics Data System (ADS)

Kimm, H.; Guan, K.; Luo, Y.; Peng, J.; Mascaro, J.; Peng, B.

2017-12-01

Monitoring crop growth conditions is of primary interest to crop yield forecasting, food production assessment, and risk management of individual farmers and agribusiness. Despite its importance, there are limited access to field level crop growth/condition information in the public domain. This scarcity of ground truth data also hampers the use of satellite remote sensing for crop monitoring due to the lack of validation. Here, we introduce a new camera network (CropInsight) to monitor crop phenology, growth, and conditions that are designed for the US Corn Belt landscape. Specifically, this network currently includes 40 sites (20 corn and 20 soybean fields) across southern half of the Champaign County, IL ( 800 km2). Its wide distribution and automatic operation enable the network to capture spatiotemporal variations of crop growth condition continuously at the regional scale. At each site, low-maintenance, and high-resolution RGB digital cameras are set up having a downward view from 4.5 m height to take continuous images. In this study, we will use these images and novel satellite data to construct daily LAI map of the Champaign County at 30 m spatial resolution. First, we will estimate LAI from the camera images and evaluate it using the LAI data collected from LAI-2200 (LI-COR, Lincoln, NE). Second, we will develop relationships between the camera-based LAI estimation and vegetation indices derived from a newly developed MODIS-Landsat fusion product (daily, 30 m resolution, RGB + NIR + SWIR bands) and the Planet Lab's high-resolution satellite data (daily, 5 meter, RGB). Finally, we will scale up the above relationships to generate high spatiotemporal resolution crop LAI map for the whole Champaign County. The proposed work has potentials to expand to other agro-ecosystems and to the broader US Corn Belt.

A coupled remote sensing and simplified surface energy balance approach to estimate actual evapotranspiration from irrigated fields

USGS Publications Warehouse

Senay, G.B.; Budde, Michael; Verdin, J.P.; Melesse, Assefa M.

2007-01-01

Accurate crop performance monitoring and production estimation are critical for timely assessment of the food balance of several countries in the world. Since 2001, the Famine Early Warning Systems Network (FEWS NET) has been monitoring crop performance and relative production using satellite-derived data and simulation models in Africa, Central America, and Afghanistan where ground-based monitoring is limited because of a scarcity of weather stations. The commonly used crop monitoring models are based on a crop water-balance algorithm with inputs from satellite-derived rainfall estimates. These models are useful to monitor rainfed agriculture, but they are ineffective for irrigated areas. This study focused on Afghanistan, where over 80 percent of agricultural production comes from irrigated lands. We developed and implemented a Simplified Surface Energy Balance (SSEB) model to monitor and assess the performance of irrigated agriculture in Afghanistan using a combination of 1-km thermal data and 250m Normalized Difference Vegetation Index (NDVI) data, both from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. We estimated seasonal actual evapotranspiration (ETa) over a period of six years (2000-2005) for two major irrigated river basins in Afghanistan, the Kabul and the Helmand, by analyzing up to 19 cloud-free thermal and NDVI images from each year. These seasonal ETa estimates were used as relative indicators of year-to-year production magnitude differences. The temporal water-use pattern of the two irrigated basins was indicative of the cropping patterns specific to each region. Our results were comparable to field reports and to estimates based on watershed-wide crop water-balance model results. For example, both methods found that the 2003 seasonal ETa was the highest of all six years. The method also captured water management scenarios where a unique year-to-year variability was identified in addition to water-use differences between upstream and downstream basins. A major advantage of the energy-balance approach is that it can be used to quantify spatial extent of irrigated fields and their water-use dynamics without reference to source of water as opposed to a water-balance model which requires knowledge of both the magnitude and temporal distribution of rainfall and irrigation applied to fields. ?? 2007 by MDPI.

A Coupled Remote Sensing and Simplified Surface Energy Balance Approach to Estimate Actual Evapotranspiration from Irrigated Fields

PubMed Central

Senay, Gabriel B.; Budde, Michael; Verdin, James P.; Melesse, Assefa M.

2007-01-01

Accurate crop performance monitoring and production estimation are critical for timely assessment of the food balance of several countries in the world. Since 2001, the Famine Early Warning Systems Network (FEWS NET) has been monitoring crop performance and relative production using satellite-derived data and simulation models in Africa, Central America, and Afghanistan where ground-based monitoring is limited because of a scarcity of weather stations. The commonly used crop monitoring models are based on a crop water-balance algorithm with inputs from satellite-derived rainfall estimates. These models are useful to monitor rainfed agriculture, but they are ineffective for irrigated areas. This study focused on Afghanistan, where over 80 percent of agricultural production comes from irrigated lands. We developed and implemented a Simplified Surface Energy Balance (SSEB) model to monitor and assess the performance of irrigated agriculture in Afghanistan using a combination of 1-km thermal data and 250-m Normalized Difference Vegetation Index (NDVI) data, both from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. We estimated seasonal actual evapotranspiration (ETa) over a period of six years (2000-2005) for two major irrigated river basins in Afghanistan, the Kabul and the Helmand, by analyzing up to 19 cloud-free thermal and NDVI images from each year. These seasonal ETa estimates were used as relative indicators of year-to-year production magnitude differences. The temporal water-use pattern of the two irrigated basins was indicative of the cropping patterns specific to each region. Our results were comparable to field reports and to estimates based on watershed-wide crop water-balance model results. For example, both methods found that the 2003 seasonal ETa was the highest of all six years. The method also captured water management scenarios where a unique year-to-year variability was identified in addition to water-use differences between upstream and downstream basins. A major advantage of the energy-balance approach is that it can be used to quantify spatial extent of irrigated fields and their water-use dynamics without reference to source of water as opposed to a water-balance model which requires knowledge of both the magnitude and temporal distribution of rainfall and irrigation applied to fields.

The potential of agricultural practices to increase C storage in cropped soils: an assessment for France

NASA Astrophysics Data System (ADS)

Chenu, Claire; Angers, Denis; Métay, Aurélie; Colnenne, Caroline; Klumpp, Katja; Bamière, Laure; Pardon, Lenaic; Pellerin, Sylvain

2014-05-01

Though large progress has been achieved in the last decades, net GHG emissions from the agricultural sector are still more poorly quantified than in other sectors. In this study, we examined i) technical mitigation options likely to store carbon in agricultural soils, ii) their potential of additional C storage per unit surface area and iii) applicable areas in mainland France. We considered only agricultural practices being technically feasible by farmers and involving no major change in either production systems or production levels. Moreover, only currently available techniques with validated efficiencies and presenting no major negative environmental impacts were taken into account. Four measures were expected to store additional C in agricultural soils: - Reducing tillage: either a switch to continuous direct seeding, direct seeding with occasional tillage once every five years, or continuous superficial (<15 cm) tillage. - Introducing cover crops in cropping systems: sown between two cash crops on arable farms, in orchards and vineyards (permanent or temporary cover cropping) . - Expanding agroforestry systems; planting of tree lines in cultivated fields and grasslands, and hedges around the field edges. - Increasing the life time of temporary sown grasslands: increase of life time to 5 years. The recent literature was reviewed in order to determine long term (>20yrs) C storage rates (MgC ha-1 y-1,) of cropping systems with and without the proposed practice. Then we analysed the conditions for potential application, in terms of feasibility, acceptance, limitation of yield losses and of other GHG emissions. According to the literature, additional C storage rates were 0.15 (0-0.3) MgC ha-1 y-1 for continuous direct seeding, 0.10 (0-0.2) MgC ha-1 y-1for occasional tillage one year in five, and 0.0 MgC ha-1 y-1 for superficial tillage. Cover crops were estimated to store 0.24 (0.13-0.37) MgC ha-1 y-1 between cash crops and 0.49 (0.23-0.72) MgC ha-1 y-1 when associated with vineyards. Hedges (i.e 60 m ha-1) stored 0.15 (0.05-0.26) Mg C ha-1 y-1. Very few estimates were available for temperate agroforestry system, and we proposed a value of 1.01 (0.11-1.36) Mg C ha-1 y-1for C stored in soil and in the tree biomass for systems comprising 30-50 trees ha-1. Increasing the life time of temporary sown grassland increased C stocls by 0.11 (0.07-0.22) Mg C ha-1 y-1. In general, practices with increased C inputs to soil through additional plant biomass (agroforestry, hedges and cover crops) resulted in higher additional C storage rates, while the reduction of soil organic matter mineralisation through reduced tillage seemed less effective. When applied to the French agricultural sector, excluding areas with soils with major technical constraints or negative environmental consequences (e.g. poorly aerated soils with high N2O emissions), the measures considered here allowed to increase French soil C stocks by 0 to more than 1 Tg C y-1. However, our estimates are associated with high uncertainties, due to the high variability in soil C storage associated with pedo-climatic conditions and cropping systems, and on the very few studies available for some practices such as agroforestry under temperate conditions.

Scaling up from field to region for wind erosion prediction using a field-scale wind erosion model and GIS

USGS Publications Warehouse

Zobeck, T.M.; Parker, N.C.; Haskell, S.; Guoding, K.

2000-01-01

Factors that affect wind erosion such as surface vegetative and other cover, soil properties and surface roughness usually change spatially and temporally at the field-scale to produce important field-scale variations in wind erosion. Accurate estimation of wind erosion when scaling up from fields to regions, while maintaining meaningful field-scale process details, remains a challenge. The objectives of this study were to evaluate the feasibility of using a field-scale wind erosion model with a geographic information system (GIS) to scale up to regional levels and to quantify the differences in wind erosion estimates produced by different scales of soil mapping used as a data layer in the model. A GIS was used in combination with the revised wind erosion equation (RWEQ), a field-scale wind erosion model, to estimate wind erosion for two 50 km2 areas. Landsat Thematic Mapper satellite imagery from 1993 with 30 m resolution was used as a base map. The GIS database layers included land use, soils, and other features such as roads. The major land use was agricultural fields. Data on 1993 crop management for selected fields of each crop type were collected from local government agency offices and used to 'train' the computer to classify land areas by crop and type of irrigation (agroecosystem) using commercially available software. The land area of the agricultural land uses was overestimated by 6.5% in one region (Lubbock County, TX, USA) and underestimated by about 21% in an adjacent region (Terry County, TX, USA). The total estimated wind erosion potential for Terry County was about four times that estimated for adjacent Lubbock County. The difference in potential erosion among the counties was attributed to regional differences in surface soil texture. In a comparison of different soil map scales in Terry County, the generalised soil map had over 20% more of the land area and over 15% greater erosion potential in loamy sand soils than did the detailed soil map. As a result, the wind erosion potential determined using the generalised soil map Was about 26% greater than the erosion potential estimated by using the detailed soil map in Terry County. This study demonstrates the feasibility of scaling up from fields to regions to estimate wind erosion potential by coupling a field-scale wind erosion model with GIS and identifies possible sources of error with this approach.

Solutions Network Formulation Report. Using NASA Sensors to Perform Crop Type Assessment for Monitoring Insect Resistance in Corn

NASA Technical Reports Server (NTRS)

Lewis, David; Copenhaver, Ken; Anderson, Daniel; Hilbert, Kent

2007-01-01

The EPA (U.S. Environmental Protection Agency) is tasked to monitor for insect pest resistance to transgenic crops. Several models have been developed to understand the resistance properties of insects. The Population Genetics Simulator model is used in the EPA PIRDSS (Pest Infestation and Resistance Decision Support System). The EPA Office of Pesticide Programs uses the DSS to help understand the potential for insect pest resistance development and the likelihood that insect pest resistance will negatively affect transgenic corn. Once the DSS identifies areas of concern, crews are deployed to collect insect pest samples, which are tested to identify whether they have developed resistance to the toxins in transgenic corn pesticides. In this candidate solution, VIIRS (Visible/Infrared Imager/Radiometer Suite) vegetation index products will be used to build hypertemporal layerstacks for crop type and phenology assessment. The current phenology attribute is determined by using the current time of year to index the expected growth stage of the crop. VIIRS might provide more accurate crop type assessment and also might give a better estimate on the crop growth stage.

Enhanced monitoring of the temporal and spatial relationships between water demand and water availability

NASA Astrophysics Data System (ADS)

Schneider, C. A.; Aggett, G. R.; Hattendorf, M. J.

2007-12-01

Better information on evapotranspiration (ET) is essential to better understanding of consumptive use of water by crops. RTi is using NASA Earth-sun System research results and METRIC (Mapping ET at high Resolution with Internalized Calibration) to increase the repeatability and accuracy of consumptive use estimates. METRIC, an image-processing model for calculating ET as a residual of the surface energy balance, utilizes the thermal band on various satellite remote sensors. Calculating actual ET from satellites can avoid many of the assumptions driving other methods of calculating ET over a large area. Because it is physically based and does not rely on explicit knowledge of crop type in the field, a large potential source of error should be eliminated. This paper assesses sources of error in current operational estimates of ET for an area of the South Platte irrigated lands of Colorado, and benchmarks potential improvements in the accuracy of ET estimates gained using METRIC, as well as the processing efficiency of consumptive use demand for large irrigated lands. Examples highlighting how better water planning decisions and water management can be achieved via enhanced monitoring of the temporal and spatial relationships between water demand and water availability are provided.

Land Use and Land Cover Change Modeling Using Remote Sensing and Soft Computing Approach to Assess Sugarcane Expansion Impacts in Tropical Agriculture

NASA Astrophysics Data System (ADS)

Vicente, L. E.; Koga-Vicente, A.; Friedel, M. J.; Victoria, D.; Zullo, J., Jr.; Gomes, D.; Bayma-Silva, G.

2014-12-01

Agriculture is related with land-use/cover changes (LUCC) over large areas and, in recent years, increase in demand of ethanol fuel has been influence in expansion of areas occupied with corn and sugar cane, raw material for ethanol production. Nevertheless, there´s a concern regarding the impacts on food security, such as, decrease in areas planted with food crops. Considering that the LUCC is highly dynamic, the use of Remote Sensing is a tool for monitoring changes quickly and precisely in order to provide information for agricultural planning. In this work, Remote Sensing techniques were used to monitor the LUCC occurred in municipalities of São Paulo state- Brazil related with sugarcane crops expansion in order to (i) evaluate and quantify the previous land cover in areas of sugarcane crop expansion, and (ii) provide information to elaborate a future land cover scenario based on Self Organizing Map (SOM) approach. The land cover classification procedure was based on Landsat 5 TM images, obtained from the Global Land Survey. The Landsat images were then segmented into homogeneous objects, with represent areas on the ground with similar spatial and spectral characteristics. These objects are related to the distinct land cover types that occur in each municipality. The segmentation procedure resulted in polygons over the three time periods along twenty years (1990-2010). The land cover for each object was visually identified, based on its shape, texture and spectral characteristics. Land cover types considered were: sugarcane plantations, pasture lands, natural cover, forest plantation, permanent crop, short cycle crop, water bodies and urban areas. SOM technique was used to estimate the values for the future land cover scenarios for the selected municipalities, using the information of land change provided by the remote sensing and data from official sources.

Sum Insured Determination for Cereal, Citrus and Vineyards in the Spanish Agricultural Insurance System

NASA Astrophysics Data System (ADS)

Lozano, C.; Tarquis, A. M.; Gómez-Barona, J. A.

2012-04-01

In general, insurance is a form of risk management used to hedge against a contingent loss. The conventional definition is the equitable transfer of a risk of loss from one entity to another in exchange for a premium or a guaranteed and quantifiable small loss to prevent a large and possibly devastating loss being agricultural insurance a special line of property insurance. Agriculture insurance, as actually are designed in the Spanish scenario, were established in 1978. At the macroeconomic insurance studies scale, it is necessary to know a basic element for the insurance actuarial components: sum insured. When a new risk assessment has to be evaluated in the insurance framework, it is essential to determinate venture capital in the total Spanish agriculture. In this study, three different crops (cereal, citrus and vineyards) cases are showed to determinate sum insured as they are representative of the cases found in the Spanish agriculture. Crop sum insured is calculated by the product of crop surface, unit surface production and crop price insured. In the cereal case, winter as spring cereal sowing, represents the highest Spanish crop surface, above to 6 millions of hectares (ha). Meanwhile, the four citrus species (oranges, mandarins, lemons and grapefruits) occupied an extension just over 275.000 ha. On the other hand, vineyard target to wine process shows almost one million of ha in Spain. A new method has been applied to estimate crop sum insured in these three cases. Under the maximum economic impact assumption, the maximum market price has been used to insurance each species. Depending on crop and reliability of the data base available, the insured area or insured production has been used in this estimation. When for a certain crop varieties or type of varieties show different insurance prices a geometric average was used as average insurance price for that particular crop. One extreme difficult case was vineyards, where differentiate prices based on Denomination of Origin (DO), varieties and autonomous communities made this estimation more complex. The macroeconomic results obtained based on MARM (Ministerio de Agricultura, Alimentación y Medio Ambiente) prices and crop data in 2009 are showed and discussed. Acknowledgements Funding provided by CEIGRAM (Research Centre for the Management of Agricultural and Environmental Risks) is greatly appreciated.

Estimating cropland NPP using national crop inventory and MODIS derived crop specific parameters

NASA Astrophysics Data System (ADS)

Bandaru, V.; West, T. O.; Ricciuto, D. M.

2011-12-01

Estimates of cropland net primary production (NPP) are needed as input for estimates of carbon flux and carbon stock changes. Cropland NPP is currently estimated using terrestrial ecosystem models, satellite remote sensing, or inventory data. All three of these methods have benefits and problems. Terrestrial ecosystem models are often better suited for prognostic estimates rather than diagnostic estimates. Satellite-based NPP estimates often underestimate productivity on intensely managed croplands and are also limited to a few broad crop categories. Inventory-based estimates are consistent with nationally collected data on crop yields, but they lack sub-county spatial resolution. Integrating these methods will allow for spatial resolution consistent with current land cover and land use, while also maintaining total biomass quantities recorded in national inventory data. The main objective of this study was to improve cropland NPP estimates by using a modification of the CASA NPP model with individual crop biophysical parameters partly derived from inventory data and MODIS 8day 250m EVI product. The study was conducted for corn and soybean crops in Iowa and Illinois for years 2006 and 2007. We used EVI as a linear function for fPAR, and used crop land cover data (56m spatial resolution) to extract individual crop EVI pixels. First, we separated mixed pixels of both corn and soybean that occur when MODIS 250m pixel contains more than one crop. Second, we substituted mixed EVI pixels with nearest pure pixel values of the same crop within 1km radius. To get more accurate photosynthetic active radiation (PAR), we applied the Mountain Climate Simulator (MTCLIM) algorithm with the use of temperature and precipitation data from the North American Land Data Assimilation System (NLDAS-2) to generate shortwave radiation data. Finally, county specific light use efficiency (LUE) values of each crop for years 2006 to 2007 were determined by application of mean county inventory NPP and EVI-derived APAR into the Monteith equation. Results indicate spatial variability in LUE values across Iowa and Illinois. Northern regions of both Iowa and Illinois have higher LUE values than southern regions. This trend is reflected in NPP estimates. Results also show that corn has higher LUE values than soybean, resulting in higher NPP for corn than for soybean. Current NPP estimates were compared with NPP estimates from MOD17A3 product and with county inventory-based NPP estimates. Results indicate that current NPP estimates closely agree with inventory-based estimates, and that current NPP estimates are higher than those of the MOD17A3 product. It was also found that when mixed pixels were substituted with nearest pure pixels, revised NPP estimates were improved showing better agreement with inventory-based estimates.

Techniques for the estimation of leaf area index using spectral data

NASA Technical Reports Server (NTRS)

Badhwar, G. D.; Shen, S. S.

1984-01-01

Based on the radiative transport theory of a homogeneous canopy, a new approach for obtaining transformations of spectral data used to estimate leaf area index (LAI), is developed. The transformations which are obtained without any ground knowledge of LAI show low sensitivity to soil variability, and are linearly related to LAI with relationships which are predictable from leaf reflectance, transmittance properties, and canopy reflectance models. Evaluation of the SAIL (scattering by arbitrarily inclined leaves) model is considered. Using only nadir view data, results obtained on winter and spring wheat and corn crops are presented.

Remote sensing in Iowa agriculture: Identification and classification of Iowa's crops, soils and forestry resources using ERTS-1 and complimentary underflight imagery

NASA Technical Reports Server (NTRS)

Mahlstede, J. P. (Principal Investigator); Carlson, R. E.; Fenton, T. E.; Thomson, G. W.

1974-01-01

The author has identified the following significant results. Springtime ERTS-1 imagery covering pre-selected test sites in Iowa showed considerable detail with respect to broad soil and land use patterns. Additional imagery has been incorporated into a state mosaic. The mosaic was used as a base for soil association lines transferred from an existing map. The regions of greatest contrast are between the Clarion-Nicollet-Webster soil association area and adjacent areas. Landscape characteristics in this area result in land use patterns with a high percentage of pasture, hay, and timber. The soil association areas of the state that have patterns interpreted to be associated with intensive row crop production are: Moody, Galva-Primghar-Sac, Clarion-Nicollet-Webter, Tama-Muscatine, Dinsdale-Tama, Cresco-Lourdes, Clyde, Kenyon-Floyd-Clyde, and the Luton-Onawa-Salix area on the Missouri River floodplain. Forestland estimates have been attained for an area in central Iowa using wintertime ERTS-1 imagery. Visual analysis of multispectral, temporal imagery indicates that temporal analysis for cropland identification and acreage analyses procedures may be a very useful tool. Combinations of wintertime, springtime, and summertime ERTS-1 imagery separate most vegetation types. Timing can be critical depending upon crop development and harvesting times because of the dynamic nature of agricultural production.

Using multi-objective robust decision making to support seasonal water management in the Chao Phraya River basin, Thailand

NASA Astrophysics Data System (ADS)

Riegels, Niels; Jessen, Oluf; Madsen, Henrik

2016-04-01

A multi-objective robust decision making approach is demonstrated that supports seasonal water management in the Chao Phraya River basin in Thailand. The approach uses multi-objective optimization to identify a Pareto-optimal set of management alternatives. Ensemble simulation is used to evaluate how each member of the Pareto set performs under a range of uncertain future conditions, and a robustness criterion is used to select a preferred alternative. Data mining tools are then used to identify ranges of uncertain factor values that lead to unacceptable performance for the preferred alternative. The approach is compared to a multi-criteria scenario analysis approach to estimate whether the introduction of additional complexity has the potential to improve decision making. Dry season irrigation in Thailand is managed through non-binding recommendations about the maximum extent of rice cultivation along with incentives for less water-intensive crops. Management authorities lack authority to prevent river withdrawals for irrigation when rice cultivation exceeds recommendations. In practice, this means that water must be provided to irrigate the actual planted area because of downstream municipal water supply requirements and water quality constraints. This results in dry season reservoir withdrawals that exceed planned withdrawals, reducing carryover storage to hedge against insufficient wet season runoff. The dry season planning problem in Thailand can therefore be framed in terms of decisions, objectives, constraints, and uncertainties. Decisions include recommendations about the maximum extent of rice cultivation and incentives for growing less water-intensive crops. Objectives are to maximize benefits to farmers, minimize the risk of inadequate carryover storage, and minimize incentives. Constraints include downstream municipal demands and water quality requirements. Uncertainties include the actual extent of rice cultivation, dry season precipitation, and precipitation in the following wet season. The multi-objective robust decision making approach is implemented as follows. First, three baseline simulation models are developed, including a crop water demand model, a river basin simulation model, and model of the impact of incentives on cropping patterns. The crop water demand model estimates irrigation water demands; the river basin simulation model estimates reservoir drawdown required to meet demands given forecasts of precipitation, evaporation, and runoff; the model of incentive impacts estimates the cost of incentives as function of marginal changes in rice yields. Optimization is used to find a set of non-dominated alternatives as a function of rice area and incentive decisions. An ensemble of uncertain model inputs is generated to represent uncertain hydrological and crop area forecasts. An ensemble of indicator values is then generated for each of the decision objectives: farmer benefits, end-of-wet-season reservoir storage, and the cost of incentives. A single alternative is selected from the Pareto set using a robustness criterion. Threshold values are defined for each of the objectives to identify ensemble members for which objective values are unacceptable, and the PRIM data mining algorithm is then used to identify input values associated with unacceptable model outcomes.

LIFE CLIMATREE project: A novel approach for accounting and monitoring carbon sequestration of tree crops and their potential as carbon sink areas

NASA Astrophysics Data System (ADS)

Stergiou, John; Tagaris, Efthimios; -Eleni Sotiropoulou, Rafaella

2016-04-01

Climate Change Mitigation is one of the most important objectives of the Kyoto Convention, and is mostly oriented towards reducing GHG emissions. However, carbon sink is retained only in the calculation of the forests capacity since agricultural land and farmers practices for securing carbon stored in soils have not been recognized in GHG accounting, possibly resulting in incorrect estimations of the carbon dioxide balance in the atmosphere. The agricultural sector, which is a key sector in the EU, presents a consistent strategic framework since 1954, in the form of Common Agricultural Policy (CAP). In its latest reform of 2013 (reg. (EU) 1305/13) CAP recognized the significance of Agriculture as a key player in Climate Change policy. In order to fill this gap the "LIFE ClimaTree" project has recently founded by the European Commission aiming to provide a novel method for including tree crop cultivations in the LULUCF's accounting rules for GHG emissions and removal. In the framework of "LIFE ClimaTree" project estimation of carbon sink within EU through the inclusion of the calculated tree crop capacity will be assessed for both current and future climatic conditions by 2050s using the GISS-WRF modeling system in a very fine scale (i.e., 9km x 9km) using RCP8.5 and RCP4.5 climate scenarios. Acknowledgement: LIFE CLIMATREE project "A novel approach for accounting and monitoring carbon sequestration of tree crops and their potential as carbon sink areas" (LIFE14 CCM/GR/000635).

An integrated soil-crop system model for water and nitrogen management in North China

PubMed Central

Liang, Hao; Hu, Kelin; Batchelor, William D.; Qi, Zhiming; Li, Baoguo

2016-01-01

An integrated model WHCNS (soil Water Heat Carbon Nitrogen Simulator) was developed to assess water and nitrogen (N) management in North China. It included five main modules: soil water, soil temperature, soil carbon (C), soil N, and crop growth. The model integrated some features of several widely used crop and soil models, and some modifications were made in order to apply the WHCNS model under the complex conditions of intensive cropping systems in North China. The WHCNS model was evaluated using an open access dataset from the European International Conference on Modeling Soil Water and N Dynamics. WHCNS gave better estimations of soil water and N dynamics, dry matter accumulation and N uptake than 14 other models. The model was tested against data from four experimental sites in North China under various soil, crop, climate, and management practices. Simulated soil water content, soil nitrate concentrations, crop dry matter, leaf area index and grain yields all agreed well with measured values. This study indicates that the WHCNS model can be used to analyze and evaluate the effects of various field management practices on crop yield, fate of N, and water and N use efficiencies in North China. PMID:27181364

Satellite Remote Sensing of Cropland Characteristics in 30m Resolution: The First North American Continental-Scale Classification on High Performance Computing Platforms

NASA Astrophysics Data System (ADS)

Massey, Richard

Cropland characteristics and accurate maps of their spatial distribution are required to develop strategies for global food security by continental-scale assessments and agricultural land use policies. North America is the major producer and exporter of coarse grains, wheat, and other crops. While cropland characteristics such as crop types are available at country-scales in North America, however, at continental-scale cropland products are lacking at fine sufficient resolution such as 30m. Additionally, applications of automated, open, and rapid methods to map cropland characteristics over large areas without the need of ground samples are needed on efficient high performance computing platforms for timely and long-term cropland monitoring. In this study, I developed novel, automated, and open methods to map cropland extent, crop intensity, and crop types in the North American continent using large remote sensing datasets on high-performance computing platforms. First, a novel method was developed in this study to fuse pixel-based classification of continental-scale Landsat data using Random Forest algorithm available on Google Earth Engine cloud computing platform with an object-based classification approach, recursive hierarchical segmentation (RHSeg) to map cropland extent at continental scale. Using the fusion method, a continental-scale cropland extent map for North America at 30m spatial resolution for the nominal year 2010 was produced. In this map, the total cropland area for North America was estimated at 275.2 million hectares (Mha). This map was assessed for accuracy using randomly distributed samples derived from United States Department of Agriculture (USDA) cropland data layer (CDL), Agriculture and Agri-Food Canada (AAFC) annual crop inventory (ACI), Servicio de Informacion Agroalimentaria y Pesquera (SIAP), Mexico's agricultural boundaries, and photo-interpretation of high-resolution imagery. The overall accuracies of the map are 93.4% with a producer's accuracy for crop class at 85.4% and user's accuracy of 74.5% across the continent. The sub-country statistics including state-wise and county-wise cropland statistics derived from this map compared well in regression models resulting in R2 > 0.84. Secondly, an automated phenological pattern matching (PPM) method to efficiently map cropping intensity was also developed in this study. This study presents a continental-scale cropping intensity map for the North American continent at 250m spatial resolution for 2010. In this map, the total areas for single crop, double crop, continuous crop, and fallow were estimated to be 123.5 Mha, 11.1 Mha, 64.0 Mha, and 83.4 Mha, respectively. This map was assessed using limited country-level reference datasets derived from United States Department of Agriculture cropland data layer and Agriculture and Agri-Food Canada annual crop inventory with overall accuracies of 79.8% and 80.2%, respectively. Third, two novel and automated decision tree classification approaches to map crop types across the conterminous United States (U.S.) using MODIS 250 m resolution data: 1) generalized, and 2) year-specific classification were developed. The classification approaches use similarities and dissimilarities in crop type phenology derived from NDVI time-series data for the two approaches. Annual crop type maps were produced for 8 major crop types in the United States using the generalized classification approach for 2001-2014 and the year-specific approach for 2008, 2010, 2011 and 2012. The year-specific classification had overall accuracies greater than 78%, while the generalized classifier had accuracies greater than 75% for the conterminous U.S. for 2008, 2010, 2011, and 2012. The generalized classifier enables automated and routine crop type mapping without repeated and expensive ground sample collection year after year with overall accuracies > 70% across all independent years. Taken together, these cropland products of extent, cropping intensity, and crop types, are significantly beneficial in agricultural and water use planning and monitoring to formulate policies towards global and North American food security issues.

First report of Raspberry bushy dwarf virus in blackberry in Ecuador

USDA-ARS?s Scientific Manuscript database

During the past two decades, several viruses have been identified from Rubus (blackberry and raspberry) in wild and commercial plantings around the world (1) In Ecuador; approximately 14 tons of blackberries (Rubus glaucus) are produced each year in an estimated area of 5,500 hectares. This crop pro...

Agricultural land use change in the Northeast

USDA-ARS?s Scientific Manuscript database

The USDA Census of Agriculture (http://www.agcensus.usda.gov/) provides county-level estimates of farm numbers, land use area and livestock and crop production every five years. In 2007, only eight of the 299 counties that make up the twelve Northeastern states had no agricultural land use. About 20...

Crop yields response to water pressures in the Ebro basin in Spain: risk and water policy implications

NASA Astrophysics Data System (ADS)

Quiroga, S.; Fernández-Haddad, Z.; Iglesias, A.

2011-02-01

The increasing pressure on water systems in the Mediterranean enhances existing water conflicts and threatens water supply for agriculture. In this context, one of the main priorities for agricultural research and public policy is the adaptation of crop yields to water pressures. This paper focuses on the evaluation of hydrological risk and water policy implications for food production. Our methodological approach includes four steps. For the first step, we estimate the impacts of rainfall and irrigation water on crop yields. However, this study is not limited to general crop production functions since it also considers the linkages between those economic and biophysical aspects which may have an important effect on crop productivity. We use statistical models of yield response to address how hydrological variables affect the yield of the main Mediterranean crops in the Ebro river basin. In the second step, this study takes into consideration the effects of those interactions and analyzes gross value added sensitivity to crop production changes. We then use Montecarlo simulations to characterize crop yield risk to water variability. Finally we evaluate some policy scenarios with irrigated area adjustments that could cope in a context of increased water scarcity. A substantial decrease in irrigated land, of up to 30% of total, results in only moderate losses of crop productivity. The response is crop and region specific and may serve to prioritise adaptation strategies.

Risk of water scarcity and water policy implications for crop production in the Ebro Basin in Spain

NASA Astrophysics Data System (ADS)

Quiroga, S.; Fernández-Haddad, Z.; Iglesias, A.

2010-08-01

The increasing pressure on water systems in the Mediterranean enhances existing water conflicts and threatens water supply for agriculture. In this context, one of the main priorities for agricultural research and public policy is the adaptation of crop yields to water pressures. This paper focuses on the evaluation of hydrological risk and water policy implications for food production. Our methodological approach includes four steps. For the first step, we estimate the impacts of rainfall and irrigation water on crop yields. However, this study is not limited to general crop production functions since it also considers the linkages between those economic and biophysical aspects which may have an important effect on crop productivity. We use statistical models of yield response to address how hydrological variables affect the yield of the main Mediterranean crops in the Ebro River Basin. In the second step, this study takes into consideration the effects of those interactions and analyzes gross value added sensitivity to crop production changes. We then use Montecarlo simulations to characterize crop yield risk to water variability. Finally we evaluate some policy scenarios with irrigated area adjustments that could cope in a context of increased water scarcity. A substantial decrease in irrigated land, of up to 30% of total, results in only moderate losses of crop productivity. The response is crop and region specific and may serve to prioritise adaptation strategies.

Supporting local farming communities and crop production resilience to climate change through giant reed (Arundo donax L.) cultivation: An Italian case study.

PubMed

Bonfante, A; Impagliazzo, A; Fiorentino, N; Langella, G; Mori, M; Fagnano, M

2017-12-01

Bioenergy crops are well known for their ability to reduce greenhouse gas emissions and increase the soil carbon stock. Although such crops are often held to be in competition with food crops and thus raise the question of current and future food security, at the same time mitigation measures are required to tackle climate change and sustain local farming communities and crop production. However, in some cases the actions envisaged for specific pedo-climatic conditions are not always economically sustainable by farmers. In this frame, energy crops with high environmental adaptability and yields, such as giant reed (Arundo donax L.), may represent an opportunity to improve farm incomes, making marginal areas not suitable for food production once again productive. In so doing, three of the 17 Sustainable Development Goals (SDGs) of the United Nations would be met, namely SDG 2 on food security and sustainable agriculture, SDG 7 on reliable, sustainable and modern energy, and SDG 13 on action to combat climate change and its impacts. In this work, the response of giant reed in the marginal areas of an agricultural district of southern Italy (Destra Sele) and expected farm incomes under climate change (2021-2050) are evaluated. The normalized water productivity index of giant reed was determined (WP; 30.1gm -2 ) by means of a SWAP agro-hydrological model, calibrated and validated on two years of a long-term field experiment. The model was used to estimate giant reed response (biomass yield) in marginal areas under climate change, and economic evaluation was performed to determine expected farm incomes (woodchips and chopped forage). The results show that woodchip production represents the most profitable option for farmers, yielding a gross margin 50% lower than ordinary high-input maize cultivation across the study area. Copyright © 2017 Elsevier B.V. All rights reserved.

A Bayesian approach to infer nitrogen loading rates from crop and land-use types surrounding private wells in the Central Valley, California

NASA Astrophysics Data System (ADS)

Ransom, Katherine M.; Bell, Andrew M.; Barber, Quinn E.; Kourakos, George; Harter, Thomas

2018-05-01

This study is focused on nitrogen loading from a wide variety of crop and land-use types in the Central Valley, California, USA, an intensively farmed region with high agricultural crop diversity. Nitrogen loading rates for several crop types have been measured based on field-scale experiments, and recent research has calculated nitrogen loading rates for crops throughout the Central Valley based on a mass balance approach. However, research is lacking to infer nitrogen loading rates for the broad diversity of crop and land-use types directly from groundwater nitrate measurements. Relating groundwater nitrate measurements to specific crops must account for the uncertainty about and multiplicity in contributing crops (and other land uses) to individual well measurements, and for the variability of nitrogen loading within farms and from farm to farm for the same crop type. In this study, we developed a Bayesian regression model that allowed us to estimate land-use-specific groundwater nitrogen loading rate probability distributions for 15 crop and land-use groups based on a database of recent nitrate measurements from 2149 private wells in the Central Valley. The water and natural, rice, and alfalfa and pasture groups had the lowest median estimated nitrogen loading rates, each with a median estimate below 5 kg N ha-1 yr-1. Confined animal feeding operations (dairies) and citrus and subtropical crops had the greatest median estimated nitrogen loading rates at approximately 269 and 65 kg N ha-1 yr-1, respectively. In general, our probability-based estimates compare favorably with previous direct measurements and with mass-balance-based estimates of nitrogen loading. Nitrogen mass-balance-based estimates are larger than our groundwater nitrate derived estimates for manured and nonmanured forage, nuts, cotton, tree fruit, and rice crops. These discrepancies are thought to be due to groundwater age mixing, dilution from infiltrating river water, or denitrification between the time when nitrogen leaves the root zone (point of reference for mass-balance-derived loading) and the time and location of groundwater measurement.

The uncertainty of crop yield projections is reduced by improved temperature response functions

USDA-ARS?s Scientific Manuscript database

Increasing the accuracy of crop productivity estimates is a key Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on cr...

Winter wheat yield estimation of remote sensing research based on WOFOST crop model and leaf area index assimilation

NASA Astrophysics Data System (ADS)

Chen, Yanling; Gong, Adu; Li, Jing; Wang, Jingmei

2017-04-01

Accurate crop growth monitoring and yield predictive information are significant to improve the sustainable development of agriculture and ensure the security of national food. Remote sensing observation and crop growth simulation models are two new technologies, which have highly potential applications in crop growth monitoring and yield forecasting in recent years. However, both of them have limitations in mechanism or regional application respectively. Remote sensing information can not reveal crop growth and development, inner mechanism of yield formation and the affection of environmental meteorological conditions. Crop growth simulation models have difficulties in obtaining data and parameterization from single-point to regional application. In order to make good use of the advantages of these two technologies, the coupling technique of remote sensing information and crop growth simulation models has been studied. Filtering and optimizing model parameters are key to yield estimation by remote sensing and crop model based on regional crop assimilation. Winter wheat of GaoCheng was selected as the experiment object in this paper. And then the essential data was collected, such as biochemical data and farmland environmental data and meteorological data about several critical growing periods. Meanwhile, the image of environmental mitigation small satellite HJ-CCD was obtained. In this paper, research work and major conclusions are as follows. (1) Seven vegetation indexes were selected to retrieve LAI, and then linear regression model was built up between each of these indexes and the measured LAI. The result shows that the accuracy of EVI model was the highest (R2=0.964 at anthesis stage and R2=0.920 at filling stage). Thus, EVI as the most optimal vegetation index to predict LAI in this paper. (2) EFAST method was adopted in this paper to conduct the sensitive analysis to the 26 initial parameters of the WOFOST model and then a sensitivity index was constructed to evaluate the influence of each parameter mentioned above on the winter wheat yield formation. Finally, six parameters that sensitivity index more than 0.1 as sensitivity factors were chose, which are TSUM1, SLATB1, SLATB2, SPAN, EFFTB3 and TMPF4. To other parameters, we confirmed them via practical measurement and calculation, available literature or WOFOST default. Eventually, we completed the regulation of WOFOST parameters. (3) Look-up table algorithm was used to realize single-point yield estimation through the assimilation of the WOFOST model and the retrieval LAI. This simulation achieved a high accuracy which perfectly meet the purpose of assimilation (R2=0.941 and RMSE=194.58kg/hm2). In this paper, the optimum value of sensitivity parameters were confirmed and the estimation of single-point yield were finished. Key words: yield estimation of winter wheat, LAI, WOFOST crop growth model, assimilation

Implications of deep drainage through saline clay for groundwater recharge and sustainable cropping in a semi-arid catchment, Australia

NASA Astrophysics Data System (ADS)

Timms, W. A.; Young, R. R.; Huth, N.

2011-11-01

The magnitude and timing of deep drainage and salt leaching through clay soils is a critical issue for dryland agriculture in semi-arid regions (<500 mm yr-1 rainfall), such as parts of Australia's Murray-Darling Basin (MDB). In this unique study, hydrogeological measurements and estimations of the historic water balance of crops grown on overlying Grey Vertosols were combined to estimate the contribution of deep drainage below crop roots to recharge and salinization of shallow groundwater. Soil sampling at two sites on the alluvial flood plain of the Lower Namoi catchment revealed significant peaks in chloride concentrations at 0.8-1.2 m depth under perennial vegetation and at 2.0-2.5 m depth under continuous cropping indicating deep drainage and salt leaching since conversion to cropping. Total salt loads of 91-229 t ha-1 NaCl equivalent were measured for perennial vegetation and cropping, with salinity to ≥10 m depth that is not detected by shallow soil surveys. Groundwater salinity varied spatially from 910 to 2430 mS m-1 at 21 to 37 m depth (N = 5), whereas deeper groundwater was less saline (290 mS m-1) with use restricted to livestock and rural domestic supplies in this area. The Agricultural Production Systems Simulator (APSIM) software package predicted deep drainage of 3.3-9.5 mm yr-1 (0.7-2.1% rainfall) based on site records of grain yields, rainfall, salt leaching and soil properties. Predicted deep drainage was highly episodic, dependent on rainfall and antecedent, and over a 39 yr period was restricted mainly to the record wet winter of 1998. During the study period, groundwater levels were unresponsive to major rainfall events (70 and 190 mm total), and most piezometers at about 18 m depth remained dry. In this area, at this time, recharge negligible due to low rainfall and large potential evapotranspiration, transient hydrological conditionsafter changes in land use and a thick clay dominated vadose zone. This is in contrast to regional groundwater modelling that assumes annual recharge of 0.5% of rainfall. Importantly, it was found that leaching from episodic deep drainage could not cause discharge of saline groundwater in the area, since the water table was several meters below the incised river bed.

Methods to estimate irrigated reference crop evapotranspiration - a review.

PubMed

Kumar, R; Jat, M K; Shankar, V

2012-01-01

Efficient water management of crops requires accurate irrigation scheduling which, in turn, requires the accurate measurement of crop water requirement. Irrigation is applied to replenish depleted moisture for optimum plant growth. Reference evapotranspiration plays an important role for the determination of water requirements for crops and irrigation scheduling. Various models/approaches varying from empirical to physically base distributed are available for the estimation of reference evapotranspiration. Mathematical models are useful tools to estimate the evapotranspiration and water requirement of crops, which is essential information required to design or choose best water management practices. In this paper the most commonly used models/approaches, which are suitable for the estimation of daily water requirement for agricultural crops grown in different agro-climatic regions, are reviewed. Further, an effort has been made to compare the accuracy of various widely used methods under different climatic conditions.

Mapping environmental land use conflict potentials and ecosystem services in agricultural watersheds.

PubMed

Kim, Ilkwon; Arnhold, Sebastian

2018-07-15

In mountainous watersheds, agricultural land use cause changes in ecosystem services, with trade-offs between crop production and erosion regulation. Management of these watersheds can generate environmental land use conflicts among regional stakeholders with different interests. Although several researches have made a start in mapping land use conflicts between human activities and conservation, spatial assessment of land use conflicts on environmental issues and ecosystem service trade-offs within agricultural areas has not been fully considered. In this study, we went further to map land use conflicts between agricultural preferences for crop production and environmental emphasis on erosion regulation. We applied an agricultural land suitability index, based on multi-criteria analysis, to estimate the spatial preference of agricultural activities, while applying the Revised Universal Soil Loss Equation (RUSLE) to reflect the environmental importance of soil erosion. Then, we classified the agricultural catchment into four levels of land use conflicts (lowest, low, high and highest) according to preference and importance of farmland areas, and we compared the classes by crop type. Soil loss in agricultural areas was estimated as 45.1thayr, and agricultural suitability as 0.873; this indicated that land use conflicts in the catchment could arise between severe soil erosion (environmental importance) and agricultural suitability (land preferences). Dry-field farms are mainly located in areas of low land use conflict level, where land preference outweighs environmental importance. When we applied farmland management scenarios with consideration of services, conversion to highest-conflict areas (Scenario 1) as 7.5% of the total area could reduce soil loss by 24.6%, while fallow land management (Scenario 2) could decrease soil loss 19.4% more than the current scenario (Business as usual). The result could maximize land management plans by extracting issues of spatial priority and use-versus-conservation conflicts as ecosystem service trade-offs from arguments over land use policy. Copyright © 2018 Elsevier B.V. All rights reserved.

Using Geostatistical Data Fusion Techniques and MODIS Data to Upscale Simulated Wheat Yield

NASA Astrophysics Data System (ADS)

Castrignano, A.; Buttafuoco, G.; Matese, A.; Toscano, P.

2014-12-01

Population growth increases food request. Assessing food demand and predicting the actual supply for a given location are critical components of strategic food security planning at regional scale. Crop yield can be simulated using crop models because is site-specific and determined by weather, management, length of growing season and soil properties. Crop models require reliable location-specific data that are not generally available. Obtaining these data at a large number of locations is time-consuming, costly and sometimes simply not feasible. An upscaling method to extend coverage of sparse estimates of crop yield to an appropriate extrapolation domain is required. This work is aimed to investigate the applicability of a geostatistical data fusion approach for merging remote sensing data with the predictions of a simulation model of wheat growth and production using ground-based data. The study area is Capitanata plain (4000 km2) located in Apulia Region, mostly cropped with durum wheat. The MODIS EVI/NDVI data products for Capitanata plain were downloaded from the Land Processes Distributed Active Archive Center (LPDAAC) remote for the whole crop cycle of durum wheat. Phenological development, biomass growth and grain quantity of durum wheat were simulated by the Delphi system, based on a crop simulation model linked to a database including soil properties, agronomical and meteorological data. Multicollocated cokriging was used to integrate secondary exhaustive information (multi-spectral MODIS data) with primary variable (sparsely distributed biomass/yield model predictions of durum wheat). The model estimates looked strongly spatially correlated with the radiance data (red and NIR bands) and the fusion data approach proved to be quite suitable and flexible to integrate data of different type and support.

Food crop production, nutrient availability, and nutrient intakes in Bangladesh: exploring the agriculture-nutrition nexus with the 2010 Household Income and Expenditure Survey.

PubMed

Fiedler, John L

2014-12-01

Systematic collection of national agricultural data has been neglected in many low- and middle-income countries for the past 20 years. Commonly conducted nationally representative household surveys collect substantial quantities of highly underutilized food crop production data. To demonstrate the potential usefulness of commonly available household survey databases for analyzing the agriculture-nutrition nexus. Using household data from the 2010 Bangladesh Household Income and Expenditure Survey, the role and significance of crop selection, area planted, yield, nutrient production, and the disposition of 34 food crops in affecting the adequacy of farming households' nutrient availability and nutrient intake status are explored. The adequacy of each farming household's available energy, vitamin A, calcium, iron, and zinc and households' apparent intakes and intake adequacies are estimated. Each household's total apparent nutrient intake adequacies are estimated, taking into account the amount of each crop that households consume from their own production, together with food purchased or obtained from other sources. Even though rice contains relatively small amounts of micronutrients, has relatively low nutrient density, and is a relatively poor source of nutrients compared with what other crops can produce on a given tract of land, because so much rice is produced in Bangladesh, it is the source of 90% of the total available energy, 85% of the zinc, 67% of the calcium, and 55% of the iron produced by the agricultural sector. The domination of agriculture and diet by rice is a major constraint to improving nutrition in Bangladesh. Simple examples of how minor changes in the five most common cropping patterns could improve farming households' nutritional status are provided. Household surveys' agricultural modules can provide a useful tool for better understanding national nutrient production realities and possibilities.

Floods and food security: A method to estimate the effect of inundation on crops availability

NASA Astrophysics Data System (ADS)

Pacetti, Tommaso; Caporali, Enrica; Rulli, Maria Cristina

2017-12-01

The inner connections between floods and food security are extremely relevant, especially in developing countries where food availability can be highly jeopardized by extreme events that damage the primary access to food, i.e. agriculture. A method for the evaluation of the effects of floods on food supply, consisting of the integration of remote sensing data, agricultural statistics and water footprint databases, is proposed and applied to two different case studies. Based on the existing literature related to extreme floods, the events in Bangladesh (2007) and in Pakistan (2010) have been selected as exemplary case studies. Results show that the use of remote sensing data combined with other sources of onsite information is particularly useful to assess the effects of flood events on food availability. The damages caused by floods on agricultural areas are estimated in terms of crop losses and then converted into lost calories and water footprint as complementary indicators. Method results are fully repeatable; whereas, for remote sensed data the sources of data are valid worldwide and the data regarding land use and crops characteristics are strongly site specific, which need to be carefully evaluated. A sensitivity analysis has been carried out for the water depth critical on the crops in Bangladesh, varying the assumed level by ±20%. The results show a difference in the energy content losses estimation of 12% underlying the importance of an accurate data choice.

Estimating riparian and agricultural evapotranspiration by reference crop evapotranspiration and MODIS Enhanced Vegetation Index

USGS Publications Warehouse

Nagler, Pamela L.; Glenn, Edward P.; Nguyen, Uyen; Scott, Russell; Doody, Tania

2013-01-01

Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor water use by both crops and natural vegetation in irrigation districts. We developed an algorithm for estimating actual evapotranspiration (ETa) based on the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the EOS-1 Terra satellite and locally-derived measurements of reference crop ET (ETo). The algorithm was calibrated with five years of ETa data from three eddy covariance flux towers set in riparian plant associations on the upper San Pedro River, Arizona, supplemented with ETa data for alfalfa and cotton from the literature. The algorithm was based on an equation of the form ETa = ETo [a(1 − e−bEVI) − c], where the term (1 − e−bEVI) is derived from the Beer-Lambert Law to express light absorption by a canopy, with EVI replacing leaf area index as an estimate of the density of light-absorbing units. The resulting algorithm capably predicted ETa across riparian plants and crops (r2 = 0.73). It was then tested against water balance data for five irrigation districts and flux tower data for two riparian zones for which season-long or multi-year ETa data were available. Predictions were within 10% of measured results in each case, with a non-significant (P = 0.89) difference between mean measured and modeled ETa of 5.4% over all validation sites. Validation and calibration data sets were combined to present a final predictive equation for application across crops and riparian plant associations for monitoring individual irrigation districts or for conducting global water use assessments of mixed agricultural and riparian biomes.

Land use policy and agricultural water management of the previous half of century in Africa

NASA Astrophysics Data System (ADS)

Valipour, Mohammad

2015-12-01

This paper examines land use policy and agricultural water management in Africa from 1962 to 2011. For this purpose, data were gathered from Food and Agriculture Organization of the United Nations (FAO) and the World Bank Group. Using the FAO database, ten indices were selected: permanent crops to cultivated area (%), rural population to total population (%), total economically active population in agriculture to total economically active population (%), human development index, national rainfall index (mm/year), value added to gross domestic product by agriculture (%), irrigation water requirement (mm/year), percentage of total cultivated area drained (%), difference between national rainfall index and irrigation water requirement (mm/year), area equipped for irrigation to cultivated area or land use policy index (%). These indices were analyzed for all 53 countries in the study area and the land use policy index was estimated by two different formulas. The results show that value of relative error is <20 %. In addition, an average index was calculated using various methods to assess countries' conditions for agricultural water management. Ability of irrigation and drainage systems was studied using other eight indices with more limited information. These indices are surface irrigation (%), sprinkler irrigation (%), localized irrigation (%), spate irrigation (%), agricultural water withdrawal (10 km3/year), conservation agriculture area as percentage of cultivated area (%), percentage of area equipped for irrigation salinized (%), and area waterlogged by irrigation (%). Finally, tendency of farmers to use irrigation systems for cultivated crops has been presented. The results show that Africa needs governments' policy to encourage farmers to use irrigation systems and raise cropping intensity for irrigated area.

Mapping paddy rice planting area in wheat-rice double-cropped areas through integration of Landsat-8 OLI, MODIS, and PALSAR images.

PubMed

Wang, Jie; Xiao, Xiangming; Qin, Yuanwei; Dong, Jinwei; Zhang, Geli; Kou, Weili; Jin, Cui; Zhou, Yuting; Zhang, Yao

2015-05-12

As farmland systems vary over space and time (season and year), accurate and updated maps of paddy rice are needed for studies of food security and environmental problems. We selected a wheat-rice double-cropped area from fragmented landscapes along the rural-urban complex (Jiangsu Province, China) and explored the potential utility of integrating time series optical images (Landsat-8, MODIS) and radar images (PALSAR) in mapping paddy rice planting areas. We first identified several main types of non-cropland land cover and then identified paddy rice fields by selecting pixels that were inundated only during paddy rice flooding periods. These key temporal windows were determined based on MODIS Land Surface Temperature and vegetation indices. The resultant paddy rice map was evaluated using regions of interest (ROIs) drawn from multiple high-resolution images, Google Earth, and in-situ cropland photos. The estimated overall accuracy and Kappa coefficient were 89.8% and 0.79, respectively. In comparison with the National Land Cover Data (China) from 2010, the resultant map better detected changes in the paddy rice fields and revealed more details about their distribution. These results demonstrate the efficacy of using images from multiple sources to generate paddy rice maps for two-crop rotation systems.

Synthetic aperture radar for a crop information system: A multipolarization and multitemporal approach

NASA Astrophysics Data System (ADS)

Ban, Yifang

Acquisition of timely information is a critical requirement for successful management of an agricultural monitoring system. Crop identification and crop-area estimation can be done fairly successfully using satellite sensors operating in the visible and near-infrared (VIR) regions of the spectrum. However, data collection can be unreliable due to problems of cloud cover at critical stages of the growing season. The all-weather capability of synthetic aperture radar (SAR) imagery acquired from satellites provides data over large areas whenever crop information is required. At the same time, SAR is sensitive to surface roughness and should be able to provide surface information such as tillage-system characteristics. With the launch of ERS-1, the first long-duration SAR system became available. The analysis of airborne multipolarization SAR data, multitemporal ERS-1 SAR data, and their combinations with VIR data, is necessary for the development of image-analysis methodologies that can be applied to RADARSAT data for extracting agricultural crop information. The overall objective of this research is to evaluate multipolarization airborne SAR data, multitemporal ERS-1 SAR data, and combinations of ERS-1 SAR and satellite VIR data for crop classification using non-conventional algorithms. The study area is situated in Norwich Township, an agricultural area in Oxford County, southern Ontario, Canada. It has been selected as one of the few representative agricultural 'supersites' across Canada at which the relationships between radar data and agriculture are being studied. The major field crops are corn, soybeans, winter wheat, oats, barley, alfalfa, hay, and pasture. Using airborne C-HH and C-HV SAR data, it was found that approaches using contextual information, texture information and per-field classification for improving agricultural crop classification proved to be effective, especially the per-field classification method. Results show that three of the four best per-field classification accuracies (\\ K=0.91) are achieved using combinations of C-HH and C-VV SAR data. This confirms the strong potential of multipolarization data for crop classification. The synergistic effects of multitemporal ERS-1 SAR and Landsat TM data are evaluated for crop classification using an artificial neural network (ANN) approach. The results show that the per-field approach using a feed-forward ANN significantly improves the overall classification accuracy of both single-date and multitemporal SAR data. Using the combination of TM3,4,5 and Aug. 5 SAR data, the best per-field ANN classification of 96.8% was achieved. It represents an 8.5% improvement over a single TM3,4,5 classification alone. Using multitemporal ERS-1 SAR data acquired during the 1992 and 1993 growing seasons, the radar backscatter characteristics of crops and their underlying soils are analyzed. The SAR temporal backscatter profiles were generated for each crop type and the earliest times of the year for differentiation of individual crop types were determined. Orbital (incidence-angle) effects were also observed on all crops. The average difference between the two orbits was about 3 dB. Thus attention should be given to the local incidence-angle effects when using ERS-1 SAR data, especially when comparing fields from different scenes or different areas within the same scene. Finally, early- and mid-season multitemporal SAR data for crop classification using sequential-masking techniques are evaluated, based on the temporal backscatter profiles. It was found that all crops studied could be identified by July 21.

Estimating crop net primary production using inventory data and MODIS-derived parameters

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

Bandaru, Varaprasad; West, Tristram O.; Ricciuto, Daniel M.

2013-06-03

National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale and over national and continental extents. Existing satellite-based NPP products tend to underestimate NPP on croplands. A new Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP. The method is documented here and evaluated for corn and soybean crops in Iowa and Illinois inmore » years 2006 and 2007. The method includes a crop-specific enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), shortwave radiation data estimated using Mountain Climate Simulator (MTCLIM) algorithm and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that correspond to the Cropland Data Layer (CDL) land cover product. The modeling framework represented well the gradient of NPP across Iowa and Illinois, and also well represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 980 g C m-2 yr-1 and 420 g C m-2 yr-1, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Estimated gross primary productivity (GPP) derived from AgI-LUE were in close agreement with eddy flux tower estimates. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents.« less

Spatial and temporal estimation of runoff in a semi-arid microwatershed of Southern India.

PubMed

Rejani, R; Rao, K V; Osman, M; Chary, G R; Pushpanjali; Reddy, K Sammi; Rao, Ch Srinivasa

2015-08-01

In a semi-arid microwatershed of Warangal district in Southern India, daily runoff was estimated spatially using Soil Conservation Service (SCS)-curve number (CN) method coupled with GIS. The groundwater status in this region is over-exploited, and precise estimation of runoff is very essential to plan interventions for this ungauged microwatershed. Rainfall is the most important factor governing runoff, and 75.8% of the daily rainfall and 92.1% of the rainy days which occurred were below 25 mm/day. The declines in rainfall and rainy days observed in recent years were 9.8 and 8.4%, respectively. The surface runoff estimated from crop land for a period of 57 years varied from 0 to 365 mm with a mean annual runoff of 103.7 mm or 14.1% of the mean annual rainfall. The mean annual runoff showed a significant reduction from 108.7 to 82.9 mm in recent years. The decadal variation of annual runoff from crop land over the years varied from 49.2 to 89.0% which showed the caution needed while planning watershed management works in this microwatershed. Among the four land use land cover conditions prevailing in the area, the higher runoff (20% of the mean annual rainfall) was observed from current fallow in clayey soil and lower runoff of 8.7% from crop land in loamy soil due to the increased canopy coverage. The drought years which occurred during recent years (1991-2007) in crop land have increased by 3.5%, normal years have increased by 15.6%, and the above normal years have decreased by 19.1%. This methodology can be adopted for estimating the runoff potential from similar ungauged watersheds with deficient data. It is concluded that in order to ensure long-term and sustainable groundwater utilization in the region, proper estimation of runoff and implementation of suitable water harvesting measures are the need of the hour.

Towards a Quantitative Use of Satellite Remote Sensing in Crop Growth Models for Large Scale Agricultural Production Estimate (Invited)

NASA Astrophysics Data System (ADS)

Defourny, P.

2013-12-01

The development of better agricultural monitoring capabilities is clearly considered as a critical step for strengthening food production information and market transparency thanks to timely information about crop status, crop area and yield forecasts. The documentation of global production will contribute to tackle price volatility by allowing local, national and international operators to make decisions and anticipate market trends with reduced uncertainty. Several operational agricultural monitoring systems are currently operating at national and international scales. Most are based on the methods derived from the pioneering experiences completed some decades ago, and use remote sensing to qualitatively compare one year to the others to estimate the risks of deviation from a normal year. The GEO Agricultural Monitoring Community of Practice described the current monitoring capabilities at the national and global levels. An overall diagram summarized the diverse relationships between satellite EO and agriculture information. There is now a large gap between the current operational large scale systems and the scientific state of the art in crop remote sensing, probably because the latter mainly focused on local studies. The poor availability of suitable in-situ and satellite data over extended areas hampers large scale demonstrations preventing the much needed up scaling research effort. For the cropland extent, this paper reports a recent research achievement using the full ENVISAT MERIS 300 m archive in the context of the ESA Climate Change Initiative. A flexible combination of classification methods depending to the region of the world allows mapping the land cover as well as the global croplands at 300 m for the period 2008 2012. This wall to wall product is then compared with regards to the FP 7-Geoland 2 results obtained using as Landsat-based sampling strategy over the IGADD countries. On the other hand, the vegetation indices and the biophysical variables such the Green Area Index (GAI), fAPAR and fcover usually retrieved from MODIS, MERIS, SPOT-Vegetation described the quality of the green vegetation development. The GLOBAM (Belgium) and EU FP-7 MOCCCASIN projects (Russia) improved the standard products and were demonstrated over large scale. The GAI retrieved from MODIS time series using a purity index criterion depicted successfully the inter-annual variability. Furthermore, the quantitative assimilation of these GAI time series into a crop growth model improved the yield estimate over years. These results showed that the GAI assimilation works best at the district or provincial level. In the context of the GEO Ag., the Joint Experiment of Crop Assessment and Monitoring (JECAM) was designed to enable the global agricultural monitoring community to compare such methods and results over a variety of regional cropping systems. For a network of test sites around the world, satellite and field measurements are currently collected and will be made available for collaborative effort. This experiment should facilitate international standards for data products and reporting, eventually supporting the development of a global system of systems for agricultural crop assessment and monitoring.

Analysis of data acquired by synthetic aperture radar over Dade County, Florida, and Acadia Parish, Louisiana

NASA Technical Reports Server (NTRS)

Wu, S. T.

1983-01-01

Results of digital processing of airborne X-band synthetic aperture radar (SAR) data acquired over Dade County, Florida, and Acadia Parish, Louisiana are presented. The goal was to investigate the utility of SAR data for land cover mapping and area estimation under the AgRISTARS Domestic Crops and Land Cover Project. In the case of the Acadia Paris study area, LANDSAT multispectral scanner (MSS) data were also used to form a combined SAR and MSS data set. The results of accuracy evaluation for the SAR, MSS, and SAR/MSS data using supervised classification show that the combined SAR/MSS data set results in an improved classification accuracy of the five land cover classes as compared with SAR-only and MSS-only data sets. In the case of the Dade County study area, the results indicate that both HH and VV polarization data are highly responsive to the row orientation of the row crop but not to the specific vegetation which forms the row structure. On the other hand, the HV polarization data are relatively insensitive to the orientation of row crop. Therefore, the HV polarization data may be used to discriminate the specific vegetation that forms the row structure.

Estimation of annual effective dose due to natural radioactive elements in ingestion of foodstuffs in tin mining area of Jos-Plateau, Nigeria.

PubMed

Jibiri, N N; Farai, I P; Alausa, S K

2007-01-01

Soils and food crops from a former tin mining location in a high background radiation area on the Jos-Plateau, Nigeria were collected and analyzed by gamma spectrometry to measure their contents of 40K, 238U and 232Th. As well as collecting samples, in situ dose rates on farms were measured using a precalibrated survey meter. Activity concentrations determined in food crops were compared with the local food derivatives or diets to investigate the possible removal or addition of radionuclides during food preparation by cooking or other means. Potassium-40 was found to contribute the highest activity in all the food products. The activity concentration of 40K, 238U and 232Th in local prepared diets ranged between 60 and 494 Bq kg-1, between BDL and 48 Bq kg-1 and between BDL and 17 Bq kg-1, respectively. The internal effective dose to individuals from the consumption of the food types was estimated on the basis of the measured radionuclide contents in the food crops. It ranged between 0.2 microSv y-1 (beans) and 2164 microSv y-1 (yam) while the annual external gamma effective dose in the farms due to soil radioactivity ranged between 228 microSv and 4065 microSv.

Transition year labeling error characterization study. [Kansas, Minnesota, Montana, North Dakota, South Dakota, and Oklahoma

NASA Technical Reports Server (NTRS)

Clinton, N. J. (Principal Investigator)

1980-01-01

Labeling errors made in the large area crop inventory experiment transition year estimates by Earth Observation Division image analysts are identified and quantified. The analysis was made from a subset of blind sites in six U.S. Great Plains states (Oklahoma, Kansas, Montana, Minnesota, North and South Dakota). The image interpretation basically was well done, resulting in a total omission error rate of 24 percent and a commission error rate of 4 percent. The largest amount of error was caused by factors beyond the control of the analysts who were following the interpretation procedures. The odd signatures, the largest error cause group, occurred mostly in areas of moisture abnormality. Multicrop labeling was tabulated showing the distribution of labeling for all crops.

Effects of a raised water table on greenhouse gas emissions and celery yield from agricultural peat under climate warming conditions

NASA Astrophysics Data System (ADS)

Matysek, Magdalena; Zona, Donatella; Leake, Jonathan; Banwart, Steven

2017-04-01

Peatlands are globally important areas for carbon preservation: covering only 3% of world's land, they store 30% of total soil carbon. At the same time, peat soils are widely utilised in agriculture: in Europe 14% of peatland area is under cultivation, 40% of UK peatlands have been drained for agricultural use and 24% of deep peat area in England is being farmed. One of the most important regions for crop production on lowland peats in the UK are the East Anglian Fenlands (the Fens): an area of drained peatlands in East England. 88% of the Fenland area is cultivated, sustaining around 4000 farms and supplying 37% of total vegetable production in England. The soils of the area are fertile (89% of agricultural land being classified as grade 1 or 2) and so crops with high nutritional demands tend to dominate. It is estimated that Fenland peats store 41 Tg of Carbon, which is lost from the ecosystem at a rate of 0.4 Tg C/yr. The Fens are at risk due to continued drainage-induced volume loss of the peat layer via shrinkage, compaction and oxidation, which are estimated to result in wastage rate of 2.1 cm/yr. Cultivation of peat soil requires drainage as most crops are intolerant of root-zone anoxia: this leads to creation of oxic conditions in which organic matter becomes vulnerable to mineralisation by aerobic microorganisms. It is, therefore, crucial to find a water table level which would minimise peat loss and at the same time allow for economically viable crop growth. Despite the importance of preservation of agricultural peats, there is a lack of studies which attempt to find water table level that strikes a balance between crop yield and greenhouse gas production. The future of the Fens is overshadowed by another uncertainty: increases in temperature brought by the climate change. It is estimated that average global temperature increase expected by the end of this century (relative to 1986-2005) would be within the range of 0.3-4.8°C, depending on the scenario. Rising temperatures should accelerate the rate of organic matter mineralisation, which will lead to higher emissions of greenhouse gases as well as enhanced plant growth due to better availability of nutrients. The effects of higher temperatures on crop growth and greenhouse gas emission have not been properly investigated in the context of agriculturally-utilised peatlands. This study was conducted on peat cores excavated from a field in the Fens and focused on the following objectives: 1. To examine effects of climate change-induced temperature rises on celery productivity and peat CO2 and CH4 emissions. 2. To find the field water table level that reduces peat emissions of CO2 and CH4 while maintaining celery productivity. The research found higher CO2 emissions from the elevated (+5°C) temperature treatment and lower CO2 emissions from the higher (-30cm) water table level, however, noted no effect on CH4 emissions of any of the treatments. The higher water table decreased aboveground celery biomass. There was no effect of increased temperature on aboveground celery yield.

Estimating millet production for famine early warning: An application of crop simulation modelling using satellite and ground-based data in Burkina Faso

USGS Publications Warehouse

Thornton, P. K.; Bowen, W. T.; Ravelo, A.C.; Wilkens, P. W.; Farmer, G.; Brock, J.; Brink, J. E.

1997-01-01

Early warning of impending poor crop harvests in highly variable environments can allow policy makers the time they need to take appropriate action to ameliorate the effects of regional food shortages on vulnerable rural and urban populations. Crop production estimates for the current season can be obtained using crop simulation models and remotely sensed estimates of rainfall in real time, embedded in a geographic information system that allows simple analysis of simulation results. A prototype yield estimation system was developed for the thirty provinces of Burkina Faso. It is based on CERES-Millet, a crop simulation model of the growth and development of millet (Pennisetum spp.). The prototype was used to estimate millet production in contrasting seasons and to derive production anomaly estimates for the 1986 season. Provincial yields simulated halfway through the growing season were generally within 15% of their final (end-of-season) values. Although more work is required to produce an operational early warning system of reasonable credibility, the methodology has considerable potential for providing timely estimates of regional production of the major food crops in countries of sub-Saharan Africa.

Assessment of Climate Suitability of Maize in South Korea

NASA Astrophysics Data System (ADS)

Hyun, S.; Choi, D.; Seo, B.

2017-12-01

Assessing suitable areas for crops would be useful to design alternate cropping systems as an adaptation option to climate change adaptation. Although suitable areas could be identified by using a crop growth model, it would require a number of input parameters including cultivar and soil. Instead, a simple climate suitability model, e.g., EcoCrop model, could be used for an assessment of climate suitability for a major grain crop. The objective of this study was to assess of climate suitability for maize using the EcoCrop model under climate change conditions in Korea. A long term climate data from 2000 - 2100 were compiled from weather data source. The EcoCrop model implemented in R was used to determine climate suitability index at each grid cell. Overall, the EcoCrop model tended to identify suitable areas for maize production near the coastal areas whereas the actual major production areas located in inland areas. It is likely that the discrepancy between assessed and actual crop production areas would result from the socioeconomic aspects of maize production. Because the price of maize is considerably low, maize has been grown in an area where moisture and temperature conditions would be less than optimum. In part, a simple algorithm to predict climate suitability for maize would caused a relatively large error in climate suitability assessment under the present climate conditions. In 2050s, the climate suitability for maize increased in a large areas in southern and western part of Korea. In particular, the plain areas near the coastal region had considerably greater suitability index in the future compared with mountainous areas. The expansion of suitable areas for maize would help crop production policy making such as the allocation of rice production area for other crops due to considerably less demand for the rice in Korea.

The review of dynamic monitoring technology for crop growth

NASA Astrophysics Data System (ADS)

Zhang, Hong-wei; Chen, Huai-liang; Zou, Chun-hui; Yu, Wei-dong

2010-10-01

In this paper, crop growth monitoring methods are described elaborately. The crop growth models, Netherlands-Wageningen model system, the United States-GOSSYM model and CERES models, Australia APSIM model and CCSODS model system in China, are introduced here more focus on the theories of mechanism, applications, etc. The methods and application of remote sensing monitoring methods, which based on leaf area index (LAI) and biomass were proposed by different scholars at home and abroad, are highly stressed in the paper. The monitoring methods of remote sensing coupling with crop growth models are talked out at large, including the method of "forced law" which using remote sensing retrieval state parameters as the crop growth model parameters input, and then to enhance the dynamic simulation accuracy of crop growth model and the method of "assimilation of Law" which by reducing the gap difference between the value of remote sensing retrieval and the simulated values of crop growth model and thus to estimate the initial value or parameter values to increasing the simulation accuracy. At last, the developing trend of monitoring methods are proposed based on the advantages and shortcomings in previous studies, it is assured that the combination of remote sensing with moderate resolution data of FY-3A, MODIS, etc., crop growth model, "3S" system and observation in situ are the main methods in refinement of dynamic monitoring and quantitative assessment techniques for crop growth in future.

Expansion of Sugarcane area for Ethanol production in Brazil: a Threat to Food Production and Environmental Sustainability?

NASA Astrophysics Data System (ADS)

Monteiro, J. M.; Coutinho, H. L.; Veiga, L. B.

2012-12-01

The raise in fossil fuels prices and the increase in Greenhouse Gas emissions is leading nations to adopt non-fossil fuels based energy sources. Sugarcane crops for biofuel production are expanding fast in Brazil, mainly through land use change (LUC) processes, in substitution of pasturelands and grain crops plantations. Would these changes affect negatively sustainability assessments of bioethanol production in the future? We estimate the extent of sugarcane cropland needed to produce sufficient ethanol to attend to market demands. This work presents a baseline scenario for sugarcane cropping area in Brazil in 2017, taking into account market forces (supply and demand). We also comment on a policy instrument targetting sustainable sugarcane production in Brazil. The expansion scenarios took into account the demand for ethanol from 2008-2017, produced by the Energy Research Corporation, of Brazil. In order to develop the expansion scenario, we estimated the amount of sugarcane needed to attend the ethanol demand. We then calculated the area needed to generate that amount of sugarcane. The analytical parameters were: 1) one tonne of sugarcane produces an average 81.6 liters of ethanol; 2) the average sugarcane crop productivity varied linearly from 81.4 tons/hectare in 2008 to 86.2 tons/hectare in 2017. We also assumed that sugarcane productivity in 2017 as the current average productivity of sugarcane in the State of São Paulo. The results show that the requirement for 3.5 million ha in 2007 will increase to 9 million ha in 2017. The Sugarcane Agroecologic Zoning (ZAECANA), published by Embrapa (2009), is a tool that not only informs the territory occupation and use policies, but also classifies land as qualified, restricted or non-qualified for the plantation of sugarcane crops. The ZAECANA is based on soil and climate suitability assessments, and is presented in a spatially-explicit format. Adopting the precautionary principle, a national policy was established restricting the Amazon and the Pantanal basin to sugarcane expansion. These eco-regions were, therefore, not considered by ZAECANA, which defined pasture lands as preferential for sugarcane crop expansion, since their majority is considered as degraded lands. ZAECANA results show that approximately 64 million ha, currently under pasture and agriculture, are suitable for sugarcane cropping in Brazil, located mainly at the Midwest and Southeast regions (35% of the national territory).Our results indicate that, if the ZAECANA instrument is implemented to drive investments for sugarcane expansion in Brazil, the projected demands for bioethanol could be met without significant impacts to food production, and environmental sustainability could be attained by the adoption of good crop, soil and water management practices.

Water use implications of biofuel scenarios

NASA Astrophysics Data System (ADS)

Teter, J.; Mishra, G. S.; Yeh, S.

2012-12-01

Existing studies rely upon attributional lifecycle analysis (LCA) approaches to estimate water intensity of biofuels in liters of irrigated/evapotranspiration water consumed for biofuel production. Such approaches can be misleading. From a policy perspective, a better approach is to compare differential water impacts among scenarios on a landscape scale. We address the shortcomings of existing studies by using consequential LCA, and incorporate direct and indirect land use (changes) of biofuel scenarios, marginal vs. average biofuel water use estimates, future climate, and geographic heterogeneity. We use the outputs of a partial equilibrium economic model, climate and soil data, and a process-based crop-soil-climate-water model to estimate differences in green water (GW - directly from precipitation to soil) and blue water (BW - supplied by irrigation) use among three scenarios: (1) business-as-usual (BAU), (2) Renewable Fuels Standard (RFS) mandates, and (3) a national Low Carbon Fuel Standard (LCFS) plus the RFS scenario. We use spatial statistical methods to interpolate key climatic variables using daily climate observations for the contiguous USA. Finally, we use FAO's crop model AquaCrop to estimate the domestic GW and BW impacts of biofuel policies from 2007-2035. We assess the differences among scenarios along the following metrics: (1) crop area expansion at the county level, including prime and marginal lands, (2) crop-specific and overall annual/seasonal water balances including (a) water inflows (irrigation & precipitation), (b) crop-atmosphere interactions: (evaporation & transpiration) and (d) soil-water flows (runoff & soil infiltration), in mm 3 /acre over the relevant time period. The functional unit of analysis is the BW and GW requirements of biofuels (mm3 per Btu biofuel) at the county level. Differential water use impacts among scenarios are a primarily a function of (1) land use conversion, in particular that of formerly uncropped land classes (2) irrigation practices, (3) feedstock water use efficiency, and (4) the longer growing season and a predominance of rainfed cultivation of dedicated biofuel feedstocks. National-level total water use is lowest in the BAU scenario and highest in the RFS2 + LCFS scenario. Figure: Million acres converted to growing miscanthus (top) & switchgrass (bottom) under the RFS + LCFS scenario in 2035. Land use classes are crop pasture (blue), idle cropland (red-purple) & prime cropland (brown).

Simultaneous state-parameter estimation supports the evaluation of data assimilation performance and measurement design for soil-water-atmosphere-plant system

NASA Astrophysics Data System (ADS)

Hu, Shun; Shi, Liangsheng; Zha, Yuanyuan; Williams, Mathew; Lin, Lin

2017-12-01

Improvements to agricultural water and crop managements require detailed information on crop and soil states, and their evolution. Data assimilation provides an attractive way of obtaining these information by integrating measurements with model in a sequential manner. However, data assimilation for soil-water-atmosphere-plant (SWAP) system is still lack of comprehensive exploration due to a large number of variables and parameters in the system. In this study, simultaneous state-parameter estimation using ensemble Kalman filter (EnKF) was employed to evaluate the data assimilation performance and provide advice on measurement design for SWAP system. The results demonstrated that a proper selection of state vector is critical to effective data assimilation. Especially, updating the development stage was able to avoid the negative effect of ;phenological shift;, which was caused by the contrasted phenological stage in different ensemble members. Simultaneous state-parameter estimation (SSPE) assimilation strategy outperformed updating-state-only (USO) assimilation strategy because of its ability to alleviate the inconsistency between model variables and parameters. However, the performance of SSPE assimilation strategy could deteriorate with an increasing number of uncertain parameters as a result of soil stratification and limited knowledge on crop parameters. In addition to the most easily available surface soil moisture (SSM) and leaf area index (LAI) measurements, deep soil moisture, grain yield or other auxiliary data were required to provide sufficient constraints on parameter estimation and to assure the data assimilation performance. This study provides an insight into the response of soil moisture and grain yield to data assimilation in SWAP system and is helpful for soil moisture movement and crop growth modeling and measurement design in practice.

Development of a decision support system for crop disease monitoring, surveillance and prediction in Bomet county, Kenya

NASA Astrophysics Data System (ADS)

Otieno, O. M.

2015-12-01

The study proposes to use Geographic Information Systems and Remote Sensing techniques to spatially model Maize Lethal Necrosis (MLN) disease in maize growing areas in Kenya. Results from this work will be used for prediction, monitoring and to guide intervention on MLN. This will minimize maize yield losses resulting from MLN infestation and thus safeguard the livelihoods of maize farmers in Kenya. MLN was first reported in Kenya in September 2011 in Bomet county. It then subsequently spread to other parts in Kenya. Maize crops are susceptible to MLN at all growth stages. Once infected the only option left for the farmers is to burn their maize plantations. Infection rate and damage is very high affecting yields and sometimes causing complete loss of maize yield.The modelling exercise will cover the period prior to and after the incidence of MLN. Specifically, the analysis will integrate spatio-temporal information on maize phenology and field surveys with the intention of delineating the extent of MLN infestation and the degree of damage as a result of MLN. Additionally, the task will identify potential predisposing factors leading to MLN resurgence and spread and to predict potential areas where MLN is likely to spread and to estimate the potential impact of MLN on the farm holders. The area of study for this task will be Bomet County. Historical and current environmental and spatial indicators including temperature, rainfall, soil moisture, vegetation health and crop cover will be fed into a model in order to determine the main factors that aide the occurrence and the spread of MLN. Multi-spectral image processing will be used to produce indices to study maize crop health whilst image classification techniques will be used to identify crop cover clusters by differentiating the variations in spectral signatures in the area of study and hence distinguish infected, unaffected maize crops and other crop cover classes. Variables from these indicators will then be weighted in a spatial model and be used as a basis for generating site-specific MLN prediction maps that will guide policy on MLN management in Kenya. The broaderobjective is to document a model that can be up-scaled and replicated in other maize producing areas in Kenya affected by MLN.

Estimated winter wheat yield from crop growth predicted by LANDSAT

NASA Technical Reports Server (NTRS)

Kanemasu, E. T.

1977-01-01

An evapotranspiration and growth model for winter wheat is reported. The inputs are daily solar radiation, maximum temperature, minimum temperature, precipitation/irrigation and leaf area index. The meteorological data were obtained from National Weather Service while LAI was obtained from LANDSAT multispectral scanner. The output provides daily estimates of potential evapotranspiration, transpiration, evaporation, soil moisture (50 cm depth), percentage depletion, net photosynthesis and dry matter production. Winter wheat yields are correlated with transpiration and dry matter accumulation.

A GIS-based approach to prevent contamination of groundwater at regional scale

NASA Astrophysics Data System (ADS)

Balderacchi, M.; Vischetti, C.; di Guardo, A.; Trevisan, M.

2009-04-01

Sustainable development is a fundamental objective of the European Union. Since 1991, the use of numerical models has been used to assess the environmental fate of pesticides (directive 91/414 EC). Since then, new approaches to assess pesticide contamination have been developed. This is an ongoing process, with approaches getting increasingly close to reality. Actually, there is a new challenge to integrate the most advanced and cost-effective monitoring strategies with simulation models so that reliable indicators of unsaturated flow and transport can be suitably mapped and coupled with other indicators related to productivity and sustainability. The most relevant role of GIS in the analysis of pesticide fate in soil is its application to process together input data and the results of distribution model based simulations of pesticide transport. FitoMarche is a GIS-based software tool that estimates pesticide movement in the unsaturated zone using MACRO 5 and it is able to simulate complex and real crop rotations at the regional scale. Crop rotation involves the sequential production of different plant species on the same land, every crop is characterized by different agricultural practices that involve the use of different pesticides at different doses. FitoMarche extracts MACRO input data from a series of geographic data sets (shapefiles) and an internal database, writes input files for MACRO, executes the simulation and extracts solute and water fluxes from MACRO output files. The study has been performed in the Marche region, located in central Italy along the Adriatic coast. Soil, climate, land use shapefiles were provided from public authorities, crop rotation schemes were estimated from ISTAT (the national statistics institute) 5th agricultural census database using a municipality detail and agricultural practices following the local customs. Two herbicides have been tested: "A" is employed on maize crop, and "B" on maize, sunflower and sugarbeet. In the first part the study focused of a definition of an indicator of groundwater contamination. The probably to exceed the groundwater quality endpoint has been chosen and it has been developed according a probabilistic approach and following a lognormal distribution of the data. After that the effect of crop rotation on pesticide leaching has been evaluated by a stepwise procedure. The tier 1 was the worst case in which the whole region is considered cropped with maize, therefore the pesticide application is every year on the crop with the highest application rate, whereas the tier 2 was a first refinement of the previous tier, the pesticide application was still every year but only in to the areas with the presence of authorised crop fore the assessed pesticide and with a crop LUA (land under agriculture) ratio higher than 10%. In the passage from tier 1 to tier 2 a contemporaneous reduction of simulated surface and pesticide leaching occurred because a relationship exists between agriculture and pesticide use. The step 3 considered a pesticide timing based on typical crop rotations. Te application followed label doses and was every time an authorised crop was found in the rotation. The passage to step 3 allowed a further percolation reduction. Step 3 blind simulations have been plotted as maps and matched with the results of the regional environment agency monitoring plan. A good correspondence between prediction and observation has got. Nevertheless herbicide "A" was detected in a larger area than assumed to be cropped with maize. However, in the past this compound was authorized for application to crops other than maize and was also used extensively in non-agricultural applications. Herbicide "B" was also detected in two wells located in areas not considered vulnerable. In the first well, water was sampled three times and the compound was detected once, in the other water was sampled once and the compound was detected. In this case point contamination, could be the origin of that. These pesticides were also researched in areas in which there is not application. This suggest that GIS approach can allow the design of new agrochemical monitoring plans that focus resources in areas with the highest probability of detection, reducing the cost to the community, and increasing the scientific value of the data collected. In the future, when well validated geographic data sets are available, it will also be possible to distinguish between point and diffuse contamination.

High-Throughput 3-D Monitoring of Agricultural-Tree Plantations with Unmanned Aerial Vehicle (UAV) Technology

PubMed Central

Torres-Sánchez, Jorge; López-Granados, Francisca; Serrano, Nicolás; Arquero, Octavio; Peña, José M.

2015-01-01

The geometric features of agricultural trees such as canopy area, tree height and crown volume provide useful information about plantation status and crop production. However, these variables are mostly estimated after a time-consuming and hard field work and applying equations that treat the trees as geometric solids, which produce inconsistent results. As an alternative, this work presents an innovative procedure for computing the 3-dimensional geometric features of individual trees and tree-rows by applying two consecutive phases: 1) generation of Digital Surface Models with Unmanned Aerial Vehicle (UAV) technology and 2) use of object-based image analysis techniques. Our UAV-based procedure produced successful results both in single-tree and in tree-row plantations, reporting up to 97% accuracy on area quantification and minimal deviations compared to in-field estimations of tree heights and crown volumes. The maps generated could be used to understand the linkages between tree grown and field-related factors or to optimize crop management operations in the context of precision agriculture with relevant agro-environmental implications. PMID:26107174

Application of agrometeorological spectral model in rice area in southern Brazil

NASA Astrophysics Data System (ADS)

Leivas, Janice F.; de C. Teixeira, Antonio Heriberto; Andrade, Ricardo G.; de C. Victoria, Daniel; Bayma-Silva, Gustavo; Bolfe, Edson L.

2015-10-01

The southern region is responsible for 70% of rice production in Brazil. In this study, rice areas of Rio Grande do Sul were selected, using the land use classification, scale 1: 100,000, provided by Brazilian Institute of Geography and Statistics (IBGE). MODIS Images were used and meteorological data, available by National Institute of Meteorology (INMET). The period of analysis was crop season 2011/2012, October to March. To obtain evapotranspiration was applied agrometeorological-spectral model SAFER (Simple Algorithm For Retrieving Evapotranspiration). From the analysis of the results, on planting and cultivation period , the average evapotranspiration (ET) daily was 1.93 +/- 0.96 mm.day-1. In the vegetative development period of rice, the daily ET has achieved 4.94 mm.day-1, with average value 2,31+/- 0.97 mm.day-1. In the period of harvest, evapotranspiration daily average was 1.84 +/- 0.80 mm.day-1. From results obtained, the estimation of evapotranspiration from satellite images may assist in monitoring the culture during the cycle, assisting in estimates of water productivity and crop yield.

High-Throughput 3-D Monitoring of Agricultural-Tree Plantations with Unmanned Aerial Vehicle (UAV) Technology.

PubMed

Torres-Sánchez, Jorge; López-Granados, Francisca; Serrano, Nicolás; Arquero, Octavio; Peña, José M

2015-01-01

The geometric features of agricultural trees such as canopy area, tree height and crown volume provide useful information about plantation status and crop production. However, these variables are mostly estimated after a time-consuming and hard field work and applying equations that treat the trees as geometric solids, which produce inconsistent results. As an alternative, this work presents an innovative procedure for computing the 3-dimensional geometric features of individual trees and tree-rows by applying two consecutive phases: 1) generation of Digital Surface Models with Unmanned Aerial Vehicle (UAV) technology and 2) use of object-based image analysis techniques. Our UAV-based procedure produced successful results both in single-tree and in tree-row plantations, reporting up to 97% accuracy on area quantification and minimal deviations compared to in-field estimations of tree heights and crown volumes. The maps generated could be used to understand the linkages between tree grown and field-related factors or to optimize crop management operations in the context of precision agriculture with relevant agro-environmental implications.

Toward irrigation retrieval by combining multi-sensor remote sensing data into a land surface model over a semi-arid region

NASA Astrophysics Data System (ADS)

Malbéteau, Y.; Lopez, O.; Houborg, R.; McCabe, M.

2017-12-01

Agriculture places considerable pressure on water resources, with the relationship between water availability and food production being critical for sustaining population growth. Monitoring water resources is particularly important in arid and semi-arid regions, where irrigation can represent up to 80% of the consumptive uses of water. In this context, it is necessary to optimize on-farm irrigation management by adjusting irrigation to crop water requirements throughout the growing season. However, in situ point measurements are not routinely available over extended areas and may not be representative at the field scale. Remote sensing approaches present as a cost-effective technique for mapping and monitoring broad areas. By taking advantage of multi-sensor remote sensing methodologies, such as those provided by MODIS, Landsat, Sentinel and Cubesats, we propose a new method to estimate irrigation input at pivot-scale. Here we explore the development of crop-water use estimates via these remote sensing data and integrate them into a land surface modeling framework, using a farm in Saudi Arabia as a demonstration of what can be achieved at larger scales.

Regional crop gross primary production and yield estimation using fused Landsat-MODIS data

NASA Astrophysics Data System (ADS)

He, M.; Kimball, J. S.; Maneta, M. P.; Maxwell, B. D.; Moreno, A.

2017-12-01

Accurate crop yield assessments using satellite-based remote sensing are of interest for the design of regional policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations are generally too coarse to capture cropland heterogeneity. Merging information from sensors with reciprocal spatial and temporal resolution can improve the accuracy of these retrievals. In this study, we estimate annual crop yields for seven important crop types -alfalfa, barley, corn, durum wheat, peas, spring wheat and winter wheat over Montana, United States (U.S.) from 2008 to 2015. Yields are estimated as the product of gross primary production (GPP) and a crop-specific harvest index (HI) at 30 m spatial resolution. To calculate GPP we used a modified form of the MOD17 LUE algorithm driven by a 30 m 8-day fused NDVI dataset constructed by blending Landsat (5 or 7) and MODIS Terra reflectance data. The fused 30-m NDVI record shows good consistency with the original Landsat and MODIS data, but provides better spatiotemporal information on cropland vegetation growth. The resulting GPP estimates capture characteristic cropland patterns and seasonal variations, while the estimated annual 30 m crop yield results correspond favorably with county-level crop yield data (r=0.96, p<0.05). The estimated crop yield performance was generally lower, but still favorable in relation to field-scale crop yield surveys (r=0.42, p<0.01). Our methods and results are suitable for operational applications at regional scales.

Earth resources data analysis program, phase 3

NASA Technical Reports Server (NTRS)

1975-01-01

Tasks were performed in two areas: (1) systems analysis and (2) algorithmic development. The major effort in the systems analysis task was the development of a recommended approach to the monitoring of resource utilization data for the Large Area Crop Inventory Experiment (LACIE). Other efforts included participation in various studies concerning the LACIE Project Plan, the utility of the GE Image 100, and the specifications for a special purpose processor to be used in the LACIE. In the second task, the major effort was the development of improved algorithms for estimating proportions of unclassified remotely sensed data. Also, work was performed on optimal feature extraction and optimal feature extraction for proportion estimation.

Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity

NASA Astrophysics Data System (ADS)

Bach, H.; Klug, P.; Ruf, T.; Migdall, S.; Schlenz, F.; Hank, T.; Mauser, W.

2015-04-01

To support food security, information products about the actual cropping area per crop type, the current status of agricultural production and estimated yields, as well as the sustainability of the agricultural management are necessary. Based on this information, well-targeted land management decisions can be made. Remote sensing is in a unique position to contribute to this task as it is globally available and provides a plethora of information about current crop status. M4Land is a comprehensive system in which a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal satellite images. SLC uses modelled surface state parameters from PROMET, such as leaf area index or phenology of different crops to simulate spatially distributed surface reflectance spectra. This is the basis for generating artificial satellite images considering sensor specific configurations (spectral bands, solar and observation geometries). Ensembles of model runs are used to represent different crop types, fertilization status, soil colour and soil moisture. By multi-temporal comparisons of simulated and real satellite images, the land cover/crop type can be classified in a dynamically, model-supervised way and without in-situ training data. The method is demonstrated in an agricultural test-site in Bavaria. Its transferability is studied by analysing PROMET model results for the rest of Germany. Especially the simulated phenological development can be verified on this scale in order to understand whether PROMET is able to adequately simulate spatial, as well as temporal (intra- and inter-season) crop growth conditions, a prerequisite for the model-supervised approach. This sophisticated new technology allows monitoring of management decisions on the field-level using high resolution optical data (presently RapidEye and Landsat). The M4Land analysis system is designed to integrate multi-mission data and is well suited for the use of Sentinel-2's continuous and manifold data stream.

The 1980 US/Canada wheat and barley exploratory experiment, volume 1

NASA Technical Reports Server (NTRS)

Bizzell, R. M.; Prior, H. L.; Payne, R. W.; Disler, J. M.

1983-01-01

The results from the U.S./Canada Wheat and Barley Exploratory Experiment which was completed during FY 1980 are presented. The results indicate that the new crop identification procedures performed well for spring small grains and that they are conductive to automation. The performance of the machine processing techniques shows a significant improvement over previously evaluated technology. However, the crop calendars will require additional development and refinements prior to integration into automated area estimation technology. The evaluation showed the integrated technology to be capable of producing accurate and consistent spring small grains proportion estimates. However, barley proportion estimation technology was not satisfactorily evaluated. The low-density segments examined were judged not to give indicative or unequivocal results. It is concluded that, generally, the spring small grains technology is ready for evaluation in a pilot experiment focusing on sensitivity analyses to a variety of agricultural and meteorological conditions representative of the global environment. It is further concluded that a strong potential exists for establishing a highly efficient technology or spring small grains.

Identification of "ever-cropped" land (1984-2010) using Landsat annual maximum NDVI image composites: Southwestern Kansas case study.

PubMed

Maxwell, Susan K; Sylvester, Kenneth M

2012-06-01

A time series of 230 intra- and inter-annual Landsat Thematic Mapper images was used to identify land that was ever cropped during the years 1984 through 2010 for a five county region in southwestern Kansas. Annual maximum Normalized Difference Vegetation Index (NDVI) image composites (NDVI(ann-max)) were used to evaluate the inter-annual dynamics of cropped and non-cropped land. Three feature images were derived from the 27-year NDVI(ann-max) image time series and used in the classification: 1) maximum NDVI value that occurred over the entire 27 year time span (NDVI(max)), 2) standard deviation of the annual maximum NDVI values for all years (NDVI(sd)), and 3) standard deviation of the annual maximum NDVI values for years 1984-1986 (NDVI(sd84-86)) to improve Conservation Reserve Program land discrimination.Results of the classification were compared to three reference data sets: County-level USDA Census records (1982-2007) and two digital land cover maps (Kansas 2005 and USGS Trends Program maps (1986-2000)). Area of ever-cropped land for the five counties was on average 11.8 % higher than the area estimated from Census records. Overall agreement between the ever-cropped land map and the 2005 Kansas map was 91.9% and 97.2% for the Trends maps. Converting the intra-annual Landsat data set to a single annual maximum NDVI image composite considerably reduced the data set size, eliminated clouds and cloud-shadow affects, yet maintained information important for discriminating cropped land. Our results suggest that Landsat annual maximum NDVI image composites will be useful for characterizing land use and land cover change for many applications.

Crop Identification Using Time Series of Landsat-8 and Radarsat-2 Images: Application in a Groundwater Irrigated Region, South India

NASA Astrophysics Data System (ADS)

Sharma, A. K.; Hubert-Moy, L.; Betbederet, J.; Ruiz, L.; Sekhar, M.; Corgne, S.

2016-08-01

Monitoring land use and land cover and more particularly irrigated cropland dynamics is of great importance for water resources management and land use planning. The objective of this study was to evaluate the combined use of multi-temporal optical and radar data with a high spatial resolution in order to improve the precision of irrigated crop identification by taking into account information on crop phenological stages. SAR and optical parameters were derived from time- series of seven quad-pol RADARSAT-2 and four Landsat-8 images which were acquired on the Berambadi catchment, South India, during the monsoon crop season at the growth stages of turmeric crop. To select the best parameter to discriminate turmeric crops, an analysis of covariance (ANCOVA) was applied on all the time-series parameters and the most discriminant ones were classified using the Support Vector Machine (SVM) technique. Results show that in absence of optical images, polarimetric parameters derived from SAR time-series can be used for the turmeric area estimates and that the combined use of SAR and optical parameters can improve the classification accuracy to identify turmeric.

Long-term trends in field level irrigation water demand in Mahanadi delta districts - a hydrological modeling approach for coping with climate change

NASA Astrophysics Data System (ADS)

Raju Pokkuluri, Venkat; Rao, Diwakar Parsi Guru; Hazra, Sugata; Srikant Kulkarni, Sunil

2017-04-01

India uses its 85 percent of available water resources for irrigation making it the country with largest net irrigated area in the world. With one of the largest delta plains, sustaining the needs of irrigation supplies is critical for food security and coping with challenges of climate change. The extensive development of upstream river basins/catchments is posing serious challenge and constrains to the water availability to delta regions, which depend on the controlled/regulated flows from the upstream catchments. The irrigation water demands vary due to changes in agricultural practices, cropping pattern and changing climate conditions. Estimation of realistic irrigation water demand and its trend over time is critical for meeting the supplementary water needs of productive agricultural lands in delta plains and there by coping the challenges of extensive upstream river basin development and climate change. The present study carried out in delta districts of Mahanadi river in Odisha State of India, wherein the long-term trends in field level irrigation water requirements were estimated, both on spatial & temporal scales, using hydrological modeling framework. This study attempts to estimate field level irrigation water requirements through simulation of soil water balance during the crop growing season through process based hydrological modeling framework. The soil water balance computations were carried out using FAO-56 framework, by modifying the crop coefficient (Kc) proportional to the water stress coefficient (Ks), which is a function of root zone depletion of water. Daily meteorological data, spatial cropping pattern, terrain are incorporated in the soil water balance simulation in the model. The irrigation water demand is derived considering the exclusion of soil water stress for each model time step. The field level irrigation water requirement at 8 day interval had been estimated for the each Rabi season (post-monsoon) spanning over 1986 to 2015. The results indicate that irrigation water requirements show spatial and temporal changes and tend to deviate from notional/envisaged demands. Validation of estimated irrigation demand is attempted through correlation of gap in supply and demand with the trends in crop water stress and crop production during the study years. Crop water Stress Index (CWSI), which is the ratio of deficit of actual evapotranspiration (AET) from the potential evapotranspiration (PET) and is derived from MODIS Evapotranspiration data. Agricultural production data is used from State/Central government statistics. The attempted methodology provides opportunities to estimate future irrigation water demand under projected climate change scenarios and for planning for basin level water resources development sustaining the delta agriculture, which are projected to be more vulnerable to climate change.

A multi-sensor burned area algorithm for crop residue burning in northwestern India: validation and sources of error

NASA Astrophysics Data System (ADS)

Liu, T.; Marlier, M. E.; Karambelas, A. N.; Jain, M.; DeFries, R. S.

2017-12-01

A leading source of outdoor emissions in northwestern India comes from crop residue burning after the annual monsoon (kharif) and winter (rabi) crop harvests. Agricultural burned area, from which agricultural fire emissions are often derived, can be poorly quantified due to the mismatch between moderate-resolution satellite sensors and the relatively small size and short burn period of the fires. Many previous studies use the Global Fire Emissions Database (GFED), which is based on the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product MCD64A1, as an outdoor fires emissions dataset. Correction factors with MODIS active fire detections have previously attempted to account for small fires. We present a new burned area classification algorithm that leverages more frequent MODIS observations (500 m x 500 m) with higher spatial resolution Landsat (30 m x 30 m) observations. Our approach is based on two-tailed Normalized Burn Ratio (NBR) thresholds, abbreviated as ModL2T NBR, and results in an estimated 104 ± 55% higher burned area than GFEDv4.1s (version 4, MCD64A1 + small fires correction) in northwestern India during the 2003-2014 winter (October to November) burning seasons. Regional transport of winter fire emissions affect approximately 63 million people downwind. The general increase in burned area (+37% from 2003-2007 to 2008-2014) over the study period also correlates with increased mechanization (+58% in combine harvester usage from 2001-2002 to 2011-2012). Further, we find strong correlation between ModL2T NBR-derived burned area and results of an independent survey (r = 0.68) and previous studies (r = 0.92). Sources of error arise from small median landholding sizes (1-3 ha), heterogeneous spatial distribution of two dominant burning practices (partial and whole field), coarse spatio-temporal satellite resolution, cloud and haze cover, and limited Landsat scene availability. The burned area estimates of this study can be used to build a new agricultural fire emissions inventory to re-evaluate the contributions of winter agricultural fires to rural and urban air quality degradation.

The method for detecting biological parameter of rice growth and early planting of paddy crop by using multi temporal remote sensing data

NASA Astrophysics Data System (ADS)

Domiri, D. D.

2017-01-01

Rice crop is the most important food crop for the Asian population, especially in Indonesia. During the growth of rice plants have four main phases, namely the early planting or inundation phase, the vegetative phase, the generative phase, and bare land phase. Monitoring the condition of the rice plant needs to be conducted in order to know whether the rice plants have problems or not in its growth. Application of remote sensing technology, which uses satellite data such as Landsat 8 and others which has a spatial and temporal resolution is high enough for monitoring the condition of crops such as paddy crop in a large area. In this study has been made an algorithm for monitoring rapidly of rice growth condition using Maximum of Vegetation Index (EVI Max). The results showed that the time of early planting can be estimated if known when EVI Max occurred. The value of EVI Max and when it occured can be known by trough spatial analysis of multitemporal EVI Landsat 8 or other medium spatial resolution satellites.

Bioregenerative food system cost based on optimized menus for advanced life support

NASA Technical Reports Server (NTRS)

Waters, Geoffrey C R.; Olabi, Ammar; Hunter, Jean B.; Dixon, Mike A.; Lasseur, Christophe

2002-01-01

Optimized menus for a bioregenerative life support system have been developed based on measures of crop productivity, food item acceptability, menu diversity, and nutritional requirements of crew. Crop-specific biomass requirements were calculated from menu recipe demands while accounting for food processing and preparation losses. Under the assumption of staggered planting, the optimized menu demanded a total crop production area of 453 m2 for six crew. Cost of the bioregenerative food system is estimated at 439 kg per menu cycle or 7.3 kg ESM crew-1 day-1, including agricultural waste processing costs. On average, about 60% (263.6 kg ESM) of the food system cost is tied up in equipment, 26% (114.2 kg ESM) in labor, and 14% (61.5 kg ESM) in power and cooling. This number is high compared to the STS and ISS (nonregenerative) systems but reductions in ESM may be achieved through intensive crop productivity improvements, reductions in equipment masses associated with crop production, and planning of production, processing, and preparation to minimize the requirement for crew labor.

Estimation of Some Bio-Physical Indicators for Sustainable Crop Production in the Eastern Nile Basin of Sudan Using Landsat-8 Imagery and SEBAL Model

NASA Astrophysics Data System (ADS)

Guma Biro Turk, Khalid

2016-07-01

Crop production under modern irrigation systems require unique management at field level and hence better utilization of agricultural inputs and water resources. This study aims to make use of remote sensing (RS) data and the surface energy balance algorithm for land (SEBAL) to improve the on-farm management. The study area is located in the Eastern part of the Blue Nile River about 60 km south of Khartoum, Sudan. Landsat-8 data were used to estimate a number of bio-physical indicators during the growing season of the year 2014/2015. Accordingly, in-situ weather data and SEBAL model were applied to calculate: the reference (ET0), actual (ETa) and potential (ETp) evapotranspiration, soil moisture (SM), crop factor (kc), nitrogen (N), biomass production (BP) and crop water productivity (CWP). Results revealed that ET0 showed steady variation throughout the year, varying from 5 to 7 mm/day. However, ETa and ETp showed clear temporal variation attributed to frequent cutting of the alfalfa, almost monthly. The BP of the alfalfa was observed to be high when there is no cutting activates were made before the image acquisition date. Nevertheless the CWP trends are following the biomass production ones, low when there is no biomass and high when the biomass is high. The application of SEBAL model within the study area using the Landsat-8 imagery indicates that it's possible to produce field-based bio-physical indicators, which can be useful in monitoring and managing the field during the growing season. However, a cross-calibration with the in-situ data should be considered in order to maintain the spatial variability within the field. Keywords: Bio-physical Indicators; Remote Sensing; SEBAL; Landsat-8; Eastern Nile Basin

Preliminary evaluation of the Environmental Research Institute of Michigan crop calendar shift algorithm for estimation of spring wheat development stage. [North Dakota, South Dakota, Montana, and Minnesota

NASA Technical Reports Server (NTRS)

Phinney, D. E. (Principal Investigator)

1980-01-01

An algorithm for estimating spectral crop calendar shifts of spring small grains was applied to 1978 spring wheat fields. The algorithm provides estimates of the date of peak spectral response by maximizing the cross correlation between a reference profile and the observed multitemporal pattern of Kauth-Thomas greenness for a field. A methodology was developed for estimation of crop development stage from the date of peak spectral response. Evaluation studies showed that the algorithm provided stable estimates with no geographical bias. Crop development stage estimates had a root mean square error near 10 days. The algorithm was recommended for comparative testing against other models which are candidates for use in AgRISTARS experiments.

New Microwave-Based Missions Applications for Rainfed Crops Characterization

NASA Astrophysics Data System (ADS)

Sánchez, N.; Lopez-Sanchez, J. M.; Arias-Pérez, B.; Valcarce-Diñeiro, R.; Martínez-Fernández, J.; Calvo-Heras, J. M.; Camps, A.; González-Zamora, A.; Vicente-Guijalba, F.

2016-06-01

A multi-temporal/multi-sensor field experiment was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain, in order to retrieve useful information from satellite Synthetic Aperture Radar (SAR) and upcoming Global Navigation Satellite Systems Reflectometry (GNSS-R) missions. The objective of the experiment was first to identify which radar observables are most sensitive to the development of crops, and then to define which crop parameters the most affect the radar signal. A wide set of radar variables (backscattering coefficients and polarimetric indicators) acquired by Radarsat-2 were analyzed and then exploited to determine variables characterizing the crops. Field measurements were fortnightly taken at seven cereals plots between February and July, 2015. This work also tried to optimize the crop characterization through Landsat-8 estimations, testing and validating parameters such as the leaf area index, the fraction of vegetation cover and the vegetation water content, among others. Some of these parameters showed significant and relevant correlation with the Landsat-derived Normalized Difference Vegetation Index (R>0.60). Regarding the radar observables, the parameters the best characterized were biomass and height, which may be explored for inversion using SAR data as an input. Moreover, the differences in the correlations found for the different crops under study types suggested a way to a feasible classification of crops.

Developing a global crop model for maize, wheat, and soybean production

NASA Astrophysics Data System (ADS)

Deryng, D.; Ramankutty, N.; Sacks, W. J.

2008-12-01

Recently, the world food supply has faced a crisis due to increasing food prices driven by rising food demand, increasing fuel prices, poor harvests due to climate factors, and the use of crops such as maize and soybean to produce biofuel. In order to assess the future of global food availability, there is a need for understanding the factors underlying food production. Farmer management practices along with climatic conditions are the main elements directly influencing crop yield. As a consequence, estimations of future world food production require the use of a global crop model that simulates reasonably the effect of both climate and management practices on yield. Only a few global crop models have been developed to date, and currently none of them represent management factors adequately, principally due to the lack of spatially explicit datasets at the global scale. In this study, we present a global crop model designed for maize, wheat, and soybean production that incorporates planting and harvest decisions, along with irrigation options based on newly available data. The crop model is built on a simple water-balance algorithm based on the Penman- Monteith equation combined with a light use efficiency approach that calculates biomass production under non-nutrient-limiting conditions. We used a world crop calendar dataset to develop statistical relationships between climate variables and planting dates for different regions of the world. Development stages are defined based on total growing degree days required to reach the beginning of each phase. Irrigation options are considered in regions where water stress occurs and irrigation infrastructures exist. We use a global dataset on irrigated areas for each crop type. The quantity of water applied is then calculated in order to avoid water stress but with an upper threshold derived from total irrigation withdrawal quantity estimated by the global water use model WaterGAP 2. Our analysis will present the model sensitivity to different scenarios of management practices, e.g. planting date and water supply, under non-nutrient limited conditions. With this study, we hope to clarify the importance of planting date and irrigation versus climate for crop yield.

Model-data integration for developing the Cropland Carbon Monitoring System (CCMS)

NASA Astrophysics Data System (ADS)

Jones, C. D.; Bandaru, V.; Pnvr, K.; Jin, H.; Reddy, A.; Sahajpal, R.; Sedano, F.; Skakun, S.; Wagle, P.; Gowda, P. H.; Hurtt, G. C.; Izaurralde, R. C.

2017-12-01

The Cropland Carbon Monitoring System (CCMS) has been initiated to improve regional estimates of carbon fluxes from croplands in the conterminous United States through integration of terrestrial ecosystem modeling, use of remote-sensing products and publically available datasets, and development of improved landscape and management databases. In order to develop these improved carbon flux estimates, experimental datasets are essential for evaluating the skill of estimates, characterizing the uncertainty of these estimates, characterizing parameter sensitivities, and calibrating specific modeling components. Experiments were sought that included flux tower measurement of CO2 fluxes under production of major agronomic crops. Currently data has been collected from 17 experiments comprising 117 site-years from 12 unique locations. Calibration of terrestrial ecosystem model parameters using available crop productivity and net ecosystem exchange (NEE) measurements resulted in improvements in RMSE of NEE predictions of between 3.78% to 7.67%, while improvements in RMSE for yield ranged from -1.85% to 14.79%. Model sensitivities were dominated by parameters related to leaf area index (LAI) and spring growth, demonstrating considerable capacity for model improvement through development and integration of remote-sensing products. Subsequent analyses will assess the impact of such integrated approaches on skill of cropland carbon flux estimates.

Virtual water trade patterns in relation to environmental and socioeconomic factors: A case study for Tunisia.

PubMed

Chouchane, Hatem; Krol, Maarten S; Hoekstra, Arjen Y

2018-02-01

Growing water demands put increasing pressure on local water resources, especially in water-short countries. Virtual water trade can play a key role in filling the gap between local demand and supply of water-intensive commodities. This study aims to analyse the dynamics in virtual water trade of Tunisia in relation to environmental and socio-economic factors such as GDP, irrigated land, precipitation, population and water scarcity. The water footprint of crop production is estimated using AquaCrop for six crops over the period 1981-2010. Net virtual water import (NVWI) is quantified at yearly basis. Regression models are used to investigate dynamics in NVWI in relation to the selected factors. The results show that NVWI during the study period for the selected crops is not influenced by blue water scarcity. NVWI correlates in two alternative models to either population and precipitation (model I) or to GDP and irrigated area (model II). The models are better in explaining NVWI of staple crops (wheat, barley, potatoes) than NVWI of cash crops (dates, olives, tomatoes). Using model I, we are able to explain both trends and inter-annual variability for rain-fed crops. Model II performs better for irrigated crops and is able to explain trends significantly; no significant relation is found, however, with variables hypothesized to represent inter-annual variability. Copyright © 2017 Elsevier B.V. All rights reserved.

A one-layer satellite surface energy balance for estimating evapotranspiration rates and crop water stress indexes.

PubMed

Barbagallo, Salvatore; Consoli, Simona; Russo, Alfonso

2009-01-01

Daily evapotranspiration fluxes over the semi-arid Catania Plain area (Eastern Sicily, Italy) were evaluated using remotely sensed data from Landsat Thematic Mapper TM5 images. A one-source parameterization of the surface sensible heat flux exchange using satellite surface temperature has been used. The transfer of sensible and latent heat is described by aerodynamic resistance and surface resistance. Required model inputs are brightness, temperature, fractional vegetation cover or leaf area index, albedo, crop height, roughness lengths, net radiation, air temperature, air humidity and wind speed. The aerodynamic resistance (r(ah)) is formulated on the basis of the Monin-Obukhov surface layer similarity theory and the surface resistance (r(s)) is evaluated from the energy balance equation. The instantaneous surface flux values were converted into evaporative fraction (EF) over the heterogeneous land surface to derive daily evapotranspiration values. Remote sensing-based assessments of crop water stress (CWSI) were also made in order to identify local irrigation requirements. Evapotranspiration data and crop coefficient values obtained from the approach were compared with: (i) data from the semi-empirical approach "K(c) reflectance-based", which integrates satellite data in the visible and NIR regions of the electromagnetic spectrum with ground-based measurements and (ii) surface energy flux measurements collected from a micrometeorological tower located in the experiment area. The expected variability associated with ET flux measurements suggests that the approach-derived surface fluxes were in acceptable agreement with the observations.

Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images

PubMed Central

Peña, José Manuel; Torres-Sánchez, Jorge; de Castro, Ana Isabel; Kelly, Maggi; López-Granados, Francisca

2013-01-01

The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r2=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (<5% weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance. PMID:24146963

Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images.

PubMed

Peña, José Manuel; Torres-Sánchez, Jorge; de Castro, Ana Isabel; Kelly, Maggi; López-Granados, Francisca

2013-01-01

The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r(2)=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (<5% weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance.

Future possible crop yield scenarios under multiple SSP and RCP scenarios.

NASA Astrophysics Data System (ADS)

Sakurai, G.; Yokozawa, M.; Nishimori, M.; Okada, M.

2016-12-01

Understanding the effect of future climate change on global crop yields is one of the most important tasks for global food security. Future crop yields would be influenced by climatic factors such as the changes of temperature, precipitation and atmospheric carbon dioxide concentration. On the other hand, the effect of the changes of agricultural technologies such as crop varieties, pesticide and fertilizer input on crop yields have large uncertainty. However, not much is available on the contribution ratio of each factor under the future climate change scenario. We estimated the future global yields of four major crops (maize, soybean, rice and wheat) under three Shared Socio Economic Pathways (SSPs) and four Representative Concentration Pathways (RCPs). For this purpose, firstly, we estimated a parameter of a process based model (PRYSBI2) using a Bayesian method for each 1.125 degree spatial grid. The model parameter is relevant to the agricultural technology (we call "technological parameter" here after). Then, we analyzed the relationship between the values of technological parameter and GDP values. We found that the estimated values of the technological parameter were positively correlated with the GDP. Using the estimated relationship, we predicted future crop yield during 2020 and 2100 under SSP1, SSP2 and SSP3 scenarios and RCP 2.6, 4.5, 6.0 and 8.5. The estimated crop yields were different among SSP scenarios. However, we found that the yield difference attributable to SSPs were smaller than those attributable to CO2 fertilization effects and climate change. Particularly, the estimated effect of the change of atmospheric carbon dioxide concentration on global yields was more than four times larger than that of GDP for C3 crops.

Whole system analysis of second generation bioenergy production and Ecosystem Services in Europe

NASA Astrophysics Data System (ADS)

Henner, Dagmar; Smith, Pete; Davies, Christian; McNamara, Niall

2017-04-01

Bioenergy crops are an important source of renewable energy and are a possible mechanism to mitigate global climate warming, by replacing fossil fuel energy that has higher greenhouse gas emissions. There is, however, uncertainty about the impacts of the growth of bioenergy crops on ecosystem services. This uncertainty is further enhanced by current climate change. It is important to establish how second generation bioenergy crops (Miscanthus, SRC willow and poplar) can contribute by closing the gap between reducing fossil fuel use and increasing the use of other renewable sources in a sustainable way. The project builds on models of energy crop production, biodiversity, soil impacts, greenhouse gas emissions and other ecosystem services, and on work undertaken in the UK on the ETI-funded ELUM project (www.elum.ac.uk). We will present estimated yields for the above named crops in Europe using the ECOSSE, DayCent, SalixFor and MiscanFor models. These yields will be brought into context with a whole system analysis, detailing trade-offs and synergies for land use change, food security, GHG emissions and soil and water security. Methods like water footprint tools, tourism value maps and ecosystem valuation tools and models (e.g. InVest, TEEB database, GREET LCA Model, World Business Council for Sustainable Development corporate ecosystem valuation, Millennium Ecosystem Assessment and the Ecosystem Services Framework) will be used to estimate and visualise the impacts of increased use of second generation bioenergy crops on the above named ecosystem services. The results will be linked to potential yields to generate "inclusion or exclusion areas" in Europe in order to establish suitable areas for bioenergy crop production and the extent of use possible. Policy is an important factor for using second generation bioenergy crops in a sustainable way. We will present how whole system analysis can be used to create scenarios for countries or on a continental scale. As an example, we will present two scenarios for the whole system on a country basis, based on current renewable energy policy, to visualise the impact of changing policy on the use of bioenergy crops. This will include the economic implications which are directly linked to renewable energy policy, best practice management recommendations, impacts on land use change and food security as well as synergies and trade-offs on other ecosystem services (GHG emission, soil C, nitrogen, water and air security). The aim is to show how second generation bioenergy crops can be used sustainably and what is needed to do this successfully on a large scale. The results can form a basis for future policy development in order to reach the goals of the Paris 2015 agreement.

Land Cover Monitoring for Water Resources Management in Angola

NASA Astrophysics Data System (ADS)

Miguel, Irina; Navarro, Ana; Rolim, Joao; Catalao, Joao; Silva, Joel; Painho, Marco; Vekerdy, Zoltan

2016-08-01

The aim of this paper is to assess the impact of improved temporal resolution and multi-source satellite data (SAR and optical) on land cover mapping and monitoring for efficient water resources management. For that purpose, we developed an integrated approach based on image classification and on NDVI and SAR backscattering (VV and VH) time series for land cover mapping and crop's irrigation requirements computation. We analysed 28 SPOT-5 Take-5 images with high temporal revisiting time (5 days), 9 Sentinel-1 dual polarization GRD images and in-situ data acquired during the crop growing season. Results show that the combination of images from different sources provides the best information to map agricultural areas. The increase of the images temporal resolution allows the improvement of the estimation of the crop parameters, and then, to calculate of the crop's irrigation requirements. However, this aspect was not fully exploited due to the lack of EO data for the complete growing season.

Impacts of projected mid-century temperatures on thermal regimes for select specialty and fieldcrops common to the southwestern U.S.

NASA Astrophysics Data System (ADS)

Elias, E.; Lopez-Brody, N.; Dialesandro, J.; Steele, C. M.; Rango, A.

2015-12-01

The impacts of projected temperature increases in agricultural ecosystems are complex, varyingby region, cropping system, crop growth stage and humidity. We analyze the impacts of mid-century temperature increases on crops grown in five southwestern states: Arizona, California,New Mexico, Nevada and Utah. Here we present a spatial impact assessment of commonsouthwestern specialty (grapes, almonds and tomatoes) and field (alfalfa, cotton and corn)crops. This analysis includes three main components: development of empirical temperaturethresholds for each crop, classification of predicted future climate conditions according to thesethresholds, and mapping the probable impacts of these climatic changes on each crop. We use30m spatial resolution 2012 crop distribution and seasonal minimum and maximumtemperature normals (1970 to 2000) to define the current thermal envelopes for each crop.These represent the temperature range for each season where 95% of each crop is presentlygrown. Seasonal period change analysis of mid-century temperatures changes downscaled from20 CMIP5 models (RCP8.5) estimate future temperatures. Change detection maps representareas predicted to become more or less suitable, or remain unchanged. Based upon mid-centurytemperature changes, total regional suitable area declined for all crops except cotton, whichincreased by 20%. For each crop there are locations which change to and from optimal thermalenvelope conditions. More than 80% of the acres currently growing tomatoes and almonds willshift outside the present 95% thermal range. Fewer acres currently growing alfalfa (14%) andcotton (20%) will shift outside the present 95% thermal range by midcentury. Crops outsidepresent thermal envelopes by midcentury may adapt, possibly aided by adaptation technologiessuch as misters or shade structures, to the new temperature regime or growers may elect togrow alternate crops better suited to future thermal envelopes.

Application of remote sensing techniques for identification of irrigated crop lands in Arizona

NASA Technical Reports Server (NTRS)

Billings, H. A.

1981-01-01

Satellite imagery was used in a project developed to demonstrate remote sensing methods of determining irrigated acreage in Arizona. The Maricopa water district, west of Phoenix, was chosen as the test area. Band rationing and unsupervised categorization were used to perform the inventory. For both techniques the irrigation district boundaries and section lines were digitized and calculated and displayed by section. Both estimation techniques were quite accurate in estimating irrigated acreage in the 1979 growing season.

Is current irrigation sustainable in the United States? An integrated assessment of climate change impact on water resources and irrigated crop yields

NASA Astrophysics Data System (ADS)

Blanc, Elodie; Caron, Justin; Fant, Charles; Monier, Erwan

2017-08-01

While climate change impacts on crop yields has been extensively studied, estimating the impact of water shortages on irrigated crop yields is challenging because the water resources management system is complex. To investigate this issue, we integrate a crop yield reduction module and a water resources model into the MIT Integrated Global System Modeling framework, an integrated assessment model linking a global economic model to an Earth system model. We assess the effects of climate and socioeconomic changes on water availability for irrigation in the U.S. as well as subsequent impacts on crop yields by 2050, while accounting for climate change projection uncertainty. We find that climate and socioeconomic changes will increase water shortages and strongly reduce irrigated yields for specific crops (i.e., cotton and forage), or in specific regions (i.e., the Southwest) where irrigation is not sustainable. Crop modeling studies that do not represent changes in irrigation availability can thus be misleading. Yet, since the most water-stressed basins represent a relatively small share of U.S. irrigated areas, the overall reduction in U.S. crop yields is small. The response of crop yields to climate change and water stress also suggests that some level of adaptation will be feasible, like relocating croplands to regions with sustainable irrigation or switching to less irrigation intensive crops. Finally, additional simulations show that greenhouse gas (GHG) mitigation can alleviate the effect of water stress on irrigated crop yields, enough to offset the reduced CO2 fertilization effect compared to an unconstrained GHG emission scenario.

Is current irrigation sustainable in the United States? An integrated assessment of climate change impact on water resources and irrigated crop yields.

PubMed

Blanc, Elodie; Caron, Justin; Fant, Charles; Monier, Erwan

2017-08-01

While climate change impacts on crop yields has been extensively studied, estimating the impact of water shortages on irrigated crop yields is challenging because the water resources management system is complex. To investigate this issue, we integrate a crop yield reduction module and a water resources model into the MIT Integrated Global System Modeling framework, an integrated assessment model linking a global economic model to an Earth system model. We assess the effects of climate and socioeconomic changes on water availability for irrigation in the U.S. as well as subsequent impacts on crop yields by 2050, while accounting for climate change projection uncertainty. We find that climate and socioeconomic changes will increase water shortages and strongly reduce irrigated yields for specific crops (i.e., cotton and forage), or in specific regions (i.e., the Southwest) where irrigation is not sustainable. Crop modeling studies that do not represent changes in irrigation availability can thus be misleading. Yet, since the most water-stressed basins represent a relatively small share of U.S. irrigated areas, the overall reduction in U.S. crop yields is small. The response of crop yields to climate change and water stress also suggests that some level of adaptation will be feasible, like relocating croplands to regions with sustainable irrigation or switching to less irrigation intensive crops. Finally, additional simulations show that greenhouse gas (GHG) mitigation can alleviate the effect of water stress on irrigated crop yields, enough to offset the reduced CO 2 fertilization effect compared to an unconstrained GHG emission scenario.

Hydro-Economic Modeling with Minimum Data Requirements: An Application to the São Francisco River Basin, Brazil

NASA Astrophysics Data System (ADS)

Torres, M.; Maneta, M.; Vosti, S.; Wallender, W.; Howitt, R.

2008-12-01

Policymakers have been charged with the efficient, equitable, and sustainable use of water resources of the São Francisco River Basin (SFRB), Brazil, and also with the promotion of economic growth and the reduction of poverty within the basin. To date, policymakers lack scientific evidence on the potential consequences for growth, poverty alleviation or environmental sustainability of alternative uses of water resources. To address these key knowledge gaps, we have linked a hydrologic and an economic model of agriculture to investigate how economic decisions affect available water, and vice versa. More specifically, the models are used to predict the effects of the application of Brazilian federal surface water use policies on farmer's net revenues and on the hydrologic system. The Economic Model of Agriculture. A spatially explicit, farm-level model capable of accommodating a broad array of farm sizes and farm/farmer characteristics is developed and used to predict the effects of alternative water policies and neighbors' water use patterns on crop mix choice. A production function comprised of seven categories of non-water-related inputs used in agriculture (land, fertilizers, pesticides, seeds, hired labor, family labor and machinery) and four water-related inputs used in agriculture (applied water, irrigation labor, irrigation capital and energy) is estimated. The parameters emerging from this estimated production function are then introduced into a non-linear, net revenue maximization positive mathematical programming algorithm that is used for simulations. The Hydrological Model. MIKE Basin, a semi-distributed hydrology model, is used to calculate water budgets for the SFRB. MIKE Basin calculates discharge at selected nodes by accumulating runoff down the river network; it simulates reservoirs using stage-area-storage and downstream release rule curves. The data used to run the model are discharge to calculate local runoff, precipitation, reference ET, crop coefficients to calculate adjusted transpiration, and reservoir operating rules. Linking the Hydro and Economic Models. Based on the crop mix and area under plow of a reference year the economic model of agriculture was calibrated. Following a Monte Carlo procedure, the statistical distribution of water flows was estimated at each of the 16 selected nodes in the SFRB. The 5th and 95th percentiles of that distribution were used as benchmarks of water availability for drought and wet years, respectively. After subtracting 2000 m3 s-1 reserved for downstream uses estimates of water availability are used as constraints in the net revenue maximization algorithm included in the model of agriculture economics. If water is binding, it will influence crop mix, area under plow and product mix choices. Results. The application of the Brazilian federal water use policies will have immediate and substantial effects on agricultural area and product mix. Agricultural incomes will fall, especially in downstream areas located near major river channels.

Integration of environmental and spectral data for sunflower stress determination. [Red River Valley, Minnesota

NASA Technical Reports Server (NTRS)

Lillesand, T.; Seeley, M.

1983-01-01

Stress in sunflowers was assessed in western and northwestern Minnesota. Weekly ground observations (acquired in 1980 and 1981) were analyzed in concert with large scale aerial photography and concurrent LANDSAT data. Using multidate supervised and unsupervised classification procedures, it was found that all crops grown in association with sunflowers in the study area are spectrally separable from one another. Under conditions of extreme drought, severely stressed plants were differentiable from those not severely stressed, but between-crop separation was not possible. Initial regression analyses to estimate sunflower seed yield showed a sensitivity to environmental stress during the flowering and seed development stages. One of the most important biological factors related to sunflower production in the Red River Valley area was found to be the extent and severity of insect infestations.

Local PM10 and PM2.5 emission inventories from agricultural tillage and harvest in northeastern China.

PubMed

Chen, Weiwei; Tong, Daniel Q; Zhang, Shichun; Zhang, Xuelei; Zhao, Hongmei

2017-07-01

Mineral particles or particulate matters (PMs) emitted during agricultural activities are major recurring sources of atmospheric aerosol loading. However, precise PM inventory from agricultural tillage and harvest in agricultural regions is challenged by infrequent local emission factor (EF) measurements. To understand PM emissions from these practices in northeastern China, we measured EFs of PM 10 and PM 2.5 from three field operations (i.e., tilling, planting and harvesting) in major crop production (i.e., corn and soybean), using portable real-time PM analyzers and weather station data. County-level PM 10 and PM 2.5 emissions from agricultural tillage and harvest were estimated, based on local EFs, crop areas and crop calendars. The EFs averaged (107±27), (17±5) and 26mg/m 2 for field tilling, planting and harvesting under relatively dry conditions (i.e., soil moisture <15%), respectively. The EFs of PM from field tillage and planting operations were negatively affected by topsoil moisture. The magnitude of PM 10 and PM 2.5 emissions from these three activities were estimated to be 35.1 and 9.8 kilotons/yr in northeastern China, respectively, of which Heilongjiang Province accounted for approximately 45%. Spatiotemporal distribution showed that most PM 10 emission occurred in April, May and October and were concentrated in the central regions of the northeastern plain, which is dominated by dryland crops. Further work is needed to estimate the contribution of agricultural dust emissions to regional air quality in northeastern China. Copyright © 2016. Published by Elsevier B.V.

Spatial estimation from remotely sensed data via empirical Bayes models

NASA Technical Reports Server (NTRS)

Hill, J. R.; Hinkley, D. V.; Kostal, H.; Morris, C. N.

1984-01-01

Multichannel satellite image data, available as LANDSAT imagery, are recorded as a multivariate time series (four channels, multiple passovers) in two spatial dimensions. The application of parametric empirical Bayes theory to classification of, and estimating the probability of, each crop type at each of a large number of pixels is considered. This theory involves both the probability distribution of imagery data, conditional on crop types, and the prior spatial distribution of crop types. For the latter Markov models indexed by estimable parameters are used. A broad outline of the general theory reveals several questions for further research. Some detailed results are given for the special case of two crop types when only a line transect is analyzed. Finally, the estimation of an underlying continuous process on the lattice is discussed which would be applicable to such quantities as crop yield.

Mapping cropland GPP in the north temperate region with space measurements of chlorophyll fluorescence

NASA Astrophysics Data System (ADS)

Guanter, L.; Zhang, Y.; Jung, M.; Joiner, J.; Voigt, M.; Huete, A. R.; Zarco-Tejada, P.; Frankenberg, C.; Lee, J.; Berry, J. A.; Moran, S. M.; Ponce-Campos, G.; Beer, C.; Camps-Valls, G.; Buchmann, N. C.; Gianelle, D.; Klumpp, K.; Cescatti, A.; Baker, J. M.; Griffis, T.

2013-12-01

Monitoring agricultural productivity is important for optimizing management practices in a world under a continuous increase of food and biofuel demand. We used new space measurements of sun-induced chlorophyll fluorescence (SIF), a vegetation parameter intrinsically linked to photosynthesis, to capture photosynthetic uptake of the crop belts in the north temperate region. The following data streams and procedures have been used in this analysis: (1) SIF retrievals have been derived from measurements of the MetOp-A / GOME-2 instrument in the 2007-2011 time period; (2) ensembles of process-based and data-driven biogeochemistry models have been analyzed in order to assess the capability of global models to represent crop gross primary production (GPP); (3) flux tower-based GPP estimates covering the 2007-2011 time period have been extracted over 18 cropland and grassland sites in the Midwest US and Western Europe from the Ameriflux and the European Fluxes Database networks; (4) large-scale NPP estimates have been derived by the agricultural inventory data sets developed by USDA-NASS and Monfreda et al. The strong linear correlation between the SIF space retrievals and the flux tower-based GPP, found to be significantly higher than that between reflectance-based vegetation indices (EVI, NDVI and MTCI) and GPP, has enabled the direct upscaling of SIF to cropland GPP maps at the synoptic scale. The new crop GPP estimates we derive from the scaling of SIF space retrievals are consistent with both flux tower GPP estimates and agricultural inventory data. These new GPP estimates show that crop productivity in the US Western Corn Belt, and most likely also in the rice production areas in the Indo-Gangetic plain and China, is up to 50-75% higher than estimates by state-of-the-art data-driven and process-oriented biogeochemistry models. From our analysis we conclude that current carbon models have difficulties in reproducing the special conditions of those highly productive crops subject to an intense management. Observational inputs closely linked to physiological condition and the photosynthetic dynamics of the vegetation, such as the fluorescence measurements presented in this study, can be an essential complement to existing models and remotely-sensed observations for the evaluation of global agricultural yields.

Comparison of Sub-Pixel Classification Approaches for Crop-Specific Mapping

EPA Science Inventory

This paper examined two non-linear models, Multilayer Perceptron (MLP) regression and Regression Tree (RT), for estimating sub-pixel crop proportions using time-series MODIS-NDVI data. The sub-pixel proportions were estimated for three major crop types including corn, soybean, a...

Evaluating the potential of vegetation indices for winter wheat LAI estimation under different fertilization and water conditions

NASA Astrophysics Data System (ADS)

Xie, Qiaoyun; Huang, Wenjiang; Dash, Jadunandan; Song, Xiaoyu; Huang, Linsheng; Zhao, Jinling; Wang, Renhong

2015-12-01

Leaf area index (LAI) is an important indicator for monitoring crop growth conditions and forecasting grain yield. Many algorithms have been developed for remote estimation of the leaf area index of vegetation, such as using spectral vegetation indices, inversion of radiative transfer models, and supervised learning techniques. Spectral vegetation indices, mathematical combination of reflectance bands, are widely used for LAI estimation due to their computational simplicity and their applications ranged from the leaf scale to the entire globe. However, in many cases, their applicability is limited to specific vegetation types or local conditions due to species specific nature of the relationship used to transfer the vegetation indices to LAI. The overall objective of this study is to investigate the most suitable vegetation index for estimating winter wheat LAI under eight different types of fertilizer and irrigation conditions. Regression models were used to estimate LAI using hyperspectral reflectance data from the Pushbroom Hyperspectral Imager (PHI) and in-situ measurements. Our results showed that, among six vegetation indices investigated, the modified soil-adjusted vegetation index (MSAVI) and the normalized difference vegetation index (NDVI) exhibited strong and significant relationships with LAI, and thus were sensitive across different nitrogen and water treatments. The modified triangular vegetation index (MTVI2) confirmed its potential on crop LAI estimation, although second to MSAVI and NDVI in our study. The enhanced vegetation index (EVI) showed moderate performance. However, the ratio vegetation index (RVI) and the modified simple ratio index (MSR) predicted the least accurate estimations of LAI, exposing the simple band ratio index's weakness under different treatment conditions. The results support the use of vegetation indices for a quick and effective LAI mapping procedure that is suitable for winter wheat under different management practices.

Effects of climate change on suitable rice cropping areas, cropping systems and crop water requirements in southern China

DOE PAGES

Ye, Qing; Yang, Xiaoguang; Dai, Shuwei; ...

2015-06-05

Here, we discuss that rice is one of the main crops grown in southern China. Global climate change has significantly altered the local water availability and temperature regime for rice production. In this study, we explored the influence of climate change on suitable rice cropping areas, rice cropping systems and crop water requirements (CWRs) during the growing season for historical (from 1951 to 2010) and future (from 2011 to 2100) time periods. The results indicated that the land areas suitable for rice cropping systems shifted northward and westward from 1951 to 2100 but with different amplitudes.

Probabilistic estimates of drought impacts on agricultural production

NASA Astrophysics Data System (ADS)

Madadgar, Shahrbanou; AghaKouchak, Amir; Farahmand, Alireza; Davis, Steven J.

2017-08-01

Increases in the severity and frequency of drought in a warming climate may negatively impact agricultural production and food security. Unlike previous studies that have estimated agricultural impacts of climate condition using single-crop yield distributions, we develop a multivariate probabilistic model that uses projected climatic conditions (e.g., precipitation amount or soil moisture) throughout a growing season to estimate the probability distribution of crop yields. We demonstrate the model by an analysis of the historical period 1980-2012, including the Millennium Drought in Australia (2001-2009). We find that precipitation and soil moisture deficit in dry growing seasons reduced the average annual yield of the five largest crops in Australia (wheat, broad beans, canola, lupine, and barley) by 25-45% relative to the wet growing seasons. Our model can thus produce region- and crop-specific agricultural sensitivities to climate conditions and variability. Probabilistic estimates of yield may help decision-makers in government and business to quantitatively assess the vulnerability of agriculture to climate variations. We develop a multivariate probabilistic model that uses precipitation to estimate the probability distribution of crop yields. The proposed model shows how the probability distribution of crop yield changes in response to droughts. During Australia's Millennium Drought precipitation and soil moisture deficit reduced the average annual yield of the five largest crops.

Mapping paddy rice area and yields over Thai Binh province in Viet Nam from MODIS, Landsat and ALOS-2/PALSAR-2

USDA-ARS?s Scientific Manuscript database

Rice is the most important staple crop grown and consumed in Southeast Asia. Timely and reliable rice production estimates are therefore important in monitoring government development plans in the region and in mitigating the effects of extreme weather and climate change to address food insecurity. ...

“FEST-C 1.0 for CMAQ Bi-directional NH3 Modeling and Apatial Allocator 4.1”

EPA Science Inventory

Accurate estimation of ammonia emissions in space and time has been a challenge in meso-scale air quality modeling. For instance, fertilizer applications vary in the date of application and amount by crop types and geographical area. With the support of the U.S EPA, we have devel...

Assessing flood damage to agriculture using color infrared aerial photography

USGS Publications Warehouse

Anderson, William H.

1977-01-01

The rationale for using color-infrared (CIR) film to assist in assessing flood damage to agriculture is demonstrated using examples prepared from photographs acquired of the 1975 flood in the Red River Valley of North Dakota and Minnesota. Information concerning flood inundation boundaries, crop damage, soil erosion, sedimentation, and other similar general features and conditions was obtained through the interpretation of CIR aerial photographs. CIR aerial photographs can be used to help improve the estimates of potential remaining production on a field by field basis, owing to the increased accuracy obtained in determining the area component of crop production as compared to conventional ground sketching methods.

Selection of the Australian indicator region

NASA Technical Reports Server (NTRS)

Reed, C. R. (Principal Investigator)

1981-01-01

Each Australian state was examined for the availability of LANDSAT data, area, yield, and production characteristics, statistics, crop calendars, and other ancillary data. Agrophysical conditions that could influence labeling and classification accuracies were identified in connection with the highest producing states as determined from available Australian crop statistics. Based primarily on these production statistics, Western Australia and New South Wales were selected as the wheat indicator region for Australia. The general characteristics of wheat in the indicator region, with potential problems anticipated for proportion estimation are considered. The varieties of wheat, the diseases and pests common to New South Wales, and the wheat growing regions of both states are examined.

Two phase sampling for wheat acreage estimation. [large area crop inventory experiment

NASA Technical Reports Server (NTRS)

Thomas, R. W.; Hay, C. M.

1977-01-01

A two phase LANDSAT-based sample allocation and wheat proportion estimation method was developed. This technique employs manual, LANDSAT full frame-based wheat or cultivated land proportion estimates from a large number of segments comprising a first sample phase to optimally allocate a smaller phase two sample of computer or manually processed segments. Application to the Kansas Southwest CRD for 1974 produced a wheat acreage estimate for that CRD within 2.42 percent of the USDA SRS-based estimate using a lower CRD inventory budget than for a simulated reference LACIE system. Factor of 2 or greater cost or precision improvements relative to the reference system were obtained.

Quantifying Impacts of Food Trade on Water Availability Considering Water Sources

NASA Astrophysics Data System (ADS)

Oki, T.; Yano, S.; Hanasaki, N.

2012-12-01

Food production requires a lot of water, and traded food potentially has external impacts on environment through reducing the water availability in the producing region. Water footprint is supposed to be an indicator to reflect the impacts of water use. However, impacts of water use on environment, resource, and sustainability are different in place and time, and according to the sources of water withdrawals. Therefore it is preferable to characterize the water withdrawals or consumptions rather than just accumulate the total amount of water use when estimating water footprint. In this study, a new methodology, global green-water equivalent method, is proposed in which regional characterization factors are determined based on the estimates of natural hydrological cycles, such as precipitation, total runoff, and sub-surface runoff, and applied for green-water, river(+reservoir) water, and non-renewable ground water uses. Water footprint of the world associated with the production of 19 major crops was estimated using an integrated hydrological and water resources modeling system (H08), with atmospheric forcing data for 1991-2000 with spatial resolution of 0.5 by 0.5 longitudinal and latitudinal degrees. The impacts is estimated to be 6 times larger than the simple summation of green and blue water uses, and reflect the climatological water scarcity conditions geographically. The results can be used to compare the possible impacts of food trade associated with various crops from various regions on environment through reducing the availability of water resources in the cropping area.

Salience Assignment for Multiple-Instance Data and Its Application to Crop Yield Prediction

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Lane, Terran

2010-01-01

An algorithm was developed to generate crop yield predictions from orbital remote sensing observations, by analyzing thousands of pixels per county and the associated historical crop yield data for those counties. The algorithm determines which pixels contain which crop. Since each known yield value is associated with thousands of individual pixels, this is a multiple instance learning problem. Because individual crop growth is related to the resulting yield, this relationship has been leveraged to identify pixels that are individually related to corn, wheat, cotton, and soybean yield. Those that have the strongest relationship to a given crop s yield values are most likely to contain fields with that crop. Remote sensing time series data (a new observation every 8 days) was examined for each pixel, which contains information for that pixel s growth curve, peak greenness, and other relevant features. An alternating-projection (AP) technique was used to first estimate the "salience" of each pixel, with respect to the given target (crop yield), and then those estimates were used to build a regression model that relates input data (remote sensing observations) to the target. This is achieved by constructing an exemplar for each crop in each county that is a weighted average of all the pixels within the county; the pixels are weighted according to the salience values. The new regression model estimate then informs the next estimate of the salience values. By iterating between these two steps, the algorithm converges to a stable estimate of both the salience of each pixel and the regression model. The salience values indicate which pixels are most relevant to each crop under consideration.

Water availability as a driver of spatial and temporal variability in vegetation in the La Mancha plain (Spain): Implications for the land-surface energy, water and carbon budget

NASA Astrophysics Data System (ADS)

Los, Sietse

2017-04-01

Vegetation is water limited in large areas of Spain and therefore a close link exists between vegetation greenness observed from satellite and moisture availability. Here we exploit this link to infer spatial and temporal variability in moisture from MODIS NDVI data and thermal data. Discrepancies in the precipitation - vegetation relationship indicate areas with an alternative supply of water (i.e. not rainfall), this can be natural where moisture is supplied by upwelling groundwater, or can be artificial where crops are irrigated. As a result spatial and temporal variability in vegetation in the La Mancha Plain appears closely linked to topography, geology, rainfall and land use. Crop land shows large variability in year-to-year vegetation greenness; for some areas this variability is linked to variability in rainfall but in other cases this variability is linked to irrigation. The differences in irrigation treatment within one plant functional type, in this case crops, will lead to errors in land surface models when ignored. The magnitude of these effects on the energy, carbon and water balance are assessed at the scale of 250 m to 200 km. Estimating the water balance correctly is of particular important since in some areas in Spain more water is used for irrigation than is supplemented by rainfall.

Large Area Crop Inventory Experiment (LACIE). LACIE third interim phase 3 accuracy assessment report. [South Dakota and U.S.S.R.

NASA Technical Reports Server (NTRS)

1978-01-01

The author has identified the following significant results. An accuracy of 90/85 was achieved with the October estimates which had a relative bias of -9.9 percent and a coefficient of variation of 5.2 percent for the total wheat production in the USGP. The probability was 0.9 that the LACIE estimate was within + or - 15 percent of true wheat production for the USGP. The LACIE spring wheat production underestimates in August, September, and October were the results of area underestimates for spring wheat in the USNGP region. The winter wheat blind study showed that the average proportion estimates were significantly different from the average dot-count, ground truth proportions at the USSGP and USGP-7 levels.

Drought-related vulnerability and risk assessment of groundwater in Belgium: estimation of the groundwater recharge and crop yield vulnerability with the B-CGMS

NASA Astrophysics Data System (ADS)

Jacquemin, Ingrid; Verbeiren, Boud; Vanderhaegen, Sven; Canters, Frank; Vermeiren, Karolien; Engelen, Guy; Huysmans, Marijke; Batelaan, Okke; Tychon, Bernard

2016-04-01

Due to common belief that regions under temperate climate are not affected by (meteorological and groundwater) drought, these events and their impacts remain poorly studied: in the GroWaDRISK, we propose to take stock of this question. We aim at providing a better understanding of the influencing factors (land use and land cover changes, water demand and climate) and the drought-related impacts on the environment, water supply and agriculture. The study area is located in the North-East of Belgium, corresponding approximatively to the Dijle and Demer catchments. To establish an overview of the groundwater situation, we assess the system input: the recharge. To achieve this goal, two models, B-CGMS and WetSpass are used to evaluate the recharge, respectively, over agricultural land and over the remaining areas, as a function of climate and for various land uses and land covers. B-CGMS, which is an adapted version for Belgium of the European Crop Growth Monitoring System, is used for assessing water recharge at a daily timestep and under different agricultural lands: arable land (winter wheat, maize...), orchards, horticulture and floriculture and for grassland. B-CGMS is designed to foresee crop yield and obviously it studies the impact of drought on crop yield and raises issues for the potential need of irrigation. For both yields and water requirements, the model proposes a potential mode, driven by temperature and solar radiation, and a water-limited mode for which water availability can limit crop growth. By this way, we can identify where and when water consumption and yield are not optimal, in addition to the Crop Water Stress Index. This index is calculated for a given crop, as the number of days affected by water stress during the growth sensitive period. Both recharge and crop yield are assessed for the current situation (1980 - 2012), taking into account the changing land use/land cover, in terms of areas and localization of the agricultural land and where the proportion of the different crops had considerably evolved through time (e.g., increase of grain maize and potatoes while winter cereals decrease). The preliminary results of the recharge lead to an average value in the area showing a significant negative trend, in both simulations with fixed (base = 1980) and changing land cover. In the same time, we could observe an increasing number of water stress periods, especially for maize, one of the main crops in the area. Finally, a preliminary test will be presented for the horizon 2040, for which we use meteorological time series (for high and low hydrologic impacts) given by the CCI-HYDR Perturbation Tool (Ntegeka V. and Willems P., 2009). This preliminary test aims to (1) evaluate the amplitude of the potential recharge deficit and, (2) especially, to define vulnerability zones, affected by frequent water stress, in connection with irrigation needs which could possibly increase the groundwater extraction.

The effect of inter-annual variability of consumption, production, trade and climate on crop-related green and blue water footprints and inter-regional virtual water trade: A study for China (1978-2008).

PubMed

Zhuo, La; Mekonnen, Mesfin M; Hoekstra, Arjen Y

2016-05-01

Previous studies into the relation between human consumption and indirect water resources use have unveiled the remote connections in virtual water (VW) trade networks, which show how communities externalize their water footprint (WF) to places far beyond their own region, but little has been done to understand variability in time. This study quantifies the effect of inter-annual variability of consumption, production, trade and climate on WF and VW trade, using China over the period 1978-2008 as a case study. Evapotranspiration, crop yields and green and blue WFs of crops are estimated at a 5 × 5 arc-minute resolution for 22 crops, for each year in the study period, thus accounting for climate variability. The results show that crop yield improvements during the study period helped to reduce the national average WF of crop consumption per capita by 23%, with a decreasing contribution to the total from cereals and increasing contribution from oil crops. The total consumptive WFs of national crop consumption and crop production, however, grew by 6% and 7%, respectively. By 2008, 28% of total water consumption in crop fields in China served the production of crops for export to other regions and, on average, 35% of the crop-related WF of a Chinese consumer was outside its own province. Historically, the net VW within China was from the water-rich South to the water-scarce North, but intensifying North-to-South crop trade reversed the net VW flow since 2000, which amounted 6% of North's WF of crop production in 2008. South China thus gradually became dependent on food supply from the water-scarce North. Besides, during the whole study period, China's domestic inter-regional VW flows went dominantly from areas with a relatively large to areas with a relatively small blue WF per unit of crop, which in 2008 resulted in a trade-related blue water loss of 7% of the national total blue WF of crop production. The case of China shows that domestic trade, as governed by economics and governmental policies rather than by regional differences in water endowments, determines inter-regional water dependencies and may worsen rather than relieve the water scarcity in a country. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Nutrient Mass Balance for the Mobile River Basin in Alabama, Georgia, and Mississippi

NASA Astrophysics Data System (ADS)

Harned, D. A.; Harvill, J. S.; McMahon, G.

2001-12-01

The source and fate of nutrients in the Mobile River drainage basin are important water-quality concerns in Alabama, Georgia, and Mississippi. Land cover in the basin is 74 percent forested, 16 percent agricultural, 2.5 percent developed, and 4 percent wetland. A nutrient mass balance calculated for 18 watersheds in the Mobile River Basin indicates that agricultural non-point nitrogen and phosphorus sources and urban non-point nitrogen sources are the most important factors associated with nutrients in the streams. Nitrogen and phosphorus inputs from atmospheric deposition, crop fertilizer, biological nitrogen fixation, animal waste, and point sources were estimated for each of the 18 drainage basins. Total basin nitrogen inputs ranged from 27 to 93 percent from atmospheric deposition (56 percent mean), 4 to 45 percent from crop fertilizer (25 percent mean), <0.01 to 31 percent from biological nitrogen fixation (8 percent mean), 2 to 14 percent from animal waste (8 percent mean), and 0.2 to 11 percent from point sources (3 percent mean). Total basin phosphorus inputs ranged from 10 to 39 percent from atmospheric deposition (26 percent mean), 7 to 51 percent from crop fertilizer (28 percent mean), 20 to 64 percent from animal waste (41 percent mean), and 0.2 to 11 percent from point sources (3 percent mean). Nutrient outputs for the watersheds were estimated by calculating instream loads and estimating nutrient uptake, or withdrawal, by crops. The difference between the total basin inputs and outputs represents nutrients that are retained or processed within the basin while moving from the point of use to the stream, or in the stream. Nitrogen output, as a percentage of the total basin nitrogen inputs, ranged from 19 to 79 percent for instream loads (35 percent mean) and from 0.01 to 32 percent for crop harvest (10 percent mean). From 53 to 87 percent (75 percent mean) of nitrogen inputs were retained within the 18 basins. Phosphorus output ranged from 9 to 29 percent for instream loads (18 percent mean) and from 0.01 to 23 percent for crop harvest (7 percent mean). The basins retained from 60 to 87 percent (74 percent mean) of phosphorous inputs. Correlation of basin nutrient output loads and concentrations with the basin inputs and correlation of output loads and concentrations with basin land use were tested using the Spearman rank test. The correlation analysis indicated that higher nitrogen concentrations in the streams are associated with urban areas and higher loads are associated with agriculture; high phosphorus output loads and concentrations are associated with agriculture. Higher nutrient loads in agricultural basins are partly an effect of basin size-- larger basins generate larger nutrient loads. Nutrient loads and concentrations showed no significant correlation to point-source inputs. Nitrogen loads were significantly (p<0.05, correlation coefficient >0.5) higher in basins with greater cropland areas. Nitrogen concentrations also increased as residential, commercial, and total urban areas increased. Phosphorus loads were positively correlated with animal-waste inputs, pasture, and total agricultural land. Phosphorus concentrations were highest in basins with the greatest amounts of row-crop agriculture.

Farm residence and lymphohematopoietic cancers in the Iowa Women’s Health Study

PubMed Central

Jones, Rena R.; Yu, Chu-Ling; Nuckols, John R.; Cerhan, James R.; Airola, Matthew; Ross, Julie A.; Robien, Kim; Ward, Mary H.

2014-01-01

Background Cancer incidence in male farmers has been studied extensively; however, less is known about risk among women residing on farms or in agricultural areas, who may be exposed to pesticides by their proximity to crop fields. We extended a previous follow-up of the Iowa Women’s Health Study cohort to examine farm residence and the incidence of lymphohematopoietic cancers. Further, we investigated crop acreage within 750 m of residences, which has been associated with higher herbicide levels in Iowa homes. Methods We analyzed data for a cohort of 37,099 Iowa women aged 55–69 years who reported their residence location (farm, rural (not a farm), town size based on population) at enrollment in 1986. We identified incident lymphohematopoietic cancers (1986–2009) by linkage with the Iowa Cancer Registry. Using a geographic information system, we geocoded addresses and calculated acreage of pasture and row crops within 750 m of homes using the 1992 National Land Cover Database. Cox regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) in multivariate analyses of cancer risk in relation to both residence location and crop acreage. Results As found in an earlier analysis of residence location, risk of acute myeloid leukemia (AML) was higher among women living on farms (HR= 2.23, 95%CI: 1.25–3.99) or rural areas (but not on a farm) (HR= 1.95, 95%CI: 0.89–4.29) compared with women living in towns of > 10,000 population. We observed no association between farm or rural residence and non-Hodgkin lymphoma (NHL; overall or for major subtypes) or multiple myeloma. In analyses of crop acreage, we observed no association between pasture or row crop acreage within 750 m of homes and risk of leukemia overall or for the AML subtype. Chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL) risk was nonsignificantly elevated among women with pasture acreage within 750 m of their home (HRs for increasing tertiles= 1.8, 1.8 and 1.5) and with row crop acreage within 750 m (HRs for increasing tertiles of acreage= 1.4, 1.5 and 1.6) compared to women with no pasture or row crop acreage, respectively. Conclusions Iowa women living on a farm or in a rural area were at increased risk of developing AML, which was not related to crop acreage near the home. Living near pasture or row crops may confer an increased risk of CLL/SLL regardless of residence location. Further investigation of specific farm-related exposures and these cancers among women living on farms and in agricultural areas is warranted. PMID:25038451

Farm residence and lymphohematopoietic cancers in the Iowa Women's Health Study.

PubMed

Jones, Rena R; Yu, Chu-Ling; Nuckols, John R; Cerhan, James R; Airola, Matthew; Ross, Julie A; Robien, Kim; Ward, Mary H

2014-08-01

Cancer incidence in male farmers has been studied extensively; however, less is known about risk among women residing on farms or in agricultural areas, who may be exposed to pesticides by their proximity to crop fields. We extended a previous follow-up of the Iowa Women's Health Study cohort to examine farm residence and the incidence of lymphohematopoietic cancers. Further, we investigated crop acreage within 750 m of residences, which has been associated with higher herbicide levels in Iowa homes. We analyzed data for a cohort of 37,099 Iowa women aged 55-69 years who reported their residence location (farm, rural (not a farm), town size based on population) at enrollment in 1986. We identified incident lymphohematopoietic cancers (1986-2009) by linkage with the Iowa Cancer Registry. Using a geographic information system, we geocoded addresses and calculated acreage of pasture and row crops within 750 m of homes using the 1992 National Land Cover Database. Cox regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) in multivariate analyses of cancer risk in relation to both residence location and crop acreage. As found in an earlier analysis of residence location, risk of acute myeloid leukemia (AML) was higher among women living on farms (HR=2.23, 95%CI: 1.25-3.99) or rural areas (but not on a farm) (HR=1.95, 95%CI: 0.89-4.29) compared with women living in towns of >10,000 population. We observed no association between farm or rural residence and non-Hodgkin lymphoma (NHL; overall or for major subtypes) or multiple myeloma. In analyses of crop acreage, we observed no association between pasture or row crop acreage within 750 m of homes and risk of leukemia overall or for the AML subtype. Chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL) risk was nonsignificantly elevated among women with pasture acreage within 750 m of their home (HRs for increasing tertiles=1.8, 1.8 and 1.5) and with row crop acreage within 750 m (HRs for increasing tertiles of acreage=1.4, 1.5 and 1.6) compared to women with no pasture or row crop acreage, respectively. Iowa women living on a farm or in a rural area were at increased risk of developing AML, which was not related to crop acreage near the home. Living near pasture or row crops may confer an increased risk of CLL/SLL regardless of residence location. Further investigation of specific farm-related exposures and these cancers among women living on farms and in agricultural areas is warranted. Copyright © 2014. Published by Elsevier Inc.

The Combination of Uav Survey and Landsat Imagery for Monitoring of Crop Vigor in Precision Agriculture

NASA Astrophysics Data System (ADS)

Lukas, V.; Novák, J.; Neudert, L.; Svobodova, I.; Rodriguez-Moreno, F.; Edrees, M.; Kren, J.

2016-06-01

Mapping of the with-in field variability of crop vigor has a long tradition with a success rate ranging from medium to high depending on the local conditions of the study. Information about the development of agronomical relevant crop parameters, such as above-ground biomass and crop nutritional status, provides high reliability for yield estimation and recommendation for variable rate application of fertilizers. The aim of this study was to utilize unmanned and satellite multispectral imaging for estimation of basic crop parameters during the growing season. The experimental part of work was carried out in 2014 at the winter wheat field with an area of 69 ha located in the South Moravia region of the Czech Republic. An UAV imaging was done in April 2014 using Sensefly eBee, which was equipped by visible and near infrared (red edge) multispectral cameras. For ground truth calibration the spectral signatures were measured on 20 sites using portable spectroradiometer ASD Handheld 2 and simultaneously plant samples were taken at BBCH 32 (April 2014) and BBCH 59 (Mai 2014) for estimation of above-ground biomass and nitrogen content. The UAV survey was later extended by selected cloud-free Landsat 8 OLI satellite imagery, downloaded from USGS web application Earth Explorer. After standard pre-processing procedures, a set of vegetation indices was calculated from remotely and ground sensed data. As the next step, a correlation analysis was computed among crop vigor parameters and vegetation indices. Both, amount of above-ground biomass and nitrogen content were highly correlated (r > 0.85) with ground spectrometric measurement by ASD Handheld 2 in BBCH 32, especially for narrow band vegetation indices (e.g. Red Edge Inflection Point). UAV and Landsat broadband vegetation indices varied in range of r = 0.5 - 0.7, highest values of the correlation coefficients were obtained for crop biomass by using GNDVI. In all cases results from BBCH 59 vegetation stage showed lower relationship to vegetation indices. Total amount of aboveground biomass was identified as the most important factor influencing the values of vegetation indices. Based on the results can be assumed that UAV and satellite monitoring provide reliable information about crop parameters for site specific crop management. The main difference of their utilization is coming from their specification and technical limits. Satellite survey can be used for periodic monitoring of crops as the indicator of their spatial heterogeneity within fields, but with low resolution (30 m per pixel for OLI). On the other hand UAV represents a special campaign aimed on the mapping of high-detailed spatial inputs for site specific crop management and variable rate application of fertilizers.

Agricultural irrigated land-use inventory for Polk County, Florida, 2016

USGS Publications Warehouse

Marella, Richard L.; Berry, Darbi; Dixon, Joann F.

2017-08-16

An accurate inventory of irrigated crop acreage is not available at the level of resolution needed to better estimate agricultural water use or to project future water demands in many Florida counties. A detailed digital map and summary of irrigated acreage was developed for Polk County, Florida, during the 2016 growing season. This cooperative project between the U.S. Geological Survey and the Office of Agricultural Water Policy of the Florida Department of Agriculture and Consumer Services is part of an effort to improve estimates of water use and projections of future demands across all counties in the State. The irrigated areas were delineated by using land-use data provided by the Florida Department of Agriculture and Consumer Services, along with information obtained from the South and Southwest Florida Water Management Districts consumptive water-use permits. Delineations were field verified between April and December 2016. Attribute data such as crop type, primary water source, and type of irrigation system were assigned to the irrigated areas.The results of this inventory and field verification indicate that during the 2016 growing seasons (spring, summer, fall, and winter), an estimated 88,652 acres were irrigated within Polk County. Of the total field-verified crops, 83,995 acres were in citrus; 2,893 acres were in other non-citrus fruit crops (blueberries, grapes, peaches, and strawberries); 621 acres were in row crops (primarily beans and watermelons); 1,117 acres were in nursery (container and tree farms) and sod production; and 26 acres were in field crops including hay and pasture. Of the total inventoried irrigated acreage within Polk County, 98 percent (86,566 acres) was in the Southwest Florida Water Management District, and the remaining 2 percent (2,086 acres) was in the South Florida Water Management District.About 85,788 acres (96.8 percent of the acreage inventoried) were irrigated by a microirrigation system, including drip, bubblers, and spray emitters. The remaining 3.2 percent of the irrigated acreage was irrigated by a sprinkler system (2,360 acres) or subsurface flood systems (504 acres). Groundwater was the primary source of water used on irrigated acreage (88 percent, or 78,050 acres); the remaining 10,602 acres (12 percent) used groundwater combined with surface water as the irrigation source.The irrigated acreage estimated by the U.S. Geological Survey (USGS) for this 2016 inventory (88,652 acres) is about 11 percent higher than the 79,869 acres estimated by the U.S. Department of Agriculture (USDA) for 2012. Citrus and pasture in Polk County show the biggest difference in irrigated acreage between the USGS and USDA totals. Irrigated citrus acreage inventoried in 2016 by the USGS totaled 83,996 acres, whereas the USDA reported 78,305 acres of citrus in 2012. The USGS identified 6 acres of irrigated pasture and 20 acres of hay, whereas the USDA reported 6,631 acres of irrigated pasture and 1,349 acres of hay for 2012. In general, differences between the 2016 USGS field-verified acreage totals and acreage published by the USDA for 2012 could be due to (1) irrigated acreage for some specific crops increased or decreased substantially during the 4-year interval between 2012 and 2016 because of production or economic changes, (2) the assumption that if an irrigation system was present, it was used in 2016, when in fact some landowners may not have used their irrigation systems during this growing period even if they had a crop in the field, or (3) the amount of irrigated acreage published by the USDA for selected crops may be underestimated as a result of how information is obtained and formulated by the agency during census compilations.

Remote Sensing Monitoring of Changes in Soil Salinity: A Case Study in Inner Mongolia, China.

PubMed

Wu, Jingwei; Vincent, Bernard; Yang, Jinzhong; Bouarfa, Sami; Vidal, Alain

2008-11-07

This study used archived remote sensing images to depict the history of changes in soil salinity in the Hetao Irrigation District in Inner Mongolia, China, with the purpose of linking these changes with land and water management practices and to draw lessons for salinity control. Most data came from LANDSAT satellite images taken in 1973, 1977, 1988, 1991, 1996, 2001, and 2006. In these years salt-affected areas were detected using a normal supervised classification method. Corresponding cropped areas were detected from NVDI (Normalized Difference Vegetation Index) values using an unsupervised method. Field samples and agricultural statistics were used to estimate the accuracy of the classification. Historical data concerning irrigation/drainage and the groundwater table were used to analyze the relation between changes in soil salinity and land and water management practices. Results showed that: (1) the overall accuracy of remote sensing in detecting soil salinity was 90.2%, and in detecting cropped area, 98%; (2) the installation/innovation of the drainage system did help to control salinity; and (3) a low ratio of cropped land helped control salinity in the Hetao Irrigation District. These findings suggest that remote sensing is a useful tool to detect soil salinity and has potential in evaluating and improving land and water management practices.

Estimating Crop Growth Stage by Combining Meteorological and Remote Sensing Based Techniques

NASA Astrophysics Data System (ADS)

Champagne, C.; Alavi-Shoushtari, N.; Davidson, A. M.; Chipanshi, A.; Zhang, Y.; Shang, J.

2016-12-01

Estimations of seeding, harvest and phenological growth stage of crops are important sources of information for monitoring crop progress and crop yield forecasting. Growth stage has been traditionally estimated at the regional level through surveys, which rely on field staff to collect the information. Automated techniques to estimate growth stage have included agrometeorological approaches that use temperature and day length information to estimate accumulated heat and photoperiod, with thresholds used to determine when these stages are most likely. These approaches however, are crop and hybrid dependent, and can give widely varying results depending on the method used, particularly if the seeding date is unknown. Methods to estimate growth stage from remote sensing have progressed greatly in the past decade, with time series information from the Normalized Difference Vegetation Index (NDVI) the most common approach. Time series NDVI provide information on growth stage through a variety of techniques, including fitting functions to a series of measured NDVI values or smoothing these values and using thresholds to detect changes in slope that are indicative of rapidly increasing or decreasing `greeness' in the vegetation cover. The key limitations of these techniques for agriculture are frequent cloud cover in optical data that lead to errors in estimating local features in the time series function, and the incongruity between changes in greenness and traditional agricultural growth stages. There is great potential to combine both meteorological approaches and remote sensing to overcome the limitations of each technique. This research will examine the accuracy of both meteorological and remote sensing approaches over several agricultural sites in Canada, and look at the potential to integrate these techniques to provide improved estimates of crop growth stage for common field crops.

Implications of deep drainage through saline clay for groundwater recharge and sustainable cropping in a semi-arid catchment, Australia

NASA Astrophysics Data System (ADS)

Timms, W. A.; Young, R. R.; Huth, N.

2012-04-01

The magnitude and timing of deep drainage and salt leaching through clay soils is a critical issue for dryland agriculture in semi-arid regions (<500 mm yr-1 rainfall, potential evapotranspiration >2000 mm yr-1) such as parts of Australia's Murray-Darling Basin (MDB). In this rare study, hydrogeological measurements and estimations of the historic water balance of crops grown on overlying Grey Vertosols were combined to estimate the contribution of deep drainage below crop roots to recharge and salinization of shallow groundwater. Soil sampling at two sites on the alluvial flood plain of the Lower Namoi catchment revealed significant peaks in chloride concentrations at 0.8-1.2 m depth under perennial vegetation and at 2.0-2.5 m depth under continuous cropping indicating deep drainage and salt leaching since conversion to cropping. Total salt loads of 91-229 t ha-1 NaCl equivalent were measured for perennial vegetation and cropping, with salinity to ≥ 10 m depth that was not detected by shallow soil surveys. Groundwater salinity varied spatially from 910 to 2430 mS m-1 at 21 to 37 m depth (N = 5), whereas deeper groundwater was less saline (290 mS m-1) with use restricted to livestock and rural domestic supplies in this area. The Agricultural Production Systems Simulator (APSIM) software package predicted deep drainage of 3.3-9.5 mm yr-1 (0.7-2.1% rainfall) based on site records of grain yields, rainfall, salt leaching and soil properties. Predicted deep drainage was highly episodic, dependent on rainfall and antecedent soil water content, and over a 39 yr period was restricted mainly to the record wet winter of 1998. During the study period, groundwater levels were unresponsive to major rainfall events (70 and 190 mm total), and most piezometers at about 18 m depth remained dry. In this area, at this time, recharge appears to be negligible due to low rainfall and large potential evapotranspiration, transient hydrological conditions after changes in land use and a thick clay dominated vadose zone. This is in contrast to regional groundwater modelling that assumes annual recharge of 0.5% of rainfall. Importantly, it was found that leaching from episodic deep drainage could not cause discharge of saline groundwater in the area, since the water table was several meters below the incised river bed.

Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models

USDA-ARS?s Scientific Manuscript database

Remote sensing technology can rapidly provide spatial information on crop growth status, which ideally could be used to invert radiative transfer models or ecophysiological models for estimating a variety of crop biophysical properties. However, the outcome of the model inversion procedure will be ...

Pumpage data from irrigation wells in eastern Laramie County, Wyoming, and Kimball County, Nebraska

USGS Publications Warehouse

Avery, Charles

1983-01-01

Quantitative information concerning pumpage by irrigation wells is an integral component of the U.S. Geological Survey High Plains Regional Aquifer System Analysis. Thus, operation time, discharge rate, and irrigated acreage were measured at approximately 450 randomly selected irrigation wells within 10 areas of the High Plains during the 1980 irrigation season. The data were used to estimate the seasonal mean application of water to crops and to project total pumpage by irrigation wells in 1980 throughout the High Plains area. As part of the sampling effort, 50 irrigation wells were randomly chosen from the area of eastern Laramie County, Wyoming, and Kimball County, Nebraska. Required information was collected on only 40 of the wells. For these wells, the seasonal mean application of water on the irrigated land was 15.2 inches. For the major crop types, the seasonal mean application, in inches, were as follows: alfalfa, 19.8; corn, 15.4; potatoes, 13.8; beans, 12.8; and small grains 10.2. (USGS)

Evaluating the Usefulness of High-Temporal Resolution Vegetation Indices to Identify Crop Types

NASA Astrophysics Data System (ADS)

Hilbert, K.; Lewis, D.; O'Hara, C. G.

2006-12-01

The National Aeronautical and Space Agency (NASA) and the United States Department of Agriculture (USDA) jointly sponsored research covering the 2004 to 2006 South American crop seasons that focused on developing methods for the USDA's Foreign Agricultural Service's (FAS) Production Estimates and Crop Assessment Division (PECAD) to identify crop types using MODIS-derived, hyper-temporal Normalized Difference Vegetation Index (NDVI) images. NDVI images were composited in 8 day intervals from daily NDVI images and aggregated to create a hyper-termporal NDVI layerstack. This NDVI layerstack was used as input to image classification algorithms. Research results indicated that creating high-temporal resolution Normalized Difference Vegetation Index (NDVI) composites from NASA's MODerate Resolution Imaging Spectroradiometer (MODIS) data products provides useful input to crop type classifications as well as potential useful input for regional crop productivity modeling efforts. A current NASA-sponsored Rapid Prototyping Capability (RPC) experiment will assess the utility of simulated future Visible Infrared Imager / Radiometer Suite (VIIRS) imagery for conducting NDVI-derived land cover and specific crop type classifications. In the experiment, methods will be considered to refine current MODIS data streams, reduce the noise content of the MODIS, and utilize the MODIS data as an input to the VIIRS simulation process. The effort also is being conducted in concert with an ISS project that will further evaluate, verify and validate the usefulness of specific data products to provide remote sensing-derived input for the Sinclair Model a semi-mechanistic model for estimating crop yield. The study area encompasses a large portion of the Pampas region of Argentina--a major world producer of crops such as corn, soybeans, and wheat which makes it a competitor to the US. ITD partnered with researchers at the Center for Surveying Agricultural and Natural Resources (CREAN) of the National University of Cordoba, Argentina, and CREAN personnel collected and continue to collect field-level, GIS-based in situ information. Current efforts involve both developing and optimizing software tools for the necessary data processing. The software includes the Time Series Product Tool (TSPT), Leica's ERDAS Imagine, and Mississippi State University's Temporal Map Algebra computational tools.

Commercial Crop Yields Reveal Strengths and Weaknesses for Organic Agriculture in the United States.

PubMed

Kniss, Andrew R; Savage, Steven D; Jabbour, Randa

2016-01-01

Land area devoted to organic agriculture has increased steadily over the last 20 years in the United States, and elsewhere around the world. A primary criticism of organic agriculture is lower yield compared to non-organic systems. Previous analyses documenting the yield deficiency in organic production have relied mostly on data generated under experimental conditions, but these studies do not necessarily reflect the full range of innovation or practical limitations that are part of commercial agriculture. The analysis we present here offers a new perspective, based on organic yield data collected from over 10,000 organic farmers representing nearly 800,000 hectares of organic farmland. We used publicly available data from the United States Department of Agriculture to estimate yield differences between organic and conventional production methods for the 2014 production year. Similar to previous work, organic crop yields in our analysis were lower than conventional crop yields for most crops. Averaged across all crops, organic yield averaged 67% of conventional yield [corrected]. However, several crops had no significant difference in yields between organic and conventional production, and organic yields surpassed conventional yields for some hay crops. The organic to conventional yield ratio varied widely among crops, and in some cases, among locations within a crop. For soybean (Glycine max) and potato (Solanum tuberosum), organic yield was more similar to conventional yield in states where conventional yield was greatest. The opposite trend was observed for barley (Hordeum vulgare), wheat (Triticum aestevum), and hay crops, however, suggesting the geographical yield potential has an inconsistent effect on the organic yield gap.

Water footprint of crop production for different crop structures in the Hebei southern plain, North China

NASA Astrophysics Data System (ADS)

Chu, Yingmin; Shen, Yanjun; Yuan, Zaijian

2017-06-01

The North China Plain (NCP) has a serious shortage of freshwater resources, and crop production consumes approximately 75 % of the region's water. To estimate water consumption of different crops and crop structures in the NCP, the Hebei southern plain (HSP) was selected as a study area, as it is a typical region of groundwater overdraft in the NCP. In this study, the water footprint (WF) of crop production, comprised of green, blue and grey water footprints, and its annual variation were analyzed. The results demonstrated the following: (1) the WF from the production of main crops was 41.8 km3 in 2012. Winter wheat, summer maize and vegetables were the top water-consuming crops in the HSP. The water footprint intensity (WFI) of cotton was the largest, and for vegetables, it was the smallest; (2) the total WF, WFblue, WFgreen and WFgrey for 13 years (2000-2012) of crop production were 604.8, 288.5, 141.3 and 175.0 km3, respectively, with an annual downtrend from 2000 to 2012; (3) winter wheat, summer maize and vegetables consumed the most groundwater, and their blue water footprint (WFblue) accounted for 74.2 % of the total WFblue in the HSP; (4) the crop structure scenarios analysis indicated that, with approximately 20 % of arable land cultivated with winter wheat-summer maize in rotation, 38.99 % spring maize, 10 % vegetables and 10 % fruiters, a sustainable utilization of groundwater resources can be promoted, and a sufficient supply of food, including vegetables and fruits, can be ensured in the HSP.

Commercial Crop Yields Reveal Strengths and Weaknesses for Organic Agriculture in the United States

PubMed Central

Savage, Steven D.; Jabbour, Randa

2016-01-01

Land area devoted to organic agriculture has increased steadily over the last 20 years in the United States, and elsewhere around the world. A primary criticism of organic agriculture is lower yield compared to non-organic systems. Previous analyses documenting the yield deficiency in organic production have relied mostly on data generated under experimental conditions, but these studies do not necessarily reflect the full range of innovation or practical limitations that are part of commercial agriculture. The analysis we present here offers a new perspective, based on organic yield data collected from over 10,000 organic farmers representing nearly 800,000 hectares of organic farmland. We used publicly available data from the United States Department of Agriculture to estimate yield differences between organic and conventional production methods for the 2014 production year. Similar to previous work, organic crop yields in our analysis were lower than conventional crop yields for most crops. Averaged across all crops, organic yield averaged 80% of conventional yield. However, several crops had no significant difference in yields between organic and conventional production, and organic yields surpassed conventional yields for some hay crops. The organic to conventional yield ratio varied widely among crops, and in some cases, among locations within a crop. For soybean (Glycine max) and potato (Solanum tuberosum), organic yield was more similar to conventional yield in states where conventional yield was greatest. The opposite trend was observed for barley (Hordeum vulgare), wheat (Triticum aestevum), and hay crops, however, suggesting the geographical yield potential has an inconsistent effect on the organic yield gap. PMID:27552217

Partitioning carbon fluxes from a Midwestern corn and soybean rotation system using footprint analysis

NASA Astrophysics Data System (ADS)

Dold, C.; Hatfield, J.; Prueger, J. H.; Wacha, K.

2017-12-01

Midwestern US agriculture is dominated by corn and soybean production. Corn has typically higher Net Ecosystem Exchange (NEE) than soybean due to increased carboxylation efficiency and different crop management. The conjoined NEE may be measured with eddy covariance (EC) stations covering both crops, however, it is often unclear what the contribution of each crop is, as the CO2 source area remains unknown. The aim of this study was to quantify the contribution of CO2 fluxes from each crop for a conventional corn-soybean rotation system from 2007 - 2015. Therefore, the combined CO2 flux of three adjacent fields with annual corn-soybean rotation was measured with a 9.1 m EC tower (Flux 30). In the center of two of these fields, additional EC towers (Flux 10 and Flux 11) were positioned above the corn and soybean canopy to validate Flux 30 NEE. For each EC system the annual 90% NEE footprint area was calculated, footprints were partitioned among fields, and NEE separated accordingly. The average annual 90% footprint area of Flux 30, and Flux 10/11 corn and soybean was estimated to 206, 11 and 7 ha, respectively. The annual average (±SE) NEE of Flux 30 was -693 ± 47 g CO2 m-2 yr-1, of which 83% out of 90% originated from the three adjacent fields. Corn and soybean NEE measured at Flux 10 and 11 was -1124 ± 95 and 173 ± 73 g CO2 m-2 yr-1, respectively, and 89% and 90% originated from these fields. That demonstrates, that Flux 30 represents the combined NEE of a corn-soybean rotation, and Flux 10 and 11 measured NEE from a single crop. However, the share of Flux 30 NEE originating from corn and soybean grown on the Flux 10/11 fields was -192 ± 16 and -205 ± 18 g CO2 m-2 yr-1, indicating a substantial difference to single crop NEE. While it was possible to measure the NEE of a corn-soybean rotation with a tall EC tower, footprint partitioning could not retrieve NEE for each crop, probably due to differences in measurement height and footprint source area.

Large area crop inventory experiment crop assessment subsystem software requirements document

NASA Technical Reports Server (NTRS)

1975-01-01

The functional data processing requirements are described for the Crop Assessment Subsystem of the Large Area Crop Inventory Experiment. These requirements are used as a guide for software development and implementation.

Drainage-return, surface-water withdrawal, and land-use data for the Sacramento-San Joaquin Delta, with emphasis on Twitchell Island, California

USGS Publications Warehouse

Templin, William E.; Cherry, Daniel E.

1997-01-01

Partial data on drainage returns and surface-water withdrawals are presented for areas of the Sacramento-San Joaquin Delta, California, for March 1994 through February 1996. These areas cover most of the delta. Data are also presented for all drainage returns and some surface-water withdrawals for Twitchell Island, which is in the western part of the delta. Changes in land use between 1968 and 1991 are also presented for the delta. Measurements of monthly drainage returns and surface-water withdrawals were made using flowmeters installed in siphons and drain pipes on Twitchell Island. Estimates of monthly returns throughout the delta were made using electric power-consumption data with pump-efficiency-test data. For Twitchell Island, monthly measured drainage returns for the 1995 calendar year totaled about 11,200 acre-feet, whereas drainage returns estimated from power-consumption data totaled 5 percent less at about 10,600 acre-feet. Monthly surface-water withdrawals onto Twitchell Island through 12 of the 21 siphons totaled about 2,400 acre-feet for 1995. For most of the delta, the monthly estimated drainage returns for 1995 totaled about 430,000 acre-feet. The area consisting of Bouldin, Brannan, Staten, Tyler, and Venice Islands had the largest estimated drainage returns for calendar year 1995. Between 1968 and 1991, native vegetation in the delta decreased by 25 percent (about 40,000 acres), and grain and hay crops increased by 340 percent (about 71,000 acres). For Twitchell Island, native vegetation decreased about 77 percent (about 850 acres), while field crop acreage increased by about 44 percent (about 780 acres).

An evaluation of time-series MODIS 250-meter vegetation index data for crop mapping in the United States Central Great Plains

NASA Astrophysics Data System (ADS)

Wardlow, Brian Douglas

The objectives of this research were to: (1) investigate time-series MODIS (Moderate Resolution Imaging Spectroradiometer) 250-meter EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index) data for regional-scale crop-related land use/land cover characterization in the U.S. Central Great Plains and (2) develop and test a MODIS-based crop mapping protocol. A pixel-level analysis of the time-series MODIS 250-m VIs for 2,000+ field sites across Kansas found that unique spectral-temporal signatures were detected for the region's major crop types, consistent with the crops' phenology. Intra-class variations were detected in VI data associated with different land use practices, climatic conditions, and planting dates for the crops. The VIs depicted similar seasonal variations and were highly correlated. A pilot study in southwest Kansas found that accurate and detailed cropping patterns could be mapped using the MODIS 250-m VI data. Overall and class-specific accuracies were generally greater than 90% for mapping crop/non-crop, general crops (alfalfa, summer crops, winter wheat, and fallow), summer crops (corn, sorghum, and soybeans), and irrigated/non-irrigated crops using either VI dataset. The classified crop areas also had a high level of agreement (<5% difference) with the USDA reported crop areas. Both VIs produced comparable crop maps with only a 1-2% difference between their classification accuracies and a high level of pixel-level agreement (>90%) between their classified crop patterns. This hierarchical crop mapping protocol was tested for Kansas and produced similar classification results over a larger and more diverse area. Overall and class-specific accuracies were typically between 85% and 95% for the crop maps. At the state level, the maps had a high level of areal agreement (<5% difference) with the USDA crop area figures and their classified patterns were consistent with the state's cropping practices. In general, the protocol's performance was relatively consistent across the state's range of environmental conditions, landscape patterns, and cropping practices. The largest areal differences occurred in eastern Kansas due to the omission of many small cropland areas that were not resolvable at MODIS' 250-m resolution. Notable regional deviations in classified areas also occurred for selected classes due to localized precipitation patterns and specific cropping practices.

Marginal cost curves for water footprint reduction in irrigated agriculture: guiding a cost-effective reduction of crop water consumption to a permit or benchmark level

NASA Astrophysics Data System (ADS)

Chukalla, Abebe D.; Krol, Maarten S.; Hoekstra, Arjen Y.

2017-07-01

Reducing the water footprint (WF) of the process of growing irrigated crops is an indispensable element in water management, particularly in water-scarce areas. To achieve this, information on marginal cost curves (MCCs) that rank management packages according to their cost-effectiveness to reduce the WF need to support the decision making. MCCs enable the estimation of the cost associated with a certain WF reduction target, e.g. towards a given WF permit (expressed in m3  ha-1 per season) or to a certain WF benchmark (expressed in m3  t-1 of crop). This paper aims to develop MCCs for WF reduction for a range of selected cases. AquaCrop, a soil-water-balance and crop-growth model, is used to estimate the effect of different management packages on evapotranspiration and crop yield and thus the WF of crop production. A management package is defined as a specific combination of management practices: irrigation technique (furrow, sprinkler, drip or subsurface drip); irrigation strategy (full or deficit irrigation); and mulching practice (no, organic or synthetic mulching). The annual average cost for each management package is estimated as the annualized capital cost plus the annual costs of maintenance and operations (i.e. costs of water, energy and labour). Different cases are considered, including three crops (maize, tomato and potato); four types of environment (humid in UK, sub-humid in Italy, semi-arid in Spain and arid in Israel); three hydrologic years (wet, normal and dry years) and three soil types (loam, silty clay loam and sandy loam). For each crop, alternative WF reduction pathways were developed, after which the most cost-effective pathway was selected to develop the MCC for WF reduction. When aiming at WF reduction one can best improve the irrigation strategy first, next the mulching practice and finally the irrigation technique. Moving from a full to deficit irrigation strategy is found to be a no-regret measure: it reduces the WF by reducing water consumption at negligible yield reduction while reducing the cost for irrigation water and the associated costs for energy and labour. Next, moving from no to organic mulching has a high cost-effectiveness, reducing the WF significantly at low cost. Finally, changing from sprinkler or furrow to drip or subsurface drip irrigation reduces the WF, but at a significant cost.

Early forecasting of crop condition using an integrative remote sensing method for corn and soybeans in Iowa and Illinois, USA

NASA Astrophysics Data System (ADS)

Seo, Bumsuk; Lee, Jihye; Kang, Sinkyu

2017-04-01

The weather-related risks in crop production is not only crucial for farmers but also for market participants and policy makers since securing food supply is an important issue for society. While crop growth condition and phenology are essential information about such risks, the extensive observations on those are often non-existent in many parts of the world. In this study, we have developed a novel integrative approach to remotely sense crop growth condition and phenology at a large scale. For corn and soybeans in Iowa and Illinois of USA (2003-2014), we assessed crop growth condition and crop phenology by EO data and validated it against the United States Department of Agriculture (USDA) National Agriculture Statistics System (NASS) crop statistics. For growth condition, we used two distinguished approaches to acquire crop condition indicators: a process-based crop growth modelling and a satellite NDVI based method. Based on their pixel-wise historic distributions, we determined relative growth conditions and scaled-down to the state-level. For crop phenology, we calculated three crop phenology metrics [i.e., start of season (SOS), end of season (EOS), and peak of season (POS)] at the pixel level from MODIS 8-day Normalized Difference Vegetation Index (NDVI). The estimates were compared with the Crop Progress and Condition (CPC) data of NASS. For the condition, the state-level 10-day estimates showed a moderate agreement (RMSE < 15.0%) and the average accuracy of the normal/bad year classification was well (> 70%). Notably, the condition estimates corresponded to the severe soybeans disease in 2003 and the drought in 2012 for both crops. For the phenology, the average RMSE of the estimates was 8.6 day for the all three metrics. The average |ME| was smaller than 1.0 day after bias correction. The proposed method enables us to evaluate crop growth at any given period and place. Global climate changes are increasing the risk in agricultural production such as long-term drought. We hope that the presented remote sensing method for crop condition and crop phenology contributes to reducing the growing risk of crop production in the Earth.

Mapping cropping patterns in irrigated rice fields in West Java: Towards mapping vulnerability to flooding using time-series MODIS imageries

NASA Astrophysics Data System (ADS)

Sianturi, Riswan; Jetten, V. G.; Sartohadi, Junun

2018-04-01

Information on the vulnerability to flooding is vital to understand the potential damages from flood events. A method to determine the vulnerability to flooding in irrigated rice fields using the Enhanced Vegetation Index (EVI) was proposed in this study. In doing so, the time-series EVI derived from time-series 8 day 500 m spatial resolution MODIS imageries (MOD09A1) was used to generate cropping patterns in irrigated rice fields in West Java. Cropping patterns were derived from the spatial distribution and phenology metrics so that it is possible to show the variation of vulnerability in space and time. Vulnerability curves and cropping patterns were used to determine the vulnerability to flooding in irrigated rice fields. Cropping patterns capture the shift in the vulnerability, which may lead to either an increase or decrease of the degree of damage in rice fields of origin and other rice fields. The comparison of rice field areas between MOD09A1 and ALOS PALSAR and MOD09A1 and Agricultural Statistics showed consistent results with R2 = 0.81 and R2 = 0.93, respectively. The estimated and observed DOYs showed RMSEs = 9.21, 9.29, and 9.69 days for the Start of Season (SOS), heading stage, and End of Season (EOS), respectively. Using the method, one can estimate the relative damage provided available information on the flood depth and velocity. The results of the study may support the efforts to reduce the potential damages from flooding in irrigated rice fields.

Mercury emissions from biomass burning in China.

PubMed

Huang, Xin; Li, Mengmeng; Friedli, Hans R; Song, Yu; Chang, Di; Zhu, Lei

2011-11-01

Biomass burning covers open fires (forest and grassland fires, crop residue burning in fields, etc.) and biofuel combustion (crop residues and wood, etc., used as fuel). As a large agricultural country, China may produce large quantities of mercury emissions from biomass burning. A new mercury emission inventory in China is needed because previous studies reflected outdated biomass burning with coarse resolution. Moreover, these studies often adopted the emission factors (mass of emitted species per mass of biomass burned) measured in North America. In this study, the mercury emissions from biomass burning in China (excluding small islands in the South China Sea) were estimated, using recently measured mercury concentrations in various biomes in China as emission factors. Emissions from crop residues and fuelwood were estimated based on annual reports distributed by provincial government. Emissions from forest and grassland fires were calculated by combining moderate resolution imaging spectroradiometer (MODIS) burned area product with combustion efficiency (ratio of fuel consumption to total available fuels) considering fuel moisture. The average annual emission from biomass burning was 27 (range from 15.1 to 39.9) Mg/year. This inventory has high spatial resolution (1 km) and covers a long period (2000-2007), making it useful for air quality modeling.

Different techniques of multispectral data analysis for vegetation fraction retrieval

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana; Georgiev, Georgi

2012-07-01

Vegetation monitoring is one of the most important applications of remote sensing technologies. In respect to farmlands, the assessment of crop condition constitutes the basis of growth, development, and yield processes monitoring. Plant condition is defined by a set of biometric variables, such as density, height, biomass amount, leaf area index, and etc. The canopy cover fraction is closely related to these variables, and is state-indicative of the growth process. At the same time it is a defining factor of the soil-vegetation system spectral signatures. That is why spectral mixtures decomposition is a primary objective in remotely sensed data processing and interpretation, specifically in agricultural applications. The actual usefulness of the applied methods depends on their prediction reliability. The goal of this paper is to present and compare different techniques for quantitative endmember extraction from soil-crop patterns reflectance. These techniques include: linear spectral unmixing, two-dimensional spectra analysis, spectral ratio analysis (vegetation indices), spectral derivative analysis (red edge position), colorimetric analysis (tristimulus values sum, chromaticity coordinates and dominant wavelength). The objective is to reveal their potential, accuracy and robustness for plant fraction estimation from multispectral data. Regression relationships have been established between crop canopy cover and various spectral estimators.

Paddy crop yield estimation in Kashmir Himalayan rice bowl using remote sensing and simulation model.

PubMed

Muslim, Mohammad; Romshoo, Shakil Ahmad; Rather, A Q

2015-06-01

The Kashmir Himalayan region of India is expected to be highly prone to the change in agricultural land use because of its geo-ecological fragility, strategic location vis-à-vis the Himalayan landscape, its trans-boundary river basins, and inherent socio-economic instabilities. Food security and sustainability of the region are thus greatly challenged by these impacts. The effect of future climate change, increased competition for land and water, labor from non-agricultural sectors, and increasing population adds to this complex problem. In current study, paddy rice yield at regional level was estimated using GIS-based environment policy integrated climate (GEPIC) model. The general approach of current study involved combining regional level crop database, regional soil data base, farm management data, and climatic data outputs with GEPIC model. The simulated yield showed that estimated production to be 4305.55 kg/ha (43.05 q h(-1)). The crop varieties like Jhelum, K-39, Chenab, China 1039, China-1007, and Shalimar rice-1 grown in plains recorded average yield of 4783.3 kg/ha (47.83 q ha(-1)). Meanwhile, high altitude areas with varieties like Kohsaar, K-78 (Barkat), and K-332 recorded yield of 4102.2 kg/ha (41.02 q ha(-1)). The observed and simulated yield showed a good match with R (2) = 0.95, RMSE = 132.24 kg/ha, respectively.

Spatial and temporal estimation of soil loss for the sustainable management of a wet semi-arid watershed cluster.

PubMed

Rejani, R; Rao, K V; Osman, M; Srinivasa Rao, Ch; Reddy, K Sammi; Chary, G R; Pushpanjali; Samuel, Josily

2016-03-01

The ungauged wet semi-arid watershed cluster, Seethagondi, lies in the Adilabad district of Telangana in India and is prone to severe erosion and water scarcity. The runoff and soil loss data at watershed, catchment, and field level are necessary for planning soil and water conservation interventions. In this study, an attempt was made to develop a spatial soil loss estimation model for Seethagondi cluster using RUSLE coupled with ARCGIS and was used to estimate the soil loss spatially and temporally. The daily rainfall data of Aphrodite for the period from 1951 to 2007 was used, and the annual rainfall varied from 508 to 1351 mm with a mean annual rainfall of 950 mm and a mean erosivity of 6789 MJ mm ha(-1) h(-1) year(-1). Considerable variation in land use land cover especially in crop land and fallow land was observed during normal and drought years, and corresponding variation in the erosivity, C factor, and soil loss was also noted. The mean value of C factor derived from NDVI for crop land was 0.42 and 0.22 in normal year and drought years, respectively. The topography is undulating and major portion of the cluster has slope less than 10°, and 85.3% of the cluster has soil loss below 20 t ha(-1) year(-1). The soil loss from crop land varied from 2.9 to 3.6 t ha(-1) year(-1) in low rainfall years to 31.8 to 34.7 t ha(-1) year(-1) in high rainfall years with a mean annual soil loss of 12.2 t ha(-1) year(-1). The soil loss from crop land was higher in the month of August with an annual soil loss of 13.1 and 2.9 t ha(-1) year(-1) in normal and drought year, respectively. Based on the soil loss in a normal year, the interventions recommended for 85.3% of area of the watershed includes agronomic measures such as contour cultivation, graded bunds, strip cropping, mixed cropping, crop rotations, mulching, summer plowing, vegetative bunds, agri-horticultural system, and management practices such as broad bed furrow, raised sunken beds, and harvesting available water using farm ponds and percolation tanks. This methodology can be adopted for estimating the soil loss from similar ungauged watersheds with deficient data and for planning suitable soil and water conservation interventions for the sustainable management of the watersheds.

Reduced uncertainty of regional scale CLM predictions of net carbon fluxes and leaf area indices with estimated plant-specific parameters

NASA Astrophysics Data System (ADS)

Post, Hanna; Hendricks Franssen, Harrie-Jan; Han, Xujun; Baatz, Roland; Montzka, Carsten; Schmidt, Marius; Vereecken, Harry

2016-04-01

Reliable estimates of carbon fluxes and states at regional scales are required to reduce uncertainties in regional carbon balance estimates and to support decision making in environmental politics. In this work the Community Land Model version 4.5 (CLM4.5-BGC) was applied at a high spatial resolution (1 km2) for the Rur catchment in western Germany. In order to improve the model-data consistency of net ecosystem exchange (NEE) and leaf area index (LAI) for this study area, five plant functional type (PFT)-specific CLM4.5-BGC parameters were estimated with time series of half-hourly NEE data for one year in 2011/2012, using the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm, a Markov Chain Monte Carlo (MCMC) approach. The parameters were estimated separately for four different plant functional types (needleleaf evergreen temperate tree, broadleaf deciduous temperate tree, C3-grass and C3-crop) at four different sites. The four sites are located inside or close to the Rur catchment. We evaluated modeled NEE for one year in 2012/2013 with NEE measured at seven eddy covariance sites in the catchment, including the four parameter estimation sites. Modeled LAI was evaluated by means of LAI derived from remotely sensed RapidEye images of about 18 days in 2011/2012. Performance indices were based on a comparison between measurements and (i) a reference run with CLM default parameters, and (ii) a 60 instance CLM ensemble with parameters sampled from the DREAM posterior probability density functions (pdfs). The difference between the observed and simulated NEE sum reduced 23% if estimated parameters instead of default parameters were used as input. The mean absolute difference between modeled and measured LAI was reduced by 59% on average. Simulated LAI was not only improved in terms of the absolute value but in some cases also in terms of the timing (beginning of vegetation onset), which was directly related to a substantial improvement of the NEE estimates in spring. In order to obtain a more comprehensive estimate of the model uncertainty, a second CLM ensemble was set up, where initial conditions and atmospheric forcings were perturbed in addition to the parameter estimates. This resulted in very high standard deviations (STD) of the modeled annual NEE sums for C3-grass and C3-crop PFTs, ranging between 24.1 and 225.9 gC m-2 y-1, compared to STD = 0.1 - 3.4 gC m-2 y-1 (effect of parameter uncertainty only, without additional perturbation of initial states and atmospheric forcings). The higher spread of modeled NEE for the C3-crop and C3-grass indicated that the model uncertainty was notably higher for those PFTs compared to the forest-PFTs. Our findings highlight the potential of parameter and uncertainty estimation to support the understanding and further development of land surface models such as CLM.

SEBAL Model Using to Estimate Irrigation Water Efficiency & Water Requirement of Alfalfa Crop

NASA Astrophysics Data System (ADS)

Zeyliger, Anatoly; Ermolaeva, Olga

2013-04-01

The sustainability of irrigation is a complex and comprehensive undertaking, requiring an attention to much more than hydraulics, chemistry, and agronomy. A special combination of human, environmental, and economic factors exists in each irrigated region and must be recognized and evaluated. A way to evaluate the efficiency of irrigation water use for crop production is to consider the so-called crop-water production functions, which express the relation between the yield of a crop and the quantity of water applied to it or consumed by it. The term has been used in a somewhat ambiguous way. Some authors have defined the Crop-Water Production Functions between yield and the total amount of water applied, whereas others have defined it as a relation between yield and seasonal evapotranspiration (ET). In case of high efficiency of irrigation water use the volume of water applied is less than the potential evapotranspiration (PET), then - assuming no significant change of soil moisture storage from beginning of the growing season to its end-the volume of water may be roughly equal to ET. In other case of low efficiency of irrigation water use the volume of water applied exceeds PET, then the excess of volume of water applied over PET must go to either augmenting soil moisture storage (end-of-season moisture being greater than start-of-season soil moisture) or to runoff or/and deep percolation beyond the root zone. In presented contribution some results of a case study of estimation of biomass and leaf area index (LAI) for irrigated alfalfa by SEBAL algorithm will be discussed. The field study was conducted with aim to compare ground biomass of alfalfa at some irrigated fields (provided by agricultural farm) at Saratov and Volgograd Regions of Russia. The study was conducted during vegetation period of 2012 from April till September. All the operations from importing the data to calculation of the output data were carried by eLEAF company and uploaded in Fieldlook web geo database and used for experiment program managment. Quite good agreement between measured and calculated biomass and LAI were obtained. Estimation of effectiveness of water efficiency as well as estimation of applied water losses were done in the base of supplied irrigation water provided by local operating irrigation water supply companies and data of soil moisture monitoring. Following analyse of the remote sensing use to estimate of crop water requirement will be presented. ACKNOWLEDGMENTS. This study was financially supported by G2G project

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

Xiong, Wei; Balkovic, Juraj; van der Velde, M.

Crop models are increasingly used to assess impacts of climate change/variability and management practices on productivity and environmental performance of alternative cropping systems. Calibration is an important procedure to improve reliability of model simulations, especially for large area applications. However, global-scale crop model calibration has rarely been exercised due to limited data availability and expensive computing cost. Here we present a simple approach to calibrate Environmental Policy Integrated Climate (EPIC) model for a global implementation of rice. We identify four parameters (potential heat unit – PHU, planting density – PD, harvest index – HI, and biomass energy ratio – BER)more » and calibrate them regionally to capture the spatial pattern of reported rice yield in 2000. Model performance is assessed by comparing simulated outputs with independent FAO national data. The comparison demonstrates that the global calibration scheme performs satisfactorily in reproducing the spatial pattern of rice yield, particularly in main rice production areas. Spatial agreement increases substantially when more parameters are selected and calibrated, but with varying efficiencies. Among the parameters, PHU and HI exhibit the highest efficiencies in increasing the spatial agreement. Simulations with different calibration strategies generate a pronounced discrepancy of 5–35% in mean yields across latitude bands, and a small to moderate difference in estimated yield variability and yield changing trend for the period of 1981–2000. Present calibration has little effects in improving simulated yield variability and trends at both regional and global levels, suggesting further works are needed to reproduce temporal variability of reported yields. This study highlights the importance of crop models’ calibration, and presents the possibility of a transparent and consistent up scaling approach for global crop simulations given current availability of global databases of weather, soil, crop calendar, fertilizer and irrigation management information, and reported yield.« less

Using thermal units for estimating critical period of weed competition in off-season maize crop.

PubMed

López-Ovejero, Ramiro Fernando; y Garcia, Axel Garcia; de Carvalho, Saul Jorge P; Christoffoleti, Pedro J; Neto, Durval Dourado; Martins, Fernando; Nicolai, Marcelo

2005-01-01

Brazilian off-season maize production is characterized by low yield due to several factors, such as climate variability and inadequate management practices, specifically weed management. Thus, the goal of this study was to determinate the critical period of weed competition in off-season maize (Zea mays L.) crop using thermal units or growing degree days (GDD) approach to characterize crop growth and development. The study was carried out in experimental area of the University of São Paulo, Brazil, with weed control (C), as well as seven coexistence periods, 2, 4, 6, 8, and 12 leaves, flowering, and all crop cycle; fourteen treatments were done. Climate data were obtained from a weather station located close to the experimental area. To determine the critical period for weed control (CPWC) logistic models were fitted to yield data obtained in both W and C, as a function of GDD. For an arbitrary maximum yield loss fixed in 2.5%, the CPWC was found between 301 and 484 GDD (7-8 leaves). Also, when the arbitrary loss yield was fixed in 5 and 10%, the period before interference (PBI) was higher than the critical weed-free period (CWFP), suggesting that the weeds control can be done with only one application, between 144 and 410 GDD and 131 and 444 GDD (3-8 leaves), respectively. The GDD approach to characterize crop growth and development was successfully used to determine the critical period of weeds control in maize sown off-season. Further works will be necessary to better characterize the interaction and complexity of maize sown off-season with weeds. However, these results are encouraging because the possibility of the results to be extrapolated and because the potential of the method on providing important results to researchers, specifically crop modelers.

7 CFR 407.12 - Area risk protection insurance for cotton.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 6 2014-01-01 2014-01-01 false Area risk protection insurance for cotton. 407.12... protection insurance for cotton. The cotton crop insurance provisions for Area Risk Protection Insurance for... Crop Insurance Corporation Area Risk Protection Insurance Cotton Crop Insurance Provisions 1...

Is current irrigation sustainable in the United States? An integrated assessment of climate change impact on water resources and irrigated crop yields

DOE PAGES

Blanc, Elodie; Caron, Justin; Fant, Charles; ...

2017-06-27

While climate change impacts on crop yields has been extensively studied, estimating the impact of water shortages on irrigated crop yields is challenging because the water resources management system is complex. To investigate this issue, we integrate a crop yield reduction module and a water resources model into the MIT Integrated Global System Modeling framework, an integrated assessment model linking a global economic model to an Earth system model. We assess the effects of climate and socioeconomic changes on water availability for irrigation in the U.S. as well as subsequent impacts on crop yields by 2050, while accounting for climatemore » change projection uncertainty. We find that climate and socioeconomic changes will increase water shortages and strongly reduce irrigated yields for specific crops (i.e., cotton and forage), or in specific regions (i.e., the Southwest) where irrigation is not sustainable. Crop modeling studies that do not represent changes in irrigation availability can thus be misleading. Yet, since the most water-stressed basins represent a relatively small share of U.S. irrigated areas, the overall reduction in U.S. crop yields is small. The response of crop yields to climate change and water stress also suggests that some level of adaptation will be feasible, like relocating croplands to regions with sustainable irrigation or switching to less irrigation intensive crops. Finally, additional simulations show that greenhouse gas (GHG) mitigation can alleviate the effect of water stress on irrigated crop yields, enough to offset the reduced CO 2 fertilization effect compared to an unconstrained GHG emission scenario.« less

Is current irrigation sustainable in the United States? An integrated assessment of climate change impact on water resources and irrigated crop yields

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

Blanc, Elodie; Caron, Justin; Fant, Charles

While climate change impacts on crop yields has been extensively studied, estimating the impact of water shortages on irrigated crop yields is challenging because the water resources management system is complex. To investigate this issue, we integrate a crop yield reduction module and a water resources model into the MIT Integrated Global System Modeling framework, an integrated assessment model linking a global economic model to an Earth system model. We assess the effects of climate and socioeconomic changes on water availability for irrigation in the U.S. as well as subsequent impacts on crop yields by 2050, while accounting for climatemore » change projection uncertainty. We find that climate and socioeconomic changes will increase water shortages and strongly reduce irrigated yields for specific crops (i.e., cotton and forage), or in specific regions (i.e., the Southwest) where irrigation is not sustainable. Crop modeling studies that do not represent changes in irrigation availability can thus be misleading. Yet, since the most water-stressed basins represent a relatively small share of U.S. irrigated areas, the overall reduction in U.S. crop yields is small. The response of crop yields to climate change and water stress also suggests that some level of adaptation will be feasible, like relocating croplands to regions with sustainable irrigation or switching to less irrigation intensive crops. Finally, additional simulations show that greenhouse gas (GHG) mitigation can alleviate the effect of water stress on irrigated crop yields, enough to offset the reduced CO 2 fertilization effect compared to an unconstrained GHG emission scenario.« less

Ascribing soil erosion of hillslope components to river sediment yield.

PubMed

Nosrati, Kazem

2017-06-01

In recent decades, soil erosion has increased in catchments of Iran. It is, therefore, necessary to understand soil erosion processes and sources in order to mitigate this problem. Geomorphic landforms play an important role in influencing water erosion. Therefore, ascribing hillslope components soil erosion to river sediment yield could be useful for soil and sediment management in order to decrease the off-site effects related to downstream sedimentation areas. The main objectives of this study were to apply radionuclide tracers and soil organic carbon to determine relative contributions of hillslope component sediment sources in two land use types (forest and crop field) by using a Bayesian-mixing model, as well as to estimate the uncertainty in sediment fingerprinting in a mountainous catchment of western Iran. In this analysis, 137 Cs, 40 K, 238 U, 226 Ra, 232 Th and soil organic carbon tracers were measured in 32 different sampling sites from four hillslope component sediment sources (summit, shoulder, backslope, and toeslope) in forested and crop fields along with six bed sediment samples at the downstream reach of the catchment. To quantify the sediment source proportions, the Bayesian mixing model was based on (1) primary sediment sources and (2) combined primary and secondary sediment sources. The results of both approaches indicated that erosion from crop field shoulder dominated the sources of river sediments. The estimated contribution of crop field shoulder for all river samples was 63.7% (32.4-79.8%) for primary sediment sources approach, and 67% (15.3%-81.7%) for the combined primary and secondary sources approach. The Bayesian mixing model, based on an optimum set of tracers, estimated that the highest contribution of soil erosion in crop field land use and shoulder-component landforms constituted the most important land-use factor. This technique could, therefore, be a useful tool for soil and sediment control management strategies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Cropland expansion in Brazil, 2000 to 2014

NASA Astrophysics Data System (ADS)

Zalles, V.; Hansen, M.; Potapov, P.; Stehman, S. V.; Tyukavina, A.; Pickens, A. H.; Okpa, C.; Aguilar, R.; John, N.; Chavez, S.

2017-12-01

Brazil has become a global leader in the production of commodity row crops such as soybean, sugarcane, cotton, and corn. Here, we employ 30m spatial resolution Landsat data to estimate cropland extent in the year 2000 and its subsequent expansion through 2014. A probability-based sample of reference data allows us to report unbiased estimates of national, biome, and state-scale area of crop expansion with associated uncertainties. We find an increase in Brazilian cropland extent from 26.0 Mha in 2000 to 46.1 Mha in 2014. The cropland frontier states of Maranhao, Tocantins, Piaui, Bahia (MATOPIBA), Mato Grosso, Mato Grosso do Sul, and Para all more than doubled in cropland extent. The states of Goias, Minas Gerais and Sao Paulo experienced >50% increases. The vast majority of expansion, 79%, occurred on repurposed pasture lands, and 20% from the conversion of natural vegetation. Area of converted Cerrado savannas was nearly 2.5 times that of Amazon forests, and accounted for over half of new cropland in MATOPIBA. Spatio-temporal dynamics of cropland expansion are reflected in market conditions, land use policies, and other factors. Continued extensification of cropland is a viable option across Brazil with attendant benefits for and challenges to development.

Possible pathways and tensions in the food and water nexus

NASA Astrophysics Data System (ADS)

Grafton, R. Quentin; Williams, John; Jiang, Qiang

2017-05-01

"Bottom-up" field-based, crop-hydrological models are used to estimate food production and irrigation water extractions under multiple scenarios of water and nitrogen use and crop yield increases from 2010 to 2050 for 19 countries. The results show: (1) a food deficit before 2050 under a worst case climate change scenario in terms of annual crop yield improvement; (2) substantial water deficits, as a result of irrigation, for major food-producing countries that will prevent these nations from meeting their domestic food requirements in the absence of investments in water infrastructure or food imports; and (3) a plateau in terms of crop food production associated with increased water extractions given no further increase in the current area of irrigated agriculture. Possible pathways to respond to the tensions in the food-water nexus are evaluated and include: (1) higher water productivity; (2) food trade; (3) improvements in both crop yield and "sustainable" total factor productivity; (4) greater investment in water infrastructure; and (5) integrative policies and decision processes. Without a combination of some, or all, of these possible pathways, appropriately adapted to bio-physical and socio-economic circumstances, the world faces grave risks in food and water security out to 2050.

Determination of irrigation pumpage in parts of Kearny and Finney Counties, southwestern Kansas

USGS Publications Warehouse

Lindgren, R.J.

1982-01-01

Irrigation pumpage was determined for parts of Kearny and Finney Counties in Southwestern Kansas using crop-acreage data and consumptive, irrigation-water requirements. Irrigated acreages for 1974-80 were compiled for wheat, grain sorghum, corn, and alfalfa using records from the U.S. Agricultural Stabilization and Conservation Service. Consumptive-irrigation requirements were computed using a soil-moisture model. The model tabulated monthly soil-moisture and crop-water demand for various crops and computed the volume of irrigation water needed to maintain the available moisture at 50% for loamy soils or at 60% for sandy soils. Irrigated acres in the study area increased from 265,000 acres during 1974 to 321,000 acres during 1980. Irrigation pumpage increased from 584,000 acre-feet during 1974 to 738,000 acre-feet during 1980. Decreased consumptive-irrigation requirements during 1979 resulted in a comparatively small irrigation-pumpage estimate of 458,000 acre-feet. (USGS)

Machine processing of remotely sensed data; Proceedings of the Fifth Annual Symposium, Purdue University, West Lafayette, Ind., June 27-29, 1979

NASA Technical Reports Server (NTRS)

Tendam, I. M. (Editor); Morrison, D. B.

1979-01-01

Papers are presented on techniques and applications for the machine processing of remotely sensed data. Specific topics include the Landsat-D mission and thematic mapper, data preprocessing to account for atmospheric and solar illumination effects, sampling in crop area estimation, the LACIE program, the assessment of revegetation on surface mine land using color infrared aerial photography, the identification of surface-disturbed features through a nonparametric analysis of Landsat MSS data, the extraction of soil data in vegetated areas, and the transfer of remote sensing computer technology to developing nations. Attention is also given to the classification of multispectral remote sensing data using context, the use of guided clustering techniques for Landsat data analysis in forest land cover mapping, crop classification using an interactive color display, and future trends in image processing software and hardware.

Separation of man-made and natural patterns in high-altitude imagery of agricultural areas

NASA Technical Reports Server (NTRS)

Samulon, A. S.

1975-01-01

A nonstationary linear digital filter is designed and implemented which extracts the natural features from high-altitude imagery of agricultural areas. Essentially, from an original image a new image is created which displays information related to soil properties, drainage patterns, crop disease, and other natural phenomena, and contains no information about crop type or row spacing. A model is developed to express the recorded brightness in a narrow-band image in terms of man-made and natural contributions and which describes statistically the spatial properties of each. The form of the minimum mean-square error linear filter for estimation of the natural component of the scene is derived and a suboptimal filter is implemented. Nonstationarity of the two-dimensional random processes contained in the model requires a unique technique for deriving the optimum filter. Finally, the filter depends on knowledge of field boundaries. An algorithm for boundary location is proposed, discussed, and implemented.

Assessment of spatially distributed values of Kc using vegetation indices derived from medium resolution satellite data

NASA Astrophysics Data System (ADS)

Greco, M.; Simoniello, T.; Lanfredi, M.; Russo, A. L.

2010-09-01

In the last years, the theme of suitable assessment of irrigation water supply has been raised relevant interest for both general principles of sustainable development and optimization of water resources techniques and management. About 99% of the water used in agriculture is lost by crops as evapotranspiration (ET). Thus, it becomes crucial to drive direct or indirect measurement in order to perform a suitable evaluation of water loss by evapotranspiration (i.e. actual evapotranspiration) as well as crop water status and its effect on the production. The main methods used to measure evapotranspiration are available only at field scale (Bowen ratio, eddy correlation system, soil water balance) confined to a small pilot area, generally due to expense and logistical constraints. This led over the last 50 years to the development of a large number of empirical methods to estimate evapotranspiration through different climatic and meteorological variables as well as combining models, based on aerodynamic theory and energy balance, taking into account both canopy properties and meteorological conditions. Among these, the Penman-Monteith equation seems to give the best results providing a robust and consistent method world wide accepted. Such conventional methods only provide accurate evapotranspiration assessment for a homogeneous region nearby the meteorological gauge station and cannot be extrapolated to other different sites; whereas remote sensing techniques allow for filling up such a gap. Some of these satellite techniques are based on the use of thermal band signals as inputs for energy balance equations. Another common approach is mainly based on the FAO method for estimating crop evapotranspiration, in which evapotranspiration data are multiplied by crop coefficients, Kc, derived from satellite multispectral vegetation indices obtained. The rationale behind such a link considers that Kc and vegetation indices are sensitive to both leaf area index and fractional ground cover. Thermal-based energy balance models are more suitable than the FAO-Kc model for estimating crop ET, especially under moisture stress conditions, but they require many inputs and detailed theoretical background knowledge; so they can be only used in regions where high quality, hourly agricultural weather data are readily available providing instantaneous values of heat fluxes corresponding to the time of the satellite overpass. Thus, FAO-Kc approach is widely used in research activities and real-time irrigation scheduling for several water applications since it does not require temporal upscaling for obtaining daily values and satellite imagery in the reflective bands used for vegetation index computation are more readily available at higher spatial resolution than thermal band data. There is no simple way to compute crop coefficients because they depend on climate, soil type, crop and its varieties, irrigation method, soil water, nutrient content and plant phenology. Consequently, specific calibrations of crop coefficient are required in various climatic regions. Many authors suggested a linear relationship between Kc and vegetation indices, but non-linear relationships have been proposed too. However, according to the radiative transfer theory, the nature of such relationships depends on the crop architecture and the definition of the adopted vegetation index, but the linear assumption can be adopted as first. Such studies, mainly investigated the possibility to use high resolution satellite data, such as Quickbird, Ikonos, TM, which are not suitable for operational purposes since in spite of the high spatial sampling they have an inadequate revisiting time over a given area. To obtain adequate temporal sampling, some authors proposed the use of a virtual constellation made by all currently available high-resolution satellites (e.g., DEMETER project). However the joint use of data from different satellites requires a carefully inter-satellite cross-calibration and co-registration. In order to avoid such problems and to generate spatially distributed values of Kc capturing field-specific crop development, the employment of vegetation indices derived from medium resolution MODIS data having a higher temporal sampling has been investigated. The spatial and temporal correlation between NDVI (Normalized Difference Vegetation Index) and crop coefficients for different herbaceous and arboreal cultivations has been investigated to define their relationships. Through this approach site-specific crop coefficients were derived taking into account the effective ground coverage and status. The analysis has been applied on the 2005-2008 time series for the Basilicata region, Southern Italy.

On estimating the economic value of insectivorous bats: Prospects and priorities for biologists

USGS Publications Warehouse

Boyles, Justin G.; Sole, Catherine L.; Cryan, Paul M.; McCracken, Gary F.

2013-01-01

Bats are among the most economically important nondomesticated mammals in the world. They are well-known pollinators and seed dispersers, but crop pest suppression is probably the most valuable ecosystem service provided by bats. Scientific literature and popular media often include reports of crop pests in the diet of bats and anecdotal or extrapolated estimates of how many insects are eaten by bats. However, quantitative estimates of the ecosystem services provided by bats in agricultural systems are rare, and the few estimates that are available are limited to a single cotton-dominated system in Texas. Despite the tremendous value for conservation and economic security of such information, surprisingly few scientific efforts have been dedicated to quantifying the economic value of bats. Here, we outline the types of information needed to better quantify the value of bats in agricultural ecosystems. Because of the complexity of the ecosystems involved, creative experimental design and innovative new methods will help advance our knowledge in this area. Experiments involving bats in agricultural systems may be needed sooner than later, before population declines associated with white-nose syndrome and wind turbines potentially render them impossible.

Operational Retrievals of Evapotranspiration: Are we there yet?

NASA Astrophysics Data System (ADS)

Neale, C. M. U.; Anderson, M. C.; Hain, C.; Schull, M.; Isidro, C., Sr.; Goncalves, I. Z.

2017-12-01

Remote sensing based retrievals of evapotranspiration (ET) have progressed significantly over the last two decades with the improvement of methods and algorithms and the availability of multiple satellite sensors with shortwave and thermal infrared bands on polar orbiting platforms. The modeling approaches include simpler vegetation index (VI) based methods such as the reflectance-based crop coefficient approach coupled with surface reference evapotranspiration estimates to derive actual evapotranspiration of crops or, direct inputs to the Penman-Monteith equation through VI relationships with certain input variables. Methods that are more complex include one-layer or two-layer energy balance approaches that make use of both shortwave and longwave spectral band information to estimate different inputs to the energy balance equation. These models mostly differ in the estimation of sensible heat fluxes. For continental and global scale applications, other satellite-based products such as solar radiation, vegetation leaf area and cover are used as inputs, along with gridded re-analysis weather information. This presentation will review the state-of-the-art in satellite-based evapotranspiration estimation, giving examples of existing efforts to obtain operational ET retrievals over continental and global scales and discussing difficulties and challenges.

Quantifying the Value of Satellite Imagery in Agriculture and other Sectors

NASA Astrophysics Data System (ADS)

Brown, M. E.; Abbott, P. C.; Escobar, V. M.

2013-12-01

This study focused on quantifying the commercial value of satellite remote sensing for agriculture. Commercial value from satellite imagery arises when improved information leads to better economic decisions. We identified five areas of application of remote sensing to agriculture where there is this potential: crop management (precision agriculture), insurance, real estate assessment, crop forecasting, and environmental monitoring. These applications can be divided between public information (crop forecasting) and those that may generate private commercial value (crop management), with both public and private information dimensions in some categories. Public information applications of remote sensing have been more successful in the past, and are likely to generate more economic value in the future. It was found that several issues have limited realization of the potential to generate private value from remote sensing in agriculture. The scale of use is small to the high cost of acquiring and interpreting large images has limited the cost effectiveness to individual farmers. Insurance, environmental monitoring, and crop management services by cooperatives or consultants may be cases overcoming this limitation. The greatest opportunities for potential commercial value from agriculture are probably in the crop forecasting area, especially where agricultural statistics services are not as well developed, since public market information benefits a broad range of economic actors, not limited to countries where forecasts are made. We estimate here the value from components of USDA's World Agricultural Supply and Demand Estimates (WASDE) forecasts for corn, indicating potential value increasing in the range of 60 to 240 million if improved satellite based information enhances those forecasts. The research was conducted by agricultural economists at Purdue University, and will be the basis for further evaluation of the use of satellite data within the NASA Carbon Monitoring System (CMS). A general evaluation framework to determine the usefulness of the CMS products to various users and to the broader community interested in managing carbon is shown in Figure 2. The first step in conducting such an analysis is to develop an understanding of the history, institutions, behaviors and other factors setting the context of an application which CMS data products inform. Decision makers are identified (who may become early adopters), and the alternative decisions they might take are elaborated. Economic models informed by biophysical models would then predict the outcome of the engagement. The new information must then be linked to a revised decision, and that decision in turn must lead to better economic or social outcomes on average. The value of the information is estimated as the predicted increase in economic surplus (profit, cost, consumer welfare) or social outcome that is a direct result of that revised decision. Alternative Monte Carlo simulations would estimate averages of key outcomes under alternative circumstances, such as differing regulations or better data, hence capturing consequences of the changes induced. These approaches will be described in the context of NASA and satellite data.

Climate change effects on agriculture: Economic responses to biophysical shocks

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

Nelson, Gerald; Valin, Hugo; Sands, Ronald

Agricultural production is sensitive to weather and will thus be directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments inmore » yields, area, consumption, and international trade. We apply biophysical shocks derived from the IPCC’s Representative Concentration Pathway that result in end-of-century radiative forcing of 8.5 watts per square meter. The mean biophysical impact on crop yield with no incremental CO2 fertilization is a 17 percent reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11 percent, increase area of major crops by 12 percent, and reduce consumption by 2 percent. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences includes model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.« less

Inventory and mapping of flood inundation using interactive digital image analysis techniques

USGS Publications Warehouse

Rohde, Wayne G.; Nelson, Charles A.; Taranik, J.V.

1979-01-01

LANDSAT digital data and color infra-red photographs were used in a multiphase sampling scheme to estimate the area of agricultural land affected by a flood. The LANDSAT data were classified with a maximum likelihood algorithm. Stratification of the LANDSAT data, prior to classification, greatly reduced misclassification errors. The classification results were used to prepare a map overlay showing the areal extent of flooding. These data also provided statistics required to estimate sample size in a two phase sampling scheme, and provided quick, accurate estimates of areas flooded for the first phase. The measurements made in the second phase, based on ground data and photo-interpretation, were used with two phase sampling statistics to estimate the area of agricultural land affected by flooding These results show that LANDSAT digital data can be used to prepare map overlays showing the extent of flooding on agricultural land and, with two phase sampling procedures, can provide acreage estimates with sampling errors of about 5 percent. This procedure provides a technique for rapidly assessing the areal extent of flood conditions on agricultural land and would provide a basis for designing a sampling framework to estimate the impact of flooding on crop production.

Evapotranspiration and Dual Crop Coefficients Sonisa Sharma1, Ayse Irmak12, Anne Parkhurst3, Elizabeth walter-Shea1 and Kenneth G. Hubbard1 1School of Natural Resources, 2Civil Engineering, 3Departments of Statistics, University of Nebraska-Lincoln

NASA Astrophysics Data System (ADS)

Sharma, S.

2012-12-01

Accurate estimation of water content in the crop root zone is most important for water conservation and management practices like irrigation. The objective of this study is to use the FA0-56 dual crop cefficients: basal crop coefficient Kcb and the soil evaporation coefficient Ke for a large corn/soybean field in the year 2005 at the Mead Turf Farm in the state of Nebraska, USA..Dual crop coefficients can be used to estimate both transpiration from crops and evaporation from soil. The Kcb has a low value of 0.15(K cb, in) during the initial period, increases rapidly to a maximum of 1.14 (K cb, mid) for the entire midseason and decreases rapidly to 0.5 at the end of the corn growing season (K cb,end). When examined together with precipitation, the dual crop coefficient was higher following rainfall or irrigation, as expected. The data suggests that the dual crop coefficient approach is a good estimation of water loss from well-watered crops. Irrigation can be scheduled to replace the loss of water from the crop/soil system. Similarly, when we compared the measured daily ET and the ET calculated from dual crop coefficients, it gives 98 % R2.; Comparision of calculated ET from dual crop coefficient appraoch with Weather Station ET

Quantifying the link between crop production and mined groundwater irrigation in China.

PubMed

Grogan, Danielle S; Zhang, Fan; Prusevich, Alexander; Lammers, Richard B; Wisser, Dominik; Glidden, Stanley; Li, Changsheng; Frolking, Steve

2015-04-01

In response to increasing demand for food, Chinese agriculture has both expanded and intensified over the past several decades. Irrigation has played a key role in increasing crop production, and groundwater is now an important source of irrigation water. Groundwater abstraction in excess of recharge (which we use here to estimate groundwater mining) has resulted in declining groundwater levels and could eventually restrict groundwater availability. In this study we used a hydrological model, WBMplus, in conjunction with a process based crop growth model, DNDC, to evaluate Chinese agriculture's recent dependence upon mined groundwater, and to quantify mined groundwater-dependent crop production across a domain that includes variation in climate, crop choice, and management practices. This methodology allowed for the direct attribution of crop production to irrigation water from rivers and reservoirs, shallow (renewable) groundwater, and mined groundwater. Simulating 20 years of weather variability and circa year 2000 crop areas, we found that mined groundwater fulfilled 20%-49% of gross irrigation water demand, assuming all demand was met. Mined groundwater accounted for 15%-27% of national total crop production. There was high spatial variability across China in irrigation water demand and crop production derived from mined groundwater. We find that climate variability and mined groundwater demand do not operate independently; rather, years in which irrigation water demand is high due to the relatively hot and dry climate also experience limited surface water supplies and therefore have less surface water with which to meet that high irrigation water demand. Copyright © 2014 Elsevier B.V. All rights reserved.

Role of fish distribution on estimates of standing crop in a cooling reservoir

USGS Publications Warehouse

Barwick, D. Hugh

1984-01-01

Estimates of fish standing crop from coves in Keowee Reservoir, South Carolina, were obtained in May and August for 3 consecutive years. Estimates were significantly higher in May than in August for most of the major species of fish collected, suggesting that considerable numbers of fish had migrated from the coves by August. This change in fish distribution may have resulted from the operation of a 2,580-megawatt nuclear power plant which altered reservoir stratification. Because fish distribution is sensitive to conditions of reservoir stratification, and because power plants often alter reservoir stratification, annual cove sampling in August may not be sufficient to produce comparable estimates of fish standing crop on which to assess the impact of power plant operations on fish populations. Comparable estimates of fish standing crop can probably be obtained from cooling reservoirs by collecting annual samples at similar water temperatures and concentrations of dissolved oxygen.

Investigations using data from LANDSAT-2. [earth resources program maps forecasting

NASA Technical Reports Server (NTRS)

Hossain, A. (Principal Investigator)

1976-01-01

The author has identified the following significant results. LANDSAT imageries have given positive indication of new land formation in the Bay of Bengal. A map of the bay region showing depth of new formations south of Patherghata test site was prepared. Winter crop estimation of the Sylhet-Mymensingh districts was made. This estimate shows an agreement of about 93% with 1973 data of the Agriculture Department. A preliminary land use map of the Sylhet-Mymensingh area using LANDSAT imageries in conjunction with aerial photographs and ground survey was also prepared.

Flood damage modeling based on expert knowledge: Insights from French damage model for agricultural sector

NASA Astrophysics Data System (ADS)

Grelot, Frédéric; Agenais, Anne-Laurence; Brémond, Pauline

2015-04-01

In France, since 2011, it is mandatory for local communities to conduct cost-benefit analysis (CBA) of their flood management projects, to make them eligible for financial support from the State. Meanwhile, as a support, the French Ministry in charge of Environment proposed a methodology to fulfill CBA. Like for many other countries, this methodology is based on the estimation of flood damage. However, existing models to estimate flood damage were judged not convenient for a national-wide use. As a consequence, the French Ministry in charge of Environment launched studies to develop damage models for different sectors, such as: residential sector, public infrastructures, agricultural sector, and commercial and industrial sector. In this presentation, we aim at presenting and discussing methodological choices of those damage models. They all share the same principle: no sufficient data from past events were available to build damage models on a statistical analysis, so modeling was based on expert knowledge. We will focus on the model built for agricultural activities and more precisely for agricultural lands. This model was based on feedback from 30 agricultural experts who experienced floods in their geographical areas. They were selected to have a representative experience of crops and flood conditions in France. The model is composed of: (i) damaging functions, which reveal physiological vulnerability of crops, (ii) action functions, which correspond to farmers' decision rules for carrying on crops after a flood, and (iii) economic agricultural data, which correspond to featured characteristics of crops in the geographical area where the flood management project studied takes place. The two first components are generic and the third one is specific to the area studied. It is, thus, possible to produce flood damage functions adapted to different agronomic and geographical contexts. In the end, the model was applied to obtain a pool of damage functions giving damage in euros by hectare for 14 agricultural lands categories. As a conclusion, we will discuss the validation step of the model. Although the model was validated by experts, we analyse how it could gain insight from comparison with past events.

Flood damage modeling based on expert knowledge: Insights from French damage model for agricultural sector

NASA Astrophysics Data System (ADS)

Grelot, Frédéric; Agenais, Anne-Laurence; Brémond, Pauline

2014-05-01

In France, since 2011, it is mandatory for local communities to conduct cost-benefit analysis (CBA) of their flood management projects, to make them eligible for financial support from the State. Meanwhile, as a support, the French Ministry in charge of Environment proposed a methodology to fulfill CBA. Like for many other countries, this methodology is based on the estimation of flood damage. Howerver, existing models to estimate flood damage were judged not convenient for a national-wide use. As a consequence, the French Ministry in charge of Environment launched studies to develop damage models for different sectors, such as: residential sector, public infrastructures, agricultural sector, and commercial and industrial sector. In this presentation, we aim at presenting and discussing methodological choices of those damage models. They all share the same principle: no sufficient data from past events were available to build damage models on a statistical analysis, so modeling was based on expert knowledge. We will focus on the model built for agricultural activities and more precisely for agricultural lands. This model was based on feedback from 30 agricultural experts who experienced floods in their geographical areas. They were selected to have a representative experience of crops and flood conditions in France. The model is composed of: (i) damaging functions, which reveal physiological vulnerability of crops, (ii) action functions, which correspond to farmers' decision rules for carrying on crops after a flood, and (iii) economic agricultural data, which correspond to featured characteristics of crops in the geographical area where the flood management project studied takes place. The two first components are generic and the third one is specific to the area studied. It is, thus, possible to produce flood damage functions adapted to different agronomic and geographical contexts. In the end, the model was applied to obtain a pool of damage functions giving damage in euros by hectare for 14 agricultural lands categories. As a conclusion, we will discuss the validation step of the model. Although the model was validated by experts, we analyse how it could gain insight from comparison with past events.

Irrigation management strategies to improve Water Use Efficiency of potatoes crop in Central Tunisia

NASA Astrophysics Data System (ADS)

Ghazouani, Hiba; Provenzano, Giuseppe; Rallo, Giovanni; Mguidiche, Amel; Douh, Boutheina; Boujelben, Abdelhamid

2015-04-01

In Tunisia, the expansion of irrigated area and the semiarid climate make it compulsory to adopt strategies of water management to increase water use efficiency. Subsurface drip irrigation (SDI), providing the application of high frequency small irrigation volumes below the soil surface have been increasingly used to enhance irrigation efficiency. At the same time, deficit irrigation (DI) has shown successful results with a large number of crop in various countries. However, for some crops like potatoes, DI is difficult to manage due to the rapid effect of water stress on tuber yield. Irrigation frequency is a key factor to schedule subsurface drip irrigation because, even maintaining the total seasonal volume, soil wetting patterns can result different during the growth period, with consequence on crop yield. Despite the need to enhance water use efficiency, only a few studies related to deficit irrigation of horticultural crops have been made in Tunisia. Objective of the paper was to assess the effects of different on-farm irrigation strategies on water use efficiency of potatoes crop irrigated with subsurface drip irrigation in a semiarid area of central Tunisia. After validation, Hydrus-2D model was used to simulate soil water status in the root zone, to evaluate actual crop evapotranspiration and then to estimate indirectly water use efficiency (IWUE), defined as the ratio between crop yield and total amount of water supplied with irrigation. Field experiments, were carried out in Central Tunisia (10° 33' 47.0" E, 35° 58' 8.1° N, 19 m a.s.l) on a potatoes crop planted in a sandy loam soil, during the growing season 2014, from January 15 (plantation of tubers) to May 6 (harvesting). Soil water status was monitored in two plots (T1 and T2) maintained under the same management, but different irrigation volumes, provided by a SDI system. In particular, irrigation was scheduled according to the average water content measured in the root zone, with a total of 8 watering, with timing ranging between one and three hours in T1, and between about half-an-hour and one-hour and a-half, in T2. The validity of Hydrus-2D model was initially assessed based on the comparison between measured and estimated soil water content at different distances from the emitter (RMSE values were not higher than 0.036). Then, model simulations allowed to verify that it is possible to enhance irrigation water use efficiency by increasing the frequency of irrigation even maintaining limited water deficit conditions during the full development stage subsequent the crop tuberization. Experimental results, joined to model simulations can therefore provide useful guidelines for a more sustainable use of irrigation water in countries characterised by semi-arid environments and limited availability of water resources.

Disaggregating and mapping crop statistics using hypertemporal remote sensing

NASA Astrophysics Data System (ADS)

Khan, M. R.; de Bie, C. A. J. M.; van Keulen, H.; Smaling, E. M. A.; Real, R.

2010-02-01

Governments compile their agricultural statistics in tabular form by administrative area, which gives no clue to the exact locations where specific crops are actually grown. Such data are poorly suited for early warning and assessment of crop production. 10-Daily satellite image time series of Andalucia, Spain, acquired since 1998 by the SPOT Vegetation Instrument in combination with reported crop area statistics were used to produce the required crop maps. Firstly, the 10-daily (1998-2006) 1-km resolution SPOT-Vegetation NDVI-images were used to stratify the study area in 45 map units through an iterative unsupervised classification process. Each unit represents an NDVI-profile showing changes in vegetation greenness over time which is assumed to relate to the types of land cover and land use present. Secondly, the areas of NDVI-units and the reported cropped areas by municipality were used to disaggregate the crop statistics. Adjusted R-squares were 98.8% for rainfed wheat, 97.5% for rainfed sunflower, and 76.5% for barley. Relating statistical data on areas cropped by municipality with the NDVI-based unit map showed that the selected crops were significantly related to specific NDVI-based map units. Other NDVI-profiles did not relate to the studied crops and represented other types of land use or land cover. The results were validated by using primary field data. These data were collected by the Spanish government from 2001 to 2005 through grid sampling within agricultural areas; each grid (block) contains three 700 m × 700 m segments. The validation showed 68%, 31% and 23% variability explained (adjusted R-squares) between the three produced maps and the thousands of segment data. Mainly variability within the delineated NDVI-units caused relatively low values; the units are internally heterogeneous. Variability between units is properly captured. The maps must accordingly be considered "small scale maps". These maps can be used to monitor crop performance of specific cropped areas because of using hypertemporal images. Early warning thus becomes more location and crop specific because of using hypertemporal remote sensing.

Landscape Level Carbon and Water Balances and Agricultural Production in Mountainous Terrain of the Haean Basin, South Korea

NASA Astrophysics Data System (ADS)

Lee, B.; Geyer, R.; Seo, B.; Lindner, S.; Walther, G.; Tenhunen, J. D.

2009-12-01

The process-based spatial simulation model PIXGRO was used to estimate gross primary production, ecosystem respiration, net ecosystem CO2 exchange and water use by forest and crop fields of Haean Basin, South Korea at landscape scale. Simulations are run for individual years from early spring to late fall, providing estimates for dry land crops and rice paddies with respect to carbon gain, biomass and leaf area development, allocation of photoproducts to the belowground ecosystem compartment, and harvest yields. In the case of deciduous oak forests, gas exchange is estimated, but spatial simulation of growth over the single annual cycles is not included. Spatial parameterization of the model is derived for forest LAI based on remote sensing, for forest and cropland fluxes via eddy covariance and chamber studies, for soil characteristics by generalization from spatial surveys, for climate drivers by generalizing observations at ca. 20 monitoring stations distributed throughout the basin and along the elevation gradient from 500 to 1000 m, and for incident radiation via modelling of the radiation components in complex terrain. Validation of the model is being carried out at point scale based on comparison of model output at selected locations with observations as well as with known trends in ecosystem response documented in the literature. The resulting modelling tool is useful for estimation of ecosystem services at landscape scale, first expressed as kg ha-1 crop yield, but via future cooperative studies also in terms of monetary gain to individual farms and farming cooperatives applying particular management strategies.

An evaluation of MODIS 250-m data for green LAI estimation in crops

NASA Astrophysics Data System (ADS)

Gitelson, Anatoly A.; Wardlow, Brian D.; Keydan, Galina P.; Leavitt, Bryan

2007-10-01

Green leaf area index (LAI) is an important variable for climate modeling, estimates of primary production, agricultural yield forecasting, and many other diverse applications. Remotely sensed data provide considerable potential for estimating LAI at local, regional, and global scales. The goal of this study was to retrieve green LAI from MODIS 250-m vegetation index (VI) data for irrigated and rainfed maize and soybeans. The performance of both MODIS-derived NDVI and Wide Dynamic Range Vegetation Index (WDRVI) were evaluated across three growing seasons (2002 through 2004) over a wide range of LAI and also compared to the performance of NDVI and WDRVI derived from reflectance data collected at close-range across the same field locations. The NDVI vs. LAI relationship showed asymptotic behavior with a sharp decrease in the sensitivity of the NDVI to LAI exceeding 2 m2/m2 for both crops. WDRVI vs. LAI relation was linear across the entire range of LAI variation with determination coefficients above 0.93. Importantly, the coefficients of the close-range WDRVI vs. LAI equation and the MODIS-retrieved WDRVI vs. LAI equation were very close. The WDRVI was found to be capable of accurately estimating LAI across a much greater LAI range than the NDVI and can be used for assessing even slight variations in LAI, which are indicative of the early stages of plant stress. These results demonstrate the new possibilities for analyzing the spatio-temporal variation of the LAI of crops using multi-temporal MODIS 250-m imagery.

A scheme for the uniform mapping and monitoring of earth resources and environmental complexes: An assessment of natural vegetation, environmental, and crop analogs. [Sierra-Lahontan and Colorado Plateaus, Northern Great Valley (CA), and Louisiana Coastal Plain

NASA Technical Reports Server (NTRS)

Poulton, C. E.; Welch, R. I. (Principal Investigator)

1975-01-01

The author has identified the following significant results. A study was performed to develop and test a procedure for the uniform mapping and monitoring of natural ecosystems in the semi-arid and wood regions of the Sierra-Lahontan and Colorado Plateau areas, and for the estimating of rice crop production in the Northern Great Valley (Ca.) and the Louisiana Coastal Plain. ERTS-1 and high flight and low flight aerial photos were used in a visual photointerpretation scheme to identify vegetation complexes, map acreages, and evaluate crop vigor and stress. Results indicated that the vegetation analog concept is valid; that depending on the kind of vegetation and its density, analogs are interpretable at different levels in the hierarchical classification from second to the fourth level. The second level uses physiognomic growth form-structural criteria, and the fourth level uses floristic or taxonomic criteria, usually at generic level. It is recommended that analog comparisons should be made in relatively small test areas where large homogeneous examples can be found of each analog.

Human health risk assessment and dietary intake of organochlorine pesticides through air, soil and food crops (wheat and rice) along two tributaries of river Chenab, Pakistan.

PubMed

Mahmood, Adeel; Malik, Riffat Naseem; Li, Jun; Zhang, Gan

2014-09-01

To assess the organochlorine pesticides (OCPs) contamination and their probable hazardous effects on human health; cereal crops (wheat and rice; n=28) agricultural soil (n=28) and air (n=6) samples were collected from Gujranwala division, Punjab Province, Pakistan. ∑OCPs concentration ranged between 123 and 635 pg m(-3), 31 and 365 ng g(-1) (dw), 2.72 and 36.6 ng g(-1) (dw), 0.55 and 15.2 ng g(-1) (dw) for air, soil, rice and wheat samples, respectively. DDTs were the predominant over other OCPSs detected from screened samples while the source apportionment analysis suggested the new inputs of DDTs in the study area. EDI (estimated daily intake) of ∑OCPs through rice and wheat was found 39 and 40 ng kg(-1) day(-1), respectively. Hazard ratios (HRs) on the basis 95th percentile concentrations were exceeding the integrity for most of the investigated OCP in rice and wheat. The results revealed that there is a severe risk to the human population of the study area through consumption of contaminated cereal crops. Copyright © 2014 Elsevier Ltd. All rights reserved.

Development of dynamic wheat crop model in ISAM and estimation of impacts of environmental factors on wheat production in India

NASA Astrophysics Data System (ADS)

Gahlot, S.; Lin, T. S.; Jain, A. K.; Baidya Roy, S.; Sehgal, V. K.; Dhakar, R.

2017-12-01

With changing environmental conditions, such as climate and elevated atmospheric CO2 concentrations, questions about food security can be answered by modeling crops based on our understanding of the dynamic crop growth processes and interactions between the crops and their environment in the form of carbon, water and energy fluxes. These interactions and their effect on cropland ecosystems are non-linear because of the feedback mechanisms. Hence, process-based modelling approach can be used to conduct numerical experiments to derive insights into these processes and interactive feedbacks. In this study we have implemented dynamic crop growth processes for wheat into a data-modeling framework, Integrated Science Assessment Model (ISAM), to estimate the impacts of different factors like CO2 fertilization, irrigation, nitrogen limitation and climate change on wheat in India. In specific, we have implemented wheat-specific phenology, C3 photosynthesis mechanism and phenology-specific carbon allocation schemes for assimilated carbon to leaf, stem, root and grain pools. Crop growth limiting stress factors like nutrients, temperature and light have been included. The impact of high temperatures on leaf senescence, anthesis and grain filling has been modeled and found to be causing significant reduction in yield in the recent years. Field data from an experimental wheat site located at the Indian Agricultural Research Institute (IARI), New Delhi, India has been collected for aboveground biomass and leaf area index (LAI) for two growing seasons 2014-15 and 2015-16. This data has been used to study the phenology, growing season length, thermal requirements and growth stages of wheat. Using the field data, the dynamic model for wheat has been evaluated for the site level seasonal variability in leaf area index (LAI) and aboveground biomass. The variations in carbon, water and energy fluxes, plant height and rooting depth have been analyzed on the site level. Model experiments have been performed to calculate the yield for wheat for India for the historical years. In order to identify wheat production regions in India that are prone to one or multiple stresses in years to come, model experiments have been performed based on future climate scenarios RCP 4.5 and 8.5.

Accuracy assessment: The statistical approach to performance evaluation in LACIE. [Great Plains corridor, United States

NASA Technical Reports Server (NTRS)

Houston, A. G.; Feiveson, A. H.; Chhikara, R. S.; Hsu, E. M. (Principal Investigator)

1979-01-01

A statistical methodology was developed to check the accuracy of the products of the experimental operations throughout crop growth and to determine whether the procedures are adequate to accomplish the desired accuracy and reliability goals. It has allowed the identification and isolation of key problems in wheat area yield estimation, some of which have been corrected and some of which remain to be resolved. The major unresolved problem in accuracy assessment is that of precisely estimating the bias of the LACIE production estimator. Topics covered include: (1) evaluation techniques; (2) variance and bias estimation for the wheat production estimate; (3) the 90/90 evaluation; (4) comparison of the LACIE estimate with reference standards; and (5) first and second order error source investigations.

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

Zhang, Xuesong; Izaurralde, Roberto C.; Manowitz, David H.

Accurate quantification and clear understanding of regional scale cropland carbon (C) cycling is critical for designing effective policies and management practices that can contribute toward stabilizing atmospheric CO2 concentrations. However, extrapolating site-scale observations to regional scales represents a major challenge confronting the agricultural modeling community. This study introduces a novel geospatial agricultural modeling system (GAMS) exploring the integration of the mechanistic Environmental Policy Integrated Climate model, spatially-resolved data, surveyed management data, and supercomputing functions for cropland C budgets estimates. This modeling system creates spatially-explicit modeling units at a spatial resolution consistent with remotely-sensed crop identification and assigns cropping systems tomore » each of them by geo-referencing surveyed crop management information at the county or state level. A parallel computing algorithm was also developed to facilitate the computationally intensive model runs and output post-processing and visualization. We evaluated GAMS against National Agricultural Statistics Service (NASS) reported crop yields and inventory estimated county-scale cropland C budgets averaged over 2000–2008. We observed good overall agreement, with spatial correlation of 0.89, 0.90, 0.41, and 0.87, for crop yields, Net Primary Production (NPP), Soil Organic C (SOC) change, and Net Ecosystem Exchange (NEE), respectively. However, we also detected notable differences in the magnitude of NPP and NEE, as well as in the spatial pattern of SOC change. By performing crop-specific annual comparisons, we discuss possible explanations for the discrepancies between GAMS and the inventory method, such as data requirements, representation of agroecosystem processes, completeness and accuracy of crop management data, and accuracy of crop area representation. Based on these analyses, we further discuss strategies to improve GAMS by updating input data and by designing more efficient parallel computing capability to quantitatively assess errors associated with the simulation of C budget components. The modularized design of the GAMS makes it flexible to be updated and adapted for different agricultural models so long as they require similar input data, and to be linked with socio-economic models to understand the effectiveness and implications of diverse C management practices and policies.« less

Cereal area and nitrogen use efficiency are drivers of future nitrogen fertilizer consumption.

PubMed

Dobermann, Achim; Cassman, Kenneth G

2005-09-01

At a global scale, cereal yields and fertilizer N consumption have increased in a near-linear fashion during the past 40 years and are highly correlated with one another. However, large differences exist in historical trends of N fertilizer usage and nitrogen use efficiency (NUE) among regions, countries, and crops. The reasons for these differences must be understood to estimate future N fertilizer requirements. Global nitrogen needs will depend on: (i) changes in cropped cereal area and the associated yield increases required to meet increasing cereal demand from population and income growth, and (ii) changes in NUE at the farm level. Our analysis indicates that the anticipated 38% increase in global cereal demand by 2025 can be met by a 30% increase in N use on cereals, provided that the steady decline in cereal harvest area is halted and the yield response to applied N can be increased by 20%. If losses of cereal cropping area continue at the rate of the past 20 years (-0.33% per year) and NUE cannot be increased substantially, a 60% increase in global N use on cereals would be required to meet cereal demand. Interventions to increase NUE and reduce N losses to the environment must be accomplished at the farm-or field-scale through a combination of improved technologies and carefully crafted local policies that contribute to the adoption of improved N management; uniform regional or national directives are unlikely to be effective at both sustaining yield increases and improving NUE. Examples from several countries show that increases in NUE at rates of 1% per year or more can be achieved if adequate investments are made in research and extension. Failure to arrest the decrease in cereal crop area and to improve NUE in the world's most important agricultural systems will likely cause severe damage to environmental services at local, regional, and global scales due to a large increase in reactive N load in the environment.

Cereal area and nitrogen use efficiency are drivers of future nitrogen fertilizer consumption.

PubMed

Dobermann, Achim; Cassman, Kenneth G

2005-12-01

At a global scale, cereal yields and fertilizer N consumption have increased in a near-linear fashion during the past 40 years and are highly correlated with one another. However, large differences exist in historical trends of N fertilizer usage and nitrogen use efficiency (NUE) among regions, countries, and crops. The reasons for these differences must be understood to estimate future N fertilizer requirements. Global nitrogen needs will depend on: (i) changes in cropped cereal area and the associated yield increases required to meet increasing cereal demand from population and income growth, and (ii) changes in NUE at the farm level. Our analysis indicates that the anticipated 38% increase in global cereal demand by 2025 can be met by a 30% increase in N use on cereals, provided that the steady decline in cereal harvest area is halted and the yield response to applied N can be increased by 20%. If losses of cereal cropping area continue at the rate of the past 20 years (-0.33% per year) and NUE cannot be increased substantially, a 60% increase in global N use on cereals would be required to meet cereal demand. Interventions to increase NUE and reduce N losses to the environment must be accomplished at the farm- or field-scale through a combination of improved technologies and carefully crafted local policies that contribute to the adoption of improved N management; uniform regional or national directives are unlikey to be effective at both sustaining yield increases and improving NUE. Examples from several countries show that increases in NUE at rates of 1% per year or more can be achieved if adequate investments are made in research and extension. Failure to arrest the decrease in cereal crop area and to improve NUE in the world's most important agricultural systems will likely cause severe damage to environmental services at local, regional, and global scales due to a large increase in reactive N load in the environment.

Climate change and world food supply, demand, and trade

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

Fischer, G.; Frohberg, K.; Parry, M.L.

This chapter summarizes the findings of a major interdisciplinary research effort by scientists in 25 countries. The study examined the potential biophysical responses of major food crops to changing atmospheric composition and climate, and projected potential socio-economic consequences. In a first step, crop models were used to estimate how changing climatic conditions may alter yields of major crops at a number of sites representing both major production areas and vulnerable regions at low, mid, and high latitudes. Then socio-economic impacts were assessed for the period 1990 up to the Year 2060 with a dynamic recursive model of the world foodmore » system. The results of the assessment suggest that a doubling of the atmospheric CO{sub 2} concentration will have little effect on global food production levels. Globally, impacts on crop production will be small compared to the required production increases between now and the middle of the next century. But, under all simulated scenarios possible negative impacts were mostly observed in low latitudes thus tending to increase the disparity between developed and developing countries.« less

Proportion estimation and classification of mixed pixels in multispectral data

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

Crouse, K.R.

1979-01-01

Remote sensing applications to crop productivity estimations are discussed with detailed instructions for developing classifier skills in multispectral data analysis for corn, soybeans, oats, and alfalfa crops. (PCS)

A Novel Approach for Forecasting Crop Production and Yield Using Remotely Sensed Satellite Images

NASA Astrophysics Data System (ADS)

Singh, R. K.; Budde, M. E.; Senay, G. B.; Rowland, J.

2017-12-01

Forecasting crop production in advance of crop harvest plays a significant role in drought impact management, improved food security, stabilizing food grain market prices, and poverty reduction. This becomes essential, particularly in Sub-Saharan Africa, where agriculture is a critical source of livelihoods, but lacks good quality agricultural statistical data. With increasing availability of low cost satellite data, faster computing power, and development of modeling algorithms, remotely sensed images are becoming a common source for deriving information for agricultural, drought, and water management. Many researchers have shown that the Normalized Difference Vegetation Index (NDVI), based on red and near-infrared reflectance, can be effectively used for estimating crop production and yield. Similarly, crop production and yield have been closely related to evapotranspiration (ET) also as there are strong linkages between production/yield and transpiration based on plant physiology. Thus, we combined NDVI and ET information from remotely sensed images for estimating total production and crop yield prior to crop harvest for Niger and Burkina Faso in West Africa. We identified the optimum time (dekads 23-29) for cumulating NDVI and ET and developed a new algorithm for estimating crop production and yield. We used the crop data from 2003 to 2008 to calibrate our model and the data from 2009 to 2013 for validation. Our results showed that total crop production can be estimated within 5% of actual production (R2 = 0.98) about 30-45 days before end of the harvest season. This novel approach can be operationalized to provide a valuable tool to decision makers for better drought impact management in drought-prone regions of the world.

Estimating Canopy Dark Respiration for Crop Models

NASA Technical Reports Server (NTRS)

Monje Mejia, Oscar Alberto

2014-01-01

Crop production is obtained from accurate estimates of daily carbon gain.Canopy gross photosynthesis (Pgross) can be estimated from biochemical models of photosynthesis using sun and shaded leaf portions and the amount of intercepted photosyntheticallyactive radiation (PAR).In turn, canopy daily net carbon gain can be estimated from canopy daily gross photosynthesis when canopy dark respiration (Rd) is known.

Evaluation of a simple method for crop evapotranspiration partitioning and comparison of different water use efficiency approaches

NASA Astrophysics Data System (ADS)

Tallec, T.; Rivalland, V.; Jarosz, N.; Boulet, G.; Gentine, P.; Ceschia, E.

2012-04-01

In the current context of climate change, intra- and inter-annual variability of precipitation can lead to major modifications of water budgets and water use efficiencies (WUE). Obtaining greater insight into how climatic variability and agricultural practices affect water budgets and their components in croplands is, thus, important for adapting crop management and limiting water losses. The principal aims of this study were 1) to assess the contribution of different components to the agro-ecosystem water budget and 2) to analyze and compare the WUE calculated from ecophysiological (WUEplt), environmental (WUEeco) and agronomical (WUEagro) points of view for various crops during the growing season and for the annual time scale. Eddy covariance (EC) measurements of CO2 and water flux were performed on winter wheat, maize and sunflower crops at two sites in southwest France: Auradé and Lamasquère. To infer WUEplt, an estimation of plant transpiration (TR) is needed. We then tested a new method for partitioning evapotranspiration (ETR), measured by means of the EC method, into soil evaporation (E) and plant transpiration (TR) based on marginal distribution sampling (MDS). We compared these estimations with calibrated simulations of the ICARE-SVAT double source mechanistic model. The two partitioning methods showed good agreement, demonstrating that MDS is a convenient, simple and robust tool for estimating E with reasonable associated uncertainties. During the growing season, the proportion of E in ETR was approximately one-third and varied mainly with crop leaf area. When calculated on an annual time scale, the proportion of E in ETR reached more than 50%, depending on crop leaf area and the duration and distribution of bare soil within the year. WUEplt values ranged between -4.1 and -5.6 g C kg-1 H2O for maize and winter wheat, respectively, and were strongly dependent on meteorological conditions at the half-hourly, daily and seasonal time scales. When normalized by the vapor pressure deficit to reduce the effect of seasonal climatic variability on WUEplt, maize had the highest efficiency. Absolute WUEeco values on the ecosystem level, including water loss through evaporation and carbon release through ecosystem respiration, were consequently lower than on the stand level. This observation was even more pronounced on an annual time scale than on the growing-season time scale because of bare soil periods. Winter wheat showed the highest absolute values of WUEeco, and sunflower showed the lowest. To account for carbon input into WUE through organic fertilization and output through biomass exportation during harvest, net biome production (NBP) was considered in the calculation of an ecosystem-level WUE (WUENBP). Considering WUENBP instead of WUEeco markedly decreased the efficiency of the ecosystem, especially for crops with important carbon exports, as observed for the maize used for silaging and pointed out the profits of organic C input. From an agronomic perspective, maize showed the best WUE, with exported (marketable) carbon per unit of water used exceeding that of other crops. Thus, the environmental and agronomical WUE approaches should be considered together in the context of global climate change and sustainable development.

Automatic corn-soybean classification using Landsat MSS data. I - Near-harvest crop proportion estimation. II - Early season crop proportion estimation

NASA Technical Reports Server (NTRS)

Badhwar, G. D.

1984-01-01

The techniques used initially for the identification of cultivated crops from Landsat imagery depended greatly on the iterpretation of film products by a human analyst. This approach was not very effective and objective. Since 1978, new methods for crop identification are being developed. Badhwar et al. (1982) showed that multitemporal-multispectral data could be reduced to a simple feature space of alpha and beta and that these features would separate corn and soybean very well. However, there are disadvantages related to the use of alpha and beta parameters. The present investigation is concerned with a suitable method for extracting the required features. Attention is given to a profile model for crop discrimination, corn-soybean separation using profile parameters, and an automatic labeling (target recognition) method. The developed technique is extended to obtain a procedure which makes it possible to estimate the crop proportion of corn and soybean from Landsat data early in the growing season.

A STELLA model to estimate water and nitrogen dynamics in a short-rotation woody crop plantation

Treesearch

Ying Ouyang; Jiaen Zhang; Theodor D. Leininger; Brent R. Frey

2015-01-01

Although short-rotation woody crop biomass production technology has demonstrated a promising potential to supply feedstocks for bioenergy production, the water and nutrient processes in the woody crop planation ecosystem are poorly understood. In this study, a computer model was developed to estimate the dynamics of water and nitrogen (N) species (e.g., NH4...

Labeling research in support of through-the-season area estimation

NASA Technical Reports Server (NTRS)

Colwell, R. N. (Principal Investigator); Hay, C. M.; Sheffner, E. J.

1982-01-01

The development of LANDSAT-based through-the-season labeling procedures for corn and soybeans is discussed. A model for predicting labeling accuracy within key time periods throughout the growing season is outlined. Two methods for establishing the starting point of one key time period, viz., early season, are described. In addition, spectral-temporal characteristics for separating crops in the early season time period are discussed.

Characterizing Brazilian fire and estimating areas burned by using the Airborne Infrared Disaster Assessment System

Treesearch

James A. Brass; Liane S. Guild; Philip J. Riggan; Vincent G. Ambrosia; Robert N. Lockwood; A. Pereira Joao

1996-01-01

Biomass burning is a common force in much of the developing tropical world and has wide-ranging environmental impacts. Fire is an important component in tropical deforestation and is often used to clear broad expanses of land for shifting agriculture, dispose of crop residue, and "clean" pastures for cattle grazing. It is part of a complex social phenomenon...

Carbon decomposition process of the residual biomass in the paddy soil of a single-crop rice field

NASA Astrophysics Data System (ADS)

Okada, K.; Iwata, T.

2014-12-01

In cultivated fields, residual organic matter is plowed into soil after harvest and decaying in fallow season. Greenhouse gases such as CO2 and CH4 is generated by the decomposition of the substantial organic matter and released into the atmosphere. In some fields, open burning is carried out by tradition, when carbon in residual matter is released into atmosphere as CO2. However, burning effect on carbon budget between crop lands and atmosphere is not entirely considered yet. In this study, coarse organic matter (COM) in paddy soil of a single-crop rice field was sampled on regular intervals between January, 2011 and August, 2014 The amount of carbon release from residual matter was estimated by analyzing of the variations in carbon content of COM. Effects of soil temperature (Ts) and soil water content (SWC) at the paddy field on the rate of carbon decomposition was investigated. Though decreasing rate of COM was much smaller in winter season, it is accelerated at the warming season between April and June every year. Decomposition was resisted for next rice cultivated season despite of highest soil temperature. In addition, the observational field was divided into two areas, and three time open burning experiments were conducted in November, 2011, 2012, and 2013. In each year, three sampling surveys, or plants before harvest and residuals before and after the burning experiment, were done. From these surveys, it is suggested that about 48±2% of carbon contents of above-ground plant was yield out as grain by harvest, and about 27±2% of carbon emitted as CO2 by burning. Carbon content of residuals plowed into soil after the harvest was estimated 293±1 and 220±36gC/m2 in no-burned and burned area, respectively, based on three-years average. It is estimated that 70 and 60% of the first input amount of COM was decomposed after a year in no-burned and burned area, respectively.

Evolution of crop production under a pseudo-space environment using model plants, Lotus japonicus

NASA Astrophysics Data System (ADS)

Tomita-Yokotani, Kaori; Motohashi, Kyohei; Omi, Naomi; Sato, Seigo; Aoki, Toshio; Hashimoto, Hirofumi; Yamashita, Masamichi

Habitation in outer space is one of our challenges. We have been studying space agriculture and/or spacecraft agriculture to provide food and oxygen for the habitation area in the space environment. However, careful investigation should be made concerning the results of exotic environmental effects on the endogenous production of biologically active substances in indi-vidual cultivated plants in a space environment. We have already reported that the production of functional substances in cultivated plants as crops are affected by gravity. The amounts of the main physiological substances in these plants grown under terrestrial control were different from that grown in a pseudo-microgravity. These results suggested that the nutrition would be changed in the plants/crops grown in the space environment when human beings eat in space. This estimation required us to investigate each of the useful components produced by each plant grown in the space environment. These estimations involved several study fields, includ-ing nutrition, plant physiology, etc. On the other hand, the analysis of model plant genomes has recently been remarkably advanced. Lotus japonicus, a leguminous plant, is also one of the model plant. The leguminosae is a large family in the plant vegetable kingdom and almost the entire genome sequence of Lotus japonicus has been determined. Nitrogen fixation would be possible even in a space environment. We are trying to determine the best conditions and evolution for crop production using the model plants.

Global and Time-Resolved Monitoring of Crop Photosynthesis with Chlorophyll Fluorescence

NASA Technical Reports Server (NTRS)

Guanter, Luis; Zhang, Yongguang; Jung, Martin; Joiner, Joanna; Voigt, Maximilian; Berry, Joseph A.; Frankenberg, Christian; Huete, Alfredo R.; Zarco-Tejada, Pablo; Lee, Jung-Eun;

Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence

PubMed Central

Guanter, Luis; Zhang, Yongguang; Jung, Martin; Joiner, Joanna; Voigt, Maximilian; Berry, Joseph A.; Frankenberg, Christian; Huete, Alfredo R.; Zarco-Tejada, Pablo; Lee, Jung-Eun; Moran, M. Susan; Ponce-Campos, Guillermo; Beer, Christian; Camps-Valls, Gustavo; Buchmann, Nina; Gianelle, Damiano; Klumpp, Katja; Cescatti, Alessandro; Baker, John M.; Griffis, Timothy J.

2014-01-01

Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50–75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle. PMID:24706867

Projected climate change threatens pollinators and crop production in Brazil

PubMed Central

Costa, Wilian França; Cordeiro, Guaraci Duran; Imperatriz-Fonseca, Vera Lucia; Saraiva, Antonio Mauro; Biesmeijer, Jacobus; Garibaldi, Lucas Alejandro

2017-01-01

Animal pollination can impact food security since many crops depend on pollinators to produce fruits and seeds. However, the effects of projected climate change on crop pollinators and therefore on crop production are still unclear, especially for wild pollinators and aggregate community responses. Using species distributional modeling, we assessed the effects of climate change on the geographic distribution of 95 pollinator species of 13 Brazilian crops, and we estimated their relative impacts on crop production. We described these effects at the municipality level, and we assessed the crops that were grown, the gross production volume of these crops, the total crop production value, and the number of inhabitants. Overall, considering all crop species, we found that the projected climate change will reduce the probability of pollinator occurrence by almost 0.13 by 2050. Our models predict that almost 90% of the municipalities analyzed will face species loss. Decreases in the pollinator occurrence probability varied from 0.08 (persimmon) to 0.25 (tomato) and will potentially affect 9% (mandarin) to 100% (sunflower) of the municipalities that produce each crop. Municipalities in central and southern Brazil will potentially face relatively large impacts on crop production due to pollinator loss. In contrast, some municipalities in northern Brazil, particularly in the northwestern Amazon, could potentially benefit from climate change because pollinators of some crops may increase. The decline in the probability of pollinator occurrence is found in a large number of municipalities with the lowest GDP and will also likely affect some places where crop production is high (20% to 90% of the GDP) and where the number of inhabitants is also high (more than 6 million people). Our study highlights key municipalities where crops are economically important and where pollinators will potentially face the worst conditions due to climate change. However, pollinators may be able to find new suitable areas that have the potential to improve crop production. The results shown here could guide policy decisions for adapting to climate change and for preventing the loss of pollinator species and crop production. PMID:28792956

Projected climate change threatens pollinators and crop production in Brazil.

PubMed

Giannini, Tereza Cristina; Costa, Wilian França; Cordeiro, Guaraci Duran; Imperatriz-Fonseca, Vera Lucia; Saraiva, Antonio Mauro; Biesmeijer, Jacobus; Garibaldi, Lucas Alejandro

2017-01-01

Animal pollination can impact food security since many crops depend on pollinators to produce fruits and seeds. However, the effects of projected climate change on crop pollinators and therefore on crop production are still unclear, especially for wild pollinators and aggregate community responses. Using species distributional modeling, we assessed the effects of climate change on the geographic distribution of 95 pollinator species of 13 Brazilian crops, and we estimated their relative impacts on crop production. We described these effects at the municipality level, and we assessed the crops that were grown, the gross production volume of these crops, the total crop production value, and the number of inhabitants. Overall, considering all crop species, we found that the projected climate change will reduce the probability of pollinator occurrence by almost 0.13 by 2050. Our models predict that almost 90% of the municipalities analyzed will face species loss. Decreases in the pollinator occurrence probability varied from 0.08 (persimmon) to 0.25 (tomato) and will potentially affect 9% (mandarin) to 100% (sunflower) of the municipalities that produce each crop. Municipalities in central and southern Brazil will potentially face relatively large impacts on crop production due to pollinator loss. In contrast, some municipalities in northern Brazil, particularly in the northwestern Amazon, could potentially benefit from climate change because pollinators of some crops may increase. The decline in the probability of pollinator occurrence is found in a large number of municipalities with the lowest GDP and will also likely affect some places where crop production is high (20% to 90% of the GDP) and where the number of inhabitants is also high (more than 6 million people). Our study highlights key municipalities where crops are economically important and where pollinators will potentially face the worst conditions due to climate change. However, pollinators may be able to find new suitable areas that have the potential to improve crop production. The results shown here could guide policy decisions for adapting to climate change and for preventing the loss of pollinator species and crop production.

Estimating nitrogen mineralization from cover crop mixtures using the Precision Nitrogen Management model

USDA-ARS?s Scientific Manuscript database

Cover crops influence soil nitrogen (N) mineralization-immobilization-turnover cycles (MIT), thus influencing N availability to a subsequent crop. Dynamic simulation models of the soil/crop system, if properly calibrated and tested, can simulate carbon (C) and N dynamics of a terminated cover crop a...

Stratum variance estimation for sample allocation in crop surveys. [Great Plains Corridor

NASA Technical Reports Server (NTRS)

Perry, C. R., Jr.; Chhikara, R. S. (Principal Investigator)

1980-01-01

The problem of determining stratum variances needed in achieving an optimum sample allocation for crop surveys by remote sensing is investigated by considering an approach based on the concept of stratum variance as a function of the sampling unit size. A methodology using the existing and easily available information of historical crop statistics is developed for obtaining initial estimates of tratum variances. The procedure is applied to estimate stratum variances for wheat in the U.S. Great Plains and is evaluated based on the numerical results thus obtained. It is shown that the proposed technique is viable and performs satisfactorily, with the use of a conservative value for the field size and the crop statistics from the small political subdivision level, when the estimated stratum variances were compared to those obtained using the LANDSAT data.

Report on 1959 forest tree seed crop in New England

Treesearch

Thomas W. McConkey

1960-01-01

The 1959 forest tree seed crop is generally light, according to observers. It is better than the 1957 crop, but only in few places is it comparable with last year's production. The better crops were reported in the northwestern Massachusetts-southwestern Vermont area and in the eastern Maine area.

Soil nutrients losses by wind erosion in a citrus crop at southeast Spain

NASA Astrophysics Data System (ADS)

Segovia, C.; Gómez, J. D.; Gallardo, P.; Lozano, F. J.; Asensio, C.

2017-06-01

The purpose of this study was to analyze the influence of wind erosion on the productivity of citric crops over gypsiric Fluvisols in Gador area (Almeria, SE Spain) by blowing air through a wind tunnel. Wind erosion varies considerably depending on time since the last tillage. This is because a physical crust forms after tilling which protects the soil from wind. Crust formation in the study area is strongly favored by dew, which causes them to form in around a week. The repeated measurements ANOVA, as a nonparametric alternative to the ANOVA, using the Geiiser method and the Friedman test shows significant differences ( P ≤ 0.05) in the fractions of very fine sand and coarse silt, which confirmed that very fine sand and coarse silt are the fractions most susceptible to loss from wind. The same statistical analysis for fertility showed smaller differences in organic carbon and K2O content, while N and P2O5 increased. Nutrients lost from wind imply an additional fertilization cost for a crop to be economically feasible. The cost of this restoration of nutrients lost from the soil because of wind erosion was based on experimental data taken in crusted soil and immediately after tilling. Losses in organic matter (O.M.), N, P2O5 and K2O were estimated based on the cost of fertilizers most commonly used in the area.