High-resolution, regional-scale crop yield simulations for the Southwestern United States

NASA Astrophysics Data System (ADS)

Stack, D. H.; Kafatos, M.; Medvigy, D.; El-Askary, H. M.; Hatzopoulos, N.; Kim, J.; Kim, S.; Prasad, A. K.; Tremback, C.; Walko, R. L.; Asrar, G. R.

2012-12-01

Over the past few decades, there have been many process-based crop models developed with the goal of better understanding the impacts of climate, soils, and management decisions on crop yields. These models simulate the growth and development of crops in response to environmental drivers. Traditionally, process-based crop models have been run at the individual farm level for yield optimization and management scenario testing. Few previous studies have used these models over broader geographic regions, largely due to the lack of gridded high-resolution meteorological and soil datasets required as inputs for these data intensive process-based models. In particular, assessment of regional-scale yield variability due to climate change requires high-resolution, regional-scale, climate projections, and such projections have been unavailable until recently. The goal of this study was to create a framework for extending the Agricultural Production Systems sIMulator (APSIM) crop model for use at regional scales and analyze spatial and temporal yield changes in the Southwestern United States (CA, AZ, and NV). Using the scripting language Python, an automated pipeline was developed to link Regional Climate Model (RCM) output with the APSIM crop model, thus creating a one-way nested modeling framework. This framework was used to combine climate, soil, land use, and agricultural management datasets in order to better understand the relationship between climate variability and crop yield at the regional-scale. Three different RCMs were used to drive APSIM: OLAM, RAMS, and WRF. Preliminary results suggest that, depending on the model inputs, there is some variability between simulated RCM driven maize yields and historical yields obtained from the United States Department of Agriculture (USDA). Furthermore, these simulations showed strong non-linear correlations between yield and meteorological drivers, with critical threshold values for some of the inputs (e.g. minimum and maximum temperature), beyond which the yields were negatively affected. These results are now being used for further regional-scale yield analysis as the aforementioned framework is adaptable to multiple geographic regions and crop types.

Yield prediction by analysis of multispectral scanner data

NASA Technical Reports Server (NTRS)

Colwell, J. E.; Suits, G. H.

1975-01-01

A preliminary model describing the growth and grain yield of wheat was developed. The modeled growth characteristics of the wheat crop were used to compute wheat canopy reflectance using a model of vegetation canopy reflectance. The modeled reflectance characteristics were compared with the corresponding growth characteristics and grain yield in order to infer their relationships. It appears that periodic wheat canopy reflectance characteristics potentially derivable from earth satellites will be useful in forecasting wheat grain yield.

Growth and yield in Eucalyptus globulus

Treesearch

James A. Rinehart; Richard B. Standiford

1983-01-01

A study of the major Eucalyptus globulus stands throughout California conducted by Woodbridge Metcalf in 1924 provides a complete and accurate data set for generating variable site-density yield models. Two models were developed using linear regression techniques. Model I depicts a linear relationship between age and yield best used for stands between five and fifteen...

LACIE: Wheat yield models for the USSR

NASA Technical Reports Server (NTRS)

Sakamoto, C. M.; Leduc, S. K.

1977-01-01

A quantitative model determining the relationship between weather conditions and wheat yield in the U.S.S.R. was studied to provide early reliable forecasts on the size of the U.S.S.R. wheat harvest. Separate models are developed for spring wheat and for winter. Differences in yield potential and responses to stress conditions and cultural improvements necessitate models for each class.

Crop weather models of barley and spring wheat yield for agrophysical units in North Dakota

NASA Technical Reports Server (NTRS)

Leduc, S. (Principal Investigator)

1982-01-01

Models based on multiple regression were developed to estimate barley yield and spring wheat yield from weather data for Agrophysical units(APU) in North Dakota. The predictor variables are derived from monthly average temperature and monthly total precipitation data at meteorological stations in the cooperative network. The models are similar in form to the previous models developed for Crop Reporting Districts (CRD). The trends and derived variables were the same and the approach to select the significant predictors was similar to that used in developing the CRD models. The APU models show sight improvements in some of the statistics of the models, e.g., explained variation. These models are to be independently evaluated and compared to the previously evaluated CRD models. The comparison will indicate the preferred model area for this application, i.e., APU or CRD.

Development of a Cadaveric Model for Arthrocentesis.

PubMed

MacIver, Melissa A; Johnson, Matthew

2015-01-01

This article reports the development of a novel cadaveric model for future use in teaching arthrocentesis. In the clinical setting, animal safety is essential and practice is thus limited. Objectives of the study were to develop and compare a model to an unmodified cadaver by injecting one of two types of fluids to increase yield. The two fluids injected, mineral oil (MO) and hypertonic saline (HS), were compared to determine any difference on yield. Lastly, aspiration immediately after (T1) or three hours after (T2) injection were compared to determine any effect on diagnostic yield. Joints used included the stifle, elbow, and carpus in eight medium dog cadavers. Arthrocentesis was performed before injection (control) and yield measured. Test joints were injected with MO or HS and yield measured after range of motion (T1) and three hours post injection to simulate lab preparation (T2). Both models had statistically significantly higher yield compared with the unmodified cadaver in all joints at T1 and T2 (p<.05) with the exception of HST2 carpus. T2 aspiration had a statistically significant lower yield when compared to T1HS carpus, T1HS elbow, and T1MO carpus. Overall, irrespective of fluid volume or type, percent yield was lower in T2 compared to T1. No statistically significant difference was seen between HS and MO in most joints with the exception of MOT1 stifle and HST2 elbow. Within the time frame assessed, both models were acceptable. However, HS arthrocentesis models proved appropriate for student trial due to the difficult aspirations with MO.

Development of a Coupled Hydrological/Sediment Yield Model for a Watershed at Regional Level

NASA Technical Reports Server (NTRS)

Rajbhandaril, Narayan; Crosson, William; Tsegaye, Teferi; Coleman, Tommy; Liu, Yaping; Soman, Vishwas

1998-01-01

Development of a hydrologic model for the study of environmental conservation requires a comprehensive understanding of individual-storm affecting hydrologic and sedimentologic processes. The hydrologic models that we are currently coupling are the Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS) and the Distributed Runoff Model (DRUM). SHEELS runs continuously to estimate surface energy fluxes and sub-surface soil water fluxes, while DRUM operates during and following precipitation events to predict surface runoff and peak flow through channel routing. The lateral re-distribution of surface water determined by DRUM is passed to SHEELS, which then adjusts soil water contents throughout the profile. The model SHEELS is well documented in Smith et al. (1993) and Laymen and Crosson (1995). The model DRUM is well documented in Vieux et al. (1990) and Vieux and Gauer (1994). The coupled hydrologic model, SHEELS/DRUM, does not simulate sedimentologic processes. The simulation of the sedimentologic process is important for environmental conservation planning and management. Therefore, we attempted to develop a conceptual frame work for coupling a sediment yield model with SHEELS/DRUM to estimate individual-storm sediment yield from a watershed at a regional level. The sediment yield model that will be used for this study is the Universal Soil Loss Equation (USLE) with some modifications to enable the model to predict individual-storm sediment yield. The predicted sediment yield does not include wind erosion and erosion caused by irrigation and snow melt. Units used for this study are those given by Foster et al. (1981) for SI units.

Remotely sensed rice yield prediction using multi-temporal NDVI data derived from NOAA's-AVHRR.

PubMed

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha(-1). Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly.

Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR

PubMed Central

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha−1. Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly. PMID:23967112

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.

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.

Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...

Brazil soybean yield covariance model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate soybean yields for the seven soybean-growing states of Brazil. The meteorological data of these seven states were pooled and the years 1975 to 1980 were used to model since there was no technological trend in the yields during these years. Predictor variables were derived from monthly total precipitation and monthly average temperature.

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.

An image based method for crop yield prediction using remotely sensed and crop canopy data: the case of Paphos district, western Cyprus

NASA Astrophysics Data System (ADS)

Papadavid, G.; Hadjimitsis, D.

2014-08-01

Remote sensing techniques development have provided the opportunity for optimizing yields in the agricultural procedure and moreover to predict the forthcoming yield. Yield prediction plays a vital role in Agricultural Policy and provides useful data to policy makers. In this context, crop and soil parameters along with NDVI index which are valuable sources of information have been elaborated statistically to test if a) Durum wheat yield can be predicted and b) when is the actual time-window to predict the yield in the district of Paphos, where Durum wheat is the basic cultivation and supports the rural economy of the area. 15 plots cultivated with Durum wheat from the Agricultural Research Institute of Cyprus for research purposes, in the area of interest, have been under observation for three years to derive the necessary data. Statistical and remote sensing techniques were then applied to derive and map a model that can predict yield of Durum wheat in this area. Indeed the semi-empirical model developed for this purpose, with very high correlation coefficient R2=0.886, has shown in practice that can predict yields very good. Students T test has revealed that predicted values and real values of yield have no statistically significant difference. The developed model can and will be further elaborated with more parameters and applied for other crops in the near future.

Statistical modelling of grapevine yield in the Port Wine region under present and future climate conditions

NASA Astrophysics Data System (ADS)

Santos, João A.; Malheiro, Aureliano C.; Karremann, Melanie K.; Pinto, Joaquim G.

2011-03-01

The impact of projected climate change on wine production was analysed for the Demarcated Region of Douro, Portugal. A statistical grapevine yield model (GYM) was developed using climate parameters as predictors. Statistically significant correlations were identified between annual yield and monthly mean temperatures and monthly precipitation totals during the growing cycle. These atmospheric factors control grapevine yield in the region, with the GYM explaining 50.4% of the total variance in the yield time series in recent decades. Anomalously high March rainfall (during budburst, shoot and inflorescence development) favours yield, as well as anomalously high temperatures and low precipitation amounts in May and June (May: flowering and June: berry development). The GYM was applied to a regional climate model output, which was shown to realistically reproduce the GYM predictors. Finally, using ensemble simulations under the A1B emission scenario, projections for GYM-derived yield in the Douro Region, and for the whole of the twenty-first century, were analysed. A slight upward trend in yield is projected to occur until about 2050, followed by a steep and continuous increase until the end of the twenty-first century, when yield is projected to be about 800 kg/ha above current values. While this estimate is based on meteorological parameters alone, changes due to elevated CO2 may further enhance this effect. In spite of the associated uncertainties, it can be stated that projected climate change may significantly benefit wine yield in the Douro Valley.

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.

Whole season compared to growth-stage resolved temperature trends: implications for US maize yield

NASA Astrophysics Data System (ADS)

Butler, E. E.; Mueller, N. D.; Huybers, P. J.

2014-12-01

The effect of temperature on maize yield has generally been considered using a single value for the entire growing season. We compare the effect of temperature trends on yield between two distinct models: a single temperature sensitivity for the whole season and a variable sensitivity across four distinct agronomic development stages. The more resolved variable-sensitivity model indicates roughly a factor of two greater influence of temperature on yield than that implied by the single-sensitivity model. The largest discrepancies occur in silking, which is demonstrated to be the most sensitive stage in the variable-sensitivity model. For instance, whereas median yields are observed to be only 53% of typical values during the hottest 1% of silking-stage temperatures, the single-sensitivity model over predicts median yields of 68% whereas the variable-sensitivity model more correctly predicts median yields of 61%. That the variable sensitivity model is also not capable of capturing the full extent of yield losses suggests that further refinement to represent the non-linear response would be useful. Results from the variable sensitivity model also indicate that management decisions regarding planting times, which have generally shifted toward earlier dates, have led to greater yield benefit than that implied by the single-sensitivity model. Together, the variation of both temperature trends and yield variability within growing stages calls for closer attention to how changes in management interact with changes in climate to ultimately affect yields.

Evaluation of the Williams-type spring wheat model in North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Leduc, S. (Principal Investigator)

1982-01-01

The Williams type model, developed similarly to previous models of C.V.D. Williams, uses monthly temperature and precipitation data as well as soil and topological variables to predict the yield of the spring wheat crop. The models are statistically developed using the regression technique. Eight model characteristics are examined in the evaluation of the model. Evaluation is at the crop reporting district level, the state level and for the entire region. A ten year bootstrap test was the basis of the statistical evaluation. The accuracy and current indication of modeled yield reliability could show improvement. There is great variability in the bias measured over the districts, but there is a slight overall positive bias. The model estimates for the east central crop reporting district in Minnesota are not accurate. The estimate of yield for 1974 were inaccurate for all of the models.

Model Identification and FE Simulations: Effect of Different Yield Loci and Hardening Laws in Sheet Forming

NASA Astrophysics Data System (ADS)

Flores, P.; Duchêne, L.; Lelotte, T.; Bouffioux, C.; El Houdaigui, F.; Van Bael, A.; He, S.; Duflou, J.; Habraken, A. M.

2005-08-01

The bi-axial experimental equipment developed by Flores enables to perform Baushinger shear tests and successive or simultaneous simple shear tests and plane-strain tests. Such experiments and classical tensile tests investigate the material behavior in order to identify the yield locus and the hardening models. With tests performed on two steel grades, the methods applied to identify classical yield surfaces such as Hill or Hosford ones as well as isotropic Swift type hardening or kinematic Armstrong-Frederick hardening models are explained. Comparison with the Taylor-Bishop-Hill yield locus is also provided. The effect of both yield locus and hardening model choice will be presented for two applications: Single Point Incremental Forming (SPIF) and a cup deep drawing.

Argentina soybean yield model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate soybean yields for the country of Argentina. A meteorological data set was obtained for the country by averaging data for stations within the soybean growing area. Predictor variables for the model were derived from monthly total precipitation and monthly average temperature. A trend variable was included for the years 1969 to 1978 since an increasing trend in yields due to technology was observed between these years.

Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments.

PubMed

Hasegawa, Toshihiro; Li, Tao; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Baker, Jeffrey; Bregaglio, Simone; Buis, Samuel; Confalonieri, Roberto; Fugice, Job; Fumoto, Tamon; Gaydon, Donald; Kumar, Soora Naresh; Lafarge, Tanguy; Marcaida Iii, Manuel; Masutomi, Yuji; Nakagawa, Hiroshi; Oriol, Philippe; Ruget, Françoise; Singh, Upendra; Tang, Liang; Tao, Fulu; Wakatsuki, Hitomi; Wallach, Daniel; Wang, Yulong; Wilson, Lloyd Ted; Yang, Lianxin; Yang, Yubin; Yoshida, Hiroe; Zhang, Zhao; Zhu, Jianguo

2017-11-01

The CO 2 fertilization effect is a major source of uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mechanisms of this uncertainty have been lacking. Here, we studied causes of uncertainty among 16 crop models in predicting rice yield in response to elevated [CO 2 ] (E-[CO 2 ]) by comparison to free-air CO 2 enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well. However, yield prediction in response to E-[CO 2 ] varied significantly among the rice models. The variation was not random: models that overestimated at one experiment simulated greater yield enhancements at the others. The variation was not associated with model structure or magnitude of photosynthetic response to E-[CO 2 ] but was significantly associated with the predictions of leaf area. This suggests that modelled secondary effects of E-[CO 2 ] on morphological development, primarily leaf area, are the sources of model uncertainty. Rice morphological development is conservative to carbon acquisition. Uncertainty will be reduced by incorporating this conservative nature of the morphological response to E-[CO 2 ] into the models. Nitrogen levels, particularly under limited situations, make the prediction more uncertain. Improving models to account for [CO 2 ] × N interactions is necessary to better evaluate management practices under climate change.

Evaluation of the CEAS trend and monthly weather data models for soybean yields in Iowa, Illinois, and Indiana

NASA Technical Reports Server (NTRS)

French, V. (Principal Investigator)

1982-01-01

The CEAS models evaluated use historic trend and meteorological and agroclimatic variables to forecast soybean yields in Iowa, Illinois, and Indiana. Indicators of yield reliability and current measures of modeled yield reliability were obtained from bootstrap tests on the end of season models. Indicators of yield reliability show that the state models are consistently better than the crop reporting district (CRD) models. One CRD model is especially poor. At the state level, the bias of each model is less than one half quintal/hectare. The standard deviation is between one and two quintals/hectare. The models are adequate in terms of coverage and are to a certain extent consistent with scientific knowledge. Timely yield estimates can be made during the growing season using truncated models. The models are easy to understand and use and are not costly to operate. Other than the specification of values used to determine evapotranspiration, the models are objective. Because the method of variable selection used in the model development is adequately documented, no evaluation can be made of the objectivity and cost of redevelopment of the model.

Development of LACIE CCEA-1 weather/wheat yield models. [regression analysis

NASA Technical Reports Server (NTRS)

Strommen, N. D.; Sakamoto, C. M.; Leduc, S. K.; Umberger, D. E. (Principal Investigator)

1979-01-01

The advantages and disadvantages of the casual (phenological, dynamic, physiological), statistical regression, and analog approaches to modeling for grain yield are examined. Given LACIE's primary goal of estimating wheat production for the large areas of eight major wheat-growing regions, the statistical regression approach of correlating historical yield and climate data offered the Center for Climatic and Environmental Assessment the greatest potential return within the constraints of time and data sources. The basic equation for the first generation wheat-yield model is given. Topics discussed include truncation, trend variable, selection of weather variables, episodic events, strata selection, operational data flow, weighting, and model results.

Operational modelling: the mechanisms influencing TB diagnostic yield in an Xpert® MTB/RIF-based algorithm.

PubMed

Dunbar, R; Naidoo, P; Beyers, N; Langley, I

2017-04-01

Cape Town, South Africa. To compare the diagnostic yield for smear/culture and Xpert® MTB/RIF algorithms and to investigate the mechanisms influencing tuberculosis (TB) yield. We developed and validated an operational model of the TB diagnostic process, first with the smear/culture algorithm and then with the Xpert algorithm. We modelled scenarios by varying TB prevalence, adherence to diagnostic algorithms and human immunodeficiency virus (HIV) status. This enabled direct comparisons of diagnostic yield in the two algorithms to be made. Routine data showed that diagnostic yield had decreased over the period of the Xpert algorithm roll-out compared to the yield when the smear/culture algorithm was in place. However, modelling yield under identical conditions indicated a 13.3% increase in diagnostic yield from the Xpert algorithm compared to smear/culture. The model demonstrated that the extensive use of culture in the smear/culture algorithm and the decline in TB prevalence are the main factors contributing to not finding an increase in diagnostic yield in the routine data. We demonstrate the benefits of an operational model to determine the effect of scale-up of a new diagnostic algorithm, and recommend that policy makers use operational modelling to make appropriate decisions before new diagnostic algorithms are scaled up.

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.

Comparison of CEAS and Williams-type models for spring wheat yields in North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Barnett, T. L. (Principal Investigator)

1982-01-01

The CEAS and Williams-type yield models are both based on multiple regression analysis of historical time series data at CRD level. The CEAS model develops a separate relation for each CRD; the Williams-type model pools CRD data to regional level (groups of similar CRDs). Basic variables considered in the analyses are USDA yield, monthly mean temperature, monthly precipitation, and variables derived from these. The Williams-type model also used soil texture and topographic information. Technological trend is represented in both by piecewise linear functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test of each model (1970-1979) demonstrate that the models are very similar in performance in all respects. Both models are about equally objective, adequate, timely, simple, and inexpensive. Both consider scientific knowledge on a broad scale but not in detail. Neither provides a good current measure of modeled yield reliability. The CEAS model is considered very slightly preferable for AgRISTARS applications.

SCS-CN based time-distributed sediment yield model

NASA Astrophysics Data System (ADS)

Tyagi, J. V.; Mishra, S. K.; Singh, Ranvir; Singh, V. P.

2008-05-01

SummaryA sediment yield model is developed to estimate the temporal rates of sediment yield from rainfall events on natural watersheds. The model utilizes the SCS-CN based infiltration model for computation of rainfall-excess rate, and the SCS-CN-inspired proportionality concept for computation of sediment-excess. For computation of sedimentographs, the sediment-excess is routed to the watershed outlet using a single linear reservoir technique. Analytical development of the model shows the ratio of the potential maximum erosion (A) to the potential maximum retention (S) of the SCS-CN method is constant for a watershed. The model is calibrated and validated on a number of events using the data of seven watersheds from India and the USA. Representative values of the A/S ratio computed for the watersheds from calibration are used for the validation of the model. The encouraging results of the proposed simple four parameter model exhibit its potential in field application.

Ranking contributing areas of salt and selenium in the Lower Gunnison River Basin, Colorado, using multiple linear regression models

USGS Publications Warehouse

Linard, Joshua I.

2013-01-01

Mitigating the effects of salt and selenium on water quality in the Grand Valley and lower Gunnison River Basin in western Colorado is a major concern for land managers. Previous modeling indicated means to improve the models by including more detailed geospatial data and a more rigorous method for developing the models. After evaluating all possible combinations of geospatial variables, four multiple linear regression models resulted that could estimate irrigation-season salt yield, nonirrigation-season salt yield, irrigation-season selenium yield, and nonirrigation-season selenium yield. The adjusted r-squared and the residual standard error (in units of log-transformed yield) of the models were, respectively, 0.87 and 2.03 for the irrigation-season salt model, 0.90 and 1.25 for the nonirrigation-season salt model, 0.85 and 2.94 for the irrigation-season selenium model, and 0.93 and 1.75 for the nonirrigation-season selenium model. The four models were used to estimate yields and loads from contributing areas corresponding to 12-digit hydrologic unit codes in the lower Gunnison River Basin study area. Each of the 175 contributing areas was ranked according to its estimated mean seasonal yield of salt and selenium.

Nonlinear programming models to optimize uneven-aged loblolly pine management

Treesearch

Benedict J. Schulte; Joseph. Buongiorno; Kenneth Skog

1999-01-01

Nonlinear programming models of uneven-aged loblolly pine (Pinus taeda L.) management were developed to identify sustainable management regimes which optimize: 1) soil expectation value (SEV), 2) tree diversity, or 3) annual sawtimber yields. The models use the equations of SouthPro, a site- and density-dependent, multi-species matrix growth and yield model that...

Argentina wheat yield model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

Five models based on multiple regression were developed to estimate wheat yields for the five wheat growing provinces of Argentina. Meteorological data sets were obtained for each province by averaging data for stations within each province. Predictor variables for the models were derived from monthly total precipitation, average monthly mean temperature, and average monthly maximum temperature. Buenos Aires was the only province for which a trend variable was included because of increasing trend in yield due to technology from 1950 to 1963.

Argentina corn yield model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate corn yields for the country of Argentina. A meteorological data set was obtained for the country by averaging data for stations within the corn-growing area. Predictor variables for the model were derived from monthly total precipitation, average monthly mean temperature, and average monthly maximum temperature. A trend variable was included for the years 1965 to 1980 since an increasing trend in yields due to technology was observed between these years.

A meteorologically-driven yield reduction model for spring and winter wheat

NASA Technical Reports Server (NTRS)

Ravet, F. W.; Cremins, W. J.; Taylor, T. W.; Ashburn, P.; Smika, D.; Aaronson, A. (Principal Investigator)

1983-01-01

A yield reduction model for spring and winter wheat was developed for large-area crop condition assessment. Reductions are expressed in percentage from a base yield and are calculated on a daily basis. The algorithm contains two integral components: a two-layer soil water budget model and a crop calendar routine. Yield reductions associated with hot, dry winds (Sukhovey) and soil moisture stress are determined. Input variables include evapotranspiration, maximum temperature and precipitation; subsequently crop-stage, available water holding percentage and stress duration are evaluated. No specific base yield is required and may be selected by the user; however, it may be generally characterized as the maximum likely to be produced commercially at a location.

Comment of "Event-based soil loss models for construction sites" by Trenouth and Gharabaghi, J. Hydrol. doi: 10.1016/jhydrol.2015.03.010

NASA Astrophysics Data System (ADS)

Kinnell, P. I. A.

2015-09-01

Trenouth and Gharabaghi (2015) present two models which replace the EI30 index used as the event erosivity index in the USLE/RUSLE with ones that include runoff and values of EI30 to powers that differ for 1.0 as the event erosivity factor in modelling soil loss for construction sites. Their analysis on the application of these models focused on data from 5 locations as a whole but did not show how the models worked at each location. Practically, the ability to predict sediment yields at a specific location is more relevant than the capacity of a model to predict sediment yields globally. Also, the mathematical structure of their proposed models shows little regard to the physical processes involved in causing erosion and sediment yield. There is still the need to develop event-based empirical models for construction sites that are robust because they give proper consideration to the erosion process involved, and take account of the fact that sediment yield is usually determined from measurements of suspended load whereas soil loss at the scale for which the USLE/RUSLE model was developed includes both suspended load and bed load.

Estimating yields of salt- and water-stressed forages with remote sensing in the visible and near infrared.

PubMed

Poss, J A; Russell, W B; Grieve, C M

2006-01-01

In arid irrigated regions, the proportion of crop production under deficit irrigation with poorer quality water is increasing as demand for fresh water soars and efforts to prevent saline water table development occur. Remote sensing technology to quantify salinity and water stress effects on forage yield can be an important tool to address yield loss potential when deficit irrigating with poor water quality. Two important forages, alfalfa (Medicago sativa L.) and tall wheatgrass (Agropyron elongatum L.), were grown in a volumetric lysimeter facility where rootzone salinity and water content were varied and monitored. Ground-based hyperspectral canopy reflectance in the visible and near infrared (NIR) were related to forage yields from a broad range of salinity and water stress conditions. Canopy reflectance spectra were obtained in the 350- to 1000-nm region from two viewing angles (nadir view, 45 degrees from nadir). Nadir view vegetation indices (VI) were not as strongly correlated with leaf area index changes attributed to water and salinity stress treatments for both alfalfa and wheatgrass. From a list of 71 VIs, two were selected for a multiple linear-regression model that estimated yield under varying salinity and water stress conditions. With data obtained during the second harvest of a three-harvest 100-d growing period, regression coefficients for each crop were developed and then used with the model to estimate fresh weights for preceding and succeeding harvests during the same 100-d interval. The model accounted for 72% of the variation in yields in wheatgrass and 94% in yields of alfalfa within the same salinity and water stress treatment period. The model successfully predicted yield in three out of four cases when applied to the first and third harvest yields. Correlations between indices and yield increased as canopy development progressed. Growth reductions attributed to simultaneous salinity and water stress were well characterized, but the corrections for effects of varying tissue nitrogen (N) and very low leaf area index (LAI) are necessary.

Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions

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

Iavarone, Salvatore; Smith, Sean T.; Smith, Philip J.

Oxy-coal combustion is an emerging low-cost “clean coal” technology for emissions reduction and Carbon Capture and Sequestration (CCS). The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of cost-effective oxy-fuel technologies and the minimization of environmental concerns at industrial scale. The coupling of detailed chemistry models and CFD simulations is still challenging, especially for large-scale plants, because of the high computational efforts required. The development of scale-bridging models is therefore necessary, to find a good compromise between computational efforts and the physical-chemical modeling precision. This paper presents a procedure for scale-bridging modeling of coal devolatilization, inmore » the presence of experimental error, that puts emphasis on the thermodynamic aspect of devolatilization, namely the final volatile yield of coal, rather than kinetics. The procedure consists of an engineering approach based on dataset consistency and Bayesian methodology including Gaussian-Process Regression (GPR). Experimental data from devolatilization tests carried out in an oxy-coal entrained flow reactor were considered and CFD simulations of the reactor were performed. Jointly evaluating experiments and simulations, a novel yield model was validated against the data via consistency analysis. In parallel, a Gaussian-Process Regression was performed, to improve the understanding of the uncertainty associated to the devolatilization, based on the experimental measurements. Potential model forms that could predict yield during devolatilization were obtained. The set of model forms obtained via GPR includes the yield model that was proven to be consistent with the data. Finally, the overall procedure has resulted in a novel yield model for coal devolatilization and in a valuable evaluation of uncertainty in the data, in the model form, and in the model parameters.« less

Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions

DOE PAGES

Iavarone, Salvatore; Smith, Sean T.; Smith, Philip J.; ...

2017-06-03

Oxy-coal combustion is an emerging low-cost “clean coal” technology for emissions reduction and Carbon Capture and Sequestration (CCS). The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of cost-effective oxy-fuel technologies and the minimization of environmental concerns at industrial scale. The coupling of detailed chemistry models and CFD simulations is still challenging, especially for large-scale plants, because of the high computational efforts required. The development of scale-bridging models is therefore necessary, to find a good compromise between computational efforts and the physical-chemical modeling precision. This paper presents a procedure for scale-bridging modeling of coal devolatilization, inmore » the presence of experimental error, that puts emphasis on the thermodynamic aspect of devolatilization, namely the final volatile yield of coal, rather than kinetics. The procedure consists of an engineering approach based on dataset consistency and Bayesian methodology including Gaussian-Process Regression (GPR). Experimental data from devolatilization tests carried out in an oxy-coal entrained flow reactor were considered and CFD simulations of the reactor were performed. Jointly evaluating experiments and simulations, a novel yield model was validated against the data via consistency analysis. In parallel, a Gaussian-Process Regression was performed, to improve the understanding of the uncertainty associated to the devolatilization, based on the experimental measurements. Potential model forms that could predict yield during devolatilization were obtained. The set of model forms obtained via GPR includes the yield model that was proven to be consistent with the data. Finally, the overall procedure has resulted in a novel yield model for coal devolatilization and in a valuable evaluation of uncertainty in the data, in the model form, and in the model parameters.« less

Estimates of nitrate loads and yields from groundwater to streams in the Chesapeake Bay watershed based on land use and geology

USGS Publications Warehouse

Terziotti, Silvia; Capel, Paul D.; Tesoriero, Anthony J.; Hopple, Jessica A.; Kronholm, Scott C.

2018-03-07

The water quality of the Chesapeake Bay may be adversely affected by dissolved nitrate carried in groundwater discharge to streams. To estimate the concentrations, loads, and yields of nitrate from groundwater to streams for the Chesapeake Bay watershed, a regression model was developed based on measured nitrate concentrations from 156 small streams with watersheds less than 500 square miles (mi2 ) at baseflow. The regression model has three predictive variables: geologic unit, percent developed land, and percent agricultural land. Comparisons of estimated and actual values within geologic units were closely matched. The coefficient of determination (R2 ) for the model was 0.6906. The model was used to calculate baseflow nitrate concentrations at over 83,000 National Hydrography Dataset Plus Version 2 catchments and aggregated to 1,966 total 12-digit hydrologic units in the Chesapeake Bay watershed. The modeled output geospatial data layers provided estimated annual loads and yields of nitrate from groundwater into streams. The spatial distribution of annual nitrate yields from groundwater estimated by this method was compared to the total watershed yields of all sources estimated from a Chesapeake Bay SPAtially Referenced Regressions On Watershed attributes (SPARROW) water-quality model. The comparison showed similar spatial patterns. The regression model for groundwater contribution had similar but lower yields, suggesting that groundwater is an important source of nitrogen for streams in the Chesapeake Bay watershed.

Heterogeneous variances in multi-environment yield trials for corn hybrids

USDA-ARS?s Scientific Manuscript database

Recent developments in statistics and computing have enabled much greater levels of complexity in statistical models of multi-environment yield trial data. One particular feature of interest to breeders is simultaneously modeling heterogeneity of variances among environments and cultivars. Our obj...

The effect of a loss of model structural detail due to network skeletonization on contamination warning system design: case studies.

PubMed

Davis, Michael J; Janke, Robert

2018-01-04

The effect of limitations in the structural detail available in a network model on contamination warning system (CWS) design was examined in case studies using the original and skeletonized network models for two water distribution systems (WDSs). The skeletonized models were used as proxies for incomplete network models. CWS designs were developed by optimizing sensor placements for worst-case and mean-case contamination events. Designs developed using the skeletonized network models were transplanted into the original network model for evaluation. CWS performance was defined as the number of people who ingest more than some quantity of a contaminant in tap water before the CWS detects the presence of contamination. Lack of structural detail in a network model can result in CWS designs that (1) provide considerably less protection against worst-case contamination events than that obtained when a more complete network model is available and (2) yield substantial underestimates of the consequences associated with a contamination event. Nevertheless, CWSs developed using skeletonized network models can provide useful reductions in consequences for contaminants whose effects are not localized near the injection location. Mean-case designs can yield worst-case performances similar to those for worst-case designs when there is uncertainty in the network model. Improvements in network models for WDSs have the potential to yield significant improvements in CWS designs as well as more realistic evaluations of those designs. Although such improvements would be expected to yield improved CWS performance, the expected improvements in CWS performance have not been quantified previously. The results presented here should be useful to those responsible for the design or implementation of CWSs, particularly managers and engineers in water utilities, and encourage the development of improved network models.

The effect of a loss of model structural detail due to network skeletonization on contamination warning system design: case studies

NASA Astrophysics Data System (ADS)

Davis, Michael J.; Janke, Robert

2018-05-01

The effect of limitations in the structural detail available in a network model on contamination warning system (CWS) design was examined in case studies using the original and skeletonized network models for two water distribution systems (WDSs). The skeletonized models were used as proxies for incomplete network models. CWS designs were developed by optimizing sensor placements for worst-case and mean-case contamination events. Designs developed using the skeletonized network models were transplanted into the original network model for evaluation. CWS performance was defined as the number of people who ingest more than some quantity of a contaminant in tap water before the CWS detects the presence of contamination. Lack of structural detail in a network model can result in CWS designs that (1) provide considerably less protection against worst-case contamination events than that obtained when a more complete network model is available and (2) yield substantial underestimates of the consequences associated with a contamination event. Nevertheless, CWSs developed using skeletonized network models can provide useful reductions in consequences for contaminants whose effects are not localized near the injection location. Mean-case designs can yield worst-case performances similar to those for worst-case designs when there is uncertainty in the network model. Improvements in network models for WDSs have the potential to yield significant improvements in CWS designs as well as more realistic evaluations of those designs. Although such improvements would be expected to yield improved CWS performance, the expected improvements in CWS performance have not been quantified previously. The results presented here should be useful to those responsible for the design or implementation of CWSs, particularly managers and engineers in water utilities, and encourage the development of improved network models.

Primary and Secondary Yield Losses Caused by Pests and Diseases: Assessment and Modeling in Coffee

PubMed Central

Gary, Christian; Tixier, Philippe; Lechevallier, Esther

2017-01-01

The assessment of crop yield losses is needed for the improvement of production systems that contribute to the incomes of rural families and food security worldwide. However, efforts to quantify yield losses and identify their causes are still limited, especially for perennial crops. Our objectives were to quantify primary yield losses (incurred in the current year of production) and secondary yield losses (resulting from negative impacts of the previous year) of coffee due to pests and diseases, and to identify the most important predictors of coffee yields and yield losses. We established an experimental coffee parcel with full-sun exposure that consisted of six treatments, which were defined as different sequences of pesticide applications. The trial lasted three years (2013–2015) and yield components, dead productive branches, and foliar pests and diseases were assessed as predictors of yield. First, we calculated yield losses by comparing actual yields of specific treatments with the estimated attainable yield obtained in plots which always had chemical protection. Second, we used structural equation modeling to identify the most important predictors. Results showed that pests and diseases led to high primary yield losses (26%) and even higher secondary yield losses (38%). We identified the fruiting nodes and the dead productive branches as the most important and useful predictors of yields and yield losses. These predictors could be added in existing mechanistic models of coffee, or can be used to develop new linear mixed models to estimate yield losses. Estimated yield losses can then be related to production factors to identify corrective actions that farmers can implement to reduce losses. The experimental and modeling approaches of this study could also be applied in other perennial crops to assess yield losses. PMID:28046054

Primary and Secondary Yield Losses Caused by Pests and Diseases: Assessment and Modeling in Coffee.

PubMed

Cerda, Rolando; Avelino, Jacques; Gary, Christian; Tixier, Philippe; Lechevallier, Esther; Allinne, Clémentine

2017-01-01

The assessment of crop yield losses is needed for the improvement of production systems that contribute to the incomes of rural families and food security worldwide. However, efforts to quantify yield losses and identify their causes are still limited, especially for perennial crops. Our objectives were to quantify primary yield losses (incurred in the current year of production) and secondary yield losses (resulting from negative impacts of the previous year) of coffee due to pests and diseases, and to identify the most important predictors of coffee yields and yield losses. We established an experimental coffee parcel with full-sun exposure that consisted of six treatments, which were defined as different sequences of pesticide applications. The trial lasted three years (2013-2015) and yield components, dead productive branches, and foliar pests and diseases were assessed as predictors of yield. First, we calculated yield losses by comparing actual yields of specific treatments with the estimated attainable yield obtained in plots which always had chemical protection. Second, we used structural equation modeling to identify the most important predictors. Results showed that pests and diseases led to high primary yield losses (26%) and even higher secondary yield losses (38%). We identified the fruiting nodes and the dead productive branches as the most important and useful predictors of yields and yield losses. These predictors could be added in existing mechanistic models of coffee, or can be used to develop new linear mixed models to estimate yield losses. Estimated yield losses can then be related to production factors to identify corrective actions that farmers can implement to reduce losses. The experimental and modeling approaches of this study could also be applied in other perennial crops to assess yield losses.

Why is it so difficult to determine the yield of indoor cannabis plantations? A case study from the Netherlands.

PubMed

Vanhove, Wouter; Maalsté, Nicole; Van Damme, Patrick

2017-07-01

Together, the Netherlands and Belgium are the largest indoor cannabis producing countries in Europe. In both countries, legal prosecution procedure of convicted illicit cannabis growers usually includes recovery of the profits gained. However, it is not easy to make a reliable estimation of the latter profits, due to the wide range of factors that determine indoor cannabis yields and eventual selling prices. In the Netherlands, since 2005, a reference model is used that assumes a constant yield (g) per plant for a given indoor cannabis plant density. Later, in 2011, a new model was developed in Belgium for yield estimation of Belgian indoor cannabis plantations that assumes a constant yield per m 2 of growth surface, provided that a number of growth conditions are met. Indoor cannabis plantations in the Netherlands and Belgium share similar technical characteristics. As a result, for indoor cannabis plantations in both countries, both aforementioned yield estimation models should yield similar yield estimations. By means of a real-case study from the Netherlands, we show that the reliability of both models is hampered by a number of flaws and unmet preconditions. The Dutch model is based on a regression equation that makes use of ill-defined plant development stages, assumes a linear plant growth, does not discriminate between different plantation size categories and does not include other important yield determining factors (such as fertilization). The Belgian model addresses some of the latter shortcomings, but its applicability is constrained by a number of pre-conditions including plantation size between 50 and 1000 plants; cultivation in individual pots with peat soil; 600W (electrical power) assimilation lamps; constant temperature between 20°C and 30°C; adequate fertilizer application and plants unaffected by pests and diseases. Judiciary in both the Netherlands and Belgium require robust indoor cannabis yield models for adequate legal prosecution of illicit indoor cannabis growth operations. To that aim, the current models should be optimized whereas the validity of their application should be examined case by case. Copyright © 2017 Elsevier B.V. All rights reserved.

Impacts of climate change on peanut yield in China simulated by CMIP5 multi-model ensemble projections

NASA Astrophysics Data System (ADS)

Xu, Hanqing; Tian, Zhan; Zhong, Honglin; Fan, Dongli; Shi, Runhe; Niu, Yilong; He, Xiaogang; Chen, Maosi

2017-09-01

Peanut is one of the major edible vegetable oil crops in China, whose growth and yield are very sensitive to climate change. In addition, agriculture climate resources are expected to be redistributed under climate change, which will further influence the growth, development, cropping patterns, distribution and production of peanut. In this study, we used the DSSAT-Peanut model to examine the climate change impacts on peanut production, oil industry and oil food security in China. This model is first calibrated using site observations including 31 years' (1981-2011) climate, soil and agronomy data. This calibrated model is then employed to simulate the future peanut yield based on 20 climate scenarios from 5 Global Circulation Models (GCMs) developed by the InterSectoral Impact Model Intercomparison Project (ISIMIP) driven by 4 Representative Concentration Pathways (RCPs). Results indicate that the irrigated peanut yield will decrease 2.6% under the RCP 2.6 scenario, 9.9% under the RCP 4.5 scenario and 29% under the RCP 8.5 scenario, respectively. Similarly, the rain-fed peanut yield will also decrease, with a 2.5% reduction under the RCP 2.6 scenario, 11.5% reduction under the RCP 4.5 scenario and 30% reduction under the RCP 8.5 scenario, respectively.

Impacts of variability in cellulosic biomass yields on energy security.

PubMed

Mullins, Kimberley A; Matthews, H Scott; Griffin, W Michael; Anex, Robert

2014-07-01

The practice of modeling biomass yields on the basis of deterministic point values aggregated over space and time obscures important risks associated with large-scale biofuel use, particularly risks related to drought-induced yield reductions that may become increasingly frequent under a changing climate. Using switchgrass as a case study, this work quantifies the variability in expected yields over time and space through switchgrass growth modeling under historical and simulated future weather. The predicted switchgrass yields across the United States range from about 12 to 19 Mg/ha, and the 80% confidence intervals range from 20 to 60% of the mean. Average yields are predicted to decrease with increased temperatures and weather variability induced by climate change. Feedstock yield variability needs to be a central part of modeling to ensure that policy makers acknowledge risks to energy supplies and develop strategies or contingency plans that mitigate those risks.

Microplume model of spatial-yield spectra. [applying to electron gas degradation in molecular nitrogen gas

NASA Technical Reports Server (NTRS)

Green, A. E. S.; Singhal, R. P.

1979-01-01

An analytic representation for the spatial (radial and longitudinal) yield spectra is developed in terms of a model containing three simple 'microplumes'. The model is applied to electron energy degradation in molecular nitrogen gas for 0.1 to 5 keV incident electrons. From the nature of the cross section input to this model it is expected that the scaled spatial yield spectra for other gases will be quite similar. The model indicates that each excitation, ionization, etc. plume should have its individual spatial and energy dependence. Extensions and aeronomical and radiological applications of the model are discussed.

Estimating climate change, CO2 and technology development effects on wheat yield in northeast Iran

NASA Astrophysics Data System (ADS)

Bannayan, M.; Mansoori, H.; Rezaei, E. Eyshi

2014-04-01

Wheat is the main food for the majority of Iran's population. Precise estimation of wheat yield change in future is essential for any possible revision of management strategies. The main objective of this study was to evaluate the effects of climate change, CO2 concentration, technology development and their integrated effects on wheat production under future climate change. This study was performed under two scenarios of the IPCC Special Report on Emission Scenarios (SRES): regional economic (A2) and global environmental (B1). Crop production was projected for three future time periods (2020, 2050 and 2080) in comparison with a baseline year (2005) for Khorasan province located in the northeast of Iran. Four study locations in the study area included Mashhad, Birjand, Bojnourd and Sabzevar. The effect of technology development was calculated by fitting a regression equation between the observed wheat yields against historical years considering yield potential increase and yield gap reduction as technology development. Yield relative increase per unit change of CO2 concentration (1 ppm-1) was considered 0.05 % and was used to implement the effect of elevated CO2. The HadCM3 general circulation model along with the CSM-CERES-Wheat crop model were used to project climate change effects on wheat crop yield. Our results illustrate that, among all the factors considered, technology development provided the highest impact on wheat yield change. Highest wheat yield increase across all locations and time periods was obtained under the A2 scenario. Among study locations, Mashhad showed the highest change in wheat yield. Yield change compared to baseline ranged from -28 % to 56 % when the integration of all factors was considered across all locations. It seems that achieving higher yield of wheat in future may be expected in northeast Iran assuming stable improvements in production technology.

Estimating climate change, CO2 and technology development effects on wheat yield in northeast Iran.

PubMed

Bannayan, M; Mansoori, H; Rezaei, E Eyshi

2014-04-01

Wheat is the main food for the majority of Iran's population. Precise estimation of wheat yield change in future is essential for any possible revision of management strategies. The main objective of this study was to evaluate the effects of climate change, CO2 concentration, technology development and their integrated effects on wheat production under future climate change. This study was performed under two scenarios of the IPCC Special Report on Emission Scenarios (SRES): regional economic (A2) and global environmental (B1). Crop production was projected for three future time periods (2020, 2050 and 2080) in comparison with a baseline year (2005) for Khorasan province located in the northeast of Iran. Four study locations in the study area included Mashhad, Birjand, Bojnourd and Sabzevar. The effect of technology development was calculated by fitting a regression equation between the observed wheat yields against historical years considering yield potential increase and yield gap reduction as technology development. Yield relative increase per unit change of CO2 concentration (1 ppm(-1)) was considered 0.05 % and was used to implement the effect of elevated CO2. The HadCM3 general circulation model along with the CSM-CERES-Wheat crop model were used to project climate change effects on wheat crop yield. Our results illustrate that, among all the factors considered, technology development provided the highest impact on wheat yield change. Highest wheat yield increase across all locations and time periods was obtained under the A2 scenario. Among study locations, Mashhad showed the highest change in wheat yield. Yield change compared to baseline ranged from -28 % to 56 % when the integration of all factors was considered across all locations. It seems that achieving higher yield of wheat in future may be expected in northeast Iran assuming stable improvements in production technology.

Precipitation-runoff modeling system; user's manual

USGS Publications Warehouse

Leavesley, G.H.; Lichty, R.W.; Troutman, B.M.; Saindon, L.G.

1983-01-01

The concepts, structure, theoretical development, and data requirements of the precipitation-runoff modeling system (PRMS) are described. The precipitation-runoff modeling system is a modular-design, deterministic, distributed-parameter modeling system developed to evaluate the impacts of various combinations of precipitation, climate, and land use on streamflow, sediment yields, and general basin hydrology. Basin response to normal and extreme rainfall and snowmelt can be simulated to evaluate changes in water balance relationships, flow regimes, flood peaks and volumes, soil-water relationships, sediment yields, and groundwater recharge. Parameter-optimization and sensitivity analysis capabilites are provided to fit selected model parameters and evaluate their individual and joint effects on model output. The modular design provides a flexible framework for continued model system enhancement and hydrologic modeling research and development. (Author 's abstract)

A network-based approach for semi-quantitative knowledge mining and its application to yield variability

NASA Astrophysics Data System (ADS)

Schauberger, Bernhard; Rolinski, Susanne; Müller, Christoph

2016-12-01

Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. A systematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields.

Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress

PubMed Central

Gu, Junfei; Yin, Xinyou; Zhang, Chengwei; Wang, Huaqi; Struik, Paul C.

2014-01-01

Background and Aims Genetic markers can be used in combination with ecophysiological crop models to predict the performance of genotypes. Crop models can estimate the contribution of individual markers to crop performance in given environments. The objectives of this study were to explore the use of crop models to design markers and virtual ideotypes for improving yields of rice (Oryza sativa) under drought stress. Methods Using the model GECROS, crop yield was dissected into seven easily measured parameters. Loci for these parameters were identified for a rice population of 94 introgression lines (ILs) derived from two parents differing in drought tolerance. Marker-based values of ILs for each of these parameters were estimated from additive allele effects of the loci, and were fed to the model in order to simulate yields of the ILs grown under well-watered and drought conditions and in order to design virtual ideotypes for those conditions. Key Results To account for genotypic yield differences, it was necessary to parameterize the model for differences in an additional trait ‘total crop nitrogen uptake’ (Nmax) among the ILs. Genetic variation in Nmax had the most significant effect on yield; five other parameters also significantly influenced yield, but seed weight and leaf photosynthesis did not. Using the marker-based parameter values, GECROS also simulated yield variation among 251 recombinant inbred lines of the same parents. The model-based dissection approach detected more markers than the analysis using only yield per se. Model-based sensitivity analysis ranked all markers for their importance in determining yield differences among the ILs. Virtual ideotypes based on markers identified by modelling had 10–36 % more yield than those based on markers for yield per se. Conclusions This study outlines a genotype-to-phenotype approach that exploits the potential value of marker-based crop modelling in developing new plant types with high yields. The approach can provide more markers for selection programmes for specific environments whilst also allowing for prioritization. Crop modelling is thus a powerful tool for marker design for improved rice yields and for ideotyping under contrasting conditions. PMID:24984712

SPATS: a model for projecting softwood timber inventories in the Southern United States.

Treesearch

David J. Brooks

1987-01-01

The yield-table projection method for modeling the development of regional timber inventories is outlined, and its application to softwood timber types in the Southern United States is described. Problems of simulating forest management practices and natural succession are discussed. A computer model that projects softwood timber inventories using yield-table...

Relation of watershed setting and stream nutrient yields at selected sites in central and eastern North Carolina, 1997-2008

USGS Publications Warehouse

Harden, Stephen L.; Cuffney, Thomas F.; Terziotti, Silvia; Kolb, Katharine R.

2013-01-01

Data collected between 1997 and 2008 at 48 stream sites were used to characterize relations between watershed settings and stream nutrient yields throughout central and eastern North Carolina. The focus of the investigation was to identify environmental variables in watersheds that influence nutrient export for supporting the development and prioritization of management strategies for restoring nutrient-impaired streams. Nutrient concentration data and streamflow data compiled for the 1997 to 2008 study period were used to compute stream yields of nitrate, total nitrogen (N), and total phosphorus (P) for each study site. Compiled environmental data (including variables for land cover, hydrologic soil groups, base-flow index, streams, wastewater treatment facilities, and concentrated animal feeding operations) were used to characterize the watershed settings for the study sites. Data for the environmental variables were analyzed in combination with the stream nutrient yields to explore relations based on watershed characteristics and to evaluate whether particular variables were useful indicators of watersheds having relatively higher or lower potential for exporting nutrients. Data evaluations included an examination of median annual nutrient yields based on a watershed land-use classification scheme developed as part of the study. An initial examination of the data indicated that the highest median annual nutrient yields occurred at both agricultural and urban sites, especially for urban sites having large percentages of point-source flow contributions to the streams. The results of statistical testing identified significant differences in annual nutrient yields when sites were analyzed on the basis of watershed land-use category. When statistical differences in median annual yields were noted, the results for nitrate, total N, and total P were similar in that highly urbanized watersheds (greater than 30 percent developed land use) and (or) watersheds with greater than 10 percent point-source flow contributions to streamflow had higher yields relative to undeveloped watersheds (having less than 10 and 15 percent developed and agricultural land uses, respectively) and watersheds with relatively low agricultural land use (between 15 and 30 percent). The statistical tests further indicated that the median annual yields for total P were statistically higher for watersheds with high agricultural land use (greater than 30 percent) compared to the undeveloped watersheds and watersheds with low agricultural land use. The total P yields also were higher for watersheds with low urban land use (between 10 and 30 percent developed land) compared to the undeveloped watersheds. The study data indicate that grouping and examining stream nutrient yields based on the land-use classifications used in this report can be useful for characterizing relations between watershed settings and nutrient yields in streams located throughout central and eastern North Carolina. Compiled study data also were analyzed with four regression tree models as a means of determining which watershed environmental variables or combination of variables result in basins that are likely to have high or low nutrient yields. The regression tree analyses indicated that some of the environmental variables examined in this study were useful for predicting yields of nitrate, total N, and total P. When the median annual nutrient yields for all 48 sites were evaluated as a group (Model 1), annual point-source flow yields had the greatest influence on nitrate and total N yields observed in streams, and annual streamflow yields had the greatest influence on yields of total P. The Model 1 results indicated that watersheds with higher annual point-source flow yields had higher annual yields of nitrate and total N, and watersheds with higher annual streamflow yields had higher annual yields of total P. When sites with high point-source flows (greater than 10 percent of total streamflow) were excluded from the regression tree analyses (Models 2–4), the percentage of forested land in the watersheds was identified as the primary environmental variable influencing stream yields for both total N and total P. Models 2, 3 and 4 did not identify any watershed environmental variables that could adequately explain the observed variability in the nitrate yields among the set of sites examined by each of these models. The results for Models 2, 3, and 4 indicated that watersheds with higher percentages of forested land had lower annual total N and total P yields compared to watersheds with lower percentages of forested land, which had higher median annual total N and total P yields. Additional environmental variables determined to further influence the stream nutrient yields included median annual percentage of point-source flow contributions to the streams, variables of land cover (percentage of forested land, agricultural land, and (or) forested land plus wetlands) in the watershed and (or) in the stream buffer, and drainage area. The regression tree models can serve as a tool for relating differences in select watershed attributes to differences in stream yields of nitrate, total N, and total P, which can provide beneficial information for improving nutrient management in streams throughout North Carolina and for reducing nutrient loads to coastal waters.

A mathematical framework for yield (vs. rate) optimization in constraint-based modeling and applications in metabolic engineering.

PubMed

Klamt, Steffen; Müller, Stefan; Regensburger, Georg; Zanghellini, Jürgen

2018-05-01

The optimization of metabolic rates (as linear objective functions) represents the methodical core of flux-balance analysis techniques which have become a standard tool for the study of genome-scale metabolic models. Besides (growth and synthesis) rates, metabolic yields are key parameters for the characterization of biochemical transformation processes, especially in the context of biotechnological applications. However, yields are ratios of rates, and hence the optimization of yields (as nonlinear objective functions) under arbitrary linear constraints is not possible with current flux-balance analysis techniques. Despite the fundamental importance of yields in constraint-based modeling, a comprehensive mathematical framework for yield optimization is still missing. We present a mathematical theory that allows one to systematically compute and analyze yield-optimal solutions of metabolic models under arbitrary linear constraints. In particular, we formulate yield optimization as a linear-fractional program. For practical computations, we transform the linear-fractional yield optimization problem to a (higher-dimensional) linear problem. Its solutions determine the solutions of the original problem and can be used to predict yield-optimal flux distributions in genome-scale metabolic models. For the theoretical analysis, we consider the linear-fractional problem directly. Most importantly, we show that the yield-optimal solution set (like the rate-optimal solution set) is determined by (yield-optimal) elementary flux vectors of the underlying metabolic model. However, yield- and rate-optimal solutions may differ from each other, and hence optimal (biomass or product) yields are not necessarily obtained at solutions with optimal (growth or synthesis) rates. Moreover, we discuss phase planes/production envelopes and yield spaces, in particular, we prove that yield spaces are convex and provide algorithms for their computation. We illustrate our findings by a small example and demonstrate their relevance for metabolic engineering with realistic models of E. coli. We develop a comprehensive mathematical framework for yield optimization in metabolic models. Our theory is particularly useful for the study and rational modification of cell factories designed under given yield and/or rate requirements. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Data-driven reduced order models for effective yield strength and partitioning of strain in multiphase materials

NASA Astrophysics Data System (ADS)

Latypov, Marat I.; Kalidindi, Surya R.

2017-10-01

There is a critical need for the development and verification of practically useful multiscale modeling strategies for simulating the mechanical response of multiphase metallic materials with heterogeneous microstructures. In this contribution, we present data-driven reduced order models for effective yield strength and strain partitioning in such microstructures. These models are built employing the recently developed framework of Materials Knowledge Systems that employ 2-point spatial correlations (or 2-point statistics) for the quantification of the heterostructures and principal component analyses for their low-dimensional representation. The models are calibrated to a large collection of finite element (FE) results obtained for a diverse range of microstructures with various sizes, shapes, and volume fractions of the phases. The performance of the models is evaluated by comparing the predictions of yield strength and strain partitioning in two-phase materials with the corresponding predictions from a classical self-consistent model as well as results of full-field FE simulations. The reduced-order models developed in this work show an excellent combination of accuracy and computational efficiency, and therefore present an important advance towards computationally efficient microstructure-sensitive multiscale modeling frameworks.

Simulating and Predicting Cereal Crop Yields in Ethiopia: Model Calibration and Verification

NASA Astrophysics Data System (ADS)

Yang, M.; Wang, G.; Ahmed, K. F.; Eggen, M.; Adugna, B.; Anagnostou, E. N.

2017-12-01

Agriculture in developing countries are extremely vulnerable to climate variability and changes. In East Africa, most people live in the rural areas with outdated agriculture techniques and infrastructure. Smallholder agriculture continues to play a key role in this area, and the rate of irrigation is among the lowest of the world. As a result, seasonal and inter-annual weather patterns play an important role in the spatiotemporal variability of crop yields. This study investigates how various climate variables (e.g., temperature, precipitation, sunshine) and agricultural practice (e.g., fertilization, irrigation, planting date) influence cereal crop yields using a process-based model (DSSAT) and statistical analysis, and focuses on the Blue Nile Basin of Ethiopia. The DSSAT model is driven with meteorological forcing from the ECMWF's latest reanalysis product that cover the past 35 years; the statistical model will be developed by linking the same meteorological reanalysis data with harvest data at the woreda level from the Ethiopian national dataset. Results from this study will set the stage for the development of a seasonal prediction system for weather and crop yields in Ethiopia, which will serve multiple sectors in coping with the agricultural impact of climate variability.

A probabilistic model framework for evaluating year-to-year variation in crop productivity

NASA Astrophysics Data System (ADS)

Yokozawa, M.; Iizumi, T.; Tao, F.

2008-12-01

Most models describing the relation between crop productivity and weather condition have so far been focused on mean changes of crop yield. For keeping stable food supply against abnormal weather as well as climate change, evaluating the year-to-year variations in crop productivity rather than the mean changes is more essential. We here propose a new framework of probabilistic model based on Bayesian inference and Monte Carlo simulation. As an example, we firstly introduce a model on paddy rice production in Japan. It is called PRYSBI (Process- based Regional rice Yield Simulator with Bayesian Inference; Iizumi et al., 2008). The model structure is the same as that of SIMRIW, which was developed and used widely in Japan. The model includes three sub- models describing phenological development, biomass accumulation and maturing of rice crop. These processes are formulated to include response nature of rice plant to weather condition. This model inherently was developed to predict rice growth and yield at plot paddy scale. We applied it to evaluate the large scale rice production with keeping the same model structure. Alternatively, we assumed the parameters as stochastic variables. In order to let the model catch up actual yield at larger scale, model parameters were determined based on agricultural statistical data of each prefecture of Japan together with weather data averaged over the region. The posterior probability distribution functions (PDFs) of parameters included in the model were obtained using Bayesian inference. The MCMC (Markov Chain Monte Carlo) algorithm was conducted to numerically solve the Bayesian theorem. For evaluating the year-to-year changes in rice growth/yield under this framework, we firstly iterate simulations with set of parameter values sampled from the estimated posterior PDF of each parameter and then take the ensemble mean weighted with the posterior PDFs. We will also present another example for maize productivity in China. The framework proposed here provides us information on uncertainties, possibilities and limitations on future improvements in crop model as well.

Modelling crop yield in Iberia under drought conditions

NASA Astrophysics Data System (ADS)

Ribeiro, Andreia; Páscoa, Patrícia; Russo, Ana; Gouveia, Célia

2017-04-01

The improved assessment of the cereal yield and crop loss under drought conditions are essential to meet the increasing economy demands. The growing frequency and severity of the extreme drought conditions in the Iberian Peninsula (IP) has been likely responsible for negative impacts on agriculture, namely on crop yield losses. Therefore, a continuous monitoring of vegetation activity and a reliable estimation of drought impacts is crucial to contribute for the agricultural drought management and development of suitable information tools. This works aims to assess the influence of drought conditions in agricultural yields over the IP, considering cereal yields from mainly rainfed agriculture for the provinces with higher productivity. The main target is to develop a strategy to model drought risk on agriculture for wheat yield at a province level. In order to achieve this goal a combined assessment was made using a drought indicator (Standardized Precipitation Evapotranspiration Index, SPEI) to evaluate drought conditions together with a widely used vegetation index (Normalized Difference Vegetation Index, NDVI) to monitor vegetation activity. A correlation analysis between detrended wheat yield and SPEI was performed in order to assess the vegetation response to each time scale of drought occurrence and also identify the moment of the vegetative cycle when the crop yields are more vulnerable to drought conditions. The time scales and months of SPEI, together with the months of NDVI, better related with wheat yield were chosen to perform a multivariate regression analysis to simulate crop yield. Model results are satisfactory and highlighted the usefulness of such analysis in the framework of developing a drought risk model for crop yields. In terms of an operational point of view, the results aim to contribute to an improved understanding of crop yield management under dry conditions, particularly adding substantial information on the advantages of combining vegetation and hydro-meteorological drought indices for the assessment of cereal yield. Moreover, the present study will provide some guidance on user's decision making process in agricultural practices in the IP, assisting farmers in deciding whether to purchase crop insurance. Acknowledgements: This work was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project IMDROFLOOD (WaterJPI/0004/2014). Ana Russo thanks FCT for granted support (SFRH/BPD/99757/2014). Andreia Ribeiro also thanks FCT for grant PD/BD/114481/2016.

Predicting Great Lakes fish yields: tools and constraints

USGS Publications Warehouse

Lewis, C.A.; Schupp, D.H.; Taylor, W.W.; Collins, J.J.; Hatch, Richard W.

1987-01-01

Prediction of yield is a critical component of fisheries management. The development of sound yield prediction methodology and the application of the results of yield prediction are central to the evolution of strategies to achieve stated goals for Great Lakes fisheries and to the measurement of progress toward those goals. Despite general availability of species yield models, yield prediction for many Great Lakes fisheries has been poor due to the instability of the fish communities and the inadequacy of available data. A host of biological, institutional, and societal factors constrain both the development of sound predictions and their application to management. Improved predictive capability requires increased stability of Great Lakes fisheries through rehabilitation of well-integrated communities, improvement of data collection, data standardization and information-sharing mechanisms, and further development of the methodology for yield prediction. Most important is the creation of a better-informed public that will in turn establish the political will to do what is required.

Drought Tolerance during Reproductive Development is Important for Increasing wheat yield Potential under Climate change in Europe.

PubMed

Senapati, Nimai; Stratonovitch, Pierre; Paul, Matthew J; Semenov, Mikhail A

2018-06-12

Drought stress during reproductive development could drastically reduce grain number and wheat yield, but quantitative evaluation of such effect is unknown under climate change. The objectives of this study were to a) evaluate potential yield benefits of drought tolerance during reproductive development for wheat ideotypes under climate change in Europe, and b) identify potential cultivar parameters for improvement. We used the Sirius wheat model to optimise drought tolerant (DT) and drought sensitive (DS) wheat ideotypes under future 2050 climate scenario at 13 contrasting sites, representing major wheat growing regions in Europe. Averaged over the sites, DT ideotypes achieved 13.4% greater yield compared to DS, with the double yield stability for DT. However, the performances of the ideotypes were site dependent. Mean yield of DT was 28-37% greater compared to DS in southern Europe. In contrast, no yield difference (≤ 1%) between ideotypes was found in north-western Europe. An intermediate yield benefit of 10-23% was found due to drought tolerance in central and eastern Europe. We conclude that tolerance to drought stress during reproductive development is important for high yield potentials and greater yield stability of wheat under climate change in Europe.

Development of predictive weather scenarios for early prediction of rice yield in South Korea

NASA Astrophysics Data System (ADS)

Shin, Y.; Cho, J.; Jung, I.

2017-12-01

International grain prices are becoming unstable due to frequent occurrence of abnormal weather phenomena caused by climate change. Early prediction of grain yield using weather forecast data is important for stabilization of international grain prices. The APEC Climate Center (APCC) is providing seasonal forecast data based on monthly climate prediction models for global seasonal forecasting services. The 3-month and 6-month seasonal forecast data using the multi-model ensemble (MME) technique are provided in their own website, ADSS (APCC Data Service System, http://adss.apcc21.org/). The spatial resolution of seasonal forecast data for each individual model is 2.5°×2.5°(about 250km) and the time scale is created as monthly. In this study, we developed customized weather forecast scenarios that are combined seasonal forecast data and observational data apply to early rice yield prediction model. Statistical downscale method was applied to produce meteorological input data of crop model because field scale crop model (ORYZA2000) requires daily weather data. In order to determine whether the forecasting data is suitable for the crop model, we produced spatio-temporal downscaled weather scenarios and evaluated the predictability by comparison with observed weather data at 57 ASOS stations in South Korea. The customized weather forecast scenarios can be applied to various application fields not only early rice yield prediction. Acknowledgement This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No: PJ012855022017)" Rural Development Administration, Republic of Korea.

Simulation of Oil Palm Shell Pyrolysis to Produce Bio-Oil with Self-Pyrolysis Reactor

NASA Astrophysics Data System (ADS)

Fika, R.; Nelwan, L. O.; Yulianto, M.

2018-05-01

A new self-pyrolysis reactor was designed to reduce the utilization of electric heater due to the energy saving for the production of bio-oil from oil palm shell. The yield of the bio- oil was then evaluated with the developed mathematical model by Sharma [1] with the characteristic of oil palm shell [2]. During the simulation, the temperature on the combustion chamber on the release of the bio-oil was utilized to determine the volatile composition from the combustion of the oil palm shell as fuel. The mass flow was assumed constant for three experiments. The model resulted in a significant difference between the simulated bio-oil and experiments. The bio-oil yields from the simulation were 22.01, 16.36, and 21.89 % (d.b.) meanwhile the experimental yields were 10.23, 9.82, and 8.41% (d.b.). The char yield varied from 30.7 % (d.b.) from the simulation to 40.9 % (d.b.) from the experiment. This phenomenon was due to the development of process temperature over time which was not considered as one of the influential factors in producing volatile matters on the simulation model. Meanwhile the real experiments highly relied on the process conditions (reactor type, temperature over time, gas flow). There was also possibilities of the occurrence of the gasification inside the reactor which caused the liquid yield was not as high as simulated. Further simulation model research on producing the bio-oil yield will be needed to predict the optimum condition and temperature development on the newly self-pyrolysis reactor.

Selected yield tables for plantations and natural stands in Inland Northwest Forests

Treesearch

Albert R. Stage; David L. Renner; Roger C. Chapman

1988-01-01

Yields arrayed by site index and age have been tabulated for plantations of 500 trees per acre, with five thinning regimes, for Douglas-fir, grand fir, and western larch. Yields were also tabulated for naturally regenerated stands of the grand fir-cedar-hemlock ecosystem of the Inland Empire. All yields were estimated with the Prognosis Model for Stand Development,...

Simultaneous or separated; comparison approach for saccharification and fermentation process in producing bio-ethanol from EFB

NASA Astrophysics Data System (ADS)

Bardant, Teuku Beuna; Dahnum, Deliana; Amaliyah, Nur

2017-11-01

Simultaneous Saccharification Fermentation (SSF) of palm oil (Elaeis guineensis) empty fruit bunch (EFB) pulp were investigated as a part of ethanol production process. SSF was investigated by observing the effect of substrate loading variation in range 10-20%w, cellulase loading 5-30 FPU/gr substrate and yeast addition 1-2%v to the ethanol yield. Mathematical model for describing the effects of these three variables to the ethanol yield were developed using Response Surface Methodology-Cheminformatics (RSM-CI). The model gave acceptable accuracy in predicting ethanol yield for Simultaneous Saccharification and Fermentation (SSF) with coefficient of determination (R2) 0.8899. Model validation based on data from previous study gave (R2) 0.7942 which was acceptable for using this model for trend prediction analysis. Trend prediction analysis based on model prediction yield showed that SSF gave trend for higher yield when the process was operated in high enzyme concentration and low substrate concentration. On the other hand, even SHF model showed better yield will be obtained if operated in lower substrate concentration, it still possible to operate in higher substrate concentration with slightly lower yield. Opportunity provided by SHF to operate in high loading substrate make it preferable option for application in commercial scale.

Neural Network Modeling for Gallium Arsenide IC Fabrication Process and Device Characteristics.

NASA Astrophysics Data System (ADS)

Creech, Gregory Lee, I.

This dissertation presents research focused on the utilization of neurocomputing technology to achieve enhanced yield and effective yield prediction in integrated circuit (IC) manufacturing. Artificial neural networks are employed to model complex relationships between material and device characteristics at critical stages of the semiconductor fabrication process. Whole wafer testing was performed on the starting substrate material and during wafer processing at four critical steps: Ohmic or Post-Contact, Post-Recess, Post-Gate and Final, i.e., at completion of fabrication. Measurements taken and subsequently used in modeling include, among others, doping concentrations, layer thicknesses, planar geometries, layer-to-layer alignments, resistivities, device voltages, and currents. The neural network architecture used in this research is the multilayer perceptron neural network (MLPNN). The MLPNN is trained in the supervised mode using the generalized delta learning rule. It has one hidden layer and uses continuous perceptrons. The research focuses on a number of different aspects. First is the development of inter-process stage models. Intermediate process stage models are created in a progressive fashion. Measurements of material and process/device characteristics taken at a specific processing stage and any previous stages are used as input to the model of the next processing stage characteristics. As the wafer moves through the fabrication process, measurements taken at all previous processing stages are used as input to each subsequent process stage model. Secondly, the development of neural network models for the estimation of IC parametric yield is demonstrated. Measurements of material and/or device characteristics taken at earlier fabrication stages are used to develop models of the final DC parameters. These characteristics are computed with the developed models and compared to acceptance windows to estimate the parametric yield. A sensitivity analysis is performed on the models developed during this yield estimation effort. This is accomplished by analyzing the total disturbance of network outputs due to perturbed inputs. When an input characteristic bears no, or little, statistical or deterministic relationship to the output characteristics, it can be removed as an input. Finally, neural network models are developed in the inverse direction. Characteristics measured after the final processing step are used as the input to model critical in-process characteristics. The modeled characteristics are used for whole wafer mapping and its statistical characterization. It is shown that this characterization can be accomplished with minimal in-process testing. The concepts and methodologies used in the development of the neural network models are presented. The modeling results are provided and compared to the actual measured values of each characteristic. An in-depth discussion of these results and ideas for future research are presented.

Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress.

PubMed

Gu, Junfei; Yin, Xinyou; Zhang, Chengwei; Wang, Huaqi; Struik, Paul C

2014-09-01

Genetic markers can be used in combination with ecophysiological crop models to predict the performance of genotypes. Crop models can estimate the contribution of individual markers to crop performance in given environments. The objectives of this study were to explore the use of crop models to design markers and virtual ideotypes for improving yields of rice (Oryza sativa) under drought stress. Using the model GECROS, crop yield was dissected into seven easily measured parameters. Loci for these parameters were identified for a rice population of 94 introgression lines (ILs) derived from two parents differing in drought tolerance. Marker-based values of ILs for each of these parameters were estimated from additive allele effects of the loci, and were fed to the model in order to simulate yields of the ILs grown under well-watered and drought conditions and in order to design virtual ideotypes for those conditions. To account for genotypic yield differences, it was necessary to parameterize the model for differences in an additional trait 'total crop nitrogen uptake' (Nmax) among the ILs. Genetic variation in Nmax had the most significant effect on yield; five other parameters also significantly influenced yield, but seed weight and leaf photosynthesis did not. Using the marker-based parameter values, GECROS also simulated yield variation among 251 recombinant inbred lines of the same parents. The model-based dissection approach detected more markers than the analysis using only yield per se. Model-based sensitivity analysis ranked all markers for their importance in determining yield differences among the ILs. Virtual ideotypes based on markers identified by modelling had 10-36 % more yield than those based on markers for yield per se. This study outlines a genotype-to-phenotype approach that exploits the potential value of marker-based crop modelling in developing new plant types with high yields. The approach can provide more markers for selection programmes for specific environments whilst also allowing for prioritization. Crop modelling is thus a powerful tool for marker design for improved rice yields and for ideotyping under contrasting conditions. © The Author 2014. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Wheat yield estimation at the farm level using TM Landsat and agrometeorological data

NASA Technical Reports Server (NTRS)

Rudorff, B. F. T.; Batista, G. T.

1991-01-01

A model for estimating wheat yields on the farm level was developed, that integrates the Landsat TM data and agrometeorological information. Results obtained for a test site in southern Brasil for years of 1986 and 1987 show that the vegetation index derived from Landsat TM could account for the 60 to 40 percent wheat-yield variability observed between the two crop years. Compared to results using either the Landsat TM vegetation index or the agrometeorological data alone, the joint use of both types of data in a single model yielded a significant improvement.

Visual Basic Growth-and-Yield Models With A Merchandising Optimizer For Planted Slash and Loblolly Pine in the West Gulf Region

Treesearch

R.L. Busby; S.J. Chang; P.R. Pasala; J.C.G. Goelz

2004-01-01

We developed two growth-and-yield models for thinned and unthinned plantations of slash pine (Pinus elliottii Engelm. var elliottii) and loblolly pine (P. taeda L.). The models, VB Merch-Slash and VB Merch-Lob, can be used to forecast product volumes and stand values for stands partitioned into 1-inch diameter-at...

Projecting water yield and ecosystem productivity across the United States by linking an ecohydrological model to WRF dynamically downscaled climate data

Treesearch

Shanlei Sun; Ge Sun; Erika Cohen Mack; Steve McNulty; Peter V. Caldwell; Kai Duan; Yang Zhang

2016-01-01

Quantifying the potential impacts of climatechange on water yield and ecosystem productivity is essential to developing sound watershed restoration plans, andecosystem adaptation and mitigation strategies. This study links an ecohydrological model (Water Supply and StressIndex, WaSSI) with WRF (Weather Research and Forecasting Model) using dynamically downscaled...

Recent climate variability and its impacts on soybean yields in Southern Brazil

NASA Astrophysics Data System (ADS)

Ferreira, Danielle Barros; Rao, V. Brahmananda

2011-08-01

Recent climate variability in rainfall, temperatures (maximum and minimum), and the diurnal temperature range is studied with emphasis on its influence over soybean yields in southern Brazil, during 1969 to 2002. The results showed that the soybean ( Glycine max L. Merril) yields are more affected by changes in temperature during summer, while changes in rainfall are more important during the beginning of plantation and at its peak of development. Furthermore, soybean yields in Paraná are more sensitive to rainfall variations, while soybean yields in the Rio Grande do Sul are more sensitive to variations in temperature. Effects of interannual climatic variability on soybean yields are evaluated through three agro-meteorological models: additive Stewart, multiplicative Rao, and multiplicative Jensen. The Jensen model is able to reproduce the interannual behavior of soybean yield reasonably well.

Numerical Simulation Of Cratering Effects In Adobe

DTIC Science & Technology

2013-07-01

DEVELOPMENT OF MATERIAL PARAMETERS .........................................................7 PROBLEM SETUP...37 PARAMETER ADJUSTMENTS ......................................................................................38 GLOSSARY...dependent yield surface with the Geological Yield Surface (GEO) modeled in CTH using well characterized adobe. By identifying key parameters that

Developing a scalable model of recombinant protein yield from Pichia pastoris: the influence of culture conditions, biomass and induction regime

PubMed Central

Holmes, William J; Darby, Richard AJ; Wilks, Martin DB; Smith, Rodney; Bill, Roslyn M

2009-01-01

Background The optimisation and scale-up of process conditions leading to high yields of recombinant proteins is an enduring bottleneck in the post-genomic sciences. Typical experiments rely on varying selected parameters through repeated rounds of trial-and-error optimisation. To rationalise this, several groups have recently adopted the 'design of experiments' (DoE) approach frequently used in industry. Studies have focused on parameters such as medium composition, nutrient feed rates and induction of expression in shake flasks or bioreactors, as well as oxygen transfer rates in micro-well plates. In this study we wanted to generate a predictive model that described small-scale screens and to test its scalability to bioreactors. Results Here we demonstrate how the use of a DoE approach in a multi-well mini-bioreactor permitted the rapid establishment of high yielding production phase conditions that could be transferred to a 7 L bioreactor. Using green fluorescent protein secreted from Pichia pastoris, we derived a predictive model of protein yield as a function of the three most commonly-varied process parameters: temperature, pH and the percentage of dissolved oxygen in the culture medium. Importantly, when yield was normalised to culture volume and density, the model was scalable from mL to L working volumes. By increasing pre-induction biomass accumulation, model-predicted yields were further improved. Yield improvement was most significant, however, on varying the fed-batch induction regime to minimise methanol accumulation so that the productivity of the culture increased throughout the whole induction period. These findings suggest the importance of matching the rate of protein production with the host metabolism. Conclusion We demonstrate how a rational, stepwise approach to recombinant protein production screens can reduce process development time. PMID:19570229

Modeling the effects of ozone on soybean growth and yield.

PubMed

Kobayashi, K; Miller, J E; Flagler, R B; Heck, W W

1990-01-01

A simple mechanistic model was developed based on an existing growth model in order to address the mechanisms of the effects of ozone on growth and yield of soybean [Glycine max. (L.) Merr. 'Davis'] and interacting effects of other environmental stresses. The model simulates daily growth of soybean plants using environmental data including shortwave radiation, temperature, precipitation, irrigation and ozone concentration. Leaf growth, dry matter accumulation, water budget, nitrogen input and seed growth linked to senescence and abscission of leaves are described in the model. The effects of ozone are modeled as reduced photosynthate production and accelerated senescence. The model was applied to the open-top chamber experiments in which soybean plants were exposed to ozone under two levels of soil moisture regimes. After calibrating the model to the growth data and seed yield, goodness-of-fit of the model was tested. The model fitted well for top dry weight in the vegetative growth phase and also at maturity. The effect of ozone on seen yield was also described satisfactorily by the model. The simulation showed apparent interaction between the effect of ozone and soil moisture stress on the seed yield. The model revealed that further work is needed concerning the effect of ozone on the senescence process and the consequences of alteration of canopy microclimate by the open-top chambers.

Spectral estimates of intercepted solar radiation by corn and soybean canopies

NASA Technical Reports Server (NTRS)

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

1982-01-01

Attention is given to the development of methods for combining spectral and meteorological data in crop yield models which are capable of providing accurate estimates of crop condition and yields throughout the growing season. The present investigation is concerned with initial tests of these concepts using spectral and agronomic data acquired in controlled experiments. The data were acquired at the Purdue University Agronomy Farm, 10 km northwest of West Lafayette, Indiana. Data were obtained throughout several growing seasons for corn and soybeans. Five methods or models for predicting yields were examined. On the basis of the obtained results, it is concluded that estimating intercepted solar radiation using spectral data is a viable approach for merging spectral and meteorological data in crop yield models.

Development of a European Ensemble System for Seasonal Prediction: Application to crop yield

NASA Astrophysics Data System (ADS)

Terres, J. M.; Cantelaube, P.

2003-04-01

Western European agriculture is highly intensive and the weather is the main source of uncertainty for crop yield assessment and for crop management. In the current system, at the time when a crop yield forecast is issued, the weather conditions leading up to harvest time are unknown and are therefore a major source of uncertainty. The use of seasonal weather forecast would bring additional information for the remaining crop season and has valuable benefit for improving the management of agricultural markets and environmentally sustainable farm practices. An innovative method for supplying seasonal forecast information to crop simulation models has been developed in the frame of the EU funded research project DEMETER. It consists in running a crop model on each individual member of the seasonal hindcasts to derive a probability distribution of crop yield. Preliminary results of cumulative probability function of wheat yield provides information on both the yield anomaly and the reliability of the forecast. Based on the spread of the probability distribution, the end-user can directly quantify the benefits and risks of taking weather-sensitive decisions.

A return mapping algorithm for isotropic and anisotropic plasticity models using a line search method

DOE PAGES

Scherzinger, William M.

2016-05-01

The numerical integration of constitutive models in computational solid mechanics codes allows for the solution of boundary value problems involving complex material behavior. Metal plasticity models, in particular, have been instrumental in the development of these codes. Here, most plasticity models implemented in computational codes use an isotropic von Mises yield surface. The von Mises, of J 2, yield surface has a simple predictor-corrector algorithm - the radial return algorithm - to integrate the model.

Crop Yield Predictions - High Resolution Statistical Model for Intra-season Forecasts Applied to Corn in the US

NASA Astrophysics Data System (ADS)

Cai, Y.

2017-12-01

Accurately forecasting crop yields has broad implications for economic trading, food production monitoring, and global food security. However, the variation of environmental variables presents challenges to model yields accurately, especially when the lack of highly accurate measurements creates difficulties in creating models that can succeed across space and time. In 2016, we developed a sequence of machine-learning based models forecasting end-of-season corn yields for the US at both the county and national levels. We combined machine learning algorithms in a hierarchical way, and used an understanding of physiological processes in temporal feature selection, to achieve high precision in our intra-season forecasts, including in very anomalous seasons. During the live run, we predicted the national corn yield within 1.40% of the final USDA number as early as August. In the backtesting of the 2000-2015 period, our model predicts national yield within 2.69% of the actual yield on average already by mid-August. At the county level, our model predicts 77% of the variation in final yield using data through the beginning of August and improves to 80% by the beginning of October, with the percentage of counties predicted within 10% of the average yield increasing from 68% to 73%. Further, the lowest errors are in the most significant producing regions, resulting in very high precision national-level forecasts. In addition, we identify the changes of important variables throughout the season, specifically early-season land surface temperature, and mid-season land surface temperature and vegetation index. For the 2017 season, we feed 2016 data to the training set, together with additional geospatial data sources, aiming to make the current model even more precise. We will show how our 2017 US corn yield forecasts converges in time, which factors affect the yield the most, as well as present our plans for 2018 model adjustments.

[Prediction of the side-cut product yield of atmospheric/vacuum distillation unit by NIR crude oil rapid assay].

PubMed

Wang, Yan-Bin; Hu, Yu-Zhong; Li, Wen-Le; Zhang, Wei-Song; Zhou, Feng; Luo, Zhi

2014-10-01

In the present paper, based on the fast evaluation technique of near infrared, a method to predict the yield of atmos- pheric and vacuum line was developed, combined with H/CAMS software. Firstly, the near-infrared (NIR) spectroscopy method for rapidly determining the true boiling point of crude oil was developed. With commercially available crude oil spectroscopy da- tabase and experiments test from Guangxi Petrochemical Company, calibration model was established and a topological method was used as the calibration. The model can be employed to predict the true boiling point of crude oil. Secondly, the true boiling point based on NIR rapid assay was converted to the side-cut product yield of atmospheric/vacuum distillation unit by H/CAMS software. The predicted yield and the actual yield of distillation product for naphtha, diesel, wax and residual oil were compared in a 7-month period. The result showed that the NIR rapid crude assay can predict the side-cut product yield accurately. The near infrared analytic method for predicting yield has the advantages of fast analysis, reliable results, and being easy to online operate, and it can provide elementary data for refinery planning optimization and crude oil blending.

Theoretical Development of an Orthotropic Elasto-Plastic Generalized Composite Material Model

NASA Technical Reports Server (NTRS)

Goldberg, Robert; Carney, Kelly; DuBois, Paul; Hoffarth, Canio; Harrington, Joseph; Rajan, Subramaniam; Blankenhorn, Gunther

2014-01-01

The need for accurate material models to simulate the deformation, damage and failure of polymer matrix composites is becoming critical as these materials are gaining increased usage in the aerospace and automotive industries. While there are several composite material models currently available within LSDYNA (Livermore Software Technology Corporation), there are several features that have been identified that could improve the predictive capability of a composite model. To address these needs, a combined plasticity and damage model suitable for use with both solid and shell elements is being developed and is being implemented into LS-DYNA as MAT_213. A key feature of the improved material model is the use of tabulated stress-strain data in a variety of coordinate directions to fully define the stress-strain response of the material. To date, the model development efforts have focused on creating the plasticity portion of the model. The Tsai-Wu composite failure model has been generalized and extended to a strain-hardening based orthotropic yield function with a nonassociative flow rule. The coefficients of the yield function, and the stresses to be used in both the yield function and the flow rule, are computed based on the input stress-strain curves using the effective plastic strain as the tracking variable. The coefficients in the flow rule are computed based on the obtained stress-strain data. The developed material model is suitable for implementation within LS-DYNA for use in analyzing the nonlinear response of polymer composites.

Forecasting economic gains from intensive plantation management using unrealistic yield over input curves

Treesearch

David B. South; Curtis L. VanderSchaaf; Larry D. Teeter

2006-01-01

Some researchers claim that continuously increasing intensive plantation management will increase profits and reduce the unit cost of wood production while others believe in the law of diminishing returns. We developed four hypothetical production models where yield is a function of silvicultural effort. Models that produced unrealistic results were (1) an exponential...

Predicting future US water yield and ecosystem productivity by linking an ecohydrological model to WRF dynamically downscaled climate projections

Treesearch

S. Sun; Ge Sun; Erika Cohen Mack; Steve McNulty; Peter Caldwell; K. Duan; Y. Zhang

2015-01-01

Quantifying the potential impacts of climate change on water yield and ecosystem productivity (i.e., carbon balances) is essential to developing sound watershed restoration plans, and climate change adaptation and mitigation strategies. This study links an ecohydrological model (Water Supply and Stress Index, WaSSI) with WRF (Weather Research and Forecasting Model)...

Coating flow of an anti-HIV microbicide gel: boundary dilution and yield stress

NASA Astrophysics Data System (ADS)

Szeri, Andrew J.; Tasoglu, Savas; Park, Su Chan; Katz, David F.

2010-11-01

A recent study has confirmed, for the first time, that a vaginal gel formulation of the antiretroviral drug Tenofovir, when topically applied, significantly inhibits sexual HIV transmission to women [1]. However, the gel for this drug, and anti-HIV microbicide gels in general, have not been designed using an understanding of how gel spreading govern successful drug delivery. Elastohydrodynamic lubrication theory can be applied to model spreading of microbicide gels [2]. Here, we extend our initial analysis: we incorporate a yield stress, and we model the effects of gel dilution due to contact with vaginal fluid produced at the gel-tissue interface. Our model developed in [2] is supplemented with a convective-diffusive transport equation to characterize dilution, and solved using a multi-step scheme in a moving domain. The association between local dilution of gel and rheological properties is obtained experimentally. To model the common yield stress property of gels, we proceed by scaling analysis first. This establishes the conditions for validity of lubrication theory of a shear thinning yield stress fluid. This involves further development of the model in [2], incorporating a biviscosity model.[4pt] [1] Karim, et al., Science, 2010.[0pt] [2] Szeri, et al., Phy. of Fluids, 2008.

Prediction of winter wheat high yield from remote sensing based model: application in United States and Ukraine

NASA Astrophysics Data System (ADS)

Franch, B.; Vermote, E.; Roger, J. C.; Skakun, S.; Becker-Reshef, I.; Justice, C. O.

2017-12-01

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. In Becker-Reshef et al. (2010) and Franch et al. (2015) we developed an empirical generalized model for forecasting winter wheat yield. It is based on the relationship between the Normalized Difference Vegetation Index (NDVI) at the peak of the growing season and the Growing Degree Day (GDD) information extracted from NCEP/NCAR reanalysis data. These methods were applied to MODIS CMG data in Ukraine, the US and China with errors around 10%. However, the NDVI is saturated for yield values higher than 4 MT/ha. As a consequence, the model had to be re-calibrated in each country and the validation of the national yields showed low correlation coefficients. In this study we present a new model based on the extrapolation of the pure wheat signal (100% of wheat within the pixel) from MODIS data at 1km resolution and using the Difference Vegetation Index (DVI). The model has been applied to monitor the national yield of winter wheat in the United States and Ukraine from 2001 to 2016.

Simulation of streamflow and the effects of brush management on water yields in the Double Mountain Fork Brazos River watershed, western Texas 1994–2013

USGS Publications Warehouse

Harwell, Glenn R.; Stengel, Victoria G.; Bumgarner, Johnathan R.

2016-04-20

The calibrated watershed model was used to perform brush-management simulations. The National Land Cover Database 2006, which was the land-cover data used to develop the watershed model, was modified to simulate shrubland replacement with grassland in each of the 35 model subbasins. After replacement of shrubland with grassland in areas with land slope less than 20 percent and excluding riparian areas, the modeled 20-year (1994 through 2013) water yields to Lake Alan Henry increased by 114,000 acre-feet or about 5,700 acre-feet per year. In terms of the increase in water yield per acre of shrubland replaced with grassland, the average annual increase in water yield was 17,300 gallons per acre. Within the modeled subbasins, the increase in average annual water yield ranged from 5,850 to 34,400 gallons per acre of shrubland replaced with grassland. Subbasins downstream from the Justiceburg gage had a higher average annual increase in water yield (21,700 gallons per acre) than subbasins upstream from the streamflow-gaging station (16,800 gallons per acre).

Relationships between primary production and crop yields in semi-arid and arid irrigated agro-ecosystems

NASA Astrophysics Data System (ADS)

Jaafar, H. H.; Ahmad, F. A.

2015-04-01

In semi-arid areas within the MENA region, food security problems are the main problematic imposed. Remote sensing can be a promising too early diagnose food shortages and further prevent the population from famine risks. This study is aimed at examining the possibility of forecasting yield before harvest from remotely sensed MODIS-derived Enhanced Vegetation Index (EVI), Net photosynthesis (net PSN), and Gross Primary Production (GPP) in semi-arid and arid irrigated agro-ecosystems within the conflict affected country of Syria. Relationships between summer yield and remotely sensed indices were derived and analyzed. Simple regression spatially-based models were developed to predict summer crop production. The validation of these models was tested during conflict years. A significant correlation (p<0.05) was found between summer crop yield and EVI, GPP and net PSN. Results indicate the efficiency of remotely sensed-based models in predicting summer yield, mostly for cotton yields and vegetables. Cumulative summer EVI-based model can predict summer crop yield during crisis period, with deviation less than 20% where vegetables are the major yield. This approach prompts to an early assessment of food shortages and lead to a real time management and decision making, especially in periods of crisis such as wars and drought.

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.

Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology

NASA Astrophysics Data System (ADS)

Najafi, Ehsan; Devineni, Naresh; Khanbilvardi, Reza M.; Kogan, Felix

2018-03-01

During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water crisis, deforestation, and climate change threaten the global food security. An understanding of the variables that caused past changes in crop yields can help improve future crop prediction models. In this article, we present a comprehensive global analysis of the changes in the crop yields and how they relate to different large-scale and regional climate variables, climate change variables and technology in a unified framework. A new multilevel model for yield prediction at the country level is developed and demonstrated. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. El Niño-southern oscillation (ENSO), Palmer drought severity index (PDSI), geopotential height anomalies (GPH), historical carbon dioxide (CO2) concentration and country-based time series of GDP per capita as an approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. Results indicate that these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out-of-sample verifications. While some countries were not generally affected by climatic factors, PDSI and GPH acted both positively and negatively in different regions for crop yields in many countries.

Variation of yield loci in finite element analysis by considering texture evolution for AA5042 aluminum sheets

NASA Astrophysics Data System (ADS)

Yoon, Jonghun; Kim, Kyungjin; Yoon, Jeong Whan

2013-12-01

Yield function has various material parameters that describe how materials respond plastically in given conditions. However, a significant number of mechanical tests are required to identify the many material parameters for yield function. In this study, an effective method using crystal plasticity through a virtual experiment is introduced to develop the anisotropic yield function for AA5042. The crystal plasticity approach was used to predict the anisotropic response of the material in order to consider a number of stress or strain modes that would not otherwise be evident through mechanical testing. A rate-independent crystal plasticity model based on a smooth single crystal yield surface, which removes the innate ambiguity problem within the rate-independent model and Taylor model for polycrystalline deformation behavior were employed to predict the material's response in the balanced biaxial stress, pure shear, and plane strain states to identify the parameters for the anisotropic yield function of AA5042.

Simulating large-scale crop yield by using perturbed-parameter ensemble method

NASA Astrophysics Data System (ADS)

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

2010-12-01

Toshichika Iizumi, Masayuki Yokozawa, Gen Sakurai, Motoki Nishimori Agro-Meteorology Division, National Institute for Agro-Environmental Sciences, Japan Abstract One of concerning issues of food security under changing climate is to predict the inter-annual variation of crop production induced by climate extremes and modulated climate. To secure food supply for growing world population, methodology that can accurately predict crop yield on a large scale is needed. However, for developing a process-based large-scale crop model with a scale of general circulation models (GCMs), 100 km in latitude and longitude, researchers encounter the difficulties in spatial heterogeneity of available information on crop production such as cultivated cultivars and management. This study proposed an ensemble-based simulation method that uses a process-based crop model and systematic parameter perturbation procedure, taking maize in U.S., China, and Brazil as examples. The crop model was developed modifying the fundamental structure of the Soil and Water Assessment Tool (SWAT) to incorporate the effect of heat stress on yield. We called the new model PRYSBI: the Process-based Regional-scale Yield Simulator with Bayesian Inference. The posterior probability density function (PDF) of 17 parameters, which represents the crop- and grid-specific features of the crop and its uncertainty under given data, was estimated by the Bayesian inversion analysis. We then take 1500 ensemble members of simulated yield values based on the parameter sets sampled from the posterior PDF to describe yearly changes of the yield, i.e. perturbed-parameter ensemble method. The ensemble median for 27 years (1980-2006) was compared with the data aggregated from the county yield. On a country scale, the ensemble median of the simulated yield showed a good correspondence with the reported yield: the Pearson’s correlation coefficient is over 0.6 for all countries. In contrast, on a grid scale, the correspondence is still high in most grids regardless of the countries. However, the model showed comparatively low reproducibility in the slope areas, such as around the Rocky Mountains in South Dakota, around the Great Xing'anling Mountains in Heilongjiang, and around the Brazilian Plateau. As there is a wide-ranging local climate conditions in the complex terrain, such as the slope of mountain, the GCM grid-scale weather inputs is likely one of major sources of error. The results of this study highlight the benefits of the perturbed-parameter ensemble method in simulating crop yield on a GCM grid scale: (1) the posterior PDF of parameter could quantify the uncertainty of parameter value of the crop model associated with the local crop production aspects; (2) the method can explicitly account for the uncertainty of parameter value in the crop model simulations; (3) the method achieve a Monte Carlo approximation of probability of sub-grid scale yield, accounting for the nonlinear response of crop yield to weather and management; (4) the method is therefore appropriate to aggregate the simulated sub-grid scale yields to a grid-scale yield and it may be a reason for high performance of the model in capturing inter-annual variation of yield.

Random Forests for Global and Regional Crop Yield Predictions.

PubMed

Jeong, Jig Han; Resop, Jonathan P; Mueller, Nathaniel D; Fleisher, David H; Yun, Kyungdahm; Butler, Ethan E; Timlin, Dennis J; Shim, Kyo-Moon; Gerber, James S; Reddy, Vangimalla R; Kim, Soo-Hyung

2016-01-01

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data.

Development of 3D and 4D Bridge Models and Plans

DOT National Transportation Integrated Search

2018-05-28

Since 2012, MDOT has been leading national efforts to modernize design development with 3D modeling. Early focus on roadway projects yielded streamlined plan production and digital data for construction. As MDOT pivots to 3D model-centric design, nat...

Increasing influence of heat stress on French maize yields from the 1960s to the 2030s

PubMed Central

Hawkins, Ed; Fricker, Thomas E; Challinor, Andrew J; Ferro, Christopher A T; Kit Ho, Chun; Osborne, Tom M

2013-01-01

Improved crop yield forecasts could enable more effective adaptation to climate variability and change. Here, we explore how to combine historical observations of crop yields and weather with climate model simulations to produce crop yield projections for decision relevant timescales. Firstly, the effects on historical crop yields of improved technology, precipitation and daily maximum temperatures are modelled empirically, accounting for a nonlinear technology trend and interactions between temperature and precipitation, and applied specifically for a case study of maize in France. The relative importance of precipitation variability for maize yields in France has decreased significantly since the 1960s, likely due to increased irrigation. In addition, heat stress is found to be as important for yield as precipitation since around 2000. A significant reduction in maize yield is found for each day with a maximum temperature above 32 °C, in broad agreement with previous estimates. The recent increase in such hot days has likely contributed to the observed yield stagnation. Furthermore, a general method for producing near-term crop yield projections, based on climate model simulations, is developed and utilized. We use projections of future daily maximum temperatures to assess the likely change in yields due to variations in climate. Importantly, we calibrate the climate model projections using observed data to ensure both reliable temperature mean and daily variability characteristics, and demonstrate that these methods work using retrospective predictions. We conclude that, to offset the projected increased daily maximum temperatures over France, improved technology will need to increase base level yields by 12% to be confident about maintaining current levels of yield for the period 2016–2035; the current rate of yield technology increase is not sufficient to meet this target. PMID:23504849

Parsing multiple processes of high temperature impacts on corn/soybean yield using a newly developed CLM-APSIM modeling framework

NASA Astrophysics Data System (ADS)

Peng, B.; Guan, K.; Chen, M.

2016-12-01

Future agricultural production faces a grand challenge of higher temperature under climate change. There are multiple physiological or metabolic processes of how high temperature affects crop yield. Specifically, we consider the following major processes: (1) direct temperature effects on photosynthesis and respiration; (2) speed-up growth rate and the shortening of growing season; (3) heat stress during reproductive stage (flowering and grain-filling); (4) high-temperature induced increase of atmospheric water demands. In this work, we use a newly developed modeling framework (CLM-APSIM) to simulate the corn and soybean growth and explicitly parse the above four processes. By combining the strength of CLM in modeling surface biophysical (e.g., hydrology and energy balance) and biogeochemical (e.g., photosynthesis and carbon-nitrogen interactions), as well as that of APSIM in modeling crop phenology and reproductive stress, the newly developed CLM-APSIM modeling framework enables us to diagnose the impacts of high temperature stress through different processes at various crop phenology stages. Ground measurements from the advanced SoyFACE facility at University of Illinois is used here to calibrate, validate, and improve the CLM-APSIM modeling framework at the site level. We finally use the CLM-APSIM modeling framework to project crop yield for the whole US Corn Belt under different climate scenarios.

Climatically driven yield variability of major crops in Khakassia (South Siberia)

NASA Astrophysics Data System (ADS)

Babushkina, Elena A.; Belokopytova, Liliana V.; Zhirnova, Dina F.; Shah, Santosh K.; Kostyakova, Tatiana V.

2018-06-01

We investigated the variability of yield of the three main crop cultures in the Khakassia Republic: spring wheat, spring barley, and oats. In terms of yield values, variability characteristics, and climatic response, the agricultural territory of Khakassia can be divided into three zones: (1) the Northern Zone, where crops yield has a high positive response to the amount of precipitation, May-July, and a moderately negative one to the temperatures of the same period; (2) the Central Zone, where crops yield depends mainly on temperatures; and (3) the Southern Zone, where climate has the least expressed impact on yield. The dominant pattern in the crops yield is caused by water stress during periods of high temperatures and low moisture supply with heat stress as additional reason. Differences between zones are due to combinations of temperature latitudinal gradient, precipitation altitudinal gradient, and the presence of a well-developed hydrological network and the irrigational system as moisture sources in the Central Zone. More detailed analysis shows differences in the climatic sensitivity of crops during phases of their vegetative growth and grain development and, to a lesser extent, during harvesting period. Multifactor linear regression models were constructed to estimate climate- and autocorrelation-induced variability of the crops yield. These models allowed prediction of the possibility of yield decreasing by at least 2-11% in the next decade due to increasing of the regional summer temperatures.

Climatically driven yield variability of major crops in Khakassia (South Siberia)

NASA Astrophysics Data System (ADS)

Babushkina, Elena A.; Belokopytova, Liliana V.; Zhirnova, Dina F.; Shah, Santosh K.; Kostyakova, Tatiana V.

2017-12-01

We investigated the variability of yield of the three main crop cultures in the Khakassia Republic: spring wheat, spring barley, and oats. In terms of yield values, variability characteristics, and climatic response, the agricultural territory of Khakassia can be divided into three zones: (1) the Northern Zone, where crops yield has a high positive response to the amount of precipitation, May-July, and a moderately negative one to the temperatures of the same period; (2) the Central Zone, where crops yield depends mainly on temperatures; and (3) the Southern Zone, where climate has the least expressed impact on yield. The dominant pattern in the crops yield is caused by water stress during periods of high temperatures and low moisture supply with heat stress as additional reason. Differences between zones are due to combinations of temperature latitudinal gradient, precipitation altitudinal gradient, and the presence of a well-developed hydrological network and the irrigational system as moisture sources in the Central Zone. More detailed analysis shows differences in the climatic sensitivity of crops during phases of their vegetative growth and grain development and, to a lesser extent, during harvesting period. Multifactor linear regression models were constructed to estimate climate- and autocorrelation-induced variability of the crops yield. These models allowed prediction of the possibility of yield decreasing by at least 2-11% in the next decade due to increasing of the regional summer temperatures.

A Growth and Yield Model for Thinned Stands of Yellow-Poplar

Treesearch

Bruce R. Knoebel; Harold E. Burkhart; Donald E. Beck

1986-01-01

Simultaneous growth and yield equations were developed for predicting basal area growth and cubic-foot volume growth and yield in thinned stands of yellow-poplar. A joint loss function involving both volume and basal area was used to estimate the coefficients in the system of equations. The estimates obtained were analytically compatible, invariant for projection...

Simulated yields for managed northern hardwood stands

Treesearch

Dale S. Solomon; William B. Leak; William B. Leak

1986-01-01

Board-foot and cubic-foot yields developed with the forest growth model SlMTlM are presented for northern hardwood stands grown with and without management. SIMTIM has been modified to include more accurate growth rates by species, a new stocking chart, and yields that reflect species values and quality classes. Treatments range from no thinning to intensive quality...

Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability

NASA Technical Reports Server (NTRS)

Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos

2016-01-01

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

Estimating sediment yield in the southern Appalachians using WCS-SED

Treesearch

Paul Bolstad; Andrew Jenks; Mark Riedel; James M. Vose

2006-01-01

We measured and modeled sediment yield over two months on five watersheds in the southern Appalachian Mountains of North Carolina. These watersheds contained first and second-order streams and are primarily forested, but span the development gradient common in this region, with up to 10 percent in suburban and transitional development and up to 27% low-intensity...

Green lumber grade yields from factory grade logs of three oak species

Treesearch

Daniel A. Yaussy

1986-01-01

Multivariate regression models were developed to predict green board foot yields for the seven common factory lumber grades processed from white, black, and chestnut oak factory grade logs. These models use the standard log measurements of grade, scaling diameter, log length, and proportion of scaling defect. Any combination of lumber grades (such as 1 Common and...

Green lumber grade yields from black cherry and red maple factory grade logs sawed at band and circular mills

Treesearch

Daniel A. Yaussy

1989-01-01

Multivariate regression models were developed to predict green board-foot yields (1 board ft. = 2.360 dm 3) for the standard factory lumber grades processed from black cherry (Prunus serotina Ehrh.) and red maple (Acer rubrum L.) factory grade logs sawed at band and circular sawmills. The models use log...

Pilot utilization plan for satellite data-based service for agriculture in Poland

NASA Astrophysics Data System (ADS)

Gatkowska, Martyna; Paradowski, Karol; Wróbel, Karolina

2017-10-01

The paper aims at demonstrating the assumptions and achievements of the Pilot Utilization Plan Activities performed within the Project ASAP "Advanced Sustainable Agricultural Production", co-financed by European Space Agency under the ARTES IAP Programme. Within the course of the project, the Pilot Utilization Plan (PilUP) activities are performed in order to develop the remote sensing based models, and further calibrate and validate them in order to achieve the accuracy, which meets the requirements of paying customers. The completion of the first PilUP resulted in development of the following models based of Landsat 8 and Sentinel 2 satellite data: model of homogenous polygons demarcation on the basis of comparison of electromagnetic scanning results and bare soil spectral reflectance, model of problematic areas indication and model for yield potential, delivered on the basis of NDVI map developed 1 month before harvest and the map of yield/collected yield derived from Users participating in PilUP. The second edition of the PilUP is being conducted between March 2017 until the end of 2017. This edition includes farmers and insurance companies. The following activities are planned: development of model for delimitation of loses due to unfavorable wintering of winter crops and validation of the model with in-situ data collected by the insurance companies in-field investigators, further enhancement of the model for homogenous polygons delimitation and primary indication of soil productivity and testing of the applicability and viability of map of problematic areas with the farmers.

Crop status evaluations and yield predictions

NASA Technical Reports Server (NTRS)

Haun, J. R.

1975-01-01

A model was developed for predicting the day 50 percent of the wheat crop is planted in North Dakota. This model incorporates location as an independent variable. The Julian date when 50 percent of the crop was planted for the nine divisions of North Dakota for seven years was regressed on the 49 variables through the step-down multiple regression procedure. This procedure begins with all of the independent variables and sequentially removes variables that are below a predetermined level of significance after each step. The prediction equation was tested on daily data. The accuracy of the model is considered satisfactory for finding the historic dates on which to initiate yield prediction model. Growth prediction models were also developed for spring wheat.

Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach.

PubMed

Cao, Hongliang; Xin, Ya; Yuan, Qiaoxia

2016-02-01

To predict conveniently the biochar yield from cattle manure pyrolysis, intelligent modeling approach was introduced in this research. A traditional artificial neural networks (ANN) model and a novel least squares support vector machine (LS-SVM) model were developed. For the identification and prediction evaluation of the models, a data set with 33 experimental data was used, which were obtained using a laboratory-scale fixed bed reaction system. The results demonstrated that the intelligent modeling approach is greatly convenient and effective for the prediction of the biochar yield. In particular, the novel LS-SVM model has a more satisfying predicting performance and its robustness is better than the traditional ANN model. The introduction and application of the LS-SVM modeling method gives a successful example, which is a good reference for the modeling study of cattle manure pyrolysis process, even other similar processes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Statistical modeling of SRAM yield performance and circuit variability

NASA Astrophysics Data System (ADS)

Cheng, Qi; Chen, Yijian

2015-03-01

In this paper, we develop statistical models to investigate SRAM yield performance and circuit variability in the presence of self-aligned multiple patterning (SAMP) process. It is assumed that SRAM fins are fabricated by a positivetone (spacer is line) self-aligned sextuple patterning (SASP) process which accommodates two types of spacers, while gates are fabricated by a more pitch-relaxed self-aligned quadruple patterning (SAQP) process which only allows one type of spacer. A number of possible inverter and SRAM structures are identified and the related circuit multi-modality is studied using the developed failure-probability and yield models. It is shown that SRAM circuit yield is significantly impacted by the multi-modality of fins' spatial variations in a SRAM cell. The sensitivity of 6-transistor SRAM read/write failure probability to SASP process variations is calculated and the specific circuit type with the highest probability to fail in the reading/writing operation is identified. Our study suggests that the 6-transistor SRAM configuration may not be scalable to 7-nm half pitch and more robust SRAM circuit design needs to be researched.

Weather-based forecasts of California crop yields

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

Lobell, D B; Cahill, K N; Field, C B

2005-09-26

Crop yield forecasts provide useful information to a range of users. Yields for several crops in California are currently forecast based on field surveys and farmer interviews, while for many crops official forecasts do not exist. As broad-scale crop yields are largely dependent on weather, measurements from existing meteorological stations have the potential to provide a reliable, timely, and cost-effective means to anticipate crop yields. We developed weather-based models of state-wide yields for 12 major California crops (wine grapes, lettuce, almonds, strawberries, table grapes, hay, oranges, cotton, tomatoes, walnuts, avocados, and pistachios), and tested their accuracy using cross-validation over themore » 1980-2003 period. Many crops were forecast with high accuracy, as judged by the percent of yield variation explained by the forecast, the number of yields with correctly predicted direction of yield change, or the number of yields with correctly predicted extreme yields. The most successfully modeled crop was almonds, with 81% of yield variance captured by the forecast. Predictions for most crops relied on weather measurements well before harvest time, allowing for lead times that were longer than existing procedures in many cases.« less

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

Kenkel, Philip; Holcomb, Rodney B.

In order for the biofuel industry to meet the RFS benchmarks for biofuels, new feedstock sources and production systems will have to be identified and evaluated. The Southern Plains has the potential to produce over a billion gallons of biofuels from regionally produced alternative crops, agricultural residues, and animal fats. While information on biofuel conversion processes is available, it is difficult for entrepreneurs, community planners and other interested individuals to determine the feasibility of biofuel processes or to match production alternatives with feed stock availability and community infrastructure. This project facilitates the development of biofuel production from these regionally availablemore » feed stocks. Project activities are concentrated in five major areas. The first component focused on demonstrating the supply of biofuel feedstocks. This involves modeling the yield and cost of production of dedicated energy crops at the county level. In 1991 the DOE selected switchgrass as a renewable source to produce transportation fuel after extensive evaluations of many plant species in multiple location (Caddel et al,. 2010). However, data on the yield and cost of production of switchgrass are limited. This deficiency in demonstrating the supply of biofuel feedstocks was addressed by modeling the potential supply and geographic variability of switchgrass yields based on relationship of available switchgrass yields to the yields of other forage crops. This model made it possible to create a database of projected switchgrass yields for five different soil types at the county level. A major advantage of this methodology is that the supply projections can be easily updated as improved varieties of switchgrass are developed and additional yield data becomes available. The modeling techniques are illustrated using the geographic area of Oklahoma. A summary of the regional supply is then provided.« less

An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data

NASA Astrophysics Data System (ADS)

Shao, Yang; Campbell, James B.; Taff, Gregory N.; Zheng, Baojuan

2015-06-01

The Midwestern United States is one of the world's most important corn-producing regions. Monitoring and forecasting of corn yields in this intensive agricultural region are important activities to support food security, commodity markets, bioenergy industries, and formation of national policies. This study aims to develop forecasting models that have the capability to provide mid-season prediction of county-level corn yields for the entire Midwestern United States. We used multi-temporal MODIS NDVI (normalized difference vegetation index) 16-day composite data as the primary input, with digital elevation model (DEM) and parameter-elevation relationships on independent slopes model (PRISM) climate data as additional inputs. The DEM and PRISM data, along with three types of cropland masks were tested and compared to evaluate their impacts on model predictive accuracy. Our results suggested that the use of general cropland masks (e.g., summer crop or cultivated crops) generated similar results compared with use of an annual corn-specific mask. Leave-one-year-out cross-validation resulted in an average R2 of 0.75 and RMSE value of 1.10 t/ha. Using a DEM as an additional model input slightly improved performance, while inclusion of PRISM climate data appeared not to be important for our regional corn-yield model. Furthermore, our model has potential for real-time/early prediction. Our corn yield esitmates are available as early as late July, which is an improvement upon previous corn-yield prediction models. In addition to annual corn yield forecasting, we examined model uncertainties through spatial and temporal analysis of the model's predictive error distribution. The magnitude of predictive error (by county) appears to be associated with the spatial patterns of corn fields in the study area.

Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database.

PubMed

Niu, Mutian; Kebreab, Ermias; Hristov, Alexander N; Oh, Joonpyo; Arndt, Claudia; Bannink, André; Bayat, Ali R; Brito, André F; Boland, Tommy; Casper, David; Crompton, Les A; Dijkstra, Jan; Eugène, Maguy A; Garnsworthy, Phil C; Haque, Md Najmul; Hellwing, Anne L F; Huhtanen, Pekka; Kreuzer, Michael; Kuhla, Bjoern; Lund, Peter; Madsen, Jørgen; Martin, Cécile; McClelland, Shelby C; McGee, Mark; Moate, Peter J; Muetzel, Stefan; Muñoz, Camila; O'Kiely, Padraig; Peiren, Nico; Reynolds, Christopher K; Schwarm, Angela; Shingfield, Kevin J; Storlien, Tonje M; Weisbjerg, Martin R; Yáñez-Ruiz, David R; Yu, Zhongtang

2018-02-16

Enteric methane (CH 4 ) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH 4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH 4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH 4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH 4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH 4 prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH 4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH 4 emission conversion factors for specific regions are required to improve CH 4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH 4 yield and intensity prediction, information on milk yield and composition is required for better estimation. © 2018 John Wiley & Sons Ltd.

New NIR Calibration Models Speed Biomass Composition and Reactivity Characterization

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

2015-09-01

Obtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. This highlight describes NREL's work to use near-infrared (NIR) spectroscopy and partial least squares multivariate analysis to develop calibration models to predict the feedstock composition and the release and yield of soluble carbohydrates generated by a bench-scale dilute acid pretreatment and enzymatic hydrolysis assay. This highlight is being developed for the September 2015 Alliance S&T Board meeting.

A new standard model for milk yield in dairy cows based on udder physiology at the milking-session level.

PubMed

Gasqui, Patrick; Trommenschlager, Jean-Marie

2017-08-21

Milk production in dairy cow udders is a complex and dynamic physiological process that has resisted explanatory modelling thus far. The current standard model, Wood's model, is empirical in nature, represents yield in daily terms, and was published in 1967. Here, we have developed a dynamic and integrated explanatory model that describes milk yield at the scale of the milking session. Our approach allowed us to formally represent and mathematically relate biological features of known relevance while accounting for stochasticity and conditional elements in the form of explicit hypotheses, which could then be tested and validated using real-life data. Using an explanatory mathematical and biological model to explore a physiological process and pinpoint potential problems (i.e., "problem finding"), it is possible to filter out unimportant variables that can be ignored, retaining only those essential to generating the most realistic model possible. Such modelling efforts are multidisciplinary by necessity. It is also helpful downstream because model results can be compared with observed data, via parameter estimation using maximum likelihood and statistical testing using model residuals. The process in its entirety yields a coherent, robust, and thus repeatable, model.

Linking crop yield anomalies to large-scale atmospheric circulation in Europe.

PubMed

Ceglar, Andrej; Turco, Marco; Toreti, Andrea; Doblas-Reyes, Francisco J

2017-06-15

Understanding the effects of climate variability and extremes on crop growth and development represents a necessary step to assess the resilience of agricultural systems to changing climate conditions. This study investigates the links between the large-scale atmospheric circulation and crop yields in Europe, providing the basis to develop seasonal crop yield forecasting and thus enabling a more effective and dynamic adaptation to climate variability and change. Four dominant modes of large-scale atmospheric variability have been used: North Atlantic Oscillation, Eastern Atlantic, Scandinavian and Eastern Atlantic-Western Russia patterns. Large-scale atmospheric circulation explains on average 43% of inter-annual winter wheat yield variability, ranging between 20% and 70% across countries. As for grain maize, the average explained variability is 38%, ranging between 20% and 58%. Spatially, the skill of the developed statistical models strongly depends on the large-scale atmospheric variability impact on weather at the regional level, especially during the most sensitive growth stages of flowering and grain filling. Our results also suggest that preceding atmospheric conditions might provide an important source of predictability especially for maize yields in south-eastern Europe. Since the seasonal predictability of large-scale atmospheric patterns is generally higher than the one of surface weather variables (e.g. precipitation) in Europe, seasonal crop yield prediction could benefit from the integration of derived statistical models exploiting the dynamical seasonal forecast of large-scale atmospheric circulation.

Hydrostatic Stress Effect on the Yield Behavior of Inconel 100

NASA Technical Reports Server (NTRS)

Allen, Phillip A.; Wilson, Christopher D.

2003-01-01

Classical metal plasticity theory assumes that hydrostatic stress has negligible effect on the yield and postyield behavior of metals. Recent reexaminations of classical theory have revealed a significant effect of hydrostatic stress on the yield behavior of various geometries. Fatigue tests and nonlinear finite element analyses (FEA) of Inconel 100 (IN100) equal-arm bend specimens and new monotonic tests and nonlinear finite element analyses of IN100 smooth tension, smooth compression, and double-edge notch tension (DENT) test specimens have revealed the effect of internal hydrostatic tensile stresses on yielding. Nonlinear FEA using the von Mises (yielding is independent of hydrostatic stress) and the Drucker-Prager (yielding is linearly dependent on hydrostatic stress) yield functions were performed. A new FEA constitutive model was developed that incorporates a pressure-dependent yield function with combined multilinear kinematic and multilinear isotropic hardening using the ABAQUS user subroutine (UMAT) utility. In all monotonic tensile test cases, the von Mises constitutive model, overestimated the load for a given displacement or strain. Considering the failure displacements or strains for the DENT specimen, the Drucker-Prager FEM s predicted loads that were approximately 3% lower than the von Mises values. For the failure loads, the Drucker Prager FEM s predicted strains that were up to 35% greater than the von Mises values. Both the Drucker-Prager model and the von Mises model performed equally-well in simulating the equal-arm bend fatigue test.

Yielding to Stress: Recent Developments in Viscoplastic Fluid Mechanics

NASA Astrophysics Data System (ADS)

Balmforth, Neil J.; Frigaard, Ian A.; Ovarlez, Guillaume

2014-01-01

The archetypal feature of a viscoplastic fluid is its yield stress: If the material is not sufficiently stressed, it behaves like a solid, but once the yield stress is exceeded, the material flows like a fluid. Such behavior characterizes materials common in industries such as petroleum and chemical processing, cosmetics, and food processing and in geophysical fluid dynamics. The most common idealization of a viscoplastic fluid is the Bingham model, which has been widely used to rationalize experimental data, even though it is a crude oversimplification of true rheological behavior. The popularity of the model is in its apparent simplicity. Despite this, the sudden transition between solid-like behavior and flow introduces significant complications into the dynamics, which, as a result, has resisted much analysis. Over recent decades, theoretical developments, both analytical and computational, have provided a better understanding of the effect of the yield stress. Simultaneously, greater insight into the material behavior of real fluids has been afforded by advances in rheometry. These developments have primed us for a better understanding of the various applications in the natural and engineering sciences.

Proceedings of the 1974 Lyndon B. Johnson Space Center Wheat-Yield Conference

NASA Technical Reports Server (NTRS)

Pitts, D. E.; Barger, G. L.

1975-01-01

The proceedings of the 1974 Lyndon B. Johnson Space Center Wheat-Yield Conference are presented. The state of art of wheat-yield forecasting and the feasibility of incorporating remote sensing into this forecasting were discussed with emphasis on formulating common approach to wheat-yield forecasting, primarily using conventional meteorological measurements, which can later include the various applications of remote sensing. Papers are presented which deal with developments in the field of crop modelling.

Investigations of the structure and electromagnetic interactions of few body systems

NASA Astrophysics Data System (ADS)

Harper, E. P.; Lehman, D. R.; Prats, F.

The structure and electromagnetic interactions of few-body systems were investigated. The structural properties of the very light nuclei are examined by developing theoretical models that begin from the basic interactions between the constituents and that are solved exactly (numerically), i.e., full three- or four-body dynamics. Such models are then used in an attempt to understand the details of the strong and electromagnetic interactions of the few-nucleon nuclei after the basic underlying reaction mechanisms are understood with simpler models. Topics included: (1) set up the equations for the low-energy photodisintegration of (3)He and (3)H including final-state interactions and the E1 plus E2 operators; (2) develop a unified picture of the p + d (YIELDS) (3)He + (GAMMA), p + d (YIELDS) (3)He + (PI) (0), p + d (YIELDS) (3)H + (PI) (+) reactions at intermediate energies; (3) calculate the elastic and inelastic (1(+) (YIELDS) 0 (+)) form factors for (6)Li with three-body ((ALPHA)NN) wave functions; (4) calculate static properties (RMS radius, magnetic moment, and quadrupole moment) of (6)Li with three-body wave functions; and (5) develop the theory for the coincidence reactions (6)Li(p,2p)n(ALPHA), (6)Li(e,e'p)n(ALPHA), and (6)Li(e,e'd)(ALPHA).

Crop status evaluations and yield predictions

NASA Technical Reports Server (NTRS)

Haun, J. R.

1975-01-01

The growth-environment relationships for greenhouse and field conditions are compared, and the development of growth-prediction models for spring wheat is discussed along with the development of models for predicting the date for spring wheat emergence in North Dakota.

A guide to the TWIGS program for the North Central United States.

Treesearch

Cynthia L. Miner; Nancy R. Walters; Monique L. Belli

1988-01-01

This is a complete reference to TWIGS, a forest growth-and-yield program with management and economic components developed for Lake and Central States tree species. The guide describes how TWIGS models growth and yield and how the model can be applied to obtain the best results. Step-by-step operating instructions are provided for TWIGS and its companion program,...

Climate-based statistical regression models for crop yield forecasting of coffee in humid tropical Kerala, India

NASA Astrophysics Data System (ADS)

Jayakumar, M.; Rajavel, M.; Surendran, U.

2016-12-01

A study on the variability of coffee yield of both Coffea arabica and Coffea canephora as influenced by climate parameters (rainfall (RF), maximum temperature (Tmax), minimum temperature (Tmin), and mean relative humidity (RH)) was undertaken at Regional Coffee Research Station, Chundale, Wayanad, Kerala State, India. The result on the coffee yield data of 30 years (1980 to 2009) revealed that the yield of coffee is fluctuating with the variations in climatic parameters. Among the species, productivity was higher for C. canephora coffee than C. arabica in most of the years. Maximum yield of C. canephora (2040 kg ha-1) was recorded in 2003-2004 and there was declining trend of yield noticed in the recent years. Similarly, the maximum yield of C. arabica (1745 kg ha-1) was recorded in 1988-1989 and decreased yield was noticed in the subsequent years till 1997-1998 due to year to year variability in climate. The highest correlation coefficient was found between the yield of C. arabica coffee and maximum temperature during January (0.7) and between C. arabica coffee yield and RH during July (0.4). Yield of C. canephora coffee had highest correlation with maximum temperature, RH and rainfall during February. Statistical regression model between selected climatic parameters and yield of C. arabica and C. canephora coffee was developed to forecast the yield of coffee in Wayanad district in Kerala. The model was validated for years 2010, 2011, and 2012 with the coffee yield data obtained during the years and the prediction was found to be good.

Ensembles modeling approach to study Climate Change impacts on Wheat

NASA Astrophysics Data System (ADS)

Ahmed, Mukhtar; Claudio, Stöckle O.; Nelson, Roger; Higgins, Stewart

2017-04-01

Simulations of crop yield under climate variability are subject to uncertainties, and quantification of such uncertainties is essential for effective use of projected results in adaptation and mitigation strategies. In this study we evaluated the uncertainties related to crop-climate models using five crop growth simulation models (CropSyst, APSIM, DSSAT, STICS and EPIC) and 14 general circulation models (GCMs) for 2 representative concentration pathways (RCP) of atmospheric CO2 (4.5 and 8.5 W m-2) in the Pacific Northwest (PNW), USA. The aim was to assess how different process-based crop models could be used accurately for estimation of winter wheat growth, development and yield. Firstly, all models were calibrated for high rainfall, medium rainfall, low rainfall and irrigated sites in the PNW using 1979-2010 as the baseline period. Response variables were related to farm management and soil properties, and included crop phenology, leaf area index (LAI), biomass and grain yield of winter wheat. All five models were run from 2000 to 2100 using the 14 GCMs and 2 RCPs to evaluate the effect of future climate (rainfall, temperature and CO2) on winter wheat phenology, LAI, biomass, grain yield and harvest index. Simulated time to flowering and maturity was reduced in all models except EPIC with some level of uncertainty. All models generally predicted an increase in biomass and grain yield under elevated CO2 but this effect was more prominent under rainfed conditions than irrigation. However, there was uncertainty in the simulation of crop phenology, biomass and grain yield under 14 GCMs during three prediction periods (2030, 2050 and 2070). We concluded that to improve accuracy and consistency in simulating wheat growth dynamics and yield under a changing climate, a multimodel ensemble approach should be used.

JULES-crop: a parametrisation of crops in the Joint UK Land Environment Simulator

NASA Astrophysics Data System (ADS)

Osborne, T.; Gornall, J.; Hooker, J.; Williams, K.; Wiltshire, A.; Betts, R.; Wheeler, T.

2014-10-01

Studies of climate change impacts on the terrestrial biosphere have been completed without recognition of the integrated nature of the biosphere. Improved assessment of the impacts of climate change on food and water security requires the development and use of models not only representing each component but also their interactions. To meet this requirement the Joint UK Land Environment Simulator (JULES) land surface model has been modified to include a generic parametrisation of annual crops. The new model, JULES-crop, is described and evaluation at global and site levels for the four globally important crops; wheat, soy bean, maize and rice is presented. JULES-crop demonstrates skill in simulating the inter-annual variations of yield for maize and soy bean at the global level, and for wheat for major spring wheat producing countries. The impact of the new parametrisation, compared to the standard configuration, on the simulation of surface heat fluxes is largely an alteration of the partitioning between latent and sensible heat fluxes during the later part of the growing season. Further evaluation at the site level shows the model captures the seasonality of leaf area index and canopy height better than in standard JULES. However, this does not lead to an improvement in the simulation of sensible and latent heat fluxes. The performance of JULES-crop from both an earth system and crop yield model perspective is encouraging however, more effort is needed to develop the parameterisation of the model for specific applications. Key future model developments identified include the specification of the yield gap to enable better representation of the spatial variability in yield.

Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil

NASA Astrophysics Data System (ADS)

Battisti, R.; Sentelhas, P. C.; Boote, K. J.

2017-12-01

Crop growth models have many uncertainties that affect the yield response to climate change. Based on that, the aim of this study was to evaluate the sensitivity of crop models to systematic changes in climate for simulating soybean attainable yield in Southern Brazil. Four crop models were used to simulate yields: AQUACROP, MONICA, DSSAT, and APSIM, as well as their ensemble. The simulations were performed considering changes of air temperature (0, + 1.5, + 3.0, + 4.5, and + 6.0 °C), [CO2] (380, 480, 580, 680, and 780 ppm), rainfall (- 30, - 15, 0, + 15, and + 30%), and solar radiation (- 15, 0, + 15), applied to daily values. The baseline climate was from 1961 to 2014, totalizing 53 crop seasons. The crop models simulated a reduction of attainable yield with temperature increase, reaching 2000 kg ha-1 for the ensemble at + 6 °C, mainly due to shorter crop cycle. For rainfall, the yield had a higher rate of reduction when it was diminished than when rainfall was increased. The crop models increased yield variability when solar radiation was changed from - 15 to + 15%, whereas [CO2] rise resulted in yield gains, following an asymptotic response, with a mean increase of 31% from 380 to 680 ppm. The models used require further attention to improvements in optimal and maximum cardinal temperature for development rate; runoff, water infiltration, deep drainage, and dynamic of root growth; photosynthesis parameters related to soil water availability; and energy balance of soil-plant system to define leaf temperature under elevated CO2.

Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil

NASA Astrophysics Data System (ADS)

Battisti, R.; Sentelhas, P. C.; Boote, K. J.

2018-05-01

Crop growth models have many uncertainties that affect the yield response to climate change. Based on that, the aim of this study was to evaluate the sensitivity of crop models to systematic changes in climate for simulating soybean attainable yield in Southern Brazil. Four crop models were used to simulate yields: AQUACROP, MONICA, DSSAT, and APSIM, as well as their ensemble. The simulations were performed considering changes of air temperature (0, + 1.5, + 3.0, + 4.5, and + 6.0 °C), [CO2] (380, 480, 580, 680, and 780 ppm), rainfall (- 30, - 15, 0, + 15, and + 30%), and solar radiation (- 15, 0, + 15), applied to daily values. The baseline climate was from 1961 to 2014, totalizing 53 crop seasons. The crop models simulated a reduction of attainable yield with temperature increase, reaching 2000 kg ha-1 for the ensemble at + 6 °C, mainly due to shorter crop cycle. For rainfall, the yield had a higher rate of reduction when it was diminished than when rainfall was increased. The crop models increased yield variability when solar radiation was changed from - 15 to + 15%, whereas [CO2] rise resulted in yield gains, following an asymptotic response, with a mean increase of 31% from 380 to 680 ppm. The models used require further attention to improvements in optimal and maximum cardinal temperature for development rate; runoff, water infiltration, deep drainage, and dynamic of root growth; photosynthesis parameters related to soil water availability; and energy balance of soil-plant system to define leaf temperature under elevated CO2.

Lameness detection based on multivariate continuous sensing of milk yield, rumination, and neck activity.

PubMed

Van Hertem, T; Maltz, E; Antler, A; Romanini, C E B; Viazzi, S; Bahr, C; Schlageter-Tello, A; Lokhorst, C; Berckmans, D; Halachmi, I

2013-07-01

The objective of this study was to develop and validate a mathematical model to detect clinical lameness based on existing sensor data that relate to the behavior and performance of cows in a commercial dairy farm. Identification of lame (44) and not lame (74) cows in the database was done based on the farm's daily herd health reports. All cows were equipped with a behavior sensor that measured neck activity and ruminating time. The cow's performance was measured with a milk yield meter in the milking parlor. In total, 38 model input variables were constructed from the sensor data comprising absolute values, relative values, daily standard deviations, slope coefficients, daytime and nighttime periods, variables related to individual temperament, and milk session-related variables. A lame group, cows recognized and treated for lameness, to not lame group comparison of daily data was done. Correlations between the dichotomous output variable (lame or not lame) and the model input variables were made. The highest correlation coefficient was obtained for the milk yield variable (rMY=0.45). In addition, a logistic regression model was developed based on the 7 highest correlated model input variables (the daily milk yield 4d before diagnosis; the slope coefficient of the daily milk yield 4d before diagnosis; the nighttime to daytime neck activity ratio 6d before diagnosis; the milk yield week difference ratio 4d before diagnosis; the milk yield week difference 4d before diagnosis; the neck activity level during the daytime 7d before diagnosis; the ruminating time during nighttime 6d before diagnosis). After a 10-fold cross-validation, the model obtained a sensitivity of 0.89 and a specificity of 0.85, with a correct classification rate of 0.86 when based on the averaged 10-fold model coefficients. This study demonstrates that existing farm data initially used for other purposes, such as heat detection, can be exploited for the automated detection of clinically lame animals on a daily basis as well. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A consistent transported PDF model for treating differential molecular diffusion

NASA Astrophysics Data System (ADS)

Wang, Haifeng; Zhang, Pei

2016-11-01

Differential molecular diffusion is a fundamentally significant phenomenon in all multi-component turbulent reacting or non-reacting flows caused by the different rates of molecular diffusion of energy and species concentrations. In the transported probability density function (PDF) method, the differential molecular diffusion can be treated by using a mean drift model developed by McDermott and Pope. This model correctly accounts for the differential molecular diffusion in the scalar mean transport and yields a correct DNS limit of the scalar variance production. The model, however, misses the molecular diffusion term in the scalar variance transport equation, which yields an inconsistent prediction of the scalar variance in the transported PDF method. In this work, a new model is introduced to remedy this problem that can yield a consistent scalar variance prediction. The model formulation along with its numerical implementation is discussed, and the model validation is conducted in a turbulent mixing layer problem.

Development of a comprehensive watershed model applied to study stream yield under drought conditions

USGS Publications Warehouse

Perkins, S.P.; Sophocleous, M.

1999-01-01

We developed a model code to simulate a watershed's hydrology and the hydraulic response of an interconnected stream-aquifer system, and applied the model code to the Lower Republican River Basin in Kansas. The model code links two well-known computer programs: MODFLOW (modular 3-D flow model), which simulates ground water flow and stream-aquifer interaction; and SWAT (soil water assessment tool), a soil water budget simulator for an agricultural watershed. SWAT represents a basin as a collection of subbasins in terms of soil, land use, and weather data, and simulates each subbasin on a daily basis to determine runoff, percolation, evaporation, irrigation, pond seepages and crop growth. Because SWAT applies a lumped hydrologic model to each subbasin, spatial heterogeneities with respect to factors such as soil type and land use are not resolved geographically, but can instead be represented statistically. For the Republican River Basin model, each combination of six soil types and three land uses, referred to as a hydrologic response unit (HRU), was simulated with a separate execution of SWAT. A spatially weighted average was then taken over these results for each hydrologic flux and time step by a separate program, SWBAVG. We wrote a package for MOD-FLOW to associate each subbasin with a subset of aquifer grid cells and stream reaches, and to distribute the hydrologic fluxes given for each subbasin by SWAT and SWBAVG over MODFLOW's stream-aquifer grid to represent tributary flow, surface and ground water diversions, ground water recharge, and evapotranspiration from ground water. The Lower Republican River Basin model was calibrated with respect to measured ground water levels, streamflow, and reported irrigation water use. The model was used to examine the relative contributions of stream yield components and the impact on stream yield and base flow of administrative measures to restrict irrigation water use during droughts. Model results indicate that tributary flow is the dominant component of stream yield and that reduction of irrigation water use produces a corresponding increase in base flow and stream yield. However, the increase in stream yield resulting from reduced water use does not appear to be of sufficient magnitude to restore minimum desirable streamflows.

Estimation efficiency of usage satellite derived and modelled biophysical products for yield forecasting

NASA Astrophysics Data System (ADS)

Kolotii, Andrii; Kussul, Nataliia; Skakun, Sergii; Shelestov, Andrii; Ostapenko, Vadim; Oliinyk, Tamara

2015-04-01

Efficient and timely crop monitoring and yield forecasting are important tasks for ensuring of stability and sustainable economic development [1]. As winter crops pay prominent role in agriculture of Ukraine - the main focus of this study is concentrated on winter wheat. In our previous research [2, 3] it was shown that usage of biophysical parameters of crops such as FAPAR (derived from Geoland-2 portal as for SPOT Vegetation data) is far more efficient for crop yield forecasting to NDVI derived from MODIS data - for available data. In our current work efficiency of usage such biophysical parameters as LAI, FAPAR, FCOVER (derived from SPOT Vegetation and PROBA-V data at resolution of 1 km and simulated within WOFOST model) and NDVI product (derived from MODIS) for winter wheat monitoring and yield forecasting is estimated. As the part of crop monitoring workflow (vegetation anomaly detection, vegetation indexes and products analysis) and yield forecasting SPIRITS tool developed by JRC is used. Statistics extraction is done for landcover maps created in SRI within FP-7 SIGMA project. Efficiency of usage satellite based and modelled with WOFOST model biophysical products is estimated. [1] N. Kussul, S. Skakun, A. Shelestov, O. Kussul, "Sensor Web approach to Flood Monitoring and Risk Assessment", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 815-818. [2] F. Kogan, N. Kussul, T. Adamenko, S. Skakun, O. Kravchenko, O. Kryvobok, A. Shelestov, A. Kolotii, O. Kussul, and A. Lavrenyuk, "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. [3] Kussul O., Kussul N., Skakun S., Kravchenko O., Shelestov A., Kolotii A, "Assessment of relative efficiency of using MODIS data to winter wheat yield forecasting in Ukraine", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 3235 - 3238.

Updated stomatal flux and flux-effect models for wheat for quantifying effects of ozone on grain yield, grain mass and protein yield.

PubMed

Grünhage, Ludger; Pleijel, Håkan; Mills, Gina; Bender, Jürgen; Danielsson, Helena; Lehmann, Yvonne; Castell, Jean-Francois; Bethenod, Olivier

2012-06-01

Field measurements and open-top chamber experiments using nine current European winter wheat cultivars provided a data set that was used to revise and improve the parameterisation of a stomatal conductance model for wheat, including a revised value for maximum stomatal conductance and new functions for phenology and soil moisture. For the calculation of stomatal conductance for ozone a diffusivity ratio between O(3) and H(2)O in air of 0.663 was applied, based on a critical review of the literature. By applying the improved parameterisation for stomatal conductance, new flux-effect relationships for grain yield, grain mass and protein yield were developed for use in ozone risk assessments including effects on food security. An example of application of the flux model at the local scale in Germany shows that negative effects of ozone on wheat grain yield were likely each year and on protein yield in most years since the mid 1980s. Copyright © 2012 Elsevier Ltd. All rights reserved.

Assessment of the Effect of Climate Change on Grain Yields in China

NASA Astrophysics Data System (ADS)

Chou, J.

2006-12-01

The paper elaborates the social background and research background; makes clear what the key scientific issues need to be resolved and where the difficulties are. In the research area of parasailing the grain yield change caused by climate change, massive works have been done both in the domestic and in the foreign. It is our upcoming work to evaluate how our countrywide climate change information provided by this pattern influence our economic and social development; and how to make related policies and countermeasures. the main idea in this paper is that the grain yield change is by no means the linear composition of social economy function effect and the climatic change function effect. This paper identifies the economic evaluation object, proposes one new concept - climate change output. The grain yields change affected by the social factors and the climatic change working together. Climate change influences the grain yields by the non ¨C linear function from both climate change and social factor changes, not only by climate change itself. Therefore, in my paper, the appraisal object is defined as: The social factors change based on actual social changing situations; under the two kinds of climate change situation, the invariable climate change situation and variable climate change situation; the difference of grain yield outputs is called " climate change output ", In order to solve this problem, we propose a method to analyze and imitate on the historical materials. Giving the condition that the climate is invariable, the social economic factor changes cause the grain yield change. However, this grain yield change is a tentative quantity index, not an actual quantity number. So we use the existing historical materials to exam the climate change output, based on the characteristic that social factor changes greater in year than in age, but the climate factor changes greater in age than in year. The paper proposes and establishes one economy - climate model (C-D-C model) to appraise the grain yield change caused by the climatic change. Also the preliminary test on this model has been done. In selection of the appraisal methods, we take the C-D production function model, which has been proved more mature in the economic research, as our fundamental model. Then, we introduce climate index (arid index) to the C-D model to develop one new model. This new model utilizes the climatic change factor in the economical model to appraise how the climatic change influence the grain yield change. The new way of appraise should have the better application prospect. The economy - climate model (The C-D-C model) has been applied on the eight Chinese regions that we divide; it has been proved satisfactory in its feasibility, rationality and the application prospect. So we can provide the theoretical fundamentals for policy-making under the more complex and uncertain climate change. Therefore, we open a new possible channel for the global climate change research moving toward the actual social, economic life.

Progress and Challenges in Predicting Crop Responses to Atmospheric [CO2

NASA Astrophysics Data System (ADS)

Kent, J.; Paustian, K.

2017-12-01

Increasing atmospheric [CO2] directly accelerates photosynthesis in C3 crops, and indirectly promotes yields by reducing stomatal conductance and associated water losses in C3 and C4 crops. Several decades of experiments have exposed crops to eCO2 in greenhouses and other enclosures and observed yield increases on the order of 33%. FACE systems were developed in the early 1990s to better replicate open-field growing conditions. Some authors contend that FACE results indicate lower crop yield responses than enclosure studies, while others maintain no significant difference or attribute differences to various methodological factors. The crop CO2 response processes in many crop models were developed using results from enclosure experiments. This work tested the ability of one such model, DayCent, to reproduce crop responses to CO2 enrichment from several FACE experiments. DayCent performed well at simulating yield and transpiration responses in C4 crops, but significantly overestimated yield responses in C3 crops. After adjustment of CO2-response parameters, DayCent was able to reproduce mean yield responses for specific crops. However, crop yield responses from FACE experiments vary widely across years and sites, and likely reflect complex interactions between conditions such as weather, soils, cultivars, and biotic stressors. Further experimental work is needed to identify the secondary variables that explain this variability so that models can more reliably forecast crop yields under climate change. Likewise, CO2 impacts on crop outcomes such as belowground biomass allocation and grain N content have implications for agricultural C fluxes and human nutrition, respectively, but are poorly understood and thus difficult to simulate with confidence.

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

NASA Technical Reports Server (NTRS)

Makowski, David; Asseng, Senthold; Ewert, Frank; Bassu, Simona; Durand, Jean-Louis; Martre, Pierre; Adam, Myriam; Aggarwal, Pramod K.; Angulo, Carlos; Baron, Chritian;

A Simple Model to Quantify Radiolytic Production following Electron Emission from Heavy-Atom Nanoparticles Irradiated in Liquid Suspensions.

PubMed

Wardlow, Nathan; Polin, Chris; Villagomez-Bernabe, Balder; Currell, Fred

2015-11-01

We present a simple model for a component of the radiolytic production of any chemical species due to electron emission from irradiated nanoparticles (NPs) in a liquid environment, provided the expression for the G value for product formation is known and is reasonably well characterized by a linear dependence on beam energy. This model takes nanoparticle size, composition, density and a number of other readily available parameters (such as X-ray and electron attenuation data) as inputs and therefore allows for the ready determination of this contribution. Several approximations are used, thus this model provides an upper limit to the yield of chemical species due to electron emission, rather than a distinct value, and this upper limit is compared with experimental results. After the general model is developed we provide details of its application to the generation of HO• through irradiation of gold nanoparticles (AuNPs), a potentially important process in nanoparticle-based enhancement of radiotherapy. This model has been constructed with the intention of making it accessible to other researchers who wish to estimate chemical yields through this process, and is shown to be applicable to NPs of single elements and mixtures. The model can be applied without the need to develop additional skills (such as using a Monte Carlo toolkit), providing a fast and straightforward method of estimating chemical yields. A simple framework for determining the HO• yield for different NP sizes at constant NP concentration and initial photon energy is also presented.

Simulating the effect of vegetation cover on the sediment yield of mediterranean catchments using SHETRAN

NASA Astrophysics Data System (ADS)

Lukey, B. T.; Sheffield, J.; Bathurst, J. C.; Lavabre, J.; Mathys, N.; Martin, C.

1995-08-01

The sediment yield of two catchments in southern France was modelled using the newly developed sediment code of SHETRAN. A fire in August 1990 denuded the Rimbaud catchment, providing an opportunity to study the effect of vegetation cover on sediment yield by running the model for both pre-and post-fire cases. Model output is in the form of upper and lower bounds on sediment discharge, reflecting the uncertainty in the erodibility of the soil. The results are encouraging since measured sediment discharge falls largely between the predicted bounds, and simulated sediment yield is dramatically lower for the catchment before the fire which matches observation. SHETRAN is also applied to the Laval catchment, which is subject to Badlands gulley erosion. Again using the principle of generating upper and lower bounds on sediment discharge, the model is shown to be capable of predicting the bulk sediment discharge over periods of months. To simulate the effect of reforestation, the model is run with vegetation cover equivalent to a neighbouring fully forested basin. The results obtained indicate that SHETRAN provides a powerful tool for predicting the impact of environmental change and land management on sediment yield.

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

PubMed Central

Liu, Xiaojun; Ferguson, Richard B.; Zheng, Hengbiao; Cao, Qiang; Tian, Yongchao; Cao, Weixing; Zhu, Yan

2017-01-01

The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: RNDVI=(1+e−15.2829×(RAGDDi−0.1944))−1−(1+e−11.6517×(RAGDDi−1.0267))−1 (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status. PMID:28338637

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China).

PubMed

Liu, Xiaojun; Ferguson, Richard B; Zheng, Hengbiao; Cao, Qiang; Tian, Yongchao; Cao, Weixing; Zhu, Yan

2017-03-24

The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: RNDVI = ( 1 + e - 15.2829 × ( R A G D D i - 0.1944 ) ) - 1 - ( 1 + e - 11.6517 × ( R A G D D i - 1.0267 ) ) - 1 (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status.

Development and Characterization of a Rate-Dependent Three-Dimensional Macroscopic Plasticity Model Suitable for Use in Composite Impact Problems

NASA Technical Reports Server (NTRS)

Goldberg, Robert K.; Carney, Kelly S.; DuBois, Paul; Hoffarth, Canio; Rajan, Subramaniam; Blankenhorn, Gunther

2015-01-01

Several key capabilities have been identified by the aerospace community as lacking in the material/models for composite materials currently available within commercial transient dynamic finite element codes such as LS-DYNA. Some of the specific desired features that have been identified include the incorporation of both plasticity and damage within the material model, the capability of using the material model to analyze the response of both three-dimensional solid elements and two dimensional shell elements, and the ability to simulate the response of composites composed with a variety of composite architectures, including laminates, weaves and braids. In addition, a need has been expressed to have a material model that utilizes tabulated experimentally based input to define the evolution of plasticity and damage as opposed to utilizing discrete input parameters (such as modulus and strength) and analytical functions based on curve fitting. To begin to address these needs, an orthotropic macroscopic plasticity based model suitable for implementation within LS-DYNA has been developed. Specifically, the Tsai-Wu composite failure model has been generalized and extended to a strain-hardening based orthotropic plasticity model with a non-associative flow rule. The coefficients in the yield function are determined based on tabulated stress-strain curves in the various normal and shear directions, along with selected off-axis curves. Incorporating rate dependence into the yield function is achieved by using a series of tabluated input curves, each at a different constant strain rate. The non-associative flow-rule is used to compute the evolution of the effective plastic strain. Systematic procedures have been developed to determine the values of the various coefficients in the yield function and the flow rule based on the tabulated input data. An algorithm based on the radial return method has been developed to facilitate the numerical implementation of the material model. The presented paper will present in detail the development of the orthotropic plasticity model and the procedures used to obtain the required material parameters. Methods in which a combination of actual testing and selective numerical testing can be combined to yield the appropriate input data for the model will be described. A specific laminated polymer matrix composite will be examined to demonstrate the application of the model.

Identification of Critical Erosion Prone Areas and Computation of Sediment Yield Using Remote Sensing and GIS: A Case Study on Sarada River Basin

NASA Astrophysics Data System (ADS)

Sundara Kumar, P.; Venkata Praveen, T.; Anjanaya Prasad, M.; Santha Rao, P.

2018-06-01

The two most important resources blessed by nature to the mankind are land and water. Undoubtedly, these gifts have to be conserved and maintained with unflinching efforts from every one of us for an effective environmental and ecological balance. The efforts and energy of water resources engineers and conservationists are going in this direction to conserve these precious resources of nature. The present study is an attempt to develop suitable methodology to facilitate decision makers to conserve the resources and also reflects the cause mentioned above has been presented here. The main focus of this study is to identify the critical prone areas for soil erosion and computation of sediment yield in a small basin using Universal Soil Loss Equation and Modified Universal Soil Loss Equation (MUSLE) respectively. The developed model has been applied on Sarada river basin which has a drainage area of 1252.99 km2. This river is located in Andhra Pradesh State (AP), India. The basin has been divided into micro basins for effective estimation and also for precise identification of the areas that are prone to soil erosion. Remote Sensing and Geographic Information Systems tools were used to generate and spatially organize the data that is required for soil erosion modeling. It was found that the micro basins with very severe soil erosion are consisting of hilly areas with high topographic factor and 38.01% of the study area has the rate erosion more than 20 t/ha/year and hence requires an immediate attention from the soil conservation point of view. In this study region, though there is one discharge measuring gauge station available at Anakapalli but there is no sediment yield gauging means available to compute the sediment yield. Therefore, to arrive at the suspended-sediment concentration was a challenge task. In the present study the sediment measurement has been carried out with an instrument (DH-48), sediment sampling equipment as per IS: 4890-1968, has been used. Suspended-sediment samples were collected and sediment yield was arrived at the site by using this instrument. The sediment yield was also computed using MUSLE. Data for this model study has been generated from the samples collected from 28 storm events spread over a time span of 1 year, at the outlet of the basin at Anakapalli for computation of sediment yield. The sediment yield as estimated by MUSLE model has been successfully compared with the sediment yield measured at the outlet of the basin by sediment yield measuring unit and found fairly good correlation between them. Hence the developed methodology will be useful to estimate the sediment yield in the hydrologically similar basins that are not gauged for sediment yield.

Simulating the effects of climate and agricultural management practices on global crop yield

NASA Astrophysics Data System (ADS)

Deryng, D.; Sacks, W. J.; Barford, C. C.; Ramankutty, N.

2011-06-01

Climate change is expected to significantly impact global food production, and it is important to understand the potential geographic distribution of yield losses and the means to alleviate them. This study presents a new global crop model, PEGASUS 1.0 (Predicting Ecosystem Goods And Services Using Scenarios) that integrates, in addition to climate, the effect of planting dates and cultivar choices, irrigation, and fertilizer application on crop yield for maize, soybean, and spring wheat. PEGASUS combines carbon dynamics for crops with a surface energy and soil water balance model. It also benefits from the recent development of a suite of global data sets and analyses that serve as model inputs or as calibration data. These include data on crop planting and harvesting dates, crop-specific irrigated areas, a global analysis of yield gaps, and harvested area and yield of major crops. Model results for present-day climate and farm management compare reasonably well with global data. Simulated planting and harvesting dates are within the range of crop calendar observations in more than 75% of the total crop-harvested areas. Correlation of simulated and observed crop yields indicates a weighted coefficient of determination, with the weighting based on crop-harvested area, of 0.81 for maize, 0.66 for soybean, and 0.45 for spring wheat. We found that changes in temperature and precipitation as predicted by global climate models for the 2050s lead to a global yield reduction if planting and harvesting dates remain unchanged. However, adapting planting dates and cultivar choices increases yield in temperate regions and avoids 7-18% of global losses.

Growth and Yield Predictions for Thinned and Unthinned Slash Pine Plantations on Cutover Sites in the West Gulf Region

Treesearch

Stanley J. Zarnoch; Donald P. Feduccia; V. Clark Baldwin; Tommy R. Dell

1991-01-01

A-growth and yield model has been developed for slash pine plantations on problem-free cutover sites in the west gulf region. The model was based on the moment-percentile method using the Weibull distribution for tree diameters. This technique was applied to untbinned and thinned stand projections and, subsequently, to the prediction of residual stands immediately...

Use of vegetation health data for estimation of aus rice yield in bangladesh.

PubMed

Rahman, Atiqur; Roytman, Leonid; Krakauer, Nir Y; Nizamuddin, Mohammad; Goldberg, Mitch

2009-01-01

Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991-2005). A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March-April (weeks 8-13 of the year), several months in advance of the rice harvest. Stepwise principal component regression (PCR) was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost.

Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh

PubMed Central

Rahman, Atiqur; Roytman, Leonid; Krakauer, Nir Y.; Nizamuddin, Mohammad; Goldberg, Mitch

2009-01-01

Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991–2005). A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March–April (weeks 8–13 of the year), several months in advance of the rice harvest. Stepwise principal component regression (PCR) was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost. PMID:22574057

What limits the yield of levoglucosan during fast pyrolysis of cellulose?

NASA Astrophysics Data System (ADS)

Proano-Aviles, Juan

The pyrolysis of cellulose to form levoglucosan is investigated in this study. Although the stoichiometric yield of levoglucosan from the pyrolysis of cellulose is expected to be 100%, only about 60 wt.% yields are reported in the literature. Several possible reasons for this limitation are investigated through experiments in micropyrolyzers and computational studies on the depolymerization of cellulose. Heat and mass transfer limitations in an experimental apparatus is one possible limitation on the yield of levoglucosan. Repolymerization of condensed phase reaction intermediates could prevent the formation and release of volatile levoglucosan. Thermohydrolysis of pyrolyzing cellulose to form non-volatile and thermally unstable glucose has also been proposed as a mechanism that reduces levoglucosan yields. Secondary reactions in the gas phase were also investigated to explain limitations on levoglucosan yields. Population balance models were developed to test ideas on how cellulose depolymerized to form levoglucosan at less than stoichiometric yields. These models were supported with chemical kinetic data obtained from transient pyrolysis experiments. Under carefully controlled experimental conditions, no evidence was found for heat and mass transfer effects limiting levoglucosan yields to 60 wt.% nor do secondary reactions in the condensed- or gas-phases appear to offer a satisfactory explanation. Based on modeling results, it appears levoglucosan-forming reaction rates that decrease as oligosaccharide chain length decreases is the most plausible explanation for limitations on levoglucosan yield from cellulose.

Statistics-based model for prediction of chemical biosynthesis yield from Saccharomyces cerevisiae

PubMed Central

2011-01-01

Background The robustness of Saccharomyces cerevisiae in facilitating industrial-scale production of ethanol extends its utilization as a platform to synthesize other metabolites. Metabolic engineering strategies, typically via pathway overexpression and deletion, continue to play a key role for optimizing the conversion efficiency of substrates into the desired products. However, chemical production titer or yield remains difficult to predict based on reaction stoichiometry and mass balance. We sampled a large space of data of chemical production from S. cerevisiae, and developed a statistics-based model to calculate production yield using input variables that represent the number of enzymatic steps in the key biosynthetic pathway of interest, metabolic modifications, cultivation modes, nutrition and oxygen availability. Results Based on the production data of about 40 chemicals produced from S. cerevisiae, metabolic engineering methods, nutrient supplementation, and fermentation conditions described therein, we generated mathematical models with numerical and categorical variables to predict production yield. Statistically, the models showed that: 1. Chemical production from central metabolic precursors decreased exponentially with increasing number of enzymatic steps for biosynthesis (>30% loss of yield per enzymatic step, P-value = 0); 2. Categorical variables of gene overexpression and knockout improved product yield by 2~4 folds (P-value < 0.1); 3. Addition of notable amount of intermediate precursors or nutrients improved product yield by over five folds (P-value < 0.05); 4. Performing the cultivation in a well-controlled bioreactor enhanced the yield of product by three folds (P-value < 0.05); 5. Contribution of oxygen to product yield was not statistically significant. Yield calculations for various chemicals using the linear model were in fairly good agreement with the experimental values. The model generally underestimated the ethanol production as compared to other chemicals, which supported the notion that the metabolism of Saccharomyces cerevisiae has historically evolved for robust alcohol fermentation. Conclusions We generated simple mathematical models for first-order approximation of chemical production yield from S. cerevisiae. These linear models provide empirical insights to the effects of strain engineering and cultivation conditions toward biosynthetic efficiency. These models may not only provide guidelines for metabolic engineers to synthesize desired products, but also be useful to compare the biosynthesis performance among different research papers. PMID:21689458

Dynamic Modeling of Yield and Particle Size Distribution in Continuous Bayer Precipitation

NASA Astrophysics Data System (ADS)

Stephenson, Jerry L.; Kapraun, Chris

Process engineers at Alcoa's Point Comfort refinery are using a dynamic model of the Bayer precipitation area to evaluate options in operating strategies. The dynamic model, a joint development effort between Point Comfort and the Alcoa Technical Center, predicts process yields, particle size distributions and occluded soda levels for various flowsheet configurations of the precipitation and classification circuit. In addition to rigorous heat, material and particle population balances, the model includes mechanistic kinetic expressions for particle growth and agglomeration and semi-empirical kinetics for nucleation and attrition. The kinetic parameters have been tuned to Point Comfort's operating data, with excellent matches between the model results and plant data. The model is written for the ACSL dynamic simulation program with specifically developed input/output graphical user interfaces to provide a user-friendly tool. Features such as a seed charge controller enhance the model's usefulness for evaluating operating conditions and process control approaches.

Earing Prediction in Cup Drawing using the BBC2008 Yield Criterion

NASA Astrophysics Data System (ADS)

Vrh, Marko; Halilovič, Miroslav; Starman, Bojan; Štok, Boris; Comsa, Dan-Sorin; Banabic, Dorel

2011-08-01

The paper deals with constitutive modelling of highly anisotropic sheet metals. It presents FEM based earing predictions in cup drawing simulation of highly anisotropic aluminium alloys where more than four ears occur. For that purpose the BBC2008 yield criterion, which is a plane-stress yield criterion formulated in the form of a finite series, is used. Thus defined criterion can be expanded to retain more or less terms, depending on the amount of given experimental data. In order to use the model in sheet metal forming simulations we have implemented it in a general purpose finite element code ABAQUS/Explicit via VUMAT subroutine, considering alternatively eight or sixteen parameters (8p and 16p version). For the integration of the constitutive model the explicit NICE (Next Increment Corrects Error) integration scheme has been used. Due to the scheme effectiveness the CPU time consumption for a simulation is comparable to the time consumption of built-in constitutive models. Two aluminium alloys, namely AA5042-H2 and AA2090-T3, have been used for a validation of the model. For both alloys the parameters of the BBC2008 model have been identified with a developed numerical procedure, based on a minimization of the developed cost function. For both materials, the predictions of the BBC2008 model prove to be in very good agreement with the experimental results. The flexibility and the accuracy of the model together with the identification and integration procedure guarantee the applicability of the BBC2008 yield criterion in industrial applications.

Short-term responses of leaf growth rate to water deficit scale up to whole-plant and crop levels: an integrated modelling approach in maize.

PubMed

Chenu, Karine; Chapman, Scott C; Hammer, Graeme L; McLean, Greg; Salah, Halim Ben Haj; Tardieu, François

2008-03-01

Physiological and genetic studies of leaf growth often focus on short-term responses, leaving a gap to whole-plant models that predict biomass accumulation, transpiration and yield at crop scale. To bridge this gap, we developed a model that combines an existing model of leaf 6 expansion in response to short-term environmental variations with a model coordinating the development of all leaves of a plant. The latter was based on: (1) rates of leaf initiation, appearance and end of elongation measured in field experiments; and (2) the hypothesis of an independence of the growth between leaves. The resulting whole-plant leaf model was integrated into the generic crop model APSIM which provided dynamic feedback of environmental conditions to the leaf model and allowed simulation of crop growth at canopy level. The model was tested in 12 field situations with contrasting temperature, evaporative demand and soil water status. In observed and simulated data, high evaporative demand reduced leaf area at the whole-plant level, and short water deficits affected only leaves developing during the stress, either visible or still hidden in the whorl. The model adequately simulated whole-plant profiles of leaf area with a single set of parameters that applied to the same hybrid in all experiments. It was also suitable to predict biomass accumulation and yield of a similar hybrid grown in different conditions. This model extends to field conditions existing knowledge of the environmental controls of leaf elongation, and can be used to simulate how their genetic controls flow through to yield.

Modeling homeorhetic trajectories of milk component yields, body composition and dry-matter intake in dairy cows: Influence of parity, milk production potential and breed.

PubMed

Daniel, J B; Friggens, N C; van Laar, H; Ingvartsen, K L; Sauvant, D

2018-06-01

The control of nutrient partitioning is complex and affected by many factors, among them physiological state and production potential. Therefore, the current model aims to provide for dairy cows a dynamic framework to predict a consistent set of reference performance patterns (milk component yields, body composition change, dry-matter intake) sensitive to physiological status across a range of milk production potentials (within and between breeds). Flows and partition of net energy toward maintenance, growth, gestation, body reserves and milk components are described in the model. The structure of the model is characterized by two sub-models, a regulating sub-model of homeorhetic control which sets dynamic partitioning rules along the lactation, and an operating sub-model that translates this into animal performance. The regulating sub-model describes lactation as the result of three driving forces: (1) use of previously acquired resources through mobilization, (2) acquisition of new resources with a priority of partition towards milk and (3) subsequent use of resources towards body reserves gain. The dynamics of these three driving forces were adjusted separately for fat (milk and body), protein (milk and body) and lactose (milk). Milk yield is predicted from lactose and protein yields with an empirical equation developed from literature data. The model predicts desired dry-matter intake as an outcome of net energy requirements for a given dietary net energy content. The parameters controlling milk component yields and body composition changes were calibrated using two data sets in which the diet was the same for all animals. Weekly data from Holstein dairy cows was used to calibrate the model within-breed across milk production potentials. A second data set was used to evaluate the model and to calibrate it for breed differences (Holstein, Danish Red and Jersey) on the mobilization/reconstitution of body composition and on the yield of individual milk components. These calibrations showed that the model framework was able to adequately simulate milk yield, milk component yields, body composition changes and dry-matter intake throughout lactation for primiparous and multiparous cows differing in their production level.

Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil.

PubMed

Battisti, R; Sentelhas, P C; Boote, K J

2018-05-01

Crop growth models have many uncertainties that affect the yield response to climate change. Based on that, the aim of this study was to evaluate the sensitivity of crop models to systematic changes in climate for simulating soybean attainable yield in Southern Brazil. Four crop models were used to simulate yields: AQUACROP, MONICA, DSSAT, and APSIM, as well as their ensemble. The simulations were performed considering changes of air temperature (0, + 1.5, + 3.0, + 4.5, and + 6.0 °C), [CO 2 ] (380, 480, 580, 680, and 780 ppm), rainfall (- 30, - 15, 0, + 15, and + 30%), and solar radiation (- 15, 0, + 15), applied to daily values. The baseline climate was from 1961 to 2014, totalizing 53 crop seasons. The crop models simulated a reduction of attainable yield with temperature increase, reaching 2000 kg ha -1 for the ensemble at + 6 °C, mainly due to shorter crop cycle. For rainfall, the yield had a higher rate of reduction when it was diminished than when rainfall was increased. The crop models increased yield variability when solar radiation was changed from - 15 to + 15%, whereas [CO 2 ] rise resulted in yield gains, following an asymptotic response, with a mean increase of 31% from 380 to 680 ppm. The models used require further attention to improvements in optimal and maximum cardinal temperature for development rate; runoff, water infiltration, deep drainage, and dynamic of root growth; photosynthesis parameters related to soil water availability; and energy balance of soil-plant system to define leaf temperature under elevated CO 2 .

Phosphorus component in AnnAGNPS

USGS Publications Warehouse

Yuan, Y.; Bingner, R.L.; Theurer, F.D.; Rebich, R.A.; Moore, P.A.

2005-01-01

The USDA Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS) has been developed to aid in evaluation of watershed response to agricultural management practices. Previous studies have demonstrated the capability of the model to simulate runoff and sediment, but not phosphorus (P). The main purpose of this article is to evaluate the performance of AnnAGNPS on P simulation using comparisons with measurements from the Deep Hollow watershed of the Mississippi Delta Management Systems Evaluation Area (MDMSEA) project. A sensitivity analysis was performed to identify input parameters whose impact is the greatest on P yields. Sensitivity analysis results indicate that the most sensitive variables of those selected are initial soil P contents, P application rate, and plant P uptake. AnnAGNPS simulations of dissolved P yield do not agree well with observed dissolved P yield (Nash-Sutcliffe coefficient of efficiency of 0.34, R2 of 0.51, and slope of 0.24); however, AnnAGNPS simulations of total P yield agree well with observed total P yield (Nash-Sutcliffe coefficient of efficiency of 0.85, R2 of 0.88, and slope of 0.83). The difference in dissolved P yield may be attributed to limitations in model simulation of P processes. Uncertainties in input parameter selections also affect the model's performance.

Varying coefficient function models to explore interactions between maternal nutritional status and prenatal methylmercury toxicity in the Seychelles Child Development Nutrition Study

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

Lynch, Miranda L., E-mail: Miranda_Lynch@urmc.rochester.edu; Huang, Li-Shan; Cox, Christopher

Maternal consumption of fish during the gestational period exposes the fetus to both nutrients, especially the long-chain polyunsaturated fatty acids (LCPUFAs), believed to be beneficial for fetal brain development, as well as to the neurotoxicant methylmercury (MeHg). We recently reported that nutrients present in fish may modify MeHg neurotoxicity. Understanding the apparent interaction of MeHg exposure and nutrients present in fish is complicated by the limitations of modeling methods. In this study we fit varying coefficient function models to data from the Seychelles Child Development Nutrition Study (SCDNS) cohort to assess the association of dietary nutrients and children's development. Thismore » cohort of mother-child pairs in the Republic of Seychelles had fish consumption averaging 9 meals per week. Maternal nutritional status was assessed for five different nutritional components known to be present in fish (n-3 LCPUFA, n-6 LCPUFA, iron status, iodine status, and choline) and associated with children's neurological development. We also included prenatal MeHg exposure (measured in maternal hair). We examined two child neurodevelopmental outcomes (Bayley Scales Infant Development-II (BSID-II) Mental Developmental Index (MDI) and Psychomotor Developmental Index (PDI)), each administered at 9 and at 30 months. The varying coefficient models allow the possible interactions between each nutritional component and MeHg to be modeled as a smoothly varying function of MeHg as an effect modifier. Iron, iodine, choline, and n-6 LCPUFA had little or no observable modulation at different MeHg exposures. In contrast the n-3 LCPUFA docosahexaenoic acid (DHA) had beneficial effects on the BSID-II PDI that were reduced or absent at higher MeHg exposures. This study presents a useful modeling method that can be brought to bear on questions involving interactions between covariates, and illustrates the continuing importance of viewing fish consumption during pregnancy as a case of multiple exposures to nutrients and to MeHg. The results encourage more emphasis on a holistic view of the risks and benefits of fish consumption as it relates to infant development. - Research highlights: {yields}Varying coefficient models are tools for examining interactions in exposure settings Associations between MeHg and fish nutrients and developmental outcomes were examined. {yields} Interactions between MeHg exposure and fish-derived nutrients were modeled using VC. {yields} Models show beneficial association of DHA with outcomes were reduced as MeHg increases. {yields} VC models show other measured nutrients unmodulated by increasing MeHg exposure.« less

Development of an online, publicly accessible naive Bayesian decision support tool for mammographic mass lesions based on the American College of Radiology (ACR) BI-RADS lexicon.

PubMed

Benndorf, Matthias; Kotter, Elmar; Langer, Mathias; Herda, Christoph; Wu, Yirong; Burnside, Elizabeth S

2015-06-01

To develop and validate a decision support tool for mammographic mass lesions based on a standardized descriptor terminology (BI-RADS lexicon) to reduce variability of practice. We used separate training data (1,276 lesions, 138 malignant) and validation data (1,177 lesions, 175 malignant). We created naïve Bayes (NB) classifiers from the training data with tenfold cross-validation. Our "inclusive model" comprised BI-RADS categories, BI-RADS descriptors, and age as predictive variables; our "descriptor model" comprised BI-RADS descriptors and age. The resulting NB classifiers were applied to the validation data. We evaluated and compared classifier performance with ROC-analysis. In the training data, the inclusive model yields an AUC of 0.959; the descriptor model yields an AUC of 0.910 (P < 0.001). The inclusive model is superior to the clinical performance (BI-RADS categories alone, P < 0.001); the descriptor model performs similarly. When applied to the validation data, the inclusive model yields an AUC of 0.935; the descriptor model yields an AUC of 0.876 (P < 0.001). Again, the inclusive model is superior to the clinical performance (P < 0.001); the descriptor model performs similarly. We consider our classifier a step towards a more uniform interpretation of combinations of BI-RADS descriptors. We provide our classifier at www.ebm-radiology.com/nbmm/index.html . • We provide a decision support tool for mammographic masses at www.ebm-radiology.com/nbmm/index.html . • Our tool may reduce variability of practice in BI-RADS category assignment. • A formal analysis of BI-RADS descriptors may enhance radiologists' diagnostic performance.

Semiparametric Thurstonian Models for Recurrent Choices: A Bayesian Analysis

ERIC Educational Resources Information Center

Ansari, Asim; Iyengar, Raghuram

2006-01-01

We develop semiparametric Bayesian Thurstonian models for analyzing repeated choice decisions involving multinomial, multivariate binary or multivariate ordinal data. Our modeling framework has multiple components that together yield considerable flexibility in modeling preference utilities, cross-sectional heterogeneity and parameter-driven…

Simulated Impacts of Climate Change on Water Use and Yield of Irrigated Sugarcane in South Africa

NASA Technical Reports Server (NTRS)

Jones, M.R; Singels, A.; Ruane, A. C.

2015-01-01

Reliable predictions of climate change impacts on water use, irrigation requirements and yields of irrigated sugarcane in South Africa (a water-scarce country) are necessary to plan adaptation strategies. Although previous work has been done in this regard, methodologies and results vary considerably. The objectives were (1) to estimate likely impacts of climate change on sugarcane yields, water use and irrigation demand at three irrigated sugarcane production sites in South Africa (Malelane, Pongola and La Mercy) for current (1980-2010) and future (2070-2100) climate scenarios, using an approach based on the Agricultural Model Inter-comparison and Improvement Project (AgMIP) protocols; and (2) to assess the suitability of this methodology for investigating climate change impacts on sugarcane production. Future climate datasets were generated using the Delta downscaling method and three Global Circulation Models (GCMs) assuming atmospheric CO2 concentration [CO2] of 734 ppm(A2 emissions scenario). Yield and water use were simulated using the DSSAT-Canegro v4.5 model. Irrigated cane yields are expected to increase at all three sites (between 11 and 14%), primarily due to increased interception of radiation as a result of accelerated canopy development. Evapotranspiration and irrigation requirements increased by 11% due to increased canopy cover and evaporative demand. Sucrose yields are expected to decline because of increased consumption of photo-assimilate for structural growth and maintenance respiration. Crop responses in canopy development and yield formation differed markedly between the crop cycles investigated. Possible agronomic implications of these results include reduced weed control costs due to shortened periods of partial canopy, a need for improved efficiency of irrigation to counter increased demands, and adjustments to ripening and harvest practices to counter decreased cane quality and optimize productivity. Although the Delta climate data downscaling method is considered robust, accurate and easily-understood, it does not change the future number of rain-days per month. The impacts of this and other climate data simplifications ought to be explored in future work. Shortcomings of the DSSAT-Canegro model include the simulated responses of phenological development, photosynthesis and respiration processes to high temperatures, and the disconnect between simulated biomass accumulation and expansive growth. Proposed methodology refinements should improve the reliability of predicted climate change impacts on sugarcane yield.

On kinetic modelling for solar redox thermochemical H2O and CO2 splitting over NiFe2O4 for H2, CO and syngas production.

PubMed

Dimitrakis, Dimitrios A; Syrigou, Maria; Lorentzou, Souzana; Kostoglou, Margaritis; Konstandopoulos, Athanasios G

2017-10-11

This study aims at developing a kinetic model that can adequately describe solar thermochemical water and carbon dioxide splitting with nickel ferrite powder as the active redox material. The kinetic parameters of water splitting of a previous study are revised to include transition times and new kinetic parameters for carbon dioxide splitting are developed. The computational results show a satisfactory agreement with experimental data and continuous multicycle operation under varying operating conditions is simulated. Different test cases are explored in order to improve the product yield. At first a parametric analysis is conducted, investigating the appropriate duration of the oxidation and the thermal reduction step that maximizes the hydrogen yield. Subsequently, a non-isothermal oxidation step is simulated and proven as an interesting option for increasing the hydrogen production. The kinetic model is adapted to simulate the production yields in structured solar reactor components, i.e. extruded monolithic structures, as well.

New criteria for isotropic and textured metals

NASA Astrophysics Data System (ADS)

Cazacu, Oana

2018-05-01

In this paper a isotropic criterion expressed in terms of both invariants of the stress deviator, J2 and J3 is proposed. This criterion involves a unique parameter, α, which depends only on the ratio between the yield stresses in uniaxial tension and pure shear. If this parameter is zero, the von Mises yield criterion is recovered; if a is positive the yield surface is interior to the von Mises yield surface whereas when a is negative, the new yield surface is exterior to it. Comparison with polycrystalline calculations using Taylor-Bishop-Hill model [1] for randomly oriented face-centered (FCC) polycrystalline metallic materials show that this new criterion captures well the numerical yield points. Furthermore, the criterion reproduces well yielding under combined tension-shear loadings for a variety of isotropic materials. An extension of this isotropic yield criterion such as to account for orthotropy in yielding is developed using the generalized invariants approach of Cazacu and Barlat [2]. This new orthotropic criterion is general and applicable to three-dimensional stress states. The procedure for the identification of the material parameters is outlined. Illustration of the predictive capabilities of the new orthotropic is demonstrated through comparison between the model predictions and data on aluminum sheet samples.

Effective model development of internal auditors in the village financial institution

NASA Astrophysics Data System (ADS)

Arsana, I. M. M.; Sugiarta, I. N.

2018-01-01

Designing an effective audit system is complex and challenging, and a focus on examining how internal audit drive improvement in three core performance dimensions ethicality, efficiency, and effectiveness in organization is needed. The problem of research is how the desain model and peripheral of supporter of effective supervation Village Credit Institution? Research of objectives is yielding the desain model and peripheral of supporter of effective supervation Village Credit Institution. Method Research use data collecting technique interview, observation and enquette. Data analysis, data qualitative before analysed to be turned into quantitative data in the form of scale. Each variable made to become five classificat pursuant to scale of likert. Data analysed descriptively to find supervation level, Structural Equation Model (SEM) to find internal and eksternal factor. So that desain model supervation with descriptive analysis. Result of research desain model and peripheral of supporter of effective supervation Village Credit Institution. The conclusion desain model supported by three sub system: sub system institute yield body supervisor of Village Credit Institution, sub system standardization and working procedure yield standard operating procedure supervisor of Village Credit Institution, sub system education and training yield supervisor professional of Village Credit Institution.

A new UK fission yield evaluation UKFY3.7

NASA Astrophysics Data System (ADS)

Mills, Robert William

2017-09-01

The JEFF neutron induced and spontaneous fission product yield evaluation is currently unchanged from JEFF-3.1.1, also known by its UK designation UKFY3.6A. It is based upon experimental data combined with empirically fitted mass, charge and isomeric state models which are then adjusted within the experimental and model uncertainties to conform to the physical constraints of the fission process. A new evaluation has been prepared for JEFF, called UKFY3.7, that incorporates new experimental data and replaces the current empirical models (multi-Gaussian fits of mass distribution and Wahl Zp model for charge distribution combined with parameter extrapolation), with predictions from GEF. The GEF model has the advantage that one set of parameters allows the prediction of many different fissioning nuclides at different excitation energies unlike previous models where each fissioning nuclide at a specific excitation energy had to be fitted individually to the relevant experimental data. The new UKFY3.7 evaluation, submitted for testing as part of JEFF-3.3, is described alongside initial results of testing. In addition, initial ideas for future developments allowing inclusion of new measurements types and changing from any neutron spectrum type to true neutron energy dependence are discussed. Also, a method is proposed to propagate uncertainties of fission product yields based upon the experimental data that underlies the fission yield evaluation. The covariance terms being determined from the evaluated cumulative and independent yields combined with the experimental uncertainties on the cumulative yield measurements.

Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network.

PubMed

Antwi, Philip; Li, Jianzheng; Boadi, Portia Opoku; Meng, Jia; Shi, En; Deng, Kaiwen; Bondinuba, Francis Kwesi

2017-03-01

Three-layered feedforward backpropagation (BP) artificial neural networks (ANN) and multiple nonlinear regression (MnLR) models were developed to estimate biogas and methane yield in an upflow anaerobic sludge blanket (UASB) reactor treating potato starch processing wastewater (PSPW). Anaerobic process parameters were optimized to identify their importance on methanation. pH, total chemical oxygen demand, ammonium, alkalinity, total Kjeldahl nitrogen, total phosphorus, volatile fatty acids and hydraulic retention time selected based on principal component analysis were used as input variables, whiles biogas and methane yield were employed as target variables. Quasi-Newton method and conjugate gradient backpropagation algorithms were best among eleven training algorithms. Coefficient of determination (R 2 ) of the BP-ANN reached 98.72% and 97.93% whiles MnLR model attained 93.9% and 91.08% for biogas and methane yield, respectively. Compared with the MnLR model, BP-ANN model demonstrated significant performance, suggesting possible control of the anaerobic digestion process with the BP-ANN model. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

Statistical rice yield modeling using blended MODIS-Landsat based crop phenology metrics in Taiwan

NASA Astrophysics Data System (ADS)

Chen, C. R.; Chen, C. F.; Nguyen, S. T.; Lau, K. V.

2015-12-01

Taiwan is a populated island with a majority of residents settled in the western plains where soils are suitable for rice cultivation. Rice is not only the most important commodity, but also plays a critical role for agricultural and food marketing. Information of rice production is thus important for policymakers to devise timely plans for ensuring sustainably socioeconomic development. Because rice fields in Taiwan are generally small and yet crop monitoring requires information of crop phenology associating with the spatiotemporal resolution of satellite data, this study used Landsat-MODIS fusion data for rice yield modeling in Taiwan. We processed the data for the first crop (Feb-Mar to Jun-Jul) and the second (Aug-Sep to Nov-Dec) in 2014 through five main steps: (1) data pre-processing to account for geometric and radiometric errors of Landsat data, (2) Landsat-MODIS data fusion using using the spatial-temporal adaptive reflectance fusion model, (3) construction of the smooth time-series enhanced vegetation index 2 (EVI2), (4) rice yield modeling using EVI2-based crop phenology metrics, and (5) error verification. The fusion results by a comparison bewteen EVI2 derived from the fusion image and that from the reference Landsat image indicated close agreement between the two datasets (R2 > 0.8). We analysed smooth EVI2 curves to extract phenology metrics or phenological variables for establishment of rice yield models. The results indicated that the established yield models significantly explained more than 70% variability in the data (p-value < 0.001). The comparison results between the estimated yields and the government's yield statistics for the first and second crops indicated a close significant relationship between the two datasets (R2 > 0.8), in both cases. The root mean square error (RMSE) and mean absolute error (MAE) used to measure the model accuracy revealed the consistency between the estimated yields and the government's yield statistics. This study demonstrates advantages of using EVI2-based phenology metrics (derived from Landsat-MODIS fusion data) for rice yield estimation in Taiwan prior to the harvest period.

Development of a recursion RNG-based turbulence model

NASA Technical Reports Server (NTRS)

Zhou, YE; Vahala, George; Thangam, S.

1993-01-01

Reynolds stress closure models based on the recursion renormalization group theory are developed for the prediction of turbulent separated flows. The proposed model uses a finite wavenumber truncation scheme to account for the spectral distribution of energy. In particular, the model incorporates effects of both local and nonlocal interactions. The nonlocal interactions are shown to yield a contribution identical to that from the epsilon-renormalization group (RNG), while the local interactions introduce higher order dispersive effects. A formal analysis of the model is presented and its ability to accurately predict separated flows is analyzed from a combined theoretical and computational stand point. Turbulent flow past a backward facing step is chosen as a test case and the results obtained based on detailed computations demonstrate that the proposed recursion -RNG model with finite cut-off wavenumber can yield very good predictions for the backstep problem.

The Norwegian Healthier Goats program--modeling lactation curves using a multilevel cubic spline regression model.

PubMed

Nagel-Alne, G E; Krontveit, R; Bohlin, J; Valle, P S; Skjerve, E; Sølverød, L S

2014-07-01

In 2001, the Norwegian Goat Health Service initiated the Healthier Goats program (HG), with the aim of eradicating caprine arthritis encephalitis, caseous lymphadenitis, and Johne's disease (caprine paratuberculosis) in Norwegian goat herds. The aim of the present study was to explore how control and eradication of the above-mentioned diseases by enrolling in HG affected milk yield by comparison with herds not enrolled in HG. Lactation curves were modeled using a multilevel cubic spline regression model where farm, goat, and lactation were included as random effect parameters. The data material contained 135,446 registrations of daily milk yield from 28,829 lactations in 43 herds. The multilevel cubic spline regression model was applied to 4 categories of data: enrolled early, control early, enrolled late, and control late. For enrolled herds, the early and late notations refer to the situation before and after enrolling in HG; for nonenrolled herds (controls), they refer to development over time, independent of HG. Total milk yield increased in the enrolled herds after eradication: the total milk yields in the fourth lactation were 634.2 and 873.3 kg in enrolled early and enrolled late herds, respectively, and 613.2 and 701.4 kg in the control early and control late herds, respectively. Day of peak yield differed between enrolled and control herds. The day of peak yield came on d 6 of lactation for the control early category for parities 2, 3, and 4, indicating an inability of the goats to further increase their milk yield from the initial level. For enrolled herds, on the other hand, peak yield came between d 49 and 56, indicating a gradual increase in milk yield after kidding. Our results indicate that enrollment in the HG disease eradication program improved the milk yield of dairy goats considerably, and that the multilevel cubic spline regression was a suitable model for exploring effects of disease control and eradication on milk yield. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Spectral estimates of solar radiation intercepted by corn canopies

NASA Technical Reports Server (NTRS)

Bauer, M. E. (Principal Investigator); Daughtry, C. S. T.; Gallo, K. P.

1982-01-01

Reflectance factor data were acquired with a Landsat band radiometer throughout two growing seasons for corn (Zea mays L.) canopies differing in planting dates, populations, and soil types. Agronomic data collected included leaf area index (LAI), biomass, development stage, and final grain yields. The spectral variable, greenness, was associated with 78 percent of the variation in LAI over all treatments. Single observations of LAI or greenness have limited value in predicting corn yields. The proportions of solar radiation intercepted (SRI) by these canopies were estimated using either measured LAI or greenness. Both SRI estimates, when accumulated over the growing season, accounted for approximately 65 percent of the variation in yields. Models which simulated the daily effects of weather and intercepted solar radiation on growth had the highest correlations to grain yields. This concept of estimating intercepted solar radiation using spectral data represents a viable approach for merging spectral and meteorological data for crop yield models.

Comparing two tools for ecosystem service assessments regarding water resources decisions.

PubMed

Dennedy-Frank, P James; Muenich, Rebecca Logsdon; Chaubey, Indrajeet; Ziv, Guy

2016-07-15

We present a comparison of two ecohydrologic models commonly used for planning land management to assess the production of hydrologic ecosystem services: the Soil and Water Assessment Tool (SWAT) and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) annual water yield model. We compare these two models at two distinct sites in the US: the Wildcat Creek Watershed in Indiana and the Upper Upatoi Creek Watershed in Georgia. The InVEST and SWAT models provide similar estimates of the spatial distribution of water yield in Wildcat Creek, but very different estimates of the spatial distribution of water yield in Upper Upatoi Creek. The InVEST model may do a poor job estimating the spatial distribution of water yield in the Upper Upatoi Creek Watershed because baseflow provides a significant portion of the site's total water yield, which means that storage dynamics which are not modeled by InVEST may be important. We also compare the ability of these two models, as well as one newly developed set of ecosystem service indices, to deliver useful guidance for land management decisions focused on providing hydrologic ecosystem services in three particular decision contexts: environmental flow ecosystem services, ecosystem services for potable water supply, and ecosystem services for rainfed irrigation. We present a simple framework for selecting models or indices to evaluate hydrologic ecosystem services as a way to formalize where models deliver useful guidance. Copyright © 2016 Elsevier Ltd. All rights reserved.

Distillation time as tool for improved antimalarial activity and differential oil composition of cumin seed oil

USDA-ARS?s Scientific Manuscript database

A steam distillation extraction kinetics experiment was conducted to estimate essential oil yield, composition, antimalarial, and antioxidant capacity of cumin (Cuminum cyminum L.) seed (fruits). Furthermore, regression models were developed to predict essential oil yield and composition for a given...

Human impact on sediment fluxes within the Blue Nile and Atbara River basins

NASA Astrophysics Data System (ADS)

Balthazar, Vincent; Vanacker, Veerle; Girma, Atkilt; Poesen, Jean; Golla, Semunesh

2013-01-01

A regional assessment of the spatial variability in sediment yields allows filling the gap between detailed, process-based understanding of erosion at field scale and empirical sediment flux models at global scale. In this paper, we focus on the intrabasin variability in sediment yield within the Blue Nile and Atbara basins as biophysical and anthropogenic factors are presumably acting together to accelerate soil erosion. The Blue Nile and Atbara River systems are characterized by an important spatial variability in sediment fluxes, with area-specific sediment yield (SSY) values ranging between 4 and 4935 t/km2/y. Statistical analyses show that 41% of the observed variation in SSY can be explained by remote sensing proxy data of surface vegetation cover, rainfall intensity, mean annual temperature, and human impact. The comparison of a locally adapted regression model with global predictive sediment flux models indicates that global flux models such as the ART and BQART models are less suited to capture the spatial variability in area-specific sediment yields (SSY), but they are very efficient to predict absolute sediment yields (SY). We developed a modified version of the BQART model that estimates the human influence on sediment yield based on a high resolution composite measure of local human impact (human footprint index) instead of countrywide estimates of GNP/capita. Our modified version of the BQART is able to explain 80% of the observed variation in SY for the Blue Nile and Atbara basins and thereby performs only slightly less than locally adapted regression models.

Yield Behavior of Solution Treated and Aged Ti-6Al-4V

NASA Technical Reports Server (NTRS)

Ring, Andrew J.; Baker, Eric H.; Salem, Jonathan A.; Thesken, John C.

2014-01-01

Post yield uniaxial tension-compression tests were run on a solution treated and aged (STA), titanium 6-percent aluminum 4-percent vanadium (Ti-6Al-4V) alloy to determine the yield behavior on load reversal. The material exhibits plastic behavior almost immediately on load reversal implying a strong Bauschinger effect. The resultant stress-strain data was compared to a 1D mechanics model and a finite element model used to design a composite overwrapped pressure vessel (COPV). Although the models and experimental data compare well for the initial loading and unloading in the tensile regime, agreement is lost in the compressive regime due to the Bauschinger effect and the assumption of perfect plasticity. The test data presented here are being used to develop more accurate cyclic hardening constitutive models for future finite element design analysis of COPVs.

Investigation and Modeling of Cranberry Weather Stress.

NASA Astrophysics Data System (ADS)

Croft, Paul Joseph

Cranberry bog weather conditions and weather-related stress were investigated for development of crop yield prediction models and models to predict daily weather conditions in the bog. Field investigations and data gathering were completed at the Rutgers University Blueberry/Cranberry Research Center experimental bogs in Chatsworth, New Jersey. Study indicated that although cranberries generally exhibit little or no stomatal response to changing atmospheric conditions, the evaluation of weather-related stress could be accomplished via use of micrometeorological data. Definition of weather -related stress was made by establishing critical thresholds of the frequencies of occurrence, and magnitudes of, temperature and precipitation in the bog based on values determined by a review of the literature and a grower questionnaire. Stress frequencies were correlated with cranberry yield to develop predictive models based on the previous season's yield, prior season data, prior and current season data, current season data; and prior and current season data through July 31 of the current season. The predictive ability of the prior season models was best and could be used in crop planning and production. Further examination of bog micrometeorological data permitted the isolation of those weather conditions conducive to cranberry scald and allowed for the institution of a pilot scald advisory program during the 1991 season. The micrometeorological data from the bog was also used to develop models to predict daily canopy temperature and precipitation, based on upper air data, for grower use. Models were developed for each month for maximum and minimum temperatures and for precipitation and generally performed well. The modeling of bog weather conditions is an important first step toward daily prediction of cranberry weather-related stress.

Estimating nutrient uptake requirements for soybean using QUEFTS model in China

PubMed Central

Yang, Fuqiang; Xu, Xinpeng; Wang, Wei; Ma, Jinchuan; Wei, Dan; He, Ping; Pampolino, Mirasol F.; Johnston, Adrian M.

2017-01-01

Estimating balanced nutrient requirements for soybean (Glycine max [L.] Merr) in China is essential for identifying optimal fertilizer application regimes to increase soybean yield and nutrient use efficiency. We collected datasets from field experiments in major soybean planting regions of China between 2001 and 2015 to assess the relationship between soybean seed yield and nutrient uptake, and to estimate nitrogen (N), phosphorus (P), and potassium (K) requirements for a target yield of soybean using the quantitative evaluation of the fertility of tropical soils (QUEFTS) model. The QUEFTS model predicted a linear–parabolic–plateau curve for the balanced nutrient uptake with a target yield increased from 3.0 to 6.0 t ha−1 and the linear part was continuing until the yield reached about 60–70% of the potential yield. To produce 1000 kg seed of soybean in China, 55.4 kg N, 7.9 kg P, and 20.1 kg K (N:P:K = 7:1:2.5) were required in the above-ground parts, and the corresponding internal efficiencies (IE, kg seed yield per kg nutrient uptake) were 18.1, 126.6, and 49.8 kg seed per kg N, P, and K, respectively. The QUEFTS model also simulated that a balanced N, P, and K removal by seed which were 48.3, 5.9, and 12.2 kg per 1000 kg seed, respectively, accounting for 87.1%, 74.1%, and 60.8% of the total above-ground parts, respectively. These results were conducive to make fertilizer recommendations that improve the seed yield of soybean and avoid excessive or deficient nutrient supplies. Field validation indicated that the QUEFTS model could be used to estimate nutrient requirements which help develop fertilizer recommendations for soybean. PMID:28498839

Gastrointestinal Spatiotemporal mRNA Expression of Ghrelin vs Growth Hormone Receptor and New Growth Yield Machine Learning Model Based on Perturbation Theory.

PubMed

Ran, Tao; Liu, Yong; Li, Hengzhi; Tang, Shaoxun; He, Zhixiong; Munteanu, Cristian R; González-Díaz, Humberto; Tan, Zhiliang; Zhou, Chuanshe

2016-07-27

The management of ruminant growth yield has economic importance. The current work presents a study of the spatiotemporal dynamic expression of Ghrelin and GHR at mRNA levels throughout the gastrointestinal tract (GIT) of kid goats under housing and grazing systems. The experiments show that the feeding system and age affected the expression of either Ghrelin or GHR with different mechanisms. Furthermore, the experimental data are used to build new Machine Learning models based on the Perturbation Theory, which can predict the effects of perturbations of Ghrelin and GHR mRNA expression on the growth yield. The models consider eight longitudinal GIT segments (rumen, abomasum, duodenum, jejunum, ileum, cecum, colon and rectum), seven time points (0, 7, 14, 28, 42, 56 and 70 d) and two feeding systems (Supplemental and Grazing feeding) as perturbations from the expected values of the growth yield. The best regression model was obtained using Random Forest, with the coefficient of determination R(2) of 0.781 for the test subset. The current results indicate that the non-linear regression model can accurately predict the growth yield and the key nodes during gastrointestinal development, which is helpful to optimize the feeding management strategies in ruminant production system.

Gastrointestinal Spatiotemporal mRNA Expression of Ghrelin vs Growth Hormone Receptor and New Growth Yield Machine Learning Model Based on Perturbation Theory

PubMed Central

Ran, Tao; Liu, Yong; Li, Hengzhi; Tang, Shaoxun; He, Zhixiong; Munteanu, Cristian R.; González-Díaz, Humberto; Tan, Zhiliang; Zhou, Chuanshe

2016-01-01

The management of ruminant growth yield has economic importance. The current work presents a study of the spatiotemporal dynamic expression of Ghrelin and GHR at mRNA levels throughout the gastrointestinal tract (GIT) of kid goats under housing and grazing systems. The experiments show that the feeding system and age affected the expression of either Ghrelin or GHR with different mechanisms. Furthermore, the experimental data are used to build new Machine Learning models based on the Perturbation Theory, which can predict the effects of perturbations of Ghrelin and GHR mRNA expression on the growth yield. The models consider eight longitudinal GIT segments (rumen, abomasum, duodenum, jejunum, ileum, cecum, colon and rectum), seven time points (0, 7, 14, 28, 42, 56 and 70 d) and two feeding systems (Supplemental and Grazing feeding) as perturbations from the expected values of the growth yield. The best regression model was obtained using Random Forest, with the coefficient of determination R2 of 0.781 for the test subset. The current results indicate that the non-linear regression model can accurately predict the growth yield and the key nodes during gastrointestinal development, which is helpful to optimize the feeding management strategies in ruminant production system. PMID:27460882

Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security

DOE PAGES

Christensen, A. J.; Srinivasan, V.; Hart, J. C.; ...

2018-03-17

Sustainable crop production is a contributing factor to current and future food security. Innovative technologies are needed to design strategies that will achieve higher crop yields on less land and with fewer resources. Computational modeling coupled with advanced scientific visualization enables researchers to explore and interact with complex agriculture, nutrition, and climate data to predict how crops will respond to untested environments. These virtual observations and predictions can direct the development of crop ideotypes designed to meet future yield and nutritional demands. This review surveys modeling strategies for the development of crop ideotypes and scientific visualization technologies that have ledmore » to discoveries in “big data” analysis. Combined modeling and visualization approaches have been used to realistically simulate crops and to guide selection that immediately enhances crop quantity and quality under challenging environmental conditions. Lastly, this survey of current and developing technologies indicates that integrative modeling and advanced scientific visualization may help overcome challenges in agriculture and nutrition data as large-scale and multidimensional data become available in these fields.« less

Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security

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

Christensen, A. J.; Srinivasan, V.; Hart, J. C.

Sustainable crop production is a contributing factor to current and future food security. Innovative technologies are needed to design strategies that will achieve higher crop yields on less land and with fewer resources. Computational modeling coupled with advanced scientific visualization enables researchers to explore and interact with complex agriculture, nutrition, and climate data to predict how crops will respond to untested environments. These virtual observations and predictions can direct the development of crop ideotypes designed to meet future yield and nutritional demands. This review surveys modeling strategies for the development of crop ideotypes and scientific visualization technologies that have ledmore » to discoveries in “big data” analysis. Combined modeling and visualization approaches have been used to realistically simulate crops and to guide selection that immediately enhances crop quantity and quality under challenging environmental conditions. Lastly, this survey of current and developing technologies indicates that integrative modeling and advanced scientific visualization may help overcome challenges in agriculture and nutrition data as large-scale and multidimensional data become available in these fields.« less

Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security.

PubMed

Christensen, A J; Srinivasan, Venkatraman; Hart, John C; Marshall-Colon, Amy

2018-05-01

Sustainable crop production is a contributing factor to current and future food security. Innovative technologies are needed to design strategies that will achieve higher crop yields on less land and with fewer resources. Computational modeling coupled with advanced scientific visualization enables researchers to explore and interact with complex agriculture, nutrition, and climate data to predict how crops will respond to untested environments. These virtual observations and predictions can direct the development of crop ideotypes designed to meet future yield and nutritional demands. This review surveys modeling strategies for the development of crop ideotypes and scientific visualization technologies that have led to discoveries in "big data" analysis. Combined modeling and visualization approaches have been used to realistically simulate crops and to guide selection that immediately enhances crop quantity and quality under challenging environmental conditions. This survey of current and developing technologies indicates that integrative modeling and advanced scientific visualization may help overcome challenges in agriculture and nutrition data as large-scale and multidimensional data become available in these fields.

Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security

PubMed Central

Christensen, A J; Srinivasan, Venkatraman; Hart, John C; Marshall-Colon, Amy

2018-01-01

Abstract Sustainable crop production is a contributing factor to current and future food security. Innovative technologies are needed to design strategies that will achieve higher crop yields on less land and with fewer resources. Computational modeling coupled with advanced scientific visualization enables researchers to explore and interact with complex agriculture, nutrition, and climate data to predict how crops will respond to untested environments. These virtual observations and predictions can direct the development of crop ideotypes designed to meet future yield and nutritional demands. This review surveys modeling strategies for the development of crop ideotypes and scientific visualization technologies that have led to discoveries in “big data” analysis. Combined modeling and visualization approaches have been used to realistically simulate crops and to guide selection that immediately enhances crop quantity and quality under challenging environmental conditions. This survey of current and developing technologies indicates that integrative modeling and advanced scientific visualization may help overcome challenges in agriculture and nutrition data as large-scale and multidimensional data become available in these fields. PMID:29562368

Physically-based strength model of tantalum incorporating effects of temperature, strain rate and pressure

DOE PAGES

Lim, Hojun; Battaile, Corbett C.; Brown, Justin L.; ...

2016-06-14

In this work, we develop a tantalum strength model that incorporates e ects of temperature, strain rate and pressure. Dislocation kink-pair theory is used to incorporate temperature and strain rate e ects while the pressure dependent yield is obtained through the pressure dependent shear modulus. Material constants used in the model are parameterized from tantalum single crystal tests and polycrystalline ramp compression experiments. It is shown that the proposed strength model agrees well with the temperature and strain rate dependent yield obtained from polycrystalline tantalum experiments. Furthermore, the model accurately reproduces the pressure dependent yield stresses up to 250 GPa.more » The proposed strength model is then used to conduct simulations of a Taylor cylinder impact test and validated with experiments. This approach provides a physically-based multi-scale strength model that is able to predict the plastic deformation of polycrystalline tantalum through a wide range of temperature, strain and pressure regimes.« less

Development of a CSP plant energy yield calculation tool applying predictive models to analyze plant performance sensitivities

NASA Astrophysics Data System (ADS)

Haack, Lukas; Peniche, Ricardo; Sommer, Lutz; Kather, Alfons

2017-06-01

At early project stages, the main CSP plant design parameters such as turbine capacity, solar field size, and thermal storage capacity are varied during the techno-economic optimization to determine most suitable plant configurations. In general, a typical meteorological year with at least hourly time resolution is used to analyze each plant configuration. Different software tools are available to simulate the annual energy yield. Software tools offering a thermodynamic modeling approach of the power block and the CSP thermal cycle, such as EBSILONProfessional®, allow a flexible definition of plant topologies. In EBSILON, the thermodynamic equilibrium for each time step is calculated iteratively (quasi steady state), which requires approximately 45 minutes to process one year with hourly time resolution. For better presentation of gradients, 10 min time resolution is recommended, which increases processing time by a factor of 5. Therefore, analyzing a large number of plant sensitivities, as required during the techno-economic optimization procedure, the detailed thermodynamic simulation approach becomes impracticable. Suntrace has developed an in-house CSP-Simulation tool (CSPsim), based on EBSILON and applying predictive models, to approximate the CSP plant performance for central receiver and parabolic trough technology. CSPsim significantly increases the speed of energy yield calculations by factor ≥ 35 and has automated the simulation run of all predefined design configurations in sequential order during the optimization procedure. To develop the predictive models, multiple linear regression techniques and Design of Experiment methods are applied. The annual energy yield and derived LCOE calculated by the predictive model deviates less than ±1.5 % from the thermodynamic simulation in EBSILON and effectively identifies the optimal range of main design parameters for further, more specific analysis.

Evaluating accuracy of DSSAT model for soybean yield estimation using satellite weather data

NASA Astrophysics Data System (ADS)

Ovando, Gustavo; Sayago, Silvina; Bocco, Mónica

2018-04-01

Crop models allow simulating the development and yield of the crops, to represent and to evaluate the influence of multiple factors. The DSSAT cropping system model is one of the most widely used and contains CROPGRO module for soybean. This crop has a great importance for many southern countries of Latin America and for Argentina. Solar radiation and rainfall are necessary variables as inputs for crop models; however these data are not as readily available. The satellital products from Clouds and Earth's Radiant Energy System (CERES) and Tropic Rainfall Measurement Mission (TRMM) provide continuous spatial and temporal information of solar radiation and precipitation, respectively. This study evaluates and quantifies the uncertainty in estimating soybean yield using a DSSAT model, when recorded weather data are replaced with CERES and TRMM ones. Different percentages of data replacements, soybean maturity groups and planting dates are considered, for 2006-2016 period in Oliveros (Argentina). Results show that CERES and TRMM products can be used for soybean yield estimation with DSSAT considering that: percentage of data replacement, campaign, planting date and maturity group, determine the amounts and trends of yield errors. Replacements with CERES data up to 30% result in %RMSE lower than 10% in 87% of the cases; while the replacement with TRMM data presents the best statisticals in campaigns with high yields. Simulations based entirely on CERES solar radiation give better results than those with TRMM. In general, similar percentages of replacement show better performance in the estimation of soybean yield for solar radiation than the replacement of precipitation values.

Predicting trends of invasive plants richness using local socio-economic data: An application in North Portugal

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

Santos, Mario, E-mail: mgsantoss@gmail.com; Freitas, Raul, E-mail: raulfreitas@portugalmail.com; Crespi, Antonio L., E-mail: aluis.crespi@gmail.com

2011-10-15

This study assesses the potential of an integrated methodology for predicting local trends in invasive exotic plant species (invasive richness) using indirect, regional information on human disturbance. The distribution of invasive plants was assessed in North Portugal using herbarium collections and local environmental, geophysical and socio-economic characteristics. Invasive richness response to anthropogenic disturbance was predicted using a dynamic model based on a sequential modeling process (stochastic dynamic methodology-StDM). Derived scenarios showed that invasive richness trends were clearly associated with ongoing socio-economic change. Simulations including scenarios of growing urbanization showed an increase in invasive richness while simulations in municipalities with decreasingmore » populations showed stable or decreasing levels of invasive richness. The model simulations demonstrate the interest and feasibility of using this methodology in disturbance ecology. - Highlights: {yields} Socio-economic data indicate human induced disturbances. {yields} Socio-economic development increase disturbance in ecosystems. {yields} Disturbance promotes opportunities for invasive plants.{yields} Increased opportunities promote richness of invasive plants.{yields} Increase in richness of invasive plants change natural ecosystems.« less

A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change.

PubMed

Ashraf, M Irfan; Meng, Fan-Rui; Bourque, Charles P-A; MacLean, David A

2015-01-01

Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm(2) 5-year(-1) and volume: 0.0008 m(3) 5-year(-1)). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm(2) 5-year(-1) and 0.0393 m(3) 5-year(-1) in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology has substantial potential in forest modelling.

A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change

PubMed Central

Ashraf, M. Irfan; Meng, Fan-Rui; Bourque, Charles P.-A.; MacLean, David A.

2015-01-01

Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm2 5-year-1 and volume: 0.0008 m3 5-year-1). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm2 5-year-1 and 0.0393 m3 5-year-1 in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology has substantial potential in forest modelling. PMID:26173081

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.

Recent Developments in the Formability of Aluminum Alloys

NASA Astrophysics Data System (ADS)

Banabic, Dorel; Cazacu, Oana; Paraianu, Liana; Jurco, Paul

2005-08-01

The paper presents a few recent contributions brought by the authors in the field of the formability of aluminum alloys. A new concept for calculating Forming Limit Diagrams (FLD) using the finite element method is presented. The article presents a new strategy for calculating both branches of an FLD, using a Hutchinson - Neale model implemented in a finite element code. The simulations have been performed with Abaqus/Standard. The constitutive model has been implemented using a UMAT subroutine. The plastic anisotropy of the sheet metal is described by the Cazacu-Barlat and the BBC2003 yield criteria. The theoretical predictions have been compared with the results given by the classical Hutchinson - Neale method and also with experimental data for different aluminum alloys. The comparison proves the capability of the finite element method to predict the strain localization. A computer program used for interactive calculation and graphical representation of different Yield Loci and Forming Limit Diagrams has also been developed. The program is based on a Hutchinson-Neale model. Different yield criteria (Hill 1948, Barlat-Lian and BBC 2003) are implemented in this model. The program consists in three modules: a graphical interface for input, a module for the identification and visualization of the yield surfaces, and a module for calculating and visualizing the forming limit curves. A useful facility offered by the program is the possibility to perform the sensitivity analysis both for the yield surface and the forming limit curves. The numerical results can be compared with experimental data, using the import/export facilities included in the program.

Recent Developments in the Formability of Aluminum Alloys

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

Banabic, Dorel; Paraianu, Liana; Jurco, Paul

The paper presents a few recent contributions brought by the authors in the field of the formability of aluminum alloys. A new concept for calculating Forming Limit Diagrams (FLD) using the finite element method is presented. The article presents a new strategy for calculating both branches of an FLD, using a Hutchinson - Neale model implemented in a finite element code. The simulations have been performed with Abaqus/Standard. The constitutive model has been implemented using a UMAT subroutine. The plastic anisotropy of the sheet metal is described by the Cazacu-Barlat and the BBC2003 yield criteria. The theoretical predictions have beenmore » compared with the results given by the classical Hutchinson - Neale method and also with experimental data for different aluminum alloys. The comparison proves the capability of the finite element method to predict the strain localization. A computer program used for interactive calculation and graphical representation of different Yield Loci and Forming Limit Diagrams has also been developed. The program is based on a Hutchinson-Neale model. Different yield criteria (Hill 1948, Barlat-Lian and BBC 2003) are implemented in this model. The program consists in three modules: a graphical interface for input, a module for the identification and visualization of the yield surfaces, and a module for calculating and visualizing the forming limit curves. A useful facility offered by the program is the possibility to perform the sensitivity analysis both for the yield surface and the forming limit curves. The numerical results can be compared with experimental data, using the import/export facilities included in the program.« less

Brachypodium seed - a potential model for studying grain development of cereal crops

USDA-ARS?s Scientific Manuscript database

Seeds of small grains are important resources for human and animal food. The understanding of seed biology is essential for crop improvement by increasing grain yields and nutritional value. In the last decade, Brachypodium distachyon has been developed as a model plant for temperate cereal grasses...

Source-sink interaction: a century old concept under the light of modern molecular systems biology.

PubMed

Chang, Tian-Gen; Zhu, Xin-Guang; Raines, Christine

2017-07-20

Many approaches to engineer source strength have been proposed to enhance crop yield potential. However, a well-co-ordinated source-sink relationship is required finally to realize the promised increase in crop yield potential in the farmer's field. Source-sink interaction has been intensively studied for decades, and a vast amount of knowledge about the interaction in different crops and under different environments has been accumulated. In this review, we first introduce the basic concepts of source, sink and their interactions, then summarize current understanding of how source and sink can be manipulated through both environmental control and genetic manipulations. We show that the source-sink interaction underlies the diverse responses of crops to the same perturbations and argue that development of a molecular systems model of source-sink interaction is required towards a rational manipulation of the source-sink relationship for increased yield. We finally discuss both bottom-up and top-down routes to develop such a model and emphasize that a community effort is needed for development of this model. © The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Supporting Crop Loss Insurance Policy of Indonesia through Rice Yield Modelling and Forecasting

NASA Astrophysics Data System (ADS)

van Verseveld, Willem; Weerts, Albrecht; Trambauer, Patricia; de Vries, Sander; Conijn, Sjaak; van Valkengoed, Eric; Hoekman, Dirk; Grondard, Nicolas; Hengsdijk, Huib; Schrevel, Aart; Vlasbloem, Pieter; Klauser, Dominik

2017-04-01

The Government of Indonesia has decided on a crop insurance policy to assist Indonesia's farmers and to boost food security. To support the Indonesian government, the G4INDO project (www.g4indo.org) is developing/constructing an integrated platform implemented in the Delft-FEWS forecasting system (Werner et al., 2013). The integrated platform brings together remote sensed data (both visible and radar) and hydrologic, crop and reservoir modelling and forecasting to improve the modelling and forecasting of rice yield. The hydrological model (wflow_sbm), crop model (wflow_lintul) and reservoir models (RTC-Tools) are coupled on time stepping basis in the OpenStreams framework (see https://github.com/openstreams/wflow) and deployed in the integrated platform to support seasonal forecasting of water availability and crop yield. First we will show the general idea about the G4INDO project, the integrated platform (including Sentinel 1 & 2 data) followed by first (reforecast) results of the coupled models for predicting water availability and crop yield in the Brantas catchment in Java, Indonesia. Werner, M., Schellekens, J., Gijsbers, P., Van Dijk, M., Van den Akker, O. and Heynert K, 2013. The Delft-FEWS flow forecasting system, Environmental Modelling & Software; 40:65-77. DOI: 10.1016/j.envsoft.2012.07.010.

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.

Yield of illicit indoor cannabis cultivation in the Netherlands.

PubMed

Toonen, Marcel; Ribot, Simon; Thissen, Jac

2006-09-01

To obtain a reliable estimation on the yield of illicit indoor cannabis cultivation in The Netherlands, cannabis plants confiscated by the police were used to determine the yield of dried female flower buds. The developmental stage of flower buds of the seized plants was described on a scale from 1 to 10 where the value of 10 indicates a fully developed flower bud ready for harvesting. Using eight additional characteristics describing the grow room and cultivation parameters, regression analysis with subset selection was carried out to develop two models for the yield of indoor cannabis cultivation. The median Dutch illicit grow room consists of 259 cannabis plants, has a plant density of 15 plants/m(2), and 510 W of growth lamps per m(2). For the median Dutch grow room, the predicted yield of female flower buds at the harvestable developmental stage (stage 10) was 33.7 g/plant or 505 g/m(2).

Uncertainty in simulating wheat yields under climate change

NASA Astrophysics Data System (ADS)

Asseng, S.; Ewert, F.; Rosenzweig, C.; Jones, J. W.; Hatfield, J. L.; Ruane, A. C.; Boote, K. J.; Thorburn, P. J.; Rötter, R. P.; Cammarano, D.; Brisson, N.; Basso, B.; Martre, P.; Aggarwal, P. K.; Angulo, C.; Bertuzzi, P.; Biernath, C.; Challinor, A. J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.; Heng, L.; Hooker, J.; Hunt, L. A.; Ingwersen, J.; Izaurralde, R. C.; Kersebaum, K. C.; Müller, C.; Naresh Kumar, S.; Nendel, C.; O'Leary, G.; Olesen, J. E.; Osborne, T. M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Semenov, M. A.; Shcherbak, I.; Steduto, P.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; Wallach, D.; White, J. W.; Williams, J. R.; Wolf, J.

2013-09-01

Projections of climate change impacts on crop yields are inherently uncertain. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development andpolicymaking.

Modeling global yield growth of major crops under multiple socioeconomic pathways

NASA Astrophysics Data System (ADS)

Iizumi, T.; Kim, W.; Zhihong, S.; Nishimori, M.

2016-12-01

Global gridded crop models (GGCMs) are a key tool in deriving global food security scenarios under climate change. However, it is difficult for GGCMs to reproduce the reported yield growth patterns—rapid growth, yield stagnation and yield collapse. Here, we propose a set of parameterizations for GGCMs to capture the contributions to yield from technological improvements at the national and multi-decadal scales. These include country annual per capita gross domestic product (GDP)-based parameterizations for the nitrogen application rate and crop tolerance to stresses associated with high temperature, low temperature, water deficit and water excess. Using a GGCM combined with the parameterizations, we present global 140-year (1961-2100) yield growth simulations for maize, soybean, rice and wheat under multiple shared socioeconomic pathways (SSPs) and no climate change. The model reproduces the major characteristics of reported global and country yield growth patterns over the 1961-2013 period. Under the most rapid developmental pathway SSP5, the simulated global yields for 2091-2100, relative to 2001-2010, are the highest (1.21-1.82 times as high, with variations across the crops), followed by SSP1 (1.14-1.56 times as high), SSP2 (1.12-1.49 times as high), SSP4 (1.08-1.38 times as high) and SSP3 (1.08-1.36 times as high). Future country yield growth varies substantially by income level as well as by crop and by SSP. These yield pathways offer a new baseline for addressing the interdisciplinary questions related to global agricultural development, food security and climate change.

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 Variability and Yields of Major Staple Food Crops in Northern Ghana

NASA Astrophysics Data System (ADS)

Amikuzuno, J.

2012-12-01

Climate variability, the short-term fluctuations in average weather conditions, and agriculture affect each other. Climate variability affects the agroecological and growing conditions of crops and livestock, and is recently believed to be the greatest impediment to the realisation of the first Millennium Development Goal of reducing poverty and food insecurity in arid and semi-arid regions of developing countries. Conversely, agriculture is a major contributor to climate variability and change by emitting greenhouse gases and reducing the agroecology's potential for carbon sequestration. What however, is the empirical evidence of this inter-dependence of climate variability and agriculture in Sub-Sahara Africa? In this paper, we provide some insight into the long run relationship between inter-annual variations in temperature and rainfall, and annual yields of the most important staple food crops in Northern Ghana. Applying pooled panel data of rainfall, temperature and yields of the selected crops from 1976 to 2010 to cointegration and Granger causality models, there is cogent evidence of cointegration between seasonal, total rainfall and crop yields; and causality from rainfall to crop yields in the Sudano-Guinea Savannah and Guinea Savannah zones of Northern Ghana. This suggests that inter-annual yields of the crops have been influenced by the total mounts of rainfall in the planting season. Temperature variability over the study period is however stationary, and is suspected to have minimal effect if any on crop yields. Overall, the results confirm the appropriateness of our attempt in modelling long-term relationships between the climate and crop yield variables.

Combining cow and bull reference populations to increase accuracy of genomic prediction and genome-wide association studies.

PubMed

Calus, M P L; de Haas, Y; Veerkamp, R F

2013-10-01

Genomic selection holds the promise to be particularly beneficial for traits that are difficult or expensive to measure, such that access to phenotypes on large daughter groups of bulls is limited. Instead, cow reference populations can be generated, potentially supplemented with existing information from the same or (highly) correlated traits available on bull reference populations. The objective of this study, therefore, was to develop a model to perform genomic predictions and genome-wide association studies based on a combined cow and bull reference data set, with the accuracy of the phenotypes differing between the cow and bull genomic selection reference populations. The developed bivariate Bayesian stochastic search variable selection model allowed for an unbalanced design by imputing residuals in the residual updating scheme for all missing records. The performance of this model is demonstrated on a real data example, where the analyzed trait, being milk fat or protein yield, was either measured only on a cow or a bull reference population, or recorded on both. Our results were that the developed bivariate Bayesian stochastic search variable selection model was able to analyze 2 traits, even though animals had measurements on only 1 of 2 traits. The Bayesian stochastic search variable selection model yielded consistently higher accuracy for fat yield compared with a model without variable selection, both for the univariate and bivariate analyses, whereas the accuracy of both models was very similar for protein yield. The bivariate model identified several additional quantitative trait loci peaks compared with the single-trait models on either trait. In addition, the bivariate models showed a marginal increase in accuracy of genomic predictions for the cow traits (0.01-0.05), although a greater increase in accuracy is expected as the size of the bull population increases. Our results emphasize that the chosen value of priors in Bayesian genomic prediction models are especially important in small data sets. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Fusion of multi-source remote sensing data for agriculture monitoring tasks

NASA Astrophysics Data System (ADS)

Skakun, S.; Franch, B.; Vermote, E.; Roger, J. C.; Becker Reshef, I.; Justice, C. O.; Masek, J. G.; Murphy, E.

2016-12-01

Remote sensing data is essential source of information for enabling monitoring and quantification of crop state at global and regional scales. Crop mapping, state assessment, area estimation and yield forecasting are the main tasks that are being addressed within GEO-GLAM. Efficiency of agriculture monitoring can be improved when heterogeneous multi-source remote sensing datasets are integrated. Here, we present several case studies of utilizing MODIS, Landsat-8 and Sentinel-2 data along with meteorological data (growing degree days - GDD) for winter wheat yield forecasting, mapping and area estimation. Archived coarse spatial resolution data, such as MODIS, VIIRS and AVHRR, can provide daily global observations that coupled with statistical data on crop yield can enable the development of empirical models for timely yield forecasting at national level. With the availability of high-temporal and high spatial resolution Landsat-8 and Sentinel-2A imagery, course resolution empirical yield models can be downscaled to provide yield estimates at regional and field scale. In particular, we present the case study of downscaling the MODIS CMG based generalized winter wheat yield forecasting model to high spatial resolution data sets, namely harmonized Landsat-8 - Sentinel-2A surface reflectance product (HLS). Since the yield model requires corresponding in season crop masks, we propose an automatic approach to extract winter crop maps from MODIS NDVI and MERRA2 derived GDD using Gaussian mixture model (GMM). Validation for the state of Kansas (US) and Ukraine showed that the approach can yield accuracies > 90% without using reference (ground truth) data sets. Another application of yearly derived winter crop maps is their use for stratification purposes within area frame sampling for crop area estimation. In particular, one can simulate the dependence of error (coefficient of variation) on the number of samples and strata size. This approach was used for estimating the area of winter crops in Ukraine for 2013-2016. The GMM-GDD approach is further extended for HLS data to provide automatic winter crop mapping at 30 m resolution for crop yield model and area estimation. In case of persistent cloudiness, addition of Sentinel-1A synthetic aperture radar (SAR) images is explored for automatic winter crop mapping.

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

Modeling of an integrated fermentation/membrane extraction process for the production of 2-phenylethanol and 2-phenylethylacetate.

PubMed

Adler, Philipp; Hugen, Thorsten; Wiewiora, Marzena; Kunz, Benno

2011-03-07

An unstructured model for an integrated fermentation/membrane extraction process for the production of the aroma compounds 2-phenylethanol and 2-phenylethylacetate by Kluyveromyces marxianus CBS 600 was developed. The extent to which this model, based only on data from the conventional fermentation and separation processes, provided an estimation of the integrated process was evaluated. The effect of product inhibition on specific growth rate and on biomass yield by both aroma compounds was approximated by multivariate regression. Simulations of the respective submodels for fermentation and the separation process matched well with experimental results. With respect to the in situ product removal (ISPR) process, the effect of reduced product inhibition due to product removal on specific growth rate and biomass yield was predicted adequately by the model simulations. Overall product yields were increased considerably in this process (4.0 g/L 2-PE+2-PEA vs. 1.4 g/L in conventional fermentation) and were even higher than predicted by the model. To describe the effect of product concentration on product formation itself, the model was extended using results from the conventional and the ISPR process, thus agreement between model and experimental data improved notably. Therefore, this model can be a useful tool for the development and optimization of an efficient integrated bioprocess. Copyright © 2010 Elsevier Inc. All rights reserved.

Unified Static and Dynamic Recrystallization Model for the Minerals of Earth's Mantle Using Internal State Variable Model

NASA Astrophysics Data System (ADS)

Cho, H. E.; Horstemeyer, M. F.; Baumgardner, J. R.

2017-12-01

In this study, we present an internal state variable (ISV) constitutive model developed to model static and dynamic recrystallization and grain size progression in a unified manner. This method accurately captures temperature, pressure and strain rate effect on the recrystallization and grain size. Because this ISV approach treats dislocation density, volume fraction of recrystallization and grain size as internal variables, this model can simultaneously track their history during the deformation with unprecedented realism. Based on this deformation history, this method can capture realistic mechanical properties such as stress-strain behavior in the relationship of microstructure-mechanical property. Also, both the transient grain size during the deformation and the steady-state grain size of dynamic recrystallization can be predicted from the history variable of recrystallization volume fraction. Furthermore, because this model has a capability to simultaneously handle plasticity and creep behaviors (unified creep-plasticity), the mechanisms (static recovery (or diffusion creep), dynamic recovery (or dislocation creep) and hardening) related to dislocation dynamics can also be captured. To model these comprehensive mechanical behaviors, the mathematical formulation of this model includes elasticity to evaluate yield stress, work hardening in treating plasticity, creep, as well as the unified recrystallization and grain size progression. Because pressure sensitivity is especially important for the mantle minerals, we developed a yield function combining Drucker-Prager shear failure and von Mises yield surfaces to model the pressure dependent yield stress, while using pressure dependent work hardening and creep terms. Using these formulations, we calibrated against experimental data of the minerals acquired from the literature. Additionally, we also calibrated experimental data for metals to show the general applicability of our model. Understanding of realistic mantle dynamics can only be acquired once the various deformation regimes and mechanisms are comprehensively modeled. The results of this study demonstrate that this ISV model is a good modeling candidate to help reveal the realistic dynamics of the Earth's mantle.

Optimisation of Croton gratissimus Oil Extraction by n-Hexane and Ethyl Acetate Using Response Surface Methodology.

PubMed

Jiyane, Phiwe Charles; Tumba, Kaniki; Musonge, Paul

2018-04-01

The extraction of oil from Croton gratissimus seeds was studied using the three-factor five-level full-factorial central composite rotatable design (CCRD) of the response surface methodology (RSM). The effect of the three factors selected, viz., extraction time, extraction temperature and solvent-to-feed ratio on the extraction oil yield was investigated when n-hexane and ethyl acetate were used as extraction solvents. The coefficients of determination (R 2 ) of the models developed were 0.98 for n-hexane extraction and 0.97 for ethyl acetate extraction. These results demonstrated that the models developed adequately represented the processes they described. From the optimized model, maximum extraction yield obtained from n-hexane and ethyl acetate extraction were 23.88% and 23.25%, respectively. In both cases the extraction temperature and solvent-to-feed ratio were 35°C and 5 mL/g, respectively. In n-hexane extraction the maximum conditions were reached only after 6 min whereas in ethyl acetate extraction it took 20 min to get the maximum extraction oil yield. Oil extraction of Croton gratissimus seeds, in this work, favoured the use of n-hexane as an extraction solvent as it offered higher oil yields at low temperatures and reduced residence times.

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.

Hydrodynamics with strength: scaling-invariant solutions for elastic-plastic cavity expansion models

NASA Astrophysics Data System (ADS)

Albright, Jason; Ramsey, Scott; Baty, Roy

2017-11-01

Spherical cavity expansion (SCE) models are used to describe idealized detonation and high-velocity impact in a variety of materials. The common theme in SCE models is the presence of a pressure-driven cavity or void within a domain comprised of plastic and elastic response sub-regions. In past work, the yield criterion characterizing material strength in the plastic sub-region is usually taken for granted and assumed to take a known functional form restrictive to certain classes of materials, e.g. ductile metals or brittle geologic materials. Our objective is to systematically determine a general functional form for the yield criterion under the additional requirement that the SCE admits a similarity solution. Solutions determined under this additional requirement have immediate implications toward development of new compressible flow algorithm verification test problems. However, more importantly, these results also provide novel insight into modeling the yield criteria from the perspective of hydrodynamic scaling.

Length-Scale Effects and Material Models at Numerical Simulations of Nanoindentation of A Metallic Alloy

NASA Astrophysics Data System (ADS)

Nikolov, N.; Avdjieva, T.; Altaparmakov, I.

2014-06-01

Some specially designed metallic alloys crystallize during process of rapid quenching which aims their amorphization. Nevertheless, change in their mechanical properties could be seen compared to these obtained during conventional technological regimes of cooling. That attracts the attention in this elaboration. Full 3-D numerical simulations of nanoindentation process of two material models are performed. The models reflect equivalent elastic and different plastic material properties. The plastic behaviour of the first one is subjected to yield criterion of Dracker-Prager and this of the second one to yield criterion of Mises. The reported numerical results depending on the nanoindentation scale length of 1000 nanometers, suggest different adequacy of the two yield criteria to the data obtained experimentally with a Zr-Al-Cu-Ni-Mo alloy. It could be speculated that the different effects developed depending on the indenter travel of 1000 nanometers and taken into account in the two yield criteria stand behind this fact and determinate three structural levels of plastic deformation.

The Assessment of Climatological Impacts on Agricultural Production and Residential Energy Demand

NASA Astrophysics Data System (ADS)

Cooter, Ellen Jean

The assessment of climatological impacts on selected economic activities is presented as a multi-step, inter -disciplinary problem. The assessment process which is addressed explicitly in this report focuses on (1) user identification, (2) direct impact model selection, (3) methodological development, (4) product development and (5) product communication. Two user groups of major economic importance were selected for study; agriculture and gas utilities. The broad agricultural sector is further defined as U.S.A. corn production. The general category of utilities is narrowed to Oklahoma residential gas heating demand. The CERES physiological growth model was selected as the process model for corn production. The statistical analysis for corn production suggests that (1) although this is a statistically complex model, it can yield useful impact information, (2) as a result of output distributional biases, traditional statistical techniques are not adequate analytical tools, (3) the model yield distribution as a whole is probably non-Gausian, particularly in the tails and (4) there appears to be identifiable weekly patterns of forecasted yields throughout the growing season. Agricultural quantities developed include point yield impact estimates and distributional characteristics, geographic corn weather distributions, return period estimates, decision making criteria (confidence limits) and time series of indices. These products were communicated in economic terms through the use of a Bayesian decision example and an econometric model. The NBSLD energy load model was selected to represent residential gas heating consumption. A cursory statistical analysis suggests relationships among weather variables across the Oklahoma study sites. No linear trend in "technology -free" modeled energy demand or input weather variables which would correspond to that contained in observed state -level residential energy use was detected. It is suggested that this trend is largely the result of non-weather factors such as population and home usage patterns rather than regional climate change. Year-to-year changes in modeled residential heating demand on the order of 10('6) Btu's per household were determined and later related to state -level components of the Oklahoma economy. Products developed include the definition of regional forecast areas, likelihood estimates of extreme seasonal conditions and an energy/climate index. This information is communicated in economic terms through an input/output model which is used to estimate changes in Gross State Product and Household income attributable to weather variability.

Integrated remote sensing imagery and two-dimensional hydraulic modeling approach for impact evaluation of flood on crop yields

NASA Astrophysics Data System (ADS)

Chen, Huili; Liang, Zhongyao; Liu, Yong; Liang, Qiuhua; Xie, Shuguang

2017-10-01

The projected frequent occurrences of extreme flood events will cause significant losses to crops and will threaten food security. To reduce the potential risk and provide support for agricultural flood management, prevention, and mitigation, it is important to account for flood damage to crop production and to understand the relationship between flood characteristics and crop losses. A quantitative and effective evaluation tool is therefore essential to explore what and how flood characteristics will affect the associated crop loss, based on accurately understanding the spatiotemporal dynamics of flood evolution and crop growth. Current evaluation methods are generally integrally or qualitatively based on statistic data or ex-post survey with less diagnosis into the process and dynamics of historical flood events. Therefore, a quantitative and spatial evaluation framework is presented in this study that integrates remote sensing imagery and hydraulic model simulation to facilitate the identification of historical flood characteristics that influence crop losses. Remote sensing imagery can capture the spatial variation of crop yields and yield losses from floods on a grid scale over large areas; however, it is incapable of providing spatial information regarding flood progress. Two-dimensional hydraulic model can simulate the dynamics of surface runoff and accomplish spatial and temporal quantification of flood characteristics on a grid scale over watersheds, i.e., flow velocity and flood duration. The methodological framework developed herein includes the following: (a) Vegetation indices for the critical period of crop growth from mid-high temporal and spatial remote sensing imagery in association with agricultural statistics data were used to develop empirical models to monitor the crop yield and evaluate yield losses from flood; (b) The two-dimensional hydraulic model coupled with the SCS-CN hydrologic model was employed to simulate the flood evolution process, with the SCS-CN model as a rainfall-runoff generator and the two-dimensional hydraulic model implementing the routing scheme for surface runoff; and (c) The spatial combination between crop yield losses and flood dynamics on a grid scale can be used to investigate the relationship between the intensity of flood characteristics and associated loss extent. The modeling framework was applied for a 50-year return period flood that occurred in Jilin province, Northeast China, which caused large agricultural losses in August 2013. The modeling results indicated that (a) the flow velocity was the most influential factor that caused spring corn, rice and soybean yield losses from extreme storm event in the mountainous regions; (b) the power function archived the best results that fit the velocity-loss relationship for mountainous areas; and (c) integrated remote sensing imagery and two-dimensional hydraulic modeling approach are helpful for evaluating the influence of historical flood event on crop production and investigating the relationship between flood characteristics and crop yield losses.

Developing a Model of Occupational Choice.

ERIC Educational Resources Information Center

Egner, Joan Roos; And Others

Review of the literature in counseling, sociology, psychology, and organizational behavior failed to yield a model satisfactory for a comprehensive research framework investigating why people choose different occupations. Rational and irrational occupational decision making models were unsatisfactory in capturing the many dimensions of the…

Grapevine canopy reflectance and yield

NASA Technical Reports Server (NTRS)

Minden, K. A.; Philipson, W. R.

1982-01-01

Field spectroradiometric and airborne multispectral scanner data were applied in a study of Concord grapevines. Spectroradiometric measurements of 18 experimental vines were collected on three dates during one growing season. Spectral reflectance, determined at 30 intervals from 0.4 to 1.1 microns, was correlated with vine yield, pruning weight, clusters/vine, and nitrogen input. One date of airborne multispectral scanner data (11 channels) was collected over commercial vineyards, and the average radiance values for eight vineyard sections were correlated with the corresponding average yields. Although some correlations were significant, they were inadequate for developing a reliable yield prediction model.

Effects of diurnal temperature range and drought on wheat yield in Spain

NASA Astrophysics Data System (ADS)

Hernandez-Barrera, S.; Rodriguez-Puebla, C.; Challinor, A. J.

2017-07-01

This study aims to provide new insight on the wheat yield historical response to climate processes throughout Spain by using statistical methods. Our data includes observed wheat yield, pseudo-observations E-OBS for the period 1979 to 2014, and outputs of general circulation models in phase 5 of the Coupled Models Inter-comparison Project (CMIP5) for the period 1901 to 2099. In investigating the relationship between climate and wheat variability, we have applied the approach known as the partial least-square regression, which captures the relevant climate drivers accounting for variations in wheat yield. We found that drought occurring in autumn and spring and the diurnal range of temperature experienced during the winter are major processes to characterize the wheat yield variability in Spain. These observable climate processes are used for an empirical model that is utilized in assessing the wheat yield trends in Spain under different climate conditions. To isolate the trend within the wheat time series, we implemented the adaptive approach known as Ensemble Empirical Mode Decomposition. Wheat yields in the twenty-first century are experiencing a downward trend that we claim is a consequence of widespread drought over the Iberian Peninsula and an increase in the diurnal range of temperature. These results are important to inform about the wheat vulnerability in this region to coming changes and to develop adaptation strategies.

Improved Crystal Quality by Detached Solidification in Microgravity

NASA Technical Reports Server (NTRS)

Regel, Liya L.; Wilcox, William R.

1999-01-01

Directional solidification in microgravity has often led to ingots that grew with little or no contact with the ampoule wall. When this occurred, crystallographic perfection was usually greatly improved -- often by several orders of magnitude. Unfortunately, until recently the true mechanisms underlying detached solidification were unknown. As a consequence, flight experiments yielded erratic results. Within the past four years, we have developed a new theoretical model that explains many of the flight results. This model gives rise to predictions of the conditions required to yield detached solidification, both in microgravity and on earth. A discussion of models of detachment, the meniscus models and results of theoretical modeling, and future plans are presented.

Optimizing Dense Plasma Focus Neutron Yields With Fast Gas Jets

NASA Astrophysics Data System (ADS)

McMahon, Matthew; Stein, Elizabeth; Higginson, Drew; Kueny, Christopher; Link, Anthony; Schmidt, Andrea

2017-10-01

We report a study using the particle-in-cell code LSP to perform fully kinetic simulations modeling dense plasma focus (DPF) devices with high density gas jets on axis. The high-density jets are modeled in the large-eddy Navier-Stokes code CharlesX, which is suitable for modeling both sub-sonic and supersonic gas flow. The gas pattern, which is essentially static on z-pinch time scales, is imported from CharlesX to LSP for neutron yield predictions. Fast gas puffs allow for more mass on axis while maintaining the optimal pressure for the DPF. As the density of a subsonic jet increases relative to the background fill, we find the neutron yield increases, as does the variability in the neutron yield. Introducing perturbations in the jet density via super-sonic flow (also known as Mach diamonds) allow for consistent seeding of the m =0 instability leading to more consistent ion acceleration and higher neutron yields with less variability. Jets with higher on axis density are found to have the greatest yield. The optimal jet configuration and the necessary jet conditions for increasing neutron yield and reducing yield variability are explored. Simulations of realistic jet profiles are performed and compared to the ideal scenario. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and supported by the Laboratory Directed Research and Development Program (15-ERD-034) at LLNL.

How model and input uncertainty impact maize yield simulations in West Africa

NASA Astrophysics Data System (ADS)

Waha, Katharina; Huth, Neil; Carberry, Peter; Wang, Enli

2015-02-01

Crop models are common tools for simulating crop yields and crop production in studies on food security and global change. Various uncertainties however exist, not only in the model design and model parameters, but also and maybe even more important in soil, climate and management input data. We analyze the performance of the point-scale crop model APSIM and the global scale crop model LPJmL with different climate and soil conditions under different agricultural management in the low-input maize-growing areas of Burkina Faso, West Africa. We test the models’ response to different levels of input information from little to detailed information on soil, climate (1961-2000) and agricultural management and compare the models’ ability to represent the observed spatial (between locations) and temporal variability (between years) in crop yields. We found that the resolution of different soil, climate and management information influences the simulated crop yields in both models. However, the difference between models is larger than between input data and larger between simulations with different climate and management information than between simulations with different soil information. The observed spatial variability can be represented well from both models even with little information on soils and management but APSIM simulates a higher variation between single locations than LPJmL. The agreement of simulated and observed temporal variability is lower due to non-climatic factors e.g. investment in agricultural research and development between 1987 and 1991 in Burkina Faso which resulted in a doubling of maize yields. The findings of our study highlight the importance of scale and model choice and show that the most detailed input data does not necessarily improve model performance.

The past and future of modeling forest dynamics: from growth and yield curves to forest landscape models

Treesearch

Stephen R. Shifley; Hong S. He; Heike Lischke; Wen J. Wang; Wenchi Jin; Eric J. Gustafson; Jonathan R. Thompson; Frank R. Thompson; William D. Dijak; Jian Yang

2017-01-01

Context. Quantitative models of forest dynamics have followed a progression toward methods with increased detail, complexity, and spatial extent. Objectives. We highlight milestones in the development of forest dynamics models and identify future research and application opportunities. Methods. We reviewed...

Integrated model for predicting rice yield with climate change

NASA Astrophysics Data System (ADS)

Park, Jin-Ki; Das, Amrita; Park, Jong-Hwa

2018-04-01

Rice is the chief agricultural product and one of the primary food source. For this reason, it is of pivotal importance for worldwide economy and development. Therefore, in a decision-support-system both for the farmers and in the planning and management of the country's economy, forecasting yield is vital. However, crop yield, which is a dependent of the soil-bio-atmospheric system, is difficult to represent in statistical language. This paper describes a novel approach for predicting rice yield using artificial neural network, spatial interpolation, remote sensing and GIS methods. Herein, the variation in the yield is attributed to climatic parameters and crop health, and the normalized difference vegetation index from MODIS is used as an indicator of plant health and growth. Due importance was given to scaling up the input parameters using spatial interpolation and GIS and minimising the sources of error in every step of the modelling. The low percentage error (2.91) and high correlation (0.76) signifies the robust performance of the proposed model. This simple but effective approach is then used to estimate the influence of climate change on South Korean rice production. As proposed in the RCP8.5 scenario, an upswing in temperature may increase the rice yield throughout South Korea.

Understanding the weather signal in national crop-yield variability

NASA Astrophysics Data System (ADS)

Frieler, Katja; Schauberger, Bernhard; Arneth, Almut; Balkovič, Juraj; Chryssanthacopoulos, James; Deryng, Delphine; Elliott, Joshua; Folberth, Christian; Khabarov, Nikolay; Müller, Christoph; Olin, Stefan; Pugh, Thomas A. M.; Schaphoff, Sibyll; Schewe, Jacob; Schmid, Erwin; Warszawski, Lila; Levermann, Anders

2017-06-01

Year-to-year variations in crop yields can have major impacts on the livelihoods of subsistence farmers and may trigger significant global price fluctuations, with severe consequences for people in developing countries. Fluctuations can be induced by weather conditions, management decisions, weeds, diseases, and pests. Although an explicit quantification and deeper understanding of weather-induced crop-yield variability is essential for adaptation strategies, so far it has only been addressed by empirical models. Here, we provide conservative estimates of the fraction of reported national yield variabilities that can be attributed to weather by state-of-the-art, process-based crop model simulations. We find that observed weather variations can explain more than 50% of the variability in wheat yields in Australia, Canada, Spain, Hungary, and Romania. For maize, weather sensitivities exceed 50% in seven countries, including the United States. The explained variance exceeds 50% for rice in Japan and South Korea and for soy in Argentina. Avoiding water stress by simulating yields assuming full irrigation shows that water limitation is a major driver of the observed variations in most of these countries. Identifying the mechanisms leading to crop-yield fluctuations is not only fundamental for dampening fluctuations, but is also important in the context of the debate on the attribution of loss and damage to climate change. Since process-based crop models not only account for weather influences on crop yields, but also provide options to represent human-management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations.

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.

Variable-Length Computerized Adaptive Testing Using the Higher Order DINA Model

ERIC Educational Resources Information Center

Hsu, Chia-Ling; Wang, Wen-Chung

2015-01-01

Cognitive diagnosis models provide profile information about a set of latent binary attributes, whereas item response models yield a summary report on a latent continuous trait. To utilize the advantages of both models, higher order cognitive diagnosis models were developed in which information about both latent binary attributes and latent…

Liquid Propellant Blast Yields for Delta IV Heavy Vehicles

DTIC Science & Technology

2010-07-01

explode simultaneously, up to 1.4 million lb of liquid oxygen and liquid hydrogen (LO2/ LH2 ) may be involved and at least partially contribute to the...in the third so as to prevent them from contributing to the blast yield. Since the PYRO LO2/ LH2 yield model was originally developed using data from...that mixing interfaces between the LO2 and LH2 tanks for all three CBCs occur simultaneously, then a reasonable argument can be made for all three

Soils Activity Mobility Study: Methodology and Application

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

None, None

2014-09-29

This report presents a three-level approach for estimation of sediment transport to provide an assessment of potential erosion risk for sites at the Nevada National Security Site (NNSS) that are posted for radiological purposes and where migration is suspected or known to occur due to storm runoff. Based on the assessed risk, the appropriate level of effort can be determined for analysis of radiological surveys, field experiments to quantify erosion and transport rates, and long-term monitoring. The method is demonstrated at contaminated sites, including Plutonium Valley, Shasta, Smoky, and T-1. The Pacific Southwest Interagency Committee (PSIAC) procedure is selected asmore » the Level 1 analysis tool. The PSIAC method provides an estimation of the total annual sediment yield based on factors derived from the climatic and physical characteristics of a watershed. If the results indicate low risk, then further analysis is not warranted. If the Level 1 analysis indicates high risk or is deemed uncertain, a Level 2 analysis using the Modified Universal Soil Loss Equation (MUSLE) is proposed. In addition, if a sediment yield for a storm event rather than an annual sediment yield is needed, then the proposed Level 2 analysis should be performed. MUSLE only provides sheet and rill erosion estimates. The U.S. Army Corps of Engineers Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) provides storm peak runoff rate and storm volumes, the inputs necessary for MUSLE. Channel Sediment Transport (CHAN-SED) I and II models are proposed for estimating sediment deposition or erosion in a channel reach from a storm event. These models require storm hydrograph associated sediment concentration and bed load particle size distribution data. When the Level 2 analysis indicates high risk for sediment yield and associated contaminant migration or when there is high uncertainty in the Level 2 results, the sites can be further evaluated with a Level 3 analysis using more complex and labor- and data-intensive methods. For the watersheds analyzed in this report using the Level 1 PSIAC method, the risk of erosion is low. The field reconnaissance surveys of these watersheds confirm the conclusion that the sediment yield of undisturbed areas at the NNSS would be low. The climate, geology, soils, ground cover, land use, and runoff potential are similar among these watersheds. There are no well-defined ephemeral channels except at the Smoky and Plutonium Valley sites. Topography seems to have the strongest influence on sediment yields, as sediment yields are higher on the steeper hill slopes. Lack of measured sediment yield data at the NNSS does not allow for a direct evaluation of the yield estimates by the PSIAC method. Level 2 MUSLE estimates in all the analyzed watersheds except Shasta are a small percentage of the estimates from PSIAC because MUSLE is not inclusive of channel erosion. This indicates that channel erosion dominates the total sediment yield in these watersheds. Annual sediment yields for these watersheds are estimated using the CHAN-SEDI and CHAN-SEDII channel sediment transport models. Both transport models give similar results and exceed the estimates obtained from PSIAC and MUSLE. It is recommended that the total watershed sediment yield of watersheds at the NNSS with flow channels be obtained by adding the washload estimate (rill and inter-rill erosion) from MUSLE to that obtained from channel transport models (bed load and suspended sediment). PSIAC will give comparable results if factor scores for channel erosion are revised towards the high erosion level. Application of the Level 3 process-based models to estimate sediment yields at the NNSS cannot be recommended at this time. Increased model complexity alone will not improve the certainty of the sediment yield estimates. Models must be calibrated against measured data before model results are accepted as certain. Because no measurements of sediment yields at the NNSS are available, model validation cannot be performed. This is also true for the models used in the Level 2 analyses presented in this study. The need to calibrate MUSLE to local conditions has been discussed. Likewise, the transport equations of CHAN-SEDI and CHAN-SEDII need to be calibrated against local data to assess their applicability under semi-arid conditions and for the ephemeral channels at the NNSS. Before these validations and calibration exercises can be undertaken, a long-term measured sediment yield data set must be developed. Development of long-term measured sediment yield data cannot be overemphasized. Long-term monitoring is essential for accurate characterization of watershed processes. It is recommended that a long-term monitoring program be set up to measure watershed erosion rates and channel sediment transport rates.« less

A standardized data structure for describing and exchanging data from remeasured growth and yield plots

Treesearch

Michael D. Sweet; John C. Byrne

1990-01-01

Proposes standard data definitions and format to facilitate the sharing of growth and yield permanent plot data for the development, testing, and improvement of tree or stand growth models. The data structure presented provides standards for documenting sampling design, plot location and summary descriptors, measurement dates, treatments, site attributes, and...

Yield Hardening of Electrorheological Fluids in Channel Flow

NASA Astrophysics Data System (ADS)

Helal, Ahmed; Qian, Bian; McKinley, Gareth H.; Hosoi, A. E.

2016-06-01

Electrorheological fluids offer potential for developing rapidly actuated hydraulic devices where shear forces or pressure-driven flow are present. In this study, the Bingham yield stress of electrorheological fluids with different particle volume fractions is investigated experimentally in wall-driven and pressure-driven flow modes using measurements in a parallel-plate rheometer and a microfluidic channel, respectively. A modified Krieger-Dougherty model can be used to describe the effects of the particle volume fraction on the yield stress and is in good agreement with the viscometric data. However, significant yield hardening in pressure-driven channel flow is observed and attributed to an increase and eventual saturation of the particle volume fraction in the channel. A phenomenological physical model linking the densification and consequent microstructure to the ratio of the particle aggregation time scale compared to the convective time scale is presented and used to predict the enhancement in yield stress in channel flow, enabling us to reconcile discrepancies in the literature between wall-driven and pressure-driven flows.

Reduced-Volume Fracture Toughness Characterization for Transparent Polymers

DTIC Science & Technology

2015-03-21

Caruthers et al. (2004) developed a thermodynamically consistent, nonlinear viscoelastic bulk constitutive model based on a potential energy clock ( PEC ...except that relaxation times change. Because of its formulation, the PEC model predicts mechanical yield as a natural consequence of relaxation...softening type of behavior, but hysteresis effects are not naturally accounted for. Adolf et al. (2009) developed a method of simplifying the PEC model

Systematic optimization of fed-batch simultaneous saccharification and fermentation at high-solid loading based on enzymatic hydrolysis and dynamic metabolic modeling of Saccharomyces cerevisiae.

PubMed

Unrean, Pornkamol; Khajeeram, Sutamat; Laoteng, Kobkul

2016-03-01

An integrative simultaneous saccharification and fermentation (SSF) modeling is a useful guiding tool for rapid process optimization to meet the techno-economic requirement of industrial-scale lignocellulosic ethanol production. In this work, we have developed the SSF model composing of a metabolic network of a Saccharomyces cerevisiae cell associated with fermentation kinetics and enzyme hydrolysis model to quantitatively capture dynamic responses of yeast cell growth and fermentation during SSF. By using model-based design of feeding profiles for substrate and yeast cell in the fed-batch SSF process, an efficient ethanol production with high titer of up to 65 g/L and high yield of 85 % of theoretical yield was accomplished. The ethanol titer and productivity was increased by 47 and 41 %, correspondingly, in optimized fed-batch SSF as compared to batch process. The developed integrative SSF model is, therefore, considered as a promising approach for systematic design of economical and sustainable SSF bioprocessing of lignocellulose.

Impact of a comprehensive population health management program on health care costs.

PubMed

Grossmeier, Jessica; Seaverson, Erin L D; Mangen, David J; Wright, Steven; Dalal, Karl; Phalen, Chris; Gold, Daniel B

2013-06-01

Assess the influence of participation in a population health management (PHM) program on health care costs. A quasi-experimental study relied on logistic and ordinary least squares regression models to compare the costs of program participants with those of nonparticipants, while controlling for differences in health care costs and utilization, demographics, and health status. Propensity score models were developed and analyses were weighted by inverse propensity scores to control for selection bias. Study models yielded an estimated savings of $60.65 per wellness participant per month and $214.66 per disease management participant per month. Program savings were combined to yield an integrated return-on-investment of $3 in savings for every dollar invested. A PHM program yielded a positive return on investment after 2 years of wellness program and 1 year of integrated disease management program launch.

Constitutive Models Based on Compressible Plastic Flows

NASA Technical Reports Server (NTRS)

Rajendran, A. M.

1983-01-01

The need for describing materials under time or cycle dependent loading conditions has been emphasized in recent years by several investigators. In response to the need, various constitutive models describing the nonlinear behavior of materials under creep, fatigue, or other complex loading conditions were developed. The developed models for describing the fully dense (non-porous) materials were mostly based on uncoupled plasticity theory. The improved characterization of materials provides a better understanding of the structual response under complex loading conditions. The pesent studies demonstrate that the rate or time dependency of the response of a porous aggregate can be incorporated into the nonlinear constitutive behavior of a porous solid by appropriately modeling the incompressible matrix behavior. It is also sown that the yield function which wads determined by a continuum mechanics approach must be verified by appropriate experiments on void containing sintered materials in order to obtain meaningful numbers for the constants that appear in the yield function.

Farmers Extension Program Effects on Yield Gap in North China Plain

NASA Astrophysics Data System (ADS)

Sum, N.; Zhao, Y.

2015-12-01

Improving crop yield of the lowest yielding smallholder farmers in developing countries is essential to both food security of the country and the farmers' livelihood. Although wheat and maize production in most developed countries have reached 80% or greater of yield potential determined by simulated models, yield gap remains high in the developing world. One of these cases is the yield gap of maize in the North China Plain (NCP), where the average farmer's yield is 41% of his or her potential yield. This large yield gap indicates opportunity to raise yields substantially by improving agronomy, especially in nutrition management, irrigation facility, and mechanization issues such as technical services. Farmers' agronomic knowledge is essential to yield performance. In order to propagate such knowledge to farmers, agricultural extension programs, especially in-the-field guidance with training programs at targeted demonstration fields, have become prevalent in China. Although traditional analyses of the effects of the extension program are done through surveys, they are limited to only one to two years and to a small area. However, the spatial analysis tool Google Earth Engine (GEE) and its extensive satellite imagery data allow for unprecedented spatial temporal analysis of yield variation. We used GEE to analyze maize yield in Quzhou county in the North China Plain from 2007 to 2013. We based our analysis on the distance from a demonstration farm plot, the source of the farmers' agronomic knowledge. Our hypothesis was that the farther the farmers' fields were from the demonstration plot, the less access they would have to the knowledge, and the less increase in yield over time. Testing this hypothesis using GEE helps us determine the effectiveness of the demonstration plot in disseminating optimal agronomic practices in addition to evaluating yield performance of the demonstration field itself. Furthermore, we can easily extend this methodology to analyze the whole NCP and any other parts of the world for any type of crop.

Modeling irrigation behavior in groundwater systems

NASA Astrophysics Data System (ADS)

Foster, Timothy; Brozović, Nicholas; Butler, Adrian P.

2014-08-01

Integrated hydro-economic models have been widely applied to water management problems in regions of intensive groundwater-fed irrigation. However, policy interpretations may be limited as most existing models do not explicitly consider two important aspects of observed irrigation decision making, namely the limits on instantaneous irrigation rates imposed by well yield and the intraseasonal structure of irrigation planning. We develop a new modeling approach for determining irrigation demand that is based on observed farmer behavior and captures the impacts on production and water use of both well yield and climate. Through a case study of irrigated corn production in the Texas High Plains region of the United States we predict optimal irrigation strategies under variable levels of groundwater supply, and assess the limits of existing models for predicting land and groundwater use decisions by farmers. Our results show that irrigation behavior exhibits complex nonlinear responses to changes in groundwater availability. Declining well yields induce large reductions in the optimal size of irrigated area and irrigation use as constraints on instantaneous application rates limit the ability to maintain sufficient soil moisture to avoid negative impacts on crop yield. We demonstrate that this important behavioral response to limited groundwater availability is not captured by existing modeling approaches, which therefore may be unreliable predictors of irrigation demand, agricultural profitability, and resilience to climate change and aquifer depletion.

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

PubMed

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

2018-05-31

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

Conjunctive-use optimization model of the Mississippi River Valley alluvial aquifer of Southeastern Arkansas

USGS Publications Warehouse

Czarnecki, John B.; Clark, Brian R.; Stanton, Gregory P.

2003-01-01

The Mississippi River Valley alluvial aquifer is a water-bearing assemblage of gravels and sands that underlies about 32,000 square miles of Missouri, Kentucky, Tennessee, Mississippi, Louisiana, and Arkansas. Because of the heavy demands placed on the aquifer, several large cones of depression have formed in the potentiometric surface, resulting in lower well yields and degraded water quality in some areas. A ground-water flow model of the alluvial aquifer was previously developed for an area covering 3,826 square miles, extending south from the Arkansas River into the southeastern corner of Arkansas, parts of northeastern Louisiana, and western Mississippi. The flow-model results indicated that continued ground-water withdrawals at rates commensurate with those of 1997 could not be sustained indefinitely without causing water levels to decline below half the original saturated thickness of the aquifer. Conjunctive-use optimization modeling was applied to the flow model of the alluvial aquifer to develop withdrawal rates that could be sustained relative to the constraints of critical ground-water area designation. These withdrawal rates form the basis for estimates of sustainable yield from the alluvial aquifer and from rivers specified within the alluvial aquifer model. A management problem was formulated as one of maximizing the sustainable yield from all ground-water and surface-water withdrawal cells within limits imposed by plausible withdrawal rates, and within specified constraints involving hydraulic head and streamflow. Steady-state conditions were selected because the maximized withdrawals are intended to represent sustainable yield of the system (a rate that can be maintained indefinitely).One point along the Arkansas River and one point along Bayou Bartholomew were specified for obtaining surface-water sustainable-yield estimates within the optimization model. Streamflow constraints were specified at two river cells based on average 7-day low flows with 10-year recurrence intervals. Sustainable-yield estimates were affected by the allowable upper limit on withdrawals from wells specified in the optimization model. Withdrawal rates were allowed to increase to 200 percent of the withdrawal rate in 1997. As the overall upper limit is increased, the sustainable yield generally increases. Tests with the optimization model show that without limits on pumping, wells adjacent to sources of water, such as large rivers, would have optimal withdrawal rates that were orders of magnitude larger than rates corresponding to those of 1997. Specifying an upper withdrawal limit of 100 percent of the 1997 withdrawal rate, the sustainable yield from ground water for the entire study area is 70.3 million cubic feet per day, which is about 96 percent of the amount withdrawn in 1997 (73.5 million cubic feet per day). If the upper withdrawal limit is increased to 150 percent of the 1997 withdrawal rate, the sustainable yield from ground water for the entire study area is 80.6 million cubic feet per day, which is about 110 percent of the amount withdrawn in 1997. If the upper withdrawal limit is increased to 200 percent of the 1997 withdrawal rate, the sustainable yield from ground water for the entire study area is 110.2 million cubic feet per day, which is about 150 percent of the amount withdrawn in 1997. Total sustainable yield from the Arkansas River and Bayou Bartholomew is about 4,900 million cubic feet per day, or about 6,700 percent of the amount of ground-water withdrawn in 1997. The large, sustainable yields from surface water represent a potential source of water that could supplement ground water and meet the total water demand. Unmet demand (defined as the difference between the optimized withdrawal rate or sustainable yield, and the anticipated demand) was calculated using different demand rates based on multiples of the 1997-withdrawal rate. Assuming that demand is the 1997 withdrawal rate, and that sustainable-

Strategies for narrowing the maize yield gap of household farms through precision fertigation under irrigated conditions using CERES-Maize model.

PubMed

Liu, Jiangang; Wang, Guangyao; Chu, Qingquan; Chen, Fu

2017-07-01

Nitrogen (N) application significantly increases maize yield; however, the unreasonable use of N fertilizer is common in China. The analysis of crop yield gaps can reveal the limiting factors for yield improvement, but there is a lack of practical strategies for narrowing yield gaps of household farms. The objectives of this study were to assess the yield gap of summer maize using an integrative method and to develop strategies for narrowing the maize yield gap through precise N fertilization. The results indicated that there was a significant difference in maize yield among fields, with a low level of variation. Additionally, significant differences in N application rate were observed among fields, with high variability. Based on long-term simulation results, the optimal N application rate was 193 kg ha -1 , with a corresponding maximum attainable yield (AY max ) of 10 318 kg ha -1 . A considerable difference between farmers' yields and AY max was observed. Low agronomic efficiency of applied N fertilizer (AE N ) in farmers' fields was exhibited. The integrative method lays a foundation for exploring the specific factors constraining crop yield gaps at the field scale and for developing strategies for rapid site-specific N management. Optimization strategies to narrow the maize yield gap include increasing N application rates and adjusting the N application schedule. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Uncertainty in Simulating Wheat Yields Under Climate Change

NASA Technical Reports Server (NTRS)

Asseng, S.; Ewert, F.; Rosenzweig, Cynthia; Jones, J. W.; Hatfield, J. W.; Ruane, A. C.; Boote, K. J.; Thornburn, P. J.; Rotter, R. P.; Cammarano, D.;

Annual Corn Yield Estimation through Multi-temporal MODIS Data

NASA Astrophysics Data System (ADS)

Shao, Y.; Zheng, B.; Campbell, J. B.

2013-12-01

This research employed 13 years of the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate annual corn yield for the Midwest of the United States. The overall objective of this study was to examine if annual corn yield could be accurately predicted using MODIS time-series NDVI (Normalized Difference Vegetation Index) and ancillary data such monthly precipitation and temperature. MODIS-NDVI 16-Day composite images were acquired from the USGS EROS Data Center for calendar years 2000 to 2012. For the same time-period, county level corn yield statistics were obtained from the National Agricultural Statistics Service (NASS). The monthly precipitation and temperature measures were derived from Precipitation-Elevation Regressions on Independent Slopes Model (PRISM) climate data. A cropland mask was derived using 2006 National Land Cover Database. For each county and within the cropland mask, the MODIS-NDVI time-series data and PRISM climate data were spatially averaged, at their respective time steps. We developed a random forest predictive model with the MODIS-NDVI and climate data as predictors and corn yield as response. To assess the model accuracy, we used twelve years of data as training and the remaining year as hold-out testing set. The training and testing procedures were repeated 13 times. The R2 ranged from 0.72 to 0.83 for testing years. It was also found that the inclusion of climate data did not improve the model predictive performance. MODIS-NDVI time-series data alone might provide sufficient information for county level corn yield prediction.

Results of a Neutronic Simulation of HTR-Proteus Core 4.2 using PEBBED and other INL Reactor Physics Tools: FY-09 Report

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

Hans D. Gougar

The Idaho National Laboratory’s deterministic neutronics analysis codes and methods were applied to the computation of the core multiplication factor of the HTR-Proteus pebble bed reactor critical facility. A combination of unit cell calculations (COMBINE-PEBDAN), 1-D discrete ordinates transport (SCAMP), and nodal diffusion calculations (PEBBED) were employed to yield keff and flux profiles. Preliminary results indicate that these tools, as currently configured and used, do not yield satisfactory estimates of keff. If control rods are not modeled, these methods can deliver much better agreement with experimental core eigenvalues which suggests that development efforts should focus on modeling control rod andmore » other absorber regions. Under some assumptions and in 1D subcore analyses, diffusion theory agrees well with transport. This suggests that developments in specific areas can produce a viable core simulation approach. Some corrections have been identified and can be further developed, specifically: treatment of the upper void region, treatment of inter-pebble streaming, and explicit (multiscale) transport modeling of TRISO fuel particles as a first step in cross section generation. Until corrections are made that yield better agreement with experiment, conclusions from core design and burnup analyses should be regarded as qualitative and not benchmark quality.« less

Mathematical model of microbicidal flow dynamics and optimization of rheological properties for intra-vaginal drug delivery: Role of tissue mechanics and fluid rheology.

PubMed

Anwar, Md Rajib; Camarda, Kyle V; Kieweg, Sarah L

2015-06-25

Topically applied microbicide gels can provide a self-administered and effective strategy to prevent sexually transmitted infections (STIs). We have investigated the interplay between vaginal tissue elasticity and the yield-stress of non-Newtonian fluids during microbicide deployment. We have developed a mathematical model of tissue deformation driven spreading of microbicidal gels based on thin film lubrication approximation and demonstrated the effect of tissue elasticity and fluid yield-stress on the spreading dynamics. Our results show that both elasticity of tissue and yield-stress rheology of gel are strong determinants of the coating behavior. An optimization framework has been demonstrated which leverages the flow dynamics of yield-stress fluid during deployment to maximize retention while reaching target coating length for a given tissue elasticity. Copyright © 2015 Elsevier Ltd. All rights reserved.

Development and Validation of a Test of Relationship Style.

ERIC Educational Resources Information Center

Tuckman, Bruce W.

A two-dimensional, four-category model for classifying the way that people relate to others, or relationship style, was developed by T. Alessandra (1987). The model characterizes style in terms of openness, with poles of open and self-contained, and directness, with poles of direct and indirect. Combining the poles of the two dimensions yields the…

Evaluation of the AnnAGNPS model for predicting runoff and sediment yield in a small Mediterranean agricultural watershed in Navarre (Spain)

USDA-ARS?s Scientific Manuscript database

AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model) is a system of computer models developed to predict non-point source pollutant loadings within agricultural watersheds. It contains a daily time step distributed parameter continuous simulation surface runoff model designed to assis...

DEVELOPMENT OF PLASTICITY MODEL USING NON ASSOCIATED FLOW RULE FOR HCP MATERIALS INCLUDING ZIRCONIUM FOR NUCLEAR APPLICATIONS

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

Michael V. Glazoff; Jeong-Whan Yoon

2013-08-01

In this report (prepared in collaboration with Prof. Jeong Whan Yoon, Deakin University, Melbourne, Australia) a research effort was made to develop a non associated flow rule for zirconium. Since Zr is a hexagonally close packed (hcp) material, it is impossible to describe its plastic response under arbitrary loading conditions with any associated flow rule (e.g. von Mises). As a result of strong tension compression asymmetry of the yield stress and anisotropy, zirconium displays plastic behavior that requires a more sophisticated approach. Consequently, a new general asymmetric yield function has been developed which accommodates mathematically the four directional anisotropies alongmore » 0 degrees, 45 degrees, 90 degrees, and biaxial, under tension and compression. Stress anisotropy has been completely decoupled from the r value by using non associated flow plasticity, where yield function and plastic potential have been treated separately to take care of stress and r value directionalities, respectively. This theoretical development has been verified using Zr alloys at room temperature as an example as these materials have very strong SD (Strength Differential) effect. The proposed yield function reasonably well models the evolution of yield surfaces for a zirconium clock rolled plate during in plane and through thickness compression. It has been found that this function can predict both tension and compression asymmetry mathematically without any numerical tolerance and shows the significant improvement compared to any reported functions. Finally, in the end of the report, a program of further research is outlined aimed at constructing tensorial relationships for the temperature and fluence dependent creep surfaces for Zr, Zircaloy 2, and Zircaloy 4.« less

A compact proton spectrometer for measurement of the absolute DD proton spectrum from which yield and ρR are determined in thin-shell inertial-confinement-fusion implosions.

PubMed

Rosenberg, M J; Zylstra, A B; Frenje, J A; Rinderknecht, H G; Johnson, M Gatu; Waugh, C J; Séguin, F H; Sio, H; Sinenian, N; Li, C K; Petrasso, R D; Glebov, V Yu; Hohenberger, M; Stoeckl, C; Sangster, T C; Yeamans, C B; LePape, S; Mackinnon, A J; Bionta, R M; Talison, B; Casey, D T; Landen, O L; Moran, M J; Zacharias, R A; Kilkenny, J D; Nikroo, A

2014-10-01

A compact, step range filter proton spectrometer has been developed for the measurement of the absolute DD proton spectrum, from which yield and areal density (ρR) are inferred for deuterium-filled thin-shell inertial confinement fusion implosions. This spectrometer, which is based on tantalum step-range filters, is sensitive to protons in the energy range 1-9 MeV and can be used to measure proton spectra at mean energies of ∼1-3 MeV. It has been developed and implemented using a linear accelerator and applied to experiments at the OMEGA laser facility and the National Ignition Facility (NIF). Modeling of the proton slowing in the filters is necessary to construct the spectrum, and the yield and energy uncertainties are ±<10% in yield and ±120 keV, respectively. This spectrometer can be used for in situ calibration of DD-neutron yield diagnostics at the NIF.

A compact proton spectrometer for measurement of the absolute DD proton spectrum from which yield and pR are determined in thin-shell inertial-confinement-fusion implosions

DOE PAGES

Rosenberg, M. J.; Zylstra, A. B.; Frenje, J. A.; ...

2014-10-10

A compact, step range filter proton spectrometer has been developed for the measurement of the absolute DD proton spectrum, from which yield and areal density (ρR) are inferred for deuterium-filled thin-shell inertial confinement fusion implosions. This spectrometer, which is based on tantalum step-range filters, is sensitive to protons in the energy range 1-9 MeV and can be used to measure proton spectra at mean energies of ~1-3 MeV. It has been developed and implemented using a linear accelerator and applied to experiments at the OMEGA laser facility and the National Ignition Facility (NIF). Modeling of the proton slowing in themore » filters is necessary to construct the spectrum, and the yield and energy uncertainties are ±<10% in yield and ±120 keV, respectively. This spectrometer can be used for in situ calibration of DD-neutron yield diagnostics at the NIF« less

A compact proton spectrometer for measurement of the absolute DD proton spectrum from which yield and ρR are determined in thin-shell inertial-confinement-fusion implosions

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

Rosenberg, M. J., E-mail: mrosenbe@mit.edu; Zylstra, A. B.; Frenje, J. A.

2014-10-01

A compact, step range filter proton spectrometer has been developed for the measurement of the absolute DD proton spectrum, from which yield and areal density (ρR) are inferred for deuterium-filled thin-shell inertial confinement fusion implosions. This spectrometer, which is based on tantalum step-range filters, is sensitive to protons in the energy range 1-9 MeV and can be used to measure proton spectra at mean energies of ~1-3 MeV. It has been developed and implemented using a linear accelerator and applied to experiments at the OMEGA laser facility and the National Ignition Facility (NIF). Modeling of the proton slowing in themore » filters is necessary to construct the spectrum, and the yield and energy uncertainties are ±<10% in yield and ±120 keV, respectively. This spectrometer can be used for in situ calibration of DD-neutron yield diagnostics at the NIF.« less

Farming and the fate of wild nature.

PubMed

Green, Rhys E; Cornell, Stephen J; Scharlemann, Jörn P W; Balmford, Andrew

2005-01-28

World food demand is expected to more than double by 2050. Decisions about how to meet this challenge will have profound effects on wild species and habitats. We show that farming is already the greatest extinction threat to birds (the best known taxon), and its adverse impacts look set to increase, especially in developing countries. Two competing solutions have been proposed: wildlife-friendly farming (which boosts densities of wild populations on farmland but may decrease agricultural yields) and land sparing (which minimizes demand for farmland by increasing yield). We present a model that identifies how to resolve the trade-off between these approaches. This shows that the best type of farming for species persistence depends on the demand for agricultural products and on how the population densities of different species on farmland change with agricultural yield. Empirical data on such density-yield functions are sparse, but evidence from a range of taxa in developing countries suggests that high-yield farming may allow more species to persist.

Empirical Behavioral Models to Support Alternative Tools for the Analysis of Mixed-Priority Pedestrian-Vehicle Interaction in a Highway Capacity Context

PubMed Central

Rouphail, Nagui M.

2011-01-01

This paper presents behavioral-based models for describing pedestrian gap acceptance at unsignalized crosswalks in a mixed-priority environment, where some drivers yield and some pedestrians cross in gaps. Logistic regression models are developed to predict the probability of pedestrian crossings as a function of vehicle dynamics, pedestrian assertiveness, and other factors. In combination with prior work on probabilistic yielding models, the results can be incorporated in a simulation environment, where they can more fully describe the interaction of these two modes. The approach is intended to supplement HCM analytical procedure for locations where significant interaction occurs between drivers and pedestrians, including modern roundabouts. PMID:21643488

A toy model for the yield of a tamped fission bomb

NASA Astrophysics Data System (ADS)

Reed, B. Cameron

2018-02-01

A simple expression is developed for estimating the yield of a tamped fission bomb, that is, a basic nuclear weapon comprising a fissile core jacketed by a surrounding neutron-reflecting tamper. This expression is based on modeling the nuclear chain reaction as a geometric progression in combination with a previously published expression for the threshold-criticality condition for such a core. The derivation is especially straightforward, as it requires no knowledge of diffusion theory and should be accessible to students of both physics and policy. The calculation can be set up as a single page spreadsheet. Application to the Little Boy and Fat Man bombs of World War II gives results in reasonable accord with published yield estimates for these weapons.

Model development and experimental validation of capnophilic lactic fermentation and hydrogen synthesis by Thermotoga neapolitana.

PubMed

Pradhan, Nirakar; Dipasquale, Laura; d'Ippolito, Giuliana; Fontana, Angelo; Panico, Antonio; Pirozzi, Francesco; Lens, Piet N L; Esposito, Giovanni

2016-08-01

The aim of the present study was to develop a kinetic model for a recently proposed unique and novel metabolic process called capnophilic (CO2-requiring) lactic fermentation (CLF) pathway in Thermotoga neapolitana. The model was based on Monod kinetics and the mathematical expressions were developed to enable the simulation of biomass growth, substrate consumption and product formation. The calibrated kinetic parameters such as maximum specific uptake rate (k), semi-saturation constant (kS), biomass yield coefficient (Y) and endogenous decay rate (kd) were 1.30 h(-1), 1.42 g/L, 0.1195 and 0.0205 h(-1), respectively. A high correlation (>0.98) was obtained between the experimental data and model predictions for both model validation and cross validation processes. An increase of the lactate production in the range of 40-80% was obtained through CLF pathway compared to the classic dark fermentation model. The proposed kinetic model is the first mechanistically based model for the CLF pathway. This model provides useful information to improve the knowledge about how acetate and CO2 are recycled back by Thermotoga neapolitana to produce lactate without compromising the overall hydrogen yield. Copyright © 2016 Elsevier Ltd. All rights reserved.

Growth and yield for managed and unmanaged stands

Treesearch

H. Michael Rauscher

1992-01-01

Yield tables were generated using STEMS (Stand and Tree Evaluation and Modeling System) developed by North Central Forest Experiment Station scientists. The data fed into STEMS came from real 1/4-acre plots. The plots were selected to characterize 40-year-old stands that averaged 58 percent of basal area in red maple, 23 percent in sugar maple, and the remaining 19...

Estimating yellow-poplar growth and yield

Treesearch

Donald E. Beck

1989-01-01

Yellow-poplar grows in essentially pure, even-aged stands, so you can make growth and yield estimates from relatively few stand characteristics. The tables and models described here require only measures of stand age, stand basal area in trees 4.5 inches and larger, and site index. They were developed by remeasuring (at 5-year intervals over a 20-year period) many...

A Unified Sediment Transport Model for Inlet Application

DTIC Science & Technology

2011-01-01

of the development was to arrive at general sediment transport formulas suitable for a wide range of hydrodynamic, sedimentologic , and morphologic...wide range of hydrodynamic, sedimentologic , and morphologic conditions that yield reliable and robust predictions. In this paper such formulas are...hydrodynamic, sedimentologic , and morphologic conditions that prevail around coastal inlets. Thus, the formulas yield transport rates under waves and currents

Comparison of an empirical forest growth and yield simulator and a forest gap simulator using actual 30-year growth from two even-aged forests in Kentucky

Treesearch

Daniel A. Yaussy

2000-01-01

Two individual-tree growth simulators are used to predict the growth and mortality on a 30-year-old forest site and an 80-year-old forest site in eastern Kentucky. The empirical growth and yield model (NE-TWIGS) was developed to simulate short-term (

A fiber-resin micromechanics analysis of the delamination front in a DCB specimen

NASA Technical Reports Server (NTRS)

Crews, J. H.; Shivakumar, K. N.; Raju, I. S.

1988-01-01

A 3-D finite element model was developed to analyze the fiber-resin behavior near the delamination front in a graphite-epoxy double cantilever beam (DCB) specimen. The specimen interior was analyzed using a typical one-fiber slice, represented by a local 3-D fiber-resin model. The resin stresses were computed for the resin-rich layer at the ply interface as well as for the regions between the fibers close to the delamination front. However, the computed strain energy release rate G sub I along the delamination front varied by less than two percent, and was within about four percent of the plane-strain value. The von Mises yield criterion was used to estimate the extent of yielding near the delamination front. The yielding extended ahead of the delamination and also developed between the fibers. Although the fibers had only a negligible effect on G sub I, they caused yielding within the ply and therefore could influence delamination fracture toughness. The normal and shear stresses at the fiber-resin interface were computed near the delamination front. These results suggest that multi-axial stress criteria may be required to analyze fiber-resin interfaces.

Alkyl nitrate formation from the reactions of C8-C14 n-alkanes with OH radicals in the presence of NO(x): measured yields with essential corrections for gas-wall partitioning.

PubMed

Yeh, Geoffrey K; Ziemann, Paul J

2014-09-18

In this study, C8-C14 n-alkanes were reacted with OH radicals in the presence of NO(x) in a Teflon film environmental chamber and isomer-specific yields of alkyl nitrates were determined using gas chromatography. Because results indicated significant losses of alkyl nitrates to chamber walls, gas-wall partitioning was investigated by monitoring the concentrations of a suite of synthesized alkyl nitrates added to the chamber. Gas-to-wall partitioning increased with increasing carbon number and with proximity of the nitrooxy group to the terminal carbon, with losses as high as 86%. The results were used to develop a structure-activity model to predict the effects of carbon number and isomer structure on gas-wall partitioning, which was used to correct the measured yields of alkyl nitrate isomers formed in chamber reactions. The resulting branching ratios for formation of secondary alkyl nitrates were similar for all isomers of a particular carbon number, and average values, which were almost identical to alkyl nitrate yields, were 0.219, 0.206, 0.254, 0.291, and 0.315 for reactions of n-octane, n-decane, n-dodecane, n-tridecane, and n-tetradecane, respectively. The increase in average branching ratios and alkyl nitrate yields with increasing carbon number to a plateau value of ∼0.30 at about C13-C14 is consistent with predictions of a previously developed model, indicating that the model is valid for alkane carbon numbers ≥C3.

An advanced constitutive model in the sheet metal forming simulation: the Teodosiu microstructural model and the Cazacu Barlat yield criterion

NASA Astrophysics Data System (ADS)

Alves, J. L.; Oliveira, M. C.; Menezes, L. F.

2004-06-01

Two constitutive models used to describe the plastic behavior of sheet metals in the numerical simulation of sheet metal forming process are studied: a recently proposed advanced constitutive model based on the Teodosiu microstructural model and the Cazacu Barlat yield criterion is compared with a more classical one, based on the Swift law and the Hill 1948 yield criterion. These constitutive models are implemented into DD3IMP, a finite element home code specifically developed to simulate sheet metal forming processes, which generically is a 3-D elastoplastic finite element code with an updated Lagrangian formulation, following a fully implicit time integration scheme, large elastoplastic strains and rotations. Solid finite elements and parametric surfaces are used to model the blank sheet and tool surfaces, respectively. Some details of the numerical implementation of the constitutive models are given. Finally, the theory is illustrated with the numerical simulation of the deep drawing of a cylindrical cup. The results show that the proposed advanced constitutive model predicts with more exactness the final shape (medium height and ears profile) of the formed part, as one can conclude from the comparison with the experimental results.

Using a water-food-energy nexus approach for optimal irrigation management during drought events in Nebraska

NASA Astrophysics Data System (ADS)

Campana, P. E.; Zhang, J.; Yao, T.; Melton, F. S.; Yan, J.

2017-12-01

Climate change and drought have severe impacts on the agricultural sector affecting crop yields, water availability, and energy consumption for irrigation. Monitoring, assessing and mitigating the effects of climate change and drought on the agricultural and energy sectors are fundamental challenges that require investigation for water, food, and energy security issues. Using an integrated water-food-energy nexus approach, this study is developing a comprehensive drought management system through integration of real-time drought monitoring with real-time irrigation management. The spatially explicit model developed, GIS-OptiCE, can be used for simulation, multi-criteria optimization and generation of forecasts to support irrigation management. To demonstrate the value of the approach, the model has been applied to one major corn region in Nebraska to study the effects of the 2012 drought on crop yield and irrigation water/energy requirements as compared to a wet year such as 2009. The water-food-energy interrelationships evaluated show that significant water volumes and energy are required to halt the negative effects of drought on the crop yield. The multi-criteria optimization problem applied in this study indicates that the optimal solutions of irrigation do not necessarily correspond to those that would produce the maximum crop yields, depending on both water and economic constraints. In particular, crop pricing forecasts are extremely important to define the optimal irrigation management strategy. The model developed shows great potential in precision agriculture by providing near real-time data products including information on evapotranspiration, irrigation volumes, energy requirements, predicted crop growth, and nutrient requirements.

Biogas production from Pongamia biomass wastes and a model to estimate biodegradability from their composition.

PubMed

Gunaseelan, Victor Nallathambi

2014-02-01

In this study, I investigated the chemical characteristics, biochemical methane potential, conversion kinetics and biodegradability of untreated and NaOH-treated Pongamia plant parts, and pod husk and press cake from the biodiesel industry to evaluate their suitability as an alternative feedstock for biogas production. The untreated Pongamia seeds exhibited the maximum CH4 yield of 473 ml g (-1) volatile solid (VS) added. Yellow, withered leaves gave a yield as low as 122 ml CH4 g (-1) VS added. There were significant variations in the CH4 production rate constants, which ranged from 0.02 to 0.15 d (-1), and biodegradability, which ranged from 0.25 to 0.98. NaOH treatment of leaf and pod husk, which were highly rich in fibers, increased the yields by 15-22% and CH4 production rate constants by 20-75%. Utilization of Pongamia wastes in biogas digesters not only influences the economics of biodiesel production but also yields CH4 fuel and protects the environment. The experimental data from this study were used to develop a multiple regression model, which could estimate biodegradability based on biochemical characteristics. The model predicted the biodegradability of previously published biomass wastes (r(2) = 0.88) from their biochemical composition. The theoretical CH4 yields estimated as 350 ml g(-1) chemical oxygen demand destroyed are much higher than the experimental yields as 100% biodegradability is assumed for each substrate. Upon correcting the theoretical CH4 yields with biodegradability data obtained from chemical analyses of substrates, their ultimate CH4 yields could be predicted rapidly.

Flux balance analysis of different carbon source fermentation with hydrogen producing Clostridium butyricum using Cell Net Analyzer.

PubMed

Rafieenia, Razieh; Chaganti, Subba Rao

2015-01-01

A metabolic network model for Clostridium butyricum was developed using six different carbon sources (sucrose, fructose, galactose, mannose, trehalose and ribose) to study the fermentative H2 production. The model was used for investigation of H2 production and the ability of growth on different substrates to predict the maximum abilities for fermentative H2 production that each substrate can support. NADH fluxes were calculated by the model as an important cofactor affecting on H2 production. Butyrate and acetate production were used as model assumptions and biomass formation was chosen as the objective function for flux analysis calculations. Among the substrates selected, sucrose and trehalose supported the maximum growth and H2 yields. The Cell Net Analyzer metabolic network model developed for H2 estimation revealed good correlation with experimental data and could be further used to study the effect of environmental conditions and substrates concentration on H2 yield. Copyright © 2014 Elsevier Ltd. All rights reserved.

Non-linear modelling and control of semi-active suspensions with variable damping

NASA Astrophysics Data System (ADS)

Chen, Huang; Long, Chen; Yuan, Chao-Chun; Jiang, Hao-Bin

2013-10-01

Electro-hydraulic dampers can provide variable damping force that is modulated by varying the command current; furthermore, they offer advantages such as lower power, rapid response, lower cost, and simple hardware. However, accurate characterisation of non-linear f-v properties in pre-yield and force saturation in post-yield is still required. Meanwhile, traditional linear or quarter vehicle models contain various non-linearities. The development of a multi-body dynamics model is very complex, and therefore, SIMPACK was used with suitable improvements for model development and numerical simulations. A semi-active suspension was built based on a belief-desire-intention (BDI)-agent model framework. Vehicle handling dynamics were analysed, and a co-simulation analysis was conducted in SIMPACK and MATLAB to evaluate the BDI-agent controller. The design effectively improved ride comfort, handling stability, and driving safety. A rapid control prototype was built based on dSPACE to conduct a real vehicle test. The test and simulation results were consistent, which verified the simulation.

Development of a Computationally Efficient, High Fidelity, Finite Element Based Hall Thruster Model

NASA Technical Reports Server (NTRS)

Jacobson, David (Technical Monitor); Roy, Subrata

2004-01-01

This report documents the development of a two dimensional finite element based numerical model for efficient characterization of the Hall thruster plasma dynamics in the framework of multi-fluid model. Effect of the ionization and the recombination has been included in the present model. Based on the experimental data, a third order polynomial in electron temperature is used to calculate the ionization rate. The neutral dynamics is included only through the neutral continuity equation in the presence of a uniform neutral flow. The electrons are modeled as magnetized and hot, whereas ions are assumed magnetized and cold. The dynamics of Hall thruster is also investigated in the presence of plasma-wall interaction. The plasma-wall interaction is a function of wall potential, which in turn is determined by the secondary electron emission and sputtering yield. The effect of secondary electron emission and sputter yield has been considered simultaneously, Simulation results are interpreted in the light of experimental observations and available numerical solutions in the literature.

Elasticity and photoelasticity relationships for polyethylene terephthalate fiber networks by molecular simulation

NASA Astrophysics Data System (ADS)

Nayak, Kapileswar; Das, Sushanta; Nanavati, Hemant

2008-01-01

We present a framework for the development of elasticity and photoelasticity relationships for polyethylene terephthalate fiber networks, incorporating aspects of the primary molecular structure. Semicrystalline polymeric fiber networks are modeled as sequentially arranged crystalline and amorphous regions. Rotational isomeric states-Monte Carlo simulations of amorphous chains of up to 360 bonds (degree of polymerization, DP =60), confined between and bridging infinite impenetrable crystalline walls, have been characterized by Ω, the probability density of the intercrystal separation h, and Δβ, the polarizability anisotropy. lnΩ and Δβ have been modeled as functions of h, yielding the chain deformation relationships. The development has been extended to the fiber network to yield the photoelasticity relationships. We execute our framework by fitting to experimental stress-elongation data and employing the single fitted parameter to directly predict the birefringence-elongation behavior, without any further fitting. Incorporating the effect of strain-induced crystallization into the framework makes it physically more meaningful and yields accurate predictions of the birefringence-elongation behavior.

Techno-economic analysis of ethanol production from sugarcane bagasse using a Liquefaction plus Simultaneous Saccharification and co-Fermentation process.

PubMed

Gubicza, Krisztina; Nieves, Ismael U; Sagues, William J; Barta, Zsolt; Shanmugam, K T; Ingram, Lonnie O

2016-05-01

A techno-economic analysis was conducted for a simplified lignocellulosic ethanol production process developed and proven by the University of Florida at laboratory, pilot, and demonstration scales. Data obtained from all three scales of development were used with Aspen Plus to create models for an experimentally-proven base-case and 5 hypothetical scenarios. The model input parameters that differed among the hypothetical scenarios were fermentation time, enzyme loading, enzymatic conversion, solids loading, and overall process yield. The minimum ethanol selling price (MESP) varied between 50.38 and 62.72 US cents/L. The feedstock and the capital cost were the main contributors to the production cost, comprising between 23-28% and 40-49% of the MESP, respectively. A sensitivity analysis showed that overall ethanol yield had the greatest effect on the MESP. These findings suggest that future efforts to increase the economic feasibility of a cellulosic ethanol process should focus on optimization for highest ethanol yield. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modeling ionization and recombination from low energy nuclear recoils in liquid argon

DOE PAGES

Foxe, M.; Hagmann, C.; Jovanovic, I.; ...

2015-03-27

Coherent elastic neutrino-nucleus scattering (CENNS) is an as-yet undetected, flavor-independent neutrino interaction predicted by the Standard Model. Detection of CENNS could offer benefits for detection of supernova and solar neutrinos in astrophysics, or for detection of antineutrinos for nuclear reactor monitoring and nuclear nonproliferation. One challenge with detecting CENNS is the low energy deposition associated with a typical CENNS nuclear recoil. In addition, nuclear recoils result in lower ionization yields than those produced by electron recoils of the same energy. While a measurement of the nuclear recoil ionization yield in liquid argon in the keV energy range has been recentlymore » reported, a corresponding model for low-energy ionization yield in liquid argon does not exist. For this reason, a Monte Carlo simulation has been developed to predict the ionization yield at sub-10 keV energies. The model consists of two distinct components: (1) simulation of the atomic collision cascade with production of ionization, and (2) the thermalization and drift of ionization electrons in an applied electric field including local recombination. As an application of our results we report updated estimates of detectable ionization in liquid argon from CENNS at a nuclear reactor.« less

Plateletpheresis efficiency and mathematical correction of software-derived platelet yield prediction: A linear regression and ROC modeling approach.

PubMed

Jaime-Pérez, José Carlos; Jiménez-Castillo, Raúl Alberto; Vázquez-Hernández, Karina Elizabeth; Salazar-Riojas, Rosario; Méndez-Ramírez, Nereida; Gómez-Almaguer, David

2017-10-01

Advances in automated cell separators have improved the efficiency of plateletpheresis and the possibility of obtaining double products (DP). We assessed cell processor accuracy of predicted platelet (PLT) yields with the goal of a better prediction of DP collections. This retrospective proof-of-concept study included 302 plateletpheresis procedures performed on a Trima Accel v6.0 at the apheresis unit of a hematology department. Donor variables, software predicted yield and actual PLT yield were statistically evaluated. Software prediction was optimized by linear regression analysis and its optimal cut-off to obtain a DP assessed by receiver operating characteristic curve (ROC) modeling. Three hundred and two plateletpheresis procedures were performed; in 271 (89.7%) occasions, donors were men and in 31 (10.3%) women. Pre-donation PLT count had the best direct correlation with actual PLT yield (r = 0.486. P < .001). Means of software machine-derived values differed significantly from actual PLT yield, 4.72 × 10 11 vs.6.12 × 10 11 , respectively, (P < .001). The following equation was developed to adjust these values: actual PLT yield= 0.221 + (1.254 × theoretical platelet yield). ROC curve model showed an optimal apheresis device software prediction cut-off of 4.65 × 10 11 to obtain a DP, with a sensitivity of 82.2%, specificity of 93.3%, and an area under the curve (AUC) of 0.909. Trima Accel v6.0 software consistently underestimated PLT yields. Simple correction derived from linear regression analysis accurately corrected this underestimation and ROC analysis identified a precise cut-off to reliably predict a DP. © 2016 Wiley Periodicals, Inc.

Nonlinear Programming Models to Optimize Uneven-Aged Shortleaf Pine Management

Treesearch

Benedict J. Schulte; Joseph Buongiorno

2002-01-01

Nonlinear programming models of uneven-aged shortleaf pine (Pinus echinata Mill.) management were developed to identify sustainable management regimes that optimize soil expectation value (SEV) or annual sawtimber yields. The models recognize three species groups (shortleaf pine and other softwoods, soft hardwoods and hard hardwoods) and 13 2-inch...

Simulation of hardwood log sawing

Treesearch

D.B. Richards; W.K. Adkins; H. Hallock; E.H. Bulgrin

1979-01-01

Mathematical modeling computer programs for several hardwood sawing systems have been developed and are described. One has judgment capabilities. Several of the subroutines are common to all of the models. These models are the basis for further research which examines the question of best-grade sawing method in terms of lumber value yield.

Behavioural modelling of irrigation decision making under water scarcity

NASA Astrophysics Data System (ADS)

Foster, T.; Brozovic, N.; Butler, A. P.

2013-12-01

Providing effective policy solutions to aquifer depletion caused by abstraction for irrigation is a key challenge for socio-hydrology. However, most crop production functions used in hydrological models do not capture the intraseasonal nature of irrigation planning, or the importance of well yield in land and water use decisions. Here we develop a method for determining stochastic intraseasonal water use that is based on observed farmer behaviour but is also theoretically consistent with dynamically optimal decision making. We use the model to (i) analyse the joint land and water use decision by farmers; (ii) to assess changes in behaviour and production risk in response to water scarcity; and (iii) to understand the limits of applicability of current methods in policy design. We develop a biophysical model of water-limited crop yield building on the AquaCrop model. The model is calibrated and applied to case studies of irrigated corn production in Nebraska and Texas. We run the model iteratively, using long-term climate records, to define two formulations of the crop-water production function: (i) the aggregate relationship between total seasonal irrigation and yield (typical of current approaches); and (ii) the stochastic response of yield and total seasonal irrigation to the choice of an intraseasonal soil moisture target and irrigated area. Irrigated area (the extensive margin decision) and per-area irrigation intensity (the intensive margin decision) are then calculated for different seasonal water restrictions (corresponding to regulatory policies) and well yield constraints on intraseasonal abstraction rates (corresponding to aquifer system limits). Profit- and utility-maximising decisions are determined assuming risk neutrality and varying degrees of risk aversion, respectively. Our results demonstrate that the formulation of the production function has a significant impact on the response to water scarcity. For low well yields, which are the major concern for farmers in areas of aquifer depletion or recurrent drought, the stochastic model demonstrates that partial-area irrigation is optimal irrespective of the size of water supply restrictions. This effect is not produced by the aggregate model, which cannot account for the variability of the production function with changes in irrigated area that control intraseasonal irrigation application rates. In addition, the aggregate model overstates the willingness of a risk-averse farmer to adjust on the intensive margin in response to water supply restrictions. This is due to the inability of aggregate models to specify correctly the production risk associated with intensive margin adjustments. Consequently, aggregate models give unrealistic estimates of water demand and underestimate the negative impacts on profitability of declining groundwater resources. Reliance on aggregate models will limit the ability of socio-hydrology to guide policy responses to groundwater scarcity. Our stochastic methodology provides a more realistic tool to study the management of groundwater in coupled human-water systems.

Transcription factor COUP-TFII is indispensable for venous and lymphatic development in zebrafish and Xenopus laevis

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

Aranguren, Xabier L., E-mail: xabier.lopezaranguren@med.kuleuven.be; Beerens, Manu, E-mail: manu.beerens@med.kuleuven.be; Vandevelde, Wouter, E-mail: woutervandevelde@gmail.com

Highlights: {yields} COUP-TFII deficiency in zebrafish affects arterio-venous EC specification. {yields} COUP-TFII is indispensable for lymphatic development in zebrafish. {yields} COUP-TFII knockdown in Xenopus disrupts lymphatic EC differentiation and migration. {yields} COUP-TFII's role in EC fate decisions is evolutionary conserved. -- Abstract: Transcription factors play a central role in cell fate determination. Gene targeting in mice revealed that Chicken Ovalbumin Upstream Promoter-Transcription Factor II (COUP-TFII, also known as Nuclear Receptor 2F2 or NR2F2) induces a venous phenotype in endothelial cells (ECs). More recently, NR2F2 was shown to be required for initiating the expression of Prox1, responsible for lymphatic commitment ofmore » venous ECs. Small animal models like zebrafish embryos and Xenopus laevis tadpoles have been very useful to elucidate mechanisms of (lymph) vascular development. Therefore, the role of NR2F2 in (lymph) vascular development was studied by eliminating its expression in these models. Like in mice, absence of NR2F2 in zebrafish resulted in distinct vascular defects including loss of venous marker expression, major trunk vessel fusion and vascular leakage. Both in zebrafish and Xenopus the development of the main lymphatic structures was severely hampered. NR2F2 knockdown significantly decreased prox1 expression in zebrafish ECs and the same manipulation affected lymphatic (L)EC commitment, migration and function in Xenopus tadpoles. Therefore, the role of NR2F2 in EC fate determination is evolutionary conserved.« less

Inference regarding multiple structural changes in linear models with endogenous regressors☆

PubMed Central

Hall, Alastair R.; Han, Sanggohn; Boldea, Otilia

2012-01-01

This paper considers the linear model with endogenous regressors and multiple changes in the parameters at unknown times. It is shown that minimization of a Generalized Method of Moments criterion yields inconsistent estimators of the break fractions, but minimization of the Two Stage Least Squares (2SLS) criterion yields consistent estimators of these parameters. We develop a methodology for estimation and inference of the parameters of the model based on 2SLS. The analysis covers the cases where the reduced form is either stable or unstable. The methodology is illustrated via an application to the New Keynesian Phillips Curve for the US. PMID:23805021

Radiochemistry and the Study of Fission

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

Rundberg, Robert S.

These are slides from a lecture given at UC Berkeley. Radiochemistry has been used to study fission since its discovery. Radiochemical methods are used to determine cumulative mass yields. These measurements have led to the two-mode fission hypothesis to model the neutron energy dependence of fission product yields. Fission product yields can be used for the nuclear forensics of nuclear explosions. The mass yield curve depends on both the fuel and the neutron spectrum of a device. Recent studies have shown that the nuclear structure of the compound nucleus can affect the mass yield distribution. The following topics are covered:more » In the beginning: the discovery of fission; forensics using fission products: what can be learned from fission products, definitions of R-values and Q-values, fission bases, K-factors and fission chambers, limitations; the neutron energy dependence of the mass yield distribution (the two mode fission hypothesis); the influence of nuclear structure on the mass yield distribution. In summary: Radiochemistry has been used to study fission since its discovery. Radiochemical measurement of fission product yields have provided the highest precision data for developing fission models and for nuclear forensics. The two-mode fission hypothesis provides a description of the neutron energy dependence of the mass yield curve. However, data is still rather sparse and more work is needed near second and third chance fission. Radiochemical measurements have provided evidence for the importance of nuclear states in the compound nucleus in predicting the mass yield curve in the resonance region.« less

Predictions of Daily Milk and Fat Yields, Major Groups of Fatty Acids, and C18:1 cis-9 from Single Milking Data without a Milking Interval

PubMed Central

Arnould, Valérie M. R.; Reding, Romain; Bormann, Jeanne; Gengler, Nicolas; Soyeurt, Hélène

2015-01-01

Simple Summary Reducing the frequency of milk recording decreases the costs of official milk recording. However, this approach can negatively affect the accuracy of predicting daily yields. Equations to predict daily yield from morning or evening data were developed in this study for fatty milk components from traits recorded easily by milk recording organizations. The correlation values ranged from 96.4% to 97.6% (96.9% to 98.3%) when the daily yields were estimated from the morning (evening) milkings. The simplicity of the proposed models which do not include the milking interval should facilitate their use by breeding and milk recording organizations. Abstract Reducing the frequency of milk recording would help reduce the costs of official milk recording. However, this approach could also negatively affect the accuracy of predicting daily yields. This problem has been investigated in numerous studies. In addition, published equations take into account milking intervals (MI), and these are often not available and/or are unreliable in practice. The first objective of this study was to propose models in which the MI was replaced by a combination of data easily recorded by dairy farmers. The second objective was to further investigate the fatty acids (FA) present in milk. Equations to predict daily yield from AM or PM data were based on a calibration database containing 79,971 records related to 51 traits [milk yield (expected AM, expected PM, and expected daily); fat content (expected AM, expected PM, and expected daily); fat yield (expected AM, expected PM, and expected daily; g/day); levels of seven different FAs or FA groups (expected AM, expected PM, and expected daily; g/dL milk), and the corresponding FA yields for these seven FA types/groups (expected AM, expected PM, and expected daily; g/day)]. These equations were validated using two distinct external datasets. The results obtained from the proposed models were compared to previously published results for models which included a MI effect. The corresponding correlation values ranged from 96.4% to 97.6% when the daily yields were estimated from the AM milkings and ranged from 96.9% to 98.3% when the daily yields were estimated from the PM milkings. The simplicity of these proposed models should facilitate their use by breeding and milk recording organizations. PMID:26479379

Theoretical Development of an Orthotropic Elasto-Plastic Generalized Composite Material Model

NASA Technical Reports Server (NTRS)

Goldberg, Robert K.; Carney, Kelly S.; DuBois, Paul; Hoffarth, Canio; Harrington, Joseph; Subramanian, Rajan; Blankenhorn, Gunther

2014-01-01

The need for accurate material models to simulate the deformation, damage and failure of polymer matrix composites is becoming critical as these materials are gaining increased usage in the aerospace and automotive industries. While there are several composite material models currently available within LS-DYNA (Registered), there are several features that have been identified that could improve the predictive capability of a composite model. To address these needs, a combined plasticity and damage model suitable for use with both solid and shell elements is being developed and is being implemented into LS-DYNA as MAT_213. A key feature of the improved material model is the use of tabulated stress-strain data in a variety of coordinate directions to fully define the stress-strain response of the material. To date, the model development efforts have focused on creating the plasticity portion of the model. The Tsai-Wu composite failure model has been generalized and extended to a strain-hardening based orthotropic material model with a non-associative flow rule. The coefficients of the yield function, and the stresses to be used in both the yield function and the flow rule, are computed based on the input stress-strain curves using the effective plastic strain as the tracking variable. The coefficients in the flow rule are computed based on the obtained stress-strain data. The developed material model is suitable for implementation within LS-DYNA for use in analyzing the nonlinear response of polymer composites.

Rainfall Intensity and Frequency Explain Production Basis Risk in Cumulative Rain Index Insurance

NASA Astrophysics Data System (ADS)

Muneepeerakul, Chitsomanus P.; Muneepeerakul, Rachata; Huffaker, Ray G.

2017-12-01

With minimal moral hazard and adverse selection, weather index insurance promises financial resilience to farmers struck by harsh weather conditions through swift compensation at affordable premium. Despite these advantages, the very nature of indexing gives rise to production basis risk as the selected weather indexes do not sufficiently correspond to actual damages. To address this problem, we develop a stochastic yield model, built upon a stochastic soil moisture model driven by marked Poisson rainfall. Our analysis shows that even under similar temperature and rainfall amount yields can differ significantly; this was empirically supported by a 2-year field experiment in which rain-fed maize was grown under very similar total rainfall. Here, the year with more intense, less-frequent rainfall produces a better yield—a rare counter evidence to most climate change projections. Through a stochastic yield model, we demonstrate the crucial roles of rainfall intensity and frequency in determining the yield. Importantly, the model allows us to compute rainfall pattern-related basis risk inherent in cumulative rain index insurance. The model results and a case study herein clearly show that total rainfall is a poor indicator of yield, imposing unnecessary production basis risk on farmers and false-positive payouts on insurers. Incorporating rainfall intensity and frequency in the design of rain index insurance can offer farmers better protection, while maintaining the attractive features of the weather index insurance and thus fulfilling its promise of financial resilience.

Improved Satellite-based Crop Yield Mapping by Spatially Explicit Parameterization of Crop Phenology

NASA Astrophysics Data System (ADS)

Jin, Z.; Azzari, G.; Lobell, D. B.

2016-12-01

Field-scale mapping of crop yields with satellite data often relies on the use of crop simulation models. However, these approaches can be hampered by inaccuracies in the simulation of crop phenology. Here we present and test an approach to use dense time series of Landsat 7 and 8 acquisitions data to calibrate various parameters related to crop phenology simulation, such as leaf number and leaf appearance rates. These parameters are then mapped across the Midwestern United States for maize and soybean, and for two different simulation models. We then implement our recently developed Scalable satellite-based Crop Yield Mapper (SCYM) with simulations reflecting the improved phenology parameterizations, and compare to prior estimates based on default phenology routines. Our preliminary results show that the proposed method can effectively alleviate the underestimation of early-season LAI by the default Agricultural Production Systems sIMulator (APSIM), and that spatially explicit parameterization for the phenology model substantially improves the SCYM performance in capturing the spatiotemporal variation in maize and soybean yield. The scheme presented in our study thus preserves the scalability of SCYM, while significantly reducing its uncertainty.

Standing wave design and experimental validation of a tandem simulated moving bed process for insulin purification.

PubMed

Xie, Yi; Mun, Sungyong; Kim, Jinhyun; Wang, Nien-Hwa Linda

2002-01-01

A tandem simulated moving bed (SMB) process for insulin purification has been proposed and validated experimentally. The mixture to be separated consists of insulin, high molecular weight proteins, and zinc chloride. A systematic approach based on the standing wave design, rate model simulations, and experiments was used to develop this multicomponent separation process. The standing wave design was applied to specify the SMB operating conditions of a lab-scale unit with 10 columns. The design was validated with rate model simulations prior to experiments. The experimental results show 99.9% purity and 99% yield, which closely agree with the model predictions and the standing wave design targets. The agreement proves that the standing wave design can ensure high purity and high yield for the tandem SMB process. Compared to a conventional batch SEC process, the tandem SMB has 10% higher yield, 400% higher throughput, and 72% lower eluant consumption. In contrast, a design that ignores the effects of mass transfer and nonideal flow cannot meet the purity requirement and gives less than 96% yield.

Climate Change Impacts on Sediment Yield in Headwaters of a High-latitude Region in China

NASA Astrophysics Data System (ADS)

Zhou, Y.; Xu, Y. J.; Wang, J., , Dr; Weihua, X.; Huang, Y.

2017-12-01

Climate change is expected to have strongest effects in higher latitude regions. Despite intensive research on possible hydrological responses to global warming in these regions, our knowledge of climate change on surface erosion and sediment yield in high-latitude headwaters is limited. In this study, we used the Soil and Water Assessment Tool (SWAT) to predict future runoff and sediment yield from the headwaters of a high-latitude river basin in China's far northeast. The SWAT model was first calibrated with historical discharge records and the model parameterization achieved satisfactory validation. The calibrated model was then applied to two greenhouse gas concentration trajectories, RCP4.5 and RCP8.5, for the period from 2020 to 2050 to estimate future runoff. Sediment yields for this period were predicted using a discharge-sediment load rating curve developed from field measurements in the past nine years. Our preliminary results show an increasing trend of sediment yield under both climate change scenarios, and that the increase is more pronounced in the summer and autumn months. Changes in precipitation and temperature seem to exert variable impacts on runoff and sediment yield at interannual and seasonal scales in these headwaters. These findings imply that the current river basin management in the region needs to be reviewed and improved in order to be effective under a changing climate.

Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield

NASA Astrophysics Data System (ADS)

Suarez, L. A.; Apan, A.; Werth, J.

2016-10-01

Yield loss in crops is often associated with plant disease or external factors such as environment, water supply and nutrient availability. Improper agricultural practices can also introduce risks into the equation. Herbicide drift can be a combination of improper practices and environmental conditions which can create a potential yield loss. As traditional assessment of plant damage is often imprecise and time consuming, the ability of remote and proximal sensing techniques to monitor various bio-chemical alterations in the plant may offer a faster, non-destructive and reliable approach to predict yield loss caused by herbicide drift. This paper examines the prediction capabilities of partial least squares regression (PLS-R) models for estimating yield. Models were constructed with hyperspectral data of a cotton crop sprayed with three simulated doses of the phenoxy herbicide 2,4-D at three different growth stages. Fibre quality, photosynthesis, conductance, and two main hormones, indole acetic acid (IAA) and abscisic acid (ABA) were also analysed. Except for fibre quality and ABA, Spearman correlations have shown that these variables were highly affected by the chemical. Four PLS-R models for predicting yield were developed according to four timings of data collection: 2, 7, 14 and 28 days after the exposure (DAE). As indicated by the model performance, the analysis revealed that 7 DAE was the best time for data collection purposes (RMSEP = 2.6 and R2 = 0.88), followed by 28 DAE (RMSEP = 3.2 and R2 = 0.84). In summary, the results of this study show that it is possible to accurately predict yield after a simulated herbicide drift of 2,4-D on a cotton crop, through the analysis of hyperspectral data, thereby providing a reliable, effective and non-destructive alternative based on the internal response of the cotton leaves.

Soil erosion and sediment yield, a double barrel problem in South Africa's only large river network without a dam

NASA Astrophysics Data System (ADS)

Le Roux, Jay

2016-04-01

Soil erosion not only involves the loss of fertile topsoil but is also coupled with sedimentation of dams, a double barrel problem in semi-arid regions where water scarcity is frequent. Due to increasing water requirements in South Africa, the Department of Water and Sanitation is planning water resource development in the Mzimvubu River Catchment, which is the only large river network in the country without a dam. Two dams are planned including a large irrigation dam and a hydropower dam. However, previous soil erosion studies indicate that large parts of the catchment is severely eroded. Previous studies, nonetheless, used mapping and modelling techniques that represent only a selection of erosion processes and provide insufficient information about the sediment yield. This study maps and models the sediment yield comprehensively by means of two approaches over a five-year timeframe between 2007 and 2012. Sediment yield contribution from sheet-rill erosion was modelled with ArcSWAT (a graphical user interface for SWAT in a GIS), whereas gully erosion contributions were estimated using time-series mapping with SPOT 5 imagery followed by gully-derived sediment yield modelling in a GIS. Integration of the sheet-rill and gully results produced a total sediment yield map, with an average of 5 300 t km-2 y-1. Importantly, the annual average sediment yield of the areas where the irrigation dam and hydropower dam will be built is around 20 000 t km-2 y-1. Without catchment rehabilitation, the life expectancy of the irrigation dam and hydropower dam could be 50 and 40 years respectively.

Local yield stress statistics in model amorphous solids

NASA Astrophysics Data System (ADS)

Barbot, Armand; Lerbinger, Matthias; Hernandez-Garcia, Anier; García-García, Reinaldo; Falk, Michael L.; Vandembroucq, Damien; Patinet, Sylvain

2018-03-01

We develop and extend a method presented by Patinet, Vandembroucq, and Falk [Phys. Rev. Lett. 117, 045501 (2016), 10.1103/PhysRevLett.117.045501] to compute the local yield stresses at the atomic scale in model two-dimensional Lennard-Jones glasses produced via differing quench protocols. This technique allows us to sample the plastic rearrangements in a nonperturbative manner for different loading directions on a well-controlled length scale. Plastic activity upon shearing correlates strongly with the locations of low yield stresses in the quenched states. This correlation is higher in more structurally relaxed systems. The distribution of local yield stresses is also shown to strongly depend on the quench protocol: the more relaxed the glass, the higher the local plastic thresholds. Analysis of the magnitude of local plastic relaxations reveals that stress drops follow exponential distributions, justifying the hypothesis of an average characteristic amplitude often conjectured in mesoscopic or continuum models. The amplitude of the local plastic rearrangements increases on average with the yield stress, regardless of the system preparation. The local yield stress varies with the shear orientation tested and strongly correlates with the plastic rearrangement locations when the system is sheared correspondingly. It is thus argued that plastic rearrangements are the consequence of shear transformation zones encoded in the glass structure that possess weak slip planes along different orientations. Finally, we justify the length scale employed in this work and extract the yield threshold statistics as a function of the size of the probing zones. This method makes it possible to derive physically grounded models of plasticity for amorphous materials by directly revealing the relevant details of the shear transformation zones that mediate this process.

Conjunctive-use optimization model of the Mississippi River Valley alluvial aquifer of northeastern Arkansas

USGS Publications Warehouse

Czarnecki, John B.; Clark, Brian R.; Reed, Thomas B.

2003-01-01

The Mississippi River Valley alluvial aquifer is a water-bearing assemblage of gravels and sands that underlies about 32,000 square miles of Missouri, Kentucky, Tennessee, Mississippi, Louisiana, and Arkansas. Because of the heavy demands placed on the aquifer, several large cones of depression over 100 feet deep have formed in the potentiometric surface, resulting in lower well yields and degraded water quality in some areas. A ground-water flow model of the alluvial aquifer was previously developed for an area covering 14,104 square miles, extending northeast from the Arkansas River into the northeast corner of Arkansas and parts of southeastern Missouri. The flow model showed that continued ground-water withdrawals at rates commensurate with those of 1997 could not be sustained indefinitely without causing water levels to decline below half the original saturated thickness of the aquifer. To develop estimates of withdrawal rates that could be sustained in compliance with the constraints of critical ground-water area designation, conjunctive-use optimization modeling was applied to the flow model of the alluvial aquifer in northeastern Arkansas. Ground-water withdrawal rates form the basis for estimates of sustainable yield from the alluvial aquifer and from rivers specified within the alluvial aquifer model. A management problem was formulated as one of maximizing the sustainable yield from all ground-water and surface-water withdrawal cells within limits imposed by plausible withdrawal rates, and within specified constraints involving hydraulic head and streamflow. Steady-state flow conditions were selected because the maximized withdrawals are intended to represent sustainable yield of the system (a rate that can be maintained indefinitely). Within the optimization model, 11 rivers are specified. Surface-water diversion rates that occurred in 2000 were subtracted from specified overland flow at the appropriate river cells. Included in these diversions were the planned diversions of 63,339,248 ft3/d for the Bayou Meto project area and 55,078,367 ft3/d for the Grand Prairie project area, which factor in an additional 30 and 40 percent transmission loss, respectively. Streamflow constraints were specified at all 1,165 river cells based on average 7-day minimum flows for 10 years. Sustainable yield for all rivers ranged from 0 (Current, Little Red, and Bayou Meto Rivers) to almost 5 billion cubic feet per day for the Arkansas River. Total sustainable yield from all rivers combined was 12.8 billion cubic feet per day, which represents a substantial source for supplementing ground water to meet the total water demand. Sustainable-yield estimates are affected by the allowable upper limit on withdrawals from wells specified in the optimization model. Ground-water withdrawal rates were allowed to vary as much as 200 percent of the withdrawal rate in 1997. As the overall upper limit on withdrawals is increased, the sustainable yield generally increases. Tests with the optimization model show that without limits on pumping, wells adjacent to sources of water would have optimized withdrawal rates that were orders of magnitude larger than rates corresponding to those of 1997. The sustainable yield from ground water for the entire study area while setting the maximum upper limit as the amount withdrawn in 1997 is 360 million cubic feet per day, which is only about 57 percent of the amount withdrawn in 1997 (635.6 million cubic feet per day). Optimal sustainable yields from within the Bayou Meto irrigation project area and within the Grand Prairie irrigation project area are 18.1 and 9.1 million cubic feet per day, respectively, assuming a maximum allowable withdrawal rate equal to 1997 rates. These values of sustainable yield represent 35 and 30 percent respectively of the amount pumped from these project areas in 1997. Unmet demand (defined as the difference between the optimized withdrawal rate or sustainable yield, a

Response of winter and spring wheat grain yields to meteorological variation

NASA Technical Reports Server (NTRS)

Feyerherm, A. M.; Kanemasu, E. T.; Paulsen, G. M.

1977-01-01

Mathematical models which quantify the relation of wheat yield to selected weather-related variables are presented. Other sources of variation (amount of applied nitrogen, improved varieties, cultural practices) have been incorporated in the models to explain yield variation both singly and in combination with weather-related variables. Separate models were developed for fall-planted (winter) and spring-planted (spring) wheats. Meteorological variation is observed, basically, by daily measurements of minimum and maximum temperatures, precipitation, and tabled values of solar radiation at the edge of the atmosphere and daylength. Two different soil moisture budgets are suggested to compute simulated values of evapotranspiration; one uses the above-mentioned inputs, the other uses the measured temperatures and precipitation but replaces the tabled values (solar radiation and daylength) by measured solar radiation and satellite-derived multispectral scanner data to estimate leaf area index. Weather-related variables are defined by phenological stages, rather than calendar periods, to make the models more universally applicable.

The kinetic origin of delayed yielding in metallic glasses

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

Ye, Y. F.; Liu, X. D.; Wang, S.

2016-06-20

Recent experiments showed that irreversible structural change or plasticity could occur in metallic glasses (MGs) even within the apparent elastic limit after a sufficiently long waiting time. To explain this phenomenon, a stochastic shear transformation model is developed based on a unified rate theory to predict delayed yielding in MGs, which is validated afterwards through extensive atomistic simulations carried out on different MGs. On a fundamental level, an analytic framework is established in this work that links time, stress, and temperature altogether into a general yielding criterion for MGs.

A CFD model for biomass fast pyrolysis in fluidized-bed reactors

NASA Astrophysics Data System (ADS)

Xue, Qingluan; Heindel, T. J.; Fox, R. O.

2010-11-01

A numerical study is conducted to evaluate the performance and optimal operating conditions of fluidized-bed reactors for fast pyrolysis of biomass to bio-oil. A comprehensive CFD model, coupling a pyrolysis kinetic model with a detailed hydrodynamics model, is developed. A lumped kinetic model is applied to describe the pyrolysis of biomass particles. Variable particle porosity is used to account for the evolution of particle physical properties. The kinetic scheme includes primary decomposition and secondary cracking of tar. Biomass is composed of reference components: cellulose, hemicellulose, and lignin. Products are categorized into groups: gaseous, tar vapor, and solid char. The particle kinetic processes and their interaction with the reactive gas phase are modeled with a multi-fluid model derived from the kinetic theory of granular flow. The gas, sand and biomass constitute three continuum phases coupled by the interphase source terms. The model is applied to investigate the effect of operating conditions on the tar yield in a fluidized-bed reactor. The influence of various parameters on tar yield, including operating temperature and others are investigated. Predicted optimal conditions for tar yield and scale-up of the reactor are discussed.

Fission yield and criticality excursion code

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

Blanchard, A.

2000-06-30

The ANSI/ANS 8.3 standard allows a maximum yield not to exceed 2 x 10 fissions to calculate requiring the alarm system to be effective. It is common practice to use this allowance or to develop some other yield based on past criticality accident history or excursion experiments. The literature on the subject of yields discusses maximum yields larger and somewhat smaller than the ANS 8.3 permissive value. The ability to model criticality excursions and vary the various parameters to determine a credible maximum yield for operational specific cases has been available for some time but is not in common usemore » by criticality safety specialists. The topic of yields for various solution, metal, oxide powders, etc. in various geometry's and containers has been published by laboratory specialists or university staff and students for many decades but have not been available to practitioners. The need for best-estimate calculations of fission yields with a well-validated criticality excursion code has long been recognized. But no coordinated effort has been made so far to develop a generalized and well-validated excursion code for different types of systems. In this paper, the current practices to estimate fission yields are summarized along with its shortcomings for the 12-Rad zone (at SRS) and Criticality Alarm System (CAS) calculations. Finally the need for a user-friendly excursion code is reemphasized.« less

Single-particle tracking of quantum dot-conjugated prion proteins inside yeast cells

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

Tsuji, Toshikazu; Kawai-Noma, Shigeko; Pack, Chan-Gi

2011-02-25

Research highlights: {yields} We develop a method to track a quantum dot-conjugated protein in yeast cells. {yields} We incorporate the conjugated quantum dot proteins into yeast spheroplasts. {yields} We track the motions by conventional or 3D tracking microscopy. -- Abstract: Yeast is a model eukaryote with a variety of biological resources. Here we developed a method to track a quantum dot (QD)-conjugated protein in the budding yeast Saccharomyces cerevisiae. We chemically conjugated QDs with the yeast prion Sup35, incorporated them into yeast spheroplasts, and tracked the motions by conventional two-dimensional or three-dimensional tracking microscopy. The method paves the way towardmore » the individual tracking of proteins of interest inside living yeast cells.« less

Suspended-Sediment Loads and Yields in the North Santiam River Basin, Oregon, Water Years 1999-2004

USGS Publications Warehouse

Bragg, Heather M.; Sobieszczyk, Steven; Uhrich, Mark A.; Piatt, David R.

2007-01-01

The North Santiam River provides drinking water to the residents and businesses of the city of Salem, Oregon, and many surrounding communities. Since 1998, water-quality data, including turbidity, were collected continuously at monitoring stations throughout the basin as part of the North Santiam River Basin Turbidity and Suspended Sediment Study. In addition, sediment samples have been collected over a range of turbidity and streamflow values. Regression models were developed between the instream turbidity and suspended-sediment concentration from the samples collected from each monitoring station. The models were then used to estimate the daily and annual suspended-sediment loads and yields. For water years 1999-2004, suspended-sediment loads and yields were estimated for each station. Annual suspended-sediment loads and yields were highest during water years 1999 and 2000. A drought during water year 2001 resulted in the lowest suspended-sediment loads and yields for all monitoring stations. High-turbidity events that were unrelated or disproportional to increased streamflow occurred at several of the monitoring stations during the period of study. These events highlight the advantage of estimating suspended-sediment loads and yields from instream turbidity rather than from streamflow alone.

Model for the separate collection of packaging waste in Portuguese low-performing recycling regions.

PubMed

Oliveira, V; Sousa, V; Vaz, J M; Dias-Ferreira, C

2018-06-15

Separate collection of packaging waste (glass; plastic/metals; paper/cardboard), is currently a widespread practice throughout Europe. It enables the recovery of good quality recyclable materials. However, separate collection performance are quite heterogeneous, with some countries reaching higher levels than others. In the present work, separate collection of packaging waste has been evaluated in a low-performance recycling region in Portugal in order to investigate which factors are most affecting the performance in bring-bank collection system. The variability of separate collection yields (kg per inhabitant per year) among 42 municipalities was scrutinized for the year 2015 against possible explanatory factors. A total of 14 possible explanatory factors were analysed, falling into two groups: socio-economic/demographic and waste collection service related. Regression models were built in an attempt to evaluate the individual effect of each factor on separate collection yields and predict changes on the collection yields by acting on those factors. The best model obtained is capable to explain 73% of the variation found in the separate collection yields. The model includes the following statistically significant indicators affecting the success of separate collection yields: i) inhabitants per bring-bank; ii) relative accessibility to bring-banks; iii) degree of urbanization; iv) number of school years attended; and v) area. The model presented in this work was developed specifically for the bring-bank system, has an explanatory power and quantifies the impact of each factor on separate collection yields. It can therefore be used as a support tool by local and regional waste management authorities in the definition of future strategies to increase collection of recyclables of good quality and to achieve national and regional targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Nut crop yield records show that budbreak-based chilling requirements may not reflect yield decline chill thresholds

NASA Astrophysics Data System (ADS)

Pope, Katherine S.; Dose, Volker; Da Silva, David; Brown, Patrick H.; DeJong, Theodore M.

2015-06-01

Warming winters due to climate change may critically affect temperate tree species. Insufficiently cold winters are thought to result in fewer viable flower buds and the subsequent development of fewer fruits or nuts, decreasing the yield of an orchard or fecundity of a species. The best existing approximation for a threshold of sufficient cold accumulation, the "chilling requirement" of a species or variety, has been quantified by manipulating or modeling the conditions that result in dormant bud breaking. However, the physiological processes that affect budbreak are not the same as those that determine yield. This study sought to test whether budbreak-based chilling thresholds can reasonably approximate the thresholds that affect yield, particularly regarding the potential impacts of climate change on temperate tree crop yields. County-wide yield records for almond ( Prunus dulcis), pistachio ( Pistacia vera), and walnut ( Juglans regia) in the Central Valley of California were compared with 50 years of weather records. Bayesian nonparametric function estimation was used to model yield potentials at varying amounts of chill accumulation. In almonds, average yields occurred when chill accumulation was close to the budbreak-based chilling requirement. However, in the other two crops, pistachios and walnuts, the best previous estimate of the budbreak-based chilling requirements was 19-32 % higher than the chilling accumulations associated with average or above average yields. This research indicates that physiological processes beyond requirements for budbreak should be considered when estimating chill accumulation thresholds of yield decline and potential impacts of climate change.

A Simple Model for Estimating Total and Merchantable Tree Heights

Treesearch

Alan R. Ek; Earl T. Birdsall; Rebecca J. Spears

1984-01-01

A model is described for estimating total and merchantable tree heights for Lake States tree species. It is intended to be used for compiling forest survey data and in conjunction with growth models for developing projections of tree product yield. Model coefficients are given for 25 species along with fit statistics. Supporting data sets are also described.

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.

A Stochastic Model for the Landing Dispersion of Hazard Detection and Avoidance Capable Flight Systems

NASA Astrophysics Data System (ADS)

Witte, L.

2014-06-01

To support landing site assessments for HDA-capable flight systems and to facilitate trade studies between the potential HDA architectures versus the yielded probability of safe landing a stochastic landing dispersion model has been developed.

Development of growth and yield models for southern hardwoods: site index determinations

Treesearch

John Paul McTague; Daniel J. Robison; David O' Loughlin; Joseph Roise; Robert Kellison

2006-01-01

Growth and yield data from across 13 southern States, collected from 1967 to 2004 from fully-stocked even-aged southern hardwood forests on a variety of site types, was used to calculate site index curves. These derived curves provide an efficient means to evaluate the productivity-age relation which varies across many sites. These curves were derived for mixed-species...

Analysis of MINIE2013 Explosion Air-Blast Data

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

Schnurr, Julie M.; Rodgers, Arthur J.; Kim, Keehoon

We report analysis of air-blast overpressure measurements from the MINIE2013 explosive experiments. The MINIE2013 experiment involved a series of nearly 70 near-surface (height-ofburst, HOB, ranging from -1 to +4 m) low-yield (W=2-20 kg TNT equivalent) chemical highexplosives tests that were recorded at local distances (230 m – 28.5 km). Many of the W and HOB combinations were repeated, allowing for quantification of the variability in air-blast features and corresponding yield estimates. We measured canonical signal features (peak overpressure, impulse per unit area, and positive pulse duration) from the air-blast data and compared these to existing air-blast models. Peak overpressure measurementsmore » showed good agreement with the models at close ranges but tended to attenuate more rapidly at longer range (~ 1 km), which is likely caused by upward refraction of acoustic waves due to a negative vertical gradient of sound speed. We estimated yields of the MINIE2013 explosions using the Integrated Yield Determination Tool (IYDT). Errors of the estimated yields were on average within 30% of the reported yields, and there were no significant differences in the accuracy of the IYDT predictions grouped by yield. IYDT estimates tend to be lower than ground truth yields, possibly because of reduced overpressure amplitudes by upward refraction. Finally, we report preliminary results on a development of a new parameterized air-blast waveform.« less

Ranking the Potential Yield of Salinity and Selenium from Subbasins in the Lower Gunnison River Basin Using Seasonal, Multi-parameter Regression Models

NASA Astrophysics Data System (ADS)

Linard, J.; Leib, K.; Colorado Water Science Center

2010-12-01

Elevated levels of salinity and dissolved selenium can detrimentally effect the quality of water where anthropogenic and natural uses are concerned. In areas, such as the lower Gunnison Basin of western Colorado, salinity and selenium are such a concern that control projects are implemented to limit their mobilization. To prioritize the locations in which control projects are implemented, multi-parameter regression models were developed to identify subbasins in the lower Gunnison River Basin that were most likely to have elevated salinity and dissolved selenium levels. The drainage area is about 5,900 mi2 and is underlain by Cretaceous marine shale, which is the most common source of salinity and dissolved selenium. To characterize the complex hydrologic and chemical processes governing constituent mobilization, geospatial variables representing 70 different environmental characteristics were correlated to mean seasonal (irrigation and nonirrigation seasons) salinity and selenium yields estimated at 154 sampling sites. The variables generally represented characteristics of the physical basin, precipitation, soil, geology, land use, and irrigation water delivery systems. Irrigation and nonirrigation seasons were selected due to documented effects of irrigation on constituent mobilization. Following a stepwise approach, combinations of the geospatial variables were used to develop four multi-parameter regression models. These models predicted salinity and selenium yield, within a 95 percent confidence range, at individual points in the Lower Gunnison Basin for irrigation and non-irrigation seasons. The corresponding subbasins were ranked according to their potential to yield salinity and selenium and rankings were used to prioritize areas that would most benefit from control projects.

Natural recharge to sustainable yield from the barind aquifer: a tool in preparing effective management plan of groundwater resources.

PubMed

Monirul Islam, Md; Kanungoe, P

2005-01-01

This paper presents the results of water balance study and aquifer simulation modeling for preliminary estimation of the recharge rate and sustainable yield for the semi arid Barind Tract region of Bangladesh. The outcomes of the study are likely to be useful for planning purposes. It is found from detailed water balance study for the area that natural recharge rates in the Barind Tract vary widely year to year. It may have resulted from the method used for the calculation. If the considered time interval had been smaller than the monthly rainfall, the results could have been different. Aquifer Simulation Modeling (ASM) for the Barind aquifer is used to estimate long-term sustainable yield of the groundwater considering limiting drawdown from the standpoint of economic pumping cost. In managing a groundwater basin efficiently and effectively, evaluation of the maximum annual groundwater yield of the basin that can be withdrawn and used without producing any undesirable effect is one of the most important issues. In investigating such recharge rate, introduction of certain terms such as sustainable yield and safe yield has been accompanied. Development of this area involves proper utilization of this vast land, which is possible only through ensured irrigation for agriculture. The Government of Bangladesh has a plan to develop irrigation facilities by optimum utilization of available ground and surface water. It is believed that the groundwater table is lowering rapidly and the whole region is in an acute state of deforestation. Indiscriminate groundwater development may accelerate deforestation trend. In this context estimation of actual natural recharge rate to the aquifer and determination of sustainable yield will assist in proper management and planning of environmentally viable abstraction schemes. It is revealed from the study that the sustainable yield of ground water (204 mm/y) is somewhat higher than the long-term annual average recharge (152.7 mm) to the groundwater reservoir. The reason behind this is that the rivers within and around the Barind Tract might have played the role of influent rivers.

The genetic and molecular basis of crop height based on a rice model.

PubMed

Liu, Fang; Wang, Pandi; Zhang, Xiaobo; Li, Xiaofei; Yan, Xiaohong; Fu, Donghui; Wu, Gang

2018-01-01

This review presents genetic and molecular basis of crop height using a rice crop model. Height is controlled by multiple genes with potential to be manipulated through breeding strategies to improve productivity. Height is an important factor affecting crop architecture, apical dominance, biomass, resistance to lodging, tolerance to crowding and mechanical harvesting. The impressive increase in wheat and rice yield during the 'green revolution' benefited from a combination of breeding for high-yielding dwarf varieties together with advances in agricultural mechanization, irrigation and agrochemical/fertilizer use. To maximize yield under irrigation and high fertilizer use, semi-dwarfing is optimal, whereas extreme dwarfing leads to decreased yield. Rice plant height is controlled by genes that lie in a complex regulatory network, mainly involved in the biosynthesis or signal transduction of phytohormones such as gibberellins, brassinosteroids and strigolactones. Additional dwarfing genes have been discovered that are involved in other pathways, some of which are uncharacterized. This review discusses our current understanding of the regulation of plant height using rice as a well-characterized model and highlights some of the most promising research that could lead to the development of new, high-yielding varieties. This knowledge underpins future work towards the genetic improvement of plant height in rice and other crops.

Characterization and modeling of the rheology of cement paste: With applications toward self-flowing materials

NASA Astrophysics Data System (ADS)

Saak, Aaron Wilbur

The objective of this research is to better understand the important mechanisms that control the rheology of cement paste. In order to understand these mechanisms, new experimental techniques are developed. The insights gained through these studies are then applied toward designing self-flowing materials, particularly self-compacting concrete (SCC). A new testing program is developed where both the peak and equilibrium stress flow curves of cement paste are obtained by testing only one sample. Additionally, the influence of wall slip on yield stress and viscoelastic measurements is determined using a vane. The results indicate that a slip layer develops when the shear stress approaches the yield point. A three-dimensional model relating slump to yield stress is derived as a function of cone geometry. The results indicate that the model fits experimental data for cylindrical slumps over a wide range of yield stress values for a variety of materials. When compared to other published models, the results suggest that a fundamental relationship exists between yield stress and slump that is material independent and largely independent of cone geometry. The affect of various mixing techniques on the rheology of cement paste is investigated using a rheometer as a highly controlled mixer. The results suggest that there is a characteristic shear rate where the viscosity of cement paste is minimized. The influence of particle packing density, morphology and surface area on the viscosity of cement paste is quantified. The data suggest that even though packing density increases with the addition of fine particles, the benefits are largely overshadowed by a dramatic increase in surface area. Finally, a new methodology is introduced for designing self-compacting concrete. This approach incorporates a "self-flow zone" where the rheology of the paste matrix provides high workability, yet segregation resistance. The flow properties of fresh concrete are measured using a U-tube apparatus to test the general applicability of the proposed methodology. Using the new design approach, concrete with a slump of 29 cm (11 inches) and slump flow diameter of 60.9 cm (24 inches) is produced.

Incorporating uncertainty into the ranking of SPARROW model nutrient yields from Mississippi/Atchafalaya River basin watersheds

USGS Publications Warehouse

Robertson, Dale M.; Schwarz, Gregory E.; Saad, David A.; Alexander, Richard B.

2009-01-01

Excessive loads of nutrients transported by tributary rivers have been linked to hypoxia in the Gulf of Mexico. Management efforts to reduce the hypoxic zone in the Gulf of Mexico and improve the water quality of rivers and streams could benefit from targeting nutrient reductions toward watersheds with the highest nutrient yields delivered to sensitive downstream waters. One challenge is that most conventional watershed modeling approaches (e.g., mechanistic models) used in these management decisions do not consider uncertainties in the predictions of nutrient yields and their downstream delivery. The increasing use of parameter estimation procedures to statistically estimate model coefficients, however, allows uncertainties in these predictions to be reliably estimated. Here, we use a robust bootstrapping procedure applied to the results of a previous application of the hybrid statistical/mechanistic watershed model SPARROW (Spatially Referenced Regression On Watershed attributes) to develop a statistically reliable method for identifying “high priority” areas for management, based on a probabilistic ranking of delivered nutrient yields from watersheds throughout a basin. The method is designed to be used by managers to prioritize watersheds where additional stream monitoring and evaluations of nutrient-reduction strategies could be undertaken. Our ranking procedure incorporates information on the confidence intervals of model predictions and the corresponding watershed rankings of the delivered nutrient yields. From this quantified uncertainty, we estimate the probability that individual watersheds are among a collection of watersheds that have the highest delivered nutrient yields. We illustrate the application of the procedure to 818 eight-digit Hydrologic Unit Code watersheds in the Mississippi/Atchafalaya River basin by identifying 150 watersheds having the highest delivered nutrient yields to the Gulf of Mexico. Highest delivered yields were from watersheds in the Central Mississippi, Ohio, and Lower Mississippi River basins. With 90% confidence, only a few watersheds can be reliably placed into the highest 150 category; however, many more watersheds can be removed from consideration as not belonging to the highest 150 category. Results from this ranking procedure provide robust information on watershed nutrient yields that can benefit management efforts to reduce nutrient loadings to downstream coastal waters, such as the Gulf of Mexico, or to local receiving streams and reservoirs.

The Importance of Juvenile Root Traits for Crop Yields

NASA Astrophysics Data System (ADS)

White, Philip; Adu, Michael; Broadley, Martin; Brown, Lawrie; Dupuy, Lionel; George, Timothy; Graham, Neil; Hammond, John; Hayden, Rory; Neugebauer, Konrad; Nightingale, Mark; Ramsay, Gavin; Thomas, Catherine; Thompson, Jacqueline; Wishart, Jane; Wright, Gladys

2014-05-01

Genetic variation in root system architecture (RSA) is an under-exploited breeding resource. This is partly a consequence of difficulties in the rapid and accurate assessment of subterranean root systems. However, although the characterisation of root systems of large plants in the field are both time-consuming and labour-intensive, high-throughput (HTP) screens of root systems of juvenile plants can be performed in the field, glasshouse or laboratory. It is hypothesised that improving the root systems of juvenile plants can accelerate access to water and essential mineral elements, leading to rapid crop establishment and, consequently, greater yields. This presentation will illustrate how aspects of the juvenile root systems of potato (Solanum tuberosum L.) and oilseed rape (OSR; Brassica napus L.) correlate with crop yields and examine the reasons for such correlations. It will first describe the significant positive relationships between early root system development, phosphorus acquisition, canopy establishment and eventual yield among potato genotypes. It will report the development of a glasshouse assay for root system architecture (RSA) of juvenile potato plants, the correlations between root system architectures measured in the glasshouse and field, and the relationships between aspects of the juvenile root system and crop yields under drought conditions. It will then describe the development of HTP systems for assaying RSA of OSR seedlings, the identification of genetic loci affecting RSA in OSR, the development of mathematical models describing resource acquisition by OSR, and the correlations between root traits recorded in the HTP systems and yields of OSR in the field.

Modelling of loading, stress relaxation and stress recovery in a shape memory polymer.

PubMed

Sweeney, J; Bonner, M; Ward, I M

2014-09-01

A multi-element constitutive model for a lactide-based shape memory polymer has been developed that represents loading to large tensile deformations, stress relaxation and stress recovery at 60, 65 and 70°C. The model consists of parallel Maxwell arms each comprising neo-Hookean and Eyring elements. Guiu-Pratt analysis of the stress relaxation curves yields Eyring parameters. When these parameters are used to define the Eyring process in a single Maxwell arm, the resulting model yields at too low a stress, but gives good predictions for longer times. Stress dip tests show a very stiff response on unloading by a small strain decrement. This would create an unrealistically high stress on loading to large strain if it were modelled by an elastic element. Instead it is modelled by an Eyring process operating via a flow rule that introduces strain hardening after yield. When this process is incorporated into a second parallel Maxwell arm, there results a model that fully represents both stress relaxation and stress dip tests at 60°C. At higher temperatures a third arm is required for valid predictions. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

Computational model of chromosome aberration yield induced by high- and low-LET radiation exposures.

PubMed

Ponomarev, Artem L; George, Kerry; Cucinotta, Francis A

2012-06-01

We present a computational model for calculating the yield of radiation-induced chromosomal aberrations in human cells based on a stochastic Monte Carlo approach and calibrated using the relative frequencies and distributions of chromosomal aberrations reported in the literature. A previously developed DNA-fragmentation model for high- and low-LET radiation called the NASARadiationTrackImage model was enhanced to simulate a stochastic process of the formation of chromosomal aberrations from DNA fragments. The current version of the model gives predictions of the yields and sizes of translocations, dicentrics, rings, and more complex-type aberrations formed in the G(0)/G(1) cell cycle phase during the first cell division after irradiation. As the model can predict smaller-sized deletions and rings (<3 Mbp) that are below the resolution limits of current cytogenetic analysis techniques, we present predictions of hypothesized small deletions that may be produced as a byproduct of properly repaired DNA double-strand breaks (DSB) by nonhomologous end-joining. Additionally, the model was used to scale chromosomal exchanges in two or three chromosomes that were obtained from whole-chromosome FISH painting analysis techniques to whole-genome equivalent values.

Statistical analysis and yield management in LED design through TCAD device simulation

NASA Astrophysics Data System (ADS)

Létay, Gergö; Ng, Wei-Choon; Schneider, Lutz; Bregy, Adrian; Pfeiffer, Michael

2007-02-01

This paper illustrates how technology computer-aided design (TCAD), which nowadays is an essential part of CMOS technology, can be applied to LED development and manufacturing. In the first part, the essential electrical and optical models inherent to LED modeling are reviewed. The second part of the work describes a methodology to improve the efficiency of the simulation procedure by using the concept of process compact models (PCMs). The last part demonstrates the capabilities of PCMs using an example of a blue InGaN LED. In particular, a parameter screening is performed to find the most important parameters, an optimization task incorporating the robustness of the design is carried out, and finally the impact of manufacturing tolerances on yield is investigated. It is indicated how the concept of PCMs can contribute to an efficient design for manufacturing DFM-aware development.

Modeling of the interactions between forest vegetation, disturbances, and sediment yields

Treesearch

Erkan Istanbulluoglu; David G. Tarboton; Robert T. Pack; Charles H. Luce

2004-01-01

The controls of forest vegetation, wildfires, and harvest vegetation disturbances on the frequency and magnitude of sediment delivery from a small watershed (~3.9 km2) in the Idaho batholith are investigated through numerical modeling. The model simulates soil development based on continuous bedrock weathering and the divergence of diffusive...

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.

Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials.

PubMed

Michel, Sebastian; Ametz, Christian; Gungor, Huseyin; Akgöl, Batuhan; Epure, Doru; Grausgruber, Heinrich; Löschenberger, Franziska; Buerstmayr, Hermann

2017-02-01

Early generation genomic selection is superior to conventional phenotypic selection in line breeding and can be strongly improved by including additional information from preliminary yield trials. The selection of lines that enter resource-demanding multi-environment trials is a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. We compared conventional phenotypic selection with various genomic selection approaches across multiple years as well as the merit of integrating phenotypic information from preliminary yield trials into the genomic selection framework. The prediction accuracy using only phenotypic data was rather low (r = 0.21) for grain yield but could be improved by modeling genetic relationships in unreplicated preliminary yield trials (r = 0.33). Genomic selection models were nevertheless found to be superior to conventional phenotypic selection for predicting grain yield performance of lines across years (r = 0.39). We subsequently simplified the problem of predicting untested lines in untested years to predicting tested lines in untested years by combining breeding values from preliminary yield trials and predictions from genomic selection models by a heritability index. This genomic assisted selection led to a 20% increase in prediction accuracy, which could be further enhanced by an appropriate marker selection for both grain yield (r = 0.48) and protein content (r = 0.63). The easy to implement and robust genomic assisted selection gave thus a higher prediction accuracy than either conventional phenotypic or genomic selection alone. The proposed method took the complex inheritance of both low and high heritable traits into account and appears capable to support breeders in their selection decisions to develop enhanced varieties more efficiently.

Refinement and evaluation of the Massachusetts firm-yield estimator model version 2.0

USGS Publications Warehouse

Levin, Sara B.; Archfield, Stacey A.; Massey, Andrew J.

2011-01-01

The firm yield is the maximum average daily withdrawal that can be extracted from a reservoir without risk of failure during an extended drought period. Previously developed procedures for determining the firm yield of a reservoir were refined and applied to 38 reservoir systems in Massachusetts, including 25 single- and multiple-reservoir systems that were examined during previous studies and 13 additional reservoir systems. Changes to the firm-yield model include refinements to the simulation methods and input data, as well as the addition of several scenario-testing capabilities. The simulation procedure was adapted to run at a daily time step over a 44-year simulation period, and daily streamflow and meteorological data were compiled for all the reservoirs for input to the model. Another change to the model-simulation methods is the adjustment of the scaling factor used in estimating groundwater contributions to the reservoir. The scaling factor is used to convert the daily groundwater-flow rate into a volume by multiplying the rate by the length of reservoir shoreline that is hydrologically connected to the aquifer. Previous firm-yield analyses used a constant scaling factor that was estimated from the reservoir surface area at full pool. The use of a constant scaling factor caused groundwater flows during periods when the reservoir stage was very low to be overestimated. The constant groundwater scaling factor used in previous analyses was replaced with a variable scaling factor that is based on daily reservoir stage. This change reduced instability in the groundwater-flow algorithms and produced more realistic groundwater-flow contributions during periods of low storage. Uncertainty in the firm-yield model arises from many sources, including errors in input data. The sensitivity of the model to uncertainty in streamflow input data and uncertainty in the stage-storage relation was examined. A series of Monte Carlo simulations were performed on 22 reservoirs to assess the sensitivity of firm-yield estimates to errors in daily-streamflow input data. Results of the Monte Carlo simulations indicate that underestimation in the lowest stream inflows can cause firm yields to be underestimated by an average of 1 to 10 percent. Errors in the stage-storage relation can arise when the point density of bathymetric survey measurements is too low. Existing bathymetric surfaces were resampled using hypothetical transects of varying patterns and point densities in order to quantify the uncertainty in stage-storage relations. Reservoir-volume calculations and resulting firm yields were accurate to within 5 percent when point densities were greater than 20 points per acre of reservoir surface. Methods for incorporating summer water-demand-reduction scenarios into the firm-yield model were developed as well as the ability to relax the no-fail reliability criterion. Although the original firm-yield model allowed monthly reservoir releases to be specified, there have been no previous studies examining the feasibility of controlled releases for downstream flows from Massachusetts reservoirs. Two controlled-release scenarios were tested—with and without a summer water-demand-reduction scenario—for a scenario with a no-fail criterion and a scenario that allows for a 1-percent failure rate over the entire simulation period. Based on these scenarios, about one-third of the reservoir systems were able to support the flow-release scenarios at their 2000–2004 usage rates. Reservoirs with higher storage ratios (reservoir storage capacity to mean annual streamflow) and lower demand ratios (mean annual water demand to annual firm yield) were capable of higher downstream release rates. For the purposes of this research, all reservoir systems were assumed to have structures which enable controlled releases, although this assumption may not be true for many of the reservoirs studied.

An assessment of climate change impacts on maize yields in Hebei Province of China.

PubMed

Chen, Yongfu; Han, Xinru; Si, Wei; Wu, Zhigang; Chien, Hsiaoping; Okamoto, Katsuo

2017-03-01

The climate change impacts on maize yields are quantified in this paper using statistical models with panel data from 3731 farmers' observations across nine sample villages in Hebei Province of China. The marginal impacts of climate change and the simulated impacts on maize yields based on scenarios of Representative Concentration Pathways 2.6, 4.5, 6.0, and 8.5 from the global climate models of Model for Interdisciplinary Research on Climate version 5 (MIROC5) and Meteorological Research Institute Coupled General Circulation Model version 3 (MRI-CGCM3) were then calculated, analyzed, and explained. The results indicate that, first, the most important finding was that climate change impacts on maize yields were significant and a 1°C warming or a 1mm decrease in precipitation resulted in a 150.255kg or a 1.941kg loss in maize yields per hectare, respectively. Second, villages with latitudes of less than 39.832 and longitudes of more than 114.839 in Hebei province suffered losses due to warm weather. Third, the simulated impacts for the full sample are all negative based on scenarios from MIROC5, and their magnitudes are more than those of MRI-CGCM3 are. Based on scenarios in the 2050s, the biggest loss for maize yields per hectare for the full sample accounts for about one-tenth of the mean maize yield from 2004 to 2010, and all of the villages are impacted. Hence, it is important to help farms adopt an adaptation strategy to tackle the risk of loss for maize yields from climate change, and it is necessary to develop agricultural synthesis services as a public adaptation policy at the village level to interact with the adaptation strategy at the farm level. Copyright © 2016 Elsevier B.V. All rights reserved.

AN INTERDISCIPLINARY APPROACH TO VALUING WATER FROM BRUSH CONTROL

EPA Science Inventory

An analytical methodology utilizing models from three disciplines is developed to assess the viability of brush control for wate yield in the Frio River Basin, TX. Ecological, hydrologic, and economic models are used to portray changes in forage production and water supply result...

Impact of Climate Change on Potential, Attainable, and Actual Wheat Yield in Oklahoma

NASA Astrophysics Data System (ADS)

Dhakal, K.; Linde, E.; Kakani, V. G.; Alderman, P. D.; Brunson, D.; Ochsner, T. E.; Carver, B.

2017-12-01

Gradually developing climatic and weather anomalies due to increasing atmospheric greenhouse gases concentration can pose threat to farmers and resource managers. This study was aimed at investigating the effects of climate change on winter wheat (Triticum aestivum L.) under the Representative Concentration Pathways 6.0 and 8.5 using downscaled climate projections from different models and their ensembles. Daily data of maximum and minimum air temperature, rainfall, and solar radiation for, four General Circulation Models (MRIOC5, MRI-CGCM3, HadGEM2-ES, CSRIO-Mk3.6.0), ensemble of four models and ensemble of 17 GCMs, at 800 m resolution, were developed for two RCPs using Marksim. We describe a methodology for rapid synthesis of GCM-based, spatially explicit, high resolution future weather data inputs for the DSSAT crop model, for cropland area across wheat growing regions of Oklahoma for the future period 2040-2060. The potential impacts of climate change and variability on potential, attainable, and actual winter wheat yield in Oklahoma is discussed.

Self-optimisation and model-based design of experiments for developing a C-H activation flow process.

PubMed

Echtermeyer, Alexander; Amar, Yehia; Zakrzewski, Jacek; Lapkin, Alexei

2017-01-01

A recently described C(sp 3 )-H activation reaction to synthesise aziridines was used as a model reaction to demonstrate the methodology of developing a process model using model-based design of experiments (MBDoE) and self-optimisation approaches in flow. The two approaches are compared in terms of experimental efficiency. The self-optimisation approach required the least number of experiments to reach the specified objectives of cost and product yield, whereas the MBDoE approach enabled a rapid generation of a process model.

Modeling the Transition From Predominantly Gas- to Predominantly Aerosol-Phase Products From OH Reactions With the Homologous Series of C10 to C15 n- Alkanes

NASA Astrophysics Data System (ADS)

Jordan, C. E.; Ziemann, P. J.; Griffin, R. J.; Lim, Y. B.; Atkinson, R.; Arey, J.

2006-12-01

Recent laboratory studies have shown significant formation of secondary organic aerosol (SOA) from OH reactions with a homologous series of n-alkanes. SOA mass yields of 56% were observed for pentadecane (C15), while only 0.5% yield was observed from octane (C8, the smallest alkane in the series). A rapid transition in SOA yield is observed from C10 to C13, with SOA yields increasing from 4% to 49%. In standard gas-aerosol partitioning theory, the vapor pressure controls the amount of material that can condense into the particle phase. However, the rapid transition observed here suggests there may also be a shift in the predominant reaction pathways for longer chain alkanes, leading to greater production of lower vapor pressure products. Here we present an investigation of the role of vapor pressure versus the role of shifting branching ratios to test the influence of each of these on SOA mass yields. We have added each of the alkanes in this series to the Caltech Atmospheric Chemistry Mechanism (CACM). This mechanism was developed in part to predict explicitly concentrations of secondary and tertiary semivolatile oxidation products that potentially form SOA. Although it is has been developed to lump similar compounds together for computational efficiency, it is nonetheless easily adapted and ideally suited for a detailed zero-dimensional modeling study of this kind. This gas-phase mechanism is linked to the aerosol partitioning module MPMPO (Model to Predict the Multi- phase Partitioning of Organics). MPMPO is a fully coupled module that allows the simultaneous partitioning of semi-volatile species to both an aqueous and an organic aerosol phase.

Development of Web tools to predict axillary lymph node metastasis and pathological response to neoadjuvant chemotherapy in breast cancer patients.

PubMed

Sugimoto, Masahiro; Takada, Masahiro; Toi, Masakazu

2014-12-09

Nomograms are a standard computational tool to predict the likelihood of an outcome using multiple available patient features. We have developed a more powerful data mining methodology, to predict axillary lymph node (AxLN) metastasis and response to neoadjuvant chemotherapy (NAC) in primary breast cancer patients. We developed websites to use these tools. The tools calculate the probability of AxLN metastasis (AxLN model) and pathological complete response to NAC (NAC model). As a calculation algorithm, we employed a decision tree-based prediction model known as the alternative decision tree (ADTree), which is an analog development of if-then type decision trees. An ensemble technique was used to combine multiple ADTree predictions, resulting in higher generalization abilities and robustness against missing values. The AxLN model was developed with training datasets (n=148) and test datasets (n=143), and validated using an independent cohort (n=174), yielding an area under the receiver operating characteristic curve (AUC) of 0.768. The NAC model was developed and validated with n=150 and n=173 datasets from a randomized controlled trial, yielding an AUC of 0.787. AxLN and NAC models require users to input up to 17 and 16 variables, respectively. These include pathological features, including human epidermal growth factor receptor 2 (HER2) status and imaging findings. Each input variable has an option of "unknown," to facilitate prediction for cases with missing values. The websites developed facilitate the use of these tools, and serve as a database for accumulating new datasets.

Climate Change and Projected Impacts in Agriculture: an Example on Mediterranean Crops

NASA Astrophysics Data System (ADS)

Ferrise, R.; Moriondo, M.; Bindi, M.

2009-04-01

Recently, the availability of multi-model ensemble prediction methods has permitted the assignment of likelihoods to future climate projections. This allowed moving from the scenario-based approach to the risk-based approach in assessing the effects of climate change, thus providing more useful information for decision-makers that, as reported by Schneider (2001), need probability estimates to assess the seriousness of the projected impacts. The probabilistic approach to evaluate crop response to climate change mainly consists in applying an impact model (such as crop growth model) to a very large number of climate projections so to provide a probabilistic distribution of the variable selected to evaluate the impact. By comparing the outputs of the multi-simulation with a critical threshold (such as minimum yield below which it is not admissible to fall), it is possible to evaluate the risk related to future climate conditions. Unfortunately, such an approach is a time-consuming process due to the large number of model runs needed for such a procedure. An alternative method relies on the set up of impact response surfaces (RS) with respect to key climatic variables on which a probabilistic representation of projected changes in the same climatic variables may be overlaid (Fronzek et al. 2008). This approach was exploited within the ENSEMBLES EU Project aiming at assessing climate change impact on typical Mediterranean crops. This work presents the results of the project with a particular concerning about the assessment of risk, of durum wheat (T. turgidum L. subsp. durum (Desf.) Husn) and grapevine (Vitis vinifera L.) yield falling below fixed thresholds, using probabilistic information about future climate. Methodology The simple mechanistic crop growth models, SIRIUS Quality (Jamieson et al., 1998) and VITE-model (Bindi et al., 1997a,b), were selected to respectively simulate durum wheat and grapevine yields in present and future scenarios. SIRIUS Quality is a wheat simulation model that calculates biomass production from photosynthetically active radiation and grain growth from simple partition rules. VITE-model is a model that uses a simplified mechanistic approach based on the accumulated degree days, the radiation use efficiency and the fruit biomass index to simulate the main processes regulating grapevine development, growth and yield. The selected crop growth models were adopted to create yield RSs of both crops over the suitable cultivated area in the Mediterranean Basin. Yield RSs were calculated performing a scenario sensitivity analysis by altering the baseline climate with respect to temperature and precipitation changes. The baseline climate consisted of 30 years (1975-2005) of daily minimum and maximum temperatures, rainfall and global radiation. Meteorological data were extracted from the MARS JRC Archive and are referred to a grid with a spatial resolution of 50 Km x 50 Km covering the whole European area. The sensitivity analysis was performed for precipitation changes (from -40% to 20%) and temperature changes (from 0°C to +8°C), uniformly applied across all the year. To take in account for the effect of rising CO2, the yield RSs for future periods, were produced considering CO2 air concentration level according to the A1B SRES emission scenario. For each rainfall and temperature combination the average yield over the 30-years period was calculated. The probabilistic distribution of future yields was estimated by applying a bilinear interpolative method to overlap, onto the RSs, the data from perturbed physics experiment of Hadley Centre for future scenarios (joint distribution of annual temperature and rainfall changes). Critical thresholds of impact were determined by calculating, for each grid cell, the distribution of the 30-years average yield according to the joint distribution data for present period (1990-2010) and selecting the values that correspond to the 20th percentile of the cumulative distribution. Finally, future yields were compared with yield threshold to assess the risk of yield shortfall that, in each time period, was defined as the percentage of projected yields that not overcome the selected threshold. Results Maps of durum wheat and grapevine low productivity risk were generated for the next century over the Mediterranean Basin. For durum wheat, with the exception of Portugal and Southern Spain, in the next 30 years risk of low crop productivity shows an overall reduction, due to the fertilizing effect of CO2 increase that counterbalances for the negative impact of rising temperature and reducing rainfall. Thereafter, these latter negative effects become greater and the risk progressively increases starting from lower latitudes. Maximum risk was estimated in 2060 when strong reductions in yield were accounted all over the study area. The smaller reductions in risk, estimated for the end of the next century, may be explained by the greater uncertainty in climate projections. South Portugal, South Spain and Peloponnesus resulted the most vulnerable areas showing increase in risk probability up to 50%, while risk in Galicia, Slovenia, Croatia and central-southern France always resulted lower then present time. As regard grapevine, in the great part of the case study area, the yield seems to have beneficial effect from future climate change. In Central-Western Europe and at lower latitudes the projected yields never fall below the risk threshold, indicating a prevailing effect of CO2 fertilisation. By the other hand, Central-Northern Italy and North of Greece result the most vulnerable areas. In these regions the likelihood of reduced yields quickly rises and remains very high (>50%) until the end of the century, denoting a greater negative effect of temperature and rainfall. Conclusions From these results it may be argued that the impact of future climate change on crop yields is the resultant of the contrasting effects of changes in temperature and precipitation, CO2 increase and uncertainty in climate projections. The intensity of these effects is very site and crop dependent and may vary with time, differently affecting the assessment of risk. As a consequence, the patterns of risk of low crop productivity will change depending on which of these effects will prevail. References Bindi M. et al., 1997a "A simple model for simulation of growth and development in grapevine (Vitis vinifera L.). I. Model description". Vitis 36:67-71 Bindi M. et al., 1997b "A simple model for simulation of growth and development in grapevine (Vitis vinifera L.). II. Model validation". Vitis 36:73-76 Carter T. et al., 2006 "". Fronzek S. et al 2008 "Applying probabilistic projections of climate change with impact models: a case study for sub-arctic palsa mires in Fennoscandia". Climatic Change (submitted) Jamieson et al., 1998 "Sirius: a mechanistic model of wheat response to environmental variation". Eur. J. Agron. 8:161-179. Schneider S. 2001 "What is ‘dangerous' climate change?". Nature 411:17-19

Climate change impacts on crop yield and quality with CO2 fertilization in China

PubMed Central

Erda, Lin; Wei, Xiong; Hui, Ju; Yinlong, Xu; Yue, Li; Liping, Bai; Liyong, Xie

2005-01-01

A regional climate change model (PRECIS) for China, developed by the UK's Hadley Centre, was used to simulate China's climate and to develop climate change scenarios for the country. Results from this project suggest that, depending on the level of future emissions, the average annual temperature increase in China by the end of the twenty-first century may be between 3 and 4 °C. Regional crop models were driven by PRECIS output to predict changes in yields of key Chinese food crops: rice, maize and wheat. Modelling suggests that climate change without carbon dioxide (CO2) fertilization could reduce the rice, maize and wheat yields by up to 37% in the next 20–80 years. Interactions of CO2 with limiting factors, especially water and nitrogen, are increasingly well understood and capable of strongly modulating observed growth responses in crops. More complete reporting of free-air carbon enrichment experiments than was possible in the Intergovernmental Panel on Climate Change's Third Assessment Report confirms that CO2 enrichment under field conditions consistently increases biomass and yields in the range of 5–15%, with CO2 concentration elevated to 550 ppm Levels of CO2 that are elevated to more than 450 ppm will probably cause some deleterious effects in grain quality. It seems likely that the extent of the CO2 fertilization effect will depend upon other factors such as optimum breeding, irrigation and nutrient applications. PMID:16433100

Does the choice of nucleotide substitution models matter topologically?

PubMed

Hoff, Michael; Orf, Stefan; Riehm, Benedikt; Darriba, Diego; Stamatakis, Alexandros

2016-03-24

In the context of a master level programming practical at the computer science department of the Karlsruhe Institute of Technology, we developed and make available an open-source code for testing all 203 possible nucleotide substitution models in the Maximum Likelihood (ML) setting under the common Akaike, corrected Akaike, and Bayesian information criteria. We address the question if model selection matters topologically, that is, if conducting ML inferences under the optimal, instead of a standard General Time Reversible model, yields different tree topologies. We also assess, to which degree models selected and trees inferred under the three standard criteria (AIC, AICc, BIC) differ. Finally, we assess if the definition of the sample size (#sites versus #sites × #taxa) yields different models and, as a consequence, different tree topologies. We find that, all three factors (by order of impact: nucleotide model selection, information criterion used, sample size definition) can yield topologically substantially different final tree topologies (topological difference exceeding 10 %) for approximately 5 % of the tree inferences conducted on the 39 empirical datasets used in our study. We find that, using the best-fit nucleotide substitution model may change the final ML tree topology compared to an inference under a default GTR model. The effect is less pronounced when comparing distinct information criteria. Nonetheless, in some cases we did obtain substantial topological differences.

Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs

Treesearch

Daniel A. Yaussy; Robert L. Brisbin

1983-01-01

A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model...

Prediction of microalgae hydrothermal liquefaction products from feedstock biochemical composition

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

Leow, Shijie; Witter, John R.; Vardon, Derek R.

Hydrothermal liquefaction (HTL) uses water under elevated temperatures and pressures (200–350 °C, 5–20 MPa) to convert biomass into liquid “biocrude” oil. Despite extensive reports on factors influencing microalgae cell composition during cultivation and separate reports on HTL products linked to cell composition, the field still lacks a quantitative model to predict HTL conversion product yield and qualities from feedstock biochemical composition; the tailoring of microalgae feedstock for downstream conversion is a unique and critical aspect of microalgae biofuels that must be leveraged upon for optimization of the whole process. This study developed predictive relationships for HTL biocrude yield and othermore » conversion product characteristics based on HTL of Nannochloropsis oculata batches harvested with a wide range of compositions (23–59% dw lipids, 58–17% dw proteins, 12–22% dw carbohydrates) and a defatted batch (0% dw lipids, 75% dw proteins, 19% dw carbohydrates). HTL biocrude yield (33–68% dw) and carbon distribution (49–83%) increased in proportion to the fatty acid (FA) content. A component additivity model (predicting biocrude yield from lipid, protein, and carbohydrates) was more accurate predicting literature yields for diverse microalgae species than previous additivity models derived from model compounds. FA profiling of the biocrude product showed strong links to the initial feedstock FA profile of the lipid component, demonstrating that HTL acts as a water-based extraction process for FAs; the remainder non-FA structural components could be represented using the defatted batch. These findings were used to introduce a new FA-based model that predicts biocrude oil yields along with other critical parameters, and is capable of adjusting for the wide variations in HTL methodology and microalgae species through the defatted batch. Lastly, the FA model was linked to an upstream cultivation model (Phototrophic Process Model), providing for the first time an integrated modeling framework to overcome a critical barrier to microalgae-derived HTL biofuels and enable predictive analysis of the overall microalgal-to-biofuel process.« less

Prediction of microalgae hydrothermal liquefaction products from feedstock biochemical composition

DOE PAGES

Leow, Shijie; Witter, John R.; Vardon, Derek R.; ...

2015-05-11

Hydrothermal liquefaction (HTL) uses water under elevated temperatures and pressures (200–350 °C, 5–20 MPa) to convert biomass into liquid “biocrude” oil. Despite extensive reports on factors influencing microalgae cell composition during cultivation and separate reports on HTL products linked to cell composition, the field still lacks a quantitative model to predict HTL conversion product yield and qualities from feedstock biochemical composition; the tailoring of microalgae feedstock for downstream conversion is a unique and critical aspect of microalgae biofuels that must be leveraged upon for optimization of the whole process. This study developed predictive relationships for HTL biocrude yield and othermore » conversion product characteristics based on HTL of Nannochloropsis oculata batches harvested with a wide range of compositions (23–59% dw lipids, 58–17% dw proteins, 12–22% dw carbohydrates) and a defatted batch (0% dw lipids, 75% dw proteins, 19% dw carbohydrates). HTL biocrude yield (33–68% dw) and carbon distribution (49–83%) increased in proportion to the fatty acid (FA) content. A component additivity model (predicting biocrude yield from lipid, protein, and carbohydrates) was more accurate predicting literature yields for diverse microalgae species than previous additivity models derived from model compounds. FA profiling of the biocrude product showed strong links to the initial feedstock FA profile of the lipid component, demonstrating that HTL acts as a water-based extraction process for FAs; the remainder non-FA structural components could be represented using the defatted batch. These findings were used to introduce a new FA-based model that predicts biocrude oil yields along with other critical parameters, and is capable of adjusting for the wide variations in HTL methodology and microalgae species through the defatted batch. Lastly, the FA model was linked to an upstream cultivation model (Phototrophic Process Model), providing for the first time an integrated modeling framework to overcome a critical barrier to microalgae-derived HTL biofuels and enable predictive analysis of the overall microalgal-to-biofuel process.« less

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.

Detailed high-resolution three-dimensional simulations of OMEGA separated reactants inertial confinement fusion experiments

DOE PAGES

Haines, Brian Michael; Grim, Gary P.; Fincke, James R.; ...

2016-07-29

Here, we present results from the comparison of high-resolution three-dimensional (3D) simulations with data from the implosions of inertial confinement fusion capsules with separated reactants performed on the OMEGA laser facility. Each capsule, referred to as a “CD Mixcap,” is filled with tritium and has a polystyrene (CH) shell with a deuterated polystyrene (CD) layer whose burial depth is varied. In these implosions, fusion reactions between deuterium and tritium ions can occur only in the presence of atomic mix between the gas fill and shell material. The simulations feature accurate models for all known experimental asymmetries and do not employmore » any adjustable parameters to improve agreement with experimental data. Simulations are performed with the RAGE radiation-hydrodynamics code using an Implicit Large Eddy Simulation (ILES) strategy for the hydrodynamics. We obtain good agreement with the experimental data, including the DT/TT neutron yield ratios used to diagnose mix, for all burial depths of the deuterated shell layer. Additionally, simulations demonstrate good agreement with converged simulations employing explicit models for plasma diffusion and viscosity, suggesting that the implicit sub-grid model used in ILES is sufficient to model these processes in these experiments. In our simulations, mixing is driven by short-wavelength asymmetries and longer-wavelength features are responsible for developing flows that transport mixed material towards the center of the hot spot. Mix material transported by this process is responsible for most of the mix (DT) yield even for the capsule with a CD layer adjacent to the tritium fuel. Consistent with our previous results, mix does not play a significant role in TT neutron yield degradation; instead, this is dominated by the displacement of fuel from the center of the implosion due to the development of turbulent instabilities seeded by long-wavelength asymmetries. Through these processes, the long-wavelength asymmetries degrade TT yield more than the DT yield and thus bring DT/TT neutron yield ratios into agreement with experiment. Finally, we present a detailed comparison of the flows in 2D and 3D simulations.« less

Detailed high-resolution three-dimensional simulations of OMEGA separated reactants inertial confinement fusion experiments

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

Haines, Brian M., E-mail: bmhaines@lanl.gov; Fincke, James R.; Shah, Rahul C.

We present results from the comparison of high-resolution three-dimensional (3D) simulations with data from the implosions of inertial confinement fusion capsules with separated reactants performed on the OMEGA laser facility. Each capsule, referred to as a “CD Mixcap,” is filled with tritium and has a polystyrene (CH) shell with a deuterated polystyrene (CD) layer whose burial depth is varied. In these implosions, fusion reactions between deuterium and tritium ions can occur only in the presence of atomic mix between the gas fill and shell material. The simulations feature accurate models for all known experimental asymmetries and do not employ anymore » adjustable parameters to improve agreement with experimental data. Simulations are performed with the RAGE radiation-hydrodynamics code using an Implicit Large Eddy Simulation (ILES) strategy for the hydrodynamics. We obtain good agreement with the experimental data, including the DT/TT neutron yield ratios used to diagnose mix, for all burial depths of the deuterated shell layer. Additionally, simulations demonstrate good agreement with converged simulations employing explicit models for plasma diffusion and viscosity, suggesting that the implicit sub-grid model used in ILES is sufficient to model these processes in these experiments. In our simulations, mixing is driven by short-wavelength asymmetries and longer-wavelength features are responsible for developing flows that transport mixed material towards the center of the hot spot. Mix material transported by this process is responsible for most of the mix (DT) yield even for the capsule with a CD layer adjacent to the tritium fuel. Consistent with our previous results, mix does not play a significant role in TT neutron yield degradation; instead, this is dominated by the displacement of fuel from the center of the implosion due to the development of turbulent instabilities seeded by long-wavelength asymmetries. Through these processes, the long-wavelength asymmetries degrade TT yield more than the DT yield and thus bring DT/TT neutron yield ratios into agreement with experiment. Finally, we present a detailed comparison of the flows in 2D and 3D simulations.« less

Detailed high-resolution three-dimensional simulations of OMEGA separated reactants inertial confinement fusion experiments

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

Haines, Brian Michael; Grim, Gary P.; Fincke, James R.

Here, we present results from the comparison of high-resolution three-dimensional (3D) simulations with data from the implosions of inertial confinement fusion capsules with separated reactants performed on the OMEGA laser facility. Each capsule, referred to as a “CD Mixcap,” is filled with tritium and has a polystyrene (CH) shell with a deuterated polystyrene (CD) layer whose burial depth is varied. In these implosions, fusion reactions between deuterium and tritium ions can occur only in the presence of atomic mix between the gas fill and shell material. The simulations feature accurate models for all known experimental asymmetries and do not employmore » any adjustable parameters to improve agreement with experimental data. Simulations are performed with the RAGE radiation-hydrodynamics code using an Implicit Large Eddy Simulation (ILES) strategy for the hydrodynamics. We obtain good agreement with the experimental data, including the DT/TT neutron yield ratios used to diagnose mix, for all burial depths of the deuterated shell layer. Additionally, simulations demonstrate good agreement with converged simulations employing explicit models for plasma diffusion and viscosity, suggesting that the implicit sub-grid model used in ILES is sufficient to model these processes in these experiments. In our simulations, mixing is driven by short-wavelength asymmetries and longer-wavelength features are responsible for developing flows that transport mixed material towards the center of the hot spot. Mix material transported by this process is responsible for most of the mix (DT) yield even for the capsule with a CD layer adjacent to the tritium fuel. Consistent with our previous results, mix does not play a significant role in TT neutron yield degradation; instead, this is dominated by the displacement of fuel from the center of the implosion due to the development of turbulent instabilities seeded by long-wavelength asymmetries. Through these processes, the long-wavelength asymmetries degrade TT yield more than the DT yield and thus bring DT/TT neutron yield ratios into agreement with experiment. Finally, we present a detailed comparison of the flows in 2D and 3D simulations.« less

Detailed high-resolution three-dimensional simulations of OMEGA separated reactants inertial confinement fusion experiments

NASA Astrophysics Data System (ADS)

Haines, Brian M.; Grim, Gary P.; Fincke, James R.; Shah, Rahul C.; Forrest, Chad J.; Silverstein, Kevin; Marshall, Frederic J.; Boswell, Melissa; Fowler, Malcolm M.; Gore, Robert A.; Hayes-Sterbenz, Anna C.; Jungman, Gerard; Klein, Andreas; Rundberg, Robert S.; Steinkamp, Michael J.; Wilhelmy, Jerry B.

2016-07-01

We present results from the comparison of high-resolution three-dimensional (3D) simulations with data from the implosions of inertial confinement fusion capsules with separated reactants performed on the OMEGA laser facility. Each capsule, referred to as a "CD Mixcap," is filled with tritium and has a polystyrene (CH) shell with a deuterated polystyrene (CD) layer whose burial depth is varied. In these implosions, fusion reactions between deuterium and tritium ions can occur only in the presence of atomic mix between the gas fill and shell material. The simulations feature accurate models for all known experimental asymmetries and do not employ any adjustable parameters to improve agreement with experimental data. Simulations are performed with the RAGE radiation-hydrodynamics code using an Implicit Large Eddy Simulation (ILES) strategy for the hydrodynamics. We obtain good agreement with the experimental data, including the DT/TT neutron yield ratios used to diagnose mix, for all burial depths of the deuterated shell layer. Additionally, simulations demonstrate good agreement with converged simulations employing explicit models for plasma diffusion and viscosity, suggesting that the implicit sub-grid model used in ILES is sufficient to model these processes in these experiments. In our simulations, mixing is driven by short-wavelength asymmetries and longer-wavelength features are responsible for developing flows that transport mixed material towards the center of the hot spot. Mix material transported by this process is responsible for most of the mix (DT) yield even for the capsule with a CD layer adjacent to the tritium fuel. Consistent with our previous results, mix does not play a significant role in TT neutron yield degradation; instead, this is dominated by the displacement of fuel from the center of the implosion due to the development of turbulent instabilities seeded by long-wavelength asymmetries. Through these processes, the long-wavelength asymmetries degrade TT yield more than the DT yield and thus bring DT/TT neutron yield ratios into agreement with experiment. Finally, we present a detailed comparison of the flows in 2D and 3D simulations.

Designing a highly active soluble PQQ-glucose dehydrogenase for efficient glucose biosensors and biofuel cells

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

Durand, Fabien; Stines-Chaumeil, Claire; Flexer, Victoria

2010-11-26

Research highlights: {yields} A new mutant of PQQ-GDH designed for glucose biosensors application. {yields} First mutant of PQQ-GDH with higher activity for D-glucose than the Wild type. {yields} Position N428 is a key point to increase the enzyme activity. {yields} Molecular modeling shows that the N428 C mutant displays a better interaction for PQQ than the WT. -- Abstract: We report for the first time a soluble PQQ-glucose dehydrogenase that is twice more active than the wild type for glucose oxidation and was obtained by combining site directed mutagenesis, modelling and steady-state kinetics. The observed enhancement is attributed to amore » better interaction between the cofactor and the enzyme leading to a better electron transfer. Electrochemical experiments also demonstrate the superiority of the new mutant for glucose oxidation and make it a promising enzyme for the development of high-performance glucose biosensors and biofuel cells.« less

Calibration of Daycent biogeochemical model for rice paddies in three agro-ecological zones in Peninsular India to optimize cropping practices and predict GHG emissions

NASA Astrophysics Data System (ADS)

Rajan, S.; Kritee, K.; Keough, C.; Parton, W. J.; Ogle, S. M.

2014-12-01

Rice is a staple for nearly half of the world population with irrigated and rainfed lowland rice accounting for about 80% of the worldwide harvested rice area. Increased atmospheric CO2 and rising temperatures are expected to adversely affect rice yields by the end of the 21st century. In addition, different crop management practices affect methane and nitrous oxide emissions from rice paddies antagonistically warranting a review of crop management practices such that farmers can adapt to the changing climate and also help mitigate climate change. The Daily DayCent is a biogeochemical model that operates on a daily time step, driven by four ecological drivers, i.e. climate, soil, vegetation, and management practices. The model is widely used to simulate daily fluxes of various gases, plant productivity, nutrient availability, and other ecosystem parameters in response to changes in land management and climate. We employed the DayCent model as a tool to optimize rice cropping practices in Peninsular India so as to develop a set of farming recommendations to ensure a triple win (i.e. higher yield, higher profit and lower GHG emissions). We applied the model to simulate both N2O and CH4 emissions, and crop yields from four rice paddies in three different agro-ecological zones under different management practices, and compared them with measured GHG and yield data from these plots. We found that, like all process based models, the biggest constraint in using the model was input data acquisition. Lack of accurate documentation of historic land use and management practices, missing historical daily weather data, and difficulty in obtaining digital records of soil and crop/vegetation parameters related to our experimental plots came in the way of our execution of this model. We will discuss utilization of estimates based on available literature, or knowledge-based values in lieu of missing measured parameters in our simulations with DayCent which could prove to be a solution to overcome data limitations in modeling with DayCent and other process based models for developing regions of the world.

Growth response analysis after early control of woody competition for 14 loblolly pine plantations in the southern U.S.

Treesearch

David B. South; James H. Miller

2007-01-01

Only a few growth and yield programs allow users to model the effects of hardwood competition on yields from pine plantations. Several of these programs were developed with the assumption that reducing hardwood competition would consistently produce a Type 2 growth response where pine volume gains increase over time. However, the actual response is not always a Type 2...

Evaluation of limited irrigation strategies to improve water use efficiency and wheat yield in the North China Plain.

PubMed

Zhang, Di; Li, Ruiqi; Batchelor, William D; Ju, Hui; Li, Yanming

2018-01-01

The North China Plain is one of the most important grain production regions in China, but is facing serious water shortages. To achieve a balance between water use and the need for food self-sufficiency, new water efficient irrigation strategies need to be developed that balance water use with farmer net return. The Crop Environment Resource Synthesis Wheat (CERES-Wheat model) was calibrated and evaluated with two years of data which consisted of 3-4 irrigation treatments, and the model was used to investigate long-term winter wheat productivity and water use from irrigation management in the North China Plain. The calibrated model simulated accurately above-ground biomass, grain yield and evapotranspiration of winter wheat in response to irrigation management. The calibrated model was then run using weather data from 1994-2016 in order to evaluate different irrigation strategies. The simulated results using historical weather data showed that grain yield and water use was sensitive to different irrigation strategies including amounts and dates of irrigation applications. The model simulated the highest yield when irrigation was applied at jointing (T9) in normal and dry rainfall years, and gave the highest simulated yields for irrigation at double ridge (T8) in wet years. A single simulated irrigation at jointing (T9) produced yields that were 88% compared to using a double irrigation treatment at T1 and T9 in wet years, 86% of that in normal years, and 91% of that in dry years. A single irrigation at jointing or double ridge produced higher water use efficiency because it obtained higher evapotranspiration. The simulated farmer irrigation practices produced the highest yield and net income. When the cost of water was taken into account, limited irrigation was found to be more profitable based on assumptions about future water costs. In order to increase farmer income, a subsidy will likely be needed to compensate farmers for yield reductions due to water savings. These results showed that there is a cost to the farmer for water conservation, but limiting irrigation to a single irrigation at jointing would minimize impact on farmer net return in North China Plain.

Model based adaptive control of a continuous capture process for monoclonal antibodies production.

PubMed

Steinebach, Fabian; Angarita, Monica; Karst, Daniel J; Müller-Späth, Thomas; Morbidelli, Massimo

2016-04-29

A two-column capture process for continuous processing of cell-culture supernatant is presented. Similar to other multicolumn processes, this process uses sequential countercurrent loading of the target compound in order maximize resin utilization and productivity for a given product yield. The process was designed using a novel mechanistic model for affinity capture, which takes both specific adsorption as well as transport through the resin beads into account. Simulations as well as experimental results for the capture of an IgG antibody are discussed. The model was able to predict the process performance in terms of yield, productivity and capacity utilization. Compared to continuous capture with two columns operated batch wise in parallel, a 2.5-fold higher capacity utilization was obtained for the same productivity and yield. This results in an equal improvement in product concentration and reduction of buffer consumption. The developed model was used not only for the process design and optimization but also for its online control. In particular, the unit operating conditions are changed in order to maintain high product yield while optimizing the process performance in terms of capacity utilization and buffer consumption also in the presence of changing upstream conditions and resin aging. Copyright © 2016 Elsevier B.V. All rights reserved.

Modelling and Forecasting of Rice Yield in support of Crop Insurance

NASA Astrophysics Data System (ADS)

Weerts, A.; van Verseveld, W.; Trambauer, P.; de Vries, S.; Conijn, S.; van Valkengoed, E.; Hoekman, D.; Hengsdijk, H.; Schrevel, A.

2016-12-01

The Government of Indonesia has embarked on a policy to bring crop insurance to all of Indonesia's farmers. To support the Indonesian government, the G4INDO project (www.g4indo.org) is developing/constructing an integrated platform for judging and handling insurance claims. The platform consists of bringing together remote sensed data (both visible and radar) and hydrologic and crop modelling and forecasting to improve predictions in one forecasting platform (i.e. Delft-FEWS, Werner et al., 2013). The hydrological model and crop model (LINTUL) are coupled on time stepping basis in the OpenStreams framework (see https://github.com/openstreams/wflow) and deployed in a Delft-FEWS forecasting platform to support seasonal forecasting of water availability and crop yield. First we will show the general idea about the project, the integrated platform (including Sentinel 1 & 2 data) followed by first (reforecast) results of the coupled models for predicting water availability and crop yield in the Brantas catchment in Java, Indonesia. Werner, M., Schellekens, J., Gijsbers, P., Van Dijk, M., Van den Akker, O. and Heynert K, 2013. The Delft-FEWS flow forecasting system, Environmental Modelling & Software; 40:65-77. DOI: 10.1016/j.envsoft.2012.07.010 .

Yield gap mapping as a support tool for risk management in agriculture

NASA Astrophysics Data System (ADS)

Lahlou, Ouiam; Imani, Yasmina; Slimani, Imane; Van Wart, Justin; Yang, Haishun

2016-04-01

The increasing frequency and magnitude of droughts in Morocco and the mounting losses from extended droughts in the agricultural sector emphasized the need to develop reliable and timely tools to manage drought and to mitigate resulting catastrophic damage. In 2011, Morocco launched a cereals multi-risk insurance with drought as the most threatening and the most frequent hazard in the country. However, and in order to assess the gap and to implement the more suitable compensation, it is essential to quantify the potential yield in each area. In collaboration with the University of Nebraska-Lincoln, a study is carried out in Morocco and aims to determine the yield potentials and the yield gaps in the different agro-climatic zones of the country. It fits into the large project: Global Yield Gap and Water Productivity Atlas: http://www.yieldgap.org/. The yield gap (Yg) is the magnitude and difference between crop yield potential (Yp) or water limited yield potential (Yw) and actual yields, reached by farmers. World Food Studies (WOFOST), which is a Crop simulation mechanistic model, has been used for this purpose. Prior to simulations, reliable information about actual yields, weather data, crop management data and soil data have been collected in 7 Moroccan buffer zones considered, each, within a circle of 100 km around a weather station point, homogenously spread across the country and where cereals are widely grown. The model calibration was also carried out using WOFOST default varieties data. The map-based results represent a robust tool, not only for drought insurance organization, but for agricultural and agricultural risk management. Moreover, accurate and geospatially granular estimates of Yg and Yw will allow to focus on regions with largest unexploited yield gaps and greatest potential to close them, and consequently to improve food security in the country.

Image analysis-based modelling for flower number estimation in grapevine.

PubMed

Millan, Borja; Aquino, Arturo; Diago, Maria P; Tardaguila, Javier

2017-02-01

Grapevine flower number per inflorescence provides valuable information that can be used for assessing yield. Considerable research has been conducted at developing a technological tool, based on image analysis and predictive modelling. However, the behaviour of variety-independent predictive models and yield prediction capabilities on a wide set of varieties has never been evaluated. Inflorescence images from 11 grapevine Vitis vinifera L. varieties were acquired under field conditions. The flower number per inflorescence and the flower number visible in the images were calculated manually, and automatically using an image analysis algorithm. These datasets were used to calibrate and evaluate the behaviour of two linear (single-variable and multivariable) and a nonlinear variety-independent model. As a result, the integrated tool composed of the image analysis algorithm and the nonlinear approach showed the highest performance and robustness (RPD = 8.32, RMSE = 37.1). The yield estimation capabilities of the flower number in conjunction with fruit set rate (R 2  = 0.79) and average berry weight (R 2  = 0.91) were also tested. This study proves the accuracy of flower number per inflorescence estimation using an image analysis algorithm and a nonlinear model that is generally applicable to different grapevine varieties. This provides a fast, non-invasive and reliable tool for estimation of yield at harvest. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

The Role of Inflation and Price Escalation Adjustments in Properly Estimating Program Costs: F-35 Case Study

DTIC Science & Technology

2016-03-01

regression models that yield hedonic price indexes is closely related to standard techniques for developing cost estimating relationships ( CERs ...October 2014). iii analysis) and derives a price index from the coefficients on variables reflecting the year of purchase. In CER development, the...index. The relevant cost metric in both cases is unit recurring flyaway (URF) costs. For the current project, we develop a “Baseline” CER model, taking

Paradise Threatened: Land Use and Erosion on St. John, US Virgin Islands

PubMed

Macdonald; Anderson; Dietrich

1997-11-01

/ Rapid development and the concomitant increases in erosion and sedimentation are believed to threaten the reefs and other marine resources that are a primary attraction of St. John and Virgin Islands National Park. Average annual sediment yields from undeveloped areas were estimated from a sediment pond and a mangrove swamp as less than 20 and less than 40 t/km2/yr, respectively. Geomorphic evidence indicates that plantation agriculture during the 18th and 19th centuries did not cause severe erosion. Since about 1950 there has been rapid growth in roads and development due to increasing tourism and second-home development. Our field investigations identified the approximately 50 km of unpaved roads as the primary source of anthropogenic sediment. Field measurements of the road network in two catchments led to the development of a vector-based GIS model to predict road surface erosion and sediment delivery. We estimate that road erosion has caused at least a fourfold increase in island-wide sediment yields and that current sedimentation rates are unprecedented. Paving the dirt roads and implementing standard sediment control practices can greatly reduce current sediment yields and possible adverse effects on the marine ecosystems surrounding St. John.KEY WORDS: Erosion; Sediment yield; Roads; Dry tropics; Development

Exploiting induced variation to dissect quantitative traits in barley.

PubMed

Druka, Arnis; Franckowiak, Jerome; Lundqvist, Udda; Bonar, Nicola; Alexander, Jill; Guzy-Wrobelska, Justyna; Ramsay, Luke; Druka, Ilze; Grant, Iain; Macaulay, Malcolm; Vendramin, Vera; Shahinnia, Fahimeh; Radovic, Slobodanka; Houston, Kelly; Harrap, David; Cardle, Linda; Marshall, David; Morgante, Michele; Stein, Nils; Waugh, Robbie

2010-04-01

The identification of genes underlying complex quantitative traits such as grain yield by means of conventional genetic analysis (positional cloning) requires the development of several large mapping populations. However, it is possible that phenotypically related, but more extreme, allelic variants generated by mutational studies could provide a means for more efficient cloning of QTLs (quantitative trait loci). In barley (Hordeum vulgare), with the development of high-throughput genome analysis tools, efficient genome-wide identification of genetic loci harbouring mutant alleles has recently become possible. Genotypic data from NILs (near-isogenic lines) that carry induced or natural variants of genes that control aspects of plant development can be compared with the location of QTLs to potentially identify candidate genes for development--related traits such as grain yield. As yield itself can be divided into a number of allometric component traits such as tillers per plant, kernels per spike and kernel size, mutant alleles that both affect these traits and are located within the confidence intervals for major yield QTLs may represent extreme variants of the underlying genes. In addition, the development of detailed comparative genomic models based on the alignment of a high-density barley gene map with the rice and sorghum physical maps, has enabled an informed prioritization of 'known function' genes as candidates for both QTLs and induced mutant genes.

The short pipe path – safe water, energy & nutrient recovery

EPA Science Inventory

The step-by-step refinement of our urban water systems has yielded unsustainable, centralized urban water services in many developed regions of the world. These large systems also provide the wrong role model and promote conservative thinking for the rapidly developing regions of...

Vulnerability of Agriculture to Climate Change as Revealed by Relationships between Simulated Crop Yield and Climate Change Indices

NASA Astrophysics Data System (ADS)

King, A. W.; Absar, S. M.; Nair, S.; Preston, B. L.

2012-12-01

The vulnerability of agriculture is among the leading concerns surrounding climate change. Agricultural production is influenced by drought and other extremes in weather and climate. In regions of subsistence farming, worst case reductions in yield lead to malnutrition and famine. Reduced surplus contributes to poverty in agrarian economies. In more economically diverse and industrialized regions, variations in agricultural yield can influence the regional economy through market mechanisms. The latter grows in importance as agriculture increasingly services the energy market in addition to markets for food and fiber. Agriculture is historically a highly adaptive enterprise and will respond to future changes in climate with a variety of adaptive mechanisms. Nonetheless, the risk, if not expectation, of increases in climate extremes and hazards exceeding historical experience motivates scientifically based anticipatory assessment of the vulnerability of agriculture to climate change. We investigate the sensitivity component of that vulnerability using EPIC, a well established field-scale model of cropping systems that includes the simulation of economic yield. The core of our analysis is the relationship between simulated yield and various indices of climate change, including the CCI/CLIVAR/JCOM ETCCDI indices, calculated from weather inputs to the model. We complement this core with analysis using the DSSAT cropping system model and exploration of relationships between historical yield statistics and climate indices calculated from weather records. Our analyses are for sites in the Southeast/Gulf Coast region of the United States. We do find "tight" monotonic relationships between annual yield and climate for some indices, especially those associated with available water. More commonly, however, we find an increase in the variability of yield as the index value becomes more extreme. Our findings contribute to understanding the sensitivity of crop yield as part of vulnerability analysis. They also contribute to considerations of adaptation, focusing attention on adapting to increased variability in yield rather than just reductions in yield. For example, in the face of increased variability or reduced reliability, hedging and risk spreading strategies may be more important than technological innovations such as drought-resistant crops or other optimization strategies. Our findings also have implications for the choice and application of climate extreme indices, demands on models used to project climate change and the development of next generation integrated assessment models (IAM) that incorporate the agricultural sector, and especially adaption within that sector, in energy and broader more general markets.

Formulating a stand-growth model for mathematical programming problems in Appalachian forests

Treesearch

Gary W. Miller; Jay Sullivan

1993-01-01

Some growth and yield simulators applicable to central hardwood forests can be formulated for use in mathematical programming models that are designed to optimize multi-stand, multi-resource management problems. Once in the required format, growth equations serve as model constraints, defining the dynamics of stand development brought about by harvesting decisions. In...

A numerical study of the effect of irrigation on land-atmosphere interactions in a spring wheat cropland in India using a coupled atmosphere-crop growth dynamics model

NASA Astrophysics Data System (ADS)

Kumari, S.; Sharma, P.; Srivastava, A.; Rastogi, D.; Sehgal, V. K.; Dhakar, R.; Roy, S. B.

2017-12-01

Vegetation dynamics and surface meteorology are tightly coupled through the exchange of momentum, moisture and heat between the land surface and the atmosphere. In this study, we use a recently developed coupled atmosphere-crop growth dynamics model to study these exchanges and their effects in a spring wheat cropland in northern India. In particular, we investigate the role of irrigation in controlling crop growth rates, surface meteorology, and sensible and latent heat fluxes. The model is developed by implementing a crop growth module based on the Simple and Universal Crop growth Simulator (SUCROS) model in the Weather Research Forecasting (WRF) mesoscale atmospheric model. The crop module calculates photosynthesis rates, carbon assimilation, and biomass partitioning as a function of environmental factors and crop development stage. The leaf area index (LAI) and root depth calculated by the crop module is then fed to the Noah-MP land module of WRF to calculate land-atmosphere fluxes. The crop model is calibrated using data from an experimental spring wheat crop site in the Indian Agriculture Research Institute. The coupled model is capable of simulating the observed spring wheat phenology. Irrigation is simulated by changing the soil moisture levels from 50% - 100% of field capacity. Results show that the yield first increases with increasing soil moisture and then starts decreasing as we further increase the soil moisture. Yield attains its maximum value with soil moisture at the level of 60% water of FC. At this level, high LAI values lead to a decrease in the Bowen Ratio because more energy is transferred to the atmosphere as latent heat rather than sensible heat resulting in a cooling effect on near-surface air temperatures. Apart from improving simulation of land-atmosphere interactions, this coupled modeling approach can form the basis for the seamless crop yield and seasonal scale weather outlook prediction system.

Prospects for Alpha Particle Heating in JET in the Hot Ion Regime

NASA Astrophysics Data System (ADS)

Cordey, J. G.; Keilhacker, M.; Watkins, M. L.

1987-01-01

The prospects for alpha particle heating in JET are discussed. A computational model is developed to represent adequately the neutron yield from JET plasmas heated by neutral beam injection. This neutral beam model, augmented by a simple plasma model, is then used to determine the neutron yields and fusion Q-values anticipated for different heating schemes in future operation of JET with tritium. The relative importance of beam-thermal and thermal-thermal reactions is pointed out and the dependence of the results on, for example, plasma density, temperature, energy confinement and purity is shown. Full 1½-D transport code calculations, based on models developed for ohmic, ICRF and NBI heated JET discharges, are used also to provide a power scan for JET operation in tritium in the low density, high ion temperature regime. The results are shown to be in good agreement with the estimates made using the simple plasma model and indicate that, based on present knowledge, a fusion Q-value in the plasma centre above unity should be achieved in JET.

Comparison of statistical models for analyzing wheat yield time series.

PubMed

Michel, Lucie; Makowski, David

2013-01-01

The world's population is predicted to exceed nine billion by 2050 and there is increasing concern about the capability of agriculture to feed such a large population. Foresight studies on food security are frequently based on crop yield trends estimated from yield time series provided by national and regional statistical agencies. Various types of statistical models have been proposed for the analysis of yield time series, but the predictive performances of these models have not yet been evaluated in detail. In this study, we present eight statistical models for analyzing yield time series and compare their ability to predict wheat yield at the national and regional scales, using data provided by the Food and Agriculture Organization of the United Nations and by the French Ministry of Agriculture. The Holt-Winters and dynamic linear models performed equally well, giving the most accurate predictions of wheat yield. However, dynamic linear models have two advantages over Holt-Winters models: they can be used to reconstruct past yield trends retrospectively and to analyze uncertainty. The results obtained with dynamic linear models indicated a stagnation of wheat yields in many countries, but the estimated rate of increase of wheat yield remained above 0.06 t ha⁻¹ year⁻¹ in several countries in Europe, Asia, Africa and America, and the estimated values were highly uncertain for several major wheat producing countries. The rate of yield increase differed considerably between French regions, suggesting that efforts to identify the main causes of yield stagnation should focus on a subnational scale.

Application of activated barrier hopping theory to viscoplastic modeling of glassy polymers

NASA Astrophysics Data System (ADS)

Sweeney, J.; Spencer, P. E.; Vgenopoulos, D.; Babenko, M.; Boutenel, F.; Caton-Rose, P.; Coates, P. D.

2018-05-01

An established statistical mechanical theory of amorphous polymer deformation has been incorporated as a plastic mechanism into a constitutive model and applied to a range of polymer mechanical deformations. The temperature and rate dependence of the tensile yield of PVC, as reported in early studies, has been modeled to high levels of accuracy. Tensile experiments on PET reported here are analyzed similarly and good accuracy is also achieved. The frequently observed increase in the gradient of the plot of yield stress against logarithm of strain rate is an inherent feature of the constitutive model. The form of temperature dependence of the yield that is predicted by the model is found to give an accurate representation. The constitutive model is developed in two-dimensional form and implemented as a user-defined subroutine in the finite element package ABAQUS. This analysis is applied to the tensile experiments on PET, in some of which strain is localized in the form of shear bands and necks. These deformations are modeled with partial success, though adiabatic heating of the instability causes inaccuracies for this isothermal implementation of the model. The plastic mechanism has advantages over the Eyring process, is equally tractable, and presents no particular difficulties in implementation with finite elements.

Application of activated barrier hopping theory to viscoplastic modeling of glassy polymers

NASA Astrophysics Data System (ADS)

Sweeney, J.; Spencer, P. E.; Vgenopoulos, D.; Babenko, M.; Boutenel, F.; Caton-Rose, P.; Coates, P. D.

2017-10-01

An established statistical mechanical theory of amorphous polymer deformation has been incorporated as a plastic mechanism into a constitutive model and applied to a range of polymer mechanical deformations. The temperature and rate dependence of the tensile yield of PVC, as reported in early studies, has been modeled to high levels of accuracy. Tensile experiments on PET reported here are analyzed similarly and good accuracy is also achieved. The frequently observed increase in the gradient of the plot of yield stress against logarithm of strain rate is an inherent feature of the constitutive model. The form of temperature dependence of the yield that is predicted by the model is found to give an accurate representation. The constitutive model is developed in two-dimensional form and implemented as a user-defined subroutine in the finite element package ABAQUS. This analysis is applied to the tensile experiments on PET, in some of which strain is localized in the form of shear bands and necks. These deformations are modeled with partial success, though adiabatic heating of the instability causes inaccuracies for this isothermal implementation of the model. The plastic mechanism has advantages over the Eyring process, is equally tractable, and presents no particular difficulties in implementation with finite elements.

Modeling olive-crop forecasting in Tunisia

NASA Astrophysics Data System (ADS)

Ben Dhiab, Ali; Ben Mimoun, Mehdi; Oteros, Jose; Garcia-Mozo, Herminia; Domínguez-Vilches, Eugenio; Galán, Carmen; Abichou, Mounir; Msallem, Monji

2017-05-01

Tunisia is the world's second largest olive oil-producing region after the European Union. This paper reports on the use of models to forecast local olive crops, using data for Tunisia's five main olive-producing areas: Mornag, Jemmel, Menzel Mhiri, Chaal, and Zarzis. Airborne pollen counts were monitored over the period 1993-2011 using a Cour trap. Forecasting models were constructed using agricultural data (harvest size in tonnes of fruit/year) and data for several weather-related and phenoclimatic variables (rainfall, humidity, temperature, Growing Degree Days, and Chilling). Analysis of these data revealed that the amount of airborne pollen emitted over the pollen season as a whole (i.e., the Pollen Index) was the variable most influencing harvest size. Findings for all local models also indicated that the amount, timing, and distribution of rainfall (except during blooming) had a positive impact on final olive harvests. Air temperature also influenced final crop yield in three study provinces (Menzel Mhiri, Chaal, and Zarzis), but with varying consequences: in the model constructed for Chaal, cumulative maximum temperature from budbreak to start of flowering contributed positively to yield; in the Menzel Mhiri model, cumulative average temperatures during fruit development had a positive impact on output; in Zarzis, by contrast, cumulative maximum temperature during the period prior to flowering negatively influenced final crop yield. Data for agricultural and phenoclimatic variables can be used to construct valid models to predict annual variability in local olive-crop yields; here, models displayed an accuracy of 98, 93, 92, 91, and 88 % for Zarzis, Mornag, Jemmel, Chaal, and Menzel Mhiri, respectively.

Contributions of atmospheric nitrogen deposition to U.S. estuaries: Summary and conclusions: Chapter 8

USGS Publications Warehouse

Stacey, Paul E.; Greening, Holly; Kremer, James N.; Peterson, David; Tomasko, David A.; Valigura, Richard A.; Alexander, Richard B.; Castro, Mark S.; Meyers, Tilden P.; Paerl, Hans W.; Stacey, Paul E.; Turner, R. Eugene

2001-01-01

A NOAA project was initiated in 1998, with support from the U.S. EPA, to develop state-of-the-art estimates of atmospheric N deposition to estuarine watersheds and water surfaces and its delivery to the estuaries. Work groups were formed to address N deposition rates, indirect (from the watershed) yields from atmospheric and other anthropogenic sources, and direct deposition on the estuarine waterbodies, and to evaluate the levels of uncertainty within the estimates. Watershed N yields were estimated using both a land-use based process approach and a national (SPARROW) model, compared to each other, and compared to estimates of N yield from the literature. The total N yields predicted by the national model were similar to values found in the literature and the land-use derived estimates were consistently higher. Atmospheric N yield estimates were within a similar range for the two approaches, but tended to be higher in the land-use based estimates and were not wellcorrelated. Median atmospheric N yields were around 15% of the total N yield for both groups, but ranged as high as 60% when both direct and indirect deposition were considered. Although not the dominant source of anthropogenic N, atmospheric N is, and will undoubtedly continue to be, an important factor in culturally eutrophied estuarine systems, warranting additional research and management attention.

Ethiopian Wheat Yield and Yield Gap Estimation: A Spatial Small Area Integrated Data Approach

NASA Astrophysics Data System (ADS)

Mann, M.; Warner, J.

2015-12-01

Despite the collection of routine annual agricultural surveys and significant advances in GIS and remote sensing products, little econometric research has been undertaken in predicting developing nation's agricultural yields. In this paper, we explore the determinants of wheat output per hectare in Ethiopia during the 2011-2013 Meher crop seasons aggregated to the woreda administrative area. Using a panel data approach, combining national agricultural field surveys with relevant GIS and remote sensing products, the model explains nearly 40% of the total variation in wheat output per hectare across the country. The model also identifies specific contributors to wheat yields that include farm management techniques (eg. area planted, improved seed, fertilizer, irrigation), weather (eg. rainfall), water availability (vegetation and moisture deficit indexes) and policy intervention. Our findings suggest that woredas produce between 9.8 and 86.5% of their potential wheat output per hectare given their altitude, weather conditions, terrain, and plant health. At the median, Amhara, Oromiya, SNNP, and Tigray produce 48.6, 51.5, 49.7, and 61.3% of their local attainable yields, respectively. This research has a broad range of applications, especially from a public policy perspective: identifying causes of yield fluctuations, remotely evaluating larger agricultural intervention packages, and analyzing relative yield potential. Overall, the combination of field surveys with spatial data can be used to identify management priorities for improving production at a variety of administrative levels.

Predicting future land cover change and its impact on streamflow and sediment load in a trans-boundary river basin

NASA Astrophysics Data System (ADS)

Wang, Jie; Wang, Hao; Ning, Shaowei; Hiroshi, Ishidaira

2018-06-01

Sediment load can provide very important perspective on erosion of river basin. The changes of human-induced vegetation cover, such as deforestation or afforestation, affect sediment yield process of a catchment. We have already evaluated that climate change and land cover change changed the historical streamflow and sediment yield, and land cover change is the main factor in Red river basin. But future streamflow and sediment yield changes under potential future land cover change scenario still have not been evaluated. For this purpose, future scenario of land cover change is developed based on historical land cover changes and land change model (LCM). In addition, future leaf area index (LAI) is simulated by ecological model (Biome-BGC) based on future land cover scenario. Then future scenarios of land cover change and LAI are used to drive hydrological model and new sediment rating curve. The results of this research provide information that decision-makers need in order to promote water resources planning efforts. Besides that, this study also contributes a basic framework for assessing climate change impacts on streamflow and sediment yield that can be applied in the other basins around the world.

Fuzzy logic modeling of the resistivity parameter and topography features for aquifer assessment in hydrogeological investigation of a crystalline basement complex

NASA Astrophysics Data System (ADS)

Adabanija, M. A.; Omidiora, E. O.; Olayinka, A. I.

2008-05-01

A linguistic fuzzy logic system (LFLS)-based expert system model has been developed for the assessment of aquifers for the location of productive water boreholes in a crystalline basement complex. The model design employed a multiple input/single output (MISO) approach with geoelectrical parameters and topographic features as input variables and control crisp value as the output. The application of the method to the data acquired in Khondalitic terrain, a basement complex in Vizianagaram District, south India, shows that potential groundwater resource zones that have control output values in the range 0.3295-0.3484 have a yield greater than 6,000 liters per hour (LPH). The range 0.3174-0.3226 gives a yield less than 4,000 LPH. The validation of the control crisp value using data acquired from Oban Massif, a basement complex in southeastern Nigeria, indicates a yield less than 3,000 LPH for control output values in the range 0.2938-0.3065. This validation corroborates the ability of control output values to predict a yield, thereby vindicating the applicability of linguistic fuzzy logic system in siting productive water boreholes in a basement complex.

Association of total mixed ration particle fractions retained on the Penn State Particle Separator with milk, fat, and protein yield lactation curves at the cow level.

PubMed

Caccamo, M; Ferguson, J D; Veerkamp, R F; Schadt, I; Petriglieri, R; Azzaro, G; Pozzebon, A; Licitra, G

2014-01-01

As part of a larger project aiming to develop management evaluation tools based on results from test-day (TD) models, the objective of this study was to examine the effect of physical composition of total mixed rations (TMR) tested quarterly from March 2006 through December 2008 on milk, fat, and protein yield curves for 25 herds in Ragusa, Sicily. A random regression sire-maternal grandsire model was used to estimate variance components for milk, fat, and protein yields fitted on a full data set, including 241,153 TD records from 9,809 animals in 42 herds recorded from 1995 through 2008. The model included parity, age at calving, year at calving, and stage of pregnancy as fixed effects. Random effects were herd × test date, sire and maternal grandsire additive genetic effect, and permanent environmental effect modeled using third-order Legendre polynomials. Model fitting was carried out using ASREML. Afterward, for the 25 herds involved in the study, 9 particle size classes were defined based on the proportions of TMR particles on the top (19-mm) and middle (8-mm) screen of the Penn State Particle Separator. Subsequently, the model with estimated variance components was used to examine the influence of TMR particle size class on milk, fat, and protein yield curves. An interaction was included with the particle size class and days in milk. The effect of the TMR particle size class was modeled using a ninth-order Legendre polynomial. Lactation curves were predicted from the model while controlling for TMR chemical composition (crude protein content of 15.5%, neutral detergent fiber of 40.7%, and starch of 19.7% for all classes), to have pure estimates of particle distribution not confounded by nutrient content of TMR. We found little effect of class of particle proportions on milk yield and fat yield curves. Protein yield was greater for sieve classes with 10.4 to 17.4% of TMR particles retained on the top (19-mm) sieve. Optimal distributions different from those recommended may reflect regional differences based on climate and types and quality of forages fed. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Prediction of County-Level Corn Yields Using an Energy-Crop Growth Index.

NASA Astrophysics Data System (ADS)

Andresen, Jeffrey A.; Dale, Robert F.; Fletcher, Jerald J.; Preckel, Paul V.

1989-01-01

Weather conditions significantly affect corn yields. while weather remains as the major uncontrolled variable in crop production, an understanding of the influence of weather on yields can aid in early and accurate assessment of the impact of weather and climate on crop yields and allow for timely agricultural extension advisories to help reduce farm management costs and improve marketing, decisions. Based on data for four representative countries in Indiana from 1960 to 1984 (excluding 1970 because of the disastrous southern corn leaf blight), a model was developed to estimate corn (Zea mays L.) yields as a function of several composite soil-crop-weather variables and a technology-trend marker, applied nitrogen fertilizer (N). The model was tested by predicting corn yields for 15 other counties. A daily energy-crop growth (ECG) variable in which different weights were used for the three crop-weather variables which make up the daily ECG-solar radiation intercepted by the canopy, a temperature function, and the ratio of actual to potential evapotranspiration-performed better than when the ECG components were weighted equally. The summation of the weighted daily ECG over a relatively short period (36 days spanning silk) was found to provide the best index for predicting county average corn yield. Numerical estimation results indicate that the ratio of actual to potential evapotranspiration (ET/PET) is much more important than the other two ECG factors in estimating county average corn yield in Indiana.

Estimating the Impact and Spillover Effect of Climate Change on Crop Yield in Northern Ghana.

NASA Astrophysics Data System (ADS)

Botchway, E.

2016-12-01

In tropical regions of the world human-induced climate change is likely to impact negatively on crop yields. To investigate the impact of climate change and its spillover effect on mean and variance of crop yields in northern Ghana, the Just and Pope stochastic production function and the Spatial Durbin model were adopted. Surprisingly, the results suggest that both precipitation and average temperature have positive effects on mean crop yield during the wet season. Wet season average temperature has a significant spillover effect in the region, whereas precipitation during the wet season has only one significant spillover effect on maize yield. Wet season precipitation does not have a strong significant effect on crop yield despite the rainfed nature of agriculture in the region. Thus, even if there are losers and winners as a result of future climate change at the regional level, future crop yield would largely depend on future technological development in agriculture, which may improve yields over time despite the changing climate. We argue, therefore, that technical improvement in farm management such as improved seeds and fertilizers, conservation tillage and better pest control, may have a more significant role in increasing observed crop productivity levels over time. So investigating the relative importance of non-climatic factors on crop yield may shed more light on where appropriate interventions can help in improving crop yields. Climate change, also, needs to be urgently assessed at the level of the household, so that poor and vulnerable people dependent on agriculture can be appropriately targeted in research and development activities whose object is poverty alleviation.

Enhanced Furfural Yields from Xylose Dehydration in the gamma-Valerolactone/Water Solvent System at Elevated Temperatures.

PubMed

Sener, Canan; Motagamwala, Ali Hussain; Alonso, David Martin; Dumesic, James

2018-05-18

High yields of furfural (>90%) were achieved from xylose dehydration in a sustainable solvent system composed of -valerolactone (GVL), a biomass derived solvent, and water. It is identified that high reaction temperatures (e.g., 498 K) are required to achieve high furfural yield. Additionally, it is shown that the furfural yield at these temperatures is independent of the initial xylose concentration, and high furfural yield is obtained for industrially relevant xylose concentrations (10 wt%). A reaction kinetics model is developed to describe the experimental data obtained with solvent system composed of 80 wt% GVL and 20 wt% water across the range of reaction conditions studied (473 - 523 K, 1-10 mM acid catalyst, 66 - 660 mM xylose concentration). The kinetic model demonstrates that furfural loss due to bimolecular condensation of xylose and furfural is minimized at elevated temperature, whereas carbon loss due to xylose degradation increases with increasing temperature. Accordingly, the optimal temperature range for xylose dehydration to furfural in the GVL/H2O solvent system is identified to be from 480 to 500 K. Under these reaction conditions, furfural yield of 93% is achieved at 97% xylan conversion from lignocellulosic biomass (maple wood). © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Statistical analysis of corn yields responding to climate variability at various spatio-temporal resolutions

NASA Astrophysics Data System (ADS)

Jiang, H.; Lin, T.

2017-12-01

Rain-fed corn production systems are subject to sub-seasonal variations of precipitation and temperature during the growing season. As each growth phase has varied inherent physiological process, plants necessitate different optimal environmental conditions during each phase. However, this temporal heterogeneity towards climate variability alongside the lifecycle of crops is often simplified and fixed as constant responses in large scale statistical modeling analysis. To capture the time-variant growing requirements in large scale statistical analysis, we develop and compare statistical models at various spatial and temporal resolutions to quantify the relationship between corn yield and weather factors for 12 corn belt states from 1981 to 2016. The study compares three spatial resolutions (county, agricultural district, and state scale) and three temporal resolutions (crop growth phase, monthly, and growing season) to characterize the effects of spatial and temporal variability. Our results show that the agricultural district model together with growth phase resolution can explain 52% variations of corn yield caused by temperature and precipitation variability. It provides a practical model structure balancing the overfitting problem in county specific model and weak explanation power in state specific model. In US corn belt, precipitation has positive impact on corn yield in growing season except for vegetative stage while extreme heat attains highest sensitivity from silking to dough phase. The results show the northern counties in corn belt area are less interfered by extreme heat but are more vulnerable to water deficiency.

Modeling the irradiance and temperature rependence of photovoltaic modules in PVsyst

DOE PAGES

Sauer, Kenneth J.; Roessler, Thomas; Hansen, Clifford W.

2014-11-10

In order to reliably simulate the energy yield of photovoltaic (PV) systems, it is necessary to have an accurate model of how the PV modules perform with respect to irradiance and cell temperature. Building on previous work that addresses the irradiance dependence, two approaches to fit the temperature dependence of module power in PVsyst have been developed and are applied here to recent multi-irradiance and -temperature data for a standard Yingli Solar PV module type. The results demonstrate that it is possible to match the measured irradiance and temperature dependence of PV modules in PVsyst. As a result, improvements inmore » energy yield prediction using the optimized models relative to the PVsyst standard model are considered significant for decisions about project financing.« less

Research and Development of a DNDC Online Model for Farmland Carbon Sequestration and GHG Emissions Mitigation in China.

PubMed

Jiang, Zaidi; Yin, Shan; Zhang, Xianxian; Li, Changsheng; Shen, Guangrong; Zhou, Pei; Liu, Chunjiang

2017-12-01

Appropriate agricultural practices for carbon sequestration and emission mitigation have a significant influence on global climate change. However, various agricultural practices on farmland carbon sequestration usually have a major impact on greenhouse gas (GHG) emissions. It is very important to accurately quantify the effect of agricultural practices. This study developed a platform-the Denitrification Decomposition (DNDC) online model-for simulating and evaluating the agricultural carbon sequestration and emission mitigation based on the scientific process of the DNDC model, which is widely used in the simulation of soil carbon and nitrogen dynamics. After testing the adaptability of the platform on two sampling fields, it turned out that the simulated values matched the measured values well for crop yields and GHG emissions. We used the platform to estimate the effect of three carbon sequestration practices in a sampling field: nitrogen fertilization reduction, straw residue and midseason drainage. The results indicated the following: (1) moderate decrement of the nitrogen fertilization in the sampling field was able to decrease the N₂O emission while maintaining the paddy rice yield; (2) ground straw residue had almost no influence on paddy rice yield, but the CH₄ emission and the surface SOC concentration increased along with the quantity of the straw residue; (3) compared to continuous flooding, midseason drainage would not decrease the paddy rice yield and could lead to a drop in CH₄ emission. Thus, this study established the DNDC online model, which is able to serve as a reference and support for the study and evaluation of the effects of agricultural practices on agricultural carbon sequestration and GHG emissions mitigation in China.

A sub-canopy structure for simulating oil palm in the Community Land Model: phenology, allocation and yield

NASA Astrophysics Data System (ADS)

Fan, Y.; Roupsard, O.; Bernoux, M.; Le Maire, G.; Panferov, O.; Kotowska, M. M.; Knohl, A.

2015-06-01

Land surface modelling has been widely used to characterize the two-way interactions between climate and human activities in terrestrial ecosystems such as deforestation, agricultural expansion, and urbanization. Towards an effort to quantify the effects of forests to oil palm conversion occurring in the tropics on land-atmosphere carbon, water and energy fluxes, we introduce a new perennial crop plant functional type (PFT) for oil palm. Due to the modular and sequential nature of oil palm growth (around 40 stacked phytomers) and yield (fruit bunches axillated on each phytomer), we developed a specific sub-canopy structure for simulating palm's growth and yield within the framework of the Community Land Model (CLM4.5). In this structure each phytomer has its own prognostic leaf growth and fruit yield capacity like a PFT but with shared stem and root components among all phytomers. Phenology and carbon and nitrogen allocation operate on the different phytomers in parallel but at unsynchronized steps, so that multiple fruit yields per annum are enabled in terms of carbon and nitrogen outputs. An important phenological phase is identified for the palm PFT - the storage growth period of bud and "spear" leaves which are photosynthetically inactive before expansion. Agricultural practices such as transplanting, fertilization, and leaf pruning are represented. Parameters introduced for the new PFT were calibrated and validated with field measurements of leaf area index (LAI) and yield from Sumatra, Indonesia. In calibration with a mature oil palm plantation, the cumulative yields from 2005 to 2014 matched perfectly between simulation and observation (mean percentage error = 4 %). Simulated inter-annual dynamics of PFT-level and phytomer-level LAI were both within the range of field measurements. Validation from eight independent oil palm sites shows the ability of the model to adequately predict the average leaf growth and fruit yield across sites but also indicates that seasonal dynamics and site-to-site variability of yield are driven by processes not yet implemented in the model. The new sub-canopy structure and phenology and allocation functions now allow exploring the effects of tropical land use change, from natural ecosystems to oil palm plantations, on carbon, water and energy cycles and regional climate.

Simulating eroded soil organic carbon with the SWAT-C model

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

Zhang, Xuesong

The soil erosion and associated lateral movement of eroded carbon (C) have been identified as a possible mechanism explaining the elusive terrestrial C sink of ca. 1.7-2.6 PgC yr(-1). Here we evaluated the SWAT-C model for simulating long-term soil erosion and associated eroded C yields. Our method couples the CENTURY carbon cycling processes with a Modified Universal Soil Loss Equation (MUSLE) to estimate C losses associated with soil erosion. The results show that SWAT-C is able to simulate well long-term average eroded C yields, as well as correctly estimate the relative magnitude of eroded C yields by crop rotations. Wemore » also evaluated three methods of calculating C enrichment ratio in mobilized sediments, and found that errors associated with enrichment ratio estimation represent a significant uncertainty in SWAT-C simulations. Furthermore, we discussed limitations and future development directions for SWAT-C to advance C cycling modeling and assessment.« less

Optimization of production conditions for activated carbons from Tamarind wood by zinc chloride using response surface methodology.

PubMed

Sahu, J N; Acharya, Jyotikusum; Meikap, B C

2010-03-01

The low-cost activated carbon was prepared from Tamarind wood an agricultural waste material, by chemical activation with zinc chloride. Activated carbon adsorption is an effective means for reducing organic chemicals, chlorine, heavy metals and unpleasant tastes and odours in effluent or colored substances from gas or liquid streams. Central composite design (CCD) was applied to study the influence of activation temperature, chemical ratio of zinc chloride to Tamarind wood and activation time on the chemical activation process of Tamarind wood. Two quadratic models were developed for yield of activated carbon and adsorption of malachite green oxalate using Design-Expert software. The models were used to calculate the optimum operating conditions for production of activated carbon providing a compromise between yield and adsorption of the process. The yield (45.26 wt.%) and adsorption (99.9%) of the activated carbon produced at these operating conditions showed an excellent agreement with the amounts predicted by the models. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

Step edge sputtering yield at grazing incidence ion bombardment.

PubMed

Hansen, Henri; Polop, Celia; Michely, Thomas; Friedrich, Andreas; Urbassek, Herbert M

2004-06-18

The surface morphology of Pt(111) was investigated by scanning tunneling microscopy after 5 keV Ar+ ion bombardment at grazing incidence in dependence of the ion fluence and in the temperature range between 625 and 720 K. The average erosion rate was found to be strongly dependent on the ion fluence and the substrate temperature during bombardment. This dependence is traced back to the variation of step concentration with temperature and fluence. We develop a simple model allowing us to determine separately the constant sputtering yields for terraces and for impact area stripes in front of ascending steps. The experimentally determined yield of these stripes--the step-edge sputtering yield--is in excellent agreement with our molecular dynamics simulations performed for the experimental situation.

Bayesian inference for the genetic control of water deficit tolerance in spring wheat by stochastic search variable selection.

PubMed

Safari, Parviz; Danyali, Syyedeh Fatemeh; Rahimi, Mehdi

2018-06-02

Drought is the main abiotic stress seriously influencing wheat production. Information about the inheritance of drought tolerance is necessary to determine the most appropriate strategy to develop tolerant cultivars and populations. In this study, generation means analysis to identify the genetic effects controlling grain yield inheritance in water deficit and normal conditions was considered as a model selection problem in a Bayesian framework. Stochastic search variable selection (SSVS) was applied to identify the most important genetic effects and the best fitted models using different generations obtained from two crosses applying two water regimes in two growing seasons. The SSVS is used to evaluate the effect of each variable on the dependent variable via posterior variable inclusion probabilities. The model with the highest posterior probability is selected as the best model. In this study, the grain yield was controlled by the main effects (additive and non-additive effects) and epistatic. The results demonstrate that breeding methods such as recurrent selection and subsequent pedigree method and hybrid production can be useful to improve grain yield.

SWAT ungauged: Hydrological budget and crop yield predictions in the Upper Mississippi River Basin

USDA-ARS?s Scientific Manuscript database

Physically based, distributed hydrologic models are increasingly used in assessments of water resources, best management practices, and climate and land use changes. Model performance evaluation in ungauged basins is an important research topic. In this study, we propose a framework for developing S...

WEPP model implementation project with the USDA-Natural Resources Conservation Service

USDA-ARS?s Scientific Manuscript database

The Water Erosion Prediction Project (WEPP) is a physical process-based soil erosion model that can be used to estimate runoff, soil loss, and sediment yield from hillslope profiles, fields, and small watersheds. Initially developed from 1985-1995, WEPP has been applied and validated across a wide r...

The Effect of Calf Gender on Milk Production in Seasonal Calving Cows and Its Impact on Genetic Evaluations

PubMed Central

Hess, Melanie K.; Hess, Andrew S.; Garrick, Dorian J.

2016-01-01

Gender of the calf whose birth initiates lactation could influence whole lactation milk yield of the dam due to hormonal influences on mammary gland development, or through calf gender effects on gestation length. Fetal gender could influence late lactation yields because cows become pregnant at peak lactation. The effects of calf gender sequences in parities 1–3 were assessed by separately fitting animal models to datasets from New Zealand comprising 274 000 Holstein Friesian and 85 000 Jersey cows, decreasing to 12 000 and 4 000 cows by parity 3. The lactation initiated by the birth of a female rather than a male calf was associated with a 0.33–1.1% (p≤0.05) higher milk yield. Female calf gender had carryover effects associated with higher milk yield in second lactations for Holstein Friesians (0.24%; p = 0.01) and third lactations for Jerseys (1.1%; p = 0.01). Cows giving birth to bull calves have 2 day longer gestations, which reduces lactation length in seasonal calving herds. Adding a covariate for lactation length to the animal model eroded some of these calf gender effects, such that calving a female led to higher milk yield only for second lactation Holstein Friesians (1.6%; p = 0.002). The interval centering method generates lower estimates of whole lactation yield when Wood’s lactation curves are shifted to the right by 2 days for male calves and this explained the higher yield in female calves when differences in lactation length were considered. Correlations of estimated breeding values between models including or excluding calf gender sequence were 1.00 for bulls or cows. Calf gender primarily influences milk yield through increased gestation length of male calves, and bias associated with the interval centering method used to estimate whole lactation milk yields. Including information on calf gender is unlikely to have an effect on selection response in New Zealand dairy cattle. PMID:26974166

Modelling the impacts of pests and diseases on agricultural systems.

PubMed

Donatelli, M; Magarey, R D; Bregaglio, S; Willocquet, L; Whish, J P M; Savary, S

2017-07-01

The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.

Towards surgeon-authored VR training: the scene-development cycle.

PubMed

Dindar, Saleh; Nguyen, Thien; Peters, Jörg

2016-01-01

Enabling surgeon-educators to themselves create virtual reality (VR) training units promises greater variety, specialization, and relevance of the units. This paper describes a software bridge that semi-automates the scene-generation cycle, a key bottleneck in authoring, modeling, and developing VR units. Augmenting an open source modeling environment with physical behavior attachment and collision specifications yields single-click testing of the full force-feedback enabled anatomical scene.

Modeling the effects of forest management on in situ and ex situ longleaf pine forest carbon stocks

Treesearch

C.A. Gonzalez-Benecke; L.J. Samuelson; T.A. Martin; W.P. Cropper Jr; Kurt Johnsen; T.A. Stokes; John Butnor; P.H. Anderson

2015-01-01

Assessment of forest carbon storage dynamics requires a variety of techniques including simulation models. We developed a hybrid model to assess the effects of silvicultural management systems on carbon (C) budgets in longleaf pine (Pinus palustris Mill.) plantations in the southeastern U.S. To simulate in situ C pools, the model integrates a growth and yield model...

A comprehensive constitutive law for waxy crude oil: a thixotropic yield stress fluid.

PubMed

Dimitriou, Christopher J; McKinley, Gareth H

2014-09-21

Guided by a series of discriminating rheometric tests, we develop a new constitutive model that can quantitatively predict the key rheological features of waxy crude oils. We first develop a series of model crude oils, which are characterized by a complex thixotropic and yielding behavior that strongly depends on the shear history of the sample. We then outline the development of an appropriate preparation protocol for carrying out rheological measurements, to ensure consistent and reproducible initial conditions. We use RheoPIV measurements of the local kinematics within the fluid under imposed deformations in order to validate the selection of a particular protocol. Velocimetric measurements are also used to document the presence of material instabilities within the model crude oil under conditions of imposed steady shearing. These instabilities are a result of the underlying non-monotonic steady flow curve of the material. Three distinct deformation histories are then used to probe the material's constitutive response. These deformations are steady shear, transient response to startup of steady shear with different aging times, and large amplitude oscillatory shear (LAOS). The material response to these three different flows is used to motivate the development of an appropriate constitutive model. This model (termed the IKH model) is based on a framework adopted from plasticity theory and implements an additive strain decomposition into characteristic reversible (elastic) and irreversible (plastic) contributions, coupled with the physical processes of isotropic and kinematic hardening. Comparisons of experimental to simulated response for all three flows show good quantitative agreement, validating the chosen approach for developing constitutive models for this class of materials.

Non-Destructive Quality Evaluation of Pepper (Capsicum annuum L.) Seeds Using LED-Induced Hyperspectral Reflectance Imaging

PubMed Central

Mo, Changyeun; Kim, Giyoung; Lee, Kangjin; Kim, Moon S.; Cho, Byoung-Kwan; Lim, Jongguk; Kang, Sukwon

2014-01-01

In this study, we developed a viability evaluation method for pepper (Capsicum annuum L.) seeds based on hyperspectral reflectance imaging. The reflectance spectra of pepper seeds in the 400–700 nm range are collected from hyperspectral reflectance images obtained using blue, green, and red LED illumination. A partial least squares–discriminant analysis (PLS-DA) model is developed to classify viable and non-viable seeds. Four spectral ranges generated with four types of LEDs (blue, green, red, and RGB), which were pretreated using various methods, are investigated to develop the classification models. The optimal PLS-DA model based on the standard normal variate for RGB LED illumination (400–700 nm) yields discrimination accuracies of 96.7% and 99.4% for viable seeds and nonviable seeds, respectively. The use of images based on the PLS-DA model with the first-order derivative of a 31.5-nm gap for red LED illumination (600–700 nm) yields 100% discrimination accuracy for both viable and nonviable seeds. The results indicate that a hyperspectral imaging technique based on LED light can be potentially applied to high-quality pepper seed sorting. PMID:24763251

Non-destructive quality evaluation of pepper (Capsicum annuum L.) seeds using LED-induced hyperspectral reflectance imaging.

PubMed

Mo, Changyeun; Kim, Giyoung; Lee, Kangjin; Kim, Moon S; Cho, Byoung-Kwan; Lim, Jongguk; Kang, Sukwon

2014-04-24

In this study, we developed a viability evaluation method for pepper (Capsicum annuum L.) seeds based on hyperspectral reflectance imaging. The reflectance spectra of pepper seeds in the 400-700 nm range are collected from hyperspectral reflectance images obtained using blue, green, and red LED illumination. A partial least squares-discriminant analysis (PLS-DA) model is developed to classify viable and non-viable seeds. Four spectral ranges generated with four types of LEDs (blue, green, red, and RGB), which were pretreated using various methods, are investigated to develop the classification models. The optimal PLS-DA model based on the standard normal variate for RGB LED illumination (400-700 nm) yields discrimination accuracies of 96.7% and 99.4% for viable seeds and nonviable seeds, respectively. The use of images based on the PLS-DA model with the first-order derivative of a 31.5-nm gap for red LED illumination (600-700 nm) yields 100% discrimination accuracy for both viable and nonviable seeds. The results indicate that a hyperspectral imaging technique based on LED light can be potentially applied to high-quality pepper seed sorting.

Dynamical System Analysis of Reynolds Stress Closure Equations

NASA Technical Reports Server (NTRS)

Girimaji, Sharath S.

1997-01-01

In this paper, we establish the causality between the model coefficients in the standard pressure-strain correlation model and the predicted equilibrium states for homogeneous turbulence. We accomplish this by performing a comprehensive fixed point analysis of the modeled Reynolds stress and dissipation rate equations. The results from this analysis will be very useful for developing improved pressure-strain correlation models to yield observed equilibrium behavior.

Estimation and application of a growth and yield model for uneven-aged mixed conifer stands in California.

Treesearch

Jingjing Liang; J. Buongiorno; R.A. Monserud

2005-01-01

A growth model for uneven-aged mixed-conifer stands in California was developed with data from 205 permanent plots. The model predicts the number of softwood and hardwood trees in nineteen diameter classes, based on equations for diameter growth rates, mortality arid recruitment. The model gave unbiased predictions of the expected number of trees by diameter class and...

Exact wave packet dynamics of singlet fission in unsubstituted and substituted polyene chains within long-range interacting models

NASA Astrophysics Data System (ADS)

Prodhan, Suryoday; Ramasesha, S.

2017-08-01

Singlet fission (SF) is a potential pathway for significant enhancement of efficiency in organic solar cells (OSC). In this paper, we study singlet fission in a pair of polyene molecules in two different stacking arrangements employing exact many-body wave packet dynamics. In the noninteracting model, the SF yield is absent. The individual molecules are treated within Hubbard and Pariser-Parr-Pople (PPP) models and the interaction between them involves transfer terms, intersite electron repulsions, and site-charge-bond-charge repulsion terms. Initial wave packet is constructed from excited singlet state of one molecule and ground state of the other. Time development of this wave packet under the influence of intermolecular interactions is followed within the Schrödinger picture by an efficient predictor-corrector scheme. In unsubstituted Hubbard and PPP chains, 2 1A excited singlet state leads to significant SF yield while the 1 1B state gives negligible fission yield. On substitution by donor-acceptor groups of moderate strength, the lowest excited state will have sufficient 2 1A character and hence results in significant SF yield. Because of rapid internal conversion, the nature of the lowest excited singlet will determine the SF contribution to OSC efficiency. Furthermore, we find the fission yield depends considerably on the stacking arrangement of the polyene molecules.

Two levels ARIMAX and regression models for forecasting time series data with calendar variation effects

NASA Astrophysics Data System (ADS)

Suhartono, Lee, Muhammad Hisyam; Prastyo, Dedy Dwi

2015-12-01

The aim of this research is to develop a calendar variation model for forecasting retail sales data with the Eid ul-Fitr effect. The proposed model is based on two methods, namely two levels ARIMAX and regression methods. Two levels ARIMAX and regression models are built by using ARIMAX for the first level and regression for the second level. Monthly men's jeans and women's trousers sales in a retail company for the period January 2002 to September 2009 are used as case study. In general, two levels of calendar variation model yields two models, namely the first model to reconstruct the sales pattern that already occurred, and the second model to forecast the effect of increasing sales due to Eid ul-Fitr that affected sales at the same and the previous months. The results show that the proposed two level calendar variation model based on ARIMAX and regression methods yields better forecast compared to the seasonal ARIMA model and Neural Networks.

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.

[Climate change impacts on yield of Cordyceps sinensis and research on yield prediction model of C. sinensis].

PubMed

Zhu, Shou-Dong; Huang, Lu-Qi; Guo, Lan-Ping; Ma, Xing-Tian; Hao, Qing-Xiu; Le, Zhi-Yong; Zhang, Xiao-Bo; Yang, Guang; Zhang, Yan; Chen, Mei-Lan

2017-04-01

Cordyceps sinensis is a Chinese unique precious herbal material, its genuine producing areas covering Naqu, Changdu in Qinghai Tibet Plateau, Yushu in Qinghai province and other regions. In recent 10 years, C. sinensis resources is decreasing as a result of the blindly and excessively perennial dug. How to rationally protect, develop and utilize of the valuable resources of C. sinensis has been referred to an important field of research on C. sinensis. The ecological environment and climate change trend of Qinghai Tibet plateau happens prior to other regions, which means that the distribution and evolution of C. sinensis are more obvious and intense than those of the other populations. Based on RS (remote sensing)/GIS(geographic information system) technology, this paper utilized the relationship between the snowline elevation, the average temperature, precipitation and sunshine hours in harvest period (April and may) of C. sinensis and the actual production of C. sinensis to establish a weighted geometric mean model. The model's prediction accuracy can reach 82.16% at least in forecasting C. sinensis year yield in Naqu area in every early June. This study can provide basic datum and information for supporting the C. sinensis industry healthful, sustainable development. Copyright© by the Chinese Pharmaceutical Association.

Discovery and process development of a novel TACE inhibitor for the topical treatment of psoriasis.

PubMed

Boiteau, Jean-Guy; Ouvry, Gilles; Arlabosse, Jean-Marie; Astri, Stéphanie; Beillard, Audrey; Bhurruth-Alcor, Yushma; Bonnary, Laetitia; Bouix-Peter, Claire; Bouquet, Karine; Bourotte, Marilyne; Cardinaud, Isabelle; Comino, Catherine; Deprez, Benoît; Duvert, Denis; Féret, Angélique; Hacini-Rachinel, Feriel; Harris, Craig S; Luzy, Anne-Pascale; Mathieu, Arnaud; Millois, Corinne; Orsini, Nicolas; Pascau, Jonathan; Pinto, Artur; Piwnica, David; Polge, Gaëlle; Reitz, Arnaud; Reversé, Kevin; Rodeville, Nicolas; Rossio, Patricia; Spiesse, Delphine; Tabet, Samuel; Taquet, Nathalie; Tomas, Loïc; Vial, Emmanuel; Hennequin, Laurent F

2018-02-15

Targeting the TNFα pathway is a validated approach to the treatment of psoriasis. In this pathway, TACE stands out as a druggable target and has been the focus of in-house research programs. In this article, we present the discovery of clinical candidate 26a. Starting from hits plagued with poor solubility or genotoxicity, 26a was identified through thorough multiparameter optimisation. Showing robust in vivo activity in an oxazolone-mediated inflammation model, the compound was selected for development. Following a polymorph screen, the hydrochloride salt was selected and the synthesis was efficiently developed to yield the API in 47% overall yield. Copyright © 2017. Published by Elsevier Ltd.

Regional Climate Change Impact on Agricultural Land Use in West Africa

NASA Astrophysics Data System (ADS)

Ahmed, K. F.; Wang, G.; You, L.

2014-12-01

Agriculture is a key element of the human-induced land use land cover change (LULCC) that is influenced by climate and can potentially influence regional climate. Temperature and precipitation directly impact the crop yield (by controlling photosynthesis, respiration and other physiological processes) that then affects agricultural land use pattern. In feedback, the resulting changes in land use and land cover play an important role to determine the direction and magnitude of global, regional and local climate change by altering Earth's radiative equilibrium. The assessment of future agricultural land use is, therefore, of great importance in climate change study. In this study, we develop a prototype land use projection model and, using this model, project the changes to land use pattern and future land cover map accounting for climate-induced yield changes for major crops in West Africa. Among the inputs to the land use projection model are crop yield changes simulated by the crop model DSSAT, driven with the climate forcing data from the regional climate model RegCM4.3.4-CLM4.5, which features a projected decrease of future mean crop yield and increase of inter-annual variability. Another input to the land use projection model is the projected changes of food demand in the future. In a so-called "dumb-farmer scenario" without any adaptation, the combined effect of decrease in crop yield and increase in food demand will lead to a significant increase in agricultural land use in future years accompanied by a decrease in forest and grass area. Human adaptation through land use optimization in an effort to minimize agricultural expansion is found to have little impact on the overall areas of agricultural land use. While the choice of the General Circulation Model (GCM) to derive initial and boundary conditions for the regional climate model can be a source of uncertainty in projecting the future LULCC, results from sensitivity experiments indicate that the changes in land use pattern are robust.

Comparison of Statistical Models for Analyzing Wheat Yield Time Series

PubMed Central

Michel, Lucie; Makowski, David

2013-01-01

The world's population is predicted to exceed nine billion by 2050 and there is increasing concern about the capability of agriculture to feed such a large population. Foresight studies on food security are frequently based on crop yield trends estimated from yield time series provided by national and regional statistical agencies. Various types of statistical models have been proposed for the analysis of yield time series, but the predictive performances of these models have not yet been evaluated in detail. In this study, we present eight statistical models for analyzing yield time series and compare their ability to predict wheat yield at the national and regional scales, using data provided by the Food and Agriculture Organization of the United Nations and by the French Ministry of Agriculture. The Holt-Winters and dynamic linear models performed equally well, giving the most accurate predictions of wheat yield. However, dynamic linear models have two advantages over Holt-Winters models: they can be used to reconstruct past yield trends retrospectively and to analyze uncertainty. The results obtained with dynamic linear models indicated a stagnation of wheat yields in many countries, but the estimated rate of increase of wheat yield remained above 0.06 t ha−1 year−1 in several countries in Europe, Asia, Africa and America, and the estimated values were highly uncertain for several major wheat producing countries. The rate of yield increase differed considerably between French regions, suggesting that efforts to identify the main causes of yield stagnation should focus on a subnational scale. PMID:24205280

The Effects of Temperature and Precipitation on the Yield of Zea Mays L. I the Southeastern United States

NASA Astrophysics Data System (ADS)

Stooksbury, David Emory

Three families of straightforward maize (Zea mays L.) yield/climate models using monthly temperature and precipitation terms are produced. One family of models uses USDA's Crop Reporting Districts (CRD) as its scale of aggregation. The other two families of models use three different district aggregates based on climate or yield patterns. The climate and yield districts are determined by using a two-stage cluster analysis. The CRD-based family of models perform as well as the climate and yield based models. All models explain between 80% and 90% of the variance in maize yield. The most important climate term affecting maize yield in the South is the daily maximum temperature at pollination time. The higher the maximum temperature, the lower the yield. Above normal minimum temperature during pollination increases yield in the Middle South. Weather that favors early planting and rapid vegetative growth increases yield. Ideal maize yield weather includes a dry period during planting followed by a warm period during vegetative growth. Moisture variables are important only during the planting and harvest periods when above normal precipitation delays field work and thereby reduces yield. The model results indicate that the dire predictions about the fate of Southern agriculture in a trace gas warmed world may not be true. This is due to the overwhelming influence of the daily maximum temperature on yield. An optimum aggregate for climate impact studies was not found. I postulate that this is due to the dynamic nature of the American maize production system. For most climate impact studies on a dynamic agricultural system, there does not need to be a concern about the model aggregation.

Evaluation of Thompson-type trend and monthly weather data models for corn yields in Iowa, Illinois, and Indiana

NASA Technical Reports Server (NTRS)

French, V. (Principal Investigator)

1982-01-01

An evaluation was made of Thompson-Type models which use trend terms (as a surrogate for technology), meteorological variables based on monthly average temperature, and total precipitation to forecast and estimate corn yields in Iowa, Illinois, and Indiana. Pooled and unpooled Thompson-type models were compared. Neither was found to be consistently superior to the other. Yield reliability indicators show that the models are of limited use for large area yield estimation. The models are objective and consistent with scientific knowledge. Timely yield forecasts and estimates can be made during the growing season by using normals or long range weather forecasts. The models are not costly to operate and are easy to use and understand. The model standard errors of prediction do not provide a useful current measure of modeled yield reliability.

Assessment of freshwater withdrawals and availability for Marcellus shale natural gas development: a case study in Pennsylvania

Treesearch

Patrick C. Eisenhauer; Nicolas P. Zegre; Samuel J. Lamont

2013-01-01

To evaluate surface water withdrawals used for Marcellus shale natural gas development and to assess potential impacts on water yield, a regional water balance model was developed for the Pine Creek watershed, located primarily in Lycoming County, Pennsylvania. Marcellus shale development has increased rapidly in Lycoming County since 2007. We used precipitation,...

Modeling sediment yield in small catchments at event scale: Model comparison, development and evaluation

NASA Astrophysics Data System (ADS)

Tan, Z.; Leung, L. R.; Li, H. Y.; Tesfa, T. K.

2017-12-01

Sediment yield (SY) has significant impacts on river biogeochemistry and aquatic ecosystems but it is rarely represented in Earth System Models (ESMs). Existing SY models focus on estimating SY from large river basins or individual catchments so it is not clear how well they simulate SY in ESMs at larger spatial scales and globally. In this study, we compare the strengths and weaknesses of eight well-known SY models in simulating annual mean SY at about 400 small catchments ranging in size from 0.22 to 200 km2 in the US, Canada and Puerto Rico. In addition, we also investigate the performance of these models in simulating event-scale SY at six catchments in the US using high-quality hydrological inputs. The model comparison shows that none of the models can reproduce the SY at large spatial scales but the Morgan model performs the better than others despite its simplicity. In all model simulations, large underestimates occur in catchments with very high SY. A possible pathway to reduce the discrepancies is to incorporate sediment detachment by landsliding, which is currently not included in the models being evaluated. We propose a new SY model that is based on the Morgan model but including a landsliding soil detachment scheme that is being developed. Along with the results of the model comparison and evaluation, preliminary findings from the revised Morgan model will be presented.

Interval Predictor Models with a Formal Characterization of Uncertainty and Reliability

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Giesy, Daniel P.; Kenny, Sean P.

2014-01-01

This paper develops techniques for constructing empirical predictor models based on observations. By contrast to standard models, which yield a single predicted output at each value of the model's inputs, Interval Predictors Models (IPM) yield an interval into which the unobserved output is predicted to fall. The IPMs proposed prescribe the output as an interval valued function of the model's inputs, render a formal description of both the uncertainty in the model's parameters and of the spread in the predicted output. Uncertainty is prescribed as a hyper-rectangular set in the space of model's parameters. The propagation of this set through the empirical model yields a range of outputs of minimal spread containing all (or, depending on the formulation, most) of the observations. Optimization-based strategies for calculating IPMs and eliminating the effects of outliers are proposed. Outliers are identified by evaluating the extent by which they degrade the tightness of the prediction. This evaluation can be carried out while the IPM is calculated. When the data satisfies mild stochastic assumptions, and the optimization program used for calculating the IPM is convex (or, when its solution coincides with the solution to an auxiliary convex program), the model's reliability (that is, the probability that a future observation would be within the predicted range of outputs) can be bounded rigorously by a non-asymptotic formula.

Advanced model for the prediction of the neutron-rich fission product yields

NASA Astrophysics Data System (ADS)

Rubchenya, V. A.; Gorelov, D.; Jokinen, A.; Penttilä, H.; Äystö, J.

2013-12-01

The consistent models for the description of the independent fission product formation cross sections in the spontaneous fission and in the neutron and proton induced fission at the energies up to 100 MeV is developed. This model is a combination of new version of the two-component exciton model and a time-dependent statistical model for fusion-fission process with inclusion of dynamical effects for accurate calculations of nucleon composition and excitation energy of the fissioning nucleus at the scission point. For each member of the compound nucleus ensemble at the scission point, the primary fission fragment characteristics: kinetic and excitation energies and their yields are calculated using the scission-point fission model with inclusion of the nuclear shell and pairing effects, and multimodal approach. The charge distribution of the primary fragment isobaric chains was considered as a result of the frozen quantal fluctuations of the isovector nuclear matter density at the scission point with the finite neck radius. Model parameters were obtained from the comparison of the predicted independent product fission yields with the experimental results and with the neutron-rich fission product data measured with a Penning trap at the Accelerator Laboratory of the University of Jyväskylä (JYFLTRAP).

Initial yield to depth relation for water wells drilled into crystalline bedrock - Pinardville quadrangle, New Hampshire

USGS Publications Warehouse

Drew, L.J.; Schuenemeyer, J.H.; Amstrong, T.R.; Sutphin, D.M.

2001-01-01

A model is proposed to explain the statistical relations between the mean initial water well yields from eight time increments from 1984 to 1998 for wells drilled into the crystalline bedrock aquifer system in the Pinardville area of southern New Hampshire and the type of bedrock, mean well depth, and mean well elevation. Statistical analyses show that the mean total yield of drilling increments is positively correlated with mean total well depth and mean well elevation. In addition, the mean total well yield varies with rock type from a minimum of 46.9 L/min (12.4 gpm) in the Damon Pond granite to a maximum of 74.5 L/min (19.7 gpm) in the Permian pegmatite and granite unit. Across the eight drilling increments that comprise 211 wells each, the percentages of very low-yield wells (1.9 L/min [0.5 gpm] or less) and high-yield wells (151.4 L/min [40 gpm] or more) increased, and those of intermediate-yield wells decreased. As housing development progressed during the 1984 to 1998 interval, the mean depth of the wells and their elevations increased, and the mix of percentages of the bedrock types drilled changed markedly. The proposed model uses a feed-forward mechanism to explain the interaction between the increasing mean elevation, mean well depth, and percentages of very low-yielding wells and the mean well yield. The increasing percentages of very low-yielding wells through time and the economics of the housing market may control the system that forces the mean well depths, percentages of high-yield wells, and mean well yields to increase. The reason for the increasing percentages of very low-yield wells is uncertain, but the explanation is believed to involve the complex structural geology and tectonic history of the Pinardville quadrangle.

Environmental limitation mapping of potential biomass resources across the conterminous United States

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

Daly, Christopher; Halbleib, Michael D.; Hannaway, David B.

Several crops have recently been identified as potential dedicated bioenergy feedstocks for the production of power, fuels, and bioproducts. Despite being identified as early as the 1980s, no systematic work has been undertaken to characterize the spatial distribution of their long-term production potentials in the United states. Such information is a starting point for planners and economic modelers, and there is a need for this spatial information to be developed in a consistent manner for a variety of crops, so that their production potentials can be intercompared to support crop selection decisions. As part of the Sun Grant Regional Feedstockmore » Partnership (RFP), an approach to mapping these potential biomass resources was developed to take advantage of the informational synergy realized when bringing together coordinated field trials, close interaction with expert agronomists, and spatial modeling into a single, collaborative effort. A modeling and mapping system called PRISM-ELM was designed to answer a basic question: How do climate and soil characteristics affect the spatial distribution and long-term production patterns of a given crop? This empirical/mechanistic/biogeographical hybrid model employs a limiting factor approach, where productivity is determined by the most limiting of the factors addressed in submodels that simulate water balance, winter low-temperature response, summer high-temperature response, and soil pH, salinity, and drainage. Yield maps are developed through linear regressions relating soil and climate attributes to reported yield data. The model was parameterized and validated using grain yield data for winter wheat and maize, which served as benchmarks for parameterizing the model for upland and lowland switchgrass, CRP grasses, Miscanthus, biomass sorghum, energycane, willow, and poplar. The resulting maps served as potential production inputs to analyses comparing the viability of biomass crops under various economic scenarios. The modeling and parameterization framework can be expanded to include other biomass crops.« less

Environmental limitation mapping of potential biomass resources across the conterminous United States

DOE PAGES

Daly, Christopher; Halbleib, Michael D.; Hannaway, David B.; ...

2017-12-22

Several crops have recently been identified as potential dedicated bioenergy feedstocks for the production of power, fuels, and bioproducts. Despite being identified as early as the 1980s, no systematic work has been undertaken to characterize the spatial distribution of their long-term production potentials in the United states. Such information is a starting point for planners and economic modelers, and there is a need for this spatial information to be developed in a consistent manner for a variety of crops, so that their production potentials can be intercompared to support crop selection decisions. As part of the Sun Grant Regional Feedstockmore » Partnership (RFP), an approach to mapping these potential biomass resources was developed to take advantage of the informational synergy realized when bringing together coordinated field trials, close interaction with expert agronomists, and spatial modeling into a single, collaborative effort. A modeling and mapping system called PRISM-ELM was designed to answer a basic question: How do climate and soil characteristics affect the spatial distribution and long-term production patterns of a given crop? This empirical/mechanistic/biogeographical hybrid model employs a limiting factor approach, where productivity is determined by the most limiting of the factors addressed in submodels that simulate water balance, winter low-temperature response, summer high-temperature response, and soil pH, salinity, and drainage. Yield maps are developed through linear regressions relating soil and climate attributes to reported yield data. The model was parameterized and validated using grain yield data for winter wheat and maize, which served as benchmarks for parameterizing the model for upland and lowland switchgrass, CRP grasses, Miscanthus, biomass sorghum, energycane, willow, and poplar. The resulting maps served as potential production inputs to analyses comparing the viability of biomass crops under various economic scenarios. The modeling and parameterization framework can be expanded to include other biomass crops.« less

Predicting watershed post-fire sediment yield with the InVEST sediment retention model: Accuracy and uncertainties

USGS Publications Warehouse

Sankey, Joel B.; McVay, Jason C.; Kreitler, Jason R.; Hawbaker, Todd J.; Vaillant, Nicole; Lowe, Scott

2015-01-01

Increased sedimentation following wildland fire can negatively impact water supply and water quality. Understanding how changing fire frequency, extent, and location will affect watersheds and the ecosystem services they supply to communities is of great societal importance in the western USA and throughout the world. In this work we assess the utility of the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) Sediment Retention Model to accurately characterize erosion and sedimentation of burned watersheds. InVEST was developed by the Natural Capital Project at Stanford University (Tallis et al., 2014) and is a suite of GIS-based implementations of common process models, engineered for high-end computing to allow the faster simulation of larger landscapes and incorporation into decision-making. The InVEST Sediment Retention Model is based on common soil erosion models (e.g., USLE – Universal Soil Loss Equation) and determines which areas of the landscape contribute the greatest sediment loads to a hydrological network and conversely evaluate the ecosystem service of sediment retention on a watershed basis. In this study, we evaluate the accuracy and uncertainties for InVEST predictions of increased sedimentation after fire, using measured postfire sediment yields available for many watersheds throughout the western USA from an existing, published large database. We show that the model can be parameterized in a relatively simple fashion to predict post-fire sediment yield with accuracy. Our ultimate goal is to use the model to accurately predict variability in post-fire sediment yield at a watershed scale as a function of future wildfire conditions.

The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and Pilot Studies

NASA Technical Reports Server (NTRS)

Rosenzweig, C.; Jones, J. W.; Hatfield, J. L.; Ruane, A. C.; Boote, K. J.; Thorburn, P.; Antle, J. M.; Nelson, G. C.; Porter, C.; Janssen, S.;

Water Ice Radiolytic O2, H2, and H2O2 Yields for Any Projectile Species, Energy, or Temperature: A Model for Icy Astrophysical Bodies

NASA Astrophysics Data System (ADS)

Teolis, B. D.; Plainaki, C.; Cassidy, T. A.; Raut, U.

2017-10-01

O2, H2, and H2O2 radiolysis from water ice is pervasive on icy astrophysical bodies, but the lack of a self-consistent, quantitative model of the yields of these water products versus irradiation projectile species and energy has been an obstacle to estimating the radiolytic oxidant sources to the surfaces and exospheres of these objects. A major challenge is the wide variation of O2 radiolysis yields between laboratory experiments, ranging over 4 orders of magnitude from 5 × 10-7 to 5 × 10-3 molecules/eV for different particles and energies. We revisit decades of laboratory data to solve this long-standing puzzle, finding an inverse projectile range dependence in the O2 yields, due to preferential O2 formation from an 30 Å thick oxygenated surface layer. Highly penetrating projectile ions and electrons with ranges ≳30 Å are therefore less efficient at producing O2 than slow/heavy ions and low-energy electrons (≲ 400 eV) which deposit most energy near the surface. Unlike O2, the H2O2 yields from penetrating projectiles fall within a comparatively narrow range of (0.1-6) × 10-3 molecules/eV and do not depend on range, suggesting that H2O2 forms deep in the ice uniformly along the projectile track, e.g., by reactions of OH radicals. We develop an analytical model for O2, H2, and H2O2 yields from pure water ice for electrons and singly charged ions of any mass and energy and apply the model to estimate possible O2 source rates on several icy satellites. The yields are upper limits for icy bodies on which surface impurities may be present.

Simulating county-level crop yields in the Conterminous United States using the Community Land Model: The effects of optimizing irrigation and fertilization

DOE PAGES

Leng, Guoyong; Zhang, Xuesong; Huang, Maoyi; ...

2016-11-12

Representing agricultural systems explicitly in Earth system models is important for understanding the water-energy-food nexus under climate change. In this study, we applied Version 4.5 of the Community Land Model (CLM) at a 0.125 degree resolution to provide the first county-scale validation of the model in simulating crop yields over the Conterminous United States (CONUS). We focused on corn and soybean that are both important grain crops and biofuel feedstocks (corn for bioethanol; soybean for biodiesel). We find that the default model substantially under- or over-estimate yields of corn and soybean as compared to the US Department of Agriculture (USDA)more » census data, with corresponding county-level root-mean square error (RMSE) of 45.3 Bu/acre and 12.9 Bu/acre, or 42% and 38% of the US mean yields for these crops, respectively. Based on the numerical experiments, the lack of proper representation of agricultural management practices, such as irrigation and fertilization, was identified as a major cause for the model's poor performance. After implementing an irrigation management scheme calibrated against county-level US Geological Survey (USGS) census data, the county-level RMSE for corn yields reduced to 42.6 Bu/acre. We then incorporated an optimized fertilizer scheme in rate and timing, which is achieved by the constraining annual total fertilizer amount against the USDA data, considering the dynamics between fertilizer demand and supply and adopting a calibrated fertilizer scheduling map. The proposed approach is shown to be effective in increasing the fertilizer use efficiency for corn yields, with county-level RMSE reduced to 23.8 Bu/acre (or 22% of the US mean yield). In regions with similar annual fertilizer applied as in the default, the improvements in corn yield simulations are mainly attributed to application of longer fertilization periods and consideration of the dynamics between fertilizer demand and supply. For soybean which is capable of fixing nitrogen to meet nitrogen demand, the reduced positive bias to 6.9 Bu/acre (or 21% of the country mean) was mainly attributed to consideration of the dynamic interactions between fertilizer demand and supply. Although large bias remains in terms of the spatial pattern (i.e. high county-level RMSE), mainly due to limited performance over the Western US, our results show that optimizing irrigation and fertilization can lead to promising improvement in crop and soybean yield simulations in terms of the mean and variability especially over the Mid-west corn belt, and subsequent evapotranspiration (ET) estimates. Finally, this study demonstrates the CLM4.5 capability for predicting crop yields and their interactions with climate, and highlights the value of continued model improvements and development to understand biogeophysical and biogeochemical impacts of land use and land cover change using an Earth system modeling framework.« less

Simulating county-level crop yields in the Conterminous United States using the Community Land Model: The effects of optimizing irrigation and fertilization

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

Leng, Guoyong; Zhang, Xuesong; Huang, Maoyi

Representing agricultural systems explicitly in Earth system models is important for understanding the water-energy-food nexus under climate change. In this study, we applied Version 4.5 of the Community Land Model (CLM) at a 0.125 degree resolution to provide the first county-scale validation of the model in simulating crop yields over the Conterminous United States (CONUS). We focused on corn and soybean that are both important grain crops and biofuel feedstocks (corn for bioethanol; soybean for biodiesel). We find that the default model substantially under- or over-estimate yields of corn and soybean as compared to the US Department of Agriculture (USDA)more » census data, with corresponding county-level root-mean square error (RMSE) of 45.3 Bu/acre and 12.9 Bu/acre, or 42% and 38% of the US mean yields for these crops, respectively. Based on the numerical experiments, the lack of proper representation of agricultural management practices, such as irrigation and fertilization, was identified as a major cause for the model's poor performance. After implementing an irrigation management scheme calibrated against county-level US Geological Survey (USGS) census data, the county-level RMSE for corn yields reduced to 42.6 Bu/acre. We then incorporated an optimized fertilizer scheme in rate and timing, which is achieved by the constraining annual total fertilizer amount against the USDA data, considering the dynamics between fertilizer demand and supply and adopting a calibrated fertilizer scheduling map. The proposed approach is shown to be effective in increasing the fertilizer use efficiency for corn yields, with county-level RMSE reduced to 23.8 Bu/acre (or 22% of the US mean yield). In regions with similar annual fertilizer applied as in the default, the improvements in corn yield simulations are mainly attributed to application of longer fertilization periods and consideration of the dynamics between fertilizer demand and supply. For soybean which is capable of fixing nitrogen to meet nitrogen demand, the reduced positive bias to 6.9 Bu/acre (or 21% of the country mean) was mainly attributed to consideration of the dynamic interactions between fertilizer demand and supply. Although large bias remains in terms of the spatial pattern (i.e. high county-level RMSE), mainly due to limited performance over the Western US, our results show that optimizing irrigation and fertilization can lead to promising improvement in crop and soybean yield simulations in terms of the mean and variability especially over the Mid-west corn belt, and subsequent evapotranspiration (ET) estimates. Finally, this study demonstrates the CLM4.5 capability for predicting crop yields and their interactions with climate, and highlights the value of continued model improvements and development to understand biogeophysical and biogeochemical impacts of land use and land cover change using an Earth system modeling framework.« less

[Predicting the impact of climate change in the next 40 years on the yield of maize in China].

PubMed

Ma, Yu-ping; Sun, Lin-li; E, You-hao; Wu, Wei

2015-01-01

Climate change will significantly affect agricultural production in China. The combination of the integral regression model and the latest climate projection may well assess the impact of future climate change on crop yield. In this paper, the correlation model of maize yield and meteorological factors was firstly established for different provinces in China by using the integral regression method, then the impact of climate change in the next 40 years on China's maize production was evaluated combined the latest climate prediction with the reason be ing analyzed. The results showed that if the current speeds of maize variety improvement and science and technology development were constant, maize yield in China would be mainly in an increasing trend of reduction with time in the next 40 years in a range generally within 5%. Under A2 climate change scenario, the region with the most reduction of maize yield would be the Northeast except during 2021-2030, and the reduction would be generally in the range of 2.3%-4.2%. Maize yield reduction would be also high in the Northwest, Southwest and middle and lower reaches of Yangtze River after 2031. Under B2 scenario, the reduction of 5.3% in the Northeast in 2031-2040 would be the greatest across all regions. Other regions with considerable maize yield reduction would be mainly in the Northwest and the Southwest. Reduction in maize yield in North China would be small, generally within 2%, under any scenarios, and that in South China would be almost unchanged. The reduction of maize yield in most regions would be greater under A2 scenario than under B2 scenario except for the period of 2021-2030. The effect of the ten day precipitation on maize yield in northern China would be almost positive. However, the effect of ten day average temperature on yield of maize in all regions would be generally negative. The main reason of maize yield reduction was temperature increase in most provinces but precipitation decrease in a few provinces. Assessments of the future change of maize yield in China based on the different methods were not consistent. Further evaluation needs to consider the change of maize variety and scientific and technological progress, and to enhance the reliability of evaluation models.

Satellite Data Inform Forecasts of Crop Growth

NASA Technical Reports Server (NTRS)

2015-01-01

During a Stennis Space Center-led program called Ag 20/20, an engineering contractor developed models for using NASA satellite data to predict crop yield. The model was eventually sold to Genscape Inc., based in Louisville, Kentucky, which has commercialized it as LandViewer. Sold under a subscription model, LandViewer software provides predictions of corn production to ethanol plants and grain traders.

The Timber Resource Inventory Model (TRIM): a projection model for timber supply and policy analysis.

Treesearch

P.L. Tedder; R.N. La Mont; J.C. Kincaid

1987-01-01

TRIM (Timber Resource Inventory Model) is a yield table projection system developed for timber supply projections and policy analysis. TRIM simulates timber growth, inventories, management and area changes, and removals over the projection period. Programs in the TRIM system, card-by-card descriptions of required inputs, table formats, and sample results are presented...

Growth and yield of all-aged Douglas-fir -- western hemlock forest stands: a matrix model with stand diversity effects.

Treesearch

Jingjing Liang; Joseph Buonglorno; Robert A. Monserud

2005-01-01

A density-dependent matrix model was developed for Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) -- western hemlock (Tsuga heterophylla (Raf.) Sarg.) forest stands in the Pacific Northwest of the United States. The model predicted the number and volume of trees for 4 species groups and 19 diameter classes. The parameters...

Nonstandard working schedules and health: the systematic search for a comprehensive model.

PubMed

Merkus, Suzanne L; Holte, Kari Anne; Huysmans, Maaike A; van Mechelen, Willem; van der Beek, Allard J

2015-10-23

Theoretical models on shift work fall short of describing relevant health-related pathways associated with the broader concept of nonstandard working schedules. Shift work models neither combine relevant working time characteristics applicable to nonstandard schedules nor include the role of rest periods and recovery in the development of health complaints. Therefore, this paper aimed to develop a comprehensive model on nonstandard working schedules to address these shortcomings. A literature review was conducted using a systematic search and selection process. Two searches were performed: one associating the working time characteristics time-of-day and working time duration with health and one associating recovery after work with health. Data extracted from the models were used to develop a comprehensive model on nonstandard working schedules and health. For models on the working time characteristics, the search strategy yielded 3044 references, of which 26 met the inclusion criteria that contained 22 distinctive models. For models on recovery after work, the search strategy yielded 896 references, of which seven met the inclusion criteria containing seven distinctive models. Of the models on the working time characteristics, three combined time-of-day with working time duration, 18 were on time-of-day (i.e. shift work), and one was on working time duration. The model developed in the paper has a comprehensive approach to working hours and other work-related risk factors and proposes that they should be balanced by positive non-work factors to maintain health. Physiological processes leading to health complaints are circadian disruption, sleep deprivation, and activation that should be counterbalanced by (re-)entrainment, restorative sleep, and recovery, respectively, to maintain health. A comprehensive model on nonstandard working schedules and health was developed. The model proposes that work and non-work as well as their associated physiological processes need to be balanced to maintain good health. The model gives researchers a useful overview over the various risk factors and pathways associated with health that should be considered when studying any form of nonstandard working schedule.

Predicting maize phenology: Intercomparison of functions for developmental response to temperature

USDA-ARS?s Scientific Manuscript database

Accurate prediction of phenological development in maize is fundamental to determining crop adaptation and yield potential. A number of thermal functions are used in crop models, but their relative precision in predicting maize development has not been quantified. The objectives of this study were t...

Constructing an Affective Tutoring System for Designing Course Learning and Evaluation

ERIC Educational Resources Information Center

Wang, Cheng-Hung; Lin, Hao-Chiang Koong

2018-01-01

Computers and the Internet are indispensable in modern life. Increasingly useful digital environments and technological developments have reshaped models of knowledge acquisition. Studies on the development of online learning have yielded valuable insights. In the design of online teaching systems that can replicate face-to-face teaching,…

Evolution of plastic anisotropy for high-strain-rate computations

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

Schiferl, S.K.; Maudlin, P.J.

1994-12-01

A model for anisotropic material strength, and for changes in the anisotropy due to plastic strain, is described. This model has been developed for use in high-rate, explicit, Lagrangian multidimensional continuum-mechanics codes. The model handles anisotropies in single-phase materials, in particular the anisotropies due to crystallographic texture--preferred orientations of the single-crystal grains. Textural anisotropies, and the changes in these anisotropies, depend overwhelmingly no the crystal structure of the material and on the deformation history. The changes, particularly for a complex deformations, are not amenable to simple analytical forms. To handle this problem, the material model described here includes a texturemore » code, or micromechanical calculation, coupled to a continuum code. The texture code updates grain orientations as a function of tensor plastic strain, and calculates the yield strength in different directions. A yield function is fitted to these yield points. For each computational cell in the continuum simulation, the texture code tracks a particular set of grain orientations. The orientations will change due to the tensor strain history, and the yield function will change accordingly. Hence, the continuum code supplies a tensor strain to the texture code, and the texture code supplies an updated yield function to the continuum code. Since significant texture changes require relatively large strains--typically, a few percent or more--the texture code is not called very often, and the increase in computer time is not excessive. The model was implemented, using a finite-element continuum code and a texture code specialized for hexagonal-close-packed crystal structures. The results for several uniaxial stress problems and an explosive-forming problem are shown.« less

Simulation of streamflow and the effects of brush management on water yields in the upper Guadalupe River watershed, south-central Texas, 1995-2010

USGS Publications Warehouse

Bumgarner, Johnathan R.; Thompson, Florence E.

2012-01-01

The U.S. Geological Survey, in cooperation with the Texas State Soil and Water Conservation Board and the Upper Guadalupe River Authority, developed and calibrated a Soil and Water Assessment Tool watershed model of the upper Guadalupe River watershed in south-central Texas to simulate streamflow and the effects of brush management on water yields in the watershed and to Canyon Lake for 1995-2010. Model simulations were done to quantify the possible change in water yield of individual subbasins in the upper Guadalupe River watershed as a result of the replacement of ashe juniper (Juniperus ashei) with grasslands. The simulation results will serve as a tool for resource managers to guide their brush-management efforts. Model hydrology was calibrated with streamflow data collected at the U.S. Geological Survey streamflow-gaging station 08167500 Guadalupe River near Spring Branch, Tex., for 1995-2010. Simulated monthly streamflow showed very good agreement with measured monthly streamflow: a percent bias of -5, a coefficient of determination of 0.91, and a Nash-Sutcliffe coefficient of model efficiency of 0.85. Modified land-cover input datasets were generated for the model in order to simulate the replacement of ashe juniper with grasslands in 23 brush-management subbasins in the watershed. Each of the 23 simulations showed an increase in simulated water yields in the targeted subbasins and to Canyon Lake. The simulated increases in average annual water yields in the subbasins ranged from 6,370 to 119,000 gallons per acre of ashe juniper replaced with grasslands with an average of 38,900 gallons. The simulated increases in average annual water yields to Canyon Lake from upstream subbasins ranged from 6,640 to 72,700 gallons per acre of ashe juniper replaced with grasslands with an average of 34,700 gallons.

Genetic analysis of milk production traits of Tunisian Holsteins using random regression test-day model with Legendre polynomials

PubMed Central

2018-01-01

Objective The objective of this study was to estimate genetic parameters of milk, fat, and protein yields within and across lactations in Tunisian Holsteins using a random regression test-day (TD) model. Methods A random regression multiple trait multiple lactation TD model was used to estimate genetic parameters in the Tunisian dairy cattle population. Data were TD yields of milk, fat, and protein from the first three lactations. Random regressions were modeled with third-order Legendre polynomials for the additive genetic, and permanent environment effects. Heritabilities, and genetic correlations were estimated by Bayesian techniques using the Gibbs sampler. Results All variance components tended to be high in the beginning and the end of lactations. Additive genetic variances for milk, fat, and protein yields were the lowest and were the least variable compared to permanent variances. Heritability values tended to increase with parity. Estimates of heritabilities for 305-d yield-traits were low to moderate, 0.14 to 0.2, 0.12 to 0.17, and 0.13 to 0.18 for milk, fat, and protein yields, respectively. Within-parity, genetic correlations among traits were up to 0.74. Genetic correlations among lactations for the yield traits were relatively high and ranged from 0.78±0.01 to 0.82±0.03, between the first and second parities, from 0.73±0.03 to 0.8±0.04 between the first and third parities, and from 0.82±0.02 to 0.84±0.04 between the second and third parities. Conclusion These results are comparable to previously reported estimates on the same population, indicating that the adoption of a random regression TD model as the official genetic evaluation for production traits in Tunisia, as developed by most Interbull countries, is possible in the Tunisian Holsteins. PMID:28823122

Genetic analysis of milk production traits of Tunisian Holsteins using random regression test-day model with Legendre polynomials.

PubMed

Ben Zaabza, Hafedh; Ben Gara, Abderrahmen; Rekik, Boulbaba

2018-05-01

The objective of this study was to estimate genetic parameters of milk, fat, and protein yields within and across lactations in Tunisian Holsteins using a random regression test-day (TD) model. A random regression multiple trait multiple lactation TD model was used to estimate genetic parameters in the Tunisian dairy cattle population. Data were TD yields of milk, fat, and protein from the first three lactations. Random regressions were modeled with third-order Legendre polynomials for the additive genetic, and permanent environment effects. Heritabilities, and genetic correlations were estimated by Bayesian techniques using the Gibbs sampler. All variance components tended to be high in the beginning and the end of lactations. Additive genetic variances for milk, fat, and protein yields were the lowest and were the least variable compared to permanent variances. Heritability values tended to increase with parity. Estimates of heritabilities for 305-d yield-traits were low to moderate, 0.14 to 0.2, 0.12 to 0.17, and 0.13 to 0.18 for milk, fat, and protein yields, respectively. Within-parity, genetic correlations among traits were up to 0.74. Genetic correlations among lactations for the yield traits were relatively high and ranged from 0.78±0.01 to 0.82±0.03, between the first and second parities, from 0.73±0.03 to 0.8±0.04 between the first and third parities, and from 0.82±0.02 to 0.84±0.04 between the second and third parities. These results are comparable to previously reported estimates on the same population, indicating that the adoption of a random regression TD model as the official genetic evaluation for production traits in Tunisia, as developed by most Interbull countries, is possible in the Tunisian Holsteins.

Skiff-based Sonar/LiDAR Survey to Calibrate Reservoir Volumes for Watershed Sediment Yield Studies: Carmel River Example

NASA Astrophysics Data System (ADS)

Smith, D. P.; Kvitek, R.; Quan, S.; Iampietro, P.; Paddock, E.; Richmond, S. F.; Gomez, K.; Aiello, I. W.; Consulo, P.

2009-12-01

Models of watershed sediment yield are complicated by spatial and temporal variability of geologic substrate, land cover, and precipitation parameters. Episodic events such as ENSO cycles and severe wildfire are frequent enough to matter in the long-term average yield, and they can produce short-lived, extreme geomorphic responses. The sediment yield from extreme events is difficult to accurately capture because of the obvious dangers associated with field measurements during flood conditions, but it is critical to include extreme values for developing realistic models of rainfall-sediment yield relations, and for calculating long term average denudation rates. Dammed rivers provide a time-honored natural laboratory for quantifying average annual sediment yield and extreme-event sediment yield. While lead-line surveys of the past provided crude estimates of reservoir sediment trapping, recent advances in geospatial technology now provide unprecedented opportunities to improve volume change measurements. High-precision digital elevation models surveyed on an annual basis, or before-and-after specific rainfall-runoff events can be used to quantify relations between rainfall and sediment yield as a function of landscape parameters, including spatially explicit fire intensity. The Basin-Complex Fire of June and July 2008 resulted in moderate to severe burns in the 114 km^2 portion of the Carmel River watershed above Los Padres Dam. The US Geological Survey produced a debris flow probability/volume model for the region indicating that the reservoir could lose considerable capacity if intense enough precipitation occurred in the 2009-10 winter. Loss of Los Padres reservoir capacity has implications for endangered steelhead and red-legged frogs, and groundwater on municipal water supply. In anticipation of potentially catastrophic erosion, we produced an accurate volume calculation of the Los Padres reservoir in fall 2009, and locally monitored hillslope and fluvial processes during winter months. The pre-runoff reservoir volume was developed by collecting and merging sonar and LiDAR data from a small research skiff equipped with a high-precision positioning and attitude-correcting system. The terrestrial LiDAR data were augmented with shore-based total station positioning. Watershed monitoring included benchmarked serial stream surveys and semi-quantitative assessment of a variety of near-channel colluvial processes. Rainfall in the 2009-10 water year was not intense enough to trigger widespread debris flows of slope failure in the burned watershed, but dry ravel was apparently accelerated. The geomorphic analysis showed that sediment yield was not significantly higher during this low-rainfall year, despite the wide-spread presence of very steep, fire-impacted slopes. Because there was little to no increase in sediment yield this year, we have postponed our second reservoir survey. A predicted ENSO event that might bring very intense rains to the watershed is currently predicted for winter 2009-10.

Development and testing of a compartmentalized reaction network model for redox zones in contaminated aquifers

USGS Publications Warehouse

Abrams , Robert H.; Loague, Keith; Kent, Douglas B.

1998-01-01

The work reported here is the first part of a larger effort focused on efficient numerical simulation of redox zone development in contaminated aquifers. The sequential use of various electron acceptors, which is governed by the energy yield of each reaction, gives rise to redox zones. The large difference in energy yields between the various redox reactions leads to systems of equations that are extremely ill-conditioned. These equations are very difficult to solve, especially in the context of coupled fluid flow, solute transport, and geochemical simulations. We have developed a general, rational method to solve such systems where we focus on the dominant reactions, compartmentalizing them in a manner that is analogous to the redox zones that are often observed in the field. The compartmentalized approach allows us to easily solve a complex geochemical system as a function of time and energy yield, laying the foundation for our ongoing work in which we couple the reaction network, for the development of redox zones, to a model of subsurface fluid flow and solute transport. Our method (1) solves the numerical system without evoking a redox parameter, (2) improves the numerical stability of redox systems by choosing which compartment and thus which reaction network to use based upon the concentration ratios of key constituents, (3) simulates the development of redox zones as a function of time without the use of inhibition factors or switching functions, and (4) can reduce the number of transport equations that need to be solved in space and time. We show through the use of various model performance evaluation statistics that the appropriate compartment choice under different geochemical conditions leads to numerical solutions without significant error. The compartmentalized approach described here facilitates the next phase of this effort where we couple the redox zone reaction network to models of fluid flow and solute transport.

Multi-phase SPH model for simulation of erosion and scouring by means of the shields and Drucker-Prager criteria.

NASA Astrophysics Data System (ADS)

Zubeldia, Elizabeth H.; Fourtakas, Georgios; Rogers, Benedict D.; Farias, Márcio M.

2018-07-01

A two-phase numerical model using Smoothed Particle Hydrodynamics (SPH) is developed to model the scouring of two-phase liquid-sediments flows with large deformation. The rheology of sediment scouring due to flows with slow kinematics and high shear forces presents a challenge in terms of spurious numerical fluctuations. This paper bridges the gap between the non-Newtonian and Newtonian flows by proposing a model that combines the yielding, shear and suspension layer mechanics which are needed to predict accurately the local erosion phenomena. A critical bed-mobility condition based on the Shields criterion is imposed to the particles located at the sediment surface. Thus, the onset of the erosion process is independent on the pressure field and eliminates the numerical problem of pressure dependant erosion at the interface. This is combined with the Drucker-Prager yield criterion to predict the onset of yielding of the sediment surface and a concentration suspension model. The multi-phase model has been implemented in the open-source DualSPHysics code accelerated with a graphics processing unit (GPU). The multi-phase model has been compared with 2-D reference numerical models and new experimental data for scour with convergent results. Numerical results for a dry-bed dam break over an erodible bed shows improved agreement with experimental scour and water surface profiles compared to well-known SPH multi-phase models.

How Do Various Maize Crop Models Vary in Their Responses to Climate Change Factors?

NASA Technical Reports Server (NTRS)

Bassu, Simona; Brisson, Nadine; Grassini, Patricio; Durand, Jean-Louis; Boote, Kenneth; Lizaso, Jon; Jones, James W.; Rosenzweig, Cynthia; Ruane, Alex C.; Adam, Myriam;

How do various maize crop models vary in their responses to climate change factors?

PubMed

Bassu, Simona; Brisson, Nadine; Durand, Jean-Louis; Boote, Kenneth; Lizaso, Jon; Jones, James W; Rosenzweig, Cynthia; Ruane, Alex C; Adam, Myriam; Baron, Christian; Basso, Bruno; Biernath, Christian; Boogaard, Hendrik; Conijn, Sjaak; Corbeels, Marc; Deryng, Delphine; De Sanctis, Giacomo; Gayler, Sebastian; Grassini, Patricio; Hatfield, Jerry; Hoek, Steven; Izaurralde, Cesar; Jongschaap, Raymond; Kemanian, Armen R; Kersebaum, K Christian; Kim, Soo-Hyung; Kumar, Naresh S; Makowski, David; Müller, Christoph; Nendel, Claas; Priesack, Eckart; Pravia, Maria Virginia; Sau, Federico; Shcherbak, Iurii; Tao, Fulu; Teixeira, Edmar; Timlin, Dennis; Waha, Katharina

2014-07-01

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information. © 2014 John Wiley & Sons Ltd.

Brazilian Soybean Yields and Yield Gaps Vary with Farm Size

NASA Astrophysics Data System (ADS)

Jeffries, G. R.; Cohn, A.; Griffin, T. S.; Bragança, A.

2017-12-01

Understanding the farm size-specific characteristics of crop yields and yield gaps may help to improve yields by enabling better targeting of technical assistance and agricultural development programs. Linking remote sensing-based yield estimates with property boundaries provides a novel view of the relationship between farm size and yield structure (yield magnitude, gaps, and stability over time). A growing literature documents variations in yield gaps, but largely ignores the role of farm size as a factor shaping yield structure. Research on the inverse farm size-productivity relationship (IR) theory - that small farms are more productive than large ones all else equal - has documented that yield magnitude may vary by farm size, but has not considered other yield structure characteristics. We examined farm size - yield structure relationships for soybeans in Brazil for years 2001-2015. Using out-of-sample soybean yield predictions from a statistical model, we documented 1) gaps between the 95th percentile of attained yields and mean yields within counties and individual fields, and 2) yield stability defined as the standard deviation of time-detrended yields at given locations. We found a direct relationship between soy yields and farm size at the national level, while the strength and the sign of the relationship varied by region. Soybean yield gaps were found to be inversely related to farm size metrics, even when yields were only compared to farms of similar size. The relationship between farm size and yield stability was nonlinear, with mid-sized farms having the most stable yields. The work suggests that farm size is an important factor in understanding yield structure and that opportunities for improving soy yields in Brazil are greatest among smaller farms.

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.

Application of wheat yield model to United States and India. [Great Plains

NASA Technical Reports Server (NTRS)

Feyerherm, A. M. (Principal Investigator)

1977-01-01

The author has identified the following significant results. The wheat yield model was applied to the major wheat-growing areas of the US and India. In the US Great Plains, estimates from the winter and spring wheat models agreed closely with USDA-SRS values in years with the lowest yields, but underestimated in years with the highest yields. Application to the Eastern Plains and Northwest indicated the importance of cultural factors, as well as meteorological ones in the model. It also demonstrated that the model could be used, in conjunction with USDA-SRRS estimates, to estimate yield losses due to factors not included in the model, particularly diseases and freezes. A fixed crop calendar for India was built from a limited amount of available plot data from that country. Application of the yield model gave measurable evidence that yield variation from state to state was due to different mixes of levels of meteorological and cultural factors.

A model-updating procedure to stimulate piezoelectric transducers accurately.

PubMed

Piranda, B; Ballandras, S; Steichen, W; Hecart, B

2001-09-01

The use of numerical calculations based on finite element methods (FEM) has yielded significant improvements in the simulation and design of piezoelectric transducers piezoelectric transducer utilized in acoustic imaging. However, the ultimate precision of such models is directly controlled by the accuracy of material characterization. The present work is dedicated to the development of a model-updating technique adapted to the problem of piezoelectric transducer. The updating process is applied using the experimental admittance of a given structure for which a finite element analysis is performed. The mathematical developments are reported and then applied to update the entries of a FEM of a two-layer structure (a PbZrTi-PZT-ridge glued on a backing) for which measurements were available. The efficiency of the proposed approach is demonstrated, yielding the definition of a new set of constants well adapted to predict the structure response accurately. Improvement of the proposed approach, consisting of the updating of material coefficients not only on the admittance but also on the impedance data, is finally discussed.

Simultaneous biosorption of chromium(VI) and copper(II) on Rhizopus arrhizus in packed column reactor: Application of the competitive Freundlich model

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

Sag, Y.; Atacoglu, I.; Kutsal, T.

1999-12-01

The simultaneous biosorption of Cr(VI) and Cu(II) on free Rhizopus arrhizus in a packed column operated in the continuous mode was investigated and compared to the single metal ion situation. The breakthrough curves were measured as a function of feed flow rate, feed pH, and different combinations of metal ion concentrations in the feed solutions. Column competitive biosorption data were evaluated in terms of the maximum (equilibrium) capacity in the column, the amount of metal loading on the R. arrhizus surface, the adsorption yield, and the total adsorption yield. In the single-ion situation the adsorption isotherms were developed for optimummore » conditions, and it was seen that the adsorption equilibrium data fit the noncompetitive Freundlich model. For the multicomponent adsorption equilibrium the competitive adsorption isotherms were also developed. The competitive Freundlich model for binary metal mixtures represented most the column adsorption equilibrium data of Cr(VI) and Cu(II) on R. arrhizus satisfactorily.« less

Spatialized Application of Remotely Sensed Data Assimilation Methods for Farmland Drought Monitoring Using Two Different Crop Models

NASA Astrophysics Data System (ADS)

Silvestro, Paolo Cosmo; Casa, Raffaele; Pignatti, Stefano; Castaldi, Fabio; Yang, Hao; Guijun, Yang

2016-08-01

The aim of this work was to develop a tool to evaluate the effect of water stress on yield losses at the farmland and regional scale, by assimilating remotely sensed biophysical variables into crop growth models. Biophysical variables were retrieved from HJ1A, HJ1B and Landsat 8 images, using an algorithm based on the training of artificial neural networks on PROSAIL.For the assimilation, two crop models of differing degree of complexity were used: Aquacrop and SAFY. For Aquacrop, an optimization procedure to reduce the difference between the remotely sensed and simulated CC was developed. For the modified version of SAFY, the assimilation procedure was based on the Ensemble Kalman Filter.These procedures were tested in a spatialized application, by using data collected in the rural area of Yangling (Shaanxi Province) between 2013 and 2015Results were validated by utilizing yield data both from ground measurements and statistical survey.

Developing a diagnostic model for estimating terrestrial vegetation gross primary productivity using the photosynthetic quantum yield and Earth Observation data.

PubMed

Ogutu, Booker O; Dash, Jadunandan; Dawson, Terence P

2013-09-01

This article develops a new carbon exchange diagnostic model [i.e. Southampton CARbon Flux (SCARF) model] for estimating daily gross primary productivity (GPP). The model exploits the maximum quantum yields of two key photosynthetic pathways (i.e. C3 and C4 ) to estimate the conversion of absorbed photosynthetically active radiation into GPP. Furthermore, this is the first model to use only the fraction of photosynthetically active radiation absorbed by photosynthetic elements of the canopy (i.e. FAPARps ) rather than total canopy, to predict GPP. The GPP predicted by the SCARF model was comparable to in situ GPP measurements (R(2)  > 0.7) in most of the evaluated biomes. Overall, the SCARF model predicted high GPP in regions dominated by forests and croplands, and low GPP in shrublands and dry-grasslands across USA and Europe. The spatial distribution of GPP from the SCARF model over Europe and conterminous USA was comparable to those from the MOD17 GPP product except in regions dominated by croplands. The SCARF model GPP predictions were positively correlated (R(2)  > 0.5) to climatic and biophysical input variables indicating its sensitivity to factors controlling vegetation productivity. The new model has three advantages, first, it prescribes only two quantum yield terms rather than species specific light use efficiency terms; second, it uses only the fraction of PAR absorbed by photosynthetic elements of the canopy (FAPARps ) hence capturing the actual PAR used in photosynthesis; and third, it does not need a detailed land cover map that is a major source of uncertainty in most remote sensing based GPP models. The Sentinel satellites planned for launch in 2014 by the European Space Agency have adequate spectral channels to derive FAPARps at relatively high spatial resolution (20 m). This provides a unique opportunity to produce global GPP operationally using the Southampton CARbon Flux (SCARF) model at high spatial resolution. © 2013 John Wiley & Sons Ltd.

Development of Large-Eddy Interaction Model for inhomogeneous turbulent flows

NASA Technical Reports Server (NTRS)

Hong, S. K.; Payne, F. R.

1987-01-01

The objective of this paper is to demonstrate the applicability of a currently proposed model, with minimum empiricism, for calculation of the Reynolds stresses and other turbulence structural quantities in a channel. The current Large-Eddy Interaction Model not only yields Reynolds stresses but also presents an opportunity to illuminate typical characteristic motions of large-scale turbulence and the phenomenological aspects of engineering models for two Reynolds numbers.

Video image analysis in the Australian meat industry - precision and accuracy of predicting lean meat yield in lamb carcasses.

PubMed

Hopkins, D L; Safari, E; Thompson, J M; Smith, C R

2004-06-01

A wide selection of lamb types of mixed sex (ewes and wethers) were slaughtered at a commercial abattoir and during this process images of 360 carcasses were obtained online using the VIAScan® system developed by Meat and Livestock Australia. Soft tissue depth at the GR site (thickness of tissue over the 12th rib 110 mm from the midline) was measured by an abattoir employee using the AUS-MEAT sheep probe (PGR). Another measure of this thickness was taken in the chiller using a GR knife (NGR). Each carcass was subsequently broken down to a range of trimmed boneless retail cuts and the lean meat yield determined. The current industry model for predicting meat yield uses hot carcass weight (HCW) and tissue depth at the GR site. A low level of accuracy and precision was found when HCW and PGR were used to predict lean meat yield (R(2)=0.19, r.s.d.=2.80%), which could be improved markedly when PGR was replaced by NGR (R(2)=0.41, r.s.d.=2.39%). If the GR measures were replaced by 8 VIAScan® measures then greater prediction accuracy could be achieved (R(2)=0.52, r.s.d.=2.17%). A similar result was achieved when the model was based on principal components (PCs) computed from the 8 VIAScan® measures (R(2)=0.52, r.s.d.=2.17%). The use of PCs also improved the stability of the model compared to a regression model based on HCW and NGR. The transportability of the models was tested by randomly dividing the data set and comparing coefficients and the level of accuracy and precision. Those models based on PCs were superior to those based on regression. It is demonstrated that with the appropriate modeling the VIAScan® system offers a workable method for predicting lean meat yield automatically.

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

Dholabhai, Pratik P., E-mail: pratik.dholabhai@asu.ed; Anwar, Shahriar, E-mail: anwar@asu.ed; Adams, James B., E-mail: jim.adams@asu.ed

Kinetic lattice Monte Carlo (KLMC) model is developed for investigating oxygen vacancy diffusion in praseodymium-doped ceria. The current approach uses a database of activation energies for oxygen vacancy migration, calculated using first-principles, for various migration pathways in praseodymium-doped ceria. Since the first-principles calculations revealed significant vacancy-vacancy repulsion, we investigate the importance of that effect by conducting simulations with and without a repulsive interaction. Initially, as dopant concentrations increase, vacancy concentration and thus conductivity increases. However, at higher concentrations, vacancies interfere and repel one another, and dopants trap vacancies, creating a 'traffic jam' that decreases conductivity, which is consistent with themore » experimental findings. The modeled effective activation energy for vacancy migration slightly increased with increasing dopant concentration in qualitative agreement with the experiment. The current methodology comprising a blend of first-principle calculations and KLMC model provides a very powerful fundamental tool for predicting the optimal dopant concentration in ceria related materials. -- graphical abstract: Ionic conductivity in praseodymium doped ceria as a function of dopant concentration calculated using the kinetic lattice Monte Carlo vacancy-repelling model, which predicts the optimal composition for achieving maximum conductivity. Display Omitted Research highlights: {yields} KLMC method calculates the accurate time-dependent diffusion of oxygen vacancies. {yields} KLMC-VR model predicts a dopant concentration of {approx}15-20% to be optimal in PDC. {yields} At higher dopant concentration, vacancies interfere and repel one another, and dopants trap vacancies. {yields} Activation energy for vacancy migration increases as a function of dopant content« less

Identification of line-specific strategies for improving carotenoid production in synthetic maize through data-driven mathematical modeling.

PubMed

Comas, Jorge; Benfeitas, Rui; Vilaprinyo, Ester; Sorribas, Albert; Solsona, Francesc; Farré, Gemma; Berman, Judit; Zorrilla, Uxue; Capell, Teresa; Sandmann, Gerhard; Zhu, Changfu; Christou, Paul; Alves, Rui

2016-09-01

Plant synthetic biology is still in its infancy. However, synthetic biology approaches have been used to manipulate and improve the nutritional and health value of staple food crops such as rice, potato and maize. With current technologies, production yields of the synthetic nutrients are a result of trial and error, and systematic rational strategies to optimize those yields are still lacking. Here, we present a workflow that combines gene expression and quantitative metabolomics with mathematical modeling to identify strategies for increasing production yields of nutritionally important carotenoids in the seed endosperm synthesized through alternative biosynthetic pathways in synthetic lines of white maize, which is normally devoid of carotenoids. Quantitative metabolomics and gene expression data are used to create and fit parameters of mathematical models that are specific to four independent maize lines. Sensitivity analysis and simulation of each model is used to predict which gene activities should be further engineered in order to increase production yields for carotenoid accumulation in each line. Some of these predictions (e.g. increasing Zmlycb/Gllycb will increase accumulated β-carotenes) are valid across the four maize lines and consistent with experimental observations in other systems. Other predictions are line specific. The workflow is adaptable to any other biological system for which appropriate quantitative information is available. Furthermore, we validate some of the predictions using experimental data from additional synthetic maize lines for which no models were developed. © 2016 The Authors The Plant Journal © 2016 John Wiley & Sons Ltd.

Electron-induced electron yields of uncharged insulating materials

NASA Astrophysics Data System (ADS)

Hoffmann, Ryan Carl

Presented here are electron-induced electron yield measurements from high-resistivity, high-yield materials to support a model for the yield of uncharged insulators. These measurements are made using a low-fluence, pulsed electron beam and charge neutralization to minimize charge accumulation. They show charging induced changes in the total yield, as much as 75%, even for incident electron fluences of <3 fC/mm2, when compared to an uncharged yield. The evolution of the yield as charge accumulates in the material is described in terms of electron recapture, based on the extended Chung and Everhart model of the electron emission spectrum and the dual dynamic layer model for internal charge distribution. This model is used to explain charge-induced total yield modification measured in high-yield ceramics, and to provide a method for determining electron yield of uncharged, highly insulating, high-yield materials. A sequence of materials with progressively greater charge susceptibility is presented. This series starts with low-yield Kapton derivative called CP1, then considers a moderate-yield material, Kapton HN, and ends with a high-yield ceramic, polycrystalline aluminum oxide. Applicability of conductivity (both radiation induced conductivity (RIC) and dark current conductivity) to the yield is addressed. Relevance of these results to spacecraft charging is also discussed.

Incremental Validity of Multidimensional Proficiency Scores from Diagnostic Classification Models: An Illustration for Elementary School Mathematics

ERIC Educational Resources Information Center

Kunina-Habenicht, Olga; Rupp, André A.; Wilhelm, Oliver

2017-01-01

Diagnostic classification models (DCMs) hold great potential for applications in summative and formative assessment by providing discrete multivariate proficiency scores that yield statistically driven classifications of students. Using data from a newly developed diagnostic arithmetic assessment that was administered to 2032 fourth-grade students…

Induced Innovation and Social Inequality: Evidence from Infant Medical Care

ERIC Educational Resources Information Center

Cutler, David M.; Meara, Ellen; Richards-Shubik, Seth

2012-01-01

We develop a model of induced innovation that applies to medical research. Our model yields three empirical predictions. First, initial death rates and subsequent research effort should be positively correlated. Second, research effort should be associated with more rapid mortality declines. Third, as a byproduct of targeting the most common…

Measuring Experiential Avoidance: A Preliminary Test of a Working Model

ERIC Educational Resources Information Center

Hayes, Steven C.; Strosahl, Kirk; Wilson, Kelly G.; Bissett, Richard T.; Pistorello, Jacqueline; Toarmino, Dosheen; Polusny, Melissa A.; Dykstra, Thane A.; Batten, Sonja V.; Bergan, John; Stewart, Sherry H.; Zvolensky, Michael J.; Eifert, Georg H.; Bond, Frank W.; Forsyth, John P.; Karekla, Maria; Mccurry, Susan M.

2004-01-01

The present study describes the development of a short, general measure of experiential avoidance, based on a specific theoretical approach to this process. A theoretically driven iterative exploratory analysis using structural equation modeling on data from a clinical sample yielded a single factor comprising 9 items. A fully confirmatory factor…

Modeling yield and biomass responses of maize cultivars to climate change under full and deficit irrigation

USDA-ARS?s Scientific Manuscript database

As climate change becomes inevitable, the agricultural community is concerned about its possible effects on crop production and developing strategies to adapt to this change. In this study, the Root Zone Water Quality Model (RZWQM2) was calibrated with four years of maize data from central Colorado ...

Effects of changing climate and cultivar on the phenology and yield of winter wheat in the North China Plain

NASA Astrophysics Data System (ADS)

Li, Kenan; Yang, Xiaoguang; Tian, Hanqin; Pan, Shufen; Liu, Zhijuan; Lu, Shuo

2016-01-01

Understanding how changing climate and cultivars influence crop phenology and potential yield is essential for crop adaptation to future climate change. In this study, crop and daily weather data collected from six sites across the North China Plain were used to drive a crop model to analyze the impacts of climate change and cultivar development on the phenology and production of winter wheat from 1981 to 2005. Results showed that both the growth period (GP) and the vegetative growth period (VGP) decreased during the study period, whereas changes in the reproductive growth period (RGP) either increased slightly or had no significant trend. Although new cultivars could prolong the winter wheat phenology (0.3˜3.8 days per decade for GP), climate warming impacts were more significant and mainly accounted for the changes. The harvest index and kernel number per stem weight have significantly increased. Model simulation indicated that the yield of winter wheat exhibited increases (5.0˜19.4 %) if new cultivars were applied. Climate change demonstrated a negative effect on winter wheat yield as suggested by the simulation driven by climate data only (-3.3 to -54.8 kg ha-1 year-1, except for Lushi). Results of this study also indicated that winter wheat cultivar development can compensate for the negative effects of future climatic change.

Effects of changing climate and cultivar on the phenology and yield of winter wheat in the North China Plain.

PubMed

Li, Kenan; Yang, Xiaoguang; Tian, Hanqin; Pan, Shufen; Liu, Zhijuan; Lu, Shuo

2016-01-01

Understanding how changing climate and cultivars influence crop phenology and potential yield is essential for crop adaptation to future climate change. In this study, crop and daily weather data collected from six sites across the North China Plain were used to drive a crop model to analyze the impacts of climate change and cultivar development on the phenology and production of winter wheat from 1981 to 2005. Results showed that both the growth period (GP) and the vegetative growth period (VGP) decreased during the study period, whereas changes in the reproductive growth period (RGP) either increased slightly or had no significant trend. Although new cultivars could prolong the winter wheat phenology (0.3∼3.8 days per decade for GP), climate warming impacts were more significant and mainly accounted for the changes. The harvest index and kernel number per stem weight have significantly increased. Model simulation indicated that the yield of winter wheat exhibited increases (5.0∼19.4%) if new cultivars were applied. Climate change demonstrated a negative effect on winter wheat yield as suggested by the simulation driven by climate data only (-3.3 to -54.8 kg ha(-1) year(-1), except for Lushi). Results of this study also indicated that winter wheat cultivar development can compensate for the negative effects of future climatic change.

Calculation of cracking under pulsed heat loads in tungsten manufactured according to ITER specifications

NASA Astrophysics Data System (ADS)

Arakcheev, A. S.; Skovorodin, D. I.; Burdakov, A. V.; Shoshin, A. A.; Polosatkin, S. V.; Vasilyev, A. A.; Postupaev, V. V.; Vyacheslavov, L. N.; Kasatov, A. A.; Huber, A.; Mertens, Ph; Wirtz, M.; Linsmeier, Ch; Kreter, A.; Löwenhoff, Th; Begrambekov, L.; Grunin, A.; Sadovskiy, Ya

2015-12-01

A mathematical model of surface cracking under pulsed heat load was developed. The model correctly describes a smooth brittle-ductile transition. The elastic deformation is described in a thin-heated-layer approximation. The plastic deformation is described with the Hollomon equation. The time dependence of the deformation and stresses is described for one heating-cooling cycle for a material without initial plastic deformation. The model can be applied to tungsten manufactured according to ITER specifications. The model shows that the stability of stress-relieved tungsten deteriorates when the base temperature increases. This proved to be a result of the close ultimate tensile and yield strengths. For a heat load of arbitrary magnitude a stability criterion was obtained in the form of condition on the relation of the ultimate tensile and yield strengths.

Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models

DOE PAGES

Blanc, Élodie

2017-01-26

This study provides statistical emulators of crop yields based on global gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. The ensemble of simulations is used to build a panel of annual crop yields from five crop models and corresponding monthly summer weather variables for over a century at the grid cell level globally. This dataset is then used to estimate, for each crop and gridded crop model, the statistical relationship between yields, temperature, precipitation and carbon dioxide. This study considers a new functional form to better capture the non-linear response of yields to weather,more » especially for extreme temperature and precipitation events, and now accounts for the effect of soil type. In- and out-of-sample validations show that the statistical emulators are able to replicate spatial patterns of yields crop levels and changes overtime projected by crop models reasonably well, although the accuracy of the emulators varies by model and by region. This study therefore provides a reliable and accessible alternative to global gridded crop yield models. By emulating crop yields for several models using parsimonious equations, the tools provide a computationally efficient method to account for uncertainty in climate change impact assessments.« less

Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models

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

Blanc, Élodie

This study provides statistical emulators of crop yields based on global gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. The ensemble of simulations is used to build a panel of annual crop yields from five crop models and corresponding monthly summer weather variables for over a century at the grid cell level globally. This dataset is then used to estimate, for each crop and gridded crop model, the statistical relationship between yields, temperature, precipitation and carbon dioxide. This study considers a new functional form to better capture the non-linear response of yields to weather,more » especially for extreme temperature and precipitation events, and now accounts for the effect of soil type. In- and out-of-sample validations show that the statistical emulators are able to replicate spatial patterns of yields crop levels and changes overtime projected by crop models reasonably well, although the accuracy of the emulators varies by model and by region. This study therefore provides a reliable and accessible alternative to global gridded crop yield models. By emulating crop yields for several models using parsimonious equations, the tools provide a computationally efficient method to account for uncertainty in climate change impact assessments.« less

A functional-structural model of rice linking quantitative genetic information with morphological development and physiological processes.

PubMed

Xu, Lifeng; Henke, Michael; Zhu, Jun; Kurth, Winfried; Buck-Sorlin, Gerhard

2011-04-01

Although quantitative trait loci (QTL) analysis of yield-related traits for rice has developed rapidly, crop models using genotype information have been proposed only relatively recently. As a first step towards a generic genotype-phenotype model, we present here a three-dimensional functional-structural plant model (FSPM) of rice, in which some model parameters are controlled by functions describing the effect of main-effect and epistatic QTLs. The model simulates the growth and development of rice based on selected ecophysiological processes, such as photosynthesis (source process) and organ formation, growth and extension (sink processes). It was devised using GroIMP, an interactive modelling platform based on the Relational Growth Grammar formalism (RGG). RGG rules describe the course of organ initiation and extension resulting in final morphology. The link between the phenotype (as represented by the simulated rice plant) and the QTL genotype was implemented via a data interface between the rice FSPM and the QTLNetwork software, which computes predictions of QTLs from map data and measured trait data. Using plant height and grain yield, it is shown how QTL information for a given trait can be used in an FSPM, computing and visualizing the phenotypes of different lines of a mapping population. Furthermore, we demonstrate how modification of a particular trait feeds back on the entire plant phenotype via the physiological processes considered. We linked a rice FSPM to a quantitative genetic model, thereby employing QTL information to refine model parameters and visualizing the dynamics of development of the entire phenotype as a result of ecophysiological processes, including the trait(s) for which genetic information is available. Possibilities for further extension of the model, for example for the purposes of ideotype breeding, are discussed.

A functional–structural model of rice linking quantitative genetic information with morphological development and physiological processes

PubMed Central

Xu, Lifeng; Henke, Michael; Zhu, Jun; Kurth, Winfried; Buck-Sorlin, Gerhard

2011-01-01

Background and Aims Although quantitative trait loci (QTL) analysis of yield-related traits for rice has developed rapidly, crop models using genotype information have been proposed only relatively recently. As a first step towards a generic genotype–phenotype model, we present here a three-dimensional functional–structural plant model (FSPM) of rice, in which some model parameters are controlled by functions describing the effect of main-effect and epistatic QTLs. Methods The model simulates the growth and development of rice based on selected ecophysiological processes, such as photosynthesis (source process) and organ formation, growth and extension (sink processes). It was devised using GroIMP, an interactive modelling platform based on the Relational Growth Grammar formalism (RGG). RGG rules describe the course of organ initiation and extension resulting in final morphology. The link between the phenotype (as represented by the simulated rice plant) and the QTL genotype was implemented via a data interface between the rice FSPM and the QTLNetwork software, which computes predictions of QTLs from map data and measured trait data. Key Results Using plant height and grain yield, it is shown how QTL information for a given trait can be used in an FSPM, computing and visualizing the phenotypes of different lines of a mapping population. Furthermore, we demonstrate how modification of a particular trait feeds back on the entire plant phenotype via the physiological processes considered. Conclusions We linked a rice FSPM to a quantitative genetic model, thereby employing QTL information to refine model parameters and visualizing the dynamics of development of the entire phenotype as a result of ecophysiological processes, including the trait(s) for which genetic information is available. Possibilities for further extension of the model, for example for the purposes of ideotype breeding, are discussed. PMID:21247905

Uncertainties in Predicting Rice Yield by Current Crop Models Under a Wide Range of Climatic Conditions

NASA Technical Reports Server (NTRS)

Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Adam, Myriam; Bregaglio, Simone; Buis, Samuel; Confalonieri, Roberto; Fumoto, Tamon;

Conduct urban agglomeration with the baton of transportation.

DOT National Transportation Integrated Search

2013-12-01

A key indicator of traffic activity patterns is commuting distance. Shorter commuting distances yield less traffic, fewer emissions, : and lower energy consumption. This study develops a spatial error seemingly unrelated regression model to investiga...

Climate risks on potato yield in Europe

NASA Astrophysics Data System (ADS)

Sun, Xun; Lall, Upmanu

2016-04-01

The yield of potatoes is affected by water and temperature during the growing season. We study the impact of a suite of climate variables on potato yield at country level. More than ten climate variables related to the growth of potato are considered, including the seasonal rainfall and temperature, but also extreme conditions at different averaging periods from daily to monthly. A Bayesian hierarchical model is developed to jointly consider the risk of heat stress, cold stress, wet and drought. Future climate risks are investigated through the projection of future climate data. This study contributes to assess the risks of present and future climate risks on potatoes yield, especially the risks of extreme events, which could be used to guide better sourcing strategy and ensure food security in the future.

A spatially explicit whole-system model of the lignocellulosic bioethanol supply chain: an assessment of decentralised processing potential

PubMed Central

Dunnett, Alex J; Adjiman, Claire S; Shah, Nilay

2008-01-01

Background Lignocellulosic bioethanol technologies exhibit significant capacity for performance improvement across the supply chain through the development of high-yielding energy crops, integrated pretreatment, hydrolysis and fermentation technologies and the application of dedicated ethanol pipelines. The impact of such developments on cost-optimal plant location, scale and process composition within multiple plant infrastructures is poorly understood. A combined production and logistics model has been developed to investigate cost-optimal system configurations for a range of technological, system scale, biomass supply and ethanol demand distribution scenarios specific to European agricultural land and population densities. Results Ethanol production costs for current technologies decrease significantly from $0.71 to $0.58 per litre with increasing economies of scale, up to a maximum single-plant capacity of 550 × 106 l year-1. The development of high-yielding energy crops and consolidated bio-processing realises significant cost reductions, with production costs ranging from $0.33 to $0.36 per litre. Increased feedstock yields result in systems of eight fully integrated plants operating within a 500 × 500 km2 region, each producing between 1.24 and 2.38 × 109 l year-1 of pure ethanol. A limited potential for distributed processing and centralised purification systems is identified, requiring developments in modular, ambient pretreatment and fermentation technologies and the pipeline transport of pure ethanol. Conclusion The conceptual and mathematical modelling framework developed provides a valuable tool for the assessment and optimisation of the lignocellulosic bioethanol supply chain. In particular, it can provide insight into the optimal configuration of multiple plant systems. This information is invaluable in ensuring (near-)cost-optimal strategic development within the sector at the regional and national scale. The framework is flexible and can thus accommodate a range of processing tasks, logistical modes, by-product markets and impacting policy constraints. Significant scope for application to real-world case studies through dynamic extensions of the formulation has been identified. PMID:18662392

Estimation of monthly water yields and flows for 1951-2012 for the United States portion of the Great Lakes Basin with AFINCH

USGS Publications Warehouse

Luukkonen, Carol L.; Holtschlag, David J.; Reeves, Howard W.; Hoard, Christopher J.; Fuller, Lori M.

2015-01-01

Monthly water yields from 105,829 catchments and corresponding flows in 107,691 stream segments were estimated for water years 1951–2012 in the Great Lakes Basin in the United States. Both sets of estimates were computed by using the Analysis of Flows In Networks of CHannels (AFINCH) application within the NHDPlus geospatial data framework. AFINCH provides an environment to develop constrained regression models to integrate monthly streamflow and water-use data with monthly climatic data and fixed basin characteristics data available within NHDPlus or supplied by the user. For this study, the U.S. Great Lakes Basin was partitioned into seven study areas by grouping selected hydrologic subregions and adjoining cataloguing units. This report documents the regression models and data used to estimate monthly water yields and flows in each study area. Estimates of monthly water yields and flows are presented in a Web-based mapper application. Monthly flow time series for individual stream segments can be retrieved from the Web application and used to approximate monthly flow-duration characteristics and to identify possible trends.

Growing Chlorella sp. on meat processing wastewater for nutrient removal and biomass production.

PubMed

Lu, Qian; Zhou, Wenguang; Min, Min; Ma, Xiaochen; Chandra, Ceria; Doan, Yen T T; Ma, Yiwei; Zheng, Hongli; Cheng, Sibo; Griffith, Richard; Chen, Paul; Chen, Chi; Urriola, Pedro E; Shurson, Gerald C; Gislerød, Hans R; Ruan, Roger

2015-12-01

In this work, Chlorella sp. (UM6151) was selected to treat meat processing wastewater for nutrient removal and biomass production. To balance the nutrient profile and improve biomass yield at low cost, an innovative algae cultivation model based on wastewater mixing was developed. The result showed that biomass yield (0.675-1.538 g/L) of algae grown on mixed wastewater was much higher than that on individual wastewater and artificial medium. Wastewater mixing eased the bottleneck for algae growth and contributed to the improved biomass yield. Furthermore, in mixed wastewater with sufficient nitrogen, ammonia nitrogen removal efficiencies (68.75-90.38%) and total nitrogen removal efficiencies (30.06-50.94%) were improved. Wastewater mixing also promoted the synthesis of protein in algal cells. Protein content of algae growing on mixed wastewater reached 60.87-68.65%, which is much higher than that of traditional protein source. Algae cultivation model based on wastewater mixing is an efficient and economical way to improve biomass yield. Copyright © 2015 Elsevier Ltd. All rights reserved.

Reliable yields of public water-supply wells in the fractured-rock aquifers of central Maryland, USA

NASA Astrophysics Data System (ADS)

Hammond, Patrick A.

2018-02-01

Most studies of fractured-rock aquifers are about analytical models used for evaluating aquifer tests or numerical methods for describing groundwater flow, but there have been few investigations on how to estimate the reliable long-term drought yields of individual hard-rock wells. During the drought period of 1998 to 2002, many municipal water suppliers in the Piedmont/Blue Ridge areas of central Maryland (USA) had to institute water restrictions due to declining well yields. Previous estimates of the yields of those wells were commonly based on extrapolating drawdowns, measured during short-term single-well hydraulic pumping tests, to the first primary water-bearing fracture in a well. The extrapolations were often made from pseudo-equilibrium phases, frequently resulting in substantially over-estimated well yields. The methods developed in the present study to predict yields consist of extrapolating drawdown data from infinite acting radial flow periods or by fitting type curves of other conceptual models to the data, using diagnostic plots, inverse analysis and derivative analysis. Available drawdowns were determined by the positions of transition zones in crystalline rocks or thin-bedded consolidated sandstone/limestone layers (reservoir rocks). Aquifer dewatering effects were detected by type-curve matching of step-test data or by breaks in the drawdown curves constructed from hydraulic tests. Operational data were then used to confirm the predicted yields and compared to regional groundwater levels to determine seasonal variations in well yields. Such well yield estimates are needed by hydrogeologists and water engineers for the engineering design of water systems, but should be verified by the collection of long-term monitoring data.

Variability in soybean yield in Brazil stemming from the interaction of heterogeneous management and climate variability

NASA Astrophysics Data System (ADS)

Cohn, A.; Bragança, A.; Jeffries, G. R.

2017-12-01

An increasing share of global agricultural production can be found in the humid tropics. Therefore, an improved understanding of the mechanisms governing variability in the output of tropical agricultural systems is of increasing importance for food security including through climate change adaptation. Yet, the long window over which many tropical crops can be sown, the diversity of crop varieties and management practices combine to challenge inference into climate risk to cropping output in analyses of tropical crop-climate sensitivity employing administrative data. In this paper, we leverage a newly developed spatially explicit dataset of soybean yields in Brazil to combat this problem. The dataset was built by training a model of remotely-sensed vegetation index data and land cover classification data using a rich in situ dataset of soybean yield and management variables collected over the period 2006 to 2016. The dataset contains soybean yields by plant date, cropping frequency, and maturity group for each 5km grid cell in Brazil. We model variation in these yields using an approach enabling the estimation of the influence of management factors on the sensitivity of soybean yields to variability in: cumulative solar radiation, extreme degree days, growing degree days, flooding rain in the harvest period, and dry spells in the rainy season. We find strong variation in climate sensitivity by management class. Planting date and maturity group each explained a great deal more variation in yield sensitivity than did cropping frequency. Brazil collects comparatively fine spatial resolution yield data. But, our attempt to replicate our results using administrative soy yield data revealed substantially lesser crop-climate sensitivity; suggesting that previous analyses employing administrative data may have underestimated climate risk to tropical soy production.

The stochastic resonance for the incidence function model of metapopulation

NASA Astrophysics Data System (ADS)

Li, Jiang-Cheng; Dong, Zhi-Wei; Zhou, Ruo-Wei; Li, Yun-Xian; Qian, Zhen-Wei

2017-06-01

A stochastic model with endogenous and exogenous periodicities is proposed in this paper on the basis of metapopulation dynamics to model the crop yield losses due to pests and diseases. The rationale is that crop yield losses occur because the physiology of the growing crop is negatively affected by pests and diseases in a dynamic way over time as crop both grows and develops. Metapopulation dynamics can thus be used to model the resultant crop yield losses. The stochastic metapopulation process is described by using the Simplified Incidence Function model (IFM). Compared to the original IFMs, endogenous and exogenous periodicities are considered in the proposed model to handle the cyclical patterns observed in pest infestations, diseases epidemics, and exogenous affecting factors such as temperature and rainfalls. Agricultural loss data in China are used to fit the proposed model. Experimental results demonstrate that: (1) Model with endogenous and exogenous periodicities is a better fit; (2) When the internal system fluctuations and external environmental fluctuations are negatively correlated, EIL or the cost of loss is monotonically increasing; when the internal system fluctuations and external environmental fluctuations are positively correlated, an outbreak of pests and diseases might occur; (3) If the internal system fluctuations and external environmental fluctuations are positively correlated, an optimal patch size can be identified which will greatly weaken the effects of external environmental influence and hence inhibit pest infestations and disease epidemics.

Evaluation of limited irrigation strategies to improve water use efficiency and wheat yield in the North China Plain

PubMed Central

Zhang, Di; Li, Ruiqi; Batchelor, William D.; Ju, Hui

2018-01-01

The North China Plain is one of the most important grain production regions in China, but is facing serious water shortages. To achieve a balance between water use and the need for food self-sufficiency, new water efficient irrigation strategies need to be developed that balance water use with farmer net return. The Crop Environment Resource Synthesis Wheat (CERES-Wheat model) was calibrated and evaluated with two years of data which consisted of 3–4 irrigation treatments, and the model was used to investigate long-term winter wheat productivity and water use from irrigation management in the North China Plain. The calibrated model simulated accurately above-ground biomass, grain yield and evapotranspiration of winter wheat in response to irrigation management. The calibrated model was then run using weather data from 1994–2016 in order to evaluate different irrigation strategies. The simulated results using historical weather data showed that grain yield and water use was sensitive to different irrigation strategies including amounts and dates of irrigation applications. The model simulated the highest yield when irrigation was applied at jointing (T9) in normal and dry rainfall years, and gave the highest simulated yields for irrigation at double ridge (T8) in wet years. A single simulated irrigation at jointing (T9) produced yields that were 88% compared to using a double irrigation treatment at T1 and T9 in wet years, 86% of that in normal years, and 91% of that in dry years. A single irrigation at jointing or double ridge produced higher water use efficiency because it obtained higher evapotranspiration. The simulated farmer irrigation practices produced the highest yield and net income. When the cost of water was taken into account, limited irrigation was found to be more profitable based on assumptions about future water costs. In order to increase farmer income, a subsidy will likely be needed to compensate farmers for yield reductions due to water savings. These results showed that there is a cost to the farmer for water conservation, but limiting irrigation to a single irrigation at jointing would minimize impact on farmer net return in North China Plain. PMID:29370186

Model development of production management unit to enhance entrepreneurship attitude of vocational school students from fashion department

NASA Astrophysics Data System (ADS)

Sumaryani, Sri

2018-03-01

The purpose of this study is to develop a model of production management unit to enhance entrepreneurship attitude of vocational school students from fashion department. This study concerns in developing students' entrepreneurship attitude in management which includes planning, organizing, applying and evaluation. The study uses Research and Development (R & D) approach with three main steps; preliminary study, development step, and product validation. Research subject was vocational school teachers from fashion department in Semarang, Salatiga and Demak. This study yields a development model of production management unit that could enhance vocational school students' entrepreneurship attitude in fashion department. The result shows that research subjects have understood about of production management unit in Vocational School (SMK).

Experimental Characterization and Material Modelling of an AZ31 Magnesium Sheet Alloy at Elevated Temperatures under Consideration of the Tension-Compression Asymmetry

NASA Astrophysics Data System (ADS)

Behrens, B.-A.; Bouguecha, A.; Bonk, C.; Dykiert, M.

2017-09-01

Magnesium sheet alloys have a great potential as a construction material in the aerospace and automotive industry. However, the current state of research regarding temperature dependent material parameters for the description of the plastic behaviour of magnesium sheet alloys is scarce in literature and accurate statements concerning yield criteria and appropriate characterization tests to describe the plastic behaviour of a magnesium sheet alloy at elevated temperatures in deep drawing processes are to define. Hence, in this paper the plastic behaviour of the well-established magnesium sheet alloy AZ31 has been characterized by means of convenient mechanical tests (e. g. tension, compression and biaxial tests) at temperatures between 180 and 230 °C. In this manner, anisotropic and hardening behaviour as well as differences between the tension-compression asymmetry of the yield locus have been estimated. Furthermore, using the evaluated data from the above mentioned tests, two different yield criteria have been parametrized; the commonly used Hill’48 and an orthotropic yield criterion, CPB2006, which was developed especially for materials with hexagonal close packed lattice structure and is able to describe an asymmetrical yielding behaviour regarding tensile and compressive stress states. Numerical simulations have been finally carried out with both yield functions in order to assess the accuracy of the material models.

The buffer value of groundwater when well yield is limited

NASA Astrophysics Data System (ADS)

Foster, T.; Brozović, N.; Speir, C.

2017-04-01

A large proportion of the total value of groundwater in conjunctive use systems is associated with the ability to smooth out shortfalls in surface water supply during droughts. Previous research has argued that aquifer depletion in these regions will impact farmers negatively by reducing the available stock of groundwater to buffer production in future periods, and also by increasing the costs of groundwater extraction. However, existing studies have not considered how depletion may impact the productivity of groundwater stocks in conjunctive use systems through reductions in well yields. In this work, we develop a hydro-economic modeling framework to quantify the effects of changes in well yields on the buffer value of groundwater, and apply this model to an illustrative case study of tomato production in California's Central Valley. Our findings demonstrate that farmers with low well yields are forced to forgo significant production and profits because instantaneous groundwater supply is insufficient to buffer surface water shortfalls in drought years. Negative economic impacts of low well yields are an increasing function of surface water variability, and are also greatest for farmers operating less efficient irrigation systems. These results indicate that impacts of well yield reductions on the productivity of groundwater are an important economic impact of aquifer depletion, and that failure to consider this feedback may lead to significant errors in estimates of the value of groundwater management in conjunctive use systems.

Towards practical application of sensors for monitoring animal health: the effect of post-calving health problems on rumination duration, activity and milk yield.

PubMed

Steensels, Machteld; Maltz, Ephraim; Bahr, Claudia; Berckmans, Daniel; Antler, Aharon; Halachmi, Ilan

2017-05-01

Three sources of sensory data: cow's individual rumination duration, activity and milk yield were evaluated as possible indicators for clinical diagnosis, focusing on post-calving health problems such as ketosis and metritis. Data were collected from a computerised dairy-management system on a commercial dairy farm with Israeli Holstein cows. In the analysis, 300 healthy and 403 sick multiparous cows were studied during the first 3 weeks after calving. A mixed model with repeated measurements was used to compare healthy cows with sick cows. In the period from 5 d before diagnosis and treatment to 2 d after it, rumination duration and activity were lower in the sick cows compared to healthy cows. The milk yield of sick cows was lower than that of the healthy cows during a period lasting from 5 d before until 5 d after the day of diagnosis and treatment. Differences in the milk yield of sick cows compared with healthy cows became greater from 5 to 1 d before diagnosis and treatment. The greatest significant differences occurred 3 d before diagnosis for rumination duration and 1 d before diagnosis for activity and milk yield. These results indicate that a model can be developed to automatically detect post-calving health problems including ketosis and metritis, based on rumination duration, activity and milk yield.

Negative impacts of climate change on cereal yields: statistical evidence from France

NASA Astrophysics Data System (ADS)

Gammans, Matthew; Mérel, Pierre; Ortiz-Bobea, Ariel

2017-05-01

In several world regions, climate change is predicted to negatively affect crop productivity. The recent statistical yield literature emphasizes the importance of flexibly accounting for the distribution of growing-season temperature to better represent the effects of warming on crop yields. We estimate a flexible statistical yield model using a long panel from France to investigate the impacts of temperature and precipitation changes on wheat and barley yields. Winter varieties appear sensitive to extreme cold after planting. All yields respond negatively to an increase in spring-summer temperatures and are a decreasing function of precipitation about historical precipitation levels. Crop yields are predicted to be negatively affected by climate change under a wide range of climate models and emissions scenarios. Under warming scenario RCP8.5 and holding growing areas and technology constant, our model ensemble predicts a 21.0% decline in winter wheat yield, a 17.3% decline in winter barley yield, and a 33.6% decline in spring barley yield by the end of the century. Uncertainty from climate projections dominates uncertainty from the statistical model. Finally, our model predicts that continuing technology trends would counterbalance most of the effects of climate change.

Optimization of a Coupling Process for Insulin Degludec According to a Quality by Design (QbD) Paradigm.

PubMed

Nie, Lei; Hu, Mingming; Yan, Xu; Guo, Tingting; Wang, Haibin; Zhang, Sheng; Qu, Haibin

2018-05-03

This case study described a successful application of the quality by design (QbD) principles to a coupling process development of insulin degludec. Failure mode effects analysis (FMEA) risk analysis was first used to recognize critical process parameters (CPPs). Five CPPs, including coupling temperature (Temp), pH of desB30 solution (pH), reaction time (Time), desB30 concentration (Conc), and molar equivalent of ester per mole of desB30 insulin (MolE), were then investigated using a fractional factorial design. The curvature effect was found significant, indicating the requirement of second-order models. Afterwards, a central composite design was built with an augmented star and center points study. Regression models were developed for the CPPs to predict the purity and yield of predegludec using above experimental data. The R 2 and adjusted R 2 were higher than 96 and 93% for the two models respectively. The Q 2 values were more than 80% indicating a good predictive ability of models. MolE was found to be the most significant factor affecting both yield and purity of predegludec. Temp, pH, and Conc were also significant for predegludec purity, while Time appeared to remarkably influence the yield model. The multi-dimensional design space and normal operating region (NOR) with a robust setpoint were determined using a probability-based Monte-Carlo simulation method. The verified experimental results showed that the design space was reliable and effective. This study enriches the understanding of acetylation process and is instructional to other complicated operations in biopharmaceutical engineering.

Winter wheat response to irrigation, nitrogen fertilization, and cold hazards in the Community Land Model 5

NASA Astrophysics Data System (ADS)

Lu, Y.

2017-12-01

Winter wheat is a staple crop for global food security, and is the dominant vegetation cover for a significant fraction of earth's croplands. As such, it plays an important role in soil carbon balance, and land-atmosphere interactions in these key regions. Accurate simulation of winter wheat growth is not only crucial for future yield prediction under changing climate, but also for understanding the energy and water cycles for winter wheat dominated regions. A winter wheat growth model has been developed in the Community Land Model 4.5 (CLM4.5), but its responses to irrigation and nitrogen fertilization have not been validated. In this study, I will validate winter wheat growth response to irrigation and nitrogen fertilization at five winter wheat field sites (TXLU, KSMA, NESA, NDMA, and ABLE) in North America, which were originally designed to understand winter wheat response to nitrogen fertilization and water treatments (4 nitrogen levels and 3 irrigation regimes). I also plan to further update the linkages between winter wheat yield and cold hazards. The previous cold damage function only indirectly affects yield through reduction on leaf area index (LAI) and hence photosynthesis, such approach could sometimes produce an unwanted higher yield when the reduced LAI saved more nutrient in the grain fill stage.

Aqua/Aura Updated Inclination Adjust Maneuver Performance Prediction Model

NASA Technical Reports Server (NTRS)

Boone, Spencer

2017-01-01

This presentation will discuss the updated Inclination Adjust Maneuver (IAM) performance prediction model that was developed for Aqua and Aura following the 2017 IAM series. This updated model uses statistical regression methods to identify potential long-term trends in maneuver parameters, yielding improved predictions when re-planning past maneuvers. The presentation has been reviewed and approved by Eric Moyer, ESMO Deputy Project Manager.

Quantifying potential yield and water-limited yield of summer maize in the North China Plain

NASA Astrophysics Data System (ADS)

Jiang, Mingnuo; Liu, Chaoshun; Chen, Maosi

2017-09-01

The North China Plain is a major food producing region in China, and climate change could pose a threat to food production in the region. Based on China Meteorological Forcing Dataset, simulating the growth of summer maize in North China Plain from 1979 to 2015 with the regional implementation of crop growth model WOFOST. The results showed that the model can reflect the potential yield and water-limited yield of Summer Maize in North China Plain through the calibration and validation of WOFOST model. After the regional implementation of model, combined with the reanalysis data, the model can better reproduce the regional history of summer maize yield in the North China Plain. The yield gap in Southeastern Beijing, southern Tianjin, southern Hebei province, Northwestern Shandong province is significant, these means the water condition is the main factor to summer maize yield in these regions.

Evaluating the capabilities of watershed-scale models in estimating sediment yield at field-scale.

PubMed

Sommerlot, Andrew R; Nejadhashemi, A Pouyan; Woznicki, Sean A; Giri, Subhasis; Prohaska, Michael D

2013-09-30

Many watershed model interfaces have been developed in recent years for predicting field-scale sediment loads. They share the goal of providing data for decisions aimed at improving watershed health and the effectiveness of water quality conservation efforts. The objectives of this study were to: 1) compare three watershed-scale models (Soil and Water Assessment Tool (SWAT), Field_SWAT, and the High Impact Targeting (HIT) model) against calibrated field-scale model (RUSLE2) in estimating sediment yield from 41 randomly selected agricultural fields within the River Raisin watershed; 2) evaluate the statistical significance among models; 3) assess the watershed models' capabilities in identifying areas of concern at the field level; 4) evaluate the reliability of the watershed-scale models for field-scale analysis. The SWAT model produced the most similar estimates to RUSLE2 by providing the closest median and the lowest absolute error in sediment yield predictions, while the HIT model estimates were the worst. Concerning statistically significant differences between models, SWAT was the only model found to be not significantly different from the calibrated RUSLE2 at α = 0.05. Meanwhile, all models were incapable of identifying priorities areas similar to the RUSLE2 model. Overall, SWAT provided the most correct estimates (51%) within the uncertainty bounds of RUSLE2 and is the most reliable among the studied models, while HIT is the least reliable. The results of this study suggest caution should be exercised when using watershed-scale models for field level decision-making, while field specific data is of paramount importance. Copyright © 2013 Elsevier Ltd. All rights reserved.

Dilution physics modeling: Dissolution/precipitation chemistry

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

Onishi, Y.; Reid, H.C.; Trent, D.S.

This report documents progress made to date on integrating dilution/precipitation chemistry and new physical models into the TEMPEST thermal-hydraulics computer code. Implementation of dissolution/precipitation chemistry models is necessary for predicting nonhomogeneous, time-dependent, physical/chemical behavior of tank wastes with and without a variety of possible engineered remediation and mitigation activities. Such behavior includes chemical reactions, gas retention, solids resuspension, solids dissolution and generation, solids settling/rising, and convective motion of physical and chemical species. Thus this model development is important from the standpoint of predicting the consequences of various engineered activities, such as mitigation by dilution, retrieval, or pretreatment, that can affectmore » safe operations. The integration of a dissolution/precipitation chemistry module allows the various phase species concentrations to enter into the physical calculations that affect the TEMPEST hydrodynamic flow calculations. The yield strength model of non-Newtonian sludge correlates yield to a power function of solids concentration. Likewise, shear stress is concentration-dependent, and the dissolution/precipitation chemistry calculations develop the species concentration evolution that produces fluid flow resistance changes. Dilution of waste with pure water, molar concentrations of sodium hydroxide, and other chemical streams can be analyzed for the reactive species changes and hydrodynamic flow characteristics.« less

Comparing Benign and Malignant Neoplasia and DSB Induction for Low-and High-LET Radiation

NASA Astrophysics Data System (ADS)

Burns, Fredric; Tang, Moon-Shong Eric; Wu, Feng

One-and 2-stage models based on DNA double strand breaks (DSBs) have been developed to describe the dose and LET dependence of cancer induction in rat skin exposed to the Bragg plateau of several ion beams or electron radiation. Data are presented showing that carcinomas (malignant) and fibromas (benign) are induced differently by low and high LET radiation. DSBs are subject to complex repair processes, including homologous and non-homologous end joining, that slowly eliminate broken chromosome ends but at the expense of elevating genomic instability that increases the risk of neoplasia. In this formulation the initial molecular lesion in radiation carcinogenesis is assumed to be a DNA double strand break (DSB). The 2-event model assumes that pairs of DSBs join to create cellular genomic instability that eventually progresses to malignancy. The 1-event model assumes that joining is insignificant but that unrepaired DSBs remain and are sufficiently destabilizing to produce low-grade neoplasias. The respective expected relationships between neoplasia yield (Y), radiation dose (D) and LET (L) are: Y(D) = CLD + BD2 (A) for 2-events and Y(D) = CLD (B) for 1-event. Respective B and C values have been evaluated empirically for carcinomas, fibromas and DSBs, the latter via the -H2Ax technique in surrogate keratinocytes, for several types of radiations, including, 40Ar ions, 56Fe ions, 20Ne ions, protons, electrons and x-rays. Fibromas outnumber carcinomas by about 6:1 but are more sensitive than carcinomas to the cytolethal effect of the radiations. The 2-event model agrees well with carcinoma yields in rat skin but fails to model fibromas correctly. Instead the fibroma yields best fitted with the 1-event model for the high LET ion radiations, but at very low LET (electron radiation), an empirical D3 component becomes apparent which is not currently incorporated into the theoretical model. At higher LET values, the D3 component was not detected. The overall results are summarized as follows: 1) DSBs predict carcinoma yields in regard to dose and LET in conformity to Equation A, 2) fibroma yields for 40Ar and 20Ne ions conform to Equation B, i.e. yield proportionality to D and L and 3) the positive slope of the fibroma yield to electron radiation is a third order discrepancy suggesting a more complicated response that has yet to be incorporated into the model. The results provide encouragement that once calibrated for humans, a short-term test of DSB yield might be capable of predicting cancer risks for a variety of space radiation exposure scenarios.

How Exemplar Counselor Advocates Develop Social Justice Interest: A Qualitative Investigation

ERIC Educational Resources Information Center

Swartz, Melissa Robinson; Limberg, Dodie; Gold, Joshua

2018-01-01

The authors examined the experiences of 10 peer-nominated exemplar counselor advocates using grounded theory methodology (Strauss & Corbin, [Strauss, A., 1998]). Analysis by the authors yielded a model of how exemplar counselor advocates develop a social justice interest and provided key insights on how counselor educators can enhance social…

Influence of conduit flow mechanics on magma rheology and the growth style of lava domes

NASA Astrophysics Data System (ADS)

Husain, Taha; Elsworth, Derek; Voight, Barry; Mattioli, Glen; Jansma, Pamela

2018-06-01

We develop a 2-D particle-mechanics model to explore different lava-dome growth styles. These range from endogenous lava dome growth comprising expansion of a ductile dome core to the exogenous extrusion of a degassed lava plug resulting in generation of a lava spine. We couple conduit flow dynamics with surface growth of the evolving lava dome, fuelled by an open-system magma chamber undergoing continuous replenishment. The conduit flow model accounts for the variation in rheology of ascending magma that results from degassing-induced crystallization. A period of reduced effusive flow rates promote enhanced degassing-induced crystallization. A degassed lava plug extrudes exogenously for magmas with crystal contents (ϕ) of 78 per cent, yield strength >1.62 MPa, and at flow rates of <0.5 m3 s-1, while endogenous dome growth is predicted at higher flow rates (Qout > 3 m3 s-1) for magma with lower relative yield strengths (<1 MPa). At moderately high flow rates (Qout = 4 m3 s-1), the extrusion of magma with lower crystal content (62 per cent) and low interparticulate yield strength (0.6 MPa) results in the development of endogenous shear lobes. Our simulations model the periodic extrusion history at Mount St. Helens (1980-1983). Endogenous growth initiates in the simulated lava dome with the extrusion of low yield strength magma (ϕ = 0.63 and τp = 0.76 MPa) after the crystallized viscous plug (ϕ = 0.87 and τp = 3 MPa) at the conduit exit is forced out by the high discharge rate pulse (2 < Qout < 12 m3 s-1). The size of the endogenous viscous plug and the occurrence of exogenous growth depend on magma yield strength and the magma chamber volume, which control the periodicity of the effusion. Our simulations generate dome morphologies similar to those observed at Mount St Helens, and demonstrate the degree to which domes can sag and spread during and following extrusion pulses. This process, which has been observed at Mount St. Helens and other locations, largely reflects gravitational loading of dome with a viscous core, with retardation by yield strength and talus friction.

Accelerated high-yield generation of limb-innervating motor neurons from human stem cells

PubMed Central

Amoroso, Mackenzie W.; Croft, Gist F.; Williams, Damian J.; O’Keeffe, Sean; Carrasco, Monica A.; Davis, Anne R.; Roybon, Laurent; Oakley, Derek H.; Maniatis, Tom; Henderson, Christopher E.; Wichterle, Hynek

2013-01-01

Human pluripotent stem cells are a promising source of differentiated cells for developmental studies, cell transplantation, disease modeling, and drug testing. However, their widespread use even for intensely studied cell types like spinal motor neurons is hindered by the long duration and low yields of existing protocols for in vitro differentiation and by the molecular heterogeneity of the populations generated. We report a combination of small molecules that within 3 weeks induce motor neurons at up to 50% abundance and with defined subtype identities of relevance to neurodegenerative disease. Despite their accelerated differentiation, motor neurons expressed combinations of HB9, ISL1 and column-specific markers that mirror those observed in vivo in human fetal spinal cord. They also exhibited spontaneous and induced activity, and projected axons towards muscles when grafted into developing chick spinal cord. Strikingly, this novel protocol preferentially generates motor neurons expressing markers of limb-innervating lateral motor column motor neurons (FOXP1+/LHX3−). Access to high-yield cultures of human limb-innervating motor neuron subtypes will facilitate in-depth study of motor neuron subtype-specific properties, disease modeling, and development of large-scale cell-based screening assays. PMID:23303937

Greenhouse studies on the phyto-extraction capacity of Cynodon nlemfuensis for lead and cadmium under irrigation with treated wastewater

NASA Astrophysics Data System (ADS)

Madyiwa, S.; Chimbari, M. J.; Schutte, C. F.; Nyamangara, J.

For over 30 years, discharge of sewage effluent and sludge on pasturelands has been used in Zimbabwe as a cheap method for secondary treatment of wastewater without any monitoring of accumulation of heavy metals in soils and grasses, let alone in animals grazing on the pastures. Cynodon nlemfuensis (star grass) has been the main grass planted on the wastewater irrigated pasturelands. This study was conducted to assess the capacity of star grass to accumulate lead (Pb) and cadmium (Cd) and develop models incorporating grass yield, metal uptake and soil bio-available (EDTA extractable) metal content, that could be used to determine critical grass and soil concentrations at which grass productivity declines. Star grass was planted in 30 fertilized pots containing sandy soil within a greenhouse. The pots consisted of nine treatments of varying levels of added inorganic Pb and Cd subjected to treated wastewater application and one control that had no added metals and received water application only. The elements were applied to the soils once just after planting the grass. Chemical analyses showed that star grass had a relatively high phyto-extraction capacity of Pb and Cd, comparable to that of hyper-accumulating grasses such as Lolium perenne (rye grass). It accumulated Pb and Cd to levels far beyond the recommended maximum limits for pasture grass. Analysis of variance on log-normal transformed data showed that bio-available soil metal concentrations correlated strongly with grass metal content and grass metal content correlated strongly with the yield. There was however a weak correlation between the yield and bio-available soil levels. The yield versus grass metal content models that were developed for the first crop and re-growth predicted similar critical metal concentrations and yields. Using the critical grass metal concentrations in the soil bio-available metal concentration versus grass metal concentration models allowed for the prediction of the corresponding critical soil concentrations.

Nation-wide assessment of climate change impacts on crops in the Philippines and Peru as part of multi-disciplinary modelling framework

NASA Astrophysics Data System (ADS)

Fujisawa, Mariko; Kanamaru, Hideki

2016-04-01

Agriculture is vulnerable to environmental changes, and climate change has been recognized as one of the most devastating factors. In many developing countries, however, few studies have focused on nation-wide assessment of crop yield and crop suitability in the future, and hence there is a large pressure on science to provide policy makers with solid predictions for major crops in the countries in support of climate risk management policies and programmes. FAO has developed the tool MOSAICC (Modelling System for Agricultural Impacts of Climate Change) where statistical climate downscaling is combined with crop yield projections under climate change scenarios. Three steps are required to get the results: 1. The historical meteorological data such as temperature and precipitation for about 30 years were collected, and future climates were statistically downscaled to the local scale, 2. The historical crop yield data were collected and regression functions were made to estimate the yield by using observed climatic data and water balance during the growing period for each crop, and 3. The yield changes in the future were estimated by using the future climate data, produced by the first step, as an input to the yield regression functions. The yield was first simulated at sub-national scale and aggregated to national scale, which is intended to provide national policies with adaptation options. The methodology considers future changes in characteristics of extreme weather events as the climate projections are on daily scale while crop simulations are on 10-daily scale. Yields were simulated with two greenhouse gas concentration pathways (RCPs) for three GCMs per crop to account for uncertainties in projections. The crop assessment constitutes a larger multi-disciplinary assessment of climate change impacts on agriculture and vulnerability of livelihoods in terms of food security (e.g. water resources, agriculture market, household-level food security from socio-economic perspective). In our presentation we will show the cases of Peru and the Philippines, and discuss the implications for agriculture policies and risk management.

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.

Yield Strength Testing in Human Cadaver Nasal Septal Cartilage and L-Strut Constructs.

PubMed

Liu, Yuan F; Messinger, Kelton; Inman, Jared C

2017-01-01

To our knowledge, yield strength testing in human nasal septal cartilage has not been reported to date. An understanding of the basic mechanics of the nasal septum may help surgeons decide how much of an L-strut to preserve and how much grafting is needed. To determine the factors correlated with yield strength of the cartilaginous nasal septum and to explore the association between L-strut width and thickness in determining yield strength. In an anatomy laboratory, yield strength of rectangular pieces of fresh cadaver nasal septal cartilage was measured, and regression was performed to identify the factors correlated with yield strength. To measure yield strength in L-shaped models, 4 bonded paper L-struts models were constructed for every possible combination of the width and thickness, for a total of 240 models. Mathematical modeling using the resultant data with trend lines and surface fitting was performed to quantify the associations among L-strut width, thickness, and yield strength. The study dates were November 1, 2015, to April 1, 2016. The factors correlated with nasal cartilage yield strength and the associations among L-strut width, thickness, and yield strength in L-shaped models. Among 95 cartilage pieces from 12 human cadavers (mean [SD] age, 67.7 [12.6] years) and 240 constructed L-strut models, L-strut thickness was the only factor correlated with nasal septal cartilage yield strength (coefficient for thickness, 5.54; 95% CI, 4.08-7.00; P < .001), with an adjusted R2 correlation coefficient of 0.37. The mean (SD) yield strength R2 varied with L-strut thickness exponentially (0.93 [0.06]) for set widths, and it varied with L-strut width linearly (0.82 [0.11]) or logarithmically (0.85 [0.17]) for set thicknesses. A 3-dimensional surface model of yield strength with L-strut width and thickness as variables was created using a 2-dimensional gaussian function (adjusted R2 = 0.94). Estimated yield strengths were generated from the model to allow determination of the desired yield strength with different permutations of L-strut width and thickness. In this study of human cadaver nasal septal cartilage, L-strut thickness was significantly associated with yield strength. In a bonded paper L-strut model, L-strut thickness had a more important role in determining yield strength than L-strut width. Surgeons should consider the thickness of potential L-struts when determining the amount of cartilaginous septum to harvest and graft. NA.

Chemometric modeling of thermogravimetric data for the compositional analysis of forest biomass

PubMed Central

Via, Brian K.; Fasina, Oladiran O.; Adhikari, Sushil; Billor, Nedret; Eckhardt, Lori G.

2017-01-01

The objective of this study was to investigated the use of chemometric modeling of thermogravimetric (TG) data as an alternative approach to estimate the chemical and proximate (i.e. volatile matter, fixed carbon and ash contents) composition of lignocellulosic biomass. Since these properties affect the conversion pathway, processing costs, yield and / or quality of products, a capability to rapidly determine these for biomass feedstock entering the process stream will be useful in the success and efficiency of bioconversion technologies. The 38-minute long methodology developed in this study enabled the simultaneous prediction of both the chemical and proximate properties of forest-derived biomass from the same TG data. Conventionally, two separate experiments had to be conducted to obtain such information. In addition, the chemometric models constructed with normalized TG data outperformed models developed via the traditional deconvolution of TG data. PLS and PCR models were especially robust in predicting the volatile matter (R2–0.92; RPD– 3.58) and lignin (R2–0.82; RPD– 2.40) contents of the biomass. The application of chemometrics to TG data also made it possible to predict some monomeric sugars in this study. Elucidation of PC loadings obtained from chemometric models also provided some insights into the thermal decomposition behavior of the chemical constituents of lignocellulosic biomass. For instance, similar loadings were noted for volatile matter and cellulose, and for fixed carbon and lignin. The findings indicate that common latent variables are shared between these chemical and thermal reactivity properties. Results from this study buttresses literature that have reported that the less thermally stable polysaccharides are responsible for the yield of volatiles whereas the more recalcitrant lignin with its higher percentage of elementary carbon contributes to the yield of fixed carbon. PMID:28253322

Chemometric modeling of thermogravimetric data for the compositional analysis of forest biomass.

PubMed

Acquah, Gifty E; Via, Brian K; Fasina, Oladiran O; Adhikari, Sushil; Billor, Nedret; Eckhardt, Lori G

2017-01-01

The objective of this study was to investigated the use of chemometric modeling of thermogravimetric (TG) data as an alternative approach to estimate the chemical and proximate (i.e. volatile matter, fixed carbon and ash contents) composition of lignocellulosic biomass. Since these properties affect the conversion pathway, processing costs, yield and / or quality of products, a capability to rapidly determine these for biomass feedstock entering the process stream will be useful in the success and efficiency of bioconversion technologies. The 38-minute long methodology developed in this study enabled the simultaneous prediction of both the chemical and proximate properties of forest-derived biomass from the same TG data. Conventionally, two separate experiments had to be conducted to obtain such information. In addition, the chemometric models constructed with normalized TG data outperformed models developed via the traditional deconvolution of TG data. PLS and PCR models were especially robust in predicting the volatile matter (R2-0.92; RPD- 3.58) and lignin (R2-0.82; RPD- 2.40) contents of the biomass. The application of chemometrics to TG data also made it possible to predict some monomeric sugars in this study. Elucidation of PC loadings obtained from chemometric models also provided some insights into the thermal decomposition behavior of the chemical constituents of lignocellulosic biomass. For instance, similar loadings were noted for volatile matter and cellulose, and for fixed carbon and lignin. The findings indicate that common latent variables are shared between these chemical and thermal reactivity properties. Results from this study buttresses literature that have reported that the less thermally stable polysaccharides are responsible for the yield of volatiles whereas the more recalcitrant lignin with its higher percentage of elementary carbon contributes to the yield of fixed carbon.

Crops in silico: A community wide multi-scale computational modeling framework of plant canopies

NASA Astrophysics Data System (ADS)

Srinivasan, V.; Christensen, A.; Borkiewic, K.; Yiwen, X.; Ellis, A.; Panneerselvam, B.; Kannan, K.; Shrivastava, S.; Cox, D.; Hart, J.; Marshall-Colon, A.; Long, S.

2016-12-01

Current crop models predict a looming gap between supply and demand for primary foodstuffs over the next 100 years. While significant yield increases were achieved in major food crops during the early years of the green revolution, the current rates of yield increases are insufficient to meet future projected food demand. Furthermore, with projected reduction in arable land, decrease in water availability, and increasing impacts of climate change on future food production, innovative technologies are required to sustainably improve crop yield. To meet these challenges, we are developing Crops in silico (Cis), a biologically informed, multi-scale, computational modeling framework that can facilitate whole plant simulations of crop systems. The Cis framework is capable of linking models of gene networks, protein synthesis, metabolic pathways, physiology, growth, and development in order to investigate crop response to different climate scenarios and resource constraints. This modeling framework will provide the mechanistic details to generate testable hypotheses toward accelerating directed breeding and engineering efforts to increase future food security. A primary objective for building such a framework is to create synergy among an inter-connected community of biologists and modelers to create a realistic virtual plant. This framework advantageously casts the detailed mechanistic understanding of individual plant processes across various scales in a common scalable framework that makes use of current advances in high performance and parallel computing. We are currently designing a user friendly interface that will make this tool equally accessible to biologists and computer scientists. Critically, this framework will provide the community with much needed tools for guiding future crop breeding and engineering, understanding the emergent implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment.

Progress in hypersonic turbulence modeling

NASA Technical Reports Server (NTRS)

Wilcox, David C.

1991-01-01

A compressibility modification is developed for k-omega (Wilcox, 1988) and k-epsilon (Jones and Launder, 1972) models, that is similar to those of Sarkar et al. (1989) and Zeman (1990). Results of the perturbation solution for the compressible wall layer demonstrate why the Sarkar and Zeman terms yield inaccurate skin friction for the flat-plate boundary layer. A new compressibility term is developed which permits accurate predictions of the compressible mixing layer, flat-plate boundary layer, and shock separated flows.

Development of an advanced anaerobic digester design and a kinetic model for biogasification of water hyacinth/sludge blends

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

Srivastava, V.; Fannin, K.F.; Biljetina, R.

1986-07-01

The Institute of Gas Technology (IGT) conducted a comprehensive laboratory-scale research program to develop and optimize the anaerobic digestion process for producing methane from water hyacinth and sludge blends. This study focused on digester design and operating techniques, which gave improved methane yields and production rates over those observed using conventional digesters. The final digester concept and the operating experience was utilized to design and operate a large-scale experimentla test unit (ETU) at Walt Disney World, Florida. This paper describes the novel digester design, operating techniques, and the results obtained in the laboratory. The paper also discusses a kinetic modelmore » which predicts methane yield, methane production rate, and digester effluent solids as a function of retention time. This model was successfully utilized to predict the performance of the ETU. 15 refs., 6 figs., 6 tabs.« less

Rejuvenation of Spent Media via Supported Emulsion Liquid Membranes

NASA Technical Reports Server (NTRS)

Wiencek, John M.

2002-01-01

The overall goal of this project was to maximize the reuseability of spent fermentation media. Supported emulsion liquid membrane separation, a highly efficient extraction technique, was used to remove inhibitory byproducts during fermentation; thus, improve the yield while reducing the need for fresh water. The key objectives of this study were: (1) Develop an emulsion liquid membrane system targeting low molecular weight organic acids which has minimal toxicity on a variety of microbial systems. (2) Conduct mass transfer studies to allow proper modeling and design of a supported emulsion liquid membrane system. (3) Investigate the effect of gravity on emulsion coalescence within the membrane unit. (4) Access the effect of water re-use on fermentation yields in a model microbial system. and (5) Develop a perfusion-type fermentor utilizing a supported emulsion liquid membrane system to control inhibitory fermentation byproducts (not completed due to lack of funds)

Positive and normative modeling for Palmer amaranth control and herbicide resistance management.

PubMed

Frisvold, George B; Bagavathiannan, Muthukumar V; Norsworthy, Jason K

2017-06-01

Dynamic optimization models are normative; they solve for what growers 'ought to do' to maximize some objective, such as long-run profits. While valuable for research, such models are difficult to solve computationally, limiting their applicability to grower resistance management education. While discussing properties of normative models in general, this study presents results of a specific positive model of herbicide resistance management, applied to Palmer amaranth control on a representative cotton farm. This positive model compares a proactive resistance management strategy to a reactive strategy with lower short-run costs, but greater risk of herbicide resistance developing. The proactive strategy can pay for itself within 1-4 years, with a yield advantage of 4% or less if the yield advantage begins within 1-2 years of adoption. Whether the proactive strategy is preferable is sensitive to resistance onset and yield losses, but less sensitive to cotton prices or baseline yields. Industry rebates to encourage residual herbicide use (to delay resistance to post-emergence treatments) may be too small to alter grower behavior or they may be paid to growers who would have used residuals anyway. Rebates change grower behavior over a relatively narrow range of model parameters. The size of rebates needed to induce a grower to adopt the proactive strategy declines significantly if growers extend their planning horizon from 1 year to 3-4 years. Whether proactive resistance management is more profitable than a reactive strategy is more sensitive to biological parameters than economic ones. Simulation results suggest growers with longer time horizons (perhaps younger ones) would be more responsive to rebate programs. More empirical work is needed to determine how much rebates increase residual use above what would occur without them. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

An adapted yield criterion for the evolution of subsequent yield surfaces

NASA Astrophysics Data System (ADS)

Küsters, N.; Brosius, A.

2017-09-01

In numerical analysis of sheet metal forming processes, the anisotropic material behaviour is often modelled with isotropic work hardening and an average Lankford coefficient. In contrast, experimental observations show an evolution of the Lankford coefficients, which can be associated with a yield surface change due to kinematic and distortional hardening. Commonly, extensive efforts are carried out to describe these phenomena. In this paper an isotropic material model based on the Yld2000-2d criterion is adapted with an evolving yield exponent in order to change the yield surface shape. The yield exponent is linked to the accumulative plastic strain. This change has the effect of a rotating yield surface normal. As the normal is directly related to the Lankford coefficient, the change can be used to model the evolution of the Lankford coefficient during yielding. The paper will focus on the numerical implementation of the adapted material model for the FE-code LS-Dyna, mpi-version R7.1.2-d. A recently introduced identification scheme [1] is used to obtain the parameters for the evolving yield surface and will be briefly described for the proposed model. The suitability for numerical analysis will be discussed for deep drawing processes in general. Efforts for material characterization and modelling will be compared to other common yield surface descriptions. Besides experimental efforts and achieved accuracy, the potential of flexibility in material models and the risk of ambiguity during identification are of major interest in this paper.

Towards Better Simulation of US Maize Yield Responses to Climate in the Community Earth System Model

NASA Astrophysics Data System (ADS)

Peng, B.; Guan, K.; Chen, M.; Lawrence, D. M.; Jin, Z.; Bernacchi, C.; Ainsworth, E. A.; DeLucia, E. H.; Lombardozzi, D. L.; Lu, Y.

2017-12-01

Global food security is undergoing continuing pressure from increased population and climate change despites the potential advancement in breeding and management technologies. Earth system models (ESMs) are essential tools to study the impacts of historical and future climate on regional and global food production, as well as to assess the effectiveness of possible adaptations and their potential feedback to climate. Here we developed an improved maize representation within the Community Earth System Model (CESM) by combining the strengths of both the Community Land Model version 4.5 (CLM4.5) and the Agricultural Production Systems sIMulator (APSIM) models. Specifically, we modified the maize planting scheme, incorporated the phenology scheme adopted from the APSIM model, added a new carbon allocation scheme into CLM4.5, and improved the estimation of canopy structure parameters including leaf area index (LAI) and canopy height. Unique features of the new model (CLM-APSIM) include more detailed phenology stages, an explicit implementation of the impacts of various abiotic environmental stresses (including nitrogen, water, temperature and heat stresses) on maize phenology and carbon allocation, as well as an explicit simulation of grain number and grain size. We conducted a regional simulation of this new model over the US Corn Belt during 1990 to 2010. The simulated maize yield as well as its responses to climate (growing season mean temperature and precipitation) are benchmarked with data from UADA NASS statistics. Our results show that the CLM-APSIM model outperforms the CLM4.5 in simulating county-level maize yield production and reproduces more realistic yield responses to climate variations than CLM4.5. However, some critical processes (such as crop failure due to frost and inundation and suboptimal growth condition due to biotic stresses) are still missing in both CLM-APSIM and CLM4.5, making the simulated yield responses to climate slightly deviate from the reality. Our results demonstrate that with improved paramterization of crop growth, the ESMs can be powerful tools for realistically simulating agricultural production, which is gaining increasing interests and critical to study of global food security and food-energy-water nexus.

Humans as Sensors: Assessing the Information Value of Qualitative Farmer's Crop Condition Surveys for Crop Yield Monitoring and Forecasting

NASA Astrophysics Data System (ADS)

Beguería, S.

2017-12-01

While large efforts are devoted to developing crop status monitoring and yield forecasting systems trough the use of Earth observation data (mostly remotely sensed satellite imagery) and observational and modeled weather data, here we focus on the information value of qualitative data on crop status from direct observations made by humans. This kind of data has a high value as it reflects the expert opinion of individuals directly involved in the development of the crop. However, they have issues that prevent their direct use in crop monitoring and yield forecasting systems, such as their non-spatially explicit nature, or most importantly their qualitative nature. Indeed, while the human brain is good at categorizing the status of physical systems in terms of qualitative scales (`very good', `good', `fair', etcetera), it has difficulties in quantifying it in physical units. This has prevented the incorporation of this kind of data into systems that make extensive use of numerical information. Here we show an example of using qualitative crop condition data to estimate yields of the most important crops in the US early in the season. We use USDA weekly crop condition reports, which are based on a sample of thousands of reporters including mostly farmers and people in direct contact with them. These reporters provide subjective evaluations of crop conditions, in a scale including five levels ranging from `very poor' to `excellent'. The USDA report indicates, for each state, the proportion of reporters fort each condition level. We show how is it possible to model the underlying non-observed quantitative variable that reflects the crop status on each state, and how this model is consistent across states and years. Furthermore, we show how this information can be used to monitor the status of the crops and to produce yield forecasts early in the season. Finally, we discuss approaches for blending this information source with other, more classical earth data sources such as remote sensing or weather data, in the context of hierarchical regression models.

Optimization of critical medium components using response surface methodology for biomass and extracellular polysaccharide production by Agaricus blazei.

PubMed

Liu, Gao-Qiang; Wang, Xiao-Ling

2007-02-01

Response surface methodology (RSM) was applied to optimize the critical medium ingredients of Agaricus blazei. A three-level Box-Behnken factorial design was employed to determine the maximum biomass and extracellular polysaccharide (EPS) yields at optimum levels for glucose, yeast extract (YE), and peptone. A mathematical model was then developed to show the effect of each medium composition and its interactions on the production of mycelial biomass and EPS. The model predicted the maximum biomass yield of 10.86 g/l that appeared at glucose, YE, peptone of 26.3, 6.84, and 6.62 g/l, respectively, while a maximum EPS yield of 348.4 mg/l appeared at glucose, YE, peptone of 28.4, 4.96, 5.60 g/l, respectively. These predicted values were also verified by validation experiments. The excellent correlation between predicted and measured values of each model justifies the validity of both the response models. The results of bioreactor fermentation also show that the optimized culture medium enhanced both biomass (13.91 +/- 0.71 g/l) and EPS (363 +/- 4.1 mg/l) production by Agaricus blazei in a large-scale fermentation process.

Assessment of 1.5°C and 2°C climate change scenarios impact on wheat production in Tunisia

NASA Astrophysics Data System (ADS)

Bergaoui, karim; Belhaj Fraj, Makram; Zaaboul, Rashyd; Allen, Myles; Mitchell, Dann; Schleussner, Carl-Friedrich; Saeed, Fahad; Mc Donnell, Rachael

2017-04-01

Wheat is the main staple crop in North Africa region and contributes the most to food security. It is almost entirely grown under rainfed conditions and its yield is highly impacted by the climate variability, e. g. dry winters, a late autumn or late spring. Irregular rainfall or drought events particularly at key stages of the growing season, lead to both early and terminal wheat stresses and high inter-year variation in yield. The goal of this study was to explore the impacts of future climate on wheat production in Tunisia using an ensemble of regional bias corrected climate models outputs for the 1.5°C and 2°C warming above the pre-industrial levels. By examining the outputs on wheat yield levels the study would help answer the question of whether the ambitious climate change mitigation efforts involved in stabilizing temperatures at 1.5°C would bring the cereal yields needed in North Africa. Tunisia was chosen as the focus country because its wheat systems are found across a wide diversity in biophysical and farming conditions so giving insight on more localized effects. Data availability across a wide range of wheat management systems from subsistence farming systems to highly mechanized agribusinesses also supported work here as model results could be readily validated for the historical period. Two scenarios were obtained using the RCP2.6 as boundary conditions for 1.5 scenario and a weighted combination of RCP2.6 and RCP4.5 for the 2°C scenario using their respective CO2 levels in the future. We calibrated and validated a dynamical crop model, DSSAT, to simulate the national wheat production and to understand the impact of drought on growth and development that causes yield variation. DSSAT simulations were driven by CHIRPS and ERA-Interim reanalysis data as daily climate forcings. The simulations were validated in a set of farmer fields which were representative of the dominant cropping systems in the country. Then, the model was validated with 10 years' state-level production data. Finally, we forced the crop model with HAPPI bias corrected outputs using ISI-MIP approach where the trend and the long-term mean are well represented and we assessed the impact of each scenario on the wheat production at the national level. The results highlighted a difference in wheat yield in some biophysical areas and farming systems. This insight is important as countries develop mitigation and adaptation strategies as the impact costs can be included.

Constitutive model for porous materials

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

Weston, A.M.; Lee, E.L.

1982-01-01

A simple pressure versus porosity compaction model is developed to calculate the response of granular porous bed materials to shock impact. The model provides a scheme for calculating compaction behavior when relatively limited material data are available. While the model was developed to study porous explosives and propellants, it has been applied to a much wider range of materials. The early development of porous material models, such as that of Hermann, required empirical dynamic compaction data. Erkman and Edwards successfully applied the early theory to unreacted porous high explosives using a Gruneisen equation of state without yield behavior and withoutmore » trapped gas in the pores. Butcher included viscoelastic rate dependance in pore collapse. The theoretical treatment of Carroll and Holt is centered on the collapse of a circular pore and includes radial inertia terms and a complex set of stress, strain and strain rate constitutive parameters. Unfortunately data required for these parameters are generally not available. The model described here is also centered on the collapse of a circular pore, but utilizes a simpler elastic-plastic static equilibrium pore collapse mechanism without strain rate dependence, or radial inertia terms. It does include trapped gas inside the pore, a solid material flow stress that creates both a yield point and a variation in solid material pressure with radius. The solid is described by a Mie-Gruneisen type EOS. Comparisons show that this model will accurately estimate major mechanical features which have been observed in compaction experiments.« less

Modelling impacts of second generation bioenergy production on Ecosystem Services in Europe

NASA Astrophysics Data System (ADS)

Henner, D. N.; Smith, P.; Davies, C.; McNamara, N. P.

2016-12-01

Bioenergy crops are an important source of renewable energy and likely to play a major role in transitioning to a lower CO2 energy system. There is, however, uncertainty about the impacts of the growth of bioenergy crops on broader sustainability encompassed by ecosystem services, further enhanced by ongoing climate change. The goal of this project is to develop a comprehensive model that covers ecosystem services at a continental scale including biodiversity and pollination, water and air security, erosion control and soil security, GHG emissions, soil C and cultural services like tourism value. The technical distribution potential and likely yield of second generation energy crops, such as Miscanthus, Short Rotation Coppice (SRC; willow and poplar) was modelled using ECOSSE, DayCent, SalixFor and MiscanFor models. In addition, methods like water footprint tools, tourism value maps and ecosystem valuation tools and models are utilised. We will present results for synergies and trade-offs between land use change and ecosystem services, impact on food security and land management. Further, we will show modelled yield maps for different cultivars of Miscanthus, willow and poplar in Europe and constraint/opportunity maps based on projected yield and other factors e.g. total economic value, technical potential, current land use, climate change and trade-offs and synergies. It will be essential to include multiple ecosystem services when assessing the potential for bioenergy production/expansion that does not impact other land uses or provisioning services. Considering that the soil GHG balance is dominated by change in soil organic carbon (SOC) and the difference among Miscanthus and SRC is largely determined by yield, an important target for management of perennial energy crops is to achieve the best possible yield using the most appropriate energy crop and cultivar for the local situation. This research could inform future policy decisions on bioenergy crops in Europe.

Modelling climate change impact: A case of bambara groundnut (Vigna subterranea)

NASA Astrophysics Data System (ADS)

Mabhaudhi, Tafadzwanashe; Chibarabada, Tendai Polite; Chimonyo, Vimbayi Grace Petrova; Modi, Albert Thembinkosi

2018-06-01

Climate change projections for southern Africa indicate low and erratic rainfall as well as increasing frequency and intensity of rainfall extremes such as drought. The 2015/16 drought devastated large parts of southern Africa and highlighted the need for drought tolerant crops. Bambara groundnut is an African indigenous crop, commonly cultivated in southern Africa, with a higher potential for drought tolerance compared to other staple legumes. AquaCrop model was used to evaluate the impacts of climate change on yield, water use (ET) and water productivity (WP) of bambara groundnut using climate change data representative of the past (1961-1991), present (1995-2025), mid-century (2030-2060) and late century (2065-2095) obtained from five global circulation models (GCMs). The carbon dioxide (CO2) file selected was for the A2 scenario. The model was run at a sub-catchment level. Model simulations showed that yield and WP of bambara groundnut will increase over time. The mean values of yield at the different time scales across the GCMs showed that yield of bambara groundnut increased by ∼9% from the past to the present, will increase by ∼15% from the present to mid-century and will increase by 6% from mid-to late-century. The simulated results of ET showed seasonal ranges of 703-796 mm. Of this, 45% was lost to soil evaporation, suggesting the need for developing bambara groundnut varieties with faster establishment and high canopy cover. Model simulations showed an increase in WP by ∼13% from the past to present and ∼15% from the present to mid-century and ∼11% from mid-century to late century. While the results of these simulations are preliminary, they confirm the view that bambara groundnut is a potential future crop suitable for cultivation in marginal agricultural production areas. Future research should focus on crop improvement to improve current yield of bambara groundnut.

Genotype × environment interaction analysis of North American shrub willow yield trials confirms superior performance of triploid hybrids

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

Fabio, Eric S.; Volk, Timothy A.; Miller, Raymond O.

Development of dedicated bioenergy crop production systems will require accurate yield estimates, which will be important for determining many of the associated environmental and economic impacts of their production. Shrub willow (Salix spp) is being promoted in areas of the USA and Canada due to its adaption to cool climates and wide genetic diversity available for breeding improvement. Willow breeding in North America is in an early stage, and selection of elite genotypes for commercialization will require testing across broad geographic regions to gain an understanding of how shrub willow interacts with the environment. We analyzed a dataset of first-rotationmore » shrub willow yields of 16 genotypes across 10 trial environments in the USA and Canada for genotype-by-environment interactions using the additive main effects and multiplicative interactions (AMMI) model. Mean genotype yields ranged from 5.22 to 8.58 oven-dry Mg ha -1 yr -1. Analysis of the main effect of genotype showed that one round of breeding improved yields by as much as 20% over check cultivars and that triploid hybrids, most notably Salix viminalis × S. miyabeana, exhibited superior yields. We also found important variability in genotypic response to environments, which suggests specific adaptability could be exploited among 16 genotypes for yield gains. Strong positive correlations were found between environment main effects and AMMI parameters and growing environment temperatures. These findings demonstrate yield improvements are possible in one generation and will be important for developing cultivar recommendations and for future breeding efforts.« less

Genotype × environment interaction analysis of North American shrub willow yield trials confirms superior performance of triploid hybrids

DOE PAGES

Fabio, Eric S.; Volk, Timothy A.; Miller, Raymond O.; ...

2016-01-30

Development of dedicated bioenergy crop production systems will require accurate yield estimates, which will be important for determining many of the associated environmental and economic impacts of their production. Shrub willow (Salix spp) is being promoted in areas of the USA and Canada due to its adaption to cool climates and wide genetic diversity available for breeding improvement. Willow breeding in North America is in an early stage, and selection of elite genotypes for commercialization will require testing across broad geographic regions to gain an understanding of how shrub willow interacts with the environment. We analyzed a dataset of first-rotationmore » shrub willow yields of 16 genotypes across 10 trial environments in the USA and Canada for genotype-by-environment interactions using the additive main effects and multiplicative interactions (AMMI) model. Mean genotype yields ranged from 5.22 to 8.58 oven-dry Mg ha -1 yr -1. Analysis of the main effect of genotype showed that one round of breeding improved yields by as much as 20% over check cultivars and that triploid hybrids, most notably Salix viminalis × S. miyabeana, exhibited superior yields. We also found important variability in genotypic response to environments, which suggests specific adaptability could be exploited among 16 genotypes for yield gains. Strong positive correlations were found between environment main effects and AMMI parameters and growing environment temperatures. These findings demonstrate yield improvements are possible in one generation and will be important for developing cultivar recommendations and for future breeding efforts.« less

Parameter-induced uncertainty quantification of crop yields, soil N2O and CO2 emission for 8 arable sites across Europe using the LandscapeDNDC model

NASA Astrophysics Data System (ADS)

Santabarbara, Ignacio; Haas, Edwin; Kraus, David; Herrera, Saul; Klatt, Steffen; Kiese, Ralf

2014-05-01

When using biogeochemical models to estimate greenhouse gas emissions at site to regional/national levels, the assessment and quantification of the uncertainties of simulation results are of significant importance. The uncertainties in simulation results of process-based ecosystem models may result from uncertainties of the process parameters that describe the processes of the model, model structure inadequacy as well as uncertainties in the observations. Data for development and testing of uncertainty analisys were corp yield observations, measurements of soil fluxes of nitrous oxide (N2O) and carbon dioxide (CO2) from 8 arable sites across Europe. Using the process-based biogeochemical model LandscapeDNDC for simulating crop yields, N2O and CO2 emissions, our aim is to assess the simulation uncertainty by setting up a Bayesian framework based on Metropolis-Hastings algorithm. Using Gelman statistics convergence criteria and parallel computing techniques, enable multi Markov Chains to run independently in parallel and create a random walk to estimate the joint model parameter distribution. Through means distribution we limit the parameter space, get probabilities of parameter values and find the complex dependencies among them. With this parameter distribution that determines soil-atmosphere C and N exchange, we are able to obtain the parameter-induced uncertainty of simulation results and compare them with the measurements data.

Assessment of climate change impact on yield of major crops in the Banas River Basin, India.

PubMed

Dubey, Swatantra Kumar; Sharma, Devesh

2018-09-01

Crop growth models like AquaCrop are useful in understanding the impact of climate change on crop production considering the various projections from global circulation models and regional climate models. The present study aims to assess the climate change impact on yield of major crops in the Banas River Basin i.e., wheat, barley and maize. Banas basin is part of the semi-arid region of Rajasthan state in India. AquaCrop model is used to calculate the yield of all the three crops for a historical period of 30years (1981-2010) and then compared with observed yield data. Root Mean Square Error (RMSE) values are calculated to assess the model accuracy in prediction of yield. Further, the calibrated model is used to predict the possible impacts of climate change and CO 2 concentration on crop yield using CORDEX-SA climate projections of three driving climate models (CNRM-CM5, CCSM4 and MPI-ESM-LR) for two different scenarios (RCP4.5 and RCP8.5) for the future period 2021-2050. RMSE values of simulated yield with respect to observed yield of wheat, barley and maize are 11.99, 16.15 and 19.13, respectively. It is predicted that crop yield of all three crops will increase under the climate change conditions for future period (2021-2050). Copyright © 2018 Elsevier B.V. All rights reserved.

A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States

USGS Publications Warehouse

Garcia, Ana Maria.; Hoos, Anne B.; Terziotti, Silvia

2011-01-01

We applied the SPARROW model to estimate phosphorus transport from catchments to stream reaches and subsequent delivery to major receiving water bodies in the Southeastern United States (U.S.). We show that six source variables and five land-to-water transport variables are significant (p < 0.05) in explaining 67% of the variability in long-term log-transformed mean annual phosphorus yields. Three land-to-water variables are a subset of landscape characteristics that have been used as transport factors in phosphorus indices developed by state agencies and are identified through experimental research as influencing land-to-water phosphorus transport at field and plot scales. Two land-to-water variables – soil organic matter and soil pH – are associated with phosphorus sorption, a significant finding given that most state-developed phosphorus indices do not explicitly contain variables for sorption processes. Our findings for Southeastern U.S. streams emphasize the importance of accounting for phosphorus present in the soil profile to predict attainable instream water quality. Regional estimates of phosphorus associated with soil-parent rock were highly significant in explaining instream phosphorus yield variability. Model predictions associate 31% of phosphorus delivered to receiving water bodies to geology and the highest total phosphorus yields in the Southeast were catchments with already high background levels that have been impacted by human activity.

Interactions of CO2, temperature and management practices: simulations with a modified version of CERES-Wheat

NASA Technical Reports Server (NTRS)

Tubiello, F. N.; Rosenzweig, C.; Volk, T.

1995-01-01

A new growth subroutine was developed for CERES-Wheat, a computer model of wheat (Triticum aestivum) growth and development. The new subroutine simulates canopy photosynthetic response to CO2 concentrations and light levels, and includes the effects of temperature on canopy light-use efficiency. Its performance was compared to the original CERES-Wheat V-2 10 in 30 different cases. Biomass and yield predictions of the two models were well correlated (correlation coefficient r > 0.95). As an application, summer growth of spring wheat was simulated at one site. Modeled crop responses to higher mean temperatures, different amounts of minimum and maximum warming, and doubled CO2 concentrations were compared to observations. The importance of irrigation and nitrogen fertilization in modulating the wheat crop climatic responses were also analyzed. Specifically, in agreement with observations, rainfed crops were found to be more sensitive to CO2 increases than irrigated ones. On the other hand, low nitrogen applications depressed the ability of the wheat crop to respond positively to CO2 increases. In general, the positive effects of high CO2 on grain yield were found to be almost completely counterbalanced by the negative effects of high temperatures. Depending on how temperature minima and maxima were increased, yield changes averaged across management practices ranged from -4% to 8%.

Combined Use of Landsat-8 and Sentinel-2A Images for Winter Crop Mapping and Winter Wheat Yield Assessment at Regional Scale

NASA Technical Reports Server (NTRS)

Skakun, Sergii; Vermote, Eric; Roger, Jean-Claude; Franch, Belen

2017-01-01

Timely and accurate information on crop yield and production is critical to many applications within agriculture monitoring. Thanks to its coverage and temporal resolution, coarse spatial resolution satellite imagery has always been a source of valuable information for yield forecasting and assessment at national and regional scales. With availability of free images acquired by Landsat-8 and Sentinel-2 remote sensing satellites, it becomes possible to provide temporal resolution of an image every 3-5 days, and therefore, to develop next generation agriculture products at higher spatial resolution (10-30 m). This paper explores the combined use of Landsat-8 and Sentinel-2A for winter crop mapping and winter wheat yield assessment at regional scale. For the former, we adapt a previously developed approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument at 250 m resolution that allows automatic mapping of winter crops taking into account a priori knowledge on crop calendar. For the latter, we use a generalized winter wheat yield forecasting model that is based on estimation of the peak Normalized Difference Vegetation Index (NDVI) from MODIS image time-series, and further downscaled to be applicable at 30 m resolution. We show that integration of Landsat-8 and Sentinel-2A improves both winter crop mapping and winter wheat yield assessment. In particular, the error of winter wheat yield estimates can be reduced up to 1.8 times compared to using a single satellite.

Structural investigation of porcine stomach mucin by X-ray fiber diffraction and homology modeling

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

Veluraja, K., E-mail: veluraja@msuniv.ac.in; Vennila, K.N.; Umamakeshvari, K.

Research highlights: {yields} Techniques to get oriented mucin fibre. {yields} X-ray fibre diffraction pattern for mucin. {yields} Molecular modeling of mucin based on X-ray fibre diffraction pattern. -- Abstract: The basic understanding of the three dimensional structure of mucin is essential to understand its physiological function. Technology has been developed to achieve orientated porcine stomach mucin molecules. X-ray fiber diffraction of partially orientated porcine stomach mucin molecules show d-spacing signals at 2.99, 4.06, 4.22, 4.7, 5.37 and 6.5 A. The high intense d-spacing signal at 4.22 A is attributed to the antiparallel {beta}-sheet structure identified in the fraction of themore » homology modeled mucin molecule (amino acid residues 800-980) using Nidogen-Laminin complex structure as a template. The X-ray fiber diffraction signal at 6.5 A reveals partial organization of oligosaccharides in porcine stomach mucin. This partial structure of mucin will be helpful in establishing a three dimensional structure for the whole mucin molecule.« less

Influence of weather and climate variables on the basal area growth of individual shortleaf pine trees

Treesearch

Pradip Saud; Thomas B. Lynch; Duncan S. Wilson; John Stewart; James M. Guldin; Bob Heinemann; Randy Holeman; Dennis Wilson; Keith Anderson

2015-01-01

An individual-tree basal area growth model previously developed for even-aged naturally occurring shortleaf pine trees (Pinus echinata Mill.) in western Arkansas and southeastern Oklahoma did not include weather variables. Individual-tree growth and yield modeling of shortleaf pine has been carried out using the remeasurements of over 200 plots...

Optimal tree increment models for the Northeastern United Statesq

Treesearch

Don C. Bragg

2003-01-01

used the potential relative increment (PRI) methodology to develop optimal tree diameter growth models for the Northeastern United States. Thirty species from the Eastwide Forest Inventory Database yielded 69,676 individuals, which were then reduced to fast-growing subsets for PRI analysis. For instance, only 14 individuals from the greater than 6,300-tree eastern...

Optimal Tree Increment Models for the Northeastern United States

Treesearch

Don C. Bragg

2005-01-01

I used the potential relative increment (PRI) methodology to develop optimal tree diameter growth models for the Northeastern United States. Thirty species from the Eastwide Forest Inventory Database yielded 69,676 individuals, which were then reduced to fast-growing subsets for PRI analysis. For instance, only 14 individuals from the greater than 6,300-tree eastern...