The Nexus Land-Use model version 1.0, an approach articulating biophysical potentials and economic dynamics to model competition for land-use

NASA Astrophysics Data System (ADS)

Souty, F.; Brunelle, T.; Dumas, P.; Dorin, B.; Ciais, P.; Crassous, R.; Müller, C.; Bondeau, A.

2012-10-01

Interactions between food demand, biomass energy and forest preservation are driving both food prices and land-use changes, regionally and globally. This study presents a new model called Nexus Land-Use version 1.0 which describes these interactions through a generic representation of agricultural intensification mechanisms within agricultural lands. The Nexus Land-Use model equations combine biophysics and economics into a single coherent framework to calculate crop yields, food prices, and resulting pasture and cropland areas within 12 regions inter-connected with each other by international trade. The representation of cropland and livestock production systems in each region relies on three components: (i) a biomass production function derived from the crop yield response function to inputs such as industrial fertilisers; (ii) a detailed representation of the livestock production system subdivided into an intensive and an extensive component, and (iii) a spatially explicit distribution of potential (maximal) crop yields prescribed from the Lund-Postdam-Jena global vegetation model for managed Land (LPJmL). The economic principles governing decisions about land-use and intensification are adapted from the Ricardian rent theory, assuming cost minimisation for farmers. In contrast to the other land-use models linking economy and biophysics, crops are aggregated as a representative product in calories and intensification for the representative crop is a non-linear function of chemical inputs. The model equations and parameter values are first described in details. Then, idealised scenarios exploring the impact of forest preservation policies or rising energy price on agricultural intensification are described, and their impacts on pasture and cropland areas are investigated.

Injury Profile SIMulator, a Qualitative Aggregative Modelling Framework to Predict Injury Profile as a Function of Cropping Practices, and Abiotic and Biotic Environment. II. Proof of Concept: Design of IPSIM-Wheat-Eyespot

PubMed Central

Robin, Marie-Hélène; Colbach, Nathalie; Lucas, Philippe; Montfort, Françoise; Cholez, Célia; Debaeke, Philippe; Aubertot, Jean-Noël

2013-01-01

IPSIM (Injury Profile SIMulator) is a generic modelling framework presented in a companion paper. It aims at predicting a crop injury profile as a function of cropping practices and abiotic and biotic environment. IPSIM's modelling approach consists of designing a model with an aggregative hierarchical tree of attributes. In order to provide a proof of concept, a model, named IPSIM-Wheat-Eyespot, has been developed with the software DEXi according to the conceptual framework of IPSIM to represent final incidence of eyespot on wheat. This paper briefly presents the pathosystem, the method used to develop IPSIM-Wheat-Eyespot using IPSIM's modelling framework, simulation examples, an evaluation of the predictive quality of the model with a large dataset (526 observed site-years) and a discussion on the benefits and limitations of the approach. IPSIM-Wheat-Eyespot proved to successfully represent the annual variability of the disease, as well as the effects of cropping practices (Efficiency = 0.51, Root Mean Square Error of Prediction = 24%; bias = 5.0%). IPSIM-Wheat-Eyespot does not aim to precisely predict the incidence of eyespot on wheat. It rather aims to rank cropping systems with regard to the risk of eyespot on wheat in a given production situation through ex ante evaluations. IPSIM-Wheat-Eyespot can also help perform diagnoses of commercial fields. Its structure is simple and permits to combine available knowledge in the scientific literature (data, models) and expertise. IPSIM-Wheat-Eyespot is now available to help design cropping systems with a low risk of eyespot on wheat in a wide range of production situations, and can help perform diagnoses of commercial fields. In addition, it provides a proof of concept with regard to the modelling approach of IPSIM. IPSIM-Wheat-Eyespot will be a sub-model of IPSIM-Wheat, a model that will predict injury profile on wheat as a function of cropping practices and the production situation. PMID:24146783

Injury profile SIMulator, a Qualitative aggregative modelling framework to predict injury profile as a function of cropping practices, and abiotic and biotic environment. II. Proof of concept: design of IPSIM-wheat-eyespot.

PubMed

Robin, Marie-Hélène; Colbach, Nathalie; Lucas, Philippe; Montfort, Françoise; Cholez, Célia; Debaeke, Philippe; Aubertot, Jean-Noël

2013-01-01

IPSIM (Injury Profile SIMulator) is a generic modelling framework presented in a companion paper. It aims at predicting a crop injury profile as a function of cropping practices and abiotic and biotic environment. IPSIM's modelling approach consists of designing a model with an aggregative hierarchical tree of attributes. In order to provide a proof of concept, a model, named IPSIM-Wheat-Eyespot, has been developed with the software DEXi according to the conceptual framework of IPSIM to represent final incidence of eyespot on wheat. This paper briefly presents the pathosystem, the method used to develop IPSIM-Wheat-Eyespot using IPSIM's modelling framework, simulation examples, an evaluation of the predictive quality of the model with a large dataset (526 observed site-years) and a discussion on the benefits and limitations of the approach. IPSIM-Wheat-Eyespot proved to successfully represent the annual variability of the disease, as well as the effects of cropping practices (Efficiency = 0.51, Root Mean Square Error of Prediction = 24%; bias = 5.0%). IPSIM-Wheat-Eyespot does not aim to precisely predict the incidence of eyespot on wheat. It rather aims to rank cropping systems with regard to the risk of eyespot on wheat in a given production situation through ex ante evaluations. IPSIM-Wheat-Eyespot can also help perform diagnoses of commercial fields. Its structure is simple and permits to combine available knowledge in the scientific literature (data, models) and expertise. IPSIM-Wheat-Eyespot is now available to help design cropping systems with a low risk of eyespot on wheat in a wide range of production situations, and can help perform diagnoses of commercial fields. In addition, it provides a proof of concept with regard to the modelling approach of IPSIM. IPSIM-Wheat-Eyespot will be a sub-model of IPSIM-Wheat, a model that will predict injury profile on wheat as a function of cropping practices and the production situation.

Test/QA plan for the validation of the verification protocol for high speed pesticide spray drift reduction technologies for row and field crops

EPA Science Inventory

This test/QA plan for evaluation the generic test protocol for high speed wind tunnel, representing aerial application, pesticide spray drift reduction technologies (DRT) for row and field crops is in conformance with EPA Requirements for Quality Assurance Project Plans (EPA QA/R...

Test/QA plan for the validation of the verification protocol for low speed pesticide spray drift reduction technologies for row and field crops

EPA Science Inventory

This test/QA plan for evaluation the generic test protocol for high speed wind tunnel, representing aerial application, pesticide spray drift reduction technologies (DRT) for row and field crops is in conformance with EPA Requirements for Quality Assurance Project Plans (EPA QA/R...

Analysis and prediction of agricultural pest dynamics with Tiko'n, a generic tool to develop agroecological food web models

NASA Astrophysics Data System (ADS)

Malard, J. J.; Rojas, M.; Adamowski, J. F.; Anandaraja, N.; Tuy, H.; Melgar-Quiñonez, H.

2016-12-01

While several well-validated crop growth models are currently widely used, very few crop pest models of the same caliber have been developed or applied, and pest models that take trophic interactions into account are even rarer. This may be due to several factors, including 1) the difficulty of representing complex agroecological food webs in a quantifiable model, and 2) the general belief that pesticides effectively remove insect pests from immediate concern. However, pests currently claim a substantial amount of harvests every year (and account for additional control costs), and the impact of insects and of their trophic interactions on agricultural crops cannot be ignored, especially in the context of changing climates and increasing pressures on crops across the globe. Unfortunately, most integrated pest management frameworks rely on very simple models (if at all), and most examples of successful agroecological management remain more anecdotal than scientifically replicable. In light of this, there is a need for validated and robust agroecological food web models that allow users to predict the response of these webs to changes in management, crops or climate, both in order to predict future pest problems under a changing climate as well as to develop effective integrated management plans. Here we present Tiko'n, a Python-based software whose API allows users to rapidly build and validate trophic web agroecological models that predict pest dynamics in the field. The programme uses a Bayesian inference approach to calibrate the models according to field data, allowing for the reuse of literature data from various sources and reducing the need for extensive field data collection. We apply the model to the cononut black-headed caterpillar (Opisina arenosella) and associated parasitoid data from Sri Lanka, showing how the modeling framework can be used to rapidly develop, calibrate and validate models that elucidate how the internal structures of food webs determine their behaviour and allow users to evaluate different integrated management options.

Research in biomass production and utilization: Systems simulation and analysis

NASA Astrophysics Data System (ADS)

Bennett, Albert Stewart

There is considerable public interest in developing a sustainable biobased economy that favors support of family farms and rural communities and also promotes the development of biorenewable energy resources. This study focuses on a number of questions related to the development and exploration of new pathways that can potentially move us toward a more sustainable biobased economy. These include issues related to biomass fuels for drying grain, economies-of-scale, new biomass harvest systems, sugar-to-ethanol crop alternatives for the Upper Midwest U.S., biomass transportation, post-harvest biomass processing and double cropping production scenarios designed to maximize biomass feedstock production. The first section of this study considers post-harvest drying of shelled corn grain both at farm-scale and at larger community-scaled installations. Currently, drying of shelled corn requires large amounts of fossil fuel energy. To address future energy concerns, this study evaluates the potential use of combined heat and power systems that use the combustion of corn stover to produce steam for drying and to generate electricity for fans, augers, and control components. Because of the large capital requirements for solid fuel boilers and steam turbines/engines, both farm-scale and larger grain elevator-scaled systems benefit by sharing boiler and power infrastructure with other processes. The second and third sections evaluate sweet sorghum as a possible "sugarcane-like" crop that can be grown in the Upper Midwest. Various harvest systems are considered including a prototype mobile juice harvester, a hypothetical one-pass unit that separates grain heads from chopped stalks and traditional forage/silage harvesters. Also evaluated were post-harvest transportation, storage and processing costs and their influence on the possible use of sweet sorghum as a supplemental feedstock for existing dry-grind ethanol plants located in the Upper Midwest. Results show that the concept of a mobile juice harvester is not economically viable due to low sugar recovery. The addition of front-end stalk processing/pressing equipment into existing ethanol facilities was found to be economically viable when combined with the plants' use of residuals as a natural gas fuel replacement. Because of high loss of fermentable carbohydrates during ensilage, storage of sweet sorghum in bunkers was not found to be economically viable. The fourth section looks at double cropping winter triticale with late-planted summer corn and compares these scenarios to traditional single cropped corn. Double cropping systems show particular promise for co-production of grain and biomass feedstocks and potentially can allow for greater utilization of grain crop residues. However, additional costs and risks associated with producing two crops instead of one could make biomass-double crops less attractive for producers despite productivity advantages. Detailed evaluation and comparisons show double cropped triticale-corn to be at a significant economic disadvantage relative to single crop corn. The cost benefits associated with using less equipment combined with availability of risk mitigating crop insurance and government subsidies will likely limit farmer interest and clearly indicate that traditional single-crop corn will provide greater financial returns to management. To evaluate the various sweet sorghum, single crop corn and double cropped triticale-corn production scenarios, a detailed but generic model was developed. The primary goal of this generic approach was to develop a modeling foundation that can be rapidly adapted, by an experienced user, to describe new and existing biomass and crop production scenarios that may be of interest to researchers. The foundation model allows input of management practices, crop production characteristics and utilizes standardized machinery performance and cost information, including farm-owned machinery and implements, and machinery and farm production operations provided by custom operators. (Abstract shortened by UMI.)

Climate-Agriculture-Modeling and Decision Tool for Disease (CAMDT-Disease) for seasonal climate forecast-based crop disease risk management in agriculture

NASA Astrophysics Data System (ADS)

Kim, K. H.; Lee, S.; Han, E.; Ines, A. V. M.

2017-12-01

Climate-Agriculture-Modeling and Decision Tool (CAMDT) is a decision support system (DSS) tool that aims to facilitate translations of probabilistic seasonal climate forecasts (SCF) to crop responses such as yield and water stress. Since CAMDT is a software framework connecting different models and algorithms with SCF information, it can be easily customized for different types of agriculture models. In this study, we replaced the DSSAT-CSM-Rice model originally incorporated in CAMDT with a generic epidemiological model, EPIRICE, to generate a seasonal pest outlook. The resulting CAMDT-Disease generates potential risks for selected fungal, viral, and bacterial diseases of rice over the next months by translating SCFs into agriculturally-relevant risk information. The integrated modeling procedure of CAMDT-Disease first disaggregates a given SCF using temporal downscaling methods (predictWTD or FResampler1), runs EPIRICE with the downscaled weather inputs, and finally visualizes the EPIRICE outputs as disease risk compared to that of the previous year and the 30-year-climatological average. In addition, the easy-to-use graphical user interface adopted from CAMDT allows users to simulate "what-if" scenarios of disease risks over different planting dates with given SCFs. Our future work includes the simulation of the effect of crop disease on yields through the disease simulation models with the DSSAT-CSM-Rice model, as disease remains one of the most critical yield-reducing factors in the field.

A generic bio-economic farm model for environmental and economic assessment of agricultural systems.

PubMed

Janssen, Sander; Louhichi, Kamel; Kanellopoulos, Argyris; Zander, Peter; Flichman, Guillermo; Hengsdijk, Huib; Meuter, Eelco; Andersen, Erling; Belhouchette, Hatem; Blanco, Maria; Borkowski, Nina; Heckelei, Thomas; Hecker, Martin; Li, Hongtao; Oude Lansink, Alfons; Stokstad, Grete; Thorne, Peter; van Keulen, Herman; van Ittersum, Martin K

2010-12-01

Bio-economic farm models are tools to evaluate ex-post or to assess ex-ante the impact of policy and technology change on agriculture, economics and environment. Recently, various BEFMs have been developed, often for one purpose or location, but hardly any of these models are re-used later for other purposes or locations. The Farm System Simulator (FSSIM) provides a generic framework enabling the application of BEFMs under various situations and for different purposes (generating supply response functions and detailed regional or farm type assessments). FSSIM is set up as a component-based framework with components representing farmer objectives, risk, calibration, policies, current activities, alternative activities and different types of activities (e.g., annual and perennial cropping and livestock). The generic nature of FSSIM is evaluated using five criteria by examining its applications. FSSIM has been applied for different climate zones and soil types (criterion 1) and to a range of different farm types (criterion 2) with different specializations, intensities and sizes. In most applications FSSIM has been used to assess the effects of policy changes and in two applications to assess the impact of technological innovations (criterion 3). In the various applications, different data sources, level of detail (e.g., criterion 4) and model configurations have been used. FSSIM has been linked to an economic and several biophysical models (criterion 5). The model is available for applications to other conditions and research issues, and it is open to be further tested and to be extended with new components, indicators or linkages to other models.

A Generic Bio-Economic Farm Model for Environmental and Economic Assessment of Agricultural Systems

PubMed Central

Louhichi, Kamel; Kanellopoulos, Argyris; Zander, Peter; Flichman, Guillermo; Hengsdijk, Huib; Meuter, Eelco; Andersen, Erling; Belhouchette, Hatem; Blanco, Maria; Borkowski, Nina; Heckelei, Thomas; Hecker, Martin; Li, Hongtao; Oude Lansink, Alfons; Stokstad, Grete; Thorne, Peter; van Keulen, Herman; van Ittersum, Martin K.

2010-01-01

Bio-economic farm models are tools to evaluate ex-post or to assess ex-ante the impact of policy and technology change on agriculture, economics and environment. Recently, various BEFMs have been developed, often for one purpose or location, but hardly any of these models are re-used later for other purposes or locations. The Farm System Simulator (FSSIM) provides a generic framework enabling the application of BEFMs under various situations and for different purposes (generating supply response functions and detailed regional or farm type assessments). FSSIM is set up as a component-based framework with components representing farmer objectives, risk, calibration, policies, current activities, alternative activities and different types of activities (e.g., annual and perennial cropping and livestock). The generic nature of FSSIM is evaluated using five criteria by examining its applications. FSSIM has been applied for different climate zones and soil types (criterion 1) and to a range of different farm types (criterion 2) with different specializations, intensities and sizes. In most applications FSSIM has been used to assess the effects of policy changes and in two applications to assess the impact of technological innovations (criterion 3). In the various applications, different data sources, level of detail (e.g., criterion 4) and model configurations have been used. FSSIM has been linked to an economic and several biophysical models (criterion 5). The model is available for applications to other conditions and research issues, and it is open to be further tested and to be extended with new components, indicators or linkages to other models. PMID:21113782

Uptake of {sup 137}Cs in vegetable crops grown on a contaminated lakebed

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

Seel, J.F.; Adriano, D.C.; Whicker, F.W.

1995-06-01

Mean concentration and plant:soil concentration ratios of {sup 137}Cs were determined for six vegetable crops grown on an exposed, contaminated lakebed of a former reactor cooling reservoir in South Carolina. Each crop species was grown with or without potassium fertilizer. Selected crops were also irrigated with either reservoir water or groundwater. Subsamples of crops were prepared for human consumption before analysis to determine the extent of any loss. Plant:soil concentration ratios (dry basis) ranged from 0.22 to 6.82, values which were substantially higher than those used in generic assessment models. While there was no statistically significant effect of irrigation sourcemore » or culinary preparation, the effect of potassium-fertilizer was dramatic. In many cases, concentrations of {sup 137}Cs in those plants receiving potassium were less than half of the concentrations in plants that did not receive potassium. Significant differences among species and {sup 131}Cs plant parts for concentrations were observed. Dose/risk calculations for the ingestion of these vegetables by a hypothetical 30-y resident indicates the possibility of a lifetime fatal cancer risk well-above the U.S. Environmental Protection Agency`s regulatory guideline of 10{sup -4}. 33 refs., 4 figs., 2 tabs.« less

Balancing competition for resources with multiple pest regulation in diversified agroecosystems: a process-based approach to reconcile diversification and productivity.

PubMed

Poeydebat, Charlotte; Carval, Dominique; de Lapeyre de Bellaire, Luc; Tixier, Philippe

2016-12-01

Agroecosystem plant diversification can enhance pest biological regulation and is a promising alternative to pesticide application. However, the costs of competition for resources between plants may exceed the benefits gained by pest regulation. To disentangle the interactions between pest regulation and competition, we developed a generic process-based approach that accounts for the effects of an associated plant and leaf and root pests on biomass production. We considered three crop-plant associations that differ in competition profiles, and we simulated biomass production under wide ranges of both pest regulation rates and resources' availability. We analyzed outputs to quantify the pest regulation service level that would be required to attain monoculture yield and other production goals. Results showed that pest regulation requirements were highly dependent on the profile of resource interception of the associated plant and on resources' availability. Pest regulation and the magnitude of competition between plants interacted in determining the balance between nitrogen and radiation uptake by the crop. Our findings suggest that productivity of diversified agroecosystems relative to monoculture should be optimized by assembling plants whose characteristics balance crops' resource acquisition. The theoretical insights from our study draw generic rules for vegetation assemblage to optimize trade-offs between pest regulation and production. Our findings and approach may have implications in understanding, theorizing and implementing agroecosystem diversification. By its generic and adaptable structure, our approach should be useful for studying the effects of diversification in many agroecosystems.

Optimal crop selection and water allocation under limited water supply in irrigation

NASA Astrophysics Data System (ADS)

Stange, Peter; Grießbach, Ulrike; Schütze, Niels

2015-04-01

Due to climate change, extreme weather conditions such as droughts may have an increasing impact on irrigated agriculture. To cope with limited water resources in irrigation systems, a new decision support framework is developed which focuses on an integrated management of both irrigation water supply and demand at the same time. For modeling the regional water demand, local (and site-specific) water demand functions are used which are derived from optimized agronomic response on farms scale. To account for climate variability the agronomic response is represented by stochastic crop water production functions (SCWPF). These functions take into account different soil types, crops and stochastically generated climate scenarios. The SCWPF's are used to compute the water demand considering different conditions, e.g., variable and fixed costs. This generic approach enables the consideration of both multiple crops at farm scale as well as of the aggregated response to water pricing at a regional scale for full and deficit irrigation systems. Within the SAPHIR (SAxonian Platform for High Performance IRrigation) project a prototype of a decision support system is developed which helps to evaluate combined water supply and demand management policies.

From field to region yield predictions in response to pedo-climatic variations in Eastern Canada

NASA Astrophysics Data System (ADS)

JÉGO, G.; Pattey, E.; Liu, J.

2013-12-01

The increase in global population coupled with new pressures to produce energy and bioproducts from agricultural land requires an increase in crop productivity. However, the influence of climate and soil variations on crop production and environmental performance is not fully understood and accounted for to define more sustainable and economical management strategies. Regional crop modeling can be a great tool for understanding the impact of climate variations on crop production, for planning grain handling and for assessing the impact of agriculture on the environment, but it is often limited by the availability of input data. The STICS ("Simulateur mulTIdisciplinaire pour les Cultures Standard") crop model, developed by INRA (France) is a functional crop model which has a built-in module to optimize several input parameters by minimizing the difference between calculated and measured output variables, such as Leaf Area Index (LAI). STICS crop model was adapted to the short growing season of the Mixedwood Plains Ecozone using field experiments results, to predict biomass and yield of soybean, spring wheat and corn. To minimize the numbers of inference required for regional applications, 'generic' cultivars rather than specific ones have been calibrated in STICS. After the calibration of several model parameters, the root mean square error (RMSE) of yield and biomass predictions ranged from 10% to 30% for the three crops. A bit more scattering was obtained for LAI (20%

Evaluating the influence of plant-specific physiological parameterizations on the partitioning of land surface energy fluxes

NASA Astrophysics Data System (ADS)

Sulis, Mauro; Langensiepen, Matthias; Shrestha, Prabhakar; Schickling, Anke; Simmer, Clemens; Kollet, Stefan

2015-04-01

Vegetation has a significant influence on the partitioning of radiative forcing, the spatial and temporal variability of soil water and soil temperature. Therefore plant physiological properties play a key role in mediating and amplifying interactions and feedback mechanisms in the soil-vegetation-atmosphere continuum. Because of the direct impact on latent heat fluxes, these properties may also influence weather generating processes, such as the evolution of the atmospheric boundary layer (ABL). In land surface models, plant physiological properties are usually obtained from literature synthesis by unifying several plant/crop species in predefined vegetation classes. In this work, crop-specific physiological characteristics, retrieved from detailed field measurements, are included in the bio-physical parameterization of the Community Land Model (CLM), which is a component of the Terrestrial Systems Modeling Platform (TerrSysMP). The measured set of parameters for two typical European mid-latitudinal crops (sugar beet and winter wheat) is validated using eddy covariance measurements (sensible heat and latent heat) over multiple years from three measurement sites located in the North Rhine-Westphalia region, Germany. We found clear improvements of CLM simulations, when using the crop-specific physiological characteristics of the plants instead of the generic crop type when compared to the measurements. In particular, the increase of latent heat fluxes in conjunction with decreased sensible heat fluxes as simulated by the two new crop-specific parameter sets leads to an improved quantification of the diurnal energy partitioning. These findings are cross-validated using estimates of gross primary production extracted from net ecosystem exchange measurements. This independent analysis reveals that the better agreement between observed and simulated latent heat using the plant-specific physiological properties largely stems from an improved simulation of the photosynthesis process owing to a better estimation of the Rubisco enzyme kinematics. Finally, to evaluate the effects of the crop-specific parameterizations on the ABL dynamics, we perform a series of semi-idealized land-atmosphere coupled simulations by hypothesizing three cropland configurations. These numerical experiments reveal different heat and moisture budgets of the ABL that clearly impact the evolution of the boundary layer when using the crop-specific physiological properties.

Implications for water use of a shift from annual to perennial crops - A stochastic modelling approach based on a trait meta-analysis

NASA Astrophysics Data System (ADS)

Vico, Giulia; Brunsell, Nathaniel

2017-04-01

The projected population growth and changes in climate and dietary habits will further increase the pressure on water resources globally. Within precision farming, a host of technical solutions has been developed to reduce water consumption for agricultural uses. The next frontier for a more sustainable agriculture is the combination of reduced water requirements with enhanced ecosystem services. Currently, staple grains are obtained from annuals crops. A shift from annual to perennial crops has been suggested as a way to enhance ecosystem services. In fact, perennial plants, with their continuous soil cover and the higher allocation of resources to the below ground, contribute to the reduction of soil erosion and nutrient losses, while enhancing carbon sequestration in the root zone. Nevertheless, the net effect of a shift to perennial crops on water use for agriculture is still unknown, despite its relevance for the sustainability of such a shift. We explore here the implications for water management at the field- to farm-scale of a shift from annual to perennial crops, under rainfed and irrigated agriculture. A probabilistic description of the soil water balance and crop development is employed to quantify water requirements and yields and their inter-annual variability, as a function of rainfall patterns, soil and crop features. Optimal irrigation strategies are thus defined in terms of maximization of yield and minimization of required irrigation volumes and their inter-annual variability. The probabilistic model is parameterized based on an extensive meta-analysis of traits of co-generic annual and perennial species to explore the consequences for water requirements of shifting from annual to perennial crops under current and future climates. We show that the larger and more developed roots of perennial crops may allow a better exploitation of soil water resources and a reduction of yield variability with respect to annual species. At the same time, perennial crops are larger and may require adequate water supply for longer periods, thus leading to higher water requirements. Furthermore, they lead to lower yields per unit area, thus requiring irrigation of larger areas.

Assessment of canopy chlorophyll content retrieval in maize and soybean: Implications of hysteresis on the development of generic algorithms

DOE PAGES

Peng, Yi; Nguy-Robertson, Anthony; Arkebauer, Timothy; ...

2017-03-02

Here, canopy chlorophyll content (Chl) closely relates to plant photosynthetic capacity, nitrogen status and productivity. The goal of this study is to develop remote sensing techniques for accurate estimation of canopy Chl during the entire growing season without re-parameterization of algorithms for two contrasting crop species, maize and soybean. These two crops represent different biochemical mechanisms of photosynthesis, leaf structure and canopy architecture. The relationships between canopy Chl and reflectance, collected at close range and resampled to bands of the Multi Spectral Instrument (MSI) aboard Sentinel-2, were analyzed in samples taken across the entirety of the growing seasons in threemore » irrigated and rainfed sites located in eastern Nebraska between 2001 and 2005. Crop phenology was a factor strongly influencing the reflectance of both maize and soybean. Substantial hysteresis of the reflectance vs. canopy Chl relationship existed between the vegetative and reproductive stages. The effect of the hysteresis on vegetation indices (VI), applied for canopy Chl estimation, depended on the bands used and their formulation. The hysteresis greatly affected the accuracy of canopy Chl estimation by widely-used VIs with near infrared (NIR) and red reflectance (e.g., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and simple ratio (SR)). VIs that use red edge and NIR bands (e.g., red edge chlorophyll index (CIred edge), red edge NDVI and the MERIS terrestrial chlorophyll index (MTCI)) were minimally affected by crop phenology (i.e., they exhibited little hysteresis) and were able to accurately estimate canopy Chl in two crops without algorithm re-parameterization and, thus, were found to be the best candidates for generic algorithms to estimate crop Chl using the surface reflectance products of MSI Sentinel-2.« less

Assessment of canopy chlorophyll content retrieval in maize and soybean: Implications of hysteresis on the development of generic algorithms

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

Peng, Yi; Nguy-Robertson, Anthony; Arkebauer, Timothy

Here, canopy chlorophyll content (Chl) closely relates to plant photosynthetic capacity, nitrogen status and productivity. The goal of this study is to develop remote sensing techniques for accurate estimation of canopy Chl during the entire growing season without re-parameterization of algorithms for two contrasting crop species, maize and soybean. These two crops represent different biochemical mechanisms of photosynthesis, leaf structure and canopy architecture. The relationships between canopy Chl and reflectance, collected at close range and resampled to bands of the Multi Spectral Instrument (MSI) aboard Sentinel-2, were analyzed in samples taken across the entirety of the growing seasons in threemore » irrigated and rainfed sites located in eastern Nebraska between 2001 and 2005. Crop phenology was a factor strongly influencing the reflectance of both maize and soybean. Substantial hysteresis of the reflectance vs. canopy Chl relationship existed between the vegetative and reproductive stages. The effect of the hysteresis on vegetation indices (VI), applied for canopy Chl estimation, depended on the bands used and their formulation. The hysteresis greatly affected the accuracy of canopy Chl estimation by widely-used VIs with near infrared (NIR) and red reflectance (e.g., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and simple ratio (SR)). VIs that use red edge and NIR bands (e.g., red edge chlorophyll index (CIred edge), red edge NDVI and the MERIS terrestrial chlorophyll index (MTCI)) were minimally affected by crop phenology (i.e., they exhibited little hysteresis) and were able to accurately estimate canopy Chl in two crops without algorithm re-parameterization and, thus, were found to be the best candidates for generic algorithms to estimate crop Chl using the surface reflectance products of MSI Sentinel-2.« less

Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots

PubMed Central

Lelong, Camille C. D.; Burger, Philippe; Jubelin, Guillaume; Roux, Bruno; Labbé, Sylvain; Baret, Frédéric

2008-01-01

This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships. PMID:27879893

Soil- and crop-dependent variation in correlation lag between precipitation and agricultural drought indices as predicted by the SWAP model

NASA Astrophysics Data System (ADS)

Wright, Azin; Cloke, Hannah; Verhoef, Anne

2017-04-01

Droughts have a devastating impact on agriculture and economy. The risk of more frequent and more severe droughts is increasing due to global warming and certain anthropogenic activities. At the same time, the global population continues to rise and the need for sustainable food production is becoming more and more pressing. In light of this, drought prediction can be of great value; in the context of early warning, preparedness and mitigation of drought impacts. Prediction of meteorological drought is associated with uncertainties around precipitation variability. As meteorological drought propagates, it can transform into agricultural drought. Determination of the maximum correlation lag between precipitation and agricultural drought indices can be useful for prediction of agricultural drought. However, the influence of soil and crop type on the lag needs to be considered, which we explored using a 1-D Soil-Vegetation-Atmosphere-Transfer model (SWAP (http://www.swap.alterra.nl/), with the following configurations, all forced with ERA-Interim weather data (1979 to 2014): i) different crop types in the UK; ii) three generic soil types (clay, loam and sand) were considered. A Sobol sensitivity analysis was carried out (perturbing the SWAP model van Genuchten soil hydraulic parameters) to study the effect of soil type uncertainty on the water balance variables. Based on the sensitivity analysis results, a few variations of each soil type were selected. Agricultural drought indices including Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) were calculated. The maximum correlation lag between precipitation and these drought indices was calculated, and analysed in the context of crop and soil model parameters. The findings of this research can be useful to UK farming, by guiding government bodies such as the Environment Agency when issuing drought warnings and implementing drought measures.

Altered pesticide use on transgenic crops and the associated general impact from an environmental perspective.

PubMed

Kleter, Gijs A; Bhula, Raj; Bodnaruk, Kevin; Carazo, Elizabeth; Felsot, Allan S; Harris, Caroline A; Katayama, Arata; Kuiper, Harry A; Racke, Kenneth D; Rubin, Baruch; Shevah, Yehuda; Stephenson, Gerald R; Tanaka, Keiji; Unsworth, John; Wauchope, R Donald; Wong, Sue-Sun

2007-11-01

The large-scale commercial cultivation of transgenic crops has undergone a steady increase since their introduction 10 years ago. Most of these crops bear introduced traits that are of agronomic importance, such as herbicide or insect resistance. These traits are likely to impact upon the use of pesticides on these crops, as well as the pesticide market as a whole. Organizations like USDA-ERS and NCFAP monitor the changes in crop pest management associated with the adoption of transgenic crops. As part of an IUPAC project on this topic, recent data are reviewed regarding the alterations in pesticide use that have been observed in practice. Most results indicate a decrease in the amounts of active ingredients applied to transgenic crops compared with conventional crops. In addition, a generic environmental indicator -- the environmental impact quotient (EIQ) -- has been applied by these authors and others to estimate the environmental consequences of the altered pesticide use on transgenic crops. The results show that the predicted environmental impact decreases in transgenic crops. With the advent of new types of agronomic trait and crops that have been genetically modified, it is useful to take also their potential environmental impacts into account.

78 FR 68018 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-13

..., Office of Management and Budget (OMB), New Executive Office Building, Washington, DC; New Executive... a currently valid OMB control number. Agricultural Marketing Service Title: Generic Fruit Crops, Marketing Order Administration Branch. OMB Control Number: 0581-0189. Summary of Collection: Industries...

Ecoclimatic indicators to study climate suitability of areas for the cultivation of specific crops

NASA Astrophysics Data System (ADS)

Caubel, J.; Garcia de Cortazar Atauri, I.; Cufi, J.; Huard, F.; Launay, M.; Ripoche, D.; Graux, A.; deNoblet, N.

2013-12-01

Climatic conditions play a fundamental role in the suitability of geographical areas for cropping. In the context of climate change, we could expect changes in overall climatic conditions and so, on the suitability for cropping. Therefore, assessing the future climate suitability of areas for cropping is decisive for anticipating agriculture in a given area. Moreover, it is crucial to have access to the split up information concerning the effect of climate on the achievement of the main ecophysiological processes and cultural practices taking place during the crop cycle. In this way, stakeholders can envisage land use adaptations under climate change conditions, such as changes in cultural practices or development of new varieties for example. We proposed an aggregation tool of ecoclimatic indicators to design evaluation trees of climate suitability of areas for cropping, GETARI (Generic Evaluation Tool of Ecoclimatic Indicators). It calculates an overall climate suitability index at the annual scale, from a designed evaluation tree. This aggregation tool allows to characterize climate suitability according to crop ecophysiology, grain/fruit quality or crop management. GETARI proposes the major ecophysiological processes and cultural practices taking place during phenological periods, together with the climatic effects that are known to affect their achievement. The climatic effects on the ecophysiological processes (or cultural practices) during phenological periods are captured by the ecoclimatic indicators, which are agroclimatic indicators calculated over phenological periods. They give information about crop response to climate through ecophysiological or agronomic thresholds. Those indices of suitability are normalized and aggregated according to aggregation rules in order to compute an overall climate index. In order to illustrate how GETARI can be used, we designed evaluation trees in order to study the climate suitability for maize cropping regarding ecophysiology, for wheat cropping regarding its management and for grape cropping regarding its quality. The designed evaluation trees were developed in accordance with expert assessment and were applied in some past climatic conditions in France to verify their consistence. To conclude, the use of indicators does not replace models but represent additional tools for understanding and spatializing some results obtained by models. Their use can provide information about suitability of geographical areas for cropping in future climatic conditions and can enable to minimize the risk of crop failure. This work is carried out under the research program ORACLE (Opportunities and Risks of Agrosystems & forests in response to CLimate, socio-economic and policy changEs in France (and Europe).

Flood damage modeling based on expert knowledge: Insights from French damage model for agricultural sector

NASA Astrophysics Data System (ADS)

Grelot, Frédéric; Agenais, Anne-Laurence; Brémond, Pauline

2015-04-01

In France, since 2011, it is mandatory for local communities to conduct cost-benefit analysis (CBA) of their flood management projects, to make them eligible for financial support from the State. Meanwhile, as a support, the French Ministry in charge of Environment proposed a methodology to fulfill CBA. Like for many other countries, this methodology is based on the estimation of flood damage. However, existing models to estimate flood damage were judged not convenient for a national-wide use. As a consequence, the French Ministry in charge of Environment launched studies to develop damage models for different sectors, such as: residential sector, public infrastructures, agricultural sector, and commercial and industrial sector. In this presentation, we aim at presenting and discussing methodological choices of those damage models. They all share the same principle: no sufficient data from past events were available to build damage models on a statistical analysis, so modeling was based on expert knowledge. We will focus on the model built for agricultural activities and more precisely for agricultural lands. This model was based on feedback from 30 agricultural experts who experienced floods in their geographical areas. They were selected to have a representative experience of crops and flood conditions in France. The model is composed of: (i) damaging functions, which reveal physiological vulnerability of crops, (ii) action functions, which correspond to farmers' decision rules for carrying on crops after a flood, and (iii) economic agricultural data, which correspond to featured characteristics of crops in the geographical area where the flood management project studied takes place. The two first components are generic and the third one is specific to the area studied. It is, thus, possible to produce flood damage functions adapted to different agronomic and geographical contexts. In the end, the model was applied to obtain a pool of damage functions giving damage in euros by hectare for 14 agricultural lands categories. As a conclusion, we will discuss the validation step of the model. Although the model was validated by experts, we analyse how it could gain insight from comparison with past events.

Flood damage modeling based on expert knowledge: Insights from French damage model for agricultural sector

NASA Astrophysics Data System (ADS)

Grelot, Frédéric; Agenais, Anne-Laurence; Brémond, Pauline

2014-05-01

In France, since 2011, it is mandatory for local communities to conduct cost-benefit analysis (CBA) of their flood management projects, to make them eligible for financial support from the State. Meanwhile, as a support, the French Ministry in charge of Environment proposed a methodology to fulfill CBA. Like for many other countries, this methodology is based on the estimation of flood damage. Howerver, existing models to estimate flood damage were judged not convenient for a national-wide use. As a consequence, the French Ministry in charge of Environment launched studies to develop damage models for different sectors, such as: residential sector, public infrastructures, agricultural sector, and commercial and industrial sector. In this presentation, we aim at presenting and discussing methodological choices of those damage models. They all share the same principle: no sufficient data from past events were available to build damage models on a statistical analysis, so modeling was based on expert knowledge. We will focus on the model built for agricultural activities and more precisely for agricultural lands. This model was based on feedback from 30 agricultural experts who experienced floods in their geographical areas. They were selected to have a representative experience of crops and flood conditions in France. The model is composed of: (i) damaging functions, which reveal physiological vulnerability of crops, (ii) action functions, which correspond to farmers' decision rules for carrying on crops after a flood, and (iii) economic agricultural data, which correspond to featured characteristics of crops in the geographical area where the flood management project studied takes place. The two first components are generic and the third one is specific to the area studied. It is, thus, possible to produce flood damage functions adapted to different agronomic and geographical contexts. In the end, the model was applied to obtain a pool of damage functions giving damage in euros by hectare for 14 agricultural lands categories. As a conclusion, we will discuss the validation step of the model. Although the model was validated by experts, we analyse how it could gain insight from comparison with past events.

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

A modelling framework to simulate foliar fungal epidemics using functional–structural plant models

PubMed Central

Garin, Guillaume; Fournier, Christian; Andrieu, Bruno; Houlès, Vianney; Robert, Corinne; Pradal, Christophe

2014-01-01

Background and Aims Sustainable agriculture requires the identification of new, environmentally responsible strategies of crop protection. Modelling of pathosystems can allow a better understanding of the major interactions inside these dynamic systems and may lead to innovative protection strategies. In particular, functional–structural plant models (FSPMs) have been identified as a means to optimize the use of architecture-related traits. A current limitation lies in the inherent complexity of this type of modelling, and thus the purpose of this paper is to provide a framework to both extend and simplify the modelling of pathosystems using FSPMs. Methods Different entities and interactions occurring in pathosystems were formalized in a conceptual model. A framework based on these concepts was then implemented within the open-source OpenAlea modelling platform, using the platform's general strategy of modelling plant–environment interactions and extending it to handle plant interactions with pathogens. New developments include a generic data structure for representing lesions and dispersal units, and a series of generic protocols to communicate with objects representing the canopy and its microenvironment in the OpenAlea platform. Another development is the addition of a library of elementary models involved in pathosystem modelling. Several plant and physical models are already available in OpenAlea and can be combined in models of pathosystems using this framework approach. Key Results Two contrasting pathosystems are implemented using the framework and illustrate its generic utility. Simulations demonstrate the framework's ability to simulate multiscaled interactions within pathosystems, and also show that models are modular components within the framework and can be extended. This is illustrated by testing the impact of canopy architectural traits on fungal dispersal. Conclusions This study provides a framework for modelling a large number of pathosystems using FSPMs. This structure can accommodate both previously developed models for individual aspects of pathosystems and new ones. Complex models are deconstructed into separate ‘knowledge sources’ originating from different specialist areas of expertise and these can be shared and reassembled into multidisciplinary models. The framework thus provides a beneficial tool for a potential diverse and dynamic research community. PMID:24925323

75 FR 69913 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-16

... methodology and assumptions used; (c) ways to enhance the quality, utility and clarity of the information to be collected; (d) ways to minimize the burden of the collection of information on those who are to... number. Agricultural Marketing Service Title: Generic Fruit Crops, Marketing Order Administration Branch...

A new generic plant growth model framework (PMF): Simulating distributed dynamic interaction of biomass production and its interaction with water and nutrients fluxes

NASA Astrophysics Data System (ADS)

Multsch, Sebastian; Kraft, Philipp; Frede, Hans-Georg; Breuer, Lutz

2010-05-01

Today, crop models have a widespread application in natural sciences, because plant growth interacts and modifies the environment. Transport processes involve water and nutrient uptake from the saturated and unsaturated zone in the pedosphere. Turnover processes include the conversion of dead root biomass into organic matter. Transpiration and the interception of radiation influence the energy exchange between atmosphere and biosphere. But many more feedback mechanisms might be of interest, including erosion, soil compaction or trace gas exchanges. Most of the existing crop models have a closed structure and do not provide interfaces or code design elements for easy data transfer or process exchange with other models during runtime. Changes in the model structure, the inclusion of alternative process descriptions or the implementation of additional functionalities requires a lot of coding. The same is true if models are being upscaled from field to landscape or catchment scale. We therefore conclude that future integrated model developments would benefit from a model structure that has the following requirements: replaceability, expandability and independency. In addition to these requirements we also propose the interactivity of models, which means that models that are being coupled are highly interacting and depending on each other, i.e. the model should be open for influences from other independent models and react on influences directly. Hence, a model which consists of building blocks seems to be reasonable. The aim of the study is the presentation of the new crop model type, the plant growth model framework, PMF. The software concept refers to an object-oriented approach, which is developed with the Unified Modeling Language (UML). The model is implemented with Python, a high level object-oriented programming language. The integration of the models with a setup code enables the data transfer on the computer memory level and direct exchange of information about changing boundary conditions. The crop model concept refers to two main elements. A plant model, which represents an abstract network of plant organs and processes and a process library, which holds mathematical solutions for the growth processes. Growth processes were mainly taken from existing, well known crop models such as SUCROS and CERES. The crop specific properties of root architecture are described based on a maximum rooting depth and a vertical growth rate. The biomass distribution depends on an interactive allocation process due to the soil layers with a daily time step. In order to show the performance and capabilities of PMF, the model is coupled with the Catchment Modeling Framework (CMF) and the simple nitrogen mineralization model DeComp. The main feature of the integrated model set up is the interaction between root growth, water uptake and nitrogen supply of the soil. We show a virtual case study on the hillslope scale and spatially dependence of water and nitrogen stress based on topographic position and seasonal development.

Development of a generic auto-calibration package for regional ecological modeling and application in the Central Plains of the United States

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang; Li, Zhengpeng; Dahal, Devendra; Young, Claudia J.; Schmidt, Gail L.; Liu, Jinxun; Davis, Brian; Sohl, Terry L.; Werner, Jeremy M.; Oeding, Jennifer

2014-01-01

Process-oriented ecological models are frequently used for predicting potential impacts of global changes such as climate and land-cover changes, which can be useful for policy making. It is critical but challenging to automatically derive optimal parameter values at different scales, especially at regional scale, and validate the model performance. In this study, we developed an automatic calibration (auto-calibration) function for a well-established biogeochemical model—the General Ensemble Biogeochemical Modeling System (GEMS)-Erosion Deposition Carbon Model (EDCM)—using data assimilation technique: the Shuffled Complex Evolution algorithm and a model-inversion R package—Flexible Modeling Environment (FME). The new functionality can support multi-parameter and multi-objective auto-calibration of EDCM at the both pixel and regional levels. We also developed a post-processing procedure for GEMS to provide options to save the pixel-based or aggregated county-land cover specific parameter values for subsequent simulations. In our case study, we successfully applied the updated model (EDCM-Auto) for a single crop pixel with a corn–wheat rotation and a large ecological region (Level II)—Central USA Plains. The evaluation results indicate that EDCM-Auto is applicable at multiple scales and is capable to handle land cover changes (e.g., crop rotations). The model also performs well in capturing the spatial pattern of grain yield production for crops and net primary production (NPP) for other ecosystems across the region, which is a good example for implementing calibration and validation of ecological models with readily available survey data (grain yield) and remote sensing data (NPP) at regional and national levels. The developed platform for auto-calibration can be readily expanded to incorporate other model inversion algorithms and potential R packages, and also be applied to other ecological models.

Adaptive management of irrigation and crops' biodiversity: a case study on tomato

NASA Astrophysics Data System (ADS)

De Lorenzi, Francesca; Alfieri, Silvia Maria; Basile, Angelo; Bonfante, Antonello; Monaco, Eugenia; Riccardi, Maria; Menenti, Massimo

2013-04-01

We have assessed the impacts of climate change and evaluated options to adapt irrigation management in the face of predicted changes of agricultural water demand. We have evaluated irrigation scheduling and its effectiveness (versus crop transpiration), and cultivars' adaptability. The spatial and temporal variations of effectiveness and adaptability were studied in an irrigated district of Southern Italy. Two climate scenarios were considered: reference (1961-90) and future (2021-2050) climate, the former from climatic statistics, and the latter from statistical downscaling of general circulation models (AOGCM). Climatic data consist of daily time series of maximum and minimum temperature, and daily rainfall on a grid with a spatial resolution of 35 km. The work was carried out in the Destra Sele irrigation scheme (18.000 ha. Twenty-five soil units were identified and their hydrological properties were determined (measured or estimated from texture through pedo-transfer functions). A tomato crop, in a rotation typical of the area, was considered. A mechanistic model of water flow in the soil-plant-atmosphere system (SWAP) was used to study crop water requirements and water consumption. The model was calibrated and validated in the same area for many different crops. Tomato crop input data and model parameters were estimated on the basis of scientific literature and assumed to be generically representative of the species. Simulations were performed for reference and future climate, and for different irrigation scheduling options. In all soil units, six levels of irrigation volumes were applied: full irrigation (100%), deficit irrigation (80%, 60%, 40%, 20%), no irrigation. From simulation runs, indicators of soil water availability were calculated, moreover the marginal increases of transpiration per unit of irrigation volume, i.e. the effectiveness of irrigation (ΔT/I), were computed, in both climate scenarios. Indicators and marginal increases were used to evaluate the tomato crop adaptability to future climate. To this purpose, for several tomato cultivars, threshold values of their yield responses to soil water availability were determined (data from scientific literature). Cultivars' threshold values were evaluated, in all soil units, against the indicators' values, for irrigation levels with different ΔT/I. Less water intensive cultivars and irrigation volumes that optimize transpiration (and yield) could thus be identified in both climate scenarios, and irrigation management scenarios were determined taking into account soils' hydrological properties, crop biodiversity, and efficient use of water resource. The work was carried out within the Italian national project AGROSCENARI funded by the Ministry for Agricultural, Food and Forest Policies (MIPAAF, D.M. 8608/7303/2008) Keywords: climate change, adaptation, simulation models, deficit irrigation, water resource efficiency, SWAP

Combining novel monitoring tools and precision application technologies for integrated high-tech crop protection in the future (a discussion document).

PubMed

Zijlstra, Carolien; Lund, Ivar; Justesen, Annemarie F; Nicolaisen, Mogens; Jensen, Peter Kryger; Bianciotto, Valeria; Posta, Katalin; Balestrini, Raffaella; Przetakiewicz, Anna; Czembor, Elzbieta; van de Zande, Jan

2011-06-01

The possibility of combining novel monitoring techniques and precision spraying for crop protection in the future is discussed. A generic model for an innovative crop protection system has been used as a framework. This system will be able to monitor the entire cropping system and identify the presence of relevant pests, diseases and weeds online, and will be location specific. The system will offer prevention, monitoring, interpretation and action which will be performed in a continuous way. The monitoring is divided into several parts. Planting material, seeds and soil should be monitored for prevention purposes before the growing period to avoid, for example, the introduction of disease into the field and to ensure optimal growth conditions. Data from previous growing seasons, such as the location of weeds and previous diseases, should also be included. During the growing season, the crop will be monitored at a macroscale level until a location that needs special attention is identified. If relevant, this area will be monitored more intensively at a microscale level. A decision engine will analyse the data and offer advice on how to control the detected diseases, pests and weeds, using precision spray techniques or alternative measures. The goal is to provide tools that are able to produce high-quality products with the minimal use of conventional plant protection products. This review describes the technologies that can be used or that need further development in order to achieve this goal. Copyright © 2011 Society of Chemical Industry.

Ecological Intensification Through Pesticide Reduction: Weed Control, Weed Biodiversity and Sustainability in Arable Farming.

PubMed

Petit, Sandrine; Munier-Jolain, Nicolas; Bretagnolle, Vincent; Bockstaller, Christian; Gaba, Sabrina; Cordeau, Stéphane; Lechenet, Martin; Mézière, Delphine; Colbach, Nathalie

2015-11-01

Amongst the biodiversity components of agriculture, weeds are an interesting model for exploring management options relying on the principle of ecological intensification in arable farming. Weeds can cause severe crop yield losses, contribute to farmland functional biodiversity and are strongly associated with the generic issue of pesticide use. In this paper, we address the impacts of herbicide reduction following a causal framework starting with herbicide reduction and triggering changes in (i) the management options required to control weeds, (ii) the weed communities and functions they provide and (iii) the overall performance and sustainability of the implemented land management options. The three components of this framework were analysed in a multidisciplinary project that was conducted on 55 experimental and farmer's fields that included conventional, integrated and organic cropping systems. Our results indicate that the reduction of herbicide use is not antagonistic with crop production, provided that alternative practices are put into place. Herbicide reduction and associated land management modified the composition of in-field weed communities and thus the functions of weeds related to biodiversity and production. Through a long-term simulation of weed communities based on alternative (?) cropping systems, some specific management pathways were identified that delivered high biodiversity gains and limited the negative impacts of weeds on crop production. Finally, the multi-criteria assessment of the environmental, economic and societal sustainability of the 55 systems suggests that integrated weed management systems fared better than their conventional and organic counterparts. These outcomes suggest that sustainable management could possibly be achieved through changes in weed management, along a pathway starting with herbicide reduction.

Ecological Intensification Through Pesticide Reduction: Weed Control, Weed Biodiversity and Sustainability in Arable Farming

NASA Astrophysics Data System (ADS)

Petit, Sandrine; Munier-Jolain, Nicolas; Bretagnolle, Vincent; Bockstaller, Christian; Gaba, Sabrina; Cordeau, Stéphane; Lechenet, Martin; Mézière, Delphine; Colbach, Nathalie

2015-11-01

Amongst the biodiversity components of agriculture, weeds are an interesting model for exploring management options relying on the principle of ecological intensification in arable farming. Weeds can cause severe crop yield losses, contribute to farmland functional biodiversity and are strongly associated with the generic issue of pesticide use. In this paper, we address the impacts of herbicide reduction following a causal framework starting with herbicide reduction and triggering changes in (i) the management options required to control weeds, (ii) the weed communities and functions they provide and (iii) the overall performance and sustainability of the implemented land management options. The three components of this framework were analysed in a multidisciplinary project that was conducted on 55 experimental and farmer's fields that included conventional, integrated and organic cropping systems. Our results indicate that the reduction of herbicide use is not antagonistic with crop production, provided that alternative practices are put into place. Herbicide reduction and associated land management modified the composition of in-field weed communities and thus the functions of weeds related to biodiversity and production. Through a long-term simulation of weed communities based on alternative (?) cropping systems, some specific management pathways were identified that delivered high biodiversity gains and limited the negative impacts of weeds on crop production. Finally, the multi-criteria assessment of the environmental, economic and societal sustainability of the 55 systems suggests that integrated weed management systems fared better than their conventional and organic counterparts. These outcomes suggest that sustainable management could possibly be achieved through changes in weed management, along a pathway starting with herbicide reduction.

A user-friendly means to scale from the biochemistry of photosynthesis to whole crop canopies and production in time and space - development of Java WIMOVAC.

PubMed

Song, Qingfeng; Chen, Dairui; Long, Stephen P; Zhu, Xin-Guang

2017-01-01

Windows Intuitive Model of Vegetation response to Atmosphere and Climate Change (WIMOVAC) has been used widely as a generic modular mechanistically rich model of plant production. It can predict the responses of leaf and canopy carbon balance, as well as production in different environmental conditions, in particular those relevant to global change. Here, we introduce an open source Java user-friendly version of WIMOVAC. This software is platform independent and can be easily downloaded to a laptop and used without any prior programming skills. In this article, we describe the structure, equations and user guide and illustrate some potential applications of WIMOVAC. © 2016 The Authors Plant, Cell & Environment Published by John Wiley & Sons Ltd.

Survival of generic E. coli and surrogate E. coli O157:H7 in manure-amended soils

USDA-ARS?s Scientific Manuscript database

Introduction: Recently released U.S. FDA standards state that untreated biological soil amendments must be applied to soil 9 months before produce crop harvest to reduce pathogen contamination risk on produce. Manure and soil type may impact survival of bacterial pathogens. Purpose: Determine survi...

The Nexus Land-Use model version 1.0, an approach articulating biophysical potentials and economic dynamics to model competition for land-use

NASA Astrophysics Data System (ADS)

Souty, F.; Brunelle, T.; Dumas, P.; Dorin, B.; Ciais, P.; Crassous, R.; Müller, C.; Bondeau, A.

2012-02-01

Interactions between food demand, biomass energy and forest preservation are driving both food prices and land-use changes, regionally and globally. This study presents a new model called Nexus Land-Use version 1.0 which describes these interactions through a generic representation of agricultural intensification mechanisms. The Nexus Land-Use model equations combine biophysics and economics into a single coherent framework to calculate crop yields, food prices, and resulting pasture and cropland areas within 12 regions inter-connected with each other by international trade. The representation of cropland and livestock production systems in each region relies on three components: (i) a biomass production function derived from the crop yield response function to inputs such as industrial fertilisers; (ii) a detailed representation of the livestock production system subdivided into an intensive and an extensive component, and (iii) a spatially explicit distribution of potential (maximal) crop yields prescribed from the Lund-Postdam-Jena global vegetation model for managed Land (LPJmL). The economic principles governing decisions about land-use and intensification are adapted from the Ricardian rent theory, assuming cost minimisation for farmers. The land-use modelling approach described in this paper entails several advantages. Firstly, it makes it possible to explore interactions among different types of biomass demand for food and animal feed, in a consistent approach, including indirect effects on land-use change resulting from international trade. Secondly, yield variations induced by the possible expansion of croplands on less suitable marginal lands are modelled by using regional land area distributions of potential yields, and a calculated boundary between intensive and extensive production. The model equations and parameter values are first described in details. Then, idealised scenarios exploring the impact of forest preservation policies or rising energy price on agricultural intensification are described, and their impacts on pasture and cropland areas are investigated.

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Simulation of an integrated system for the production of methane and single cell protein from biomass

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

Thomas, M.V.

1989-01-01

A numerical model was developed to simulate the operation of an integrated system for the production of methane and single-cell algal protein from a variety of biomass energy crops or waste streams. Economic analysis was performed at the end of each simulation. The model was capable of assisting in the determination of design parameters by providing relative economic information for various strategies. Three configurations of anaerobic reactors were simulated. These included fed-bed reactors, conventional stirred tank reactors, and continuously expanding reactors. A generic anaerobic digestion process model, using lumped substrate parameters, was developed for use by type-specific reactor models. Themore » generic anaerobic digestion model provided a tool for the testing of conversion efficiencies and kinetic parameters for a wide range of substrate types and reactor designs. Dynamic growth models were used to model the growth of algae and Eichornia crassipes was modeled as a function of daily incident radiation and temperature. The growth of Eichornia crassipes was modeled for the production of biomass as a substrate for digestion. Computer simulations with the system model indicated that tropical or subtropical locations offered the most promise for a viable system. The availability of large quantities of digestible waste and low land prices were found to be desirable in order to take advantage of the economies of scale. Other simulations indicated that poultry and swine manure produced larger biogas yields than cattle manure. The model was created in a modular fashion to allow for testing of a wide variety of unit operations. Coding was performed in the Pascal language for use on personal computers.« less

Modeling Spatial and Temporal Variability in Ammonia Emissions from Agricultural Fertilization

NASA Astrophysics Data System (ADS)

Balasubramanian, S.; Koloutsou-Vakakis, S.; Rood, M. J.

2013-12-01

Ammonia (NH3), is an important component of the reactive nitrogen cycle and a precursor to formation of atmospheric particulate matter (PM). Predicting regional PM concentrations and deposition of nitrogen species to ecosystems requires representative emission inventories. Emission inventories have traditionally been developed using top down approaches and more recently from data assimilation based on satellite and ground based ambient concentrations and wet deposition data. The National Emission Inventory (NEI) indicates agricultural fertilization as the predominant contributor (56%) to NH3 emissions in Midwest USA, in 2002. However, due to limited understanding of the complex interactions between fertilizer usage, farm practices, soil and meteorological conditions and absence of detailed statistical data, such emission estimates are currently based on generic emission factors, time-averaged temporal factors and coarse spatial resolution. Given the significance of this source, our study focuses on developing an improved NH3 emission inventory for agricultural fertilization at finer spatial and temporal scales for air quality modeling studies. Firstly, a high-spatial resolution 4 km x 4 km NH3 emission inventory for agricultural fertilization has been developed for Illinois by modifying spatial allocation of emissions based on combining crop-specific fertilization rates with cropland distribution in the Sparse Matrix Operator Kernel Emissions model. Net emission estimates of our method are within 2% of NEI, since both methods are constrained by fertilizer sales data. However, we identified localized crop-specific NH3 emission hotspots at sub-county resolutions absent in NEI. Secondly, we have adopted the use of the DeNitrification-DeComposition (DNDC) Biogeochemistry model to simulate the physical and chemical processes that control volatilization of nitrogen as NH3 to the atmosphere after fertilizer application and resolve the variability at the hourly scale. Representative temporal factors are being developed to capture crop-specific NH3 emission variability by combining knowledge of local crop management practices with high resolution cropland and soil maps. This improved spatially and temporally dependent NH3 emission inventory for agricultural fertilization is being prepared as a direct input to a state of the art air quality model to evaluate the effects of agricultural fertilization on regional air quality and atmospheric deposition of reactive nitrogen species.

Two-year-olds use the generic/non-generic distinction to guide their inferences about novel kinds

PubMed Central

Graham, Susan A.; Nayer, Samantha L.; Gelman, Susan A.

2011-01-01

These studies investigated 24- and 30-month-olds’ sensitivity to generic versus nongeneric language when acquiring knowledge about novel kinds. Toddlers were administered an inductive inference task, during which they heard a generic noun-phrase (e.g., “Blicks drink milk”) or a non-generic noun-phrase (e.g., “This blick drinks milk”) paired with an action (e.g., drinking) modeled on an object. They were then provided with the model and a non-model exemplar and asked to imitate the action. After hearing non-generic phrases, 30-month-olds, but not 24-month-olds, imitated more often with the model than with the non-model exemplar. In contrast, after hearing generic phrases, 30-month-olds imitated equally often with both exemplars. These results suggest that 30-month-olds use the generic/non-generic distinction to guide their inferences about novel kinds. PMID:21410928

Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach

NASA Astrophysics Data System (ADS)

Bellón, Beatriz; Bégué, Agnès; Lo Seen, Danny; Lebourgeois, Valentine; Evangelista, Balbino Antônio; Simões, Margareth; Demonte Ferraz, Rodrigo Peçanha

2018-06-01

Cropping systems' maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014-2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape-clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems' mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions.

Modelling Management Practices in Viticulture while Considering Resource Limitations: The Dhivine Model

PubMed Central

Martin-Clouaire, Roger; Rellier, Jean-Pierre; Paré, Nakié; Voltz, Marc; Biarnès, Anne

2016-01-01

Many farming-system studies have investigated the design and evaluation of crop-management practices with respect to economic performance and reduction in environmental impacts. In contrast, little research has been devoted to analysing these practices in terms of matching the recurrent context-dependent demand for resources (labour in particular) with those available on the farm. This paper presents Dhivine, a simulation model of operational management of grape production at the vineyard scale. Particular attention focuses on representing a flexible plan, which organises activities temporally, the resources available to the vineyard manager and the process of scheduling and executing the activities. The model relies on a generic production-system ontology used in several agricultural production domains. The types of investigations that the model supports are briefly illustrated. The enhanced realism of the production-management situations simulated makes it possible to examine and understand properties of resource-constrained work-organisation strategies and possibilities for improving them. PMID:26990089

Detecting in-field variation in photosynthetic capacity of trangenically modifed plants with hyperspectral imaging.

NASA Astrophysics Data System (ADS)

Meacham, K.; Montes, C.; Pederson, T.; Wu, J.; Guan, K.; Bernacchi, C.

2017-12-01

Improved photosynthetic rates have been shown to increase crop biomass, making improved photosynthesis a focus for driving future grain yield increases. Improving the photosynthetic pathway offers opportunity to meet food demand, but requires high throughput measurement techniques to detect photosynthetic variation in natural accessions and transgenically modified plants. Gas exchange measurements are the most widely used method of measuring photosynthesis in field trials but this process is laborious and slow, and requires further modeling to estimate meaningful parameters and to upscale to the plot or canopy level. In field trials of tobacco with modifications made to the photosynthetic pathway, we infer the maximum carboxylation rate of Rubisco (Vcmax) and maximum electron transport rate (Jmax) and detect photosynthetic variation from hyperspectral imaging with a partial least squares regression technique. Ground-truth measurements from photosynthetic gas exchange, a full-range (400-2500nm) handheld spectroadiometer with leaf clip, hyperspectral indices, and extractions of leaf pigments support the model. The results from a range of wild-type cultivars and from genetically modified germplasm suggest that the opportunity for rapid selection of top performing genotypes from among thousands of plots. This research creates the opportunity to extend agroecosystem models from simplified "one-cultivar" generic parameterization to better represent a full suite of current and future crop cultivars for a wider range of environmental conditions.

Integrating NASA Earth Science Enterprise (ESE) Data Into Global Agricultural Decision Support Systems

NASA Astrophysics Data System (ADS)

Teng, W.; Kempler, S.; Chiu, L.; Doraiswamy, P.; Liu, Z.; Milich, L.; Tetrault, R.

2003-12-01

Monitoring global agricultural crop conditions during the growing season and estimating potential seasonal production are critically important for market development of U.S. agricultural products and for global food security. Two major operational users of satellite remote sensing for global crop monitoring are the USDA Foreign Agricultural Service (FAS) and the U.N. World Food Programme (WFP). The primary goal of FAS is to improve foreign market access for U.S. agricultural products. The WFP uses food to meet emergency needs and to support economic and social development. Both use global agricultural decision support systems that can integrate and synthesize a variety of data sources to provide accurate and timely information on global crop conditions. The Goddard Space Flight Center Earth Sciences Distributed Active Archive Center (GES DAAC) has begun a project to provide operational solutions to FAS and WFP, by fully leveraging results from previous work, as well as from existing capabilities of the users. The GES DAAC has effectively used its recently developed prototype TRMM Online Visualization and Analysis System (TOVAS) to provide ESE data and information to the WFP for its agricultural drought monitoring efforts. This prototype system will be evolved into an Agricultural Information System (AIS), which will operationally provide ESE and other data products (e.g., rainfall, land productivity) and services, to be integrated into and thus enhance the existing GIS-based, decision support systems of FAS and WFP. Agriculture-oriented, ESE data products (e.g., MODIS-based, crop condition assessment product; TRMM derived, drought index product) will be input to a crop growth model in collaboration with the USDA Agricultural Research Service, to generate crop condition and yield prediction maps. The AIS will have the capability for remotely accessing distributed data, by being compliant with community-based interoperability standards, enabling easy access to agriculture-related products from other data producers. The AIS? system approach will provide a generic mechanism for easily incorporating new products and making them accessible to users.

Analysis and prediction of pest dynamics in an agroforestry context using Tiko'n, a generic tool to develop food web models

NASA Astrophysics Data System (ADS)

Rojas, Marcela; Malard, Julien; Adamowski, Jan; Carrera, Jaime Luis; Maas, Raúl

2017-04-01

While it is known that climate change will impact future plant-pest population dynamics, potentially affecting crop damage, agroforestry with its enhanced biodiversity is said to reduce the outbreaks of pest insects by providing natural enemies for the control of pest populations. This premise is known in the literature as the natural enemy hypothesis and has been widely studied qualitatively. However, disagreement still exists on whether biodiversity enhancement reduces pest outbreaks, showing the need of quantitatively understanding the mechanisms behind the interactions between pests and natural enemies, also known as trophic interactions. Crop pest models that study insect population dynamics in agroforestry contexts are very rare, and pest models that take trophic interactions into account are even rarer. This may be due to the difficulty of representing complex food webs in a quantifiable model. There is therefore a need for validated food web models that allow users to predict the response of these webs to changes in climate in agroforestry systems. In this study we present Tiko'n, a Python-based software whose API allows users to rapidly build and validate trophic web models; the program uses a Bayesian inference approach to calibrate the models according to field data, allowing for the reuse of literature data from various sources and reducing the need for extensive field data collection. Tiko'n was run using coffee leaf miner (Leucoptera coffeella) and associated parasitoid data from a shaded coffee plantation, showing the mechanisms of insect population dynamics within a tri-trophic food web in an agroforestry system.

Common vole (Microtus arvalis) ecology and management: implications for risk assessment of plant protection products.

PubMed

Jacob, Jens; Manson, Phil; Barfknecht, Ralf; Fredricks, Timothy

2014-06-01

Common voles (Microtus arvalis) are common small mammals in some European landscapes. They can be a major rodent pest in European agriculture and they are also a representative generic focal small herbivorous mammal species used in risk assessment for plant protection products. In this paper, common vole population dynamics, habitat and food preferences, pest potential and use of the common vole as a model small wild mammal species in the risk assessment process are reviewed. Common voles are a component of agroecosystems in many parts of Europe, inhabiting agricultural areas (secondary habitats) when the carrying capacity of primary grassland habitats is exceeded. Colonisation of secondary habitats occurs during multiannual outbreaks, when population sizes can exceed 1000 individuals ha(-1) . In such cases, in-crop common vole population control management has been practised to avoid significant crop damage. The species' status as a crop pest, high fecundity, resilience to disturbance and intermittent colonisation of crop habitats are important characteristics that should be reflected in risk assessment. Based on the information provided in the scientific literature, it seems justified to modify elements of the current risk assessment scheme for plant protection products, including the use of realistic food intake rates, reduced assessment factors or the use of alternativee focal rodent species in particular European regions. Some of these adjustments are already being applied in some EU member states. Therefore, it seems reasonable consistently to apply such pragmatic and realistic approaches in risk assessments for plant protection products across the EU. © 2013 Society of Chemical Industry.

Space Generic Open Avionics Architecture (SGOAA) reference model technical guide

NASA Technical Reports Server (NTRS)

Wray, Richard B.; Stovall, John R.

1993-01-01

This report presents a full description of the Space Generic Open Avionics Architecture (SGOAA). The SGOAA consists of a generic system architecture for the entities in spacecraft avionics, a generic processing architecture, and a six class model of interfaces in a hardware/software system. The purpose of the SGOAA is to provide an umbrella set of requirements for applying the generic architecture interface model to the design of specific avionics hardware/software systems. The SGOAA defines a generic set of system interface points to facilitate identification of critical interfaces and establishes the requirements for applying appropriate low level detailed implementation standards to those interface points. The generic core avionics system and processing architecture models provided herein are robustly tailorable to specific system applications and provide a platform upon which the interface model is to be applied.

Space Generic Open Avionics Architecture (SGOAA) standard specification

NASA Technical Reports Server (NTRS)

Wray, Richard B.; Stovall, John R.

1994-01-01

This standard establishes the Space Generic Open Avionics Architecture (SGOAA). The SGOAA includes a generic functional model, processing structural model, and an architecture interface model. This standard defines the requirements for applying these models to the development of spacecraft core avionics systems. The purpose of this standard is to provide an umbrella set of requirements for applying the generic architecture models to the design of a specific avionics hardware/software processing system. This standard defines a generic set of system interface points to facilitate identification of critical services and interfaces. It establishes the requirement for applying appropriate low level detailed implementation standards to those interfaces points. The generic core avionics functions and processing structural models provided herein are robustly tailorable to specific system applications and provide a platform upon which the interface model is to be applied.

Generic domain models in software engineering

NASA Technical Reports Server (NTRS)

Maiden, Neil

1992-01-01

This paper outlines three research directions related to domain-specific software development: (1) reuse of generic models for domain-specific software development; (2) empirical evidence to determine these generic models, namely elicitation of mental knowledge schema possessed by expert software developers; and (3) exploitation of generic domain models to assist modelling of specific applications. It focuses on knowledge acquisition for domain-specific software development, with emphasis on tool support for the most important phases of software development.

Optimization of black-box models with uncertain climatic inputs—Application to sunflower ideotype design

PubMed Central

Picheny, Victor; Trépos, Ronan; Casadebaig, Pierre

2017-01-01

Accounting for the interannual climatic variations is a well-known issue for simulation-based studies of environmental systems. It often requires intensive sampling (e.g., averaging the simulation outputs over many climatic series), which hinders many sequential processes, in particular optimization algorithms. We propose here an approach based on a subset selection in a large basis of climatic series, using an ad-hoc similarity function and clustering. A non-parametric reconstruction technique is introduced to estimate accurately the distribution of the output of interest using only the subset sampling. The proposed strategy is non-intrusive and generic (i.e. transposable to most models with climatic data inputs), and can be combined to most “off-the-shelf” optimization solvers. We apply our approach to sunflower ideotype design using the crop model SUNFLO. The underlying optimization problem is formulated as a multi-objective one to account for risk-aversion. Our approach achieves good performances even for limited computational budgets, outperforming significantly standard strategies. PMID:28542198

Is there a relationship between patient beliefs or communication about generic drugs and medication utilization?

PubMed

Shrank, William H; Cadarette, Suzanne M; Cox, Emily; Fischer, Michael A; Mehta, Jyotsna; Brookhart, Alan M; Avorn, Jerry; Choudhry, Niteesh K

2009-03-01

Insurers and policymakers strive to stimulate more cost-effective prescribing and, increasingly, are educating beneficiaries about generics. To evaluate the relationship between patient beliefs and communication about generic drugs and actual drug use. We performed a national mailed survey of a random sample of 2500 commercially-insured adults. Patient responses were linked to pharmacy claims data to assess actual generic medication use. We used factor analysis to develop 5 multi-item scales from patient survey responses that measured: (1) general preferences for generics, (2) generic safety/effectiveness, (3) generic cost/value, (4) comfort with generic substitution, and (5) communication with providers about generics. The relationship between each scale and the proportion of prescriptions filled for generics was assessed using linear regression, controlling for demographic, health, and insurance characteristics. Separate models were created for each scale and then all 5 scales were included simultaneously in a fully-adjusted model. The usable response rate was 48%. When evaluated independently, a 1 SD increase in each of the 5 scales was associated with a 3.1% to 6.3% increase in generic drug use (P < 0.05 for each). In the fully adjusted model, only 2 scales were significantly associated with generic drug use: comfort with generic substitution (P = 0.021) and communication with providers about generic drugs (P = 0.012). Generic drug use is most closely associated with the 2 actionable items we evaluated: communication with providers about generics and comfort with generic substitution. Educational campaigns that focus on these 2 domains may be most effective at influencing generic drug use.

PERPHECLIM ACCAF Project - Perennial fruit crops and forest phenology evolution facing climatic changes

NASA Astrophysics Data System (ADS)

Garcia de Cortazar-Atauri, Iñaki; Audergon, Jean Marc; Bertuzzi, Patrick; Anger, Christel; Bonhomme, Marc; Chuine, Isabelle; Davi, Hendrik; Delzon, Sylvain; Duchêne, Eric; Legave, Jean Michel; Raynal, Hélène; Pichot, Christian; Van Leeuwen, Cornelis; Perpheclim Team

2015-04-01

Phenology is a bio-indicator of climate evolutions. Measurements of phenological stages on perennial species provide actually significant illustrations and assessments of the impact of climate change. Phenology is also one of the main key characteristics of the capacity of adaptation of perennial species, generating questions about their consequences on plant growth and development or on fruit quality. Predicting phenology evolution and adaptative capacities of perennial species need to override three main methodological limitations: 1) existing observations and associated databases are scattered and sometimes incomplete, rendering difficult implementation of multi-site study of genotype-environment interaction analyses; 2) there are not common protocols to observe phenological stages; 3) access to generic phenological models platforms is still very limited. In this context, the PERPHECLIM project, which is funded by the Adapting Agriculture and Forestry to Climate Change Meta-Program (ACCAF) from INRA (French National Institute of Agronomic Research), has the objective to develop the necessary infrastructure at INRA level (observatories, information system, modeling tools) to enable partners to study the phenology of various perennial species (grapevine, fruit trees and forest trees). Currently the PERPHECLIM project involves 27 research units in France. The main activities currently developed are: define protocols and observation forms to observe phenology for various species of interest for the project; organizing observation training; develop generic modeling solutions to simulate phenology (Phenological Modelling Platform and modelling platform solutions); support in building research projects at national and international level; develop environment/genotype observation networks for fruit trees species; develop an information system managing data and documentation concerning phenology. Finally, PERPHECLIM project aims to build strong collaborations with public (Observatoire des Saisons) and private sector partners (technical institutes) in order to allow a more direct transfer of knowledge.

Consequences of gene flow between oilseed rape (Brassica napus) and its relatives.

PubMed

Liu, Yongbo; Wei, Wei; Ma, Keping; Li, Junsheng; Liang, Yuyong; Darmency, Henri

2013-10-01

Numerous studies have focused on the probability of occurrence of gene flow between transgenic crops and their wild relatives and the likelihood of transgene escape, which should be assessed before the commercial release of transgenic crops. This review paper focuses on this issue for oilseed rape, Brassica napus L., a species that produces huge numbers of pollen grains and seeds. We analyze separately the distinct steps of gene flow: (1) pollen and seeds as vectors of gene flow; (2) spontaneous hybridization; (3) hybrid behavior, fitness cost due to hybridization and mechanisms of introgression; (4) and fitness benefit due to transgenes (e.g. herbicide resistance and Bt toxin). Some physical, biological and molecular means of transgene containment are also described. Although hybrids and first generation progeny are difficult to identify in fields and non-crop habitats, the literature shows that transgenes could readily introgress into Brassica rapa, Brassica juncea and Brassica oleracea, while introgression is expected to be rare with Brassica nigra, Hirschfeldia incana and Raphanus raphanistrum. The hybrids grow well but produce less seed than their wild parent. The difference declines with increasing generations. However, there is large uncertainty about the evolution of chromosome numbers and recombination, and many parameters of life history traits of hybrids and progeny are not determined with satisfactory confidence to build generic models capable to really cover the wide diversity of situations. We show that more studies are needed to strengthen and organize biological knowledge, which is a necessary prerequisite for model simulations to assess the practical and evolutionary outputs of introgression, and to provide guidelines for gene flow management. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Transactions in domain-specific information systems

NASA Astrophysics Data System (ADS)

Zacek, Jaroslav

2017-07-01

Substantial number of the current information system (IS) implementations is based on transaction approach. In addition, most of the implementations are domain-specific (e.g. accounting IS, resource planning IS). Therefore, we have to have a generic transaction model to build and verify domain-specific IS. The paper proposes a new transaction model for domain-specific ontologies. This model is based on value oriented business process modelling technique. The transaction model is formalized by the Petri Net theory. First part of the paper presents common business processes and analyses related to business process modeling. Second part defines the transactional model delimited by REA enterprise ontology paradigm and introduces states of the generic transaction model. The generic model proposal is defined and visualized by the Petri Net modelling tool. Third part shows application of the generic transaction model. Last part of the paper concludes results and discusses a practical usability of the generic transaction model.

Identifying obstacles and ranking common biological control research priorities for Europe to manage most economically important pests in arable, vegetable and perennial crops.

PubMed

Lamichhane, Jay Ram; Bischoff-Schaefer, Monika; Bluemel, Sylvia; Dachbrodt-Saaydeh, Silke; Dreux, Laure; Jansen, Jean-Pierre; Kiss, Jozsef; Köhl, Jürgen; Kudsk, Per; Malausa, Thibaut; Messéan, Antoine; Nicot, Philippe C; Ricci, Pierre; Thibierge, Jérôme; Villeneuve, François

2017-01-01

EU agriculture is currently in transition from conventional crop protection to integrated pest management (IPM). Because biocontrol is a key component of IPM, many European countries recently have intensified their national efforts on biocontrol research and innovation (R&I), although such initiatives are often fragmented. The operational outputs of national efforts would benefit from closer collaboration among stakeholders via transnationally coordinated approaches, as most economically important pests are similar across Europe. This paper proposes a common European framework on biocontrol R&I. It identifies generic R&I bottlenecks and needs as well as priorities for three crop types (arable, vegetable and perennial crops). The existing gap between the market offers of biocontrol solutions and the demand of growers, the lengthy and expensive registration process for biocontrol solutions and their varying effectiveness due to variable climatic conditions and site-specific factors across Europe are key obstacles hindering the development and adoption of biocontrol solutions in Europe. Considering arable, vegetable and perennial crops, a dozen common target pests are identified for each type of crop and ranked by order of importance at European level. Such a ranked list indicates numerous topics on which future joint transnational efforts would be justified. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Is There a Relationship Between Patient Beliefs or Communication About Generic Drugs and Medication Utilization?

PubMed Central

Shrank, William H.; Cadarette, Suzanne M.; Cox, Emily; Fischer, Michael A.; Mehta, Jyotsna; Brookhart, Alan M.; Avorn, Jerry; Choudhry, Niteesh K.

2009-01-01

Background Insurers and policymakers strive to stimulate more cost-effective prescribing and, increasingly, are educating beneficiaries about generics. Objectives To evaluate the relationship between patient beliefs and communication about generic drugs and actual drug use. Research Design and Subjects We performed a national mailed survey of a random sample of 2500 commercially-insured adults. Patient responses were linked to pharmacy claims data to assess actual generic medication use. Measures We used factor analysis to develop 5 multi-item scales from patient survey responses that measured: (1) general preferences for generics, (2) generic safety/effectiveness, (3) generic cost/value, (4) comfort with generic substitution, and (5) communication with providers about generics. The relationship between each scale and the proportion of prescriptions filled for generics was assessed using linear regression, controlling for demographic, health, and insurance characteristics. Separate models were created for each scale and then all 5 scales were included simultaneously in a fully-adjusted model. Results The usable response rate was 48%. When evaluated independently, a 1 SD increase in each of the 5 scales was associated with a 3.1% to 6.3% increase in generic drug use (P < 0.05 for each). In the fully adjusted model, only 2 scales were significantly associated with generic drug use: comfort with generic substitution (P = 0.021) and communication with providers about generic drugs (P = 0.012). Conclusions Generic drug use is most closely associated with the 2 actionable items we evaluated: communication with providers about generics and comfort with generic substitution. Educational campaigns that focus on these 2 domains may be most effective at influencing generic drug use. PMID:19194329

Do we know how to reconcile preservation of landscapes with adaptation of agriculture to climate change? A case-study in a hilly area in Southern Italy

NASA Astrophysics Data System (ADS)

Menenti, Massimo; Alfieri, Silvia; Basile, Angelo; Bonfante, Antonello; Monaco, Eugenia; Riccardi, Maria; De Lorenzi, Francesca

2013-04-01

Limited impacts of climate change on agricultural yields are unlikely to induce any significant changes in current landscapes. Larger impacts, unacceptable on economic or social ground, are likely to trigger interventions towards adaptation of agricultural production systems by reducing or removing vulnerabilities to climate variability and change. Such interventions may require a transition to a different production system, i.e. complete substitution of current crops, or displacement of current crops at their current location towards other locations, e.g. at higher elevations within the landscape. We have assessed the impacts of climate change and evaluated options for adaptation of a valley in Southern Italy, dominated by vine and olive orchards with a significant presence of wheat. We have first estimated the climatic requirements of several varieties for each dominant species. Next, to identify options for adaptation we have evaluated the compatibility of such requirements with indicators of a reference (current) climate and of future climate. This climate - compatibility assessment was done for each soil unit within the valley, leading to maps of locations where each crop is expected to be compatible with climate. This leads to identify both potential crop substitutions within the entire valley and crop displacements from one location to another within the valley. Two climate scenarios were considered: reference (1961-90) and future (2021-2050) climate, the former from climatic statistics, and the latter from statistical downscaling of general circulation models (AOGCM). Climatic data consists of daily time series of maximum and minimum temperature, and daily rainfall on a grid with a spatial resolution of 35 km. We evaluated the adaptive capacity of the "Valle Telesina" (Campania Region, Southern Italy). A mechanistic model of water flow in the soil-plant-atmosphere system (SWAP) was used to describe the hydrological conditions in response to climate for each soil unit. Crop-specific input data and model parameters were estimated on the basis of local experiments and of scientific literature and assumed to be generically representative of the species. Time series of MODIS TIR data were used to downscale gridded climate data on air temperature for both the reference and the future climate. The results indicate that no complete crop substitution will be required within this time frame, i.e. the Valle Telesina will preserve its typical landscape features of a vine - olive orchards dominated production system, typical of many regions in Mediterranean Europe. On the other hand very significant crop displacements will be necessary to grow each variety under optimal hydrothermal conditions, from the point of view of both quantity and quality of yield. The work was carried out within the Italian national project AGROSCENARI funded by the Ministry for Agricultural, Food and Forest Policies (MIPAAF, D.M. 8608/7303/2008)

The effect of measured and estimated soil hydraulic properties on simulated water regime in the analysis of grapevine adaptability to future climate

NASA Astrophysics Data System (ADS)

Bonfante, Antonello; Alfieri, Silvia Maria; Agrillo, Antonietta; Dragonetti, Giovanna; Mileti, Antonio; Monaco, Eugenia; De Lorenzi, Francesca

2013-04-01

In the last years many research works have been addressed to evaluate the impact of future climate on crop productivity and plant water use at different spatial scales (global, regional, field) by means of simulation models of agricultural crop systems. Most of these approaches use estimated soil hydraulic properties, through pedotransfer functions (PTF). This choice is related to soil data availability: soil data bases lack measured soil hydraulic properties, but generally they contain information that allow the application of PTF . Although the reliability of the predicted future climate scenarios cannot be immediately validated, we address to evaluate the effects of a simplification of the soil system by using PTF. Thus we compare simulations performed with measured soil hydraulic properties versus simulations carried out with estimated properties. The water regimes resulting from the two procedures are evaluated with respect to crop adaptability to future climate. In particular we will examine if the two procedures bring about different seasonal and spatial variations in the soil water regime patterns, and if these patterns influence adaptation options. The present case study uses the agro-hydrological model SWAP (soil-water-atmosphere and plant) and studies future adaptability of grapevine. The study area is a viticultural area of Southern Italy (Valle Telesina, BN) devoted to the production of high quality wines (DOC and DOCG), and characterized by a complex geomorphology and pedology. The future climate scenario (2021-2050) was constructed applying statistical downscaling techniques to GCMs scenarios. The moisture regime for 25 soils of the selected study area was calculated by means of SWAP model, using both measured and estimated soil hydraulic properties. In the simulation, the upper boundary conditions were derived from the regional climate scenarios. Unit gradient in soil water potential was set as lower boundary condition. Crop-specific input data and model parameters were estimated on the basis of scientific literature and assumed to be generically representative of the species. From the output of the simulation runs, the relative evapotranspiration deficit (or Crop Water Stress Index - CWSI) of the soil units was calculated. Since CWSI is considered an important indicator of the qualitative grapevine responses, its pattern in both simulation procedures has been evaluated. The work was carried out within the Italian national project AGROSCENARI funded by the Ministry for Agricultural, Food and Forest Policies (MIPAAF, D.M. 8608/7303/2008)

Pleiotropic effects of herbicide-resistance genes on crop yield: a review.

PubMed

Darmency, Henri

2013-08-01

The rapid adoption of genetically engineered herbicide-resistant crop varieties (HRCVs)-encompassing 83% of all GM crops and nearly 8% of the worldwide arable area-is due to technical efficiency and higher returns. Other herbicide-resistant varieties obtained from genetic resources and mutagenesis have also been successfully released. Although the benefit for weed control is the main criteria for choosing HRCVs, the pleiotropic costs of genes endowing resistance have rarely been investigated in crops. Here the available data of comparisons between isogenic resistant and susceptible varieties are reviewed. Pleiotropic harmful effects on yield are reported in half of the cases, mostly with resistance mechanisms that originate from genetic resources and mutagenesis (atrazine in oilseed rape and millet, trifluralin in millet, imazamox in cotton) rather than genetic engineering (chlorsulfuron and glufosinate in some oilseed rape varieties, glyphosate in soybean). No effect was found for sethoxydim and bromoxynil resistance. Variable minor effects were found for imazamox, chlorsulfuron, glufosinate and glyphosate resistance. The importance of the breeding plan and the genetic background on the emergence of these effects is pointed out. Breeders' efforts to produce better varieties could compensate for the yield loss, which eliminates any possibility of formulating generic conclusions on pleiotropic effects that can be applied to all resistant crops. © 2013 Society of Chemical Industry.

Enhancements Needed in GE Crop and Food Regulation in the U.S.

PubMed Central

Benbrook, Charles

2016-01-01

Genetically engineered (GE) crops, multi-ingredient foods derived from one or more GE ingredients, and GE agricultural inputs are regulated in the United States under a “Coordinated Framework” that was literally cobbled together in the early 1990s. Via this Framework, responsibility is spread across three federal agencies for the assessment and management of potential risks arising from the planting of GE crops, the raising of GE animals, or uses of GE inputs. The Framework was incomplete and conceptually flawed from the beginning. Despite multiple, piecemeal efforts to update aspects of GE risk assessment and regulatory policy, the Coordinated Framework survives to this day largely unchanged. Its shortcomings are recognized in both the scientific and legal communities, but meaningful reforms thus far remain out of reach, blocked by the intense controversy now surrounding all things biotech. Five generic reforms and another five specific initiatives are described to create a more robust, science-driven GE regulatory infrastructure in the U.S. PMID:27066473

Building generic anatomical models using virtual model cutting and iterative registration.

PubMed

Xiao, Mei; Soh, Jung; Meruvia-Pastor, Oscar; Schmidt, Eric; Hallgrímsson, Benedikt; Sensen, Christoph W

2010-02-08

Using 3D generic models to statistically analyze trends in biological structure changes is an important tool in morphometrics research. Therefore, 3D generic models built for a range of populations are in high demand. However, due to the complexity of biological structures and the limited views of them that medical images can offer, it is still an exceptionally difficult task to quickly and accurately create 3D generic models (a model is a 3D graphical representation of a biological structure) based on medical image stacks (a stack is an ordered collection of 2D images). We show that the creation of a generic model that captures spatial information exploitable in statistical analyses is facilitated by coupling our generalized segmentation method to existing automatic image registration algorithms. The method of creating generic 3D models consists of the following processing steps: (i) scanning subjects to obtain image stacks; (ii) creating individual 3D models from the stacks; (iii) interactively extracting sub-volume by cutting each model to generate the sub-model of interest; (iv) creating image stacks that contain only the information pertaining to the sub-models; (v) iteratively registering the corresponding new 2D image stacks; (vi) averaging the newly created sub-models based on intensity to produce the generic model from all the individual sub-models. After several registration procedures are applied to the image stacks, we can create averaged image stacks with sharp boundaries. The averaged 3D model created from those image stacks is very close to the average representation of the population. The image registration time varies depending on the image size and the desired accuracy of the registration. Both volumetric data and surface model for the generic 3D model are created at the final step. Our method is very flexible and easy to use such that anyone can use image stacks to create models and retrieve a sub-region from it at their ease. Java-based implementation allows our method to be used on various visualization systems including personal computers, workstations, computers equipped with stereo displays, and even virtual reality rooms such as the CAVE Automated Virtual Environment. The technique allows biologists to build generic 3D models of their interest quickly and accurately.

Customization of a generic 3D model of the distal femur using diagnostic radiographs.

PubMed

Schmutz, B; Reynolds, K J; Slavotinek, J P

2008-01-01

A method for the customization of a generic 3D model of the distal femur is presented. The customization method involves two steps: acquisition of calibrated orthogonal planar radiographs; and linear scaling of the generic model based on the width of a subject's femoral condyles as measured on the planar radiographs. Planar radiographs of seven intact lower cadaver limbs were obtained. The customized generic models were validated by comparing their surface geometry with that of CT-reconstructed reference models. The overall mean error was 1.2 mm. The results demonstrate that uniform scaling as a first step in the customization process produced a base model of accuracy comparable to other models reported in the literature.

Space Generic Open Avionics Architecture (SGOAA) standard specification

NASA Technical Reports Server (NTRS)

Wray, Richard B.; Stovall, John R.

1993-01-01

The purpose of this standard is to provide an umbrella set of requirements for applying the generic architecture interface model to the design of a specific avionics hardware/software system. This standard defines a generic set of system interface points to facilitate identification of critical interfaces and establishes the requirements for applying appropriate low level detailed implementation standards to those interface points. The generic core avionics system and processing architecture models provided herein are robustly tailorable to specific system applications and provide a platform upon which the interface model is to be applied.

Environmental assessment model for shallow land disposal of low-level radioactive wastes

NASA Astrophysics Data System (ADS)

Little, C. A.; Fields, D. E.; Emerson, C. J.; Hiromoto, G.

1981-09-01

The PRESTO (Prediction of Radiation Effects from Shallow Trench Operations) computer code developed to evaluate health effects from shallow land burial trenches is described. This generic model assesses radionuclide transport, ensuing exposure, and health impact to a static local population for a 1000 y period following the end of burial operations. Human exposure scenarios considered include normal releases (including leaching and operational spillage), human intrusion, and site farming or reclamation. Pathways and processes of transit from the trench to an individual or population includes ground water transport overland flow, erosion, surface water dilution, resuspension, atmospheric transport, deposition, inhalation, and ingestion of contaminated beef, milk, crops, and water. Both population doses and individual doses are calculated as well as doses to the intruder and farmer. Cumulative health effects in terms of deaths from cancer are calculated for the population over the 1000 y period using a life table approach. Data bases for three shallow land burial sites (Barnwell, South Carolina, Beatty, Nevada, and West Valley, New York) are under development. The interim model, includes coding for environmental transport through air, surface water, and ground water.

Modeling light and temperature effects on leaf emergence in wheat and barley

NASA Technical Reports Server (NTRS)

Volk, T.; Bugbee, B.

1991-01-01

Phenological development affects canopy structure, radiation interception, and dry matter production; most crop simulation models therefore incorporate leaf emergence rate as a basic parameter. A recent study examined leaf emergence rate as a function of temperature and daylength among wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) cultivars. Leaf emergence rate and phyllochron were modeled as functions of temperature alone, daylength alone, and the interaction between temperature and daylength. The resulting equations contained an unwieldy number of constants. Here we simplify by reducing the constants by > 70%, and show leaf emergence rate as a single response surface with temperature and daylength. In addition, we incorporate the effect of photosynthetic photon flux into the model. Generic fits for wheat and barley show cultivar differences less than +/- 5% for wheat and less than +/- 10% for barley. Barley is more sensitive to daylength changes than wheat for common environmental values of daylength, which may be related to the difference in sensitivity to daylength between spring and winter cultivars. Differences in leaf emergence rate between cultivars can be incorporated into the model by means of a single, nondimensional factor for each cultivar.

Vehicle Surveillance with a Generic, Adaptive, 3D Vehicle Model.

PubMed

Leotta, Matthew J; Mundy, Joseph L

2011-07-01

In automated surveillance, one is often interested in tracking road vehicles, measuring their shape in 3D world space, and determining vehicle classification. To address these tasks simultaneously, an effective approach is the constrained alignment of a prior model of 3D vehicle shape to images. Previous 3D vehicle models are either generic but overly simple or rigid and overly complex. Rigid models represent exactly one vehicle design, so a large collection is needed. A single generic model can deform to a wide variety of shapes, but those shapes have been far too primitive. This paper uses a generic 3D vehicle model that deforms to match a wide variety of passenger vehicles. It is adjustable in complexity between the two extremes. The model is aligned to images by predicting and matching image intensity edges. Novel algorithms are presented for fitting models to multiple still images and simultaneous tracking while estimating shape in video. Experiments compare the proposed model to simple generic models in accuracy and reliability of 3D shape recovery from images and tracking in video. Standard techniques for classification are also used to compare the models. The proposed model outperforms the existing simple models at each task.

AgMIP: Next Generation Models and Assessments

NASA Astrophysics Data System (ADS)

Rosenzweig, C.

2014-12-01

Next steps in developing next-generation crop models fall into several categories: significant improvements in simulation of important crop processes and responses to stress; extension from simplified crop models to complex cropping systems models; and scaling up from site-based models to landscape, national, continental, and global scales. Crop processes that require major leaps in understanding and simulation in order to narrow uncertainties around how crops will respond to changing atmospheric conditions include genetics; carbon, temperature, water, and nitrogen; ozone; and nutrition. The field of crop modeling has been built on a single crop-by-crop approach. It is now time to create a new paradigm, moving from 'crop' to 'cropping system.' A first step is to set up the simulation technology so that modelers can rapidly incorporate multiple crops within fields, and multiple crops over time. Then the response of these more complex cropping systems can be tested under different sustainable intensification management strategies utilizing the updated simulation environments. Model improvements for diseases, pests, and weeds include developing process-based models for important diseases, frameworks for coupling air-borne diseases to crop models, gathering significantly more data on crop impacts, and enabling the evaluation of pest management strategies. Most smallholder farming in the world involves integrated crop-livestock systems that cannot be represented by crop modeling alone. Thus, next-generation cropping system models need to include key linkages to livestock. Livestock linkages to be incorporated include growth and productivity models for grasslands and rangelands as well as the usual annual crops. There are several approaches for scaling up, including use of gridded models and development of simpler quasi-empirical models for landscape-scale analysis. On the assessment side, AgMIP is leading a community process for coordinated contributions to IPCC AR6 that involves the key modeling groups from around the world including North America, Europe, South America, Sub-Saharan Africa, South Asia, East Asia, and Australia and Oceania. This community process will lead to mutually agreed protocols for coordinated global and regional assessments.

An optimal generic model for multi-parameters and big data optimizing: a laboratory experimental study

NASA Astrophysics Data System (ADS)

Utama, D. N.; Ani, N.; Iqbal, M. M.

2018-03-01

Optimization is a process for finding parameter (parameters) that is (are) able to deliver an optimal value for an objective function. Seeking an optimal generic model for optimizing is a computer science study that has been being practically conducted by numerous researchers. Generic model is a model that can be technically operated to solve any varieties of optimization problem. By using an object-oriented method, the generic model for optimizing was constructed. Moreover, two types of optimization method, simulated-annealing and hill-climbing, were functioned in constructing the model and compared to find the most optimal one then. The result said that both methods gave the same result for a value of objective function and the hill-climbing based model consumed the shortest running time.

Connecting Biochemical Photosynthesis Models with Crop Models to Support Crop Improvement

PubMed Central

Wu, Alex; Song, Youhong; van Oosterom, Erik J.; Hammer, Graeme L.

2016-01-01

The next advance in field crop productivity will likely need to come from improving crop use efficiency of resources (e.g., light, water, and nitrogen), aspects of which are closely linked with overall crop photosynthetic efficiency. Progress in genetic manipulation of photosynthesis is confounded by uncertainties of consequences at crop level because of difficulties connecting across scales. Crop growth and development simulation models that integrate across biological levels of organization and use a gene-to-phenotype modeling approach may present a way forward. There has been a long history of development of crop models capable of simulating dynamics of crop physiological attributes. Many crop models incorporate canopy photosynthesis (source) as a key driver for crop growth, while others derive crop growth from the balance between source- and sink-limitations. Modeling leaf photosynthesis has progressed from empirical modeling via light response curves to a more mechanistic basis, having clearer links to the underlying biochemical processes of photosynthesis. Cross-scale modeling that connects models at the biochemical and crop levels and utilizes developments in upscaling leaf-level models to canopy models has the potential to bridge the gap between photosynthetic manipulation at the biochemical level and its consequences on crop productivity. Here we review approaches to this emerging cross-scale modeling framework and reinforce the need for connections across levels of modeling. Further, we propose strategies for connecting biochemical models of photosynthesis into the cross-scale modeling framework to support crop improvement through photosynthetic manipulation. PMID:27790232

Connecting Biochemical Photosynthesis Models with Crop Models to Support Crop Improvement.

PubMed

Wu, Alex; Song, Youhong; van Oosterom, Erik J; Hammer, Graeme L

2016-01-01

The next advance in field crop productivity will likely need to come from improving crop use efficiency of resources (e.g., light, water, and nitrogen), aspects of which are closely linked with overall crop photosynthetic efficiency. Progress in genetic manipulation of photosynthesis is confounded by uncertainties of consequences at crop level because of difficulties connecting across scales. Crop growth and development simulation models that integrate across biological levels of organization and use a gene-to-phenotype modeling approach may present a way forward. There has been a long history of development of crop models capable of simulating dynamics of crop physiological attributes. Many crop models incorporate canopy photosynthesis (source) as a key driver for crop growth, while others derive crop growth from the balance between source- and sink-limitations. Modeling leaf photosynthesis has progressed from empirical modeling via light response curves to a more mechanistic basis, having clearer links to the underlying biochemical processes of photosynthesis. Cross-scale modeling that connects models at the biochemical and crop levels and utilizes developments in upscaling leaf-level models to canopy models has the potential to bridge the gap between photosynthetic manipulation at the biochemical level and its consequences on crop productivity. Here we review approaches to this emerging cross-scale modeling framework and reinforce the need for connections across levels of modeling. Further, we propose strategies for connecting biochemical models of photosynthesis into the cross-scale modeling framework to support crop improvement through photosynthetic manipulation.

A study on agricultural drought vulnerability at disaggregated level in a highly irrigated and intensely cropped state of India.

PubMed

Murthy, C S; Yadav, Manoj; Mohammed Ahamed, J; Laxman, B; Prawasi, R; Sesha Sai, M V R; Hooda, R S

2015-03-01

Drought is an important global hazard, challenging the sustainable agriculture and food security of nations. Measuring agricultural drought vulnerability is a prerequisite for targeting interventions to improve and sustain the agricultural performance of both irrigated and rain-fed agriculture. In this study, crop-generic agricultural drought vulnerability status is empirically measured through a composite index approach. The study area is Haryana state, India, a prime agriculture state of the country, characterised with low rainfall, high irrigation support and stable cropping pattern. By analysing the multiyear rainfall and crop condition data of kharif crop season (June-October) derived from satellite data and soil water holding capacity and groundwater quality, nine contributing indicators were generated for 120 blocks (sub-district administrative units). Composite indices for exposure, sensitivity and adaptive capacity components were generated after assigning variance-based weightages to the respective input indicators. Agricultural Drought Vulnerability Index (ADVI) was developed through a linear combination of the three component indices. ADVI-based vulnerability categorisation revealed that 51 blocks are with vulnerable to very highly vulnerable status. These blocks are located in the southern and western parts of the state, where groundwater quality is saline and water holding capacity of soils is less. The ADVI map has effectively captured the spatial pattern of agricultural drought vulnerability in the state. Districts with large number of vulnerable blocks showed considerably larger variability of de-trended crop yields. Correlation analysis reveals that crop condition variability, groundwater quality and soil factors are closely associated with ADVI. The vulnerability index is useful to prioritise the blocks for implementation of long-term drought management plans. There is scope for improving the methodology by adding/fine-tuning the indicators and by optimising the weights.

Association of authorized generic marketing with prescription drug spending on antidepressants from 2000 to 2011.

PubMed

Cheng, Ning; Banerjee, Tannista; Qian, Jingjing; Hansen, Richard A

Prior research suggests that authorized generic drugs increase competition and decrease prices, but little empirical evidence supports this conclusion. This study evaluated the impact of authorized generic marketing on brand and generic prices. Longitudinal analysis of the household component of the Medical Expenditure Panel Survey. Interview panels over 12 years, with a new panel each year. For each panel, 5 rounds of household interviews were conducted over 30 months. Nationally representative sample of the U.S. civilian noninstitutionalized population, focusing on people using 1 of 5 antidepressant drugs that became generically available between 2000 to 2011. Drugs and dose/formulations with versus without an authorized generic drug marketed. Multiple linear regression models with lagged variables evaluated the effect of an authorized generic on average inflation-adjusted brand and generic price, adjusting for payment sources, generic entry time, competitor price, and year. During 2000-2011, annual brand antidepressant utilization decreased from 51.47 to 7.52 million prescriptions, and generic antidepressant utilization increased from 0 to 88.83 million prescriptions. Over time, payment per prescription for brand prescriptions increased 25% overall, and generic payments decreased 70% for all payer types. With unadjusted data, after generic entry the average brand price decreased $0.59 per year with and $3.62 per year without an authorized generic in the market. Average generic prices decreased $10.30 per year with and $8.47 per year without an authorized generic in the market. In multiple regression models with lagged variables adjusted for heteroscedasticity, payer source, time since generic entry, competitor price, and year, authorized generics significantly reduced average payment for generic (-$3.03) and brand (-$60.64) prescriptions, and over time this price change slowly diminished. Availability of an authorized generic was associated with reduced average generic and brand price in the antidepressant market, supporting prior evidences. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Statistical analysis of modeling error in structural dynamic systems

NASA Technical Reports Server (NTRS)

Hasselman, T. K.; Chrostowski, J. D.

1990-01-01

The paper presents a generic statistical model of the (total) modeling error for conventional space structures in their launch configuration. Modeling error is defined as the difference between analytical prediction and experimental measurement. It is represented by the differences between predicted and measured real eigenvalues and eigenvectors. Comparisons are made between pre-test and post-test models. Total modeling error is then subdivided into measurement error, experimental error and 'pure' modeling error, and comparisons made between measurement error and total modeling error. The generic statistical model presented in this paper is based on the first four global (primary structure) modes of four different structures belonging to the generic category of Conventional Space Structures (specifically excluding large truss-type space structures). As such, it may be used to evaluate the uncertainty of predicted mode shapes and frequencies, sinusoidal response, or the transient response of other structures belonging to the same generic category.

Generic NICA-Donnan model parameters for metal-ion binding by humic substances.

PubMed

Milne, Christopher J; Kinniburgh, David G; van Riemsdijk, Willem H; Tipping, Edward

2003-03-01

A total of 171 datasets of literature and experimental data for metal-ion binding by fulvic and humic acids have been digitized and re-analyzed using the NICA-Donnan model. Generic parameter values have been derived that can be used for modeling in the absence of specific metalion binding measurements. These values complement the previously derived generic descriptions of proton binding. For ions where the ranges of pH, concentration, and ionic strength conditions are well covered by the available data,the generic parameters successfully describe the metalion binding behavior across a very wide range of conditions and for different humic and fulvic acids. Where published data for other metal ions are too sparse to constrain the model well, generic parameters have been estimated by interpolating trends observable in the parameter values of the well-defined data. Recommended generic NICA-Donnan model parameters are provided for 23 metal ions (Al, Am, Ba, Ca, Cd, Cm, Co, CrIII, Cu, Dy, Eu, FeII, FeIII, Hg, Mg, Mn, Ni, Pb, Sr, Thv, UVIO2, VIIIO, and Zn) for both fulvic and humic acids. These parameters probably represent the best NICA-Donnan description of metal-ion binding that can be achieved using existing data.

Even apparently insignificant chemical deviations among bioequivalent generic antibiotics can lead to therapeutic nonequivalence: the case of meropenem.

PubMed

Agudelo, M; Rodriguez, C A; Pelaez, C A; Vesga, O

2014-01-01

Several studies with animal models have demonstrated that bioequivalence of generic products of antibiotics like vancomycin, as currently defined, do not guarantee therapeutic equivalence. However, the amounts and characteristics of impurities and degradation products in these formulations do not violate the requirements of the U.S. Pharmacopeia (USP). Here, we provide experimental data with three generic products of meropenem that help in understanding how these apparently insignificant chemical differences affect the in vivo efficacy. Meropenem generics were compared with the innovator in vitro by microbiological assay, susceptibility testing, and liquid chromatography/mass spectrometry (LC/MS) analysis and in vivo with the neutropenic guinea pig soleus infection model (Pseudomonas aeruginosa) and the neutropenic mouse thigh (P. aeruginosa), brain (P. aeruginosa), and lung (Klebisella pneumoniae) infection models, adding the dihydropeptidase I (DHP-I) inhibitor cilastatin in different proportions to the carbapenem. We found that the concentration and potency of the active pharmaceutical ingredient, in vitro susceptibility testing, and mouse pharmacokinetics were identical for all products; however, two generics differed significantly from the innovator in the guinea pig and mouse models, while the third generic was therapeutically equivalent under all conditions. Trisodium adducts in a bioequivalent generic made it more susceptible to DHP-I hydrolysis and less stable at room temperature, explaining its therapeutic nonequivalence. We conclude that the therapeutic nonequivalence of generic products of meropenem is due to greater susceptibility to DHP-I hydrolysis. These failing generics are compliant with USP requirements and would remain undetectable under current regulations.

Organic marker compounds in surface soils of crop fields from the San Joaquin Valley fugitive dust characterization study

NASA Astrophysics Data System (ADS)

Rogge, Wolfgang F.; Medeiros, Patricia M.; Simoneit, Bernd R. T.

Fugitive dust from the erosion of arid and fallow land, after harvest and during agricultural activities, can at times be the dominant source of airborne particulate matter. In order to assess the source contributions to a given site, chemical mass balance (CMB) modeling is typically used together with source-specific profiles for organic and inorganic constituents. Yet, the mass balance closure can be achieved only if emission profiles for all major sources are considered. While a higher degree of mass balance closure has been achieved by adding individual organic marker compounds to elements, ions, EC, and organic carbon (OC), major source profiles for fugitive dust are not available. Consequently, neither the exposure of the population living near fugitive dust sources from farm land, nor its chemical composition is known. Surface soils from crop fields are enriched in plant detritus from both above and below ground plant parts; therefore, surface soil dust contains natural organic compounds from the crops and soil microbiota. Here, surface soils derived from fields growing cotton, safflower, tomato, almonds, and grapes have been analyzed for more than 180 organic compounds, including natural lipids, saccharides, pesticides, herbicides, and polycyclic aromatic hydrocarbon (PAH). The major result of this study is that selective biogenically derived organic compounds are suitable markers of fugitive dust from major agricultural crop fields in the San Joaquin Valley. Aliphatic homologs exhibit the typical biogenic signatures of epicuticular plant waxes and are therefore indicative of fugitive dust emissions and mechanical abrasion of wax protrusions from leaf surfaces. Saccharides, among which α- and β-glucose, sucrose, and mycose show the highest concentrations in surface soils, have been proposed to be generic markers for fugitive dust from cultivated land. Similarly, steroids are strongly indicative of fugitive dust. Yet, triterpenoids reveal the most pronounced distribution differences for all types of cultivated soils examined here and are by themselves powerful markers for fugitive dust that allow differentiation between the types of crops cultivated. PAHs are also found in some surface soils, as well as persistent pesticides, e.g., DDE, Fosfall, and others.

Leveraging consumer's behaviour to promote generic drugs in Italy.

PubMed

Zerbini, Cristina; Luceri, Beatrice; Vergura, Donata Tania

2017-04-01

The aim of this study was to fill the lack of knowledge regarding a more grounded exploration of the consumer's decision-making process in the context of generic drugs. In this perspective, a model, within the theoretical framework of the Theory of Planned Behaviour (TPB), for studying the consumers' purchase intention of generic drugs was developed. An online survey on 2,222 Italian people who bought drugs in the past was conducted. The proposed model was tested through structural equation modelling (SEM). Almost all the constructs considered in the model, except the perceived behavioural control, contribute to explain the consumer's purchase intention of generic drugs, after controlling for demographic variables (age, income, education). Specifically, attitude, subjective norm, past behaviour, self-identity and trust in the pharmacist have a positive influence on the intention to buy generic drugs. On the contrary, perceived risk towards products and brand sensitivity act negatively. The results of the present study could be useful to public policy makers in developing effective policies and educational campaigns aimed at promoting generic drugs. Specifically, marketing efforts should be directed to inform consumers about the generic drugs' characteristics to mitigate the perceived risk towards these products and to raise awareness during their decision-making process. Copyright © 2017 Elsevier B.V. All rights reserved.

SuMoToRI, an Ecophysiological Model to Predict Growth and Sulfur Allocation and Partitioning in Oilseed Rape (Brassica napus L.) Until the Onset of Pod Formation

PubMed Central

Brunel-Muguet, Sophie; Mollier, Alain; Kauffmann, François; Avice, Jean-Christophe; Goudier, Damien; Sénécal, Emmanuelle; Etienne, Philippe

2015-01-01

Sulfur (S) nutrition in rapeseed (Brassica napus L.) is a major concern for this high S-demanding crop, especially in the context of soil S oligotrophy. Therefore, predicting plant growth, S plant allocation (between the plant’s compartments) and S pool partitioning (repartition of the mobile-S vs. non-mobile-S fractions) until the onset of reproductive phase could help in the diagnosis of S deficiencies during the early stages. For this purpose, a process-based model, SuMoToRI (Sulfur Model Toward Rapeseed Improvement), was developed up to the onset of pod formation. The key features rely on (i) the determination of the S requirements used for growth (structural and metabolic functions) through critical S dilution curves and (ii) the estimation of a mobile pool of S that is regenerated by daily S uptake and remobilization from senescing leaves. This study describes the functioning of the model and presents the model’s calibration and evaluation. SuMoToRI was calibrated and evaluated with independent datasets from greenhouse experiments under contrasting S supply conditions. It is run with a small number of parameters with generic values, except in the case of the radiation use efficiency, which was shown to be modulated by S supply. The model gave satisfying predictions of the dynamics of growth, S allocation between compartments and S partitioning, such as the mobile-S fraction in the leaves, which is an indicator of the remobilization potential toward growing sinks. The mechanistic features of SuMoToRI provide a process-based framework that has enabled the description of the S remobilizing process in a species characterized by senescence during the vegetative phase. We believe that this model structure could be useful for modeling S dynamics in other arable crops that have similar senescence-related characteristics. PMID:26635825

Modelling Hydrology and Erosion in a Changing Socio-Economic Environment

NASA Astrophysics Data System (ADS)

Kirkby, M. J.; van Delden, H.; Hahn, B. M.; Irvine, B. J.

2009-12-01

Although forecasting systems have a limited time horizon due to the impact of unforeseen events, a rationally based model is able to provide some insights into likely short term behaviour, taking account of the dynamic interactions between climate, physical processes and land use decisions. The biophysical model (PESERA) takes land use decisions as inputs, together with climatic data or scenarios, topography and soils, to generate estimates of runoff, soil erosion, crop or natural vegetation growth and physical suitability, primarily based on crop yields. Estimates are made at a spatial resolution of 100 - 1000 m according to the area involved, the former for a catchment of say 1000 km2, the latter for a continental region. The generic dynamic land-use (change) model (METRONAMICA) has been fully integrated with PESERA to create the ‘Integrated Assessment Model’ (IAM) within the DESURVEY European project. The IAM takes biophysical performance and other spatial characteristics as an input and combines them with socio-economic data to determine potential suitability for each possible residential, commercial, agricultural etc use. The model then allocates each land use type and detailed agricultural class to the locations with the highest potential, based on neighbouring and historic choices as well as short-term economic advantage to estimate probabilities of change to alternative uses. Suitability, accessibility and zoning regulations are included in the decision process to provide the locational characteristics. Change of land use over time is thus determined within a cellular automaton model that generates rational spatial patterns of land use choice. The IAM is being applied at country scale in southern Europe and to smaller regions in North Africa. Although, in the long term, climate change is likely to dominate physical and economic impacts, in the shorter term over which this type of model can most reliably be used, the significance of land use change is much stronger.

Factors influencing U.S. consumer support for genetic modification to prevent crop disease.

PubMed

McComas, Katherine A; Besley, John C; Steinhardt, Joseph

2014-07-01

This study examines support for the genetic modification (GM) of crops in the context of preventing "late blight," a devastating potato and tomato disease that caused the Irish Potato Famine in the 1850s and results in substantial crop loss today. We surveyed U.S. adults who do the primary grocery shopping in their household (n = 859). Half of the respondents were randomly assigned to read a vignette describing late blight before responding to questions about GM, whereas the other half read a vignette about generic crop disease before responding to questions. We also examine how the perceived fairness of decision makers relates to GM support and the perceived legitimacy of GM decision making. We found that disease specificity mattered less to support and legitimacy than the perceived fairness of decision makers. The perceived risks of GM to human and environmental health negatively related to GM support and legitimacy, whereas the perceived benefits (e.g. reduced threats to crops and a more secure food supply) positively related to support and legitimacy. Objective knowledge about GM had a small, negative relationship with legitimacy whereas self-assessed familiarity with GM had a positive relationship. Overall, the results offer additional confirmation of past findings from more localized settings that perceived fairness of decision makers matters to support for GM and underscore the importance of considering how risk managers' behaviors and actions are perceived alongside individuals' perceptions about the risks and benefits. Copyright © 2014 Elsevier Ltd. All rights reserved.

Performance of STICS model to predict rainfed corn evapotranspiration and biomass evaluated for 6 years between 1995 and 2006 using daily aggregated eddy covariance fluxes and ancillary measurements.

NASA Astrophysics Data System (ADS)

Pattey, Elizabeth; Jégo, Guillaume; Bourgeois, Gaétan

2010-05-01

Verifying the performance of process-based crop growth models to predict evapotranspiration and crop biomass is a key component of the adaptation of agricultural crop production to climate variations. STICS, developed by INRA, was part of the models selected by Agriculture and Agri-Food Canada to be implemented for environmental assessment studies on climate variations, because of its built-in ability to assimilate biophysical descriptors such as LAI derived from satellite imagery and its open architecture. The model prediction of shoot biomass was calibrated using destructive biomass measurements over one season, by adjusting six cultivar parameters and three generic plant parameters to define two grain corn cultivars adapted to the 1000-km long Mixedwood Plains ecozone. Its performance was then evaluated using a database of 40 years-sites of corn destructive biomass and yield. In this study we evaluate the temporal response of STICS evapotranspiration and biomass accumulation predictions against estimates using daily aggregated eddy covariance fluxes. The flux tower was located in an experimental farm south of Ottawa and measurements carried out over corn fields in 1995, 1996, 1998, 2000, 2002 and 2006. Daytime and nighttime fluxes were QC/QA and gap-filled separately. Soil respiration was partitioned to calculate the corn net daily CO2 uptake, which was converted into dry biomass. Out of the six growing seasons, three (1995, 1998, 2002) had water stress periods during corn grain filling. Year 2000 was cool and wet, while 1996 had heat and rainfall distributed evenly over the season and 2006 had a wet spring. STICS can predict evapotranspiration using either crop coefficients, when wind speed and air moisture are not available, or resistance. The first approach provided higher prediction for all the years than the resistance approach and the flux measurements. The dynamic of evapotranspiration prediction of STICS was very good for the growing seasons without water stress and was overestimated by 12-34% when rainfall deficit occurred. The preliminary comparison with intra-seasonal biomass accumulation showed that the total corn biomass derived from eddy fluxes was closer to the shoot biomass predicted by STICS than to the total biomass. The root to shoot ratio predicted by STICS was higher (30-40%) than the ratio reported in the literature (~20%). Some of the parameters controlling root growth might need a better calibration. The assembled database will help us identify the areas of greater uncertainty requiring improvement.

Site-specific temporal and spatial validation of a generic plant pest forecast system with observations of Bactrocera dorsalis (oriental fruit fly).

USDA-ARS?s Scientific Manuscript database

This study introduces a simple generic model, the Generic Pest Forecast System (GPFS), for simulatingthe relative populations of non-indigenousarthropod pests in space and time. The model was designed to calculate the population index or relative population using hourly weather dataas influenced by...

The Utilization of a Space Flight Plant Growth Chamber in the Cultivation of Salad Crop Species: A Prelude to a Salad Machine

NASA Technical Reports Server (NTRS)

Heyenga, A. G.; Hoehn, A.; Stodieck, L. S.; Kliss, M.; Arnold, James O. (Technical Monitor)

1998-01-01

The application of bioregenerative life support systems provides an attractive approach to minimize resupply requirement and ultimate self-sufficiency on long duration manned missions in space. The on-board cultivation of salad-type vegetables for crew consumption has been proposed as a first step approach towards reducing a total reliance on the resupply of food. The recent advances in the development of space flight plant growth facilities such as the Plant Generic Bioprocessing Apparatus (PGBA) have established a firm technical basis upon which the implementation of a 'salad machine' concept may be achieved. A presentation on ground based studies will be made evaluating (a) the operational performance of the PGBA facility in a crop production mode and (b) the qualitative and quantitative value of salad plant material produced within the chamber.

Essential equivalence of the general equation for the nonequilibrium reversible-irreversible coupling (GENERIC) and steepest-entropy-ascent models of dissipation for nonequilibrium thermodynamics.

PubMed

Montefusco, Alberto; Consonni, Francesco; Beretta, Gian Paolo

2015-04-01

By reformulating the steepest-entropy-ascent (SEA) dynamical model for nonequilibrium thermodynamics in the mathematical language of differential geometry, we compare it with the primitive formulation of the general equation for the nonequilibrium reversible-irreversible coupling (GENERIC) model and discuss the main technical differences of the two approaches. In both dynamical models the description of dissipation is of the "entropy-gradient" type. SEA focuses only on the dissipative, i.e., entropy generating, component of the time evolution, chooses a sub-Riemannian metric tensor as dissipative structure, and uses the local entropy density field as potential. GENERIC emphasizes the coupling between the dissipative and nondissipative components of the time evolution, chooses two compatible degenerate structures (Poisson and degenerate co-Riemannian), and uses the global energy and entropy functionals as potentials. As an illustration, we rewrite the known GENERIC formulation of the Boltzmann equation in terms of the square root of the distribution function adopted by the SEA formulation. We then provide a formal proof that in more general frameworks, whenever all degeneracies in the GENERIC framework are related to conservation laws, the SEA and GENERIC models of the dissipative component of the dynamics are essentially interchangeable, provided of course they assume the same kinematics. As part of the discussion, we note that equipping the dissipative structure of GENERIC with the Leibniz identity makes it automatically SEA on metric leaves.

Risk assessment and spread of the potentially invasive Ceratitis rosa Karsch and Ceratitis quilicii De Meyer, Mwatawala & Virgilio sp. Nov. using life-cycle simulation models: Implications for phytosanitary measures and management

PubMed Central

Tanga, Chrysantus Mbi; Khamis, Fathiya Mbarak; Tonnang, Henri E. Z.; Rwomushana, Ivan; Mosomtai, Gladys; Mohamed, Samira A.; Ekesi, Sunday

2018-01-01

Integrative taxonomy has resolved the species status of the potentially invasive Ceratitis rosa Karsch into two separate species with distinct ecological requirements: C. rosa “lowland type” and the newly described species Ceratitis quilicii De Meyer, Mwatawala & Virgilio sp. nov. “highland type”. Both species are tephritid pests threatening the production of horticultural crops in Africa and beyond. Studies were carried out by constructing thermal reaction norms for each life stage of both species at constant and fluctuating temperatures. Non-linear functions were fitted to continuously model species development, mortality, longevity and oviposition to establish phenology models that were stochastically simulated to estimate the life table parameters of each species. For spatial analysis of pest risk, three generic risk indices were visualized using the advanced Insect Life Cycle Modeling software. The study revealed that the highest fecundity, intrinsic rate of natural increase and net reproductive rate for C. rosa and C. quilicii was at 25 and 30°C, respectively. The resulting model successfully fits the known distribution of C. rosa and C. quilicii in Africa and the two Indian Ocean islands of La Réunion and Mauritius. Globally, the model highlights the substantial invasion risk posed by C. rosa and C. quilicii to cropping regions in the Americas, Australia, India, China, Southeast Asia, Europe, and West and Central Africa. However, the proportion of the regions predicted to be climatically suitable for both pests is narrower for C. rosa in comparison with C. quilicii, suggesting that C. quilicii will be more tolerant to a wider range of climatic conditions than C. rosa. This implies that these pests are of significant concern to biosecurity agencies in the uninvaded regions. Therefore, these findings provide important information to enhance monitoring/surveillance and designing pest management strategies to limit the spread and reduce their impact in the invaded range. PMID:29304084

Demand driven decision support for efficient water resources allocation in irrigated agriculture

NASA Astrophysics Data System (ADS)

Schuetze, Niels; Grießbach, Ulrike Ulrike; Röhm, Patric; Stange, Peter; Wagner, Michael; Seidel, Sabine; Werisch, Stefan; Barfus, Klemens

2014-05-01

Due to climate change, extreme weather conditions, such as longer dry spells in the summer months, may have an increasing impact on the agriculture in Saxony (Eastern Germany). For this reason, and, additionally, declining amounts of rainfall during the growing season the use of irrigation will be more important in future in Eastern Germany. To cope with this higher demand of water, a new decision support framework is developed which focuses on an integrated management of both irrigation water supply and demand. For modeling the regional water demand, local (and site-specific) water demand functions are used which are derived from the optimized agronomic response at farms scale. To account for climate variability the agronomic response is represented by stochastic crop water production functions (SCWPF) which provide the estimated yield subject to the minimum amount of irrigation water. These functions take into account the different soil types, crops and stochastically generated climate scenarios. By applying mathematical interpolation and optimization techniques, the SCWPF's are used to compute the water demand considering different constraints, for instance variable and fix costs or the producer price. This generic approach enables the computation for both multiple crops at farm scale as well as of the aggregated response to water pricing at a regional scale for full and deficit irrigation systems. Within the SAPHIR (SAxonian Platform for High Performance Irrigation) project a prototype of a decision support system is developed which helps to evaluate combined water supply and demand management policies for an effective and efficient utilization of water in order to meet future demands. The prototype is implemented as a web-based decision support system and it is based on a service-oriented geo-database architecture.

Enabling Philippine Farmers to Adapt to Climate Variability Using Seasonal Climate and Weather Forecast with a Crop Simulation Model in an SMS-based Farmer Decision Support System

NASA Astrophysics Data System (ADS)

Ebardaloza, J. B. R.; Trogo, R.; Sabido, D. J.; Tongson, E.; Bagtasa, G.; Balderama, O. F.

2015-12-01

Corn farms in the Philippines are rainfed farms, hence, it is of utmost importance to choose the start of planting date so that the critical growth stages that are in need of water will fall on dates when there is rain. Most farmers in the Philippines use superstitions and traditions as basis for farming decisions such as when to start planting [1]. Before climate change, superstitions like planting after a feast day of a saint has worked for them but with the recent progression of climate change, farmers now recognize that there is a need for technological intervention [1]. The application discussed in this paper presents a solution that makes use of meteorological station sensors, localized seasonal climate forecast, localized weather forecast and a crop simulation model to provide recommendations to farmers based on the crop cultivar, soil type and fertilizer type used by farmers. It is critical that the recommendations given to farmers are not generic as each farmer would have different needs based on their cultivar, soil, fertilizer, planting schedule and even location [2]. This application allows the farmer to inquire about whether it will rain in the next seven days, the best date to start planting based on the potential yield upon harvest, when to apply fertilizer and by how much, when to water and by how much. Short messaging service (SMS) is the medium chosen for this application because while mobile penetration in the Philippines is as high as 101%, the smart phone penetration is only at 15% [3]. SMS has been selected as it has been identified as the most effective way of reaching farmers with timely agricultural information and knowledge [4,5]. The recommendations while derived from making use of Automated Weather Station (AWS) sensor data, Weather Research Forecasting (WRF) models and DSSAT 4.5 [9], are translated into the local language of the farmers and in a format that is easily understood as recommended in [6,7,8]. A pilot study has been started in May 2015 and the harvest of this pilot season will be September 2015.

Generic Airplane Model Concept and Four Specific Models Developed for Use in Piloted Simulation Studies

NASA Technical Reports Server (NTRS)

Hoffler, Keith D.; Fears, Scott P.; Carzoo, Susan W.

1997-01-01

A generic airplane model concept was developed to allow configurations with various agility, performance, handling qualities, and pilot vehicle interface to be generated rapidly for piloted simulation studies. The simple concept allows stick shaping and various stick command types or modes to drive an airplane with both linear and nonlinear components. Output from the stick shaping goes to linear models or a series of linear models that can represent an entire flight envelope. The generic model also has provisions for control power limitations, a nonlinear feature. Therefore, departures from controlled flight are possible. Note that only loss of control is modeled, the generic airplane does not accurately model post departure phenomenon. The model concept is presented herein, along with four example airplanes. Agility was varied across the four example airplanes without altering specific excess energy or significantly altering handling qualities. A new feedback scheme to provide angle-of-attack cueing to the pilot, while using a pitch rate command system, was implemented and tested.

Optimisation of a Generic Ionic Model of Cardiac Myocyte Electrical Activity

PubMed Central

Guo, Tianruo; Al Abed, Amr; Lovell, Nigel H.; Dokos, Socrates

2013-01-01

A generic cardiomyocyte ionic model, whose complexity lies between a simple phenomenological formulation and a biophysically detailed ionic membrane current description, is presented. The model provides a user-defined number of ionic currents, employing two-gate Hodgkin-Huxley type kinetics. Its generic nature allows accurate reconstruction of action potential waveforms recorded experimentally from a range of cardiac myocytes. Using a multiobjective optimisation approach, the generic ionic model was optimised to accurately reproduce multiple action potential waveforms recorded from central and peripheral sinoatrial nodes and right atrial and left atrial myocytes from rabbit cardiac tissue preparations, under different electrical stimulus protocols and pharmacological conditions. When fitted simultaneously to multiple datasets, the time course of several physiologically realistic ionic currents could be reconstructed. Model behaviours tend to be well identified when extra experimental information is incorporated into the optimisation. PMID:23710254

Longitudinal factorial invariance of the PedsQL 4.0 Generic Core Scales child self-report Version: one year prospective evidence from the California State Children's Health Insurance Program (SCHIP).

PubMed

Varni, James W; Limbers, Christine A; Newman, Daniel A; Seid, Michael

2008-11-01

The measurement of health-related quality of life (HRQOL) in pediatric medicine and health services research has grown significantly over the past decade. The paradigm shift toward patient-reported outcomes (PROs) has provided the opportunity to emphasize the value and critical need for pediatric patient self-report. In order for changes in HRQOL/PRO outcomes to be meaningful over time, it is essential to demonstrate longitudinal factorial invariance. This study examined the longitudinal factor structure of the PedsQL 4.0 Generic Core Scales over a one-year period for child self-report ages 5-17 in 2,887 children from a statewide evaluation of the California State Children's Health Insurance Program (SCHIP) utilizing a structural equation modeling framework. Specifying four- and five-factor measurement models, longitudinal structural equation modeling was used to compare factor structures over a one-year interval on the PedsQL 4.0 Generic Core Scales. While the four-factor conceptually-derived measurement model for the PedsQL 4.0 Generic Core Scales produced an acceptable fit, the five-factor empirically-derived measurement model from the initial field test of the PedsQL 4.0 Generic Core Scales produced a marginally superior fit in comparison to the four-factor model. For the five-factor measurement model, the best fitting model, strict factorial invariance of the PedsQL 4.0 Generic Core Scales across the two measurement occasions was supported by the stability of the comparative fit index between the unconstrained and constrained models, and several additional indices of practical fit including the root mean squared error of approximation, the non-normed fit index, and the parsimony normed fit index. The findings support an equivalent factor structure on the PedsQL 4.0 Generic Core Scales over time. Based on these data, it can be concluded that over a one-year period children in our study interpreted items on the PedsQL 4.0 Generic Core Scales in a similar manner.

Determining generic velocity and density models for crustal amplification calculations, with an update of the Boore and Joyner (1997) Generic Site Amplification for Graphic Site Amplification

USGS Publications Warehouse

Boore, David

2016-01-01

This short note contains two contributions related to deriving depth‐dependent velocity and density models for use in computing generic crustal amplifications. The first contribution is a method for interpolating two velocity profiles to obtain a third profile with a time‐averaged velocity  to depth Z that is equal to a specified value (e.g., for shear‐wave velocity VS,  for Z=30  m, in which the subscript S has been added to indicate that the average is for shear‐wave velocities). The second contribution is a procedure for obtaining densities from VS. The first contribution is used to extend and revise the Boore and Joyner (1997) generic rock VS model, for which , to a model with the more common . This new model is then used with the densities from the second contribution to compute crustal amplifications for a generic site with .

Generic Science Skills Enhancement of Students through Implementation of IDEAL Problem Solving Model on Genetic Information Course

NASA Astrophysics Data System (ADS)

Zirconia, A.; Supriyanti, F. M. T.; Supriatna, A.

2018-04-01

This study aims to determine generic science skills enhancement of students through implementation of IDEAL problem-solving model on genetic information course. Method of this research was mixed method, with pretest-posttest nonequivalent control group design. Subjects of this study were chemistry students enrolled in biochemistry course, consisted of 22 students in the experimental class and 19 students in control class. The instrument in this study was essayed involves 6 indicators generic science skills such as indirect observation, causality thinking, logical frame, self-consistent thinking, symbolic language, and developing concept. The results showed that genetic information course using IDEAL problem-solving model have been enhancing generic science skills in low category with of 20,93%. Based on result for each indicator, showed that there are indicators of generic science skills classified in the high category.

Sustainability of irrigated crops under future climate: the interplay of irrigation strategies and cultivar responses

NASA Astrophysics Data System (ADS)

De Lorenzi, F.; Bonfante, A.; Alfieri, S.; Patanè, C.; Basile, A.; Di Tommasi, P.; Monaco, E.; Menenti, M.

2012-04-01

Climate evolution will cause significant changes in the quality and availability of water resources, affecting many sectors including food production, where available water resources for irrigation play a crucial role. Strategies focused on managing and conserving water are one way to deal with the impact; moreover concurring adaptation measurements will be needed to cope with the foreseen decline of water resource. This work deals with i) the impacts of climate change on water requirements of an horticultural crop, determined in an irrigated district in Southern Italy, ii) the possible irrigation scheduling options and their sustainability in the future, iii) the adaptation measurements that can be undertaken to protect production, relying on intra-specific biodiversity of agricultural crops. Two climate scenarios were considered: present climate (1961-90) and future climate (2021-2050), the former from climatic statistics, and the latter from statistical downscaling of general circulation models (AOGCM). Climatic data set consists of daily time series of maximum and minimum temperature, and rainfall on a grid with spatial resolution of 35 km. The analysis of climate scenarios showed that significant increases in summer maximum daily temperature could be expected in 2021-2050 period. Soil water regime was determined by means of a mechanistic model (SWAP) of water flow in the soil-plant-atmosphere system. Twenty? soil units were identified in the district (in Sele Plain, Campania Region) and simulations were performed accounting for hydro-pedological properties of different soil units. Parameters of a generic tomato crop, in a rotation typical of the area, were used in simulations. Soil water balance was simulated in the present and future climate, both with optimal water availability and under constrains that irrigation schemes will pose. Indicators of soil water availability were calculated, in terms of soil water or evapotranspiration deficit. For several tomato cultivars, quantitative yield response functions to water availability were determined through the re-analysis of experimental data, derived from scientific literature. Variety-specific threshold values of yield reduction in dependence of soil water and evapotranspiration deficit were determined. The spatial pattern of soil water availability indicators was calculated., for present and future climate scenarios and for different irrigation scheduling options. Cultivars' threshold values were matched with indicators' values in all soil units. The future adaptability of the crop in the area is thus evaluated, and adaptation options that exploit the intra-specific biodiversity of the crop are indicated. The work was carried out within the Italian national project AGROSCENARI funded by the Ministry for Agricultural, Food and Forest Policies (MIPAAF, D.M. 8608/7303/2008) Keywords: climate change, tomato, deficit irrigation, biodiversity

Impact of climate, vegetation, soil and crop management variables on multi-year ISBA-A-gs simulations of evapotranspiration over a Mediterranean crop site

NASA Astrophysics Data System (ADS)

Garrigues, S.; Olioso, A.; Carrer, D.; Decharme, B.; Calvet, J.-C.; Martin, E.; Moulin, S.; Marloie, O.

2015-10-01

Generic land surface models are generally driven by large-scale data sets to describe the climate, the soil properties, the vegetation dynamic and the cropland management (irrigation). This paper investigates the uncertainties in these drivers and their impacts on the evapotranspiration (ET) simulated from the Interactions between Soil, Biosphere, and Atmosphere (ISBA-A-gs) land surface model over a 12-year Mediterranean crop succession. We evaluate the forcing data sets used in the standard implementation of ISBA over France where the model is driven by the SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie) high spatial resolution atmospheric reanalysis, the leaf area index (LAI) time courses derived from the ECOCLIMAP-II land surface parameter database and the soil texture derived from the French soil database. For climate, we focus on the radiations and rainfall variables and we test additional data sets which include the ERA-Interim (ERA-I) low spatial resolution reanalysis, the Global Precipitation Climatology Centre data set (GPCC) and the MeteoSat Second Generation (MSG) satellite estimate of downwelling shortwave radiations. The evaluation of the drivers indicates very low bias in daily downwelling shortwave radiation for ERA-I (2.5 W m-2) compared to the negative biases found for SAFRAN (-10 W m-2) and the MSG satellite (-12 W m-2). Both SAFRAN and ERA-I underestimate downwelling longwave radiations by -12 and -16 W m-2, respectively. The SAFRAN and ERA-I/GPCC rainfall are slightly biased at daily and longer timescales (1 and 0.5 % of the mean rainfall measurement). The SAFRAN rainfall is more precise than the ERA-I/GPCC estimate which shows larger inter-annual variability in yearly rainfall error (up to 100 mm). The ECOCLIMAP-II LAI climatology does not properly resolve Mediterranean crop phenology and underestimates the bare soil period which leads to an overall overestimation of LAI over the crop succession. The simulation of irrigation by the model provides an accurate irrigation amount over the crop cycle but the timing of irrigation occurrences is frequently unrealistic. Errors in the soil hydrodynamic parameters and the lack of irrigation in the simulation have the largest influence on ET compared to uncertainties in the large-scale climate reanalysis and the LAI climatology. Among climate variables, the errors in yearly ET are mainly related to the errors in yearly rainfall. The underestimation of the available water capacity and the soil hydraulic diffusivity induce a large underestimation of ET over 12 years. The underestimation of radiations by the reanalyses and the absence of irrigation in the simulation lead to the underestimation of ET while the overall overestimation of LAI by the ECOCLIMAP-II climatology induces an overestimation of ET over 12 years. This work shows that the key challenges to monitor the water balance of cropland at regional scale concern the representation of the spatial distribution of the soil hydrodynamic parameters, the variability of the irrigation practices, the seasonal and inter-annual dynamics of vegetation and the spatiotemporal heterogeneity of rainfall.

A Nonlinear Dynamic Inversion Predictor-Based Model Reference Adaptive Controller for a Generic Transport Model

NASA Technical Reports Server (NTRS)

Campbell, Stefan F.; Kaneshige, John T.

2010-01-01

Presented here is a Predictor-Based Model Reference Adaptive Control (PMRAC) architecture for a generic transport aircraft. At its core, this architecture features a three-axis, non-linear, dynamic-inversion controller. Command inputs for this baseline controller are provided by pilot roll-rate, pitch-rate, and sideslip commands. This paper will first thoroughly present the baseline controller followed by a description of the PMRAC adaptive augmentation to this control system. Results are presented via a full-scale, nonlinear simulation of NASA s Generic Transport Model (GTM).

Impact of alternative interventions on changes in generic dispensing rates.

PubMed

O'Malley, A James; Frank, Richard G; Kaddis, Atheer; Rothenberg, Barbara M; McNeil, Barbara J

2006-10-01

To evaluate the effectiveness of four alternative interventions (member mailings, advertising campaigns, free generic drug samples to physicians, and physician financial incentives) used by a major health insurer to encourage its members to switch to generic drugs. Using claim-level data from Blue Cross Blue Shield of Michigan, we evaluated the success of four interventions implemented during 2000-2003 designed to increase the use of generic drugs among its members. Around 13 million claims involving seven important classes of drugs were used to assess the effectiveness of the interventions. For each intervention a control group was developed that most closely resembled the corresponding intervention group. Logistic regression models with interaction effects between the treatment group (intervention versus control) and the status of the intervention (active versus not active) were used to evaluate if the interventions had an effect on the generic dispensing rate (GDR). Because the mail order pharmacy was considered more aggressive at converting prescriptions to generics, separate generic purchasing models were fitted to retail and mail order claims. In secondary analyses separate models were also fitted to claims involving a new condition and claims refilled for preexisting conditions. The interventions did not appear to increase the market penetration of generic drugs for either retail or mail order claims, or for claims involving new or preexisting conditions. In addition, we found that the ratio of copayments for brand name to generic drugs had a large positive effect on the GDR. The interventions did not appear to directly influence the GDR. Financial incentives expressed to consumers through benefit designs have a large influence on their switching to generic drugs and on the less-costly mail-order mode of purchase.

PRESTO low-level waste transport and risk assessment code

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

Little, C.A.; Fields, D.E.; McDowell-Boyer, L.M.

1981-01-01

PRESTO (Prediction of Radiation Effects from Shallow Trench Operations) is a computer code developed under US Environmental Protection Agency (EPA) funding to evaluate possible health effects from shallow land burial trenches. The model is intended to be generic and to assess radionuclide transport, ensuing exposure, and health impact to a static local population for a 1000-y period following the end of burial operations. Human exposure scenarios considered by the model include normal releases (including leaching and operational spillage), human intrusion, and site farming or reclamation. Pathways and processes of transit from the trench to an individual or population inlude: groundwatermore » transport, overland flow, erosion, surface water dilution, resuspension, atmospheric transport, deposition, inhalation, and ingestion of contaminated beef, milk, crops, and water. Both population doses and individual doses are calculated as well as doses to the intruder and farmer. Cumulative health effects in terms of deaths from cancer are calculated for the population over the thousand-year period using a life-table approach. Data bases are being developed for three extant shallow land burial sites: Barnwell, South Carolina; Beatty, Nevada; and West Valley, New York.« less

Generic Surface-to-Air Missile Model.

DTIC Science & Technology

1979-10-01

describes the Generic Surface-to-Air Missile Model (GENSAM) which evaluates the outcome of an engagement between a surface-to-air missile system and an...DETAILS OF THE GENERIC SAM MODEL 3-1 3.1 Coordinate Transformations 3-1 3.1.1 Coordinate Systems 3-1 3.1.2 Coordinate Transformations 3-4 3.1.3 Functions...Tracking Radars 3-54 3.3.11 Deception Jamming and Tracking Radars 3-55 3.3.12 Jaming and Track Radar Downlinks 3-56 3.3.13 Infrared Surveillance Systems 3

Can generic knee joint models improve the measurement of osteoarthritic knee kinematics during squatting activity?

PubMed

Clément, Julien; Dumas, Raphaël; Hagemeister, Nicola; de Guise, Jaques A

2017-01-01

Knee joint kinematics derived from multi-body optimisation (MBO) still requires evaluation. The objective of this study was to corroborate model-derived kinematics of osteoarthritic knees obtained using four generic knee joint models used in musculoskeletal modelling - spherical, hinge, degree-of-freedom coupling curves and parallel mechanism - against reference knee kinematics measured by stereo-radiography. Root mean square errors ranged from 0.7° to 23.4° for knee rotations and from 0.6 to 9.0 mm for knee displacements. Model-derived knee kinematics computed from generic knee joint models was inaccurate. Future developments and experiments should improve the reliability of osteoarthritic knee models in MBO and musculoskeletal modelling.

Common Cause Failure Modeling: Aerospace Versus Nuclear

NASA Technical Reports Server (NTRS)

Stott, James E.; Britton, Paul; Ring, Robert W.; Hark, Frank; Hatfield, G. Spencer

2010-01-01

Aggregate nuclear plant failure data is used to produce generic common-cause factors that are specifically for use in the common-cause failure models of NUREG/CR-5485. Furthermore, the models presented in NUREG/CR-5485 are specifically designed to incorporate two significantly distinct assumptions about the methods of surveillance testing from whence this aggregate failure data came. What are the implications of using these NUREG generic factors to model the common-cause failures of aerospace systems? Herein, the implications of using the NUREG generic factors in the modeling of aerospace systems are investigated in detail and strong recommendations for modeling the common-cause failures of aerospace systems are given.

Modelling impacts of climate change on arable crop diseases: progress, challenges and applications.

PubMed

Newbery, Fay; Qi, Aiming; Fitt, Bruce Dl

2016-08-01

Combining climate change, crop growth and crop disease models to predict impacts of climate change on crop diseases can guide planning of climate change adaptation strategies to ensure future food security. This review summarises recent developments in modelling climate change impacts on crop diseases, emphasises some major challenges and highlights recent trends. The use of multi-model ensembles in climate change modelling and crop modelling is contributing towards measures of uncertainty in climate change impact projections but other aspects of uncertainty remain largely unexplored. Impact assessments are still concentrated on few crops and few diseases but are beginning to investigate arable crop disease dynamics at the landscape level. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Geo-referenced multimedia environmental fate model (G-CIEMS): model formulation and comparison to the generic model and monitoring approaches.

PubMed

Suzuki, Noriyuki; Murasawa, Kaori; Sakurai, Takeo; Nansai, Keisuke; Matsuhashi, Keisuke; Moriguchi, Yuichi; Tanabe, Kiyoshi; Nakasugi, Osami; Morita, Masatoshi

2004-11-01

A spatially resolved and geo-referenced dynamic multimedia environmental fate model, G-CIEMS (Grid-Catchment Integrated Environmental Modeling System) was developed on a geographical information system (GIS). The case study for Japan based on the air grid cells of 5 x 5 km resolution and catchments with an average area of 9.3 km2, which corresponds to about 40,000 air grid cells and 38,000 river segments/catchment polygons, were performed for dioxins, benzene, 1,3-butadiene, and di-(2-ethyhexyl)phthalate. The averaged concentration of the model and monitoring output were within a factor of 2-3 for all the media. Outputs from G-CIEMS and the generic model were essentially comparable when identical parameters were employed, whereas the G-CIEMS model gave explicit information of distribution of chemicals in the environment. Exposure-weighted averaged concentrations (EWAC) in air were calculated to estimate the exposure ofthe population, based on the results of generic, G-CIEMS, and monitoring approaches. The G-CIEMS approach showed significantly better agreement with the monitoring-derived EWAC than the generic model approach. Implication for the use of a geo-referenced modeling approach in the risk assessment scheme is discussed as a generic-spatial approach, which can be used to provide more accurate exposure estimation with distribution information, using generally available data sources for a wide range of chemicals.

A scheme for the uniform mapping and monitoring of earth resources and environmental complexes: An assessment of natural vegetation, environmental, and crop analogs. [Sierra-Lahontan and Colorado Plateaus, Northern Great Valley (CA), and Louisiana Coastal Plain

NASA Technical Reports Server (NTRS)

Poulton, C. E.; Welch, R. I. (Principal Investigator)

1975-01-01

The author has identified the following significant results. A study was performed to develop and test a procedure for the uniform mapping and monitoring of natural ecosystems in the semi-arid and wood regions of the Sierra-Lahontan and Colorado Plateau areas, and for the estimating of rice crop production in the Northern Great Valley (Ca.) and the Louisiana Coastal Plain. ERTS-1 and high flight and low flight aerial photos were used in a visual photointerpretation scheme to identify vegetation complexes, map acreages, and evaluate crop vigor and stress. Results indicated that the vegetation analog concept is valid; that depending on the kind of vegetation and its density, analogs are interpretable at different levels in the hierarchical classification from second to the fourth level. The second level uses physiognomic growth form-structural criteria, and the fourth level uses floristic or taxonomic criteria, usually at generic level. It is recommended that analog comparisons should be made in relatively small test areas where large homogeneous examples can be found of each analog.

Impacts of Stratospheric Black Carbon on Agriculture

NASA Astrophysics Data System (ADS)

Xia, L.; Robock, A.; Elliott, J. W.

2017-12-01

A regional nuclear war between India and Pakistan could inject 5 Tg of soot into the stratosphere, which would absorb sunlight, decrease global surface temperature by about 1°C for 5-10 years and have major impacts on precipitation and the amount of solar radiation reaching Earth's surface. Using two global gridded crop models forced by one global climate model simulation, we investigate the impacts on agricultural productivity in various nations. The crop model in the Community Land Model 4.5 (CLM-crop4.5) and the parallel Decision Support System for Agricultural Technology (pDSSAT) in the parallel System for Integrating Impact Models and Sectors are participating in the Global Gridded Crop Model Intercomparison. We force these two crop models with output from the Whole Atmospheric Community Climate Model to characterize the global agricultural impact from climate changes due to a regional nuclear war. Crops in CLM-crop4.5 include maize, rice, soybean, cotton and sugarcane, and crops in pDSSAT include maize, rice, soybean and wheat. Although the two crop models require a different time frequency of weather input, we downscale the climate model output to provide consistent temperature, precipitation and solar radiation inputs. In general, CLM-crop4.5 simulates a larger global average reduction of maize and soybean production relative to pDSSAT. Global rice production shows negligible change with climate anomalies from a regional nuclear war. Cotton and sugarcane benefit from a regional nuclear war from CLM-crop4.5 simulation, and global wheat production would decrease significantly in the pDSSAT simulation. The regional crop yield responses to a regional nuclear conflict are different for each crop, and we present the changes in production on a national basis. These models do not include the crop responses to changes in ozone, ultraviolet radiation, or diffuse radiation, and we would like to encourage more modelers to improve crop models to account for those impacts. We present these results as a demonstration of using different crop models to study this problem, and we invite more global crop modeling groups to use the same climate forcing, which we would be happy to provide, to gain a better understanding of global agricultural responses under different future climate scenarios with stratospheric aerosols.

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

Comparison of Six Generic Vancomycin Products for Treatment of Methicillin-Resistant Staphylococcus aureus Experimental Endocarditis in Rabbits

PubMed Central

Tattevin, P.; Saleh-Mghir, A.; Davido, B.; Ghout, I.; Massias, L.; Garcia de la Maria, C.; Miró, J. M.; Perronne, C.; Laurent, F.

2013-01-01

Concerns have recently emerged about the potency and the quality of generic vancomycin (VAN) products approved for use in humans, based on experiments in a neutropenic mouse thigh infection model. However, other animal models may be more appropriate to decipher the bactericidal activities of VAN generics in vivo and to predict their efficacy in humans. We aimed to compare the bactericidal activities of six generic VAN products currently used in France (Mylan and Sandoz), Spain (Hospira), Switzerland (Teva), and the United States (Akorn-Strides and American Pharmaceutical Products [APP]) in a rabbit model of aortic valve endocarditis induced by 8 × 107 CFU of methicillin-resistant Staphylococcus aureus (MRSA) strain COL (VAN MIC, 1.5 μg/ml). In vitro, there were no significant differences in the time-kill curve studies performed with the six generic VAN products. Ten rabbits in each group were treated with intravenous (i.v.) VAN, 60 mg/kg of body weight twice a day (b.i.d.) for 4 days. Mean peak serum VAN levels, measured 45 min after the last injection, ranged from 35.5 (APP) to 45.9 μg/ml (Teva). Mean trough serum VAN levels, measured 12 h after the last injection, ranged from 2.3 (Hospira) to 9.2 (APP) μg/ml. All generic VAN products were superior to controls (no treatment) in terms of residual organisms in vegetations (P < 0.02 for each comparison) and in the spleen (P < 0.005 for each comparison). Pairwise comparisons of generic VAN products found no significant differences. In conclusion, a stringent MRSA endocarditis model found no significant differences in the bactericidal activities of six generic VAN products currently used in Europe and America. PMID:23254435

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

NASA Astrophysics Data System (ADS)

Dhungel, S.; Barber, M. E.

2016-12-01

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

Rapid Crop Cover Mapping for the Conterminous United States.

PubMed

Dahal, Devendra; Wylie, Bruce; Howard, Danny

2018-06-05

Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM and implementing a 'two model mapping' approach, we found it viable to generate conterminous United States-wide rapid crop cover maps at a resolution of 250 m for the current year by the month of September. In this approach, we divided the CCM model into one 'crop type model' to handle the classification of nine specific crops and a second, binary model to classify the presence or absence of 'other' crops. Under the two model mapping approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4%, respectively. With spatial mapping accuracies for annual maps reaching upwards of 70%, this approach demonstrated a strong potential for generating rapid crop cover maps by the 1 st of September.

Development of a district information system for water management planning and strategic decision making

NASA Astrophysics Data System (ADS)

Loukas, A.; Tzabiras, J.; Spiliotopoulos, M.; Kokkinos, K.; Fafoutis, C.; Mylopoulos, N.

2015-06-01

The overall objective of this work is the development of a District Information System (DIS) which could be used by stakeholders for the purposes of a district day-to-day water management as well as for planning and strategic decisionmaking. The DIS was developed from a GIS-based modeling approach, which integrates a generic crop model and a hydraulic model of the transport/distribution system, using land use maps generated by Landsat TM imagery. The main sub-objectives are: (i) the development of an operational algorithm to retrieve crop evapotranspiration from remote sensing data, (ii) the development of an information system with friendly user interface for the data base, the crop module and the hydraulic module and (iii) the analysis and validation of management scenarios from model simulations predicting the respective behavior. The Lake Karla watershed is used in this study, but the overall methodology could be used as a basis for future analysis elsewhere. Surface Energy Balance Algorithm for Land (SEBAL) was used to derive monthly actual evapotranspiration (ET) values from Landsat TM imagery. Meteorological data from the archives of the Institute for Research and Technology, Thessaly (I.RE.TE.TH) has also been used. The methodology was developed using high quality Landsat TM images during 2007 growing season. Monthly ET values are used as an input to CROPWAT model. Outputs of CROPWAT model are then used as input for WEAP model. The developed scenario is based on the actual situation of the surface irrigation network of the Local Administration of Land Reclamation (LALR) of Pinios for the year of 2007. The DIS is calibrated with observed data of this year and the district parameterization is conducted based on the actual operation of the network. The operation of the surface irrigation network of Pinios LALR is simulated using Technologismiki Works, while the operation of closed pipe irrigation network of Lake Karla LALR is simulated using Watercad. Four alternative scenarios have been tested with the DIS: reduction of channel losses, alteration of irrigation methods, Introduction of greenhouse cultivation, and operation of the future Lake Karla network. The results of the simulation for the historical period indicate that the water pumped from Pinios LALR is not enough to serve irrigation requirements. The spatial and temporal variation of the unmet and unsatisfied water demand has been estimated. Simulation of the four alternative scenarios indicated that the alteration of irrigation methods scenario mainly increases the efficiency of the irrigation network.

Mathematical Modeling Of Life-Support Systems

NASA Technical Reports Server (NTRS)

Seshan, Panchalam K.; Ganapathi, Balasubramanian; Jan, Darrell L.; Ferrall, Joseph F.; Rohatgi, Naresh K.

1994-01-01

Generic hierarchical model of life-support system developed to facilitate comparisons of options in design of system. Model represents combinations of interdependent subsystems supporting microbes, plants, fish, and land animals (including humans). Generic model enables rapid configuration of variety of specific life support component models for tradeoff studies culminating in single system design. Enables rapid evaluation of effects of substituting alternate technologies and even entire groups of technologies and subsystems. Used to synthesize and analyze life-support systems ranging from relatively simple, nonregenerative units like aquariums to complex closed-loop systems aboard submarines or spacecraft. Model, called Generic Modular Flow Schematic (GMFS), coded in such chemical-process-simulation languages as Aspen Plus and expressed as three-dimensional spreadsheet.

Space Generic Open Avionics Architecture (SGOAA): Overview

NASA Technical Reports Server (NTRS)

Wray, Richard B.; Stovall, John R.

1992-01-01

A space generic open avionics architecture created for NASA is described. It will serve as the basis for entities in spacecraft core avionics, capable of being tailored by NASA for future space program avionics ranging from small vehicles such as Moon ascent/descent vehicles to large ones such as Mars transfer vehicles or orbiting stations. The standard consists of: (1) a system architecture; (2) a generic processing hardware architecture; (3) a six class architecture interface model; (4) a system services functional subsystem architectural model; and (5) an operations control functional subsystem architectural model.

Competition in prescription drug markets: the roles of trademarks, advertising, and generic names.

PubMed

Feldman, Roger; Lobo, Félix

2013-08-01

We take on two subjects of controversy among economists-advertising and trademarks-in the context of the market for generic drugs. We outline a model in which trademarks for drug names reduce search costs but increase product differentiation. In this particular framework, trademarks may not benefit consumers. In contrast, the generic names of drugs or "International Nonproprietary Names" (INN) have unquestionable benefits in both economic theory and empirical studies. We offer a second model where advertising of a brand-name drug creates recognition for the generic name. The monopoly patent-holder advertises less than in the absence of a competitive spillover.

Global Gridded Crop Model Evaluation: Benchmarking, Skills, Deficiencies and Implications.

NASA Technical Reports Server (NTRS)

Muller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe; Deryng, Delphine; Folberth, Christian; Glotter, Michael; Hoek, Steven;

Causal Models of Role Stressor Antecedents and Consequences: The Importance of Occupational Differences.

ERIC Educational Resources Information Center

Bacharach, Samuel; Bamberger, Peter

1992-01-01

Survey data from 215 nurses (10 male) and 430 civil engineers (10 female) supported the plausibility of occupation-specific models (positing direct paths between role stressors, antecedents, and consequences) compared to generic models. A weakness of generic models is the tendency to ignore differences in occupational structure and culture. (SK)

A generic model for evaluation of the Federal Aviation Administration air traffic control specialist training programs.

DOT National Transportation Integrated Search

1982-03-01

The Systems Analysis Research Unit at the Civil Aeromedical Institute (CAMI) has developed a generic model for Federal Aviation Administration (FAA) Academy training program evaluation. The model will serve as a basis for integrating the total data b...

Incorporating agricultural management into an earth system model for the Pacific Northwest region: Interactions between climate, hydrology, agriculture, and economics

NASA Astrophysics Data System (ADS)

Chinnayakanahalli, K.; Adam, J. C.; Stockle, C.; Nelson, R.; Brady, M.; Rajagopalan, K.; Barber, M. E.; Dinesh, S.; Malek, K.; Yorgey, G.; Kruger, C.; Marsh, T.; Yoder, J.

2011-12-01

For better management and decision making in the face of climate change, earth system models must explicitly account for natural resource and agricultural management activities. Including crop system, water management, and economic models into an earth system modeling framework can help in answering questions related to the impacts of climate change on irrigation water and crop productivity, how agricultural producers can adapt to anticipated climate change, and how agricultural practices can mitigate climate change. Herein we describe the coupling of the Variability Infiltration Capacity (VIC) land surface model, which solves the water and energy balances of the hydrologic cycle at regional scales, with a crop-growth model, CropSyst. This new model, VIC-CropSyst, is the land surface model that will be used in a new regional-scale model development project focused on the Pacific Northwest, termed BioEarth. Here we describe the VIC-CropSyst coupling process and its application over the Columbia River basin (CRB) using agricultural-specific land cover information. The Washington State Department of Agriculture (WSDA) and U. S. Department of Agriculture (USDA) cropland data layers were used to identify agricultural land use patterns, in which both irrigated and dry land crops were simulated. The VIC-CropSyst model was applied over the CRB for the historical period of 1976 - 2006 to establish a baseline for surface water availability, irrigation demand, and crop production. The model was then applied under future (2030s) climate change scenarios derived from statistically-downscaled Global Circulation Models output under two emission scenarios (A1B and B1). Differences between simulated future and historical irrigation demand, irrigation water availability, and crop production were used in an economics model to identify the most economically-viable future cropping pattern. The economics model was run under varying scenarios of regional growth, trade, water pricing, and water capacity providing a spectrum of possible future cropping patterns. The resulting cropping patterns were then used in VIC-CropSyst to quantify the impacts of climate change, economic, and water management scenarios on crop production, and water resources availability. This modeling framework provides opportunities to study the interactions between human activities and complex natural processes and is a valuable tool for inclusion in an earth system model with the goal of informing land use and water management.

Delay correlation analysis and representation for vital complaint VHDL models

DOEpatents

Rich, Marvin J.; Misra, Ashutosh

2004-11-09

A method and system unbind a rise/fall tuple of a VHDL generic variable and create rise time and fall time generics of each generic variable that are independent of each other. Then, according to a predetermined correlation policy, the method and system collect delay values in a VHDL standard delay file, sort the delay values, remove duplicate delay values, group the delay values into correlation sets, and output an analysis file. The correlation policy may include collecting all generic variables in a VHDL standard delay file, selecting each generic variable, and performing reductions on the set of delay values associated with each selected generic variable.

Modeling of Protection in Dynamic Simulation Using Generic Relay Models and Settings

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

Samaan, Nader A.; Dagle, Jeffery E.; Makarov, Yuri V.

This paper shows how generic protection relay models available in planning tools can be augmented with settings that are based on NERC standards or best engineering practice. Selected generic relay models in Siemens PSS®E have been used in dynamic simulations in the proposed approach. Undervoltage, overvoltage, underfrequency, and overfrequency relays have been modeled for each generating unit. Distance-relay protection was modeled for transmission system protection. Two types of load-shedding schemes were modeled: underfrequency (frequency-responsive non-firm load shedding) and underfrequency and undervoltage firm load shedding. Several case studies are given to show the impact of protection devices on dynamic simulations. Thismore » is useful for simulating cascading outages.« less

US/Canada wheat and barley crop calender exploratory experiment implementation plan

NASA Technical Reports Server (NTRS)

1980-01-01

A plan is detailed for a supplemental experiment to evaluate several crop growth stage models and crop starter models. The objective of this experiment is to provide timely information to aid in understanding crop calendars and to provide data that will allow a selection between current crop calendar models.

Dynamic drought risk assessment using crop model and remote sensing techniques

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

Influence of ecohydrologic feedbacks from simulated crop growth on integrated regional hydrologic simulations under climate scenarios

NASA Astrophysics Data System (ADS)

van Walsum, P. E. V.; Supit, I.

2012-06-01

Hydrologic climate change modelling is hampered by climate-dependent model parameterizations. To reduce this dependency, we extended the regional hydrologic modelling framework SIMGRO to host a two-way coupling between the soil moisture model MetaSWAP and the crop growth simulation model WOFOST, accounting for ecohydrologic feedbacks in terms of radiation fraction that reaches the soil, crop coefficient, interception fraction of rainfall, interception storage capacity, and root zone depth. Except for the last, these feedbacks are dependent on the leaf area index (LAI). The influence of regional groundwater on crop growth is included via a coupling to MODFLOW. Two versions of the MetaSWAP-WOFOST coupling were set up: one with exogenous vegetation parameters, the "static" model, and one with endogenous crop growth simulation, the "dynamic" model. Parameterization of the static and dynamic models ensured that for the current climate the simulated long-term averages of actual evapotranspiration are the same for both models. Simulations were made for two climate scenarios and two crops: grass and potato. In the dynamic model, higher temperatures in a warm year under the current climate resulted in accelerated crop development, and in the case of potato a shorter growing season, thus partly avoiding the late summer heat. The static model has a higher potential transpiration; depending on the available soil moisture, this translates to a higher actual transpiration. This difference between static and dynamic models is enlarged by climate change in combination with higher CO2 concentrations. Including the dynamic crop simulation gives for potato (and other annual arable land crops) systematically higher effects on the predicted recharge change due to climate change. Crop yields from soils with poor water retention capacities strongly depend on capillary rise if moisture supply from other sources is limited. Thus, including a crop simulation model in an integrated hydrologic simulation provides a valuable addition for hydrologic modelling as well as for crop modelling.

Guide for Regional Integrated Assessments: Handbook of Methods and Procedures, Version 5.1. Appendix 1

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia E.; Jones, James W.; Hatfield, Jerry; Antle, John; Ruane, Alex; Boote, Ken; Thorburn, Peter; Valdivia, Roberto; Porter, Cheryl; Janssen, Sander;

Effects of input uncertainty on cross-scale crop modeling

NASA Astrophysics Data System (ADS)

Waha, Katharina; Huth, Neil; Carberry, Peter

2014-05-01

The quality of data on climate, soils and agricultural management in the tropics is in general low or data is scarce leading to uncertainty in process-based modeling of cropping systems. Process-based crop models are common tools for simulating crop yields and crop production in climate change impact studies, studies on mitigation and adaptation options or food security studies. Crop modelers are concerned about input data accuracy as this, together with an adequate representation of plant physiology processes and choice of model parameters, are the key factors for a reliable simulation. For example, assuming an error in measurements of air temperature, radiation and precipitation of ± 0.2°C, ± 2 % and ± 3 % respectively, Fodor & Kovacs (2005) estimate that this translates into an uncertainty of 5-7 % in yield and biomass simulations. In our study we seek to answer the following questions: (1) are there important uncertainties in the spatial variability of simulated crop yields on the grid-cell level displayed on maps, (2) are there important uncertainties in the temporal variability of simulated crop yields on the aggregated, national level displayed in time-series, and (3) how does the accuracy of different soil, climate and management information influence the simulated crop yields in two crop models designed for use at different spatial scales? The study will help to determine whether more detailed information improves the simulations and to advise model users on the uncertainty related to input data. We analyse the performance of the point-scale crop model APSIM (Keating et al., 2003) and the global scale crop model LPJmL (Bondeau et al., 2007) with different climate information (monthly and daily) and soil conditions (global soil map and African soil map) under different agricultural management (uniform and variable sowing dates) for the low-input maize-growing areas in Burkina Faso/West Africa. We test the models' response to different levels of input data from very little to very detailed information, and compare the models' abilities to represent the spatial variability and temporal variability in crop yields. We display the uncertainty in crop yield simulations from different input data and crop models in Taylor diagrams which are a graphical summary of the similarity between simulations and observations (Taylor, 2001). The observed spatial variability can be represented well from both models (R=0.6-0.8) but APSIM predicts higher spatial variability than LPJmL due to its sensitivity to soil parameters. Simulations with the same crop model, climate and sowing dates have similar statistics and therefore similar skill to reproduce the observed spatial variability. Soil data is less important for the skill of a crop model to reproduce the observed spatial variability. However, the uncertainty in simulated spatial variability from the two crop models is larger than from input data settings and APSIM is more sensitive to input data then LPJmL. Even with a detailed, point-scale crop model and detailed input data it is difficult to capture the complexity and diversity in maize cropping systems.

Experiment of Enzyme Kinetics Using Guided Inquiry Model for Enhancing Generic Science Skills

NASA Astrophysics Data System (ADS)

Amida, N.; Supriyanti, F. M. T.; Liliasari

2017-02-01

This study aims to enhance generic science skills of students using guided inquiry model through experiments of enzyme kinetics. This study used quasi-experimental methods, with pretest-posttestnonequivalent control group design. Subjects of this study were chemistry students enrolled in biochemistry lab course, consisted of 18 students in experimental class and 19 students in control class. Instrument in this study were essay test that involves 5 indicators of generic science skills (i.e. direct observation, causality, symbolic language, mathematical modeling, and concepts formation) and also student worksheets. The results showed that the experiments of kinetics enzyme using guided inquiry model have been enhance generic science skills in high category with a value of average of 0.77. Four indicators classified in the high category are direct observation, causality, symbolic language, and mathematical modeling with the value of 0,73 0,70; 0,96; dan 0,85. Meanwhile, indicator of concepts formation in the medium category with a value of 0.62

An avionics scenario and command model description for Space Generic Open Avionics Architecture (SGOAA)

NASA Technical Reports Server (NTRS)

Stovall, John R.; Wray, Richard B.

1994-01-01

This paper presents a description of a model for a space vehicle operational scenario and the commands for avionics. This model will be used in developing a dynamic architecture simulation model using the Statemate CASE tool for validation of the Space Generic Open Avionics Architecture (SGOAA). The SGOAA has been proposed as an avionics architecture standard to NASA through its Strategic Avionics Technology Working Group (SATWG) and has been accepted by the Society of Automotive Engineers (SAE) for conversion into an SAE Avionics Standard. This architecture was developed for the Flight Data Systems Division (FDSD) of the NASA Johnson Space Center (JSC) by the Lockheed Engineering and Sciences Company (LESC), Houston, Texas. This SGOAA includes a generic system architecture for the entities in spacecraft avionics, a generic processing external and internal hardware architecture, and a nine class model of interfaces. The SGOAA is both scalable and recursive and can be applied to any hierarchical level of hardware/software processing systems.

Multimodel ensembles of wheat growth: many models are better than one

USDA-ARS?s Scientific Manuscript database

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but suc...

Participatory data collection and monitoring of agricultural pest dynamics for climate-resilient coffee production using Tiko'n, a generic tool to develop agroecological food web models

NASA Astrophysics Data System (ADS)

Rojas, M.; Malard, J. J.; Adamowski, J. F.; Tuy, H.

2016-12-01

Climate variability impacts agricultural processes through many mechanisms. For example, the proliferation of pests and diseases increases with warmer climate and alternated wind patterns, as longer growing seasons allow pest species to complete more reproductive cycles and changes in the weather patterns alter the stages and rates of development of pests and pathogens. Several studies suggest that enhancing plant diversity and complexity in farming systems, such as in agroforestry systems, reduces the vulnerability of farms to extreme climatic events. On the other hand, other authors have argued that vegetation diversity does not necessarily reduce the incidence of pests and diseases, highlighting the importance of understanding how, where and when it is recommendable to diversify vegetation to improve pest and disease control, and emphasising the need for tools to develop, monitor and evaluate agroecosystems. In order to understand how biodiversity can enhance ecosystem services provided by the agroecosystem in the context of climatic variability, it is important to develop comprehensive models that include the role of trophic chains in the regulation of pests, which can be achieved by integrating crop models with pest-predator models, also known as agroecosystem network (AEN) models. Here we present a methodology for the participatory data collection and monitoring necessary for running Tiko'n, an AEN model that can also be coupled to a crop model such as DSSAT. This methodology aims to combine the local and practical knowledge of farmers with the scientific knowledge of entomologists and agronomists, allowing for the simplification of complex ecological networks of plant and insect interactions. This also increases the acceptability, credibility, and comprehension of the model by farmers, allowing them to understand their relationship with the local agroecosystem and their potential to use key agroecosystem principles such as functional diversity to mitigate climate variability impacts. Preliminary results of a study currently being conducted in a coffee agroforestry system in El Quebracho, Guatemala, will be presented, where the data was directly collected by farmers during eight consecutive months. Finally, future recommendations from lessons learnt during this study will be discussed.

Generic versus branded medicines: An observational study among patients with chronic diseases attending a public hospital outpatient department

PubMed Central

Das, Manisha; Choudhury, Supriyo; Maity, Somnath; Hazra, Avijit; Pradhan, Tirthankar; Pal, Aishee; Roy, Ranendra Kumar

2017-01-01

Background: The concept of generic prescription is widely accepted in various parts of the world. Nevertheless, it has failed to gain popularity in India due to factors such as nonavailability and distrust on the product quality. However, since 2012, the Government of West Bengal, India, has initiated exclusive generic drug outlets called “fair price medicine shop” (FPMS) inside the government hospital premises in a “public-private-partnership” model. This study was undertaken to evaluate the experience and attitude of patients who were consuming generic drugs purchased from these FPMS. Materials and Methods: It was a questionnaire-based cross-sectional study where we have interviewed 100 patients each consuming generic and branded drugs, respectively. The perceived effectiveness, reported safety, medication adherence, cost of therapy, and availability of drugs was compared between two mentioned groups. Medication adherence was estimated through Drug Attitude Inventory-10. Results: 93% of generic and 87% branded drug users believed that their drugs were effective (P = 0.238) in controlling their ailments. No significant difference (9% generic, 10% branded drug users, P = 1.000) was observed in reported adverse effects between generic and branded drug users. 82% and 77% of patients were adherent generic and branded drugs, respectively (P = 0.289). As expected, a significantly lower cost of generic drugs was observed compared to its branded counterpart. Conclusion: The policy of FPMS implemented by the Government of West Bengal, India appeared to be promising in terms of perceived effectiveness, safety, and adherence of generic drugs from FPMS compared to drugs purchased from open market retailers. Therefore, this study might act as an impetus for the policy-makers to initiate similar models across the country. PMID:28250671

Comparison of generic-to-brand switchback patterns for generic and authorized generic drugs

PubMed Central

Hansen, Richard A.; Qian, Jingjing; Berg, Richard; Linneman, James; Seoane-Vazquez, Enrique; Dutcher, Sarah K.; Raofi, Saeid; Page, C. David; Peissig, Peggy

2018-01-01

Background While generic drugs are therapeutically equivalent to brand drugs, some patients and healthcare providers remain uncertain about whether they produce identical outcomes. Authorized generics, which are identical in formulation to corresponding brand drugs but marketed as a generic, provide a unique post-marketing opportunity to study whether utilization patterns are influenced by perceptions of generic drugs. Objectives To compare generic-to-brand switchback rates between generics and authorized generics. Methods A retrospective cohort study was conducted using claims and electronic health records data from a regional U.S. healthcare system. Ten drugs with authorized generics and generics marketed between 1999 and 2014 were evaluated. Eligible adult patients received a brand drug during the 6 months preceding generic entry, and then switched to a generic or authorized generic. Patients in this cohort were followed for up to 30 months from the index switch date to evaluate occurrence of generic-to-brand switchbacks. Switchback rates were compared between patients on authorized generics versus generics using Kaplan-Meier curves and Cox proportional hazards models, controlling for individual drug effects, age, sex, Charlson comorbidity score, pre-index drug use characteristics, and pre-index healthcare utilization. Results Among 5,542 unique patients that switched from brand-to-generic or brand-to-authorized generic, 264 (4.8%) switched back to the brand drug. Overall switchback rates were similar for authorized generics compared with generics (HR=0.86; 95% CI 0.65-1.15). The likelihood of switchback was higher for alendronate (HR=1.64; 95% CI 1.20-2.23) and simvastatin (HR=1.81; 95% CI 1.30-2.54) and lower for amlodipine (HR=0.27; 95% CI 0.17-0.42) compared with other drugs in the cohort. Conclusions Overall switchback rates were similar between authorized generic and generic drug users, indirectly supporting similar efficacy and tolerability profiles for brand and generic drugs. Reasons for differences in switchback rates among specific products need to be further explored. PMID:28152215

The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

NASA Technical Reports Server (NTRS)

Shukla, Sonali P.; Ruane, Alexander Clark

2014-01-01

Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, and water (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models' responses to CTW changes (Rotter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012). To fulfill this need, the Coordinated Climate-Crop Modeling Project (C3MP) (Ruane et al., 2014) was initiated within the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013). The submitted results from C3MP Phase 1 (February 15, 2013-December 31, 2013) are currently being analyzed. This chapter serves to present and update the C3MP protocols, discuss the initial participation and general findings, comment on needed adjustments, and describe continued and future development. AgMIP aims to improve substantially the climate, crop, and economic simulation tools that are used to characterize the agricultural sector, to assess future world food security under changing climate conditions, and to enhance adaptation capacity both globally and regionally. To understand better and improve the modeled crop responses, AgMIP has conducted detailed crop model intercomparisons at closely observed field sites for wheat (Asseng et al., 2013), rice (Li et al., in review), maize (Bassu et al., 2014), and sugarcane (Singels et al., 2013). A coordinated modeling exercise was one of the original motivations for AgMIP, and C3MP provides rapid estimation of crop responses to CO2, water, and temperature (CTW) changes, adding dimension and insight into the crop model intercomparisons, while facilitating interactions within the global community of modelers. C3MP also contributes a fast-track, multi-model climate sensitivity assessment for the AgMIP climate and crop modeling teams on Research Track 2 (Fig. 1), which seeks to understand the impact of projected climatic changes on crop production and food security (Rosenzweig et al., 2013; Ruane et al., 2014).

The dynamics of hydroponic crops for simulation studies of the CELSS initial reference configurations

NASA Technical Reports Server (NTRS)

Volk, Tyler

1992-01-01

The goal of this research is to develop a progressive series of mathematical models for the CELSS hydroponic crops. These models will systematize the experimental findings from the crop researchers in the CELSS Program into a form useful to investigate system-level considerations, for example, dynamic studies of the CELSS Initial Reference Configurations. The crop models will organize data from different crops into a common modeling framework. This is the fifth semiannual report for this project. The following topics are discussed: (1) use of field crop models to explore phasic control of CELSS crops for optimizing yield; (2) seminar presented at Purdue CELSS NSCORT; and (3) paper submitted on analysis of bioprocessing of inedible plant materials.

Integrated approaches to climate-crop modelling: needs and challenges.

PubMed

Betts, Richard A

2005-11-29

This paper discusses the need for a more integrated approach to modelling changes in climate and crops, and some of the challenges posed by this. While changes in atmospheric composition are expected to exert an increasing radiative forcing of climate change leading to further warming of global mean temperatures and shifts in precipitation patterns, these are not the only climatic processes which may influence crop production. Changes in the physical characteristics of the land cover may also affect climate; these may arise directly from land use activities and may also result from the large-scale responses of crops to seasonal, interannual and decadal changes in the atmospheric state. Climate models used to drive crop models may, therefore, need to consider changes in the land surface, either as imposed boundary conditions or as feedbacks from an interactive climate-vegetation model. Crops may also respond directly to changes in atmospheric composition, such as the concentrations of carbon dioxide (CO2), ozone (03) and compounds of sulphur and nitrogen, so crop models should consider these processes as well as climate change. Changes in these, and the responses of the crops, may be intimately linked with meteorological processes so crop and climate models should consider synergies between climate and atmospheric chemistry. Some crop responses may occur at scales too small to significantly influence meteorology, so may not need to be included as feedbacks within climate models. However, the volume of data required to drive the appropriate crop models may be very large, especially if short-time-scale variability is important. Implementation of crop models within climate models would minimize the need to transfer large quantities of data between separate modelling systems. It should also be noted that crop responses to climate change may interact with other impacts of climate change, such as hydrological changes. For example, the availability of water for irrigation may be affected by changes in runoff as a direct consequence of climate change, and may also be affected by climate-related changes in demand for water for other uses. It is, therefore, necessary to consider the interactions between the responses of several impacts sectors to climate change. Overall, there is a strong case for a much closer coupling between models of climate, crops and hydrology, but this in itself poses challenges arising from issues of scale and errors in the models. A strategy is proposed whereby the pursuit of a fully coupled climate-chemistry-crop-hydrology model is paralleled by continued use of separate climate and land surface models but with a focus on consistency between the models.

Impact on Bacterial Resistance of Therapeutically Nonequivalent Generics: The Case of Piperacillin-Tazobactam

PubMed Central

Rodriguez, Carlos A.; Agudelo, Maria; Aguilar, Yudy A.; Zuluaga, Andres F.

2016-01-01

Previous studies have demonstrated that pharmaceutical equivalence and pharmacokinetic equivalence of generic antibiotics are necessary but not sufficient conditions to guarantee therapeutic equivalence (better called pharmacodynamic equivalence). In addition, there is scientific evidence suggesting a direct link between pharmacodynamic nonequivalence of generic vancomycin and promotion of resistance in Staphylococcus aureus. To find out if even subtle deviations from the expected pharmacodynamic behavior with respect to the innovator could favor resistance, we studied a generic product of piperacillin-tazobactam characterized by pharmaceutical and pharmacokinetic equivalence but a faulty fit of Hill’s Emax sigmoid model that could be interpreted as pharmacodynamic nonequivalence. We determined the impact in vivo of this generic product on the resistance of a mixed Escherichia coli population composed of ∼99% susceptible cells (ATCC 35218 strain) and a ∼1% isogenic resistant subpopulation that overproduces TEM-1 β-lactamase. After only 24 hours of treatment in the neutropenic murine thigh infection model, the generic amplified the resistant subpopulation up to 20-times compared with the innovator, following an inverted-U dose-response relationship. These findings highlight the critical role of therapeutic nonequivalence of generic antibiotics as a key factor contributing to the global problem of bacterial resistance. PMID:27191163

Canada's New Generic Pricing Policy: A Reasoned Approach to a Challenging Problem.

PubMed

Hollis, Aidan; Grootendorst, Paul

2015-08-01

Alberta, quickly followed by other Canadian provinces, has introduced a new pricing model for generic drugs, in which prices are inversely related to the number of generic manufacturers of the drug. This paper examines the rationale for the new policy. Copyright © 2015 Longwoods Publishing.

An integrated model for assessing both crop productivity and agricultural water resources at a large scale

NASA Astrophysics Data System (ADS)

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

2012-12-01

Agricultural production utilizes regional resources (e.g. river water and ground water) as well as local resources (e.g. temperature, rainfall, solar energy). Future climate changes and increasing demand due to population increases and economic developments would intensively affect the availability of water resources for agricultural production. While many studies assessed the impacts of climate change on agriculture, there are few studies that dynamically account for changes in water resources and crop production. This study proposes an integrated model for assessing both crop productivity and agricultural water resources at a large scale. Also, the irrigation management to subseasonal variability in weather and crop response varies for each region and each crop. To deal with such variations, we used the Markov Chain Monte Carlo technique to quantify regional-specific parameters associated with crop growth and irrigation water estimations. We coupled a large-scale crop model (Sakurai et al. 2012), with a global water resources model, H08 (Hanasaki et al. 2008). The integrated model was consisting of five sub-models for the following processes: land surface, crop growth, river routing, reservoir operation, and anthropogenic water withdrawal. The land surface sub-model was based on a watershed hydrology model, SWAT (Neitsch et al. 2009). Surface and subsurface runoffs simulated by the land surface sub-model were input to the river routing sub-model of the H08 model. A part of regional water resources available for agriculture, simulated by the H08 model, was input as irrigation water to the land surface sub-model. The timing and amount of irrigation water was simulated at a daily step. The integrated model reproduced the observed streamflow in an individual watershed. Additionally, the model accurately reproduced the trends and interannual variations of crop yields. To demonstrate the usefulness of the integrated model, we compared two types of impact assessment of climate change on crop productivity in a watershed. The first was carried out by the large-scale crop model alone. The second was carried out by the integrated model of the large-scale crop model and the H08 model. The former projected that changes in temperature and precipitation due to future climate change would give rise to increasing the water stress in crops. Nevertheless, the latter projected that the increasing amount of agricultural water resources in the watershed would supply sufficient amount of water for irrigation, consequently reduce the water stress. The integrated model demonstrated the importance of taking into account the water circulation in watershed when predicting the regional crop production.

Simulating crop growth with Expert-N-GECROS under different site conditions in Southwest Germany

NASA Astrophysics Data System (ADS)

Poyda, Arne; Ingwersen, Joachim; Demyan, Scott; Gayler, Sebastian; Streck, Thilo

2016-04-01

When feedbacks between the land surface and the atmosphere are investigated by Atmosphere-Land surface-Crop-Models (ALCM) it is fundamental to accurately simulate crop growth dynamics as plants directly influence the energy partitioning at the plant-atmosphere interface. To study both the response and the effect of intensive agricultural crop production systems on regional climate change in Southwest Germany, the crop growth model GECROS (YIN & VAN LAAR, 2005) was calibrated based on multi-year field data from typical crop rotations in the Kraichgau and Swabian Alb regions. Additionally, the SOC (soil organic carbon) model DAISY (MÜLLER et al., 1998) was implemented in the Expert-N model tool (ENGEL & PRIESACK, 1993) and combined with GECROS. The model was calibrated based on a set of plant (BBCH, LAI, plant height, aboveground biomass, N content of biomass) and weather data for the years 2010 - 2013 and validated with the data of 2014. As GECROS adjusts the root-shoot partitioning in response to external conditions (water, nitrogen, CO2), it is suitable to simulate crop growth dynamics under changing climate conditions and potentially more frequent stress situations. As C and N pools and turnover rates in soil as well as preceding crop effects were expected to considerably influence crop growth, the model was run in a multi-year, dynamic way. Crop residues and soil mineral N (nitrate, ammonium) available for the subsequent crop were accounted for. The model simulates growth dynamics of winter wheat, winter rape, silage maize and summer barley at the Kraichgau and Swabian Alb sites well. The Expert-N-GECROS model is currently parameterized for crops with potentially increasing shares in future crop rotations. First results will be shown.

Rapid crop cover mapping for the conterminous United States

USGS Publications Warehouse

Dahal, Devendra; Wylie, Bruce K.; Howard, Daniel

2018-01-01

Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM and implementing a ‘two model mapping’ approach, we found it viable to generate conterminous United States-wide rapid crop cover maps at a resolution of 250 m for the current year by the month of September. In this approach, we divided the CCM model into one ‘crop type model’ to handle the classification of nine specific crops and a second, binary model to classify the presence or absence of ‘other’ crops. Under the two model mapping approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4%, respectively. With spatial mapping accuracies for annual maps reaching upwards of 70%, this approach demonstrated a strong potential for generating rapid crop cover maps by the 1st of September.

A Deformable Generic 3D Model of Haptoral Anchor of Monogenean

PubMed Central

Teo, Bee Guan; Dhillon, Sarinder Kaur; Lim, Lee Hong Susan

2013-01-01

In this paper, a digital 3D model which allows for visualisation in three dimensions and interactive manipulation is explored as a tool to help us understand the structural morphology and elucidate the functions of morphological structures of fragile microorganisms which defy live studies. We developed a deformable generic 3D model of haptoral anchor of dactylogyridean monogeneans that can subsequently be deformed into different desired anchor shapes by using direct manipulation deformation technique. We used point primitives to construct the rectangular building blocks to develop our deformable 3D model. Point primitives are manually marked on a 2D illustration of an anchor on a Cartesian graph paper and a set of Cartesian coordinates for each point primitive is manually extracted from the graph paper. A Python script is then written in Blender to construct 3D rectangular building blocks based on the Cartesian coordinates. The rectangular building blocks are stacked on top or by the side of each other following their respective Cartesian coordinates of point primitive. More point primitives are added at the sites in the 3D model where more structural variations are likely to occur, in order to generate complex anchor structures. We used Catmull-Clark subdivision surface modifier to smoothen the surface and edge of the generic 3D model to obtain a smoother and more natural 3D shape and antialiasing option to reduce the jagged edges of the 3D model. This deformable generic 3D model can be deformed into different desired 3D anchor shapes through direct manipulation deformation technique by aligning the vertices (pilot points) of the newly developed deformable generic 3D model onto the 2D illustrations of the desired shapes and moving the vertices until the desire 3D shapes are formed. In this generic 3D model all the vertices present are deployed for displacement during deformation. PMID:24204903

A deformable generic 3D model of haptoral anchor of Monogenean.

PubMed

Teo, Bee Guan; Dhillon, Sarinder Kaur; Lim, Lee Hong Susan

2013-01-01

In this paper, a digital 3D model which allows for visualisation in three dimensions and interactive manipulation is explored as a tool to help us understand the structural morphology and elucidate the functions of morphological structures of fragile microorganisms which defy live studies. We developed a deformable generic 3D model of haptoral anchor of dactylogyridean monogeneans that can subsequently be deformed into different desired anchor shapes by using direct manipulation deformation technique. We used point primitives to construct the rectangular building blocks to develop our deformable 3D model. Point primitives are manually marked on a 2D illustration of an anchor on a Cartesian graph paper and a set of Cartesian coordinates for each point primitive is manually extracted from the graph paper. A Python script is then written in Blender to construct 3D rectangular building blocks based on the Cartesian coordinates. The rectangular building blocks are stacked on top or by the side of each other following their respective Cartesian coordinates of point primitive. More point primitives are added at the sites in the 3D model where more structural variations are likely to occur, in order to generate complex anchor structures. We used Catmull-Clark subdivision surface modifier to smoothen the surface and edge of the generic 3D model to obtain a smoother and more natural 3D shape and antialiasing option to reduce the jagged edges of the 3D model. This deformable generic 3D model can be deformed into different desired 3D anchor shapes through direct manipulation deformation technique by aligning the vertices (pilot points) of the newly developed deformable generic 3D model onto the 2D illustrations of the desired shapes and moving the vertices until the desire 3D shapes are formed. In this generic 3D model all the vertices present are deployed for displacement during deformation.

Temporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier

USGS Publications Warehouse

Friesz, Aaron M.; Wylie, Bruce K.; Howard, Daniel M.

2017-01-01

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008 to 2013. In this investigation, we sought to contribute to the availability of consistent CONUS crop cover maps by extending temporal coverage of the NASS CDL archive back eight additional years to 2000 by creating annual NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million records to train a classification tree algorithm and develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000–2013 at 250 m spatial resolution. The CCM and the maps for years 2008–2013 were assessed for accuracy relative to resampled NASS CDLs. The CCM performed well against a withheld test data set with a model prediction accuracy of over 90%. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains; however, the model did show a bias towards the ‘Other’ crop cover class, which caused frequent misclassifications of pixels around the periphery of large crop cover patch clusters and of pixels that form small, sparsely dispersed crop cover patches.

Improving ecophysiological simulation models to predict the impact of elevated atmospheric CO2 concentration on crop productivity

PubMed Central

Yin, Xinyou

2013-01-01

Background Process-based ecophysiological crop models are pivotal in assessing responses of crop productivity and designing strategies of adaptation to climate change. Most existing crop models generally over-estimate the effect of elevated atmospheric [CO2], despite decades of experimental research on crop growth response to [CO2]. Analysis A review of the literature indicates that the quantitative relationships for a number of traits, once expressed as a function of internal plant nitrogen status, are altered little by the elevated [CO2]. A model incorporating these nitrogen-based functional relationships and mechanisms simulated photosynthetic acclimation to elevated [CO2], thereby reducing the chance of over-estimating crop response to [CO2]. Robust crop models to have small parameterization requirements and yet generate phenotypic plasticity under changing environmental conditions need to capture the carbon–nitrogen interactions during crop growth. Conclusions The performance of the improved models depends little on the type of the experimental facilities used to obtain data for parameterization, and allows accurate projections of the impact of elevated [CO2] and other climatic variables on crop productivity. PMID:23388883

Improving Crop Productions Using the Irrigation & Crop Production Model Under Drought

NASA Astrophysics Data System (ADS)

Shin, Y.; Lee, T.; Lee, S. H.; Kim, J.; Jang, W.; Park, S.

2017-12-01

We aimed to improve crop productions by providing optimal irrigation water amounts (IWAs) for various soils and crops using the Irrigation & Crop Production (ICP) model under various hydro-climatic regions. We selected the Little Washita (LW 13/21) and Bangdong-ri sites in Oklahoma (United States of America) and Chuncheon (Republic of Korea) for the synthetic studies. Our results showed that the ICP model performed well for improving crop productions by providing optimal IWAs during the study period (2000 to 2016). Crop productions were significantly affected by the solar radiation and precipitation, but the maximum and minimum temperature showed less impact on crop productions. When we considerd that the weather variables cannot be adjusted by artifical activities, irrigation might be the only solution for improving crop productions under drought. Also, the presence of shallow ground water (SGW) table depths higlhy influences on crop production. Although certainties exist in the synthetic studies, our results showed the robustness of the ICP model for improving crop productions under the drought condition. Thus, the ICP model can contribute to efficient water management plans under drought in regions at where water availability is limited.

Parameterization models for pesticide exposure via crop consumption.

PubMed

Fantke, Peter; Wieland, Peter; Juraske, Ronnie; Shaddick, Gavin; Itoiz, Eva Sevigné; Friedrich, Rainer; Jolliet, Olivier

2012-12-04

An approach for estimating human exposure to pesticides via consumption of six important food crops is presented that can be used to extend multimedia models applied in health risk and life cycle impact assessment. We first assessed the variation of model output (pesticide residues per kg applied) as a function of model input variables (substance, crop, and environmental properties) including their possible correlations using matrix algebra. We identified five key parameters responsible for between 80% and 93% of the variation in pesticide residues, namely time between substance application and crop harvest, degradation half-lives in crops and on crop surfaces, overall residence times in soil, and substance molecular weight. Partition coefficients also play an important role for fruit trees and tomato (Kow), potato (Koc), and lettuce (Kaw, Kow). Focusing on these parameters, we develop crop-specific models by parametrizing a complex fate and exposure assessment framework. The parametric models thereby reflect the framework's physical and chemical mechanisms and predict pesticide residues in harvest using linear combinations of crop, crop surface, and soil compartments. Parametric model results correspond well with results from the complex framework for 1540 substance-crop combinations with total deviations between a factor 4 (potato) and a factor 66 (lettuce). Predicted residues also correspond well with experimental data previously used to evaluate the complex framework. Pesticide mass in harvest can finally be combined with reduction factors accounting for food processing to estimate human exposure from crop consumption. All parametric models can be easily implemented into existing assessment frameworks.

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

NASA Technical Reports Server (NTRS)

Lin, K. K. (Principal Investigator)

1982-01-01

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

The review of dynamic monitoring technology for crop growth

NASA Astrophysics Data System (ADS)

Zhang, Hong-wei; Chen, Huai-liang; Zou, Chun-hui; Yu, Wei-dong

2010-10-01

In this paper, crop growth monitoring methods are described elaborately. The crop growth models, Netherlands-Wageningen model system, the United States-GOSSYM model and CERES models, Australia APSIM model and CCSODS model system in China, are introduced here more focus on the theories of mechanism, applications, etc. The methods and application of remote sensing monitoring methods, which based on leaf area index (LAI) and biomass were proposed by different scholars at home and abroad, are highly stressed in the paper. The monitoring methods of remote sensing coupling with crop growth models are talked out at large, including the method of "forced law" which using remote sensing retrieval state parameters as the crop growth model parameters input, and then to enhance the dynamic simulation accuracy of crop growth model and the method of "assimilation of Law" which by reducing the gap difference between the value of remote sensing retrieval and the simulated values of crop growth model and thus to estimate the initial value or parameter values to increasing the simulation accuracy. At last, the developing trend of monitoring methods are proposed based on the advantages and shortcomings in previous studies, it is assured that the combination of remote sensing with moderate resolution data of FY-3A, MODIS, etc., crop growth model, "3S" system and observation in situ are the main methods in refinement of dynamic monitoring and quantitative assessment techniques for crop growth in future.

Scenario tree model for animal disease freedom framed in the OIE context using the example of a generic swine model for Aujeszky's disease in commercial swine in Canada.

PubMed

Christensen, Jette; Vallières, André

2016-01-01

"Freedom from animal disease" is an ambiguous concept that may have a different meaning in trade and science. For trade alone, there are different levels of freedom from OIE listed diseases. A country can: be recognized by OIE to be "officially free"; self-declare freedom, with no official recognition by the OIE; or report animal disease as absent (no occurrence) in six-monthly reports. In science, we apply scenario tree models to calculate the probability of a population being free from disease at a given prevalence to provide evidence of freedom from animal disease. Here, we link science with application by describing how a scenario tree model may contribute to a country's claim of freedom from animal disease. We combine the idea of a standardized presentation of scenario tree models for disease freedom and having a similar model for two different animal diseases to suggest that a simple generic model may help veterinary authorities to build and evaluate scenario tree models for disease freedom. Here, we aim to develop a generic scenario tree model for disease freedom that is: animal species specific, population specific, and has a simple structure. The specific objectives were: to explore the levels of freedom described in the OIE Terrestrial Animal Health Code; to describe how scenario tree models may contribute to a country's claim of freedom from animal disease; and to present a generic swine scenario tree model for disease freedom in Canada's domestic (commercial) swine applied to Aujeszky's disease (AD). In particular, to explore how historical survey data, and data mining may affect the probability of freedom and to explore different sampling strategies. Finally, to frame the generic scenario tree model in the context of Canada's claim of freedom from AD. We found that scenario tree models are useful to support a country's claim of freedom either as "recognized officially free" or as part of a self-declaration but the models should not stand alone in a claim. The generic AD scenario tree model demonstrated the benefit of combining three sources of surveillance data and helped to design the surveillance for the next year. The generic AD scenario model is one piece in Canada's self-declaration of freedom from AD. The model is strongly supported by the fact that AD has never been detected in Canada. Crown Copyright © 2015. Published by Elsevier B.V. All rights reserved.

Modeling and control for closed environment plant production systems

NASA Technical Reports Server (NTRS)

Fleisher, David H.; Ting, K. C.; Janes, H. W. (Principal Investigator)

2002-01-01

A computer program was developed to study multiple crop production and control in controlled environment plant production systems. The program simulates crop growth and development under nominal and off-nominal environments. Time-series crop models for wheat (Triticum aestivum), soybean (Glycine max), and white potato (Solanum tuberosum) are integrated with a model-based predictive controller. The controller evaluates and compensates for effects of environmental disturbances on crop production scheduling. The crop models consist of a set of nonlinear polynomial equations, six for each crop, developed using multivariate polynomial regression (MPR). Simulated data from DSSAT crop models, previously modified for crop production in controlled environments with hydroponics under elevated atmospheric carbon dioxide concentration, were used for the MPR fitting. The model-based predictive controller adjusts light intensity, air temperature, and carbon dioxide concentration set points in response to environmental perturbations. Control signals are determined from minimization of a cost function, which is based on the weighted control effort and squared-error between the system response and desired reference signal.

A pharmaco-economic analysis of patients with schizophrenia switching to generic risperidone involving a possible compliance loss.

PubMed

Treur, Maarten; Heeg, Bart; Möller, Hans-Jürgen; Schmeding, Annette; van Hout, Ben

2009-02-18

As schizophrenia patients are typically suspicious of, or are hostile to changes they may be reluctant to accept generic substitution, possibly affecting compliance. This may counteract drug costs savings due to less symptom control and increased hospitalization risk. Although compliance losses following generic substitution have not been quantified so far, one can estimate the possible health-economic consequences. The current study aims to do so by considering the case of risperidone in Germany. An existing DES model was adapted to compare staying on branded risperidone with generic substitution. Differences include the probability of non-compliance and medication costs. Incremental probability of non-compliance after generic substitution was varied between 2.5% and 10%, while generic medication costs were assumed to be 40% lower. Effect of medication price was assessed as well as the effect of applying compliance losses to all treatment settings. The probability of staying on branded risperidone being cost-effective was calculated for various outcomes of a hypothetical study that would investigate non-compliance following generic substitution of risperidone. If the incremental probability of non-compliance after generic substitution is 2.5%, 5.0%, 7.5% and 10% respectively, incremental effects of staying on branded risperidone are 0.004, 0.007, 0.011 and 0.015 Quality Adjusted Life Years (QALYs). Incremental costs are euro757, euro343, -euro123 and -euro554 respectively. Benefits of staying on branded risperidone include improved symptom control and fewer hospitalizations. If generic substitution results in a 5.2% higher probability of non-compliance, the model predicts staying on branded risperidone to be cost-effective (NICE threshold of 30,000 per QALY gained). Compliance losses of more than 6.9% makes branded risperidone the dominant alternative. Results are sensitive to the locations at which compliance loss is applied and the price of generic risperidone. The probability that staying on branded risperidone is cost-effective would increase with larger compliance differences and more patients included in the hypothetical study. The model predicts that it is cost-effective to keep a patient with schizophrenia in Germany on branded risperidone instead of switching him/her to generic risperidone (assuming a 40% reduction in medication costs), if the incremental probability of becoming non-compliant after generic substitution exceeds 5.2%.

A Generic Modeling Process to Support Functional Fault Model Development

NASA Technical Reports Server (NTRS)

Maul, William A.; Hemminger, Joseph A.; Oostdyk, Rebecca; Bis, Rachael A.

2016-01-01

Functional fault models (FFMs) are qualitative representations of a system's failure space that are used to provide a diagnostic of the modeled system. An FFM simulates the failure effect propagation paths within a system between failure modes and observation points. These models contain a significant amount of information about the system including the design, operation and off nominal behavior. The development and verification of the models can be costly in both time and resources. In addition, models depicting similar components can be distinct, both in appearance and function, when created individually, because there are numerous ways of representing the failure space within each component. Generic application of FFMs has the advantages of software code reuse: reduction of time and resources in both development and verification, and a standard set of component models from which future system models can be generated with common appearance and diagnostic performance. This paper outlines the motivation to develop a generic modeling process for FFMs at the component level and the effort to implement that process through modeling conventions and a software tool. The implementation of this generic modeling process within a fault isolation demonstration for NASA's Advanced Ground System Maintenance (AGSM) Integrated Health Management (IHM) project is presented and the impact discussed.

Identifying traits for genotypic adaptation using crop models.

PubMed

Ramirez-Villegas, Julian; Watson, James; Challinor, Andrew J

2015-06-01

Genotypic adaptation involves the incorporation of novel traits in crop varieties so as to enhance food productivity and stability and is expected to be one of the most important adaptation strategies to future climate change. Simulation modelling can provide the basis for evaluating the biophysical potential of crop traits for genotypic adaptation. This review focuses on the use of models for assessing the potential benefits of genotypic adaptation as a response strategy to projected climate change impacts. Some key crop responses to the environment, as well as the role of models and model ensembles for assessing impacts and adaptation, are first reviewed. Next, the review describes crop-climate models can help focus the development of future-adapted crop germplasm in breeding programmes. While recently published modelling studies have demonstrated the potential of genotypic adaptation strategies and ideotype design, it is argued that, for model-based studies of genotypic adaptation to be used in crop breeding, it is critical that modelled traits are better grounded in genetic and physiological knowledge. To this aim, two main goals need to be pursued in future studies: (i) a better understanding of plant processes that limit productivity under future climate change; and (ii) a coupling between genetic and crop growth models-perhaps at the expense of the number of traits analysed. Importantly, the latter may imply additional complexity (and likely uncertainty) in crop modelling studies. Hence, appropriately constraining processes and parameters in models and a shift from simply quantifying uncertainty to actually quantifying robustness towards modelling choices are two key aspects that need to be included into future crop model-based analyses of genotypic adaptation. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

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

NASA Astrophysics Data System (ADS)

Chakrabarty, Abhisek

2016-07-01

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

Relevance of various animal models of human infections to establish therapeutic equivalence of a generic product of piperacillin/tazobactam.

PubMed

Agudelo, Maria; Rodriguez, Carlos A; Zuluaga, Andres F; Vesga, Omar

2015-02-01

After demonstrating with diverse intravenous antibacterials that pharmaceutical equivalence (PE) does not predict therapeutic equivalence, we tested a single generic product of piperacillin/tazobactam (TZP) in terms of PE, pharmacokinetics and in vitro/vivo pharmacodynamics against several pathogens in neutropenic mouse thigh, lung and brain infection models. A generic product was compared head-to-head against the innovator. PE was evaluated by microbiological assay. Single-dose serum pharmacokinetics were determined in infected mice, and the MIC/MBC were determined by broth microdilution. In vivo experiments were done in a blind fashion. Reproducibility was tested on different days using different infecting organisms and animal models. Neutropenic MPF mice were infected in the thighs with Staphylococcus aureus GRP-0057 or Pseudomonas aeruginosa PA01 and in the lungs or brain with Klebsiella pneumoniae ATCC 10031. Treatment started 2h (thigh and brain) or 14 h (lung) after infection and was administered every 3h over 24h (thigh and lung) or 48 h (brain). Both products exhibited the same MIC/MBC against each strain, yielded overlaid curves in the microbiological assay (P>0.21) and were bioequivalent (IC90 83-117% for AUC test/reference ratio). In vivo, the generic product and innovator were again undistinguishable in all models and against the different bacterial pathogens involved. The relevance of these neutropenic murine models of infection was established by demonstrating their accuracy to predict the biological response following simultaneous treatment with a generic product or the innovator of TZP. Therapeutic equivalence of the generic product was proved in every model and against different pathogens. Copyright © 2014 Elsevier B.V. and the International Society of Chemotherapy. All rights reserved.

Nonequilibrium thermodynamics of the shear-transformation-zone model

NASA Astrophysics Data System (ADS)

Luo, Alan M.; Ã-ttinger, Hans Christian

2014-02-01

The shear-transformation-zone (STZ) model has been applied numerous times to describe the plastic deformation of different types of amorphous systems. We formulate this model within the general equation for nonequilibrium reversible-irreversible coupling (GENERIC) framework, thereby clarifying the thermodynamic structure of the constitutive equations and guaranteeing thermodynamic consistency. We propose natural, physically motivated forms for the building blocks of the GENERIC, which combine to produce a closed set of time evolution equations for the state variables, valid for any choice of free energy. We demonstrate an application of the new GENERIC-based model by choosing a simple form of the free energy. In addition, we present some numerical results and contrast those with the original STZ equations.

A generic simulation model to assess the performance of sterilization services in health establishments.

PubMed

Di Mascolo, Maria; Gouin, Alexia

2013-03-01

The work presented here is with a view to improving performance of sterilization services in hospitals. We carried out a survey in a large number of health establishments in the Rhône-Alpes region in France. Based on the results of this survey and a detailed study of a specific service, we have built a generic model. The generic nature of the model relies on a common structure with a high level of detail. This model can be used to improve the performance of a specific sterilization service and/or to dimension its resources. It can also serve for quantitative comparison of performance indicators of various sterilization services.

Simulating canopy temperature for modelling heat stress in cereals

USDA-ARS?s Scientific Manuscript database

Crop models must be improved to account for the large effects of heat stress effects on crop yields. To date, most approaches in crop models use air temperature despite evidence that crop canopy temperature better explains yield reductions associated with high temperature events. This study presents...

Integrated approaches to climate–crop modelling: needs and challenges

PubMed Central

A. Betts, Richard

2005-01-01

This paper discusses the need for a more integrated approach to modelling changes in climate and crops, and some of the challenges posed by this. While changes in atmospheric composition are expected to exert an increasing radiative forcing of climate change leading to further warming of global mean temperatures and shifts in precipitation patterns, these are not the only climatic processes which may influence crop production. Changes in the physical characteristics of the land cover may also affect climate; these may arise directly from land use activities and may also result from the large-scale responses of crops to seasonal, interannual and decadal changes in the atmospheric state. Climate models used to drive crop models may, therefore, need to consider changes in the land surface, either as imposed boundary conditions or as feedbacks from an interactive climate–vegetation model. Crops may also respond directly to changes in atmospheric composition, such as the concentrations of carbon dioxide (CO2), ozone (O3) and compounds of sulphur and nitrogen, so crop models should consider these processes as well as climate change. Changes in these, and the responses of the crops, may be intimately linked with meteorological processes so crop and climate models should consider synergies between climate and atmospheric chemistry. Some crop responses may occur at scales too small to significantly influence meteorology, so may not need to be included as feedbacks within climate models. However, the volume of data required to drive the appropriate crop models may be very large, especially if short-time-scale variability is important. Implementation of crop models within climate models would minimize the need to transfer large quantities of data between separate modelling systems. It should also be noted that crop responses to climate change may interact with other impacts of climate change, such as hydrological changes. For example, the availability of water for irrigation may be affected by changes in runoff as a direct consequence of climate change, and may also be affected by climate-related changes in demand for water for other uses. It is, therefore, necessary to consider the interactions between the responses of several impacts sectors to climate change. Overall, there is a strong case for a much closer coupling between models of climate, crops and hydrology, but this in itself poses challenges arising from issues of scale and errors in the models. A strategy is proposed whereby the pursuit of a fully coupled climate–chemistry–crop–hydrology model is paralleled by continued use of separate climate and land surface models but with a focus on consistency between the models. PMID:16433093

Stimulating innovation for global monitoring of agriculture and its impact on the environment in support of GEOGLAM

NASA Astrophysics Data System (ADS)

Bydekerke, Lieven; Gilliams, Sven; Gobin, Anne

2015-04-01

There is an urgent need to ensure food supply for a growing global population. To enable a sustainable growth of agricultural production, effective and timely information is required to support decision making and to improve management of agricultural resources. This requires innovative ways and monitoring methods that will not only improve short-term crop production forecasts, but also allow to assess changes in cultivation practices, agricultural areas, agriculture in general and, its impact on the environment. The G20 launched in June 2011 the "GEO Global Agricultural Monitoring initiative (GEOGLAM), requesting the GEO (Group on Earth Observations) Agricultural Community of Practice to implement GEOGLAM with the main objective to improve crop yield forecasts as an input to the Agricultural Market Information System (AMIS), in order to foster stabilisation of markets and increase transparency on agricultural production. In response to this need, the European Commission decided in 2013 to fund an international partnership to contribute to GEOGLAM and its research agenda. The resulting SIGMA project (Stimulating Innovation for Global Monitoring of Agriculture), a partnership of 23 globally distributed expert organisations, focusses on developing datasets and innovative techniques in support of agricultural monitoring and its impact on the environment in support of GEOGLAM. SIGMA has 3 generic objectives which are: (i) develop and test methods to characterise cropland and assess its changes at various scales; (ii) develop and test methods to assess changes in agricultural production levels; and; (iii) study environmental impacts of agriculture. Firstly, multi-scale remote sensing data sets, in combination with field and other ancillary data, will be used to generate an improved (global) agro-ecological zoning map and crop mask. Secondly, a combination of agro-meteorological models, satellite-based information and long-term time series will be explored to assess crop yield gaps and shifts in cultivation. The third research topic entails the development of best practices for assessing the impact of crop land and cropping system change on the environment. In support of the GEO JECAM (Joint Experiment for Crop Assessment and Monitoring) initiative, SIGMA has selected case studies in Ukraine, Russia, Europe, Africa, Latin America and China, coinciding with the JECAM sites in these area, to explore possible methodological synergies and particularities according to different cropping systems. In combination with research conducted at regional and global scale, it is one of the goals to improve the understanding of dynamics, interactions and validity of the developed methods at the various scales. In addition, specific activities will be dedicated to raising awareness and strengthening capacity for what concerns agro-environmental monitoring, data accessibility and interoperability in line with the GEOSS Data-core principles. The SIGMA project will also anticipate on the availability of the SENTINEL satellites for agricultural applications as open-data in the near future. References http://proba-v.vgt.vito.be/ http://www.geoglam-sigma.info/

Designing Crop Simulation Web Service with Service Oriented Architecture Principle

NASA Astrophysics Data System (ADS)

Chinnachodteeranun, R.; Hung, N. D.; Honda, K.

2015-12-01

Crop simulation models are efficient tools for simulating crop growth processes and yield. Running crop models requires data from various sources as well as time-consuming data processing, such as data quality checking and data formatting, before those data can be inputted to the model. It makes the use of crop modeling limited only to crop modelers. We aim to make running crop models convenient for various users so that the utilization of crop models will be expanded, which will directly improve agricultural applications. As the first step, we had developed a prototype that runs DSSAT on Web called as Tomorrow's Rice (v. 1). It predicts rice yields based on a planting date, rice's variety and soil characteristics using DSSAT crop model. A user only needs to select a planting location on the Web GUI then the system queried historical weather data from available sources and expected yield is returned. Currently, we are working on weather data connection via Sensor Observation Service (SOS) interface defined by Open Geospatial Consortium (OGC). Weather data can be automatically connected to a weather generator for generating weather scenarios for running the crop model. In order to expand these services further, we are designing a web service framework consisting of layers of web services to support compositions and executions for running crop simulations. This framework allows a third party application to call and cascade each service as it needs for data preparation and running DSSAT model using a dynamic web service mechanism. The framework has a module to manage data format conversion, which means users do not need to spend their time curating the data inputs. Dynamic linking of data sources and services are implemented using the Service Component Architecture (SCA). This agriculture web service platform demonstrates interoperability of weather data using SOS interface, convenient connections between weather data sources and weather generator, and connecting various services for running crop models for decision support.

Climate driven crop planting date in the ACME Land Model (ALM): Impacts on productivity and yield

NASA Astrophysics Data System (ADS)

Drewniak, B.

2017-12-01

Climate is one of the key drivers of crop suitability and productivity in a region. The influence of climate and weather on the growing season determine the amount of time crops spend in each growth phase, which in turn impacts productivity and, more importantly, yields. Planting date can have a strong influence on yields with earlier planting generally resulting in higher yields, a sensitivity that is also present in some crop models. Furthermore, planting date is already changing and may continue, especially if longer growing seasons caused by future climate change drive early (or late) planting decisions. Crop models need an accurate method to predict plant date to allow these models to: 1) capture changes in crop management to adapt to climate change, 2) accurately model the timing of crop phenology, and 3) improve crop simulated influences on carbon, nutrient, energy, and water cycles. Previous studies have used climate as a predictor for planting date. Climate as a plant date predictor has more advantages than fixed plant dates. For example, crop expansion and other changes in land use (e.g., due to changing temperature conditions), can be accommodated without additional model inputs. As such, a new methodology to implement a predictive planting date based on climate inputs is added to the Accelerated Climate Model for Energy (ACME) Land Model (ALM). The model considers two main sources of climate data important for planting: precipitation and temperature. This method expands the current temperature threshold planting trigger and improves the estimated plant date in ALM. Furthermore, the precipitation metric for planting, which synchronizes the crop growing season with the wettest months, allows tropical crops to be introduced to the model. This presentation will demonstrate how the improved model enhances the ability of ALM to capture planting date compared with observations. More importantly, the impact of changing the planting date and introducing tropical crops will be explored. Those impacts include discussions on productivity, yield, and influences on carbon and energy fluxes.

Characterizing Learning Environments Capable of Nurturing Generic Capabilities in Higher Education

ERIC Educational Resources Information Center

Kember, David; Leung, Doris Y. P.; Ma, Rosa S. F.

2007-01-01

There has been wide recognition that today's graduates need the type of generic capabilities necessary for lifelong learning. However, the mechanism by which universities can develop these generic skills is not clearly established. This study aimed to investigate the mechanism for their development. Structural equation modeling (SEM) was used to…

Evaluation of the Community Land Model (CLM-Crop) in the United States Corn Belt

NASA Astrophysics Data System (ADS)

Chen, M.; Griffis, T.

2013-12-01

An accurate representation of crop phenology in land surface models is crucial for predicting the carbon, water and energy budgets of managed ecosystems. Soybean and corn are cultivated in approximately 600,000 km2 in the Corn Belt- an area greater than the entire State of California. Accurate prediction of the radiation, energy, and carbon budgets of this region is especially important for understanding its influence on radiative forcing, the thermodynamic properties of the atmospheric boundary layer, and changes in climate. Recently, key algorithms describing crop biophysics and interactive crop management (planting, fertilization, irrigation, harvesting) have been implemented in the Community Land Model (CLM-Crop). CLM-Crop provides a framework for prognostic simulation of crop phenology and evaluation of human management decisions under future climate scenarios. However, there is an important need to evaluate CLM-Crop against a broad range of agricultural site observations in order to understand its limitations and to help optimize the crop biophysical parameterization. Here we evaluated CLM-Crop version 4.5 at 9 AmeriFlux corn/soybean sites that are located within the United States Corn Belt. The following questions were addressed: 1) How well does CLM perform for the 9 crop sites with different management techniques (e.g., tillage vs. no-till, rainfed vs. irrigated)? 2) What are the model's strengths and weaknesses of simulating crop phenology, energy fluxes and carbon fluxes? 3) What steps are needed in order to improve the reliability of the CLM-Crop simulations? Our preliminary results indicate that CLM-Crop can simulate the radiation, energy, and carbon fluxes with reasonable accuracy during the mid growing season. The model performance degrades substantially during the early and late growing seasons, which we attribute to a bias in crop phenology. For instance, we observed that the simulated corn and soybean phenology (LAI) has an earlier phase than the observations by about 15 days at many sites. Here, we show how the optimization of carbon allocation and crop phenology influences the modeled radiation, energy, and carbon fluxes and discuss other model deficiencies associated with the crop biophysics scheme.

Forecasting the (un)productivity of the 2014 M 6.0 South Napa aftershock sequence

USGS Publications Warehouse

Llenos, Andrea L.; Michael, Andrew J.

2017-01-01

The 24 August 2014 Mw 6.0 South Napa mainshock produced fewer aftershocks than expected for a California earthquake of its magnitude. In the first 4.5 days, only 59 M≥1.8 aftershocks occurred, the largest of which was an M 3.9 that happened a little over two days after the mainshock. We investigate the aftershock productivity of the South Napa sequence and compare it with other M≥5.5 California strike‐slip mainshock–aftershock sequences. While the productivity of the South Napa sequence is among the lowest, northern California mainshocks generally have fewer aftershocks than mainshocks further south, although the productivities vary widely in both regions. An epidemic‐type aftershock sequence (ETAS) model (Ogata, 1988) fit to Napa seismicity from 1980 to 23 August 2014 fits the sequence well and suggests that low‐productivity sequences are typical of this area. Utilizing regional variations in productivity could improve operational earthquake forecasting (OEF) by improving the model used immediately after the mainshock. We show this by comparing the daily rate of M≥2 aftershocks to forecasts made with the generic California model (Reasenberg and Jones, 1989; hereafter, RJ89), RJ89 models with productivity updated daily, a generic California ETAS model, an ETAS model based on premainshock seismicity, and ETAS models updated daily following the mainshock. RJ89 models for which only the productivity is updated provide better forecasts than the generic RJ89 California model, and the Napa‐specific ETAS models forecast the aftershock rates more accurately than either generic model. Therefore, forecasts that use localized initial parameters and that rapidly update the productivity may be better for OEF than using a generic model and/or updating all parameters.

A generic open-source toolbox to help long term irrigation monitoring for integrated water management in semi-arid Mediterranean areas.

NASA Astrophysics Data System (ADS)

Le Page, Michel; Gosset, Cindy; Oueslati, Ines; Calvez, Roger; Zribi, Mehrez; Lili Chabaane, Zohra

2016-04-01

In semi arid areas, irrigated plains are often the major consumer of water well beyond other water demands. Traditionally fed by surface water, irrigation has massively shifted to a more reliable resource: groundwater. This shift occurred in the late thirty years has also provoked an extension and intensification of irrigation, often translated into impressive groundwater table decreases. Integrated water management needs a systematic and robust way to estimate the water demands by the agricultural sector. We propose a generic toolbox based on the FAO-56 method and the Crop Coefficient/NDVI approach used in Remote Sensing. The toolbox can be separated in three main areas: 1) It facilitates the preparation of different input datasets: download, domain extraction, homogenization of formats, or spatial interpolation. 2) A collection of algorithms based on the analysis of NDVI time series is proposed: Separation of irrigated vs non-irrigated area, a simplified annual land cover classification, Crop Coefficient, Fraction Cover and Efficient Rainfall. 3) Synthesis against points or areas produces the output data at the desired spatial and temporal resolution for Integrated Water Modeling or data analysis and comparison. The toolbox has been used in order to build a WEAP21 model of the Merguellil basin in Tunisia for the period of 2000-2014. Different meteorological forcings were easily used and compared: WFDEI, AGRI4CAST, MED-CORDEX. A local rain gauges database was used to produce a daily rainfall gridded dataset. MODIS MOD13Q1 (16 days, 250m) data was used to produce the NDVI derived datasets (Kc, Fc, RainEff). Punctual evapotranspiration was compared to actual measurements obtained by flux towers on wheat and barley showing good agreements on a daily basis (r2=0.77). Finally, the comparison to monthly statistics of three irrigated commands was performed over 4 years. This late comparison showed a bad agreement which led us to suppose two things: First, the simple approach of (Evapotranspiration minus Efficient Rainfall) to estimate Irrigation at the monthly time step is not pertinent because only a subset of the irrigated commands is actually irrigated. Hence, a higher spatial resolution of remote sensing imagery is needed. Second, in this particular area, farmers have a different rationale about rainfall and irrigation water needs. Those two aspects need to be further investigated. The toolbox has proven to be an interesting tool to integrate different sources of data, efficiently process them and easily produce input data for the WEAP1 model on a long term range. Yet some new challenges have been raised.

Estimating nitrogen mineralization from cover crop mixtures using the Precision Nitrogen Management model

USDA-ARS?s Scientific Manuscript database

Cover crops influence soil nitrogen (N) mineralization-immobilization-turnover cycles (MIT), thus influencing N availability to a subsequent crop. Dynamic simulation models of the soil/crop system, if properly calibrated and tested, can simulate carbon (C) and N dynamics of a terminated cover crop a...

AquaCrop-OS: A tool for resilient management of land and water resources in agriculture

NASA Astrophysics Data System (ADS)

Foster, Timothy; Brozovic, Nicholas; Butler, Adrian P.; Neale, Christopher M. U.; Raes, Dirk; Steduto, Pasquale; Fereres, Elias; Hsiao, Theodore C.

2017-04-01

Water managers, researchers, and other decision makers worldwide are faced with the challenge of increasing food production under population growth, drought, and rising water scarcity. Crop simulation models are valuable tools in this effort, and, importantly, provide a means of quantifying rapidly crop yield response to water, climate, and field management practices. Here, we introduce a new open-source crop modelling tool called AquaCrop-OS (Foster et al., 2017), which extends the functionality of the globally used FAO AquaCrop model. Through case studies focused on groundwater-fed irrigation in the High Plains and Central Valley of California in the United States, we demonstrate how AquaCrop-OS can be used to understand the local biophysical, behavioural, and institutional drivers of water risks in agricultural production. Furthermore, we also illustrate how AquaCrop-OS can be combined effectively with hydrologic and economic models to support drought risk mitigation and decision-making around water resource management at a range of spatial and temporal scales, and highlight future plans for model development and training. T. Foster, et al. (2017) AquaCrop-OS: An open source version of FAO's crop water productivity model. Agricultural Water Management. 181: 18-22. http://dx.doi.org/10.1016/j.agwat.2016.11.015.

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;

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.

Estimating the impact of mineral aerosols on crop yields in food insecure regions using statistical crop models

NASA Astrophysics Data System (ADS)

Hoffman, A.; Forest, C. E.; Kemanian, A.

2016-12-01

A significant number of food-insecure nations exist in regions of the world where dust plays a large role in the climate system. While the impacts of common climate variables (e.g. temperature, precipitation, ozone, and carbon dioxide) on crop yields are relatively well understood, the impact of mineral aerosols on yields have not yet been thoroughly investigated. This research aims to develop the data and tools to progress our understanding of mineral aerosol impacts on crop yields. Suspended dust affects crop yields by altering the amount and type of radiation reaching the plant, modifying local temperature and precipitation. While dust events (i.e. dust storms) affect crop yields by depleting the soil of nutrients or by defoliation via particle abrasion. The impact of dust on yields is modeled statistically because we are uncertain which impacts will dominate the response on national and regional scales considered in this study. Multiple linear regression is used in a number of large-scale statistical crop modeling studies to estimate yield responses to various climate variables. In alignment with previous work, we develop linear crop models, but build upon this simple method of regression with machine-learning techniques (e.g. random forests) to identify important statistical predictors and isolate how dust affects yields on the scales of interest. To perform this analysis, we develop a crop-climate dataset for maize, soybean, groundnut, sorghum, rice, and wheat for the regions of West Africa, East Africa, South Africa, and the Sahel. Random forest regression models consistently model historic crop yields better than the linear models. In several instances, the random forest models accurately capture the temperature and precipitation threshold behavior in crops. Additionally, improving agricultural technology has caused a well-documented positive trend that dominates time series of global and regional yields. This trend is often removed before regression with traditional crop models, but likely at the cost of removing climate information. Our random forest models consistently discover the positive trend without removing any additional data. The application of random forests as a statistical crop model provides insight into understanding the impact of dust on yields in marginal food producing regions.

Baldrige Theory into Practice: A Generic Model

ERIC Educational Resources Information Center

Arif, Mohammed

2007-01-01

Purpose: The education system globally has moved from a push-based or producer-centric system to a pull-based or customer centric system. Malcolm Baldrige Quality Award (MBQA) model happens to be one of the latest additions to the pull based models. The purpose of this paper is to develop a generic framework for MBQA that can be used by…

Developing Social Competence and Other Generic Skills in Teacher Education: Applying the Model of Integrative Pedagogy

ERIC Educational Resources Information Center

Tynjälä, Päivi; Virtanen, Anne; Klemola, Ulla; Kostiainen, Emma; Rasku-Puttonen, Helena

2016-01-01

The purpose of the study was to examine how social competence and other generic skills can be developed in teacher education using a pedagogical model called Integrative Pedagogy. This model is based on the idea of integrating the four basic components of expertise: Theoretical knowledge, practical knowledge, self-regulative knowledge, and…

Generic Sensor Failure Modeling for Cooperative Systems.

PubMed

Jäger, Georg; Zug, Sebastian; Casimiro, António

2018-03-20

The advent of cooperative systems entails a dynamic composition of their components. As this contrasts current, statically composed systems, new approaches for maintaining their safety are required. In that endeavor, we propose an integration step that evaluates the failure model of shared information in relation to an application's fault tolerance and thereby promises maintainability of such system's safety. However, it also poses new requirements on failure models, which are not fulfilled by state-of-the-art approaches. Consequently, this work presents a mathematically defined generic failure model as well as a processing chain for automatically extracting such failure models from empirical data. By examining data of an Sharp GP2D12 distance sensor, we show that the generic failure model not only fulfills the predefined requirements, but also models failure characteristics appropriately when compared to traditional techniques.

Generic Sensor Failure Modeling for Cooperative Systems

PubMed Central

Jäger, Georg; Zug, Sebastian

2018-01-01

The advent of cooperative systems entails a dynamic composition of their components. As this contrasts current, statically composed systems, new approaches for maintaining their safety are required. In that endeavor, we propose an integration step that evaluates the failure model of shared information in relation to an application’s fault tolerance and thereby promises maintainability of such system’s safety. However, it also poses new requirements on failure models, which are not fulfilled by state-of-the-art approaches. Consequently, this work presents a mathematically defined generic failure model as well as a processing chain for automatically extracting such failure models from empirical data. By examining data of an Sharp GP2D12 distance sensor, we show that the generic failure model not only fulfills the predefined requirements, but also models failure characteristics appropriately when compared to traditional techniques. PMID:29558435

Evaluating gridded crop model simulations of evapotranspiration and irrigation using survey and remotely sensed data

NASA Astrophysics Data System (ADS)

Lopez Bobeda, J. R.

2017-12-01

The increasing use of groundwater for irrigation of crops has exacerbated groundwater sustainability issues faced by water limited regions. Gridded, process-based crop models have the potential to help farmers and policymakers asses the effects water shortages on yield and devise new strategies for sustainable water use. Gridded crop models are typically calibrated and evaluated using county-level survey data of yield, planting dates, and maturity dates. However, little is known about the ability of these models to reproduce observed crop evapotranspiration and water use at regional scales. The aim of this work is to evaluate a gridded version of the Decision Support System for Agrotechnology Transfer (DSSAT) crop model over the continental United States. We evaluated crop seasonal evapotranspiration over 5 arc-minute grids, and irrigation water use at the county level. Evapotranspiration was assessed only for rainfed agriculture to test the model evapotranspiration equations separate from the irrigation algorithm. Model evapotranspiration was evaluated against the Atmospheric Land Exchange Inverse (ALEXI) modeling product. Using a combination of the USDA crop land data layer (CDL) and the USGS Moderate Resolution Imaging Spectroradiometer Irrigated Agriculture Dataset for the United States (MIrAD-US), we selected only grids with more than 60% of their area planted with the simulated crops (corn, cotton, and soybean), and less than 20% of their area irrigated. Irrigation water use was compared against the USGS county level irrigated agriculture water use survey data. Simulated gridded data were aggregated to county level using USDA CDL and USGS MIrAD-US. Only counties where 70% or more of the irrigated land was corn, cotton, or soybean were selected for the evaluation. Our results suggest that gridded crop models can reasonably reproduce crop evapotranspiration at the country scale (RRMSE = 10%).

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

NASA Astrophysics Data System (ADS)

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

2016-08-01

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

Cost-effectiveness analysis of valsartan versus losartan and the effect of switching.

PubMed

Baker, Timothy M; Goh, Jowern; Johnston, Atholl; Falvey, Heather; Brede, Yvonne; Brown, Ruth E

2012-01-01

Losartan will shortly become generic, and this may encourage switching to the generic drug. However, valsartan was shown in a meta-analysis to be statistically superior in lowering blood pressure (BP) to losartan. This paper examines the costs of treatment with these two drugs and the potential consequences of switching established valsartan patients to generic losartan. A US payer cost-effectiveness model was developed incorporating the risk of cardiovascular disease (CVD) events related to systolic blood pressure (SBP) control comparing valsartan to continual losartan and switching from valsartan to generic losartan. The model, based upon a meta-analysis by Nixon et al. and Framingham equations, included first CVD event costs calculated from US administrative data sets and utility values from published sources. The modeled outcomes were number of CVD events, costs and incremental cost per quality-adjusted life-year (QALY) and life-year (LY). Fewer patients had fatal and non-fatal CVD events with valsartan therapy compared with continual losartan and with patients switched from valsartan to generic losartan. The base-case model results indicated that continued treatment with valsartan had an incremental cost-effectiveness ratio of $27,268 and $25,460 per life year gained, and $32,313 and $30,170 per QALY gained, relative to continual losartan and switching treatments, respectively. Sensitivity analyses found that patient discontinuation post-switching was a sensitive parameter. Including efficacy offsets with lowered adherence or discontinuation resulted in more favorable ratios for valsartan compared to switching therapy. The model does not evaluate post-primary CVD events and considers change in SBP from baseline level as the sole predictor of CVD risk. Valsartan appears to be cost-effective compared to switching to generic losartan and switching to the generic drug does not support a cost offset argument over the longer term. Physicians should continue to consider the needs of individual patient and not cost offsets.

Rocketdyne/Westinghouse nuclear thermal rocket engine modeling

NASA Technical Reports Server (NTRS)

Glass, James F.

1993-01-01

The topics are presented in viewgraph form and include the following: systems approach needed for nuclear thermal rocket (NTR) design optimization; generic NTR engine power balance codes; rocketdyne nuclear thermal system code; software capabilities; steady state model; NTR engine optimizer code-logic; reactor power calculation logic; sample multi-component configuration; NTR design code output; generic NTR code at Rocketdyne; Rocketdyne NTR model; and nuclear thermal rocket modeling directions.

Using peer-assisted learning and role-playing to teach generic skills to dental students: the health care simulation model.

PubMed

El Tantawi, Maha M A; Abdelaziz, Hytham; AbdelRaheem, Amira S; Mahrous, Ahmed A

2014-01-01

Increasing importance is attached to teaching generic skills to undergraduate students in various disciplines. This article describes an extracurricular, student-led activity for teaching generic skills using the Model United Nations over three months. The activity used the Health Care Simulation Model (HCSM) with peer learning and role-playing to accomplish its objectives. An interview was used to select from undergraduate and postgraduate dental students at Alexandria University, Alexandria, Egypt, to develop a group of staff to act as peer teachers after receiving training (n=77). These peer teachers provided training for 123 undergraduate dental students to serve as delegates who acted as trainees or peer learners. At the end of the training sessions, a conference was held in which the students played the roles of delegates representing officials responsible for health care systems in ten countries. The students reported improvement in generic skills, enjoyed several aspects of the experience, and disliked other aspects of the model to a lesser extent. In multivariate analysis, perceived usefulness of the HCSM was significantly greater for staff than delegates and increased as self-reported improvement in knowledge of health care systems increased. This study suggests that innovative, student-centered educational methods can be effective for teaching generic skills and factual information.

Evaluating high temporal and spatial resolution vegetation index for crop yield prediction

USDA-ARS?s Scientific Manuscript database

Remote sensing data have been widely used in estimating crop yield. Remote sensing derived parameters such as Vegetation Index (VI) were used either directly in building empirical models or by assimilating with crop growth models to predict crop yield. The abilities of remote sensing VI in crop yiel...

The formal verification of generic interpreters

NASA Technical Reports Server (NTRS)

Windley, P.; Levitt, K.; Cohen, G. C.

1991-01-01

The task assignment 3 of the design and validation of digital flight control systems suitable for fly-by-wire applications is studied. Task 3 is associated with formal verification of embedded systems. In particular, results are presented that provide a methodological approach to microprocessor verification. A hierarchical decomposition strategy for specifying microprocessors is also presented. A theory of generic interpreters is presented that can be used to model microprocessor behavior. The generic interpreter theory abstracts away the details of instruction functionality, leaving a general model of what an interpreter does.

AgMIP Training in Multiple Crop Models and Tools

NASA Technical Reports Server (NTRS)

Boote, Kenneth J.; Porter, Cheryl H.; Hargreaves, John; Hoogenboom, Gerrit; Thornburn, Peter; Mutter, Carolyn

2015-01-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has the goal of using multiple crop models to evaluate climate impacts on agricultural production and food security in developed and developing countries. There are several major limitations that must be overcome to achieve this goal, including the need to train AgMIP regional research team (RRT) crop modelers to use models other than the ones they are currently familiar with, plus the need to harmonize and interconvert the disparate input file formats used for the various models. Two activities were followed to address these shortcomings among AgMIP RRTs to enable them to use multiple models to evaluate climate impacts on crop production and food security. We designed and conducted courses in which participants trained on two different sets of crop models, with emphasis on the model of least experience. In a second activity, the AgMIP IT group created templates for inputting data on soils, management, weather, and crops into AgMIP harmonized databases, and developed translation tools for converting the harmonized data into files that are ready for multiple crop model simulations. The strategies for creating and conducting the multi-model course and developing entry and translation tools are reviewed in this chapter.

Reconstruction of genome-scale human metabolic models using omics data.

PubMed

Ryu, Jae Yong; Kim, Hyun Uk; Lee, Sang Yup

2015-08-01

The impact of genome-scale human metabolic models on human systems biology and medical sciences is becoming greater, thanks to increasing volumes of model building platforms and publicly available omics data. The genome-scale human metabolic models started with Recon 1 in 2007, and have since been used to describe metabolic phenotypes of healthy and diseased human tissues and cells, and to predict therapeutic targets. Here we review recent trends in genome-scale human metabolic modeling, including various generic and tissue/cell type-specific human metabolic models developed to date, and methods, databases and platforms used to construct them. For generic human metabolic models, we pay attention to Recon 2 and HMR 2.0 with emphasis on data sources used to construct them. Draft and high-quality tissue/cell type-specific human metabolic models have been generated using these generic human metabolic models. Integration of tissue/cell type-specific omics data with the generic human metabolic models is the key step, and we discuss omics data and their integration methods to achieve this task. The initial version of the tissue/cell type-specific human metabolic models can further be computationally refined through gap filling, reaction directionality assignment and the subcellular localization of metabolic reactions. We review relevant tools for this model refinement procedure as well. Finally, we suggest the direction of further studies on reconstructing an improved human metabolic model.

The development, evaluation, and application of O3 flux and flux-response models for additional agricultural crops

Treesearch

L. D. Emberson; W. J. Massman; P. Buker; G. Soja; I. Van De Sand; G. Mills; C. Jacobs

2006-01-01

Currently, stomatal O3 flux and flux-response models only exist for wheat and potato (LRTAP Convention, 2004), as such there is a need to extend these models to include additional crop types. The possibility of establishing robust stomatal flux models for five agricultural crops (tomato, grapevine, sugar beet, maize and sunflower) was investigated. These crops were...

Uncertainty of Wheat Water Use: Simulated Patterns and Sensitivity to Temperature and CO2

NASA Technical Reports Server (NTRS)

Cammarano, Davide; Roetter, Reimund P.; Asseng, Senthold; Ewert, Frank; Wallach, Daniel; Martre, Pierre; Hatfield, Jerry L.; Jones, James W.; Rosenzweig, Cynthia E.; Ruane, Alex C.;

Using Bayesian Networks to Improve Knowledge Assessment

ERIC Educational Resources Information Center

Millan, Eva; Descalco, Luis; Castillo, Gladys; Oliveira, Paula; Diogo, Sandra

2013-01-01

In this paper, we describe the integration and evaluation of an existing generic Bayesian student model (GBSM) into an existing computerized testing system within the Mathematics Education Project (PmatE--Projecto Matematica Ensino) of the University of Aveiro. This generic Bayesian student model had been previously evaluated with simulated…

Embedding Academic Literacy Skills: Towards a Best Practice Model

ERIC Educational Resources Information Center

McWilliams, Robyn; Allan, Quentin

2014-01-01

Learning advisors provide academic literacy development support in a variety of configurations, ranging from one-on-one consultations through to large-scale lectures. Such lectures can be generic, stand-alone modules or embedded within a discipline-specific course. Pragmatic and institutional considerations suggest that a generic model of delivery…

Leaf photosynthesis and respiration of three bioenergy crops in relation to temperature and leaf nitrogen: how conserved are biochemical model parameters among crop species?

PubMed Central

Archontoulis, S. V.; Yin, X.; Vos, J.; Danalatos, N. G.; Struik, P. C.

2012-01-01

Given the need for parallel increases in food and energy production from crops in the context of global change, crop simulation models and data sets to feed these models with photosynthesis and respiration parameters are increasingly important. This study provides information on photosynthesis and respiration for three energy crops (sunflower, kenaf, and cynara), reviews relevant information for five other crops (wheat, barley, cotton, tobacco, and grape), and assesses how conserved photosynthesis parameters are among crops. Using large data sets and optimization techniques, the C3 leaf photosynthesis model of Farquhar, von Caemmerer, and Berry (FvCB) and an empirical night respiration model for tested energy crops accounting for effects of temperature and leaf nitrogen were parameterized. Instead of the common approach of using information on net photosynthesis response to CO2 at the stomatal cavity (An–Ci), the model was parameterized by analysing the photosynthesis response to incident light intensity (An–Iinc). Convincing evidence is provided that the maximum Rubisco carboxylation rate or the maximum electron transport rate was very similar whether derived from An–Ci or from An–Iinc data sets. Parameters characterizing Rubisco limitation, electron transport limitation, the degree to which light inhibits leaf respiration, night respiration, and the minimum leaf nitrogen required for photosynthesis were then determined. Model predictions were validated against independent sets. Only a few FvCB parameters were conserved among crop species, thus species-specific FvCB model parameters are needed for crop modelling. Therefore, information from readily available but underexplored An–Iinc data should be re-analysed, thereby expanding the potential of combining classical photosynthetic data and the biochemical model. PMID:22021569

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

PubMed Central

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

2016-01-01

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

Impact of medicare part D plan features on use of generic drugs.

PubMed

Tang, Yan; Gellad, Walid F; Men, Aiju; Donohue, Julie M

2014-06-01

Little is known about how Medicare Part D plan features influence choice of generic versus brand drugs. To examine the association between Part D plan features and generic medication use. Data from a 2009 random sample of 1.6 million fee-for-service, Part D enrollees aged 65 years and above, who were not dually eligible or receiving low-income subsidies, were used to examine the association between plan features (generic cost-sharing, difference in brand and generic copay, prior authorization, step therapy) and choice of generic antidepressants, antidiabetics, and statins. Logistic regression models accounting for plan-level clustering were adjusted for sociodemographic and health status. Generic cost-sharing ranged from $0 to $9 for antidepressants and statins, and from $0 to $8 for antidiabetics (across 5th-95th percentiles). Brand-generic cost-sharing differences were smallest for statins (5th-95th percentiles: $16-$37) and largest for antidepressants ($16-$64) across plans. Beneficiaries with higher generic cost-sharing had lower generic use [adjusted odds ratio (OR)=0.97, 95% confidence interval (CI), 0.95-0.98 for antidepressants; OR=0.97, 95% CI, 0.96-0.98 for antidiabetics; OR=0.94, 95% CI, 0.92-0.95 for statins]. Larger brand-generic cost-sharing differences and prior authorization were significantly associated with greater generic use in all categories. Plans could increase generic use by 5-12 percentage points by reducing generic cost-sharing from the 75th ($7) to 25th percentiles ($4-$5), increasing brand-generic cost-sharing differences from the 25th ($25-$26) to 75th ($32-$33) percentiles, and using prior authorization and step therapy. Cost-sharing features and utilization management tools were significantly associated with generic use in 3 commonly used medication categories.

Noah-MP-Crop: Enhancing cropland representation in the community land surface modeling system

NASA Astrophysics Data System (ADS)

Liu, X.; Chen, F.; Barlage, M. J.; Zhou, G.; Niyogi, D.

2015-12-01

Croplands are important in land-atmosphere interactions and in modifying local and regional weather and climate. Despite their importance, croplands are poorly represented in the current version of the coupled Weather Research and Forecasting (WRF)/ Noah land-surface modeling system, resulting in significant surface temperature and humidity biases across agriculture- dominated regions of the United States. This study aims to improve the WRF weather forecasting and regional climate simulations during the crop growing season by enhancing the representation of cropland in the Noah-MP land model. We introduced dynamic crop growth parameterization into Noah-MP and evaluated the enhanced model (Noah-MP-Crop) at both the field and regional scales with multiple crop biomass datasets, surface fluxes and soil moisture/temperature observations. We also integrated a detailed cropland cover map into WRF, enabling the model to simulate corn and soybean field across the U.S. Great Plains. Results show marked improvement in the Noah-MP-Crop performance in simulating leaf area index (LAI), crop biomass, soil temperature, and surface fluxes. Enhanced cropland representation is not only crucial for improving weather forecasting but can also help assess potential impacts of weather variability on regional hydrometeorology and crop yields. In addition to its applications to WRF, Noah-MP-Crop can be applied in high-spatial-resolution regional crop yield modeling and drought assessments

A Generic Evaluation Model for Semantic Web Services

NASA Astrophysics Data System (ADS)

Shafiq, Omair

Semantic Web Services research has gained momentum over the last few Years and by now several realizations exist. They are being used in a number of industrial use-cases. Soon software developers will be expected to use this infrastructure to build their B2B applications requiring dynamic integration. However, there is still a lack of guidelines for the evaluation of tools developed to realize Semantic Web Services and applications built on top of them. In normal software engineering practice such guidelines can already be found for traditional component-based systems. Also some efforts are being made to build performance models for servicebased systems. Drawing on these related efforts in component-oriented and servicebased systems, we identified the need for a generic evaluation model for Semantic Web Services applicable to any realization. The generic evaluation model will help users and customers to orient their systems and solutions towards using Semantic Web Services. In this chapter, we have presented the requirements for the generic evaluation model for Semantic Web Services and further discussed the initial steps that we took to sketch such a model. Finally, we discuss related activities for evaluating semantic technologies.

Consideration in selecting crops for the human-rated life support system: a Linear Programming model

NASA Technical Reports Server (NTRS)

Wheeler, E. F.; Kossowski, J.; Goto, E.; Langhans, R. W.; White, G.; Albright, L. D.; Wilcox, D.; Henninger, D. L. (Principal Investigator)

1996-01-01

A Linear Programming model has been constructed which aids in selecting appropriate crops for CELSS (Controlled Environment Life Support System) food production. A team of Controlled Environment Agriculture (CEA) faculty, staff, graduate students and invited experts representing more than a dozen disciplines, provided a wide range of expertise in developing the model and the crop production program. The model incorporates nutritional content and controlled-environment based production yields of carefully chosen crops into a framework where a crop mix can be constructed to suit the astronauts' needs. The crew's nutritional requirements can be adequately satisfied with only a few crops (assuming vitamin mineral supplements are provided) but this will not be satisfactory from a culinary standpoint. This model is flexible enough that taste and variety driven food choices can be built into the model.

Consideration in selecting crops for the human-rated life support system: a linear programming model

NASA Astrophysics Data System (ADS)

Wheeler, E. F.; Kossowski, J.; Goto, E.; Langhans, R. W.; White, G.; Albright, L. D.; Wilcox, D.

A Linear Programming model has been constructed which aids in selecting appropriate crops for CELSS (Controlled Environment Life Support System) food production. A team of Controlled Environment Agriculture (CEA) faculty, staff, graduate students and invited experts representing more than a dozen disciplines, provided a wide range of expertise in developing the model and the crop production program. The model incorporates nutritional content and controlled-environment based production yields of carefully chosen crops into a framework where a crop mix can be constructed to suit the astronauts' needs. The crew's nutritional requirements can be adequately satisfied with only a few crops (assuming vitamin mineral supplements are provided) but this will not be satisfactory from a culinary standpoint. This model is flexible enough that taste and variety driven food choices can be built into the model.

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.;

Performance degradation of a model helicopter rotor with a generic ice shape

NASA Technical Reports Server (NTRS)

Korkan, K. D.; Cross, E. J., Jr.; Miller, T. L.

1984-01-01

An experimental program using a commercially available remotely controlled model helicopter in the Texas A&M University (TAMU) subsonic wind tunnel has been conducted to investigate the performance degradation resulting from the simulated formation of ice on the leading edge of the main rotor blades in both hover and forward flight. The rotor blades utilized a NACA 0012 airfoil with a 2.5-in. constant chord. A generic ice shape derived from a predetermined natural ice condition was applied to the 53.375-in.-diameter main rotor, and thrust and torque coefficients were measured for the main rotor as functions of velocity, main rotor rpm, fuselage angle of incidence, collective pitch angle, and spanwise extent of icing. The model helicopter test exhibited significant performance degradation of the main rotor when generic ice was added. An increase of approximately 150 percent in torque coefficient to maintain a constant thrust coefficient was noted when generic ice had been applied to the 85 percent rotor radial location. Also, considerable additional degradation occurred when generic ice was applied to the 100 percent rotor radial location, as compared with the 85 percent simulated ice performance values, indicating the sensitivity of the rotor tip region.

Investigation of an alternative generic model for predicting pharmacokinetic changes during physiological stress.

PubMed

Peng, Henry T; Edginton, Andrea N; Cheung, Bob

2013-10-01

Physiologically based pharmacokinetic models were developed using MATLAB Simulink® and PK-Sim®. We compared the capability and usefulness of these two models by simulating pharmacokinetic changes of midazolam under exercise and heat stress to verify the usefulness of MATLAB Simulink® as a generic PBPK modeling software. Although both models show good agreement with experimental data obtained under resting condition, their predictions of pharmacokinetics changes are less accurate in the stressful conditions. However, MATLAB Simulink® may be more flexible to include physiologically based processes such as oral absorption and simulate various stress parameters such as stress intensity, duration and timing of drug administration to improve model performance. Further work will be conducted to modify algorithms in our generic model developed using MATLAB Simulink® and to investigate pharmacokinetics under other physiological stress such as trauma. © The Author(s) 2013.

Contribution of Crop Models to Adaptation in Wheat.

PubMed

Chenu, Karine; Porter, John Roy; Martre, Pierre; Basso, Bruno; Chapman, Scott Cameron; Ewert, Frank; Bindi, Marco; Asseng, Senthold

2017-06-01

With world population growing quickly, agriculture needs to produce more with fewer inputs while being environmentally friendly. In a context of changing environments, crop models are useful tools to simulate crop yields. Wheat (Triticum spp.) crop models have been evolving since the 1960s to translate processes related to crop growth and development into mathematical equations. These have been used over decades for agronomic purposes, and have more recently incorporated advances in the modeling of environmental footprints, biotic constraints, trait and gene effects, climate change impact, and the upscaling of global change impacts. This review outlines the potential and limitations of modern wheat crop models in assisting agronomists, breeders, and policymakers to address the current and future challenges facing agriculture. Copyright © 2017 Elsevier Ltd. All rights reserved.

Satellite passive microwave detection of surface water inundation changes over the pan-Arctic from AMSR

NASA Astrophysics Data System (ADS)

Du, J.; Kimball, J. S.; Jones, L. A.; Watts, J. D.

2016-12-01

Climate is one of the key drivers of crop suitability and productivity in a region. The influence of climate and weather on the growing season determine the amount of time crops spend in each growth phase, which in turn impacts productivity and, more importantly, yields. Planting date can have a strong influence on yields with earlier planting generally resulting in higher yields, a sensitivity that is also present in some crop models. Furthermore, planting date is already changing and may continue, especially if longer growing seasons caused by future climate change drive early (or late) planting decisions. Crop models need an accurate method to predict plant date to allow these models to: 1) capture changes in crop management to adapt to climate change, 2) accurately model the timing of crop phenology, and 3) improve crop simulated influences on carbon, nutrient, energy, and water cycles. Previous studies have used climate as a predictor for planting date. Climate as a plant date predictor has more advantages than fixed plant dates. For example, crop expansion and other changes in land use (e.g., due to changing temperature conditions), can be accommodated without additional model inputs. As such, a new methodology to implement a predictive planting date based on climate inputs is added to the Accelerated Climate Model for Energy (ACME) Land Model (ALM). The model considers two main sources of climate data important for planting: precipitation and temperature. This method expands the current temperature threshold planting trigger and improves the estimated plant date in ALM. Furthermore, the precipitation metric for planting, which synchronizes the crop growing season with the wettest months, allows tropical crops to be introduced to the model. This presentation will demonstrate how the improved model enhances the ability of ALM to capture planting date compared with observations. More importantly, the impact of changing the planting date and introducing tropical crops will be explored. Those impacts include discussions on productivity, yield, and influences on carbon and energy fluxes.

Impacts of crop rotations on soil organic carbon sequestration

NASA Astrophysics Data System (ADS)

Gobin, Anne; Vos, Johan; Joris, Ingeborg; Van De Vreken, Philippe

2013-04-01

Agricultural land use and crop rotations can greatly affect the amount of carbon sequestered in the soil. We developed a framework for modelling the impacts of crop rotations on soil carbon sequestration at the field scale with test case Flanders. A crop rotation geo-database was constructed covering 10 years of crop rotation in Flanders using the IACS parcel registration (Integrated Administration and Control System) to elicit the most common crop rotation on major soil types in Flanders. In order to simulate the impact of crop cover on carbon sequestration, the Roth-C model was adapted to Flanders' environment and coupled to common crop rotations extracted from the IACS geodatabases and statistical databases on crop yield. Crop allometric models were used to calculate crop residues from common crops in Flanders and subsequently derive stable organic matter fluxes to the soil (REGSOM). The REGSOM model was coupled to Roth-C model was run for 30 years and for all combinations of seven main arable crops, two common catch crops and two common dosages of organic manure. The common crops are winter wheat, winter barley, sugar beet, potato, grain maize, silage maize and winter rapeseed; the catch crops are yellow mustard and Italian ryegrass; the manure dosages are 35 ton/ha cattle slurry and 22 ton/ha pig slurry. Four common soils were simulated: sand, loam, sandy loam and clay. In total more than 2.4 million simulations were made with monthly output of carbon content for 30 years. Results demonstrate that crop cover dynamics influence carbon sequestration for a very large percentage. For the same rotations carbon sequestration is highest on clay soils and lowest on sandy soils. Crop residues of grain maize and winter wheat followed by catch crops contribute largely to the total carbon sequestered. This implies that agricultural policies that impact on agricultural land management influence soil carbon sequestration for a large percentage. The framework is therefore suited for further scenario analysis and impact assessment in order to support agri-environmental policy decisions.

Putting mechanisms into crop production models.

PubMed

Boote, Kenneth J; Jones, James W; White, Jeffrey W; Asseng, Senthold; Lizaso, Jon I

2013-09-01

Crop growth models dynamically simulate processes of C, N and water balance on daily or hourly time-steps to predict crop growth and development and at season-end, final yield. Their ability to integrate effects of genetics, environment and crop management have led to applications ranging from understanding gene function to predicting potential impacts of climate change. The history of crop models is reviewed briefly, and their level of mechanistic detail for assimilation and respiration, ranging from hourly leaf-to-canopy assimilation to daily radiation-use efficiency is discussed. Crop models have improved steadily over the past 30-40 years, but much work remains. Improvements are needed for the prediction of transpiration response to elevated CO₂ and high temperature effects on phenology and reproductive fertility, and simulation of root growth and nutrient uptake under stressful edaphic conditions. Mechanistic improvements are needed to better connect crop growth to genetics and to soil fertility, soil waterlogging and pest damage. Because crop models integrate multiple processes and consider impacts of environment and management, they have excellent potential for linking research from genomics and allied disciplines to crop responses at the field scale, thus providing a valuable tool for deciphering genotype by environment by management effects. © 2013 John Wiley & Sons Ltd.

Generic Competencies in the Education of Engineers: The Case of Engineering Program in a Public University in Mexico

ERIC Educational Resources Information Center

Serna, Alejandra García; Vega, José Luis Arcos; García, Juan José Sevilla; Ruiz, María Amparo Oliveros

2018-01-01

We present an analysis regarding generic skills on engineering program offered in a public state university in Mexico (UABC). The university implemented a new educational model changing rigid programs to flexible programs based on competencies. The goal is to determine generic skills related to the four pillars of learning: learning to do,…

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

Impacts of crop growth dynamics on soil quality at the regional scale

NASA Astrophysics Data System (ADS)

Gobin, Anne

2014-05-01

Agricultural land use and in particular crop growth dynamics can greatly affect soil quality. Both the amount of soil lost from erosion by water and soil organic matter are key indicators for soil quality. The aim was to develop a modelling framework for quantifying the impacts of crop growth dynamics on soil quality at the regional scale with test case Flanders. A framework for modelling the impacts of crop growth on soil erosion and soil organic matter was developed by coupling the dynamic crop cover model REGCROP (Gobin, 2010) to the PESERA soil erosion model (Kirkby et al., 2009) and to the RothC carbon model (Coleman and Jenkinson, 1999). All three models are process-based, spatially distributed and intended as a regional diagnostic tool. A geo-database was constructed covering 10 years of crop rotation in Flanders using the IACS parcel registration (Integrated Administration and Control System). Crop allometric models were developed from variety trials to calculate crop residues for common crops in Flanders and subsequently derive stable organic matter fluxes to the soil. Results indicate that crop growth dynamics and crop rotations influence soil quality for a very large percentage. soil erosion mainly occurs in the southern part of Flanders, where silty to loamy soils and a hilly topography are responsible for soil loss rates of up to 40 t/ha. Parcels under maize, sugar beet and potatoes are most vulnerable to soil erosion. Crop residues of grain maize and winter wheat followed by catch crops contribute most to the total carbon sequestered in agricultural soils. For the same rotations carbon sequestration is highest on clay soils and lowest on sandy soils. This implies that agricultural policies that impact on agricultural land management influence soil quality for a large percentage. The coupled REGCROP-PESERA-ROTHC model allows for quantifying the impact of seasonal and year-to-year crop growth dynamics on soil quality. When coupled to a multi-annual crop rotation database both spatial and temporal analysis becomes possible and allows for decision support at both farm and regional level. The framework is therefore suited for further scenario analysis and impact assessment. The research is funded by the Belgian Science Policy Organisation (Belspo) under contract nr SD/RI/03A.

Simulation of crop yield variability by improved root-soil-interaction modelling

NASA Astrophysics Data System (ADS)

Duan, X.; Gayler, S.; Priesack, E.

2009-04-01

Understanding the processes and factors that govern the within-field variability in crop yield has attached great importance due to applications in precision agriculture. Crop response to environment at field scale is a complex dynamic process involving the interactions of soil characteristics, weather conditions and crop management. The numerous static factors combined with temporal variations make it very difficult to identify and manage the variability pattern. Therefore, crop simulation models are considered to be useful tools in analyzing separately the effects of change in soil or weather conditions on the spatial variability, in order to identify the cause of yield variability and to quantify the spatial and temporal variation. However, tests showed that usual crop models such as CERES-Wheat and CERES-Maize were not able to quantify the observed within-field yield variability, while their performance on crop growth simulation under more homogeneous and mainly non-limiting conditions was sufficent to simulate average yields at the field-scale. On a study site in South Germany, within-field variability in crop growth has been documented since years. After detailed analysis and classification of the soil patterns, two site specific factors, the plant-available-water and the O2 deficiency, were considered as the main causes of the crop growth variability in this field. Based on our measurement of root distribution in the soil profile, we hypothesize that in our case the insufficiency of the applied crop models to simulate the yield variability can be due to the oversimplification of the involved root models which fail to be sensitive to different soil conditions. In this study, the root growth model described by Jones et al. (1991) was adapted by using data of root distributions in the field and linking the adapted root model to the CERES crop model. The ability of the new root model to increase the sensitivity of the CERES crop models to different enviromental conditions was then evaluated by means of comparison of the simualtion results with measured data and by scenario calculations.

Predictors of generic substitution: The role of psychological, sociodemographic, and contextual factors.

PubMed

Drozdowska, Aleksandra; Hermanowski, Tomasz

2016-01-01

Escalating pharmaceutical costs have become a global challenge for both governments and patients. Generic substitution is one way of decreasing these costs. The aim of this study was to investigate factors associated with patients' choice between generic drugs and innovator drugs. The survey was conducted in June 2013, 1000 people from across Poland were chosen as a representative population sample. The outcome (a preference for generics/a preference for innovator pharmaceuticals/no preference) was modeled by multinomial logistic regression, adjusted for several variables describing patients' sensitivity to selected generic features (price, brand, and country of origin), to third-party opinions about generics (information on generics in the mass media, opinions of health professionals (i.e. physicians, pharmacists), relatives/friends), as well as patients' personal experiences and income per household. The results supported the predictive capacity of most independent variables (except for patient sensitivity to the country of origin and to the information on generics in the mass media), denoting patients' preferences toward generic substitution. Patient sensitivity to recommendations by physicians, generic brand, and household income were the strongest predictors of the choice between generic and innovator pharmaceuticals (P < 0.001). The probability of choosing generics over innovator drugs was significantly higher among respondents with the lowest income levels, in those who were indifferent to generic brand or their physician's opinion, as well as in respondents who were sensitive to recommendations by pharmacists or attached a greater value to a past experience with generics (their own experience or that of relatives/friends). In consideration of the foregoing, awareness-raising campaigns may be recommended, supported by a variety of systemic solutions and tools to encourage generic substitution. Copyright © 2016 Elsevier Inc. All rights reserved.

Comparison of Standard Wind Turbine Models with Vendor Models for Power System Stability Analysis: Preprint

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

Honrubia-Escribano, A.; Gomez Lazaro, E.; Jimenez-Buendia, F.

The International Electrotechnical Commission Standard 61400-27-1 was published in February 2015. This standard deals with the development of generic terms and parameters to specify the electrical characteristics of wind turbines. Generic models of very complex technological systems, such as wind turbines, are thus defined based on the four common configurations available in the market. Due to its recent publication, the comparison of the response of generic models with specific vendor models plays a key role in ensuring the widespread use of this standard. This paper compares the response of a specific Gamesa dynamic wind turbine model to the corresponding genericmore » IEC Type III wind turbine model response when the wind turbine is subjected to a three-phase voltage dip. This Type III model represents the doubly-fed induction generator wind turbine, which is not only one of the most commonly sold and installed technologies in the current market but also a complex variable-speed operation implementation. In fact, active and reactive power transients are observed due to the voltage reduction. Special attention is given to the reactive power injection provided by the wind turbine models because it is a requirement of current grid codes. Further, the boundaries of the generic models associated with transient events that cannot be represented exactly are included in the paper.« less

Reconciling the Mitscherlich's law of diminishing returns with Liebig's law of the minimum. Some results on crop modeling.

PubMed

Ferreira, Iuri E P; Zocchi, Silvio S; Baron, Daniel

2017-11-01

Reliable fertilizer recommendations depend on the correctness of the crop production models fitted to the data, but generally the crop models are built empirically, neglecting important physiological aspects related with response to fertilizers, or they are based in laws of plant mineral nutrition seen by many authors as conflicting theories: the Liebig's Law of the Minimum and Mitscherlich's Law of Diminishing Returns. We developed a new approach to modelling the crop response to fertilizers that reconcile these laws. In this study, the Liebig's Law is applied at the cellular level to explain plant production and, as a result, crop models compatible with the Law of Diminishing Returns are derived. Some classical crop models appear here as special cases of our methodology, and a new interpretation for Mitscherlich's Law is also provided. Copyright © 2017 Elsevier Inc. All rights reserved.

The Challenge of Improving Soil Fertility in Yam Cropping Systems of West Africa

PubMed Central

Frossard, Emmanuel; Aighewi, Beatrice A.; Aké, Sévérin; Barjolle, Dominique; Baumann, Philipp; Bernet, Thomas; Dao, Daouda; Diby, Lucien N.; Floquet, Anne; Hgaza, Valérie K.; Ilboudo, Léa J.; Kiba, Delwende I.; Mongbo, Roch L.; Nacro, Hassan B.; Nicolay, Gian L.; Oka, Esther; Ouattara, Yabile F.; Pouya, Nestor; Senanayake, Ravinda L.; Six, Johan; Traoré, Orokya I.

2017-01-01

Yam (Dioscorea spp.) is a tuber crop grown for food security, income generation, and traditional medicine. This crop has a high cultural value for some of the groups growing it. Most of the production comes from West Africa where the increased demand has been covered by enlarging cultivated surfaces while the mean yield remained around 10 t tuber ha−1. In West Africa, yam is traditionally cultivated without input as the first crop after a long-term fallow as it is considered to require a high soil fertility. African soils, however, are being more and more degraded. The aims of this review were to show the importance of soil fertility for yam, discuss barriers that might limit the adoption of integrated soil fertility management (ISFM) in yam-based systems in West Africa, present the concept of innovation platforms (IPs) as a tool to foster collaboration between actors for designing innovations in yam-based systems and provide recommendations for future research. This review shows that the development of sustainable, feasible, and acceptable soil management innovations for yam requires research to be conducted in interdisciplinary teams including natural and social sciences and in a transdisciplinary manner involving relevant actors from the problem definition, to the co-design of soil management innovations, the evaluation of research results, their communication and their implementation. Finally, this research should be conducted in diverse biophysical and socio-economic settings to develop generic rules on soil/plant relationships in yam as affected by soil management and on how to adjust the innovation supply to specific contexts. PMID:29209341

Is current irrigation sustainable in the United States? An integrated assessment of climate change impact on water resources and irrigated crop yields

NASA Astrophysics Data System (ADS)

Blanc, Elodie; Caron, Justin; Fant, Charles; Monier, Erwan

2017-08-01

While climate change impacts on crop yields has been extensively studied, estimating the impact of water shortages on irrigated crop yields is challenging because the water resources management system is complex. To investigate this issue, we integrate a crop yield reduction module and a water resources model into the MIT Integrated Global System Modeling framework, an integrated assessment model linking a global economic model to an Earth system model. We assess the effects of climate and socioeconomic changes on water availability for irrigation in the U.S. as well as subsequent impacts on crop yields by 2050, while accounting for climate change projection uncertainty. We find that climate and socioeconomic changes will increase water shortages and strongly reduce irrigated yields for specific crops (i.e., cotton and forage), or in specific regions (i.e., the Southwest) where irrigation is not sustainable. Crop modeling studies that do not represent changes in irrigation availability can thus be misleading. Yet, since the most water-stressed basins represent a relatively small share of U.S. irrigated areas, the overall reduction in U.S. crop yields is small. The response of crop yields to climate change and water stress also suggests that some level of adaptation will be feasible, like relocating croplands to regions with sustainable irrigation or switching to less irrigation intensive crops. Finally, additional simulations show that greenhouse gas (GHG) mitigation can alleviate the effect of water stress on irrigated crop yields, enough to offset the reduced CO2 fertilization effect compared to an unconstrained GHG emission scenario.

Is current irrigation sustainable in the United States? An integrated assessment of climate change impact on water resources and irrigated crop yields.

PubMed

Blanc, Elodie; Caron, Justin; Fant, Charles; Monier, Erwan

2017-08-01

While climate change impacts on crop yields has been extensively studied, estimating the impact of water shortages on irrigated crop yields is challenging because the water resources management system is complex. To investigate this issue, we integrate a crop yield reduction module and a water resources model into the MIT Integrated Global System Modeling framework, an integrated assessment model linking a global economic model to an Earth system model. We assess the effects of climate and socioeconomic changes on water availability for irrigation in the U.S. as well as subsequent impacts on crop yields by 2050, while accounting for climate change projection uncertainty. We find that climate and socioeconomic changes will increase water shortages and strongly reduce irrigated yields for specific crops (i.e., cotton and forage), or in specific regions (i.e., the Southwest) where irrigation is not sustainable. Crop modeling studies that do not represent changes in irrigation availability can thus be misleading. Yet, since the most water-stressed basins represent a relatively small share of U.S. irrigated areas, the overall reduction in U.S. crop yields is small. The response of crop yields to climate change and water stress also suggests that some level of adaptation will be feasible, like relocating croplands to regions with sustainable irrigation or switching to less irrigation intensive crops. Finally, additional simulations show that greenhouse gas (GHG) mitigation can alleviate the effect of water stress on irrigated crop yields, enough to offset the reduced CO 2 fertilization effect compared to an unconstrained GHG emission scenario.

Simulating the spread of malaria using a generic transmission model for mosquito-borne infectious diseases

NASA Astrophysics Data System (ADS)

Kon, Cynthia Mui Lian; Labadin, Jane

2016-06-01

Malaria is a critical infection caused by parasites which are spread to humans through mosquito bites. Approximately half of the world's population is in peril of getting infected by malaria. Mosquito-borne diseases have a standard behavior where they are transmitted in the same manner, only through vector mosquito. Taking this into account, a generic spatial-temporal model for transmission of multiple mosquito-borne diseases had been formulated. Our interest is to reproduce the actual cases of different mosquito-borne diseases using the generic model and then predict future cases so as to improve control and target measures competently. In this paper, we utilize notified weekly malaria cases in four districts in Sarawak, Malaysia, namely Kapit, Song, Belaga and Marudi. The actual cases for 36 weeks, which is from week 39 in 2012 to week 22 in 2013, are compared with simulations of the generic spatial-temporal transmission mosquito-borne diseases model. We observe that the simulation results display corresponding result to the actual malaria cases in the four districts.

A reactive transport modelling approach to assess the leaching potential of hydraulic fracturing fluids associated with coal seam gas extraction

NASA Astrophysics Data System (ADS)

Mallants, Dirk; Simunek, Jirka; Gerke, Kirill

2015-04-01

Coal Seam Gas production generates large volumes of "produced" water that may contain compounds originating from the use of hydraulic fracturing fluids. Such produced water also contains elevated concentrations of naturally occurring inorganic and organic compounds, and usually has a high salinity. Leaching of produced water from storage ponds may occur as a result of flooding or containment failure. Some produced water is used for irrigation of specific crops tolerant to elevated salt levels. These chemicals may potentially contaminate soil, shallow groundwater, and groundwater, as well as receiving surface waters. This paper presents an application of scenario modelling using the reactive transport model for variably-saturated media HP1 (coupled HYDRUS-1D and PHREEQC). We evaluate the fate of hydraulic fracturing chemicals and naturally occurring chemicals in soil as a result of unintentional release from storage ponds or when produced water from Coal Seam Gas operations is used in irrigation practices. We present a review of exposure pathways and relevant hydro-bio-geo-chemical processes, a collation of physico-chemical properties of organic/inorganic contaminants as input to a set of generic simulations of transport and attenuation in variably saturated soil profiles. We demonstrate the ability to model the coupled processes of flow and transport in soil of contaminants associated with hydraulic fracturing fluids and naturally occurring contaminants.

Generic Raman-based calibration models enabling real-time monitoring of cell culture bioreactors.

PubMed

Mehdizadeh, Hamidreza; Lauri, David; Karry, Krizia M; Moshgbar, Mojgan; Procopio-Melino, Renee; Drapeau, Denis

2015-01-01

Raman-based multivariate calibration models have been developed for real-time in situ monitoring of multiple process parameters within cell culture bioreactors. Developed models are generic, in the sense that they are applicable to various products, media, and cell lines based on Chinese Hamster Ovarian (CHO) host cells, and are scalable to large pilot and manufacturing scales. Several batches using different CHO-based cell lines and corresponding proprietary media and process conditions have been used to generate calibration datasets, and models have been validated using independent datasets from separate batch runs. All models have been validated to be generic and capable of predicting process parameters with acceptable accuracy. The developed models allow monitoring multiple key bioprocess metabolic variables, and hence can be utilized as an important enabling tool for Quality by Design approaches which are strongly supported by the U.S. Food and Drug Administration. © 2015 American Institute of Chemical Engineers.

Generic substitution: micro evidence from register data in Norway.

PubMed

Dalen, Dag Morten; Furu, Kari; Locatelli, Marilena; Strøm, Steinar

2011-02-01

The importance of prices, doctor and patient characteristics, and market institutions for the likelihood of choosing generic drugs instead of the more expensive original brand-name version are examined. Using an extensive dataset extracted from The Norwegian Prescription Database containing all prescriptions dispensed to individuals in February 2004 and 2006 on 23 different drugs (chemical substances) in Norway, we find strong evidence for the importance of both doctor and patient characteristics for the choice probabilities. The price difference between brand and generic versions and insurance coverage both affect generic substitution. Moreover, controlling for the retail chain affiliation of the dispensing pharmacy, we find that pharmacies play an important role in promoting generic substitution. In markets with more recent entry of generic drugs, brand-name loyalty proves to be much stronger, giving less explanatory power to our demand model.

Comparison of Generic-to-Brand Switchback Rates Between Generic and Authorized Generic Drugs.

PubMed

Hansen, Richard A; Qian, Jingjing; Berg, Richard; Linneman, James; Seoane-Vazquez, Enrique; Dutcher, Sarah K; Raofi, Saeid; Page, C David; Peissig, Peggy

2017-04-01

Generic drugs contain identical active ingredients as their corresponding brand drugs and are pharmaceutically equivalent and bioequivalent, whereas authorized generic drugs (AGs) contain both identical active and inactive ingredients as their corresponding brand drugs but are marketed as generics. This study compares generic-to-brand switchback rates between generic and AGs. Retrospective cohort study. Claims and electronic health record data from a regional U.S. health care system. The full cohort consisted of 5542 unique patients who received select branded drugs during the 6 months prior to their generic drug market availability (between 1999 and 2014) and then were switched to an AG or generic drug within 30 months of generic drug entry. For these patients, 5929 unique patient-drug combinations (867 with AGs and 5062 with generic drugs) were evaluated. Ten drugs with AGs and generics marketed between 1999 and 2014 were evaluated. The date of the first generic prescription was considered the index date for each drug, and it marked the beginning of follow-up to evaluate the occurrence of generic-to-brand switchback patterns over the subsequent 30 months. Switchback rates were compared between patients receiving AGs versus those receiving generics using multivariable Cox proportional hazards models, controlling for individual drug effects, age, sex, Charlson Comorbidity Score, pre-index drug use characteristics, and pre-index health care utilization. Among the 5542 unique patients who switched from brand to generic or brand to AG, 264 (4.8%) switched back to the brand drug. Overall switchback rates were similar for AGs compared with generics (hazard ratio [HR] 0.86, 95% confidence interval [CI] 0.65-1.15). The likelihood of switchback was higher for alendronate (HR 1.64, 95% CI 1.20-2.23) and simvastatin (HR 1.81, 95% CI 1.30-2.54) and lower for amlodipine (HR 0.27, 95% CI 0.17-0.42) compared with the other drugs evaluated. Overall switchback rates were similar between AG and generic drug users, indirectly supporting similar efficacy and tolerability profiles for brand and generic drugs. Reasons for differences in switchback rates among specific products need to be explored further. © 2017 Pharmacotherapy Publications, Inc.

Development and validation of a tool to assess knowledge and attitudes towards generic medicines among students in Greece: The ATtitude TOwards GENerics (ATTOGEN) questionnaire.

PubMed

Domeyer, Philip J; Aletras, Vassilis; Anagnostopoulos, Fotios; Katsari, Vasiliki; Niakas, Dimitris

2017-01-01

The use of generic medicines is a cost-effective policy, often dictated by fiscal restraints. To our knowledge, no fully validated tool exploring the students' knowledge and attitudes towards generic medicines exists. The aim of our study was to develop and validate a questionnaire exploring the knowledge and attitudes of M.Sc. in Health Care Management students and recent alumni's towards generic drugs in Greece. The development of the questionnaire was a result of literature review and pilot-testing of its preliminary versions to researchers and students. The final version of the questionnaire contains 18 items measuring the respondents' knowledge and attitude towards generic medicines on a 5-point Likert scale. Given the ordinal nature of the data, ordinal alpha and polychoric correlations were computed. The sample was randomly split into two halves. Exploratory factor analysis, performed in the first sample, was used for the creation of multi-item scales. Confirmatory factor analysis and Generalized Linear Latent and Mixed Model analysis (GLLAMM) with the use of the rating scale model were used in the second sample to assess goodness of fit. An assessment of internal consistency reliability, test-retest reliability, and construct validity was also performed. Among 1402 persons contacted, 986 persons completed our questionnaire (response rate = 70.3%). Overall Cronbach's alpha was 0.871. The conjoint use of exploratory and confirmatory factor analysis resulted in a six-scale model, which seemed to fit the data well. Five of the six scales, namely trust, drug quality, state audit, fiscal impact and drug substitution were found to be valid and reliable, while the knowledge scale suffered only from low inter-scale correlations and a ceiling effect. However, the subsequent confirmatory factor and GLLAMM analyses indicated a good fit of the model to the data. The ATTOGEN instrument proved to be a reliable and valid tool, suitable for assessing students' knowledge and attitudes towards generic medicines.

Requirements analysis notebook for the flight data systems definition in the Real-Time Systems Engineering Laboratory (RSEL)

NASA Astrophysics Data System (ADS)

Wray, Richard B.

1991-12-01

A hybrid requirements analysis methodology was developed, based on the practices actually used in developing a Space Generic Open Avionics Architecture. During the development of this avionics architecture, a method of analysis able to effectively define the requirements for this space avionics architecture was developed. In this methodology, external interfaces and relationships are defined, a static analysis resulting in a static avionics model was developed, operating concepts for simulating the requirements were put together, and a dynamic analysis of the execution needs for the dynamic model operation was planned. The systems engineering approach was used to perform a top down modified structured analysis of a generic space avionics system and to convert actual program results into generic requirements. CASE tools were used to model the analyzed system and automatically generate specifications describing the model's requirements. Lessons learned in the use of CASE tools, the architecture, and the design of the Space Generic Avionics model were established, and a methodology notebook was prepared for NASA. The weaknesses of standard real-time methodologies for practicing systems engineering, such as Structured Analysis and Object Oriented Analysis, were identified.

Requirements analysis notebook for the flight data systems definition in the Real-Time Systems Engineering Laboratory (RSEL)

NASA Technical Reports Server (NTRS)

Wray, Richard B.

1991-01-01

A hybrid requirements analysis methodology was developed, based on the practices actually used in developing a Space Generic Open Avionics Architecture. During the development of this avionics architecture, a method of analysis able to effectively define the requirements for this space avionics architecture was developed. In this methodology, external interfaces and relationships are defined, a static analysis resulting in a static avionics model was developed, operating concepts for simulating the requirements were put together, and a dynamic analysis of the execution needs for the dynamic model operation was planned. The systems engineering approach was used to perform a top down modified structured analysis of a generic space avionics system and to convert actual program results into generic requirements. CASE tools were used to model the analyzed system and automatically generate specifications describing the model's requirements. Lessons learned in the use of CASE tools, the architecture, and the design of the Space Generic Avionics model were established, and a methodology notebook was prepared for NASA. The weaknesses of standard real-time methodologies for practicing systems engineering, such as Structured Analysis and Object Oriented Analysis, were identified.

Growth/reflectance model interface for wheat and corresponding model

NASA Technical Reports Server (NTRS)

Suits, G. H.; Sieron, R.; Odenweller, J.

1984-01-01

The use of modeling to explore the possibility of discovering new and useful crop condition indicators which might be available from the Thematic Mapper and to connect these symptoms to the biological causes in the crop is discussed. A crop growth model was used to predict the day to day growth features of the crop as it responds biologically to the various environmental factors. A reflectance model was used to predict the character of the interaction of daylight with the predicted growth features. An atmospheric path radiance was added to the reflected daylight to simulate the radiance appearing at the sensor. Finally, the digitized data sent to a ground station were calculated. The crop under investigation is wheat.

Innovation strategies for generic drug companies: moving into supergenerics.

PubMed

Ross, Malcolm S F

2010-04-01

Pharmaceutical companies that market generic products generally are not regarded as innovators, but rather as companies that produce copies of originator products to be launched at patent expiration. However, many generics companies have developed excellent scientific innovative skills in an effort to circumvent the defense patents of originator companies. More patents per product, in terms of both drug substances (process patents and polymorph patents) and formulations, are issued to generics companies than to companies that are traditionally considered to be 'innovators'. This quantity of issued patents highlights the technical knowledge and skill sets that are available in generics companies. In order to adopt a completely innovative model (ie, the development of NCEs), a generics company would require a completely new set of skills in several fields, including a sufficient knowledge base, project and risk management experience, and capability for clinical data evaluation. However, with relatively little investment, generics companies should be able to progress into the so-called 'supergeneric' drug space - an area of innovation that reflects the existing competencies of both innovative and generics companies.

Evaluation of two evapotranspiration approaches simulated with the CSM-CERES-Maize model under different irrigation strategies and the impact on maize growth, development and soil moisture content for semi-arid conditions

USDA-ARS?s Scientific Manuscript database

Water deficit is the most common adverse environmental condition that can seriously reduce crop productivity. Crop simulation models could assist in determining alternate crop management scenarios to deal with water-limited conditions. However, prior to the application of crop models, the appropriat...

Comparative approaches from empirical to mechanistic simulation modelling in Land Evaluation studies

NASA Astrophysics Data System (ADS)

Manna, P.; Basile, A.; Bonfante, A.; Terribile, F.

2009-04-01

The Land Evaluation (LE) comprise the evaluation procedures to asses the attitudes of the land to a generic or specific use (e.g. biomass production). From local to regional and national scale the approach to the land use planning should requires a deep knowledge of the processes that drive the functioning of the soil-plant-atmosphere system. According to the classical approaches the assessment of attitudes is the result of a qualitative comparison between the land/soil physical properties and the land use requirements. These approaches have a quick and inexpensive applicability; however, they are based on empirical and qualitative models with a basic knowledge structure specifically built for a specific landscape and for the specific object of the evaluation (e.g. crop). The outcome from this situation is the huge difficulties in the spatial extrapolation of the LE results and the rigidity of the system. Modern techniques instead, rely on the application of mechanistic and quantitative simulation modelling that allow a dynamic characterisation of the interrelated physical and chemical processes taking place in the soil landscape. Moreover, the insertion of physical based rules in the LE procedure may make it less difficult in terms of both extending spatially the results and changing the object (e.g. crop species, nitrate dynamics, etc.) of the evaluation. On the other side these modern approaches require high quality and quantity of input data that cause a significant increase in costs. In this scenario nowadays the LE expert is asked to choose the best LE methodology considering costs, complexity of the procedure and benefits in handling a specific land evaluation. In this work we performed a forage maize land suitability study by comparing 9 different methods having increasing complexity and costs. The study area, of about 2000 ha, is located in North Italy in the Lodi plain (Po valley). The range of the 9 employed methods ranged from standard LE approaches to the extensive use of simulation modelling (SWAP and CropSyst), using as data input pre-existing soil information (soil map 1:50000) and also hydraulic properties measured as well estimated by PTF. The comparison between the different methods was based on both cost and predictive ability of each of the methods. The latter was evaluated by comparison to the estimate of forage maize biomass obtained by using locally tested remote sensing measurements. Statistical indexes like correlation, relative variance and ANOVA test were applied. As expected, higher method complexity corresponds to higher quality/quantity of input parameters and as consequence higher costs. Generally, results show that more complex methods gave better results in terms of their predictive ability and those operating on measurements gave better performance than those operating on PTF. Moreover, the best predictive results were obtained abandoning the support of the soil mapping units, incrementing dramatically the number of sampling and analysis and applying the simulation modelling on real benchmark soils rather than averaging more soils observations. Keywords: Land Evaluation, simulation modelling, CropSyst, SWAP, NDVI.

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

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

Limitation of using Angstrom exponent for source apportionment of black carbon in complex environments - A case study from the North West Indo- Gangetic plain

NASA Astrophysics Data System (ADS)

Garg, S.; Sinha, B.; Sinha, V.; Chandra, P.; Sarda Esteve, R.; Gros, V.

2015-12-01

Determining the contribution of different sources to the total BC is necessary for targeted mitigation. Absorption Angstrom exponent (αabs) measurements of black carbon (BC) have recently been introduced as a novel tool to apportion the contribution of biomass burning sources to BC. Two-component Aethalometer model for apportioning BC to biomass burning sources and fossil fuel combustion sources, which uses αabs as a generic indicator of the source type, is widely used for determining the contribution of the two types of sources to the total BC. Our work studies BC emissions in the highly-populated, anthropogenic emissions-dominated Indo-Gangetic Plain and demonstrates that the αabs cannot be used as a generic tracer for biomass burning emissions in a complex environment. Simultaneously collected high time resolution data from a 7-wavelength Aethalometer (AE 42, Magee Scientific, USA) and a high sensitivity Proton Transfer Reaction- Quadrupole Mass Spectrometer (PTR-MS) installed at a sub-urban site in Mohali (Punjab), India, were used to identify a number of biomass combustion plumes during which BC enhancements correlated strongly with an increase in acetonitrile (a well-established biomass burning tracer) mixing ratio. Each type of biomass combustion is classified and characterized by distinct emission ratios of aromatic compounds and oxygenated VOCs to acetonitrile. The identified types of biomass combustion include two different types of crop residue burning (paddy and wheat), burning of leaf-litter, and garbage burning. Traffic (fossil-fuel burning) plumes were also selected for comparison. We find that the two-component Aethalometer source-apportionment method cannot be extrapolated to all types of biomass combustion and αabs of traffic plumes can be >1 in developing countries like India, where use of adulterated fuel in vehicles is common. Thus in a complex environment, where multiple anthropogenic BC sources and air masses of variable photochemical age impact a receptor site, the angstrom exponent is not representative of the combustion type and therefore, cannot be used as a generic tracer to constrain source contributions.

Factors influencing the preference for purchasing generic drugs in a Southern Brazilian city.

PubMed

Guttier, Marília Cruz; Silveira, Marysabel Pinto Telis; Luiza, Vera Lucia; Bertoldi, Andréa Dâmaso

2017-06-26

The objective of this study is to identify factors associated with the preference for purchasing generic drugs in a medium-sized municipality in Southern Brazil. We have analyzed data from a population-based cross-sectional study conducted in 2012 with a sample of 2,856 adults (≥ 20 years old). The preference for purchasing generic drugs was the main outcome. The explanatory variables were the demographic and socioeconomic variables. Statistical analyses included Poisson regressions. The preference for purchasing generic drugs was 63.2% (95%CI 61.4-64.9). The variables correlated with this preference in the fully adjusted models were: male (prevalence ratio [PR] = 1.08; 95%CI 1.03-1.14), age of 20-39 years (PR = 1.10; 95%CI 1.02-1.20), low socioeconomic status (PR = 1.15; 95%CI 1.03-1.28), and good knowledge about generic drugs (PR= 4.66; 95%CI 2.89-7.52). Among those who preferred to purchase generic drugs, 55.1% have reported accepting to replace the prescribed drug (if not a generic) with the equivalent generic drug. Another correlate of the preference for purchasing generic drugs was because individuals consider their quality equivalent to reference medicines (PR = 2.15; 95%CI 1.93-2.41). Knowledge about generic drugs was the main correlate of the preference for purchasing generic drugs. The greater the knowledge or positive perception about generic drugs, the greater is the preference to purchase them. Therefore, educational campaigns for healthcare professionals and consumers appear to be the best strategy for expanding the use of generic drugs in Brazil.

Radiometer footprint model to estimate sunlit and shaded components for row crops

USDA-ARS?s Scientific Manuscript database

This paper describes a geometric model for computing the relative proportion of sunlit vegetation, shaded vegetation, sunlit soil, and shaded soil appearing in a circular or elliptical radiometer footprint for row crops, where the crop rows were modeled as continuous ellipses. The model was validate...

Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models

USDA-ARS?s Scientific Manuscript database

Remote sensing technology can rapidly provide spatial information on crop growth status, which ideally could be used to invert radiative transfer models or ecophysiological models for estimating a variety of crop biophysical properties. However, the outcome of the model inversion procedure will be ...

The Impacts of Various Environments Factors and Adaptive Management Strategies on Food Crops in the 21st Century Based on a Land Surface Model

NASA Astrophysics Data System (ADS)

Jain, A. K.; Lin, T. S.; Lawrence, P.; Kheshgi, H. S.

2017-12-01

Environmental factors - characterized by increasing levels of CO2, and changes in temperature and precipitation patterns - present potential risks to global food supply. To date, understanding of environmental factors' effects on crop production remains uncertain due to (1) uncertainties in projected trends of these factors and their spatial and temporal variability; (2) uncertainties in the physiological, genetic and molecular basis of crop adaptation to adaptive management practices (e.g. change in planting time, irrigation and N fertilization etc.) and (3) uncertainties in current land surface models to estimate the response of crop production to changes in environmental factors and management strategies. In this study we apply a process-based land surface model, the Integrated Science Assessment model (ISAM), to assess the impact of various environmental factors and management strategies on the production of row crops (corn, soybean and wheat) at regional and global scales. Results are compared to corresponding simulations performed with the crop model in the Community Land Model (CLM4.5). Each model is driven with historical atmospheric forcing data (1901-2005), and projected atmospheric forcing data under RCP 4.5 or RCP 8.5 (2006-2100) from CESM CMIP5 simulations to estimate the effects of different climate change projections on potential productivity of food crops at a global scale. For each set of atmospheric forcing data, production of each crop is simulated with and without inclusion of adaptive management practices (e.g. application of irrigation, N fertilization, change in planting time and crop cultivars etc.) to assess the effect of adaptation on projected crop production over the 21st century. In detail, three questions are addressed: (1) what is the impact of different climate change projections on global crop production; (2) what is the effect of adaptive management practices on projected crop production; and (3) how do differences in model mechanisms in ISAM and CLM4.5 impact projected global crop production and adaptive management practices (irrigation and N fertilizer) over the 21st century. The major outcomes of this study will help to understand the uncertainties in potential productivity of food crops under different environmental conditions and management practices.

GMODWeb: a web framework for the generic model organism database

PubMed Central

O'Connor, Brian D; Day, Allen; Cain, Scott; Arnaiz, Olivier; Sperling, Linda; Stein, Lincoln D

2008-01-01

The Generic Model Organism Database (GMOD) initiative provides species-agnostic data models and software tools for representing curated model organism data. Here we describe GMODWeb, a GMOD project designed to speed the development of model organism database (MOD) websites. Sites created with GMODWeb provide integration with other GMOD tools and allow users to browse and search through a variety of data types. GMODWeb was built using the open source Turnkey web framework and is available from . PMID:18570664

Physical robustness of canopy temperature models for crop heat stress simulation across environments and production conditions

USDA-ARS?s Scientific Manuscript database

Despite widespread application in studying climate change impacts, most crop models ignore complex interactions among air temperature, crop and soil water status, CO2 concentration and atmospheric conditions that influence crop canopy temperature. The current study extended previous studies by evalu...

Economic Benefits of Predictive Models for Pest Control in Agricultural Crops

USDA-ARS?s Scientific Manuscript database

Various forms of crop models or decision making tools for managing crops have existed for many years. The potential advantage of all of these decision making tools is that more informed and economically improved crop management or decision making is accomplished. However, examination of some of thes...

Digital Modeling and Testing Research on Digging Mechanism of Deep Rootstalk Crops

NASA Astrophysics Data System (ADS)

Yang, Chuanhua; Xu, Ma; Wang, Zhoufei; Yang, Wenwu; Liao, Xinglong

The digital model of the laboratory bench parts of digging deep rootstalk crops were established through adopting the parametric model technology based on feature. The virtual assembly of the laboratory bench of digging deep rootstalk crops was done and the digital model of the laboratory bench parts of digging deep rootstalk crops was gained. The vibrospade, which is the key part of the laboratory bench of digging deep rootstalk crops was simulated and the movement parametric curves of spear on the vibrospade were obtained. The results show that the spear was accorded with design requirements. It is propitious to the deep rootstalk.

NASA Earth Science Research Results for Improved Regional Crop Yield Prediction

NASA Astrophysics Data System (ADS)

Mali, P.; O'Hara, C. G.; Shrestha, B.; Sinclair, T. R.; G de Goncalves, L. G.; Salado Navarro, L. R.

2007-12-01

National agencies such as USDA Foreign Agricultural Service (FAS), Production Estimation and Crop Assessment Division (PECAD) work specifically to analyze and generate timely crop yield estimates that help define national as well as global food policies. The USDA/FAS/PECAD utilizes a Decision Support System (DSS) called CADRE (Crop Condition and Data Retrieval Evaluation) mainly through an automated database management system that integrates various meteorological datasets, crop and soil models, and remote sensing data; providing significant contribution to the national and international crop production estimates. The "Sinclair" soybean growth model has been used inside CADRE DSS as one of the crop models. This project uses Sinclair model (a semi-mechanistic crop growth model) for its potential to be effectively used in a geo-processing environment with remote-sensing-based inputs. The main objective of this proposed work is to verify, validate and benchmark current and future NASA earth science research results for the benefit in the operational decision making process of the PECAD/CADRE DSS. For this purpose, the NASA South American Land Data Assimilation System (SALDAS) meteorological dataset is tested for its applicability as a surrogate meteorological input in the Sinclair model meteorological input requirements. Similarly, NASA sensor MODIS products is tested for its applicability in the improvement of the crop yield prediction through improving precision of planting date estimation, plant vigor and growth monitoring. The project also analyzes simulated Visible/Infrared Imager/Radiometer Suite (VIIRS, a future NASA sensor) vegetation product for its applicability in crop growth prediction to accelerate the process of transition of VIIRS research results for the operational use of USDA/FAS/PECAD DSS. The research results will help in providing improved decision making capacity to the USDA/FAS/PECAD DSS through improved vegetation growth monitoring from high spatial and temporal resolution remote sensing datasets; improved time-series meteorological inputs required for crop growth models; and regional prediction capability through geo-processing-based yield modeling.

Integrated Modeling of Crop Growth and Water Resource Management to Project Climate Change Impacts on Crop Production and Irrigation Water Supply and Demand in African Nations

NASA Astrophysics Data System (ADS)

Dale, A. L.; Boehlert, B.; Reisenauer, M.; Strzepek, K. M.; Solomon, S.

2017-12-01

Climate change poses substantial risks to African agriculture. These risks are exacerbated by concurrent risks to water resources, with water demand for irrigation comprising 80 to 90% of water withdrawals across the continent. Process-based crop growth models are able to estimate both crop demand for irrigation water and crop yields, and are therefore well-suited to analyses of climate change impacts at the food-water nexus. Unfortunately, impact assessments based on these models generally focus on either yields or water demand, rarely both. For this work, we coupled a crop model to a water resource management model in order to predict national trends in the impact of climate change on crop production, irrigation water demand, and the availability of water for irrigation across Africa. The crop model FAO AquaCrop-OS was run at 2ox2o resolution for 17 different climate futures from the CMIP5 archive, nine for Representative Concentration Pathway (RCP) 4.5 and eight for RCP8.5. Percent changes in annual rainfed and irrigated crop production and temporal shifts in monthly irrigation water demand were estimated for the years 2030, 2050, 2070, and 2090 for maize, sorghum, rice, wheat, cotton, sugarcane, fruits & vegetables, roots & tubers, and legumes & soybeans. AquaCrop was then coupled to a water management model (WEAP) in order to project changes in the ability of seven major river basins (the Congo, Niger, Nile, Senegal, Upper Orange, Volta, and Zambezi) to meet irrigation water demand out to 2050 in both average and dry years in the face of both climate change and irrigation expansion. Spatial and temporal trends were identified and interpreted through the lens of potential risk management strategies. Uncertainty in model estimates is reported and discussed.

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.

Observed and modelled solar radiation components in sugarcane crop grown under tropical conditions

NASA Astrophysics Data System (ADS)

Santos, Marcos A. dos; Souza, José L. de; Lyra, Gustavo B.; Teodoro, Iêdo; Ferreira, Ricardo A.; Santos Almeida, Alexsandro C. dos; Lyra, Guilherme B.; Souza, Renan C. de; Lemes, Marco A. Maringolo

2017-04-01

The net radiation over vegetated surfaces is one of the major input variables in many models of soil evaporation, evapotranspiration as well as leaf wetness duration. In the literature there are relatively few studies on net radiation over sugarcane crop in tropical climates. The main objective of the present study was to assess the solar radiation components measured and modelled for two crop stages of a sugarcane crop in the region of Rio Largo, Alagoas, North-eastern Brazil. The measurements of the radiation components were made with a net radiometer during the dry and rainy seasons and two models were used to estimate net radiation: the Ortega-Farias model and the Monteith and Unsworth model. The highest values of net radiation were observed at the crop development stage, due mainly to the high indices of incoming solar radiation. The daily average albedos of sugarcane at the crop development and mid-season stages were 0.16 and 0.20, respectively. Both models showed a better fit for the crop development stage than for the mid-season stage. When they were inter-compared, Monteith and Unsworth model was more efficient than Ortega-Farias model, despite the dispersion of their simulated radiation components which was similar.

Spatial Sampling of Weather Data for Regional Crop Yield Simulations

NASA Technical Reports Server (NTRS)

Van Bussel, Lenny G. J.; Ewert, Frank; Zhao, Gang; Hoffmann, Holger; Enders, Andreas; Wallach, Daniel; Asseng, Senthold; Baigorria, Guillermo A.; Basso, Bruno; Biernath, Christian;

Is current irrigation sustainable in the United States? An integrated assessment of climate change impact on water resources and irrigated crop yields

DOE PAGES

Blanc, Elodie; Caron, Justin; Fant, Charles; ...

2017-06-27

While climate change impacts on crop yields has been extensively studied, estimating the impact of water shortages on irrigated crop yields is challenging because the water resources management system is complex. To investigate this issue, we integrate a crop yield reduction module and a water resources model into the MIT Integrated Global System Modeling framework, an integrated assessment model linking a global economic model to an Earth system model. We assess the effects of climate and socioeconomic changes on water availability for irrigation in the U.S. as well as subsequent impacts on crop yields by 2050, while accounting for climatemore » change projection uncertainty. We find that climate and socioeconomic changes will increase water shortages and strongly reduce irrigated yields for specific crops (i.e., cotton and forage), or in specific regions (i.e., the Southwest) where irrigation is not sustainable. Crop modeling studies that do not represent changes in irrigation availability can thus be misleading. Yet, since the most water-stressed basins represent a relatively small share of U.S. irrigated areas, the overall reduction in U.S. crop yields is small. The response of crop yields to climate change and water stress also suggests that some level of adaptation will be feasible, like relocating croplands to regions with sustainable irrigation or switching to less irrigation intensive crops. Finally, additional simulations show that greenhouse gas (GHG) mitigation can alleviate the effect of water stress on irrigated crop yields, enough to offset the reduced CO 2 fertilization effect compared to an unconstrained GHG emission scenario.« less

Assessment of Climate Suitability of Maize in South Korea

NASA Astrophysics Data System (ADS)

Hyun, S.; Choi, D.; Seo, B.

2017-12-01

Assessing suitable areas for crops would be useful to design alternate cropping systems as an adaptation option to climate change adaptation. Although suitable areas could be identified by using a crop growth model, it would require a number of input parameters including cultivar and soil. Instead, a simple climate suitability model, e.g., EcoCrop model, could be used for an assessment of climate suitability for a major grain crop. The objective of this study was to assess of climate suitability for maize using the EcoCrop model under climate change conditions in Korea. A long term climate data from 2000 - 2100 were compiled from weather data source. The EcoCrop model implemented in R was used to determine climate suitability index at each grid cell. Overall, the EcoCrop model tended to identify suitable areas for maize production near the coastal areas whereas the actual major production areas located in inland areas. It is likely that the discrepancy between assessed and actual crop production areas would result from the socioeconomic aspects of maize production. Because the price of maize is considerably low, maize has been grown in an area where moisture and temperature conditions would be less than optimum. In part, a simple algorithm to predict climate suitability for maize would caused a relatively large error in climate suitability assessment under the present climate conditions. In 2050s, the climate suitability for maize increased in a large areas in southern and western part of Korea. In particular, the plain areas near the coastal region had considerably greater suitability index in the future compared with mountainous areas. The expansion of suitable areas for maize would help crop production policy making such as the allocation of rice production area for other crops due to considerably less demand for the rice in Korea.

Is current irrigation sustainable in the United States? An integrated assessment of climate change impact on water resources and irrigated crop yields

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

Blanc, Elodie; Caron, Justin; Fant, Charles

While climate change impacts on crop yields has been extensively studied, estimating the impact of water shortages on irrigated crop yields is challenging because the water resources management system is complex. To investigate this issue, we integrate a crop yield reduction module and a water resources model into the MIT Integrated Global System Modeling framework, an integrated assessment model linking a global economic model to an Earth system model. We assess the effects of climate and socioeconomic changes on water availability for irrigation in the U.S. as well as subsequent impacts on crop yields by 2050, while accounting for climatemore » change projection uncertainty. We find that climate and socioeconomic changes will increase water shortages and strongly reduce irrigated yields for specific crops (i.e., cotton and forage), or in specific regions (i.e., the Southwest) where irrigation is not sustainable. Crop modeling studies that do not represent changes in irrigation availability can thus be misleading. Yet, since the most water-stressed basins represent a relatively small share of U.S. irrigated areas, the overall reduction in U.S. crop yields is small. The response of crop yields to climate change and water stress also suggests that some level of adaptation will be feasible, like relocating croplands to regions with sustainable irrigation or switching to less irrigation intensive crops. Finally, additional simulations show that greenhouse gas (GHG) mitigation can alleviate the effect of water stress on irrigated crop yields, enough to offset the reduced CO 2 fertilization effect compared to an unconstrained GHG emission scenario.« less

ALAMEDA, a Structural–Functional Model for Faba Bean Crops: Morphological Parameterization and Verification

PubMed Central

RUIZ-RAMOS, MARGARITA; MÍNGUEZ, M. INÉS

2006-01-01

• Background Plant structural (i.e. architectural) models explicitly describe plant morphology by providing detailed descriptions of the display of leaf and stem surfaces within heterogeneous canopies and thus provide the opportunity for modelling the functioning of plant organs in their microenvironments. The outcome is a class of structural–functional crop models that combines advantages of current structural and process approaches to crop modelling. ALAMEDA is such a model. • Methods The formalism of Lindenmayer systems (L-systems) was chosen for the development of a structural model of the faba bean canopy, providing both numerical and dynamic graphical outputs. It was parameterized according to the results obtained through detailed morphological and phenological descriptions that capture the detailed geometry and topology of the crop. The analysis distinguishes between relationships of general application for all sowing dates and stem ranks and others valid only for all stems of a single crop cycle. • Results and Conclusions The results reveal that in faba bean, structural parameterization valid for the entire plant may be drawn from a single stem. ALAMEDA was formed by linking the structural model to the growth model ‘Simulation d'Allongement des Feuilles’ (SAF) with the ability to simulate approx. 3500 crop organs and components of a group of nine plants. Model performance was verified for organ length, plant height and leaf area. The L-system formalism was able to capture the complex architecture of canopy leaf area of this indeterminate crop and, with the growth relationships, generate a 3D dynamic crop simulation. Future development and improvement of the model are discussed. PMID:16390842

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

Treesearch

Ram Deo; Matthew Russell; Grant Domke; Hans-Erik Andersen; Warren Cohen; Christopher Woodall

2017-01-01

Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory plot data with spatial predictors from Landsat time-...

Projected economic impact of clinical findings of generic entry of topiramate on G4 European countries.

PubMed

Paradis, Pierre Emmanuel; Latrémouille-Viau, Dominick; Moore, Yuliya; Mishagina, Natalia; Lafeuille, Marie-Hélène; Lefebvre, Patrick; Gaudig, Maren; Duh, Mei Sheng

2009-07-01

To explore the effects of generic substitution of the antiepileptic drug (AED) topiramate (Topamax) in Canada; to convert observed Canadian costs into the settings of France, Germany, Italy, and the United Kingdom (UK); and to forecast the economic impact of generic topiramate entry in these four European countries. Health claims from Régie de l'assurance maladie du Québec (RAMQ) plan (1/2006-9/2008) and IMS Health data (1998-2008) were used. Patients with epilepsy and > or = 2 topiramate dispensings were selected. An open-cohort design was used to classify observation into mutually-exclusive periods of branded versus generic use of topiramate. Canadian healthcare utilization and costs (2007 CAN$/person-year) were compared between periods using multivariate models. Annualized per-patient costs (2007 euro or 2007 pound sterling/person-year) were converted using Canadian utilization rates, European prices and service-use ratios. Non-parametric bootstrap served to assess statistical significance of cost differences. Topiramate market was forecasted following generic entry (09/2009-09/2010) using autoregressive models based on the European experience. The economic impact of generic topiramate entry was estimated for each country. A total of 1164 patients (mean age: 39.8 years, 61.7% female) were observed for 2.6 years on average. After covariates adjustment, generic-use periods were associated with increased pharmacy dispensings (other AEDs: +0.95/person-year, non-AEDs: +12.28/person-year, p < 0.001), hospitalizations ( + 0.08/person-year, p = 0.015), and lengths of hospital stays (+0.51 days/person-year, p < 0.001). Adjusted costs, excluding topiramate, were CAN$1060/person-year higher during generic use (p = 0.005). Converted per-patient costs excluding topiramate were significantly higher for generic relative to brand periods in all European countries (adjusted cost differences per person-year: 706-815 euro, p < 0.001 for all comparisons). System-wide costs would increase from 3.5 to 24.4% one year after generic entry. Study limitations include the absence of indirect costs, possible claim inaccuracies, and IMS data limitations. Higher health costs were projected for G4 European countries from the Canadian experience following the generic entry of topiramate.

Impacts of Future Climate Change on California Perennial Crop Yields: Model Projections with Climate and Crop Uncertainties

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

Lobell, D; Field, C; Cahill, K

2006-01-10

Most research on the agricultural impacts of climate change has focused on the major annual crops, yet perennial cropping systems are less adaptable and thus potentially more susceptible to damage. Improved assessments of yield responses to future climate are needed to prioritize adaptation strategies in the many regions where perennial crops are economically and culturally important. These impact assessments, in turn, must rely on climate and crop models that contain often poorly defined uncertainties. We evaluated the impact of climate change on six major perennial crops in California: wine grapes, almonds, table grapes, oranges, walnuts, and avocados. Outputs from multiplemore » climate models were used to evaluate climate uncertainty, while multiple statistical crop models, derived by resampling historical databases, were used to address crop response uncertainties. We find that, despite these uncertainties, climate change in California is very likely to put downward pressure on yields of almonds, walnuts, avocados, and table grapes by 2050. Without CO{sub 2} fertilization or adaptation measures, projected losses range from 0 to >40% depending on the crop and the trajectory of climate change. Climate change uncertainty generally had a larger impact on projections than crop model uncertainty, although the latter was substantial for several crops. Opportunities for expansion into cooler regions are identified, but this adaptation would require substantial investments and may be limited by non-climatic constraints. Given the long time scales for growth and production of orchards and vineyards ({approx}30 years), climate change should be an important factor in selecting perennial varieties and deciding whether and where perennials should be planted.« less

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Assessing the impact of climate change upon hydrology and agriculture in the Indrawati Basin, Nepal.

NASA Astrophysics Data System (ADS)

Palazzoli, Irene; Bocchiola, Daniele; Nana, Ester; Maskey, Shreedhar; Uhlenbrook, Stefan

2014-05-01

Agriculture is sensitive to climate change, especially to temperature and precipitation changes. The purpose of this study was to evaluate the climate change impacts upon rain-fed crops production in the Indrawati river basin, Nepal. The Soil and Water Assessment Tool SWAT model was used to model hydrology and cropping systems in the catchment, and to predict the influence of different climate change scenarios therein. Daily weather data collected from about 13 weather stations during 4 decades were used to constrain the SWAT model, and data from two hydrometric stations used to calibrate/validate it. Then management practices (crop calendar) were applied to specific Hydrological Response Units (HRUs) for the main crops of the region, rice, corn and wheat. Manual calibration of crop production was also carried, against values of crop yield in the area from literature. The calibrated and validated model was further applied to assess the impact of three future climate change scenarios (RCPs) upon the crop productivity in the region. Three climate models (GCMs) were adopted, each with three RCPs (2.5, 4.5, 8.5). Hence, impacts of climate change were assessed considering three time windows, namely a baseline period (1995-2004), the middle of century (2045-2054) and the end of century (2085-2094). For each GCM and RCP future hydrology and yield was compared to baseline scenario. The results displayed slightly modified hydrological cycle, and somewhat small variation in crop production, variable with models and RCPs, and for crop type, the largest being for wheat. Keywords: Climate Change, Nepal, hydrological cycle, crop yield.

Progress in modelling agricultural impacts of and adaptations to climate change.

PubMed

Rötter, R P; Hoffmann, M P; Koch, M; Müller, C

2018-06-01

Modelling is a key tool to explore agricultural impacts of and adaptations to climate change. Here we report recent progress made especially referring to the large project initiatives MACSUR and AgMIP; in particular, in modelling potential crop impacts from field to global using multi-model ensembles. We identify two main fields where further progress is necessary: a more mechanistic understanding of climate impacts and management options for adaptation and mitigation; and focusing on cropping systems and integrative multi-scale assessments instead of single season and crops, especially in complex tropical and neglected but important cropping systems. Stronger linking of experimentation with statistical and eco-physiological crop modelling could facilitate the necessary methodological advances. Copyright © 2018 Elsevier Ltd. All rights reserved.

Virtual water trade patterns in relation to environmental and socioeconomic factors: A case study for Tunisia.

PubMed

Chouchane, Hatem; Krol, Maarten S; Hoekstra, Arjen Y

2018-02-01

Growing water demands put increasing pressure on local water resources, especially in water-short countries. Virtual water trade can play a key role in filling the gap between local demand and supply of water-intensive commodities. This study aims to analyse the dynamics in virtual water trade of Tunisia in relation to environmental and socio-economic factors such as GDP, irrigated land, precipitation, population and water scarcity. The water footprint of crop production is estimated using AquaCrop for six crops over the period 1981-2010. Net virtual water import (NVWI) is quantified at yearly basis. Regression models are used to investigate dynamics in NVWI in relation to the selected factors. The results show that NVWI during the study period for the selected crops is not influenced by blue water scarcity. NVWI correlates in two alternative models to either population and precipitation (model I) or to GDP and irrigated area (model II). The models are better in explaining NVWI of staple crops (wheat, barley, potatoes) than NVWI of cash crops (dates, olives, tomatoes). Using model I, we are able to explain both trends and inter-annual variability for rain-fed crops. Model II performs better for irrigated crops and is able to explain trends significantly; no significant relation is found, however, with variables hypothesized to represent inter-annual variability. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting optimum crop designs using crop models and seasonal climate forecasts.

PubMed

Rodriguez, D; de Voil, P; Hudson, D; Brown, J N; Hayman, P; Marrou, H; Meinke, H

2018-02-02

Expected increases in food demand and the need to limit the incorporation of new lands into agriculture to curtail emissions, highlight the urgency to bridge productivity gaps, increase farmers profits and manage risks in dryland cropping. A way to bridge those gaps is to identify optimum combination of genetics (G), and agronomic managements (M) i.e. crop designs (GxM), for the prevailing and expected growing environment (E). Our understanding of crop stress physiology indicates that in hindsight, those optimum crop designs should be known, while the main problem is to predict relevant attributes of the E, at the time of sowing, so that optimum GxM combinations could be informed. Here we test our capacity to inform that "hindsight", by linking a tested crop model (APSIM) with a skillful seasonal climate forecasting system, to answer "What is the value of the skill in seasonal climate forecasting, to inform crop designs?" Results showed that the GCM POAMA-2 was reliable and skillful, and that when linked with APSIM, optimum crop designs could be informed. We conclude that reliable and skillful GCMs that are easily interfaced with crop simulation models, can be used to inform optimum crop designs, increase farmers profits and reduce risks.

Simulating crop phenology in the Community Land Model and its impact on energy and carbon fluxes

USDA-ARS?s Scientific Manuscript database

A reasonable representation of crop phenology and biophysical processes in land surface models is necessary to accurately simulate energy, water and carbon budgets at the field, regional, and global scales. However, the evaluation of crop models that can be coupled to earth system models is relative...

How do various maize crop models vary in their responses to climate change factors?

USDA-ARS?s Scientific Manuscript database

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 give similar grain yield responses to changes in climatic factors, or whether they agr...

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

USDA-ARS?s Scientific Manuscript database

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

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.

[Evaluation of Gastric Mucosal Injury Model Animals of Rebamipide Formulation--Study of Therapeutic Equivalence].

PubMed

Abe, Noriaki; Funato, Hiroki; Hirata, Ayumu; Nakai, Megumi; Iizuka, Michiro; Shiraishi, Hisashi; Jobu, Kohei; Yagi, Yusuke; Kadota, Aki; Ogi, Kyoko; Yokota, Junko; Miyamura, Mitsuhiko

2016-01-01

The introduction of generic drugs is promoted from the perspective of medical economics. In this context, we need to understand not only the bioequivalence of generic drugs specified in "the Guidelines for Bioequivalence Studies of Generic Products", but also formulation properties to consider their effect on pharmacological therapy. We evaluated the pharmaceutical characteristics of rebamipide formulations, a brand-name drug and two generic drugs, and their clinical functionality by using rat models of gastric mucosal injury induced by non-steroidal anti-inflammatory drugs (NSAIDs). Pharmaceutical evaluation showed significant differences in hardness. The inter-lot variation was small in all rebamipide formulations. In the clinical functionality study, biochemistry test values 7 d after the administration of rebamipide showed no differences among formulations. Higher levels of mucosal fluid secretion and antioxidative enzymes were observed in the groups administered rebamipide than in the control group. The levels of lipid peroxide were lower in the groups administered rebamipide than the control group. Multivariate analysis showed slight divergence between the brand-name and generic drugs. In future, it will be necessary to select generic drugs after careful consideration of bioequivalence, clinical functionality, and therapeutic equivalence by reviewing scientific evidence such as indication and formulation design, not to mention stable provision.

Putting mechanisms into crop production models

USDA-ARS?s Scientific Manuscript database

Crop simulation models dynamically predict processes of carbon, nitrogen, and water balance on daily or hourly time-steps to the point of predicting yield and production at crop maturity. A brief history of these models is reviewed, and their level of mechanism for assimilation and respiration, ran...

Impact of climate change on crop yield and role of model for achieving food security.

PubMed

Kumar, Manoj

2016-08-01

In recent times, several studies around the globe indicate that climatic changes are likely to impact the food production and poses serious challenge to food security. In the face of climate change, agricultural systems need to adapt measures for not only increasing food supply catering to the growing population worldwide with changing dietary patterns but also to negate the negative environmental impacts on the earth. Crop simulation models are the primary tools available to assess the potential consequences of climate change on crop production and informative adaptive strategies in agriculture risk management. In consideration with the important issue, this is an attempt to provide a review on the relationship between climate change impacts and crop production. It also emphasizes the role of crop simulation models in achieving food security. Significant progress has been made in understanding the potential consequences of environment-related temperature and precipitation effect on agricultural production during the last half century. Increased CO2 fertilization has enhanced the potential impacts of climate change, but its feasibility is still in doubt and debates among researchers. To assess the potential consequences of climate change on agriculture, different crop simulation models have been developed, to provide informative strategies to avoid risks and understand the physical and biological processes. Furthermore, they can help in crop improvement programmes by identifying appropriate future crop management practises and recognizing the traits having the greatest impact on yield. Nonetheless, climate change assessment through model is subjected to a range of uncertainties. The prediction uncertainty can be reduced by using multimodel, incorporating crop modelling with plant physiology, biochemistry and gene-based modelling. For devloping new model, there is a need to generate and compile high-quality field data for model testing. Therefore, assessment of agricultural productivity to sustain food security for generations is essential to maintain a collective knowledge and resources for preventing negative impact as well as managing crop practises.

Adverse weather impacts on arable cropping systems

NASA Astrophysics Data System (ADS)

Gobin, Anne

2016-04-01

Damages due to extreme or adverse weather strongly depend on crop type, crop stage, soil conditions and management. The impact is largest during the sensitive periods of the farming calendar, and requires a modelling approach to capture the interactions between the crop, its environment and the occurrence of the meteorological event. The hypothesis is that extreme and adverse weather events can be quantified and subsequently incorporated in current crop models. Since crop development is driven by thermal time and photoperiod, a regional crop model was used to examine the likely frequency, magnitude and impacts of frost, drought, heat stress and waterlogging in relation to the cropping season and crop sensitive stages. Risk profiles and associated return levels were obtained by fitting generalized extreme value distributions to block maxima for air humidity, water balance and temperature variables. The risk profiles were subsequently confronted with yields and yield losses for the major arable crops in Belgium, notably winter wheat, winter barley, winter oilseed rape, sugar beet, potato and maize at the field (farm records) to regional scale (statistics). The average daily vapour pressure deficit (VPD) and reference evapotranspiration (ET0) during the growing season is significantly lower (p < 0.001) and has a higher variability before 1988 than after 1988. Distribution patterns of VPD and ET0 have relevant impacts on crop yields. The response to rising temperatures depends on the crop's capability to condition its microenvironment. Crops short of water close their stomata, lose their evaporative cooling potential and ultimately become susceptible to heat stress. Effects of heat stress therefore have to be combined with moisture availability such as the precipitation deficit or the soil water balance. Risks of combined heat and moisture deficit stress appear during the summer. These risks are subsequently related to crop damage. The methodology of defining meteorological risks and subsequently relating the risk to the cropping calendar will be demonstrated for major arable crops in Belgium. Physically based crop models assist in understanding the links between adverse weather events, sensitive crop stages and crop damage. Financial support was obtained from Belspo under research contract SD/RI/03A.

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.

Soybean Physiology Calibration in the Community Land Model

NASA Astrophysics Data System (ADS)

Drewniak, B. A.; Bilionis, I.; Constantinescu, E. M.

2014-12-01

With the large influence of agricultural land use on biophysical and biogeochemical cycles, integrating cultivation into Earth System Models (ESMs) is increasingly important. The Community Land Model (CLM) was augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. However, the strong nonlinearity of ESMs makes parameter fitting a difficult task. In this study, our goal is to calibrate ten of the CLM-Crop parameters for one crop type, soybean, in order to improve model projection of plant development and carbon fluxes. We used measurements of gross primary productivity, net ecosystem exchange, and plant biomass from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). Our scheme can perform model calibration using very few evaluations and, by exploiting parallelism, at a fraction of the time required by plain vanilla Markov Chain Monte Carlo (MCMC). We present the results from a twin experiment (self-validation) and calibration results and validation using real observations from an AmeriFlux tower site in the Midwestern United States, for the soybean crop type. The improved model will help researchers understand how climate affects crop production and resulting carbon fluxes, and additionally, how cultivation impacts climate.

Development of the Transport Class Model (TCM) Aircraft Simulation From a Sub-Scale Generic Transport Model (GTM) Simulation

NASA Technical Reports Server (NTRS)

Hueschen, Richard M.

2011-01-01

A six degree-of-freedom, flat-earth dynamics, non-linear, and non-proprietary aircraft simulation was developed that is representative of a generic mid-sized twin-jet transport aircraft. The simulation was developed from a non-proprietary, publicly available, subscale twin-jet transport aircraft simulation using scaling relationships and a modified aerodynamic database. The simulation has an extended aerodynamics database with aero data outside the normal transport-operating envelope (large angle-of-attack and sideslip values). The simulation has representative transport aircraft surface actuator models with variable rate-limits and generally fixed position limits. The simulation contains a generic 40,000 lb sea level thrust engine model. The engine model is a first order dynamic model with a variable time constant that changes according to simulation conditions. The simulation provides a means for interfacing a flight control system to use the simulation sensor variables and to command the surface actuators and throttle position of the engine model.

Changes in crop yields and their variability at different levels of global warming

NASA Astrophysics Data System (ADS)

Ostberg, Sebastian; Schewe, Jacob; Childers, Katelin; Frieler, Katja

2018-05-01

An assessment of climate change impacts at different levels of global warming is crucial to inform the policy discussion about mitigation targets, as well as for the economic evaluation of climate change impacts. Integrated assessment models often use global mean temperature change (ΔGMT) as a sole measure of climate change and, therefore, need to describe impacts as a function of ΔGMT. There is already a well-established framework for the scalability of regional temperature and precipitation changes with ΔGMT. It is less clear to what extent more complex biological or physiological impacts such as crop yield changes can also be described in terms of ΔGMT, even though such impacts may often be more directly relevant for human livelihoods than changes in the physical climate. Here we show that crop yield projections can indeed be described in terms of ΔGMT to a large extent, allowing for a fast estimation of crop yield changes for emissions scenarios not originally covered by climate and crop model projections. We use an ensemble of global gridded crop model simulations for the four major staple crops to show that the scenario dependence is a minor component of the overall variance of projected yield changes at different levels of ΔGMT. In contrast, the variance is dominated by the spread across crop models. Varying CO2 concentrations are shown to explain only a minor component of crop yield variability at different levels of global warming. In addition, we find that the variability in crop yields is expected to increase with increasing warming in many world regions. We provide, for each crop model, geographical patterns of mean yield changes that allow for a simplified description of yield changes under arbitrary pathways of global mean temperature and CO2 changes, without the need for additional climate and crop model simulations.

Early Season Large-Area Winter Crop Mapping Using MODIS NDVI Data, Growing Degree Days Information and a Gaussian Mixture Model

NASA Technical Reports Server (NTRS)

Skakun, Sergii; Franch, Belen; Vermote, Eric; Roger, Jean-Claude; Becker-Reshef, Inbal; Justice, Christopher; Kussul, Nataliia

2017-01-01

Knowledge on geographical location and distribution of crops at global, national and regional scales is an extremely valuable source of information applications. Traditional approaches to crop mapping using remote sensing data rely heavily on reference or ground truth data in order to train/calibrate classification models. As a rule, such models are only applicable to a single vegetation season and should be recalibrated to be applicable for other seasons. This paper addresses the problem of early season large-area winter crop mapping using Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI) time-series and growing degree days (GDD) information derived from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product. The model is based on the assumption that winter crops have developed biomass during early spring while other crops (spring and summer) have no biomass. As winter crop development is temporally and spatially non-uniform due to the presence of different agro-climatic zones, we use GDD to account for such discrepancies. A Gaussian mixture model (GMM) is applied to discriminate winter crops from other crops (spring and summer). The proposed method has the following advantages: low input data requirements, robustness, applicability to global scale application and can provide winter crop maps 1.5-2 months before harvest. The model is applied to two study regions, the State of Kansas in the US and Ukraine, and for multiple seasons (2001-2014). Validation using the US Department of Agriculture (USDA) Crop Data Layer (CDL) for Kansas and ground measurements for Ukraine shows that accuracies of greater than 90% can be achieved in mapping winter crops 1.5-2 months before harvest. Results also show good correspondence to official statistics with average coefficients of determination R(exp. 2) greater than 0.85.

Monitoring Crop Productivity over the U.S. Corn Belt using an Improved Light Use Efficiency Model

NASA Astrophysics Data System (ADS)

Wu, X.; Xiao, X.; Zhang, Y.; Qin, Y.; Doughty, R.

2017-12-01

Large-scale monitoring of crop yield is of great significance for forecasting food production and prices and ensuring food security. Satellite data that provide temporally and spatially continuous information that by themselves or in combination with other data or models, raises possibilities to monitor and understand agricultural productivity regionally. In this study, we first used an improved light use efficiency model-Vegetation Photosynthesis Model (VPM) to simulate the gross primary production (GPP). Model evaluation showed that the simulated GPP (GPPVPM) could well captured the spatio-temporal variation of GPP derived from FLUXNET sites. Then we applied the GPPVPM to further monitor crop productivity for corn and soybean over the U.S. Corn Belt and benchmarked with county-level crop yield statistics. We found VPM-based approach provides pretty good estimates (R2 = 0.88, slope = 1.03). We further showed the impacts of climate extremes on the crop productivity and carbon use efficiency. The study indicates the great potential of VPM in estimating crop yield and in understanding of crop yield responses to climate variability and change.

Leaf wetness distribution within a potato crop

NASA Astrophysics Data System (ADS)

Heusinkveld, B. G.

2010-07-01

The Netherlands has a mild maritime climate and therefore the major interest in leaf wetness is associated with foliar plant diseases. During moist micrometeorological conditions (i.e. dew, fog, rain), foliar fungal diseases may develop quickly and thereby destroy a crop quickly. Potato crop monocultures covering several hectares are especially vulnerable to such diseases. Therefore understanding and predicting leaf wetness in potato crops is crucial in crop disease control strategies. A field experiment was carried out in a large homogeneous potato crop in the Netherlands during the growing season of 2008. Two innovative sensor networks were installed as a 3 by 3 grid at 3 heights covering an area of about 2 hectares within two larger potato crops. One crop was located on a sandy soil and one crop on a sandy peat soil. In most cases leaf wetting starts in the top layer and then progresses downward. Leaf drying takes place in the same order after sunrise. A canopy dew simulation model was applied to simulate spatial leaf wetness distribution. The dew model is based on an energy balance model. The model can be run using information on the above-canopy wind speed, air temperature, humidity, net radiation and within canopy air temperature, humidity and soil moisture content and temperature conditions. Rainfall was accounted for by applying an interception model. The results of the dew model agreed well with the leaf wetness sensors if all local conditions were considered. The measurements show that the spatial correlation of leaf wetness decreases downward.

A Generic Nonlinear Aerodynamic Model for Aircraft

NASA Technical Reports Server (NTRS)

Grauer, Jared A.; Morelli, Eugene A.

2014-01-01

A generic model of the aerodynamic coefficients was developed using wind tunnel databases for eight different aircraft and multivariate orthogonal functions. For each database and each coefficient, models were determined using polynomials expanded about the state and control variables, and an othgonalization procedure. A predicted squared-error criterion was used to automatically select the model terms. Modeling terms picked in at least half of the analyses, which totalled 45 terms, were retained to form the generic nonlinear aerodynamic (GNA) model. Least squares was then used to estimate the model parameters and associated uncertainty that best fit the GNA model to each database. Nonlinear flight simulations were used to demonstrate that the GNA model produces accurate trim solutions, local behavior (modal frequencies and damping ratios), and global dynamic behavior (91% accurate state histories and 80% accurate aerodynamic coefficient histories) under large-amplitude excitation. This compact aerodynamics model can be used to decrease on-board memory storage requirements, quickly change conceptual aircraft models, provide smooth analytical functions for control and optimization applications, and facilitate real-time parametric system identification.

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.

Factors influencing the preference for purchasing generic drugs in a Southern Brazilian city

PubMed Central

Guttier, Marília Cruz; Silveira, Marysabel Pinto Telis; Luiza, Vera Lucia; Bertoldi, Andréa Dâmaso

2017-01-01

ABSTRACT OBJECTIVE The objective of this study is to identify factors associated with the preference for purchasing generic drugs in a medium-sized municipality in Southern Brazil. METHODS We have analyzed data from a population-based cross-sectional study conducted in 2012 with a sample of 2,856 adults (≥ 20 years old). The preference for purchasing generic drugs was the main outcome. The explanatory variables were the demographic and socioeconomic variables. Statistical analyses included Poisson regressions. RESULTS The preference for purchasing generic drugs was 63.2% (95%CI 61.4–64.9). The variables correlated with this preference in the fully adjusted models were: male (prevalence ratio [PR] = 1.08; 95%CI 1.03–1.14), age of 20–39 years (PR = 1.10; 95%CI 1.02–1.20), low socioeconomic status (PR = 1.15; 95%CI 1.03–1.28), and good knowledge about generic drugs (PR= 4.66; 95%CI 2.89–7.52). Among those who preferred to purchase generic drugs, 55.1% have reported accepting to replace the prescribed drug (if not a generic) with the equivalent generic drug. Another correlate of the preference for purchasing generic drugs was because individuals consider their quality equivalent to reference medicines (PR = 2.15; 95%CI 1.93–2.41). CONCLUSIONS Knowledge about generic drugs was the main correlate of the preference for purchasing generic drugs. The greater the knowledge or positive perception about generic drugs, the greater is the preference to purchase them. Therefore, educational campaigns for healthcare professionals and consumers appear to be the best strategy for expanding the use of generic drugs in Brazil. PMID:28678909

Determinants of generic drug substitution in Switzerland.

PubMed

Decollogny, Anne; Eggli, Yves; Halfon, Patricia; Lufkin, Thomas M

2011-01-26

Since generic drugs have the same therapeutic effect as the original formulation but at generally lower costs, their use should be more heavily promoted. However, a considerable number of barriers to their wider use have been observed in many countries. The present study examines the influence of patients, physicians and certain characteristics of the generics' market on generic substitution in Switzerland. We used reimbursement claims' data submitted to a large health insurer by insured individuals living in one of Switzerland's three linguistic regions during 2003. All dispensed drugs studied here were substitutable. The outcome (use of a generic or not) was modelled by logistic regression, adjusted for patients' characteristics (gender, age, treatment complexity, substitution groups) and with several variables describing reimbursement incentives (deductible, co-payments) and the generics' market (prices, packaging, co-branded original, number of available generics, etc.). The overall generics' substitution rate for 173,212 dispensed prescriptions was 31%, though this varied considerably across cantons. Poor health status (older patients, complex treatments) was associated with lower generic use. Higher rates were associated with higher out-of-pocket costs, greater price differences between the original and the generic, and with the number of generics on the market, while reformulation and repackaging were associated with lower rates. The substitution rate was 13% lower among hospital physicians. The adoption of the prescribing practices of the canton with the highest substitution rate would increase substitution in other cantons to as much as 26%. Patient health status explained a part of the reluctance to substitute an original formulation by a generic. Economic incentives were efficient, but with a moderate global effect. The huge interregional differences indicated that prescribing behaviours and beliefs are probably the main determinant of generic substitution.

Stress-strain analysis of a (0/90)sub 2 symmetric titanium matrix laminate subjected to a generic hypersonic flight profile

NASA Technical Reports Server (NTRS)

Mirdamadi, Massoud; Johnson, W. Steven

1992-01-01

Cross ply laminate behavior of Ti-15V-3Cr-3Al-3Sn (Ti-15-3) matrix reinforced with continuous silicon carbide fibers (SCS-6) subjected to a generic hypersonic flight profile was evaluated experimentally and analytically. Thermomechanical fatigue test techniques were developed to conduct a simulation of a generic hypersonic flight profile. A micromechanical analysis was used. The analysis predicts the stress-strain response of the laminate and of the constituents in each ply during thermal and mechanical cycling by using only constituent properties as input. The fiber was modeled using a thermo-viscoplastic constitutive relation. The fiber transverse modulus was reduced in the analysis to simulate the fiber matrix interface failure. Excellent correlation was found between measured and predicted laminate stress-strain response due to generic hypersonic flight profile when fiber debonding was modeled.

Pose estimation of teeth through crown-shape matching

NASA Astrophysics Data System (ADS)

Mok, Vevin; Ong, Sim Heng; Foong, Kelvin W. C.; Kondo, Toshiaki

2002-05-01

This paper presents a technique for determining a tooth's pose given a dental plaster cast and a set of generic tooth models. The ultimate goal of pose estimation is to obtain information about the sizes and positions of the roots, which lie hidden within the gums, without the use of X-rays, CT or MRI. In our approach, the tooth of interest is first extracted from the 3D dental cast image through segmentation. 2D views are then generated from the extracted tooth and are matched against a target view generated from the generic model with known pose. Additional views are generated in the vicinity of the best view and the entire process is repeated until convergence. Upon convergence, the generic tooth is superimposed onto the dental cast to show the position of the root. The results of applying the technique to canines demonstrate the excellent potential of the algorithm for generic tooth fitting.

Developing multiple-choices test items as tools for measuring the scientific-generic skills on solar system

NASA Astrophysics Data System (ADS)

Bhakti, Satria Seto; Samsudin, Achmad; Chandra, Didi Teguh; Siahaan, Parsaoran

2017-05-01

The aim of research is developing multiple-choices test items as tools for measuring the scientific of generic skills on solar system. To achieve the aim that the researchers used the ADDIE model consisting Of: Analyzing, Design, Development, Implementation, dan Evaluation, all of this as a method research. While The scientific of generic skills limited research to five indicator including: (1) indirect observation, (2) awareness of the scale, (3) inference logic, (4) a causal relation, and (5) mathematical modeling. The participants are 32 students at one of junior high schools in Bandung. The result shown that multiple-choices that are constructed test items have been declared valid by the expert validator, and after the tests show that the matter of developing multiple-choices test items be able to measuring the scientific of generic skills on solar system.

National Variation in Crop Yield Production Functions

NASA Astrophysics Data System (ADS)

Devineni, N.; Rising, J. A.

2017-12-01

A new multilevel model for yield prediction at the county scale using regional climate covariates is presented in this paper. A new crop specific water deficit index, growing degree days, extreme degree days, and time-trend as an approximation of technology improvements are used as predictors to estimate annual crop yields for each county from 1949 to 2009. Every county in the United States is allowed to have unique parameters describing how these weather predictors are related to yield outcomes. County-specific parameters are further modeled as varying according to climatic characteristics, allowing the prediction of parameters in regions where crops are not currently grown and into the future. The structural relationships between crop yield and regional climate as well as trends are estimated simultaneously. All counties are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. The model captures up to 60% of the variability in crop yields after removing the effect of technology, does well in out of sample predictions and is useful in relating the climate responses to local bioclimatic factors. We apply the predicted growing models in a cost-benefit analysis to identify the most economically productive crop in each county.

Global Simulation of Bioenergy Crop Productivity: Analytical Framework and Case Study for Switchgrass

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

Kang, Shujiang; Kline, Keith L; Nair, S. Surendran

A global energy crop productivity model that provides geospatially explicit quantitative details on biomass potential and factors affecting sustainability would be useful, but does not exist now. This study describes a modeling platform capable of meeting many challenges associated with global-scale agro-ecosystem modeling. We designed an analytical framework for bioenergy crops consisting of six major components: (i) standardized natural resources datasets, (ii) global field-trial data and crop management practices, (iii) simulation units and management scenarios, (iv) model calibration and validation, (v) high-performance computing (HPC) simulation, and (vi) simulation output processing and analysis. The HPC-Environmental Policy Integrated Climate (HPC-EPIC) model simulatedmore » a perennial bioenergy crop, switchgrass (Panicum virgatum L.), estimating feedstock production potentials and effects across the globe. This modeling platform can assess soil C sequestration, net greenhouse gas (GHG) emissions, nonpoint source pollution (e.g., nutrient and pesticide loss), and energy exchange with the atmosphere. It can be expanded to include additional bioenergy crops (e.g., miscanthus, energy cane, and agave) and food crops under different management scenarios. The platform and switchgrass field-trial dataset are available to support global analysis of biomass feedstock production potential and corresponding metrics of sustainability.« less

The Crop Risk Zones Monitoring System for resilience to drought in the Sahel

NASA Astrophysics Data System (ADS)

Vignaroli, Patrizio; Rocchi, Leandro; De Filippis, Tiziana; Tarchiani, Vieri; Bacci, Maurizio; Toscano, Piero; Pasqui, Massimiliano; Rapisardi, Elena

2016-04-01

Food security is still one of the major concerns that Sahelian populations have to face. In the Sahel, agriculture is primarily based on rainfed crops and it is often structurally inadequate to manage the climatic variability. The predominantly rainfed cropping system of Sahel region is dependent on season quality on a year-to-year basis, and susceptible to weather extremes of droughts and extreme temperatures. Low water-storage capacity and high dependence on rainfed agriculture leave the agriculture sector even more vulnerable to climate risks. Crop yields may suffer significantly with either a late onset or early cessation of the rainy season, as well as with a high frequency of damaging dry spells. Early rains at the beginning of the season are frequently followed by dry spells which may last a week or longer. As the amount of water stored in the soil at this time of the year is negligible, early planted crops can suffer water shortage stresses during a prolonged dry spell. Therefore, the choice of the sowing date is of fundamental importance for farmers. The ability to estimate effectively the onset of the season and potentially dangerous dry spells becomes therefore vital for planning rainfed agriculture practices aiming to minimize risks and maximize yields. In this context, advices to farmers are key drivers for prevention allowing a better adaptation of traditional crop calendar to climatic variability. In the Sahel, particularly in CILSS (Permanent Interstates Committee for Drought Control in the Sahel) countries, national Early Warning System (EWS) for food security are underpinned by Multidisciplinary Working Groups (MWGs) lead by National Meteorological Services (NMS). The EWSs are mainly based on tools and models utilizing numeric forecasts and satellite data to outlook and monitor the growing season. This approach is focused on the early identification of risks and on the production of information within the prescribed time period for decision-making. Since the '90s, analysis tools and models based on meteorological satellites data have been developed within different regional and national initiatives to allow near-real-time monitoring of the cropping season. The software was in general stand-alone applications, transferred to MWGs without continuous user support and updates. Currently MWGs in the Sahel do not have any working operational tool for drought risk identification and forecast, because such tools are by now obsolete from the IT perspective. The challenge and the objective of this work is to provide to MWGs and local end-users an open access/source Crop Risk Zones Monitoring System (CRZ-MS) supporting decision making for drought risk reduction and resilience improvement. A first prototype has been developed for Niger and Mali NMSs, based on a coherent Open Source web-based infrastructure to treat all input and output data in a interoperable, platform-independent and uniform way. The System architecture and functions are based on a agro-meteorological model, running in two different modes: 1) diagnostic mode for the drought monitoring during the agro-pastoral campaign allowing MWGs to identify agricultural drought risk areas in order to support decision making at local and national level in agricultural drought management. This early warning information also represents an input for estimating the nutritional food insecurity, for the identification of potentially vulnerable populations and assessing food crises risks by National EWSs put in place by CILSS with EU, FAO and WFP. 2) predictive mode for "advisory-support" activities to the farmers by the Agricultural Extension Services, in order to implement the most appropriate strategies for minimizing drought risk on crops (i.e. identification of the optimal period of sowing, choice of varieties based on the expected length of the growing season, adoption of suitable cultural practices for soil water management) and to build farmers resilience. To increase the accessibility of appropriate and targeted drought risk information, it is essential to move from generic information to specific advises for end-users at different decision-making levels, bridging the gap between available technology and local users' needs. Thus, advices to farmers are a fundamental component of prevention allowing a better country's preparedness to cope with weather variability.

VIC-CropSyst-v2: A regional-scale modeling platform to simulate the nexus of climate, hydrology, cropping systems, and human decisions

NASA Astrophysics Data System (ADS)

Malek, Keyvan; Stöckle, Claudio; Chinnayakanahalli, Kiran; Nelson, Roger; Liu, Mingliang; Rajagopalan, Kirti; Barik, Muhammad; Adam, Jennifer C.

2017-08-01

Food supply is affected by a complex nexus of land, atmosphere, and human processes, including short- and long-term stressors (e.g., drought and climate change, respectively). A simulation platform that captures these complex elements can be used to inform policy and best management practices to promote sustainable agriculture. We have developed a tightly coupled framework using the macroscale variable infiltration capacity (VIC) hydrologic model and the CropSyst agricultural model. A mechanistic irrigation module was also developed for inclusion in this framework. Because VIC-CropSyst combines two widely used and mechanistic models (for crop phenology, growth, management, and macroscale hydrology), it can provide realistic and hydrologically consistent simulations of water availability, crop water requirements for irrigation, and agricultural productivity for both irrigated and dryland systems. This allows VIC-CropSyst to provide managers and decision makers with reliable information on regional water stresses and their impacts on food production. Additionally, VIC-CropSyst is being used in conjunction with socioeconomic models, river system models, and atmospheric models to simulate feedback processes between regional water availability, agricultural water management decisions, and land-atmosphere interactions. The performance of VIC-CropSyst was evaluated on both regional (over the US Pacific Northwest) and point scales. Point-scale evaluation involved using two flux tower sites located in agricultural fields in the US (Nebraska and Illinois). The agreement between recorded and simulated evapotranspiration (ET), applied irrigation water, soil moisture, leaf area index (LAI), and yield indicated that, although the model is intended to work on regional scales, it also captures field-scale processes in agricultural areas.

Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform.

PubMed

Marshall-Colon, Amy; Long, Stephen P; Allen, Douglas K; Allen, Gabrielle; Beard, Daniel A; Benes, Bedrich; von Caemmerer, Susanne; Christensen, A J; Cox, Donna J; Hart, John C; Hirst, Peter M; Kannan, Kavya; Katz, Daniel S; Lynch, Jonathan P; Millar, Andrew J; Panneerselvam, Balaji; Price, Nathan D; Prusinkiewicz, Przemyslaw; Raila, David; Shekar, Rachel G; Shrivastava, Stuti; Shukla, Diwakar; Srinivasan, Venkatraman; Stitt, Mark; Turk, Matthew J; Voit, Eberhard O; Wang, Yu; Yin, Xinyou; Zhu, Xin-Guang

2017-01-01

Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental perturbations. These models have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts. Outcomes will potentially accelerate improvement of crop yield, sustainability, and increase future food security. It is time for a paradigm shift in plant modeling, from largely isolated efforts to a connected community that takes advantage of advances in high performance computing and mechanistic understanding of plant processes. Tools for guiding future crop breeding and engineering, understanding the implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment are urgently needed. The purpose of this perspective is to introduce Crops in silico (cropsinsilico.org), an integrative and multi-scale modeling platform, as one solution that combines isolated modeling efforts toward the generation of virtual crops, which is open and accessible to the entire plant biology community. The major challenges involved both in the development and deployment of a shared, multi-scale modeling platform, which are summarized in this prospectus, were recently identified during the first Crops in silico Symposium and Workshop.

Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform

PubMed Central

Marshall-Colon, Amy; Long, Stephen P.; Allen, Douglas K.; Allen, Gabrielle; Beard, Daniel A.; Benes, Bedrich; von Caemmerer, Susanne; Christensen, A. J.; Cox, Donna J.; Hart, John C.; Hirst, Peter M.; Kannan, Kavya; Katz, Daniel S.; Lynch, Jonathan P.; Millar, Andrew J.; Panneerselvam, Balaji; Price, Nathan D.; Prusinkiewicz, Przemyslaw; Raila, David; Shekar, Rachel G.; Shrivastava, Stuti; Shukla, Diwakar; Srinivasan, Venkatraman; Stitt, Mark; Turk, Matthew J.; Voit, Eberhard O.; Wang, Yu; Yin, Xinyou; Zhu, Xin-Guang

2017-01-01

Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental perturbations. These models have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts. Outcomes will potentially accelerate improvement of crop yield, sustainability, and increase future food security. It is time for a paradigm shift in plant modeling, from largely isolated efforts to a connected community that takes advantage of advances in high performance computing and mechanistic understanding of plant processes. Tools for guiding future crop breeding and engineering, understanding the implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment are urgently needed. The purpose of this perspective is to introduce Crops in silico (cropsinsilico.org), an integrative and multi-scale modeling platform, as one solution that combines isolated modeling efforts toward the generation of virtual crops, which is open and accessible to the entire plant biology community. The major challenges involved both in the development and deployment of a shared, multi-scale modeling platform, which are summarized in this prospectus, were recently identified during the first Crops in silico Symposium and Workshop. PMID:28555150

Modeling water scarcity over south Asia: Incorporating crop growth and irrigation models into the Variable Infiltration Capacity (VIC) model

NASA Astrophysics Data System (ADS)

Troy, Tara J.; Ines, Amor V. M.; Lall, Upmanu; Robertson, Andrew W.

2013-04-01

Large-scale hydrologic models, such as the Variable Infiltration Capacity (VIC) model, are used for a variety of studies, from drought monitoring to projecting the potential impact of climate change on the hydrologic cycle decades in advance. The majority of these models simulates the natural hydrological cycle and neglects the effects of human activities such as irrigation, which can result in streamflow withdrawals and increased evapotranspiration. In some parts of the world, these activities do not significantly affect the hydrologic cycle, but this is not the case in south Asia where irrigated agriculture has a large water footprint. To address this gap, we incorporate a crop growth model and irrigation model into the VIC model in order to simulate the impacts of irrigated and rainfed agriculture on the hydrologic cycle over south Asia (Indus, Ganges, and Brahmaputra basin and peninsular India). The crop growth model responds to climate signals, including temperature and water stress, to simulate the growth of maize, wheat, rice, and millet. For the primarily rainfed maize crop, the crop growth model shows good correlation with observed All-India yields (0.7) with lower correlations for the irrigated wheat and rice crops (0.4). The difference in correlation is because irrigation provides a buffer against climate conditions, so that rainfed crop growth is more tied to climate than irrigated crop growth. The irrigation water demands induce hydrologic water stress in significant parts of the region, particularly in the Indus, with the streamflow unable to meet the irrigation demands. Although rainfall can vary significantly in south Asia, we find that water scarcity is largely chronic due to the irrigation demands rather than being intermittent due to climate variability.

Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany

NASA Astrophysics Data System (ADS)

Waldhoff, Guido; Lussem, Ulrike; Bareth, Georg

2017-09-01

Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. Annual crop maps of eight consecutive years (2008-2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. In most years, the remote sensing data basis was highly fragmented. Nevertheless, our method enabled satisfying crop mapping results. As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. For the generation of the crop sequence dataset, the eight annual crop maps were geometrically smoothened and integrated into a single vector data layer. The resulting dataset informs about the occurring crop sequence for individual areas on arable land, so that crop rotation schemes can be derived. The resulting dataset reveals that the spectrum of the practiced crop rotations is extremely heterogeneous and contains a large amount of crop sequences, which strongly diverge from model crop rotations. Consequently, the integration of remote sensing-based crop rotation data can considerably reduce uncertainties regarding the management in regional agro-ecosystem modeling. Finally, the developed methods and the results are discussed in detail.

Evaluation of the "e-rater"® Scoring Engine for the "GRE"® Issue and Argument Prompts. Research Report. ETS RR-12-02

ERIC Educational Resources Information Center

Ramineni, Chaitanya; Trapani, Catherine S.; Williamson, David M.; Davey, Tim; Bridgeman, Brent

2012-01-01

Automated scoring models for the "e-rater"® scoring engine were built and evaluated for the "GRE"® argument and issue-writing tasks. Prompt-specific, generic, and generic with prompt-specific intercept scoring models were built and evaluation statistics such as weighted kappas, Pearson correlations, standardized difference in…

Therapy Processes and Outcomes of Psychological Interventions for Women Diagnosed with Gynecological Cancers: A Test of the Generic Process Model of Psychotherapy

ERIC Educational Resources Information Center

Manne, Sharon; Winkel, Gary; Zaider, Talia; Rubin, Stephen; Hernandez, Enrique; Bergman, Cynthia

2010-01-01

Objective: Little attention has been paid to the role of nonspecific therapy processes in the efficacy of psychological interventions for individuals diagnosed with cancer. The goal of the current study was to examine the three constructs from the generic model of psychotherapy (GMP): therapeutic alliance, therapeutic realizations, and therapeutic…

Race and Gender Matter: A Multidimensional Approach to Conceptualizing and Measuring Stress in African American Women

PubMed Central

Woods-Giscombé, Cheryl L.; Lobel, Marci

2008-01-01

Based on prior research and theory, the authors constructed a multidimensional model of stress in African American women comprised of race-related, gender-related, and generic stress. Exposure to and appraisal of these three types of stress were combined into a higher-order global stress factor. Using structural equation modeling, the fit of this stress factor and its ability to predict distress symptoms were examined in 189 socioeconomically diverse African American women aged 21 to 78. Results support the multidimensional conceptualization and operationalization of stress. Race-related, gender-related, and generic stress contributed equally to the global stress factor, and global stress predicted a significant amount of variance in distress symptoms and intensity. This model exhibited better fit than a model without a global stress factor, in which each stress component predicted distress directly. Furthermore, race-related, gender-related, and generic stress did not contribute to distress beyond their representation in the global stress factor. These findings illustrate that stress related to central elements of identity, namely race and gender, cohere with generic stress to define the stress experience of African American women. PMID:18624581

Race and gender matter: a multidimensional approach to conceptualizing and measuring stress in African American women.

PubMed

Woods-Giscombé, Cheryl L; Lobel, Marci

2008-07-01

Based on prior research and theory, the authors constructed a multidimensional model of stress in African American women comprised of race-related, gender-related, and generic stress. Exposure to and appraisal of these three types of stress were combined into a higher-order global stress factor. Using structural equation modeling, the fit of this stress factor and its ability to predict distress symptoms were examined in 189 socioeconomically diverse African American women aged 21 to 78. Results support the multidimensional conceptualization and operationalization of stress. Race-related, gender-related, and generic stress contributed equally to the global stress factor, and global stress predicted a significant amount of variance in distress symptoms and intensity. This model exhibited better fit than a model without a global stress factor, in which each stress component predicted distress directly. Furthermore, race-related, gender-related, and generic stress did not contribute to distress beyond their representation in the global stress factor. These findings illustrate that stress related to central elements of identity, namely race and gender, cohere with generic stress to define the stress experience of African American women. Copyright (c) 2008 APA, all rights reserved.

Impacts on Water Management and Crop Production of Regional Cropping System Adaptation to Climate Change

NASA Astrophysics Data System (ADS)

Zhong, H.; Sun, L.; Tian, Z.; Liang, Z.; Fischer, G.

2014-12-01

China is one of the most populous and fast developing countries, also faces a great pressure on grain production and food security. Multi-cropping system is widely applied in China to fully utilize agro-climatic resources and increase land productivity. As the heat resource keep improving under climate warming, multi-cropping system will also shifting northward, and benefit crop production. But water shortage in North China Plain will constrain the adoption of new multi-cropping system. Effectiveness of multi-cropping system adaptation to climate change will greatly depend on future hydrological change and agriculture water management. So it is necessary to quantitatively express the water demand of different multi-cropping systems under climate change. In this paper, we proposed an integrated climate-cropping system-crops adaptation framework, and specifically focused on: 1) precipitation and hydrological change under future climate change in China; 2) the best multi-cropping system and correspondent crop rotation sequence, and water demand under future agro-climatic resources; 3) attainable crop production with water constraint; and 4) future water management. In order to obtain climate projection and precipitation distribution, global climate change scenario from HADCAM3 is downscaled with regional climate model (PRECIS), historical climate data (1960-1990) was interpolated from more than 700 meteorological observation stations. The regional Agro-ecological Zone (AEZ) model is applied to simulate the best multi-cropping system and crop rotation sequence under projected climate change scenario. Finally, we use the site process-based DSSAT model to estimate attainable crop production and the water deficiency. Our findings indicate that annual land productivity may increase and China can gain benefit from climate change if multi-cropping system would be adopted. This study provides a macro-scale view of agriculture adaptation, and gives suggestions to national agriculture adaptation strategy decisions.

Sequential use of the STICS crop model and of the MACRO pesticide fate model to simulate pesticides leaching in cropping systems.

PubMed

Lammoglia, Sabine-Karen; Moeys, Julien; Barriuso, Enrique; Larsbo, Mats; Marín-Benito, Jesús-María; Justes, Eric; Alletto, Lionel; Ubertosi, Marjorie; Nicolardot, Bernard; Munier-Jolain, Nicolas; Mamy, Laure

2017-03-01

The current challenge in sustainable agriculture is to introduce new cropping systems to reduce pesticides use in order to reduce ground and surface water contamination. However, it is difficult to carry out in situ experiments to assess the environmental impacts of pesticide use for all possible combinations of climate, crop, and soils; therefore, in silico tools are necessary. The objective of this work was to assess pesticides leaching in cropping systems coupling the performances of a crop model (STICS) and of a pesticide fate model (MACRO). STICS-MACRO has the advantage of being able to simulate pesticides fate in complex cropping systems and to consider some agricultural practices such as fertilization, mulch, or crop residues management, which cannot be accounted for with MACRO. The performance of STICS-MACRO was tested, without calibration, from measurements done in two French experimental sites with contrasted soil and climate properties. The prediction of water percolation and pesticides concentrations with STICS-MACRO was satisfactory, but it varied with the pedoclimatic context. The performance of STICS-MACRO was shown to be similar or better than that of MACRO. The improvement of the simulation of crop growth allowed better estimate of crop transpiration therefore of water balance. It also allowed better estimate of pesticide interception by the crop which was found to be crucial for the prediction of pesticides concentrations in water. STICS-MACRO is a new promising tool to improve the assessment of the environmental risks of pesticides used in cropping systems.

Building a statistical emulator for prediction of crop yield response to climate change: a global gridded panel data set approach

NASA Astrophysics Data System (ADS)

Mistry, Malcolm; De Cian, Enrica; Wing, Ian Sue

2015-04-01

There is widespread concern that trends and variability in weather induced by climate change will detrimentally affect global agricultural productivity and food supplies. Reliable quantification of the risks of negative impacts at regional and global scales is a critical research need, which has so far been met by forcing state-of-the-art global gridded crop models with outputs of global climate model (GCM) simulations in exercises such as the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP)-Fastrack. Notwithstanding such progress, it remains challenging to use these simulation-based projections to assess agricultural risk because their gridded fields of crop yields are fundamentally denominated as discrete combinations of warming scenarios, GCMs and crop models, and not as model-specific or model-averaged yield response functions of meteorological shifts, which may have their own independent probability of occurrence. By contrast, the empirical climate economics literature has adeptly represented agricultural responses to meteorological variables as reduced-form statistical response surfaces which identify the crop productivity impacts of additional exposure to different intervals of temperature and precipitation [cf Schlenker and Roberts, 2009]. This raises several important questions: (1) what do the equivalent reduced-form statistical response surfaces look like for crop model outputs, (2) do they exhibit systematic variation over space (e.g., crop suitability zones) or across crop models with different characteristics, (3) how do they compare to estimates based on historical observations, and (4) what are the implications for the characterization of climate risks? We address these questions by estimating statistical yield response functions for four major crops (maize, rice, wheat and soybeans) over the historical period (1971-2004) as well as future climate change scenarios (2005-2099) using ISIMIP-Fastrack data for five GCMs and seven crop models under rain-fed and irrigated management regimes. Our approach, which is patterned after Lobell and Burke [2010], is a novel application of cross-section/time-series statistical techniques from the climate economics literature to large, high-dimension, multi-model datasets, and holds considerable promise as a diagnostic methodology to elucidate uncertainties in the processes simulated by crop models, and to support the development of climate impact intercomparison exercises.

Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission

USGS Publications Warehouse

Mariotto, Isabella; Thenkabail, Prasad S.; Huete, Alfredo; Slonecker, E. Terrence; Platonov, Alexander

2013-01-01

Precise monitoring of agricultural crop biomass and yield quantities is critical for crop production management and prediction. The goal of this study was to compare hyperspectral narrowband (HNB) versus multispectral broadband (MBB) reflectance data in studying irrigated cropland characteristics of five leading world crops (cotton, wheat, maize, rice, and alfalfa) with the objectives of: 1. Modeling crop productivity, and 2. Discriminating crop types. HNB data were obtained from Hyperion hyperspectral imager and field ASD spectroradiometer, and MBB data were obtained from five broadband sensors: Landsat-7 Enhanced Thematic Mapper Plus (ETM +), Advanced Land Imager (ALI), Indian Remote Sensing (IRS), IKONOS, and QuickBird. A large collection of field spectral and biophysical variables were gathered for the 5 crops in Central Asia throughout the growing seasons of 2006 and 2007. Overall, the HNB and hyperspectral vegetation index (HVI) crop biophysical models explained about 25% greater variability when compared with corresponding MBB models. Typically, 3 to 7 HNBs, in multiple linear regression models of a given crop variable, explained more than 93% of variability in crop models. The evaluation of λ1 (400–2500 nm) versus λ2 (400–2500 nm) plots of various crop biophysical variables showed that the best two-band normalized difference HVIs involved HNBs centered at: (i) 742 nm and 1175 nm (HVI742-1175), (ii) 1296 nm and 1054 nm (HVI1296-1054), (iii) 1225 nm and 697 nm (HVI1225-697), and (iv) 702 nm and 1104 nm (HVI702-1104). Among the most frequently occurring HNBs in various crop biophysical models, 74% were located in the 1051–2331 nm spectral range, followed by 10% in the moisture sensitive 970 nm, 6% in the red and red-edge (630–752 nm), and the remaining 10% distributed between blue (400–500 nm), green (501–600 nm), and NIR (760–900 nm).Discriminant models, used for discriminating 3 or 4 or 5 crop types, showed significantly higher accuracies when using HNBs (> 90%) over MBBs data (varied between 45 and 84%).Finally, the study highlighted 29 HNBs of Hyperion that are optimal in the study of agricultural crops and potentially significant to the upcoming NASA HyspIRI mission. Determining optimal and redundant bands for a given application will help overcoming the Hughes' phenomenon (or curse of high dimensionality of data).

Yield model development project implementation plan

NASA Technical Reports Server (NTRS)

Ambroziak, R. A.

1982-01-01

Tasks remaining to be completed are summarized for the following major project elements: (1) evaluation of crop yield models; (2) crop yield model research and development; (3) data acquisition processing, and storage; (4) related yield research: defining spectral and/or remote sensing data requirements; developing input for driving and testing crop growth/yield models; real time testing of wheat plant process models) and (5) project management and support.

Downscaled climate change impacts on agricultural water resources in Puerto Rico

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

Harmsen, E.W.; Miller, N.L.; Schlegel, N.J.

2009-04-01

The purpose of this study is to estimate reference evapotranspiration (ET{sub o}), rainfall deficit (rainfall - ET{sub o}) and relative crop yield reduction for a generic crop under climate change conditions for three locations in Puerto Rico: Adjuntas, Mayaguez, and Lajas. Reference evapotranspiration is estimated by the Penman-Monteith method. Rainfall and temperature data were statistically downscaled and evaluated using the DOE/NCAR PCM global circulation model projections for the B1 (low), A2 (mid-high) and A1fi (high) emission scenarios of the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios. Relative crop yield reductions were estimated from a function dependent watermore » stress factor, which is a function of soil moisture content. Average soil moisture content for the three locations was determined by means of a simple water balance approach. Results from the analysis indicate that the rainy season will become wetter and the dry season will become drier. The 20-year mean 1990-2010 September rainfall excess (i.e., rainfall - ET{sub o} > 0) increased for all scenarios and locations from 149.8 to 356.4 mm for 2080-2100. Similarly, the 20-year average February rainfall deficit (i.e., rainfall - ET{sub o} < 0) decreased from a -26.1 mm for 1990-2010 to -72.1 mm for the year 2080-2100. The results suggest that additional water could be saved during the wet months to offset increased irrigation requirements during the dry months. Relative crop yield reduction did not change significantly under the B1 projected emissions scenario, but increased by approximately 20% during the summer months under the A1fi emissions scenario. Components of the annual water balance for the three climate change scenarios are rainfall, evapotranspiration (adjusted for soil moisture), surface runoff, aquifer recharge and change in soil moisture storage. Under the A1fi scenario, for all locations, annual evapotranspiration decreased owing to lower soil moisture, surface runoff decreased, and aquifer recharge increased. Aquifer recharge increased at all three locations because the majority of recharge occurs during the wet season and the wet season became wetter. This is good news from a groundwater production standpoint. Increasing aquifer recharge also suggests that groundwater levels may increase and this may help to minimize saltwater intrusion near the coasts as sea levels increase, provided that groundwater use is not over-subscribed.« less

Synthesis and identification of three-dimensional faces from image(s) and three-dimensional generic models

NASA Astrophysics Data System (ADS)

Liu, Zexi; Cohen, Fernand

2017-11-01

We describe an approach for synthesizing a three-dimensional (3-D) face structure from an image or images of a human face taken at a priori unknown poses using gender and ethnicity specific 3-D generic models. The synthesis process starts with a generic model, which is personalized as images of the person become available using preselected landmark points that are tessellated to form a high-resolution triangular mesh. From a single image, two of the three coordinates of the model are reconstructed in accordance with the given image of the person, while the third coordinate is sampled from the generic model, and the appearance is made in accordance with the image. With multiple images, all coordinates and appearance are reconstructed in accordance with the observed images. This method allows for accurate pose estimation as well as face identification in 3-D rendering of a difficult two-dimensional (2-D) face recognition problem into a much simpler 3-D surface matching problem. The estimation of the unknown pose is achieved using the Levenberg-Marquardt optimization process. Encouraging experimental results are obtained in a controlled environment with high-resolution images under a good illumination condition, as well as for images taken in an uncontrolled environment under arbitrary illumination with low-resolution cameras.

Long-term observations of crop water use with eddy covariance stations and coupling with crop simulation models

USDA-ARS?s Scientific Manuscript database

Understanding crop water use is critical to being able to determine crop water requirements and when water is limiting crop productivity. There have been many different techniques used to quantify crop water use and the eddy covariance approach is one method that has the capacity to measure crop wat...

The use of statistical tools in field testing of putative effects of genetically modified plants on nontarget organisms

PubMed Central

Semenov, Alexander V; Elsas, Jan Dirk; Glandorf, Debora C M; Schilthuizen, Menno; Boer, Willem F

2013-01-01

Abstract To fulfill existing guidelines, applicants that aim to place their genetically modified (GM) insect-resistant crop plants on the market are required to provide data from field experiments that address the potential impacts of the GM plants on nontarget organisms (NTO's). Such data may be based on varied experimental designs. The recent EFSA guidance document for environmental risk assessment (2010) does not provide clear and structured suggestions that address the statistics of field trials on effects on NTO's. This review examines existing practices in GM plant field testing such as the way of randomization, replication, and pseudoreplication. Emphasis is placed on the importance of design features used for the field trials in which effects on NTO's are assessed. The importance of statistical power and the positive and negative aspects of various statistical models are discussed. Equivalence and difference testing are compared, and the importance of checking the distribution of experimental data is stressed to decide on the selection of the proper statistical model. While for continuous data (e.g., pH and temperature) classical statistical approaches – for example, analysis of variance (ANOVA) – are appropriate, for discontinuous data (counts) only generalized linear models (GLM) are shown to be efficient. There is no golden rule as to which statistical test is the most appropriate for any experimental situation. In particular, in experiments in which block designs are used and covariates play a role GLMs should be used. Generic advice is offered that will help in both the setting up of field testing and the interpretation and data analysis of the data obtained in this testing. The combination of decision trees and a checklist for field trials, which are provided, will help in the interpretation of the statistical analyses of field trials and to assess whether such analyses were correctly applied. We offer generic advice to risk assessors and applicants that will help in both the setting up of field testing and the interpretation and data analysis of the data obtained in field testing. PMID:24567836

The use of statistical tools in field testing of putative effects of genetically modified plants on nontarget organisms.

PubMed

Semenov, Alexander V; Elsas, Jan Dirk; Glandorf, Debora C M; Schilthuizen, Menno; Boer, Willem F

2013-08-01

To fulfill existing guidelines, applicants that aim to place their genetically modified (GM) insect-resistant crop plants on the market are required to provide data from field experiments that address the potential impacts of the GM plants on nontarget organisms (NTO's). Such data may be based on varied experimental designs. The recent EFSA guidance document for environmental risk assessment (2010) does not provide clear and structured suggestions that address the statistics of field trials on effects on NTO's. This review examines existing practices in GM plant field testing such as the way of randomization, replication, and pseudoreplication. Emphasis is placed on the importance of design features used for the field trials in which effects on NTO's are assessed. The importance of statistical power and the positive and negative aspects of various statistical models are discussed. Equivalence and difference testing are compared, and the importance of checking the distribution of experimental data is stressed to decide on the selection of the proper statistical model. While for continuous data (e.g., pH and temperature) classical statistical approaches - for example, analysis of variance (ANOVA) - are appropriate, for discontinuous data (counts) only generalized linear models (GLM) are shown to be efficient. There is no golden rule as to which statistical test is the most appropriate for any experimental situation. In particular, in experiments in which block designs are used and covariates play a role GLMs should be used. Generic advice is offered that will help in both the setting up of field testing and the interpretation and data analysis of the data obtained in this testing. The combination of decision trees and a checklist for field trials, which are provided, will help in the interpretation of the statistical analyses of field trials and to assess whether such analyses were correctly applied. We offer generic advice to risk assessors and applicants that will help in both the setting up of field testing and the interpretation and data analysis of the data obtained in field testing.

Influence of feedbacks from simulated crop growth on integrated regional hydrologic simulations under climate scenarios

NASA Astrophysics Data System (ADS)

van Walsum, P. E. V.

2011-11-01

Climate change impact modelling of hydrologic responses is hampered by climate-dependent model parameterizations. Reducing this dependency was one of the goals of extending the regional hydrologic modelling system SIMGRO with a two-way coupling to the crop growth simulation model WOFOST. The coupling includes feedbacks to the hydrologic model in terms of the root zone depth, soil cover, leaf area index, interception storage capacity, crop height and crop factor. For investigating whether such feedbacks lead to significantly different simulation results, two versions of the model coupling were set up for a test region: one with exogenous vegetation parameters, the "static" model, and one with endogenous simulation of the crop growth, the "dynamic" model WOFOST. The used parameterization methods of the static/dynamic vegetation models ensure that for the current climate the simulated long-term average of the actual evapotranspiration is the same for both models. Simulations were made for two climate scenarios. Owing to the higher temperatures in combination with a higher CO2-concentration of the atmosphere, a forward time shift of the crop development is simulated in the dynamic model; the used arable land crop, potatoes, also shows a shortening of the growing season. For this crop, a significant reduction of the potential transpiration is simulated compared to the static model, in the example by 15% in a warm, dry year. In consequence, the simulated crop water stress (the unit minus the relative transpiration) is lower when the dynamic model is used; also the simulated increase of crop water stress due to climate change is lower; in the example, the simulated increase is 15 percentage points less (of 55) than when a static model is used. The static/dynamic models also simulate different absolute values of the transpiration. The difference is most pronounced for potatoes at locations with ample moisture supply; this supply can either come from storage release of a good soil or from capillary rise. With good supply of moisture, the dynamic model simulates up to 10% less actual evapotranspiration than the static one in the example. This can lead to cases where the dynamic model predicts a slight increase of the recharge in a climate scenario, where the static model predicts a decrease. The use of a dynamic model also affects the simulated demand for surface water from external sources; especially the timing is affected. The proposed modelling approach uses postulated relationships that require validation with controlled field trials. In the Netherlands there is a lack of experimental facilities for performing such validations.

A review of crop canopy reflectance models

NASA Technical Reports Server (NTRS)

Goel, N. S. (Principal Investigator)

1982-01-01

Various models for calculating crop canopy reflectance, in the visible and infrared wavelengths, from the optical and geometrical properties of a canopy and its constituents are reviewed. The radiative transfer equation is discussed as well as both analytical and numerical crop reflectance models which are manifestations of the solution of this equation. Recommendations are made for further work in modeling of canopy reflectance.

Comparing crop growth and carbon budgets simulated across AmeriFlux agricultural sites using the community land model (CLM)

USDA-ARS?s Scientific Manuscript database

Improving process-based crop models is needed to achieve high fidelity forecasts of regional energy, water, and carbon exchange. However, most state-of-the-art Land Surface Models (LSMs) assessed in the fifth phase of the Coupled Model Inter-comparison project (CMIP5) simulated crops as simple C3 or...

Crop modeling applications in agricultural water management

USGS Publications Warehouse

Kisekka, Isaya; DeJonge, Kendall C.; Ma, Liwang; Paz, Joel; Douglas-Mankin, Kyle R.

2017-01-01

This article introduces the fourteen articles that comprise the “Crop Modeling and Decision Support for Optimizing Use of Limited Water” collection. This collection was developed from a special session on crop modeling applications in agricultural water management held at the 2016 ASABE Annual International Meeting (AIM) in Orlando, Florida. In addition, other authors who were not able to attend the 2016 ASABE AIM were also invited to submit papers. The articles summarized in this introductory article demonstrate a wide array of applications in which crop models can be used to optimize agricultural water management. The following section titles indicate the topics covered in this collection: (1) evapotranspiration modeling (one article), (2) model development and parameterization (two articles), (3) application of crop models for irrigation scheduling (five articles), (4) coordinated water and nutrient management (one article), (5) soil water management (two articles), (6) risk assessment of water-limited irrigation management (one article), and (7) regional assessments of climate impact (two articles). Changing weather and climate, increasing population, and groundwater depletion will continue to stimulate innovations in agricultural water management, and crop models will play an important role in helping to optimize water use in agriculture.

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.

(abstract) Generic Modeling of a Life Support System for Process Technology Comparisons

NASA Technical Reports Server (NTRS)

Ferrall, J. F.; Seshan, P. K.; Rohatgi, N. K.; Ganapathi, G. B.

1993-01-01

This paper describes a simulation model called the Life Support Systems Analysis Simulation Tool (LiSSA-ST), the spreadsheet program called the Life Support Systems Analysis Trade Tool (LiSSA-TT), and the Generic Modular Flow Schematic (GMFS) modeling technique. Results of using the LiSSA-ST and the LiSSA-TT will be presented for comparing life support systems and process technology options for a Lunar Base and a Mars Exploration Mission.

Quantifying the effect of crop spatial arrangement on weed suppression using functional-structural plant modelling.

PubMed

Evers, Jochem B; Bastiaans, Lammert

2016-05-01

Suppression of weed growth in a crop canopy can be enhanced by improving crop competitiveness. One way to achieve this is by modifying the crop planting pattern. In this study, we addressed the question to what extent a uniform planting pattern increases the ability of a crop to compete with weed plants for light compared to a random and a row planting pattern, and how this ability relates to crop and weed plant density as well as the relative time of emergence of the weed. To this end, we adopted the functional-structural plant modelling approach which allowed us to explicitly include the 3D spatial configuration of the crop-weed canopy and to simulate intra- and interspecific competition between individual plants for light. Based on results of simulated leaf area development, canopy photosynthesis and biomass growth of the crop, we conclude that differences between planting pattern were small, particularly if compared to the effects of relative time of emergence of the weed, weed density and crop density. Nevertheless, analysis of simulated weed biomass demonstrated that a uniform planting of the crop improved the weed-suppression ability of the crop canopy. Differences in weed suppressiveness between planting patterns were largest with weed emergence before crop emergence, when the suppressive effect of the crop was only marginal. With simultaneous emergence a uniform planting pattern was 8 and 15 % more competitive than a row and a random planting pattern, respectively. When weed emergence occurred after crop emergence, differences between crop planting patterns further decreased as crop canopy closure was reached early on regardless of planting pattern. We furthermore conclude that our modelling approach provides promising avenues to further explore crop-weed interactions and aid in the design of crop management strategies that aim at improving crop competitiveness with weeds.

Generation of multi annual land use and crop rotation data for regional agro-ecosystem modeling

NASA Astrophysics Data System (ADS)

Waldhoff, G.; Lussem, U.; Sulis, M.; Bareth, G.

2017-12-01

For agro-ecosystem modeling on a regional scale with systems like the Community Land Model (CLM), detailed crop type and crop rotation information on the parcel-level is of key importance. Only with this, accurate assessments of the fluxes associated with the succession of crops and their management are possible. However, sophisticated agro-ecosystem modeling for large regions is only feasible at grid resolutions, which are much coarser than the spatial resolution of modern land use maps (usually ca. 30 m). As a result, much of the original information content of the maps has to be dismissed during resampling. Here we present our mapping approach for the Rur catchment (located in the west of Germany), which was developed to address these demands and issues. We integrated remote sensing and geographic information system (GIS) methods to classify multi temporal images of (e.g.) Landsat, RapidEye and Sentinel-2 to generate annual crop maps for the years 2008-2017 at 15 m spatial resolution (accuracy always ca. 90 %). A key aspect of our method is the consideration of crop phenology for the data selection and the analysis. In a GIS, the annul crop maps were integrated to a crop sequence dataset from which the major crop rotations were derived (based on the 10-years). To retain the multi annual crop succession and crop area information at coarser grid resolutions, cell-based land use fractions, including other land use classes were calculated for each year and for various target cell sizes (1-32 arc seconds). The resulting datasets contain the contribution (in percent) of every land use class to each cell. Our results show that parcels with the major crop types can be differentiated with a high accuracy and on an annual basis. The analysis of the crop sequence data revealed a very large number of different crop rotations, but only relatively few crop rotations cover larger areas. This strong diversity emphasizes the importance of information on crop rotations to reduce uncertainties in agro-ecosystem modeling. Through the combination of the multi annual land use fractions, the resulting datasets additionally inform about land use changes and trends within the coarser grid cells. We see this as a major advantage, because we are able to maintain much more precise land use information when a coarser cell size is used.

Issues of Spatial and Temporal Scale in Modeling the Effects of Field Operatiions on Soil Properties

USDA-ARS?s Scientific Manuscript database

Tillage is an important procedure for modifying the soil environment in order to enhance crop growth and conserve soil and water resources. Process-based models of crop production are widely used in decision support, but few explicitly simulate tillage. The Cropping Systems Model (CSM) was modified ...

Determining the potential productivity of food crops in controlled environments

NASA Technical Reports Server (NTRS)

Bugbee, Bruce

1992-01-01

The quest to determine the maximum potential productivity of food crops is greatly benefitted by crop growth models. Many models have been developed to analyze and predict crop growth in the field, but it is difficult to predict biological responses to stress conditions. Crop growth models for the optimal environments of a Controlled Environment Life Support System (CELSS) can be highly predictive. This paper discusses the application of a crop growth model to CELSS; the model is used to evaluate factors limiting growth. The model separately evaluates the following four physiological processes: absorption of PPF by photosynthetic tissue, carbon fixation (photosynthesis), carbon use (respiration), and carbon partitioning (harvest index). These constituent processes determine potentially achievable productivity. An analysis of each process suggests that low harvest index is the factor most limiting to yield. PPF absorption by plant canopies and respiration efficiency are also of major importance. Research concerning productivity in a CELSS should emphasize: (1) the development of gas exchange techniques to continuously monitor plant growth rates and (2) environmental techniques to reduce plant height in communities.

Development and validation of a tool to assess knowledge and attitudes towards generic medicines among students in Greece: The ATtitude TOwards GENerics (ATTOGEN) questionnaire

PubMed Central

Katsari, Vasiliki; Niakas, Dimitris

2017-01-01

Introduction The use of generic medicines is a cost-effective policy, often dictated by fiscal restraints. To our knowledge, no fully validated tool exploring the students’ knowledge and attitudes towards generic medicines exists. The aim of our study was to develop and validate a questionnaire exploring the knowledge and attitudes of M.Sc. in Health Care Management students and recent alumni’s towards generic drugs in Greece. Materials and methods The development of the questionnaire was a result of literature review and pilot-testing of its preliminary versions to researchers and students. The final version of the questionnaire contains 18 items measuring the respondents’ knowledge and attitude towards generic medicines on a 5-point Likert scale. Given the ordinal nature of the data, ordinal alpha and polychoric correlations were computed. The sample was randomly split into two halves. Exploratory factor analysis, performed in the first sample, was used for the creation of multi-item scales. Confirmatory factor analysis and Generalized Linear Latent and Mixed Model analysis (GLLAMM) with the use of the rating scale model were used in the second sample to assess goodness of fit. An assessment of internal consistency reliability, test-retest reliability, and construct validity was also performed. Results Among 1402 persons contacted, 986 persons completed our questionnaire (response rate = 70.3%). Overall Cronbach’s alpha was 0.871. The conjoint use of exploratory and confirmatory factor analysis resulted in a six-scale model, which seemed to fit the data well. Five of the six scales, namely trust, drug quality, state audit, fiscal impact and drug substitution were found to be valid and reliable, while the knowledge scale suffered only from low inter-scale correlations and a ceiling effect. However, the subsequent confirmatory factor and GLLAMM analyses indicated a good fit of the model to the data. Conclusions The ATTOGEN instrument proved to be a reliable and valid tool, suitable for assessing students’ knowledge and attitudes towards generic medicines. PMID:29186163

Toward an Integrated Design, Inspection and Redundancy Research Program.

DTIC Science & Technology

1984-01-01

William Creelman William H. Silcox National Marine Service Standard Oil Company of California St. Louis, Missouri San Francisco, California .-- N...develop physical models and generic tools for analyzing the effects of redundancy, reserve strength, and residual strength on the system behavior of marine...probabilistic analyses to be applicable to real-world problems, this program needs to provide - the deterministic physical models and generic tools upon

Exact solution of the XXX Gaudin model with generic open boundaries

NASA Astrophysics Data System (ADS)

Hao, Kun; Cao, Junpeng; Yang, Tao; Yang, Wen-Li

2015-03-01

The XXX Gaudin model with generic integrable open boundaries specified by the most general non-diagonal reflecting matrices is studied. Besides the inhomogeneous parameters, the associated Gaudin operators have six free parameters which break the U(1) -symmetry. With the help of the off-diagonal Bethe ansatz, we successfully obtained the eigenvalues of these Gaudin operators and the corresponding Bethe ansatz equations.

Naturalness of Electroweak Symmetry Breaking while Waiting for the LHC

NASA Astrophysics Data System (ADS)

Espinosa, J. R.

2007-06-01

After revisiting the hierarchy problem of the Standard Model and its implications for the scale of New Physics, I consider the finetuning problem of electroweak symmetry breaking in several scenarios beyond the Standard Model: SUSY, Little Higgs and "improved naturalness" models. The main conclusions are that: New Physics should appear on the reach of the LHC; some SUSY models can solve the hierarchy problem with acceptable residual tuning; Little Higgs models generically suffer from large tunings, many times hidden; and, finally, that "improved naturalness" models do not generically improve the naturalness of the SM.

Simulating crop phenology in the Community Land Model and its impact on energy and carbon fluxes

NASA Astrophysics Data System (ADS)

Chen, Ming; Griffis, Tim J.; Baker, John; Wood, Jeffrey D.; Xiao, Ke

2015-02-01

A reasonable representation of crop phenology and biophysical processes in land surface models is necessary to accurately simulate energy, water, and carbon budgets at the field, regional, and global scales. However, the evaluation of crop models that can be coupled to Earth system models is relatively rare. Here we evaluated two such models (CLM4-Crop and CLM3.5-CornSoy), both implemented within the Community Land Model (CLM) framework, at two AmeriFlux corn-soybean sites to assess their ability to simulate phenology, energy, and carbon fluxes. Our results indicated that the accuracy of net ecosystem exchange and gross primary production simulations was intimately connected to the phenology simulations. The CLM4-Crop model consistently overestimated early growing season leaf area index, causing an overestimation of gross primary production, to such an extent that the model simulated a carbon sink instead of the measured carbon source for corn. The CLM3.5-CornSoy-simulated leaf area index (LAI), energy, and carbon fluxes showed stronger correlations with observations compared to CLM4-Crop. Net radiation was biased high in both models and was especially pronounced for soybeans. This was primarily caused by the positive LAI bias, which led to a positive net long-wave radiation bias. CLM4-Crop underestimated soil water content during midgrowing season in all soil layers at the two sites, which caused unrealistic water stress, especially for soybean. Future work regarding the mechanisms that drive early growing season phenology and soil water dynamics is needed to better represent crops including their net radiation balance, energy partitioning, and carbon cycle processes.

Simulating Crop Evapotranspiration Response under Different Planting Scenarios by Modified SWAT Model in an Irrigation District, Northwest China.

PubMed

Liu, Xin; Wang, Sufen; Xue, Han; Singh, Vijay P

2015-01-01

Modelling crop evapotranspiration (ET) response to different planting scenarios in an irrigation district plays a significant role in optimizing crop planting patterns, resolving agricultural water scarcity and facilitating the sustainable use of water resources. In this study, the SWAT model was improved by transforming the evapotranspiration module. Then, the improved model was applied in Qingyuan Irrigation District of northwest China as a case study. Land use, soil, meteorology, irrigation scheduling and crop coefficient were considered as input data, and the irrigation district was divided into subdivisions based on the DEM and local canal systems. On the basis of model calibration and verification, the improved model showed better simulation efficiency than did the original model. Therefore, the improved model was used to simulate the crop evapotranspiration response under different planting scenarios in the irrigation district. Results indicated that crop evapotranspiration decreased by 2.94% and 6.01% under the scenarios of reducing the planting proportion of spring wheat (scenario 1) and summer maize (scenario 2) by keeping the total cultivated area unchanged. However, the total net output values presented an opposite trend under different scenarios. The values decreased by 3.28% under scenario 1, while it increased by 7.79% under scenario 2, compared with the current situation. This study presents a novel method to estimate crop evapotranspiration response under different planting scenarios using the SWAT model, and makes recommendations for strategic agricultural water management planning for the rational utilization of water resources and development of local economy by studying the impact of planting scenario changes on crop evapotranspiration and output values in the irrigation district of northwest China.

Simulating Crop Evapotranspiration Response under Different Planting Scenarios by Modified SWAT Model in an Irrigation District, Northwest China

PubMed Central

Liu, Xin; Wang, Sufen; Xue, Han; Singh, Vijay P.

2015-01-01

Modelling crop evapotranspiration (ET) response to different planting scenarios in an irrigation district plays a significant role in optimizing crop planting patterns, resolving agricultural water scarcity and facilitating the sustainable use of water resources. In this study, the SWAT model was improved by transforming the evapotranspiration module. Then, the improved model was applied in Qingyuan Irrigation District of northwest China as a case study. Land use, soil, meteorology, irrigation scheduling and crop coefficient were considered as input data, and the irrigation district was divided into subdivisions based on the DEM and local canal systems. On the basis of model calibration and verification, the improved model showed better simulation efficiency than did the original model. Therefore, the improved model was used to simulate the crop evapotranspiration response under different planting scenarios in the irrigation district. Results indicated that crop evapotranspiration decreased by 2.94% and 6.01% under the scenarios of reducing the planting proportion of spring wheat (scenario 1) and summer maize (scenario 2) by keeping the total cultivated area unchanged. However, the total net output values presented an opposite trend under different scenarios. The values decreased by 3.28% under scenario 1, while it increased by 7.79% under scenario 2, compared with the current situation. This study presents a novel method to estimate crop evapotranspiration response under different planting scenarios using the SWAT model, and makes recommendations for strategic agricultural water management planning for the rational utilization of water resources and development of local economy by studying the impact of planting scenario changes on crop evapotranspiration and output values in the irrigation district of northwest China. PMID:26439928

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

NASA Astrophysics Data System (ADS)

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

2010-05-01

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

How should we build a generic open-source water management simulator?

NASA Astrophysics Data System (ADS)

Khadem, M.; Meier, P.; Rheinheimer, D. E.; Padula, S.; Matrosov, E.; Selby, P. D.; Knox, S.; Harou, J. J.

2014-12-01

Increasing water needs for agriculture, industry and cities mean effective and flexible water resource system management tools will remain in high demand. Currently many regions or countries use simulators that have been adapted over time to their unique system properties and water management rules and realities. Most regions operate with a preferred short-list of water management and planning decision support systems. Is there scope for a simulator, shared within the water management community, that could be adapted to different contexts, integrate community contributions, and connect to generic data and model management software? What role could open-source play in such a project? How could a genericuser-interface and data/model management software sustainably be attached to this model or suite of models? Finally, how could such a system effectively leverage existing model formulations, modeling technologies and software? These questions are addressed by the initial work presented here. We introduce a generic water resource simulation formulation that enables and integrates both rule-based and optimization driven technologies. We suggest how it could be linked to other sub-models allowing for detailed agent-based simulation of water management behaviours. An early formulation is applied as an example to the Thames water resource system in the UK. The model uses centralised optimisation to calculate allocations but allows for rule-based operations as well in an effort to represent observed behaviours and rules with fidelity. The model is linked through import/export commands to a generic network model platform named Hydra. Benefits and limitations of the approach are discussed and planned work and potential use cases are outlined.

Rice crop mapping and change prediction using multi-temporal satellite images in the Mekong Delta, Vietnam

NASA Astrophysics Data System (ADS)

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

2014-12-01

The rice cropping systems in the Vietnamese Mekong Delta (VMD) has been undergoing major changes to cope with developing agro-economics, increasing population and changing climate. Information on rice cropping practices and changes in cropping systems is critical for policymakers to devise successful strategies to ensure food security and rice grain exports for the country. The primary objective of this research is to map rice cropping systems and predict future dynamics of rice cropping systems using the MODIS time-series data of 2002, 2006, and 2010. First, a phenology-based classification approach was applied for the classification and assessment of rice cropping systems in study region. Second, the Cellular Automata-Markov (CA-Markov) models was used to simulate the rice-cropping system map of VMD for 2010. The comparisons between the classification maps and the ground reference data indicated satisfactory results with overall accuracies and Kappa coefficients, respectively, of 81.4% and 0.75 for 2002, 80.6% and 0.74 for 2006 and 85.5% and 0.81 for 2010. The simulated map of rice cropping system for 2010 was extrapolated by CA-Markov model based on the trend of rice cropping systems during 2002~2006. The comparison between predicted scenario and classification map for 2010 presents a reasonably closer agreement. In conclusion, the CA-Markov model performs a powerful tool for the dynamic modeling of changes in rice cropping systems, and the results obtained demonstrate that the approach produces satisfactory results in terms of accuracy, quantitative forecast and spatial pattern changes. Meanwhile, the projections of the future changes would provide useful inputs to the agricultural policy for effective management of the rice cropping practices in VMD.

Climate Change Impacts on Crop Production in Nigeria

NASA Astrophysics Data System (ADS)

Mereu, V.; Gallo, A.; Carboni, G.; Spano, D.

2011-12-01

The agricultural sector in Nigeria is particularly important for the country's food security, natural resources, and growth agenda. The cultivable areas comprise more than 70% of the total area; however, the cultivated area is about the 35% of the total area. The most important components in the food basket of the nation are cereals and tubers, which include rice, maize, corn, millet, sorghum, yam, and cassava. These crops represent about 80% of the total agricultural product in Nigeria (from NPAFS). The major crops grown in the country can be divided into food crops (produced for consumption) and export products. Despite the importance of the export crops, the primary policy of agriculture is to make Nigeria self-sufficient in its food and fiber requirements. The projected impacts of future climate change on agriculture and water resources are expected to be adverse and extensive in these area. This implies the need for actions and measures to adapt to climate change impacts, and especially as they affect agriculture, the primary sector for Nigerian economy. In the framework of the Project Climate Risk Analysis in Nigeria (founded by World Bank Contract n.7157826), a study was made to assess the potential impact of climate change on the main crops that characterize Nigerian agriculture. The DSSAT-CSM (Decision Support System for Agrotechnology Transfer - Cropping System Model) software, version 4.5 was used for the analysis. Crop simulation models included in DSSAT are tools that simulate physiological processes of crop growth, development and production by combining genetic crop characteristics and environmental (soil and weather) conditions. For each selected crop, the models were calibrated to evaluate climate change impacts on crop production. The climate data used for the analysis are derived by the Regional Circulation Model COSMO-CLM, from 1971 to 2065, at 8 km of spatial resolution. The RCM model output was "perturbed" with 10 Global Climate Models to have a wide variety of possible climate projections for the impact analysis. Multiple combinations of soil and climate conditions and crop management and varieties were considered for each Agro-Ecological Zone (AEZ) of Nigeria. A sensitivity analysis was made to evaluate the model response to changes in precipitation and temperature. The climate impact assessment was made by comparing the yield obtained with the climate data for the present period and the yield obtainable under future climate conditions. The results were analyzed at state, AEZ and country levels. The analysis shows a general reduction in crop yields in particular in the dryer regions of northern Nigeria.

Urban-Rural Disparity of Generics Prescription in Taiwan: The Example of Dihydropyridine Derivatives

PubMed Central

Chiang, Shu-Chiung; Chou, Li-Fang

2014-01-01

The aim of the current study was to investigate the urban-rural disparity of prescribing generics, which were usually cheaper than branded drugs, within the universal health insurance system in Taiwan. Data sources were the cohort datasets of National Health Insurance Research Database with claims data in 2010. The generic prescribing ratios of dihydropyridine (DHP) derivatives (the proportion of DHP prescribed as generics to all prescribed DHP) of medical facilities were examined against the urbanization levels of the clinic location. Among the total 21,606,914 defined daily doses of DHP, 35.7% belonged to generics. The aggregate generic prescribing ratio rose from 6.7% at academic medical centers to 15.3% at regional hospitals, 29.4% at community hospital, and 66.1% at physician clinics. Among physician clinics, the generic prescribing ratio in urban areas was 63.9 ± 41.0% (mean ± standard deviation), lower than that in suburban (69.6 ± 38.7%) and in rural (74.1% ± 35.3%). After adjusting the related factors in the linear regression model, generic prescribing ratios of suburban and rural clinics were significantly higher than those of urban clinics (β = 0.043 and 0.077; P = 0.024 and 0.008, resp.). The generic prescribing ratio of the most popular antihypertensive agents at a clinic was reversely associated with the urbanization level. PMID:24683364

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

Development of an Integrated Nonlinear Aeroservoelastic Flight Dynamic Model of the NASA Generic Transport Model

NASA Technical Reports Server (NTRS)

Nguyen, Nhan; Ting, Eric

2018-01-01

This paper describes a recent development of an integrated fully coupled aeroservoelastic flight dynamic model of the NASA Generic Transport Model (GTM). The integrated model couples nonlinear flight dynamics to a nonlinear aeroelastic model of the GTM. The nonlinearity includes the coupling of the rigid-body aircraft states in the partial derivatives of the aeroelastic angle of attack. Aeroservoelastic modeling of the control surfaces which are modeled by the Variable Camber Continuous Trailing Edge Flap is also conducted. The R.T. Jones' method is implemented to approximate unsteady aerodynamics. Simulations of the GTM are conducted with simulated continuous and discrete gust loads..

Two-way Coupling of a Process-Based Crop Growth Model (BioCro) and a Biogeochemistry Model (DayCent) and its Application to an Energy Crop Site in the mid-west USA

NASA Astrophysics Data System (ADS)

Jaiswal, D.; Long, S.; Parton, W. J.; Hartman, M.

2012-12-01

A coupled modeling system of crop growth model (BioCro) and biogeochemical model (DayCent) has been developed to assess the two-way interactions between plant growth and biogeochemistry. Crop growth in BioCro is simulated using a detailed mechanistic biochemical and biophysical multi-layer canopy model and partitioning of dry biomass into different plant organs according to phenological stages. Using hourly weather records, the model partitions light between dynamically changing sunlit and shaded portions of the canopy and computes carbon and water exchange with the atmosphere and through the canopy for each hour of the day, each day of the year. The model has been parameterized for the bioenergy crops sugarcane, Miscanthus and switchgrass, and validation has shown it to predict growth cycles and partitioning of biomass to a high degree of accuracy. As such it provides an ideal input for a soil biogeochemical model. DayCent is an established model for predicting long-term changes in soil C & N and soil-atmosphere exchanges of greenhouse gases. At present, DayCent uses a relatively simple productivity model. In this project BioCro has replaced this simple model to provide DayCent with a productivity and growth model equal in detail to its biogeochemistry. Dynamic coupling of these two models to produce CroCent allows for differential C: N ratios of litter fall (based on rates of senescence of different plant organs) and calibration of the model for realistic plant productivity in a mechanistic way. A process-based approach to modeling plant growth is needed for bioenergy crops because research on these crops (especially second generation feedstocks) has started only recently, and detailed agronomic information for growth, yield and management is too limited for effective empirical models. The coupled model provides means to test and improve the model against high resolution data, such as that obtained by eddy covariance and explore yield implications of different crop and soil management.

Effects of dynamic agricultural decision making in an ecohydrological model

NASA Astrophysics Data System (ADS)

Reichenau, T. G.; Krimly, T.; Schneider, K.

2012-04-01

Due to various interdependencies between the cycles of water, carbon, nitrogen, and energy the impacts of climate change on ecohydrological systems can only be investigated in an integrative way. Furthermore, the human intervention in the environmental processes makes the system even more complex. On the one hand human impact affects natural systems. On the other hand the changing natural systems have a feedback on human decision making. One of the most important examples for this kind of interaction can be found in the agricultural sector. Management dates (planting, fertilization, harvesting) are chosen based on meteorological conditions and yield expectations. A faster development of crops under a warmer climate causes shorter cropping seasons. The choice of crops depends on their profitability, which is mainly determined by market prizes, the agro-political framework, and the (climate dependent) crop yield. This study investigates these relations for the district Günzburg located in the Upper Danube catchment in southern Germany. The modeling system DANUBIA was used to perform dynamically coupled simulations of plant growth, surface and soil hydrological processes, soil nitrogen transformations, and agricultural decision making. The agro-economic model simulates decisions on management dates (based on meteorological conditions and the crops' development state), on fertilization intensities (based on yield expectations), and on choice of crops (based on profitability). The environmental models included in DANUBIA are to a great extent process based to enable its use in a climate change scenario context. Scenario model runs until 2058 were performed using an IPCC A1B forcing. In consecutive runs, dynamic crop management, dynamic crop selection, and a changing agro-political framework were activated. Effects of these model features on hydrological and ecological variables were analyzed separately by comparing the results to a model run with constant crop distribution and constant management. Results show that the influence of the modeled dynamic management adaptation on variables like transpiration, carbon uptake, or nitrate leaching from the vadose zone is stronger than the influence of a dynamic choice of crops. Climate change was found to have a stronger impact on this modeled choice of crops than the agro-political framework. These results suggest that scenario studies in areas with a large share of arable land should take into account management adaptations to changing climate.

Generic Safety Requirements for Developing Safe Insulin Pump Software

PubMed Central

Zhang, Yi; Jetley, Raoul; Jones, Paul L; Ray, Arnab

2011-01-01

Background The authors previously introduced a highly abstract generic insulin infusion pump (GIIP) model that identified common features and hazards shared by most insulin pumps on the market. The aim of this article is to extend our previous work on the GIIP model by articulating safety requirements that address the identified GIIP hazards. These safety requirements can be validated by manufacturers, and may ultimately serve as a safety reference for insulin pump software. Together, these two publications can serve as a basis for discussing insulin pump safety in the diabetes community. Methods In our previous work, we established a generic insulin pump architecture that abstracts functions common to many insulin pumps currently on the market and near-future pump designs. We then carried out a preliminary hazard analysis based on this architecture that included consultations with many domain experts. Further consultation with domain experts resulted in the safety requirements used in the modeling work presented in this article. Results Generic safety requirements for the GIIP model are presented, as appropriate, in parameterized format to accommodate clinical practices or specific insulin pump criteria important to safe device performance. Conclusions We believe that there is considerable value in having the diabetes, academic, and manufacturing communities consider and discuss these generic safety requirements. We hope that the communities will extend and revise them, make them more representative and comprehensive, experiment with them, and use them as a means for assessing the safety of insulin pump software designs. One potential use of these requirements is to integrate them into model-based engineering (MBE) software development methods. We believe, based on our experiences, that implementing safety requirements using MBE methods holds promise in reducing design/implementation flaws in insulin pump development and evolutionary processes, therefore improving overall safety of insulin pump software. PMID:22226258

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.

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.

Modeling salt movement and halophytic crop growth on marginal lands with the APEX model

NASA Astrophysics Data System (ADS)

Goehring, N.; Saito, L.; Verburg, P.; Jeong, J.; Garrett, A.

2016-12-01

Saline soils negatively impact crop productivity in nearly 20% of irrigated agricultural lands worldwide. At these saline sites, cultivation of highly salt-tolerant plants, known as halophytes, may increase productivity compared to conventional salt-sensitive crops (i.e., glycophytes), thereby increasing the economic potential of marginal lands. Through a variety of mechanisms, halophytes are more effective than glycophytes at excluding, accumulating, and secreting salts from their tissues. Each mechanism can have a different impact on the salt balance in the plant-soil-water system. To date, little information is available to understand the long-term impacts of halophyte cultivation on environmental quality. This project utilizes the Agricultural Policy/Environmental Extender (APEX) model, developed by the US Department of Agriculture, to model the growth and production of two halophytic crops. The crops being modeled include quinoa (Chenopodium quinoa), which has utilities for human consumption and forage, and AC Saltlander green wheatgrass (Elymus hoffmannii), which has forage utility. APEX simulates salt movement between soil layers and accounts for the salt balance in the plant-soil-water system, including salinity in irrigation water and crop-specific salt uptake. Key crop growth parameters in APEX are derived from experimental growth data obtained under non-stressed conditions. Data from greenhouse and field experiments in which quinoa and AC Saltlander were grown under various soil salinity and irrigation salinity treatments are being used to parameterize, calibrate, and test the model. This presentation will discuss progress on crop parameterization and completed model runs under different salt-affected soil and irrigation conditions.

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.

An Integrated Approach to Conversion, Verification, Validation and Integrity of AFRL Generic Engine Model and Simulation (Postprint)

DTIC Science & Technology

2007-02-01

and Astronautics 11 PS3C W3 P3 T3 FAR3 Ps3 W41 P41 T41 FAR41 Ps41 W4 P4 T4 FAR4 Ps4 7 NozFlow 6 Flow45 5 Flow44 4 Flow41 3 Flow4 2 Flow3 1 N2Bal... Motivation for Modeling and Simulation Work The Augmented Generic Engine Model (AGEM) Model Verification and Validation (V&V) Assessment of AGEM V&V

Analysis of Wind Turbine Simulation Models: Assessment of Simplified versus Complete Methodologies: Preprint

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

Honrubia-Escribano, A.; Jimenez-Buendia, F.; Molina-Garcia, A.

This paper presents the current status of simplified wind turbine models used for power system stability analysis. This work is based on the ongoing work being developed in IEC 61400-27. This international standard, for which a technical committee was convened in October 2009, is focused on defining generic (also known as simplified) simulation models for both wind turbines and wind power plants. The results of the paper provide an improved understanding of the usability of generic models to conduct power system simulations.

General description and operation of the agro-environmental system: Crop management modeling. [Virginia

NASA Technical Reports Server (NTRS)

Gross, E.; Scott, J. H., Jr.

1981-01-01

Input for a data management system to provide farmers with information to improve crop management practices in Virginia requires monitoring of control crops at field stations, crop surveys derived from remotely sensed aircraft data, meteorological data from synchronous satellites, and details of local agricultural conditions. Presently models are under development for determining pest problems, water balance in the soil, stages of plant maturity, and optimum planting date. The status of the Cerospora leafspot model for peanut crop management is considered. Other models under development planned relate to Cylindtocladium Blackrot and Sclerotinia blight of peanuts, cyst nematode (Globerdena solanacearum) of tobacco, and red crown rot of soybeans. A software for program for estimating precipitation and solar radiation on a statewise basis is also being developed.

PRZM-2, A MODEL FOR PREDICTING PESTICIDE FATE IN THE CROP ROOT AND UNSATURATED SOIL ZONES: USERS MANUAL FOR RELEASE 2.0

EPA Science Inventory

PRZM-2 links two subordinate models--PRZM and VADOFT--in order to predict pesticide transport and transformation down through the crop root and unsaturated zones. RZM is a one-dimensional, finite difference model that accounts for pesticide fate in the crop root zone. his release...

Three-Dimension Visualization for Primary Wheat Diseases Based on Simulation Model

NASA Astrophysics Data System (ADS)

Shijuan, Li; Yeping, Zhu

Crop simulation model has been becoming the core of agricultural production management and resource optimization management. Displaying crop growth process makes user observe the crop growth and development intuitionisticly. On the basis of understanding and grasping the occurrence condition, popularity season, key impact factors for main wheat diseases of stripe rust, leaf rust, stem rust, head blight and powdery mildew from research material and literature, we designed 3D visualization model for wheat growth and diseases occurrence. The model system will help farmer, technician and decision-maker to use crop growth simulation model better and provide decision-making support. Now 3D visualization model for wheat growth on the basis of simulation model has been developed, and the visualization model for primary wheat diseases is in the process of development.

Modeling ecohydrological dynamics of smallholder strategies for food production in dryland agricultural systems

NASA Astrophysics Data System (ADS)

Gower, Drew B.; Dell'Angelo, Jampel; McCord, Paul F.; Caylor, Kelly K.; Evans, Tom P.

2016-11-01

In dryland environments, characterized by low and frequently variable rainfall, smallholder farmers must take crop water sensitivity into account along with other characteristics like seed availability and market price when deciding what to plant. In this paper we use the results of surveys conducted among smallholders located near Mount Kenya to identify clusters of farmers devoting different fractions of their land to subsistence and market crops. Additionally, we explore the tradeoffs between water-insensitive but low-value subsistence crops and a water-sensitive but high-value market crop using a numerical model that simulates soil moisture dynamics and crop production over multiple growing seasons. The cluster analysis shows that most farmers prefer to plant either only subsistence crops or only market crops, with a minority choosing to plant substantial fractions of both. The model output suggests that the value a farmer places on a successful growing season, a measure of risk aversion, plays a large role in whether the farmer chooses a subsistence or market crop strategy. Furthermore, access to irrigation, makes market crops more appealing, even to very risk-averse farmers. We then conclude that the observed clustering may result from different levels of risk aversion and access to irrigation.

Frost risk for overwintering crops in a changing climate

NASA Astrophysics Data System (ADS)

Vico, Giulia; Weih, Martin

2013-04-01

Climate change scenarios predict a general increase in daily temperatures and a decline in snow cover duration. On the one hand, higher temperature in fall and spring may facilitate the development of overwintering crops and allow the expansion of winter cropping in locations where the growing season is currently too short. On the other hand, higher temperatures prior to winter crop dormancy slow down frost hardening, enhancing crop vulnerability to temperature fluctuation. Such vulnerability may be exacerbated by reduced snow cover, with potential further negative impacts on yields in extremely low temperatures. We propose a parsimonious probabilistic model to quantify the winter frost damage risk for overwintering crops, based on a coupled model of air temperature, snow cover, and crop minimum tolerable temperature. The latter is determined by crop features, previous history of temperature, and snow cover. The temperature-snow cover model is tested against meteorological data collected over 50 years in Sweden and applied to winter wheat varieties differing in their ability to acquire frost resistance. Hence, exploiting experimental results assessing crop frost damage under limited temperature and snow cover realizations, this probabilistic framework allows the quantification of frost risk for different crop varieties, including in full temperature and precipitation unpredictability. Climate change scenarios are explored to quantify the effects of changes in temperature mean and variance and precipitation regime over crops differing in winter frost resistance and response to temperature.

Assessing the potential of economic instruments for managing drought risk at river basin scale

NASA Astrophysics Data System (ADS)

Pulido-Velazquez, M.; Lopez-Nicolas, A.; Macian-Sorribes, H.

2015-12-01

Economic instruments work as incentives to adapt individual decisions to collectively agreed goals. Different types of economic instruments have been applied to manage water resources, such as water-related taxes and charges (water pricing, environmental taxes, etc.), subsidies, markets or voluntary agreements. Hydroeconomic models (HEM) provide useful insight on optimal strategies for coping with droughts by simultaneously analysing engineering, hydrology and economics of water resources management. We use HEMs for evaluating the potential of economic instruments on managing drought risk at river basin scale, considering three criteria for assessing drought risk: reliability, resilience and vulnerability. HEMs allow to calculate water scarcity costs as the economic losses due to water deliveries below the target demands, which can be used as a vulnerability descriptor of drought risk. Two generic hydroeconomic DSS tools, SIMGAMS and OPTIGAMS ( both programmed in GAMS) have been developed to evaluate water scarcity cost at river basin scale based on simulation and optimization approaches. The simulation tool SIMGAMS allocates water according to the system priorities and operating rules, and evaluate the scarcity costs using economic demand functions. The optimization tool allocates water resources for maximizing net benefits (minimizing total water scarcity plus operating cost of water use). SIMGAS allows to simulate incentive water pricing policies based on water availability in the system (scarcity pricing), while OPTIGAMS is used to simulate the effect of ideal water markets by economic optimization. These tools have been applied to the Jucar river system (Spain), highly regulated and with high share of water use for crop irrigation (greater than 80%), where water scarcity, irregular hydrology and groundwater overdraft cause droughts to have significant economic, social and environmental consequences. An econometric model was first used to explain the variation of the production value of irrigated agriculture during droughts, assessing revenue responses to varying crop prices and water availability. Hydroeconomic approaches were then used to show the potential of economic instruments in setting incentives for a more efficient management of water resources systems.

Developing COMET-Farm and the DayCent Model for California Specialty Crops

NASA Astrophysics Data System (ADS)

Steenwerth, K. L.; Barker, X. Z.; Carlson, M.; Killian, K.; Easter, M.; Swan, A.; Thompson, L.; Williams, S.; Paustian, K.

2016-12-01

Specialty crops are hugely important to the agricultural economy of California, which grows over 400 specialty crops and produces at least a third of the nations' vegetables and more than two thirds of its fruit and nut tree crops. Since the passage of AB32 Global Warming Solutions Act in 2006, the state has made strong investments in reducing greenhouse gas emissions and developing climate adaptation solutions. Most recently, Governor J. Brown (CA) has issued an executive order to establish reductions to 40% below 1990 levels. While agriculture in California is not regulated for greenhouse gas emissions under AB32, efforts are being made to develop tools to support practices that can enhance soil health and reduce greenhouse gas emissions. USDA-NRCS supports one such tool known as COMET-Farm, which is intended for future use with incentive programs and soil conservation plans managed by the agency. The underlying model that that simulates entity-scale greenhouse gas emissions in COMET-Farm is DayCent. Members of the California Climate Hub are collaborating with the Natural Resource Ecology Laboratory at Colorado State University in Fort Collins, CO to develop DayCent for 15 California specialty crops. These specialty crops include woody perennials like stone fruit like almonds and peaches, walnuts, citrus, wine grapes, raisins and table grapes. Annual specialty crops include cool season vegetables like lettuce and broccoli, tomatoes, and strawberries. DayCent has been parameterized for these crops using existing published and unpublished studies. Practice based information has also been gathered in consultation with growers. Aspects of the model have been developed for woody biomass production and competition between herbaceous vegetation and woody perennial crops. We will report on model performance for these crops and opportunities for model improvement.

Phase I Experimental Testing of a Generic Submarine Model in the DSTO Low Speed Wind Tunnel

DTIC Science & Technology

2012-07-01

used during the tests , along with the test methodology . A sub-set of the data gathered is also presented and briefly discussed. Overall, the force...total pressure probe when positioned close to the model. 4. Results Selected results from the testing of the generic submarine model in the...Appendix B summaries the test conditions. 4.3.1 Smoke Generator and Probe An Aerotech smoke generator and probe were used for visualisation of

Improving plot- and regional-scale crop models for simulating impacts of climate variability and extremes

NASA Astrophysics Data System (ADS)

Tao, F.; Rötter, R.

2013-12-01

Many studies on global climate report that climate variability is increasing with more frequent and intense extreme events1. There are quite large uncertainties from both the plot- and regional-scale models in simulating impacts of climate variability and extremes on crop development, growth and productivity2,3. One key to reducing the uncertainties is better exploitation of experimental data to eliminate crop model deficiencies and develop better algorithms that more adequately capture the impacts of extreme events, such as high temperature and drought, on crop performance4,5. In the present study, in a first step, the inter-annual variability in wheat yield and climate from 1971 to 2012 in Finland was investigated. Using statistical approaches the impacts of climate variability and extremes on wheat growth and productivity were quantified. In a second step, a plot-scale model, WOFOST6, and a regional-scale crop model, MCWLA7, were calibrated and validated, and applied to simulate wheat growth and yield variability from 1971-2012. Next, the estimated impacts of high temperature stress, cold damage, and drought stress on crop growth and productivity based on the statistical approaches, and on crop simulation models WOFOST and MCWLA were compared. Then, the impact mechanisms of climate extremes on crop growth and productivity in the WOFOST model and MCWLA model were identified, and subsequently, the various algorithm and impact functions were fitted against the long-term crop trial data. Finally, the impact mechanisms, algorithms and functions in WOFOST model and MCWLA model were improved to better simulate the impacts of climate variability and extremes, particularly high temperature stress, cold damage and drought stress for location-specific and large area climate impact assessments. Our studies provide a good example of how to improve, in parallel, the plot- and regional-scale models for simulating impacts of climate variability and extremes, as needed for better informed decision-making on adaptation strategies. References 1. Coumou, D. & Rahmstorf, S. A decade of extremes. Nature Clim. Change, 2, 491-496 (2012). 2. Rötter, R. P., Carter, T. R., Olesen, J. E. & Porter, J. R. Crop-climate models need an overhaul. Nature Clim. Change, 1, 175-177 (2011). 3. Asseng, S. et al., Uncertainty in simulating wheat yields under climate change. Nature Clim. Change. 10.1038/nclimate1916. (2013). 4. Porter, J.R., & Semenov, M., Crop responses to climatic variation . Trans. R. Soc. B., 360, 2021-2035 (2005). 5. Porter, J.R. & Christensen, S. Deconstructing crop processes and models via identities. Plant, Cell and Environment . doi: 10.1111/pce.12107 (2013). 6. Boogaard, H.L., van Diepen C.A., Rötter R.P., Cabrera J.M. & van Laar H.H. User's guide for the WOFOST 7.1 crop growth simulation model and Control Center 1.5, Alterra, Wageningen, The Netherlands. (1998) 7. Tao, F. & Zhang, Z. Climate change, wheat productivity and water use in the North China Plain: a new super-ensemble-based probabilistic projection. Agric. Forest Meteorol., 170, 146-165. (2013).

The Climate-Agriculture-Modeling and Decision Tool (CAMDT) for Climate Risk Management in Agriculture

NASA Astrophysics Data System (ADS)

Ines, A. V. M.; Han, E.; Baethgen, W.

2017-12-01

Advances in seasonal climate forecasts (SCFs) during the past decades have brought great potential to improve agricultural climate risk managements associated with inter-annual climate variability. In spite of popular uses of crop simulation models in addressing climate risk problems, the models cannot readily take seasonal climate predictions issued in the format of tercile probabilities of most likely rainfall categories (i.e, below-, near- and above-normal). When a skillful SCF is linked with the crop simulation models, the informative climate information can be further translated into actionable agronomic terms and thus better support strategic and tactical decisions. In other words, crop modeling connected with a given SCF allows to simulate "what-if" scenarios with different crop choices or management practices and better inform the decision makers. In this paper, we present a decision support tool, called CAMDT (Climate Agriculture Modeling and Decision Tool), which seamlessly integrates probabilistic SCFs to DSSAT-CSM-Rice model to guide decision-makers in adopting appropriate crop and agricultural water management practices for given climatic conditions. The CAMDT has a functionality to disaggregate a probabilistic SCF into daily weather realizations (either a parametric or non-parametric disaggregation method) and to run DSSAT-CSM-Rice with the disaggregated weather realizations. The convenient graphical user-interface allows easy implementation of several "what-if" scenarios for non-technical users and visualize the results of the scenario runs. In addition, the CAMDT also translates crop model outputs to economic terms once the user provides expected crop price and cost. The CAMDT is a practical tool for real-world applications, specifically for agricultural climate risk management in the Bicol region, Philippines, having a great flexibility for being adapted to other crops or regions in the world. CAMDT GitHub: https://github.com/Agro-Climate/CAMDT

Using a basin-scale hydrological model to estimate crop transpiration and soil evaporation

NASA Astrophysics Data System (ADS)

Kite, G.

2000-03-01

Increasing populations and expectations, declining crop yields and the resulting increased competition for water necesitate improvements in irrigation management and productivity. A key factor in defining agricultural productivity is to be able to simulate soil evaporation and crop transpiration. In agribusiness terms, crop transpiration is a useful process while soil and open-water evaporations are wasteful processes. In this study a distributed hydrological model was used to compute daily evaporation and transpiration for a variety of crops and other land covers within the 17,200 km 2 Gediz Basin in western Turkey. The model, SLURP, describes the complete hydrological cycle for each land cover within a series of sub-basins including all dams, reservoirs, regulators and irrigation schemes in the basin. The sub-basins and land covers are defined by analysing a digital elevation model and NOAA AVHRR satellite data. In this study, the model uses the FAO implementation of the Penman-Monteith equation to simulate soil evaporation and crop transpiration. The results of the model runs provide time series of data on streamflow at many points along the river system, abstractions and return flows from crops within the irrigation schemes and areally distributed soil evaporation and crop transpiration across the entire basin on each day of an 11 year period. The results show that evaporation and transpiration vary widely across the basin on any one day and over the irrigation season and can be used to evaluate the effectiveness of the various irrigation strategies used in the basin. The advantages of using such a model as compared to deriving evapotranspiration from satellite data are that the model obtains results for each day of an indefinitely long period, as opposed to occasional snapshots, and can also be used to simulate alternate scenarios.

A Spatial Allocation Procedure to Downscale Regional Crop Production Estimates from an Integrated Assessment Model

NASA Astrophysics Data System (ADS)

Moulds, S.; Djordjevic, S.; Savic, D.

2017-12-01

The Global Change Assessment Model (GCAM), an integrated assessment model, provides insight into the interactions and feedbacks between physical and human systems. The land system component of GCAM, which simulates land use activities and the production of major crops, produces output at the subregional level which must be spatially downscaled in order to use with gridded impact assessment models. However, existing downscaling routines typically consider cropland as a homogeneous class and do not provide information about land use intensity or specific management practices such as irrigation and multiple cropping. This paper presents a spatial allocation procedure to downscale crop production data from GCAM to a spatial grid, producing a time series of maps which show the spatial distribution of specific crops (e.g. rice, wheat, maize) at four input levels (subsistence, low input rainfed, high input rainfed and high input irrigated). The model algorithm is constrained by available cropland at each time point and therefore implicitly balances extensification and intensification processes in order to meet global food demand. It utilises a stochastic approach such that an increase in production of a particular crop is more likely to occur in grid cells with a high biophysical suitability and neighbourhood influence, while a fall in production will occur more often in cells with lower suitability. User-supplied rules define the order in which specific crops are downscaled as well as allowable transitions. A regional case study demonstrates the ability of the model to reproduce historical trends in India by comparing the model output with district-level agricultural inventory data. Lastly, the model is used to predict the spatial distribution of crops globally under various GCAM scenarios.

Improvement of Alternative Crop Phenology Detection Algorithms using MODIS NDVI Time Series Data in US Corn Belt Region

NASA Astrophysics Data System (ADS)

Lee, J.; Kang, S.; Seo, B.; Lee, K.

2017-12-01

Predicting crop phenology is important for understanding of crop development and growth processes and improving the accuracy of crop model. Remote sensing offers a feasible tool for monitoring spatio-temporal patterns of crop phenology in region and continental scales. Various methods have been developed to determine the timing of crop phenological stages using spectral vegetation indices (i.e. NDVI and EVI) derived from satellite data. In our study, it was compared four alternative detection methods to identify crop phenological stages (i.e. the emergence and harvesting date) using high quality NDVI time series data derived from MODIS. Also we investigated factors associated with crop development rate. Temperature and photoperiod are the two main factors which would influence the crop's growth pattern expressed in the VI data. Only the effect of temperature on crop development rate was considered. The temperature response function in the Wang-Engel (WE) model was used, which simulates crop development using nonlinear models with response functions that range from zero to one. It has attempted at the state level over 14 years (2003-2016) in Iowa and Illinois state of USA, where the estimated phenology date by using four methods for both corn and soybean. Weekly crop progress reports produced by the USDA NASS were used to validate phenology detection algorithms effected by temperature. All methods showed substantial uncertainty but the threshold method showed relatively better agreement with the State-level data for soybean phenology.

An Intelligent Crop Planning Tool for Controlled Ecological Life Support Systems

NASA Technical Reports Server (NTRS)

Whitaker, Laura O.; Leon, Jorge

1996-01-01

This paper describes a crop planning tool developed for the Controlled Ecological Life Support Systems (CELSS) project which is in the research phases at various NASA facilities. The Crop Planning Tool was developed to assist in the understanding of the long term applications of a CELSS environment. The tool consists of a crop schedule generator as well as a crop schedule simulator. The importance of crop planning tools such as the one developed is discussed. The simulator is outlined in detail while the schedule generator is touched upon briefly. The simulator consists of data inputs, plant and human models, and various other CELSS activity models such as food consumption and waste regeneration. The program inputs such as crew data and crop states are discussed. References are included for all nominal parameters used. Activities including harvesting, planting, plant respiration, and human respiration are discussed using mathematical models. Plans provided to the simulator by the plan generator are evaluated for their 'fitness' to the CELSS environment with an objective function based upon daily reservoir levels. Sample runs of the Crop Planning Tool and future needs for the tool are detailed.

Methods to estimate irrigated reference crop evapotranspiration - a review.

PubMed

Kumar, R; Jat, M K; Shankar, V

2012-01-01

Efficient water management of crops requires accurate irrigation scheduling which, in turn, requires the accurate measurement of crop water requirement. Irrigation is applied to replenish depleted moisture for optimum plant growth. Reference evapotranspiration plays an important role for the determination of water requirements for crops and irrigation scheduling. Various models/approaches varying from empirical to physically base distributed are available for the estimation of reference evapotranspiration. Mathematical models are useful tools to estimate the evapotranspiration and water requirement of crops, which is essential information required to design or choose best water management practices. In this paper the most commonly used models/approaches, which are suitable for the estimation of daily water requirement for agricultural crops grown in different agro-climatic regions, are reviewed. Further, an effort has been made to compare the accuracy of various widely used methods under different climatic conditions.

Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity

NASA Astrophysics Data System (ADS)

Bach, H.; Klug, P.; Ruf, T.; Migdall, S.; Schlenz, F.; Hank, T.; Mauser, W.

2015-04-01

To support food security, information products about the actual cropping area per crop type, the current status of agricultural production and estimated yields, as well as the sustainability of the agricultural management are necessary. Based on this information, well-targeted land management decisions can be made. Remote sensing is in a unique position to contribute to this task as it is globally available and provides a plethora of information about current crop status. M4Land is a comprehensive system in which a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal satellite images. SLC uses modelled surface state parameters from PROMET, such as leaf area index or phenology of different crops to simulate spatially distributed surface reflectance spectra. This is the basis for generating artificial satellite images considering sensor specific configurations (spectral bands, solar and observation geometries). Ensembles of model runs are used to represent different crop types, fertilization status, soil colour and soil moisture. By multi-temporal comparisons of simulated and real satellite images, the land cover/crop type can be classified in a dynamically, model-supervised way and without in-situ training data. The method is demonstrated in an agricultural test-site in Bavaria. Its transferability is studied by analysing PROMET model results for the rest of Germany. Especially the simulated phenological development can be verified on this scale in order to understand whether PROMET is able to adequately simulate spatial, as well as temporal (intra- and inter-season) crop growth conditions, a prerequisite for the model-supervised approach. This sophisticated new technology allows monitoring of management decisions on the field-level using high resolution optical data (presently RapidEye and Landsat). The M4Land analysis system is designed to integrate multi-mission data and is well suited for the use of Sentinel-2's continuous and manifold data stream.

Estimating irrigation water use in the humid eastern United States

USGS Publications Warehouse

Levin, Sara B.; Zarriello, Phillip J.

2013-01-01

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

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.

Coupling sensing to crop models for closed-loop plant production in advanced life support systems

NASA Astrophysics Data System (ADS)

Cavazzoni, James; Ling, Peter P.

1999-01-01

We present a conceptual framework for coupling sensing to crop models for closed-loop analysis of plant production for NASA's program in advanced life support. Crop status may be monitored through non-destructive observations, while models may be independently applied to crop production planning and decision support. To achieve coupling, environmental variables and observations are linked to mode inputs and outputs, and monitoring results compared with model predictions of plant growth and development. The information thus provided may be useful in diagnosing problems with the plant growth system, or as a feedback to the model for evaluation of plant scheduling and potential yield. In this paper, we demonstrate this coupling using machine vision sensing of canopy height and top projected canopy area, and the CROPGRO crop growth model. Model simulations and scenarios are used for illustration. We also compare model predictions of the machine vision variables with data from soybean experiments conducted at New Jersey Agriculture Experiment Station Horticulture Greenhouse Facility, Rutgers University. Model simulations produce reasonable agreement with the available data, supporting our illustration.

Assessing the Application of a Geographic Presence-Only Model for Land Suitability Mapping

PubMed Central

Heumann, Benjamin W.; Walsh, Stephen J.; McDaniel, Phillip M.

2011-01-01

Recent advances in ecological modeling have focused on novel methods for characterizing the environment that use presence-only data and machine-learning algorithms to predict the likelihood of species occurrence. These novel methods may have great potential for land suitability applications in the developing world where detailed land cover information is often unavailable or incomplete. This paper assesses the adaptation and application of the presence-only geographic species distribution model, MaxEnt, for agricultural crop suitability mapping in a rural Thailand where lowland paddy rice and upland field crops predominant. To assess this modeling approach, three independent crop presence datasets were used including a social-demographic survey of farm households, a remote sensing classification of land use/land cover, and ground control points, used for geodetic and thematic reference that vary in their geographic distribution and sample size. Disparate environmental data were integrated to characterize environmental settings across Nang Rong District, a region of approximately 1,300 sq. km in size. Results indicate that the MaxEnt model is capable of modeling crop suitability for upland and lowland crops, including rice varieties, although model results varied between datasets due to the high sensitivity of the model to the distribution of observed crop locations in geographic and environmental space. Accuracy assessments indicate that model outcomes were influenced by the sample size and the distribution of sample points in geographic and environmental space. The need for further research into accuracy assessments of presence-only models lacking true absence data is discussed. We conclude that the Maxent model can provide good estimates of crop suitability, but many areas need to be carefully scrutinized including geographic distribution of input data and assessment methods to ensure realistic modeling results. PMID:21860606

The development of an advanced generic solar dynamic heat receiver thermal model

NASA Technical Reports Server (NTRS)

Wu, Y. C.; Roschke, E. J.; Kohout, L.

1988-01-01

An advanced generic solar dynamic heat receiver thermal model under development which can analyze both orbital transient and orbital average conditions is discussed. This model can be used to study advanced receiver concepts, evaluate receiver concepts under development, analyze receiver thermal characteristics under various operational conditions, and evaluate solar dynamic system thermal performances in various orbit conditions. The model and the basic considerations that led to its creation are described, and results based on a set of baseline orbit, configuration, and operational conditions are presented to demonstrate the working of the receiver model.

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.

SPECTRAL data-based estimation of soil heat flux

USGS Publications Warehouse

Singh, Ramesh K.; Irmak, A.; Walter-Shea, Elizabeth; Verma, S.B.; Suyker, A.E.

2011-01-01

Numerous existing spectral-based soil heat flux (G) models have shown wide variation in performance for maize and soybean cropping systems in Nebraska, indicating the need for localized calibration and model development. The objectives of this article are to develop a semi-empirical model to estimate G from a normalized difference vegetation index (NDVI) and net radiation (Rn) for maize (Zea mays L.) and soybean (Glycine max L.) fields in the Great Plains, and present the suitability of the developed model to estimate G under similar and different soil and management conditions. Soil heat fluxes measured in both irrigated and rainfed fields in eastern and south-central Nebraska were used for model development and validation. An exponential model that uses NDVI and Rn was found to be the best to estimate G based on r2 values. The effect of geographic location, crop, and water management practices were used to develop semi-empirical models under four case studies. Each case study has the same exponential model structure but a different set of coefficients and exponents to represent the crop, soil, and management practices. Results showed that the semi-empirical models can be used effectively for G estimation for nearby fields with similar soil properties for independent years, regardless of differences in crop type, crop rotation, and irrigation practices, provided that the crop residue from the previous year is more than 4000 kg ha-1. The coefficients calibrated from particular fields can be used at nearby fields in order to capture temporal variation in G. However, there is a need for further investigation of the models to account for the interaction effects of crop rotation and irrigation. Validation at an independent site having different soil and crop management practices showed the limitation of the semi-empirical model in estimating G under different soil and environment conditions.

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.

Plant, soil, and shadow reflectance components of row crops

NASA Technical Reports Server (NTRS)

Richardson, A. J.; Wiegand, C. L.; Gausman, H. W.; Cuellar, J. A.; Gerbermann, A. H.

1975-01-01

Data from the first Earth Resource Technology Satellite (LANDSAT-1) multispectral scanner (MSS) were used to develop three plant canopy models (Kubelka-Munk (K-M), regression, and combined K-M and regression models) for extracting plant, soil, and shadow reflectance components of cropped fields. The combined model gave the best correlation between MSS data and ground truth, by accounting for essentially all of the reflectance of plants, soil, and shadow between crop rows. The principles presented can be used to better forecast crop yield and to estimate acreage.

Retailing policies for generic medicines.

PubMed

Narciso, Susana

2005-06-01

As there is general disagreement about the way generic medicines should be commercialized, two retailing policies are analyzed, taking into account their effects on the welfare of patients, government, pharmacies and physicians. In the first policy scenario, pharmacies are allowed to substitute generic medicines for branded ones, while in the second, substitution is forbidden. In both cases a pharmacies association is allowed to have a share in the production of generic medicines. The model predicts that under some conditions patients may prefer substitution by pharmacies but when doctors' decisions are binding, they are never "excessively bad". However, the policy choice belongs to the government, which prefers to allow for substitution more often than patients would like.

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

Crop physiology calibration in the CLM

DOE PAGES

Bilionis, I.; Drewniak, B. A.; Constantinescu, E. M.

2015-04-15

Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurementsmore » of gross primary productivity (GPP) and net ecosystem exchange (NEE) from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper, we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites.« less

Crop physiology calibration in the CLM

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

Bilionis, I.; Drewniak, B. A.; Constantinescu, E. M.

Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurementsmore » of gross primary productivity (GPP) and net ecosystem exchange (NEE) from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper, we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites.« less

GIS and crop simulation modelling applications in climate change research

USDA-ARS?s Scientific Manuscript database

The challenges that climate change presents humanity require an unprecedented ability to predict the responses of crops to environment and management. Geographic information systems (GIS) and crop simulation models are two powerful and highly complementary tools that are increasingly used for such p...

Estimating winter wheat phenological parameters: Implications for crop modeling

USDA-ARS?s Scientific Manuscript database

Crop parameters, such as the timing of developmental events, are critical for accurate simulation results in crop simulation models, yet uncertainty often exists in determining the parameters. Factors contributing to the uncertainty include: a) sources of variation within a plant (i.e., within diffe...

The uncertainty of crop yield projections is reduced by improved temperature response functions.

PubMed

Wang, Enli; Martre, Pierre; Zhao, Zhigan; Ewert, Frank; Maiorano, Andrea; Rötter, Reimund P; Kimball, Bruce A; Ottman, Michael J; Wall, Gerard W; White, Jeffrey W; Reynolds, Matthew P; Alderman, Phillip D; Aggarwal, Pramod K; Anothai, Jakarat; Basso, Bruno; Biernath, Christian; Cammarano, Davide; Challinor, Andrew J; De Sanctis, Giacomo; Doltra, Jordi; Fereres, Elias; Garcia-Vila, Margarita; Gayler, Sebastian; Hoogenboom, Gerrit; Hunt, Leslie A; Izaurralde, Roberto C; Jabloun, Mohamed; Jones, Curtis D; Kersebaum, Kurt C; Koehler, Ann-Kristin; Liu, Leilei; Müller, Christoph; Naresh Kumar, Soora; Nendel, Claas; O'Leary, Garry; Olesen, Jørgen E; Palosuo, Taru; Priesack, Eckart; Eyshi Rezaei, Ehsan; Ripoche, Dominique; Ruane, Alex C; Semenov, Mikhail A; Shcherbak, Iurii; Stöckle, Claudio; Stratonovitch, Pierre; Streck, Thilo; Supit, Iwan; Tao, Fulu; Thorburn, Peter; Waha, Katharina; Wallach, Daniel; Wang, Zhimin; Wolf, Joost; Zhu, Yan; Asseng, Senthold

2017-07-17

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

The Uncertainty of Crop Yield Projections Is Reduced by Improved Temperature Response Functions

NASA Technical Reports Server (NTRS)

Wang, Enli; Martre, Pierre; Zhao, Zhigan; Ewert, Frank; Maiorano, Andrea; Rotter, Reimund P.; Kimball, Bruce A.; Ottman, Michael J.; White, Jeffrey W.; Reynolds, Matthew P.;

Generic Software Architecture for Prognostics (GSAP) User Guide

NASA Technical Reports Server (NTRS)

Teubert, Christopher Allen; Daigle, Matthew John; Watkins, Jason; Sankararaman, Shankar; Goebel, Kai

2016-01-01

The Generic Software Architecture for Prognostics (GSAP) is a framework for applying prognostics. It makes applying prognostics easier by implementing many of the common elements across prognostic applications. The standard interface enables reuse of prognostic algorithms and models across systems using the GSAP framework.

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

PubMed

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

2012-01-01

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

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.

Impact of price deregulation policy on the affordability of essential medicines for women's health: a panel data analysis.

PubMed

Liu, Junjie; Wang, Liming; Liu, Chenxi; Zhang, Xinping

2017-12-01

A new policy which required deregulation on prices of off-patent medicines for women's health during procurement was introduced in China in September 2015. The current study examines this policy's impact on the affordability of essential medicines for women's health. Based on product-level panel data, a fixed effect regression model is employed by using procurement records from Hubei Centralist Tender for Drug Purchase platform. In the model, Affordability was measured with prices. The Competition consists of two parts: generic competition and therapeutic class competition which are measured with generic competitors and therapeutic substitutes. Instrument variable is used to deal with endogeneity. The policy helped control prices of essential medicines for women's health. Generic competition helped control prices, however, therapeutic class competition caused higher prices. The new policy helped enhance the affordability of essential medicines for women's health as expected, which provides empirical evidence on price deregulation. Besides, generic competition is important in price control despite strict regulatory system in China.

Direct and indirect impacts of crop-livestock organization on mixed crop-livestock systems sustainability: a model-based study.

PubMed

Sneessens, I; Veysset, P; Benoit, M; Lamadon, A; Brunschwig, G

2016-11-01

Crop-livestock production is claimed more sustainable than specialized production systems. However, the presence of controversial studies suggests that there must be conditions of mixing crop and livestock productions to allow for higher sustainable performances. Whereas previous studies focused on the impact of crop-livestock interactions on performances, we posit here that crop-livestock organization is a key determinant of farming system sustainability. Crop-livestock organization refers to the percentage of the agricultural area that is dedicated to each production. Our objective is to investigate if crop-livestock organization has both a direct and an indirect impact on mixed crop-livestock (MC-L) sustainability. In that objective, we build a whole-farm model parametrized on representative French sheep and crop farming systems in plain areas (Vienne, France). This model permits simulating contrasted MC-L systems and their subsequent sustainability through the following indicators of performance: farm income, production, N balance, greenhouse gas (GHG) emissions (/kg product) and MJ consumption (/kg product). Two MC-L systems were simulated with contrasted crop-livestock organizations (MC20-L80: 20% of crops; MC80-L20: 80% of crops). A first scenario - constraining no crop-livestock interactions in both MC-L systems - permits highlighting that crop-livestock organization has a significant direct impact on performances that implies trade-offs between objectives of sustainability. Indeed, the MC80-L20 system is showing higher performances for farm income (+44%), livestock production (+18%) and crop GHG emissions (-14%) whereas the MC20-L80 system has a better N balance (-53%) and a lower livestock MJ consumption (-9%). A second scenario - allowing for crop-livestock interactions in both MC20-L80 and MC80-L20 systems - stated that crop-livestock organization has a significant indirect impact on performances. Indeed, even if crop-livestock interactions permit improving performances, crop-livestock organization influences the capacity of MC-L systems to benefit from crop-livestock interactions. As a consequence, we observed a decreasing performance trade-off between MC-L systems for farm income (-4%) and crop GHG emissions (-10%) whereas the gap increases for nitrogen balance (+23%), livestock production (+6%) - MJ consumption (+16%) - GHG emissions (+5%) and crop MJ consumption (+5%). However, the indirect impact of crop-livestock organization doesn't reverse the trend of trade-offs between objectives of sustainability determined by the direct impact of crop-livestock organization. As a conclusion, crop-livestock organization is a key factor that has to be taken into account when studying the sustainability of mixed crop-livestock systems.

Early warning and crop condition assessment research

NASA Technical Reports Server (NTRS)

Boatwright, G. O.; Whitehead, V. S.

1986-01-01

The Early Warning Crop Condition Assessment Project of AgRISTARS was a multiagency and multidisciplinary effort. Its mission and objectives were centered around development and testing of remote-sensing techniques that enhance operational methodologies for global crop-condition assessments. The project developed crop stress indicators models that provide data filter and alert capabilities for monitoring global agricultural conditions. The project developed a technique for using NOAA-n satellite advanced very-high-resolution radiometer (AVHRR) data for operational crop-condition assessments. This technology was transferred to the Foreign Agricultural Service of the USDA. The project developed a U.S. Great Plains data base that contains various meteorological parameters and vegetative index numbers (VIN) derived from AVHRR satellite data. It developed cloud screening techniques and scan angle correction models for AVHRR data. It also developed technology for using remotely acquired thermal data for crop water stress indicator modeling. The project provided basic technology including spectral characteristics of soils, water, stressed and nonstressed crop and range vegetation, solar zenith angle, and atmospheric and canopy structure effects.

Modelling irrigated maize with a combination of coupled-model simulation and uncertainty analysis, in the northwest of China

NASA Astrophysics Data System (ADS)

Li, Y.; Kinzelbach, W.; Zhou, J.; Cheng, G. D.; Li, X.

2012-05-01

The hydrologic model HYDRUS-1-D and the crop growth model WOFOST are coupled to efficiently manage water resources in agriculture and improve the prediction of crop production. The results of the coupled model are validated by experimental studies of irrigated-maize done in the middle reaches of northwest China's Heihe River, a semi-arid to arid region. Good agreement is achieved between the simulated evapotranspiration, soil moisture and crop production and their respective field measurements made under current maize irrigation and fertilization. Based on the calibrated model, the scenario analysis reveals that the most optimal amount of irrigation is 500-600 mm in this region. However, for regions without detailed observation, the results of the numerical simulation can be unreliable for irrigation decision making owing to the shortage of calibrated model boundary conditions and parameters. So, we develop a method of combining model ensemble simulations and uncertainty/sensitivity analysis to speculate the probability of crop production. In our studies, the uncertainty analysis is used to reveal the risk of facing a loss of crop production as irrigation decreases. The global sensitivity analysis is used to test the coupled model and further quantitatively analyse the impact of the uncertainty of coupled model parameters and environmental scenarios on crop production. This method can be used for estimation in regions with no or reduced data availability.

Reduction of predictive uncertainty in estimating irrigation water requirement through multi-model ensembles and ensemble averaging

NASA Astrophysics Data System (ADS)

Multsch, S.; Exbrayat, J.-F.; Kirby, M.; Viney, N. R.; Frede, H.-G.; Breuer, L.

2015-04-01

Irrigation agriculture plays an increasingly important role in food supply. Many evapotranspiration models are used today to estimate the water demand for irrigation. They consider different stages of crop growth by empirical crop coefficients to adapt evapotranspiration throughout the vegetation period. We investigate the importance of the model structural versus model parametric uncertainty for irrigation simulations by considering six evapotranspiration models and five crop coefficient sets to estimate irrigation water requirements for growing wheat in the Murray-Darling Basin, Australia. The study is carried out using the spatial decision support system SPARE:WATER. We find that structural model uncertainty among reference ET is far more important than model parametric uncertainty introduced by crop coefficients. These crop coefficients are used to estimate irrigation water requirement following the single crop coefficient approach. Using the reliability ensemble averaging (REA) technique, we are able to reduce the overall predictive model uncertainty by more than 10%. The exceedance probability curve of irrigation water requirements shows that a certain threshold, e.g. an irrigation water limit due to water right of 400 mm, would be less frequently exceeded in case of the REA ensemble average (45%) in comparison to the equally weighted ensemble average (66%). We conclude that multi-model ensemble predictions and sophisticated model averaging techniques are helpful in predicting irrigation demand and provide relevant information for decision making.

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.

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;

Food Crops Response to Climate Change

NASA Astrophysics Data System (ADS)

Butler, E.; Huybers, P.

2009-12-01

Projections of future climate show a warming world and heterogeneous changes in precipitation. Generally, warming temperatures indicate a decrease in crop yields where they are currently grown. However, warmer climate will also open up new areas at high latitudes for crop production. Thus, there is a question whether the warmer climate with decreased yields but potentially increased growing area will produce a net increase or decrease of overall food crop production. We explore this question through a multiple linear regression model linking temperature and precipitation to crop yield. Prior studies have emphasised temporal regression which indicate uniformly decreased yields, but neglect the potentially increased area opened up for crop production. This study provides a compliment to the prior work by exploring this spatial variation. We explore this subject with a multiple linear regression model from temperature, precipitation and crop yield data over the United States. The United States was chosen as the training region for the model because there are good crop data available over the same time frame as climate data and presumably the yield from crops in the United States is optimized with respect to potential yield. We study corn, soybeans, sorghum, hard red winter wheat and soft red winter wheat using monthly averages of temperature and precipitation from NCEP reanalysis and yearly yield data from the National Agriculture Statistics Service for 1948-2008. The use of monthly averaged temperature and precipitation, which neglect extreme events that can have a significant impact on crops limits this study as does the exclusive use of United States agricultural data. The GFDL 2.1 model under a 720ppm CO2 scenario provides temperature and precipitation fields for 2040-2100 which are used to explore how the spatial regions available for crop production will change under these new conditions.

Hydrological Responses of Weather Conditions and Crop Change of Agricultural Area in the Rincon Valley, New Mexico

NASA Astrophysics Data System (ADS)

Ahn, S.; Sheng, Z.; Abudu, S.

2017-12-01

Hydrologic cycle of agricultural area has been changing due to the impacts of climate and land use changes (crop coverage changes) in an arid region of Rincon Valley, New Mexico. This study is to evaluate the impacts of weather condition and crop coverage change on hydrologic behavior of agricultural area in Rincon Valley (2,466km2) for agricultural watershed management using a watershed-scale hydrologic model, SWAT (Soil and Water Assessment Tool). The SWAT model was developed to incorporate irrigation of different crops using auto irrigation function. For the weather condition and crop coverage change evaluation, three spatial crop coverages including a normal (2008), wet (2009), and dry (2011) years were prepared using USDA crop data layer (CDL) for fourteen different crops. The SWAT model was calibrated for the period of 2001-2003 and validated for the period of 2004-2006 using daily-observed streamflow data. Scenario analysis was performed for wet and dry years based on the unique combinations of crop coverages and releases from Caballo Reservoir. The SWAT model simulated the present vertical water budget and horizontal water transfer considering irrigation practices in the Rincon Valley. Simulation results indicated the temporal and spatial variability for irrigation and non-irrigation seasons of hydrologic cycle in agricultural area in terms of surface runoff, evapotranspiration, infiltration, percolation, baseflow, soil moisture, and groundwater recharge. The water supply of the dry year could not fully cover whole irrigation period due to dry weather conditions, resulting in reduction of crop acreage. For extreme weather conditions, the temporal variation of water budget became robust, which requires careful irrigation management of the agricultural area. The results could provide guidelines for farmers to decide crop patterns in response to different weather conditions and water availability.

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

NASA Technical Reports Server (NTRS)

Andrews, J.

1977-01-01

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

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 .

Water for food and nature in drought-prone tropics: vapour shift in rain-fed agriculture.

PubMed Central

Rockström, Johan

2003-01-01

This paper quantifies the eco-hydrological challenge up until 2050 of producing food in balance with goods and services generated by water-dependent ecosystems in nature. Particular focus is given to the savannah zone, covering 40% of the land area in the world, where water scarcity constitutes a serious constraint to sustainable development. The analysis indicates an urgent need for a new green revolution, which focuses on upgrading rain-fed agriculture. Water requirements to produce adequate diets for humans are shown to be relatively generic irrespective of hydro-climate, amounting to a global average of 1,300 m(3) cap(-1) yr(-1). Present food production requires an estimated 6,800 km(3) yr(-1) of consumptive green water (5,000 km(3) yr(-1) in rain-fed agriculture and 1,800 km(3) yr(-1) from irrigated crops). Without considering water productivity gains, an additional 5,800 km(3) yr(-1) of water is needed to feed a growing population in 2,050 and eradicate malnutrition. It is shown that the bulk of this water will be used in rain-fed agriculture. A dynamic analysis of water productivity and management options indicates that large 'crop per drop' improvements can be achieved at the farm level. Vapour shift in favour of productive green water flow as crop transpiration could result in relative water savings of 500 km(3) yr(-1) in semi-arid rain-fed agriculture. PMID:14728794

Water for food and nature in drought-prone tropics: vapour shift in rain-fed agriculture.

PubMed

Rockström, Johan

2003-12-29

This paper quantifies the eco-hydrological challenge up until 2050 of producing food in balance with goods and services generated by water-dependent ecosystems in nature. Particular focus is given to the savannah zone, covering 40% of the land area in the world, where water scarcity constitutes a serious constraint to sustainable development. The analysis indicates an urgent need for a new green revolution, which focuses on upgrading rain-fed agriculture. Water requirements to produce adequate diets for humans are shown to be relatively generic irrespective of hydro-climate, amounting to a global average of 1,300 m(3) cap(-1) yr(-1). Present food production requires an estimated 6,800 km(3) yr(-1) of consumptive green water (5,000 km(3) yr(-1) in rain-fed agriculture and 1,800 km(3) yr(-1) from irrigated crops). Without considering water productivity gains, an additional 5,800 km(3) yr(-1) of water is needed to feed a growing population in 2,050 and eradicate malnutrition. It is shown that the bulk of this water will be used in rain-fed agriculture. A dynamic analysis of water productivity and management options indicates that large 'crop per drop' improvements can be achieved at the farm level. Vapour shift in favour of productive green water flow as crop transpiration could result in relative water savings of 500 km(3) yr(-1) in semi-arid rain-fed agriculture.

Injury Profile SIMulator, a Qualitative Aggregative Modelling Framework to Predict Crop Injury Profile as a Function of Cropping Practices, and the Abiotic and Biotic Environment. I. Conceptual Bases

PubMed Central

Aubertot, Jean-Noël; Robin, Marie-Hélène

2013-01-01

The limitation of damage caused by pests (plant pathogens, weeds, and animal pests) in any agricultural crop requires integrated management strategies. Although significant efforts have been made to i) develop, and to a lesser extent ii) combine genetic, biological, cultural, physical and chemical control methods in Integrated Pest Management (IPM) strategies (vertical integration), there is a need for tools to help manage Injury Profiles (horizontal integration). Farmers design cropping systems according to their goals, knowledge, cognition and perception of socio-economic and technological drivers as well as their physical, biological, and chemical environment. In return, a given cropping system, in a given production situation will exhibit a unique injury profile, defined as a dynamic vector of the main injuries affecting the crop. This simple description of agroecosystems has been used to develop IPSIM (Injury Profile SIMulator), a modelling framework to predict injury profiles as a function of cropping practices, abiotic and biotic environment. Due to the tremendous complexity of agroecosystems, a simple holistic aggregative approach was chosen instead of attempting to couple detailed models. This paper describes the conceptual bases of IPSIM, an aggregative hierarchical framework and a method to help specify IPSIM for a given crop. A companion paper presents a proof of concept of the proposed approach for a single disease of a major crop (eyespot on wheat). In the future, IPSIM could be used as a tool to help design ex-ante IPM strategies at the field scale if coupled with a damage sub-model, and a multicriteria sub-model that assesses the social, environmental, and economic performances of simulated agroecosystems. In addition, IPSIM could also be used to help make diagnoses on commercial fields. It is important to point out that the presented concepts are not crop- or pest-specific and that IPSIM can be used on any crop. PMID:24019908

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

PubMed Central

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

2016-01-01

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

Injury Profile SIMulator, a qualitative aggregative modelling framework to predict crop injury profile as a function of cropping practices, and the abiotic and biotic environment. I. Conceptual bases.

PubMed

Aubertot, Jean-Noël; Robin, Marie-Hélène

2013-01-01

The limitation of damage caused by pests (plant pathogens, weeds, and animal pests) in any agricultural crop requires integrated management strategies. Although significant efforts have been made to i) develop, and to a lesser extent ii) combine genetic, biological, cultural, physical and chemical control methods in Integrated Pest Management (IPM) strategies (vertical integration), there is a need for tools to help manage Injury Profiles (horizontal integration). Farmers design cropping systems according to their goals, knowledge, cognition and perception of socio-economic and technological drivers as well as their physical, biological, and chemical environment. In return, a given cropping system, in a given production situation will exhibit a unique injury profile, defined as a dynamic vector of the main injuries affecting the crop. This simple description of agroecosystems has been used to develop IPSIM (Injury Profile SIMulator), a modelling framework to predict injury profiles as a function of cropping practices, abiotic and biotic environment. Due to the tremendous complexity of agroecosystems, a simple holistic aggregative approach was chosen instead of attempting to couple detailed models. This paper describes the conceptual bases of IPSIM, an aggregative hierarchical framework and a method to help specify IPSIM for a given crop. A companion paper presents a proof of concept of the proposed approach for a single disease of a major crop (eyespot on wheat). In the future, IPSIM could be used as a tool to help design ex-ante IPM strategies at the field scale if coupled with a damage sub-model, and a multicriteria sub-model that assesses the social, environmental, and economic performances of simulated agroecosystems. In addition, IPSIM could also be used to help make diagnoses on commercial fields. It is important to point out that the presented concepts are not crop- or pest-specific and that IPSIM can be used on any crop.

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.

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.

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.

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

EPA Science Inventory

Crop residue burning is a common land management practice that results in emissions of a variety of pollutants with negative health impacts. Modeling systems are used to estimate air quality impacts of crop residue burning to support retrospective regulatory assessments and also ...

Spontaneous symmetry breaking in active droplets provides a generic route to motility

PubMed Central

Tjhung, Elsen; Marenduzzo, Davide; Cates, Michael E.

2012-01-01

We explore a generic mechanism whereby a droplet of active matter acquires motility by the spontaneous breakdown of a discrete symmetry. The model we study offers a simple representation of a “cell extract” comprising, e.g., a droplet of actomyosin solution. (Such extracts are used experimentally to model the cytoskeleton). Actomyosin is an active gel whose polarity describes the mean sense of alignment of actin fibres. In the absence of polymerization and depolymerization processes (‘treadmilling’), the gel’s dynamics arises solely from the contractile motion of myosin motors; this should be unchanged when polarity is inverted. Our results suggest that motility can arise in the absence of treadmilling, by spontaneous symmetry breaking (SSB) of polarity inversion symmetry. Adapting our model to wall-bound cells in two dimensions, we find that as wall friction is reduced, treadmilling-induced motility falls but SSB-mediated motility rises. The latter might therefore be crucial in three dimensions where frictional forces are likely to be modest. At a supracellular level, the same generic mechanism can impart motility to aggregates of nonmotile but active bacteria; we show that SSB in this (extensile) case leads generically to rotational as well as translational motion. PMID:22797894

Therapeutic Equivalence Requires Pharmaceutical, Pharmacokinetic, and Pharmacodynamic Identities: True Bioequivalence of a Generic Product of Intravenous Metronidazole

PubMed Central

Agudelo, M.

2012-01-01

Animal models of infection have been used to demonstrate the therapeutic failure of “bioequivalent” generic products, but their applicability for this purpose requires the accurate identification of those products that are truly bioequivalent. Here, we present data comparing one intravenous generic product of metronidazole with the innovator product in a neutropenic mouse thigh anaerobic infection model. Simultaneous experiments allowed comparisons (generic versus innovator) of potency and the concentration of the active pharmaceutical ingredient (API), analytical chemistry (liquid chromatography/mass spectrometry [LC/MS]), in vitro susceptibility testing, single-dose serum pharmacokinetics (PK) in infected mice, and in vivo pharmacodynamics (PD) against Bacteroides fragilis ATCC 25825 in synergy with Escherichia coli SIG-1 in the neutropenic mouse thigh anaerobic infection model. The Hill dose-response model followed by curve-fitting analysis was used to calculate and compare primary and secondary PD parameters. The generic and the innovator products were identical in terms of the concentration and potency of the API, chromatographic and spectrographic profiles, MIC and minimal bactericidal concentrations (MBC) (2.0 mg/liter), and mouse PK. We found no differences between products in bacteriostatic doses (BD) (15 to 22 mg/kg of body weight per day) or the doses needed to kill 1 log (1LKD) (21 to 29 mg/kg per day) or 2 logs (2LKD) (28 to 54 mg/kg per day) of B. fragilis under dosing schedules of every 12 h (q12h), q8h, or q6h. The area under the concentration-time curve over 24 h in the steady state divided by the MIC (AUC/MIC ratio) was the best PD index to predict the antibacterial efficacy of metronidazole (adjusted coefficient of determination [AdjR2] = 84.6%), and its magnitude to reach bacteriostasis in vivo (56.6 ± 5.17 h) or to kill the first (90.8 ± 9.78 h) and second (155.5 ± 22.2 h) logs was the same for both products. Animal models of infection allow a thorough demonstration of the therapeutic equivalence of generic antimicrobials. PMID:22330928

Validation of the Korean version of the pediatric quality of life inventory 4.0 (PedsQL) generic core scales in school children and adolescents using the Rasch model.

PubMed

Kook, Seung Hee; Varni, James W

2008-06-02

The Pediatric Quality of Life Inventory (PedsQL) is a child self-report and parent proxy-report instrument designed to assess health-related quality of life (HRQOL) in healthy and ill children and adolescents. It has been translated into over 70 international languages and proposed as a valid and reliable pediatric HRQOL measure. This study aimed to assess the psychometric properties of the Korean translation of the PedsQL 4.0 Generic Core Scales. Following the guidelines for linguistic validation, the original US English scales were translated into Korean and cognitive interviews were administered. The field testing responses of 1425 school children and adolescents and 1431 parents to the Korean version of PedsQL 4.0 Generic Core Scales were analyzed utilizing confirmatory factor analysis and the Rasch model. Consistent with studies using the US English instrument and other translation studies, score distributions were skewed toward higher HRQOL in a predominantly healthy population. Confirmatory factor analysis supported a four-factor and a second order-factor model. The analysis using the Rasch model showed that person reliabilities are low, item reliabilities are high, and the majority of items fit the model's expectation. The Rasch rating scale diagnostics showed that PedsQL 4.0 Generic Core Scales in general have the optimal number of response categories, but category 4 (almost always a problem) is somewhat problematic for the healthy school sample. The agreements between child self-report and parent proxy-report were moderate. The results demonstrate the feasibility, validity, item reliability, item fit, and agreement between child self-report and parent proxy-report of the Korean version of PedsQL 4.0 Generic Core Scales for school population health research in Korea. However, the utilization of the Korean version of the PedsQL 4.0 Generic Core Scales for healthy school populations needs to consider low person reliability, ceiling effects and cultural differences, and further validation studies on Korean clinical samples are required.

Comparison of SVAT models for simulating and optimizing deficit irrigation systems in arid and semi-arid countries under climate variability

NASA Astrophysics Data System (ADS)

Kloss, Sebastian; Schuetze, Niels; Schmitz, Gerd H.

2010-05-01

The strong competition for fresh water in order to fulfill the increased demand for food worldwide has led to a renewed interest in techniques to improve water use efficiency (WUE) such as controlled deficit irrigation. Furthermore, as the implementation of crop models into complex decision support systems becomes more and more common, it is imperative to reliably predict the WUE as ratio of water consumption and yield. The objective of this paper is the assessment of the problems the crop models - such as FAO-33, DAISY, and APSIM in this study - face when maximizing the WUE. We applied these crop models for calculating the risk in yield reduction in view of different sources of uncertainty (e.g. climate) employing a stochastic framework for decision support for the planning of water supply in irrigation. The stochastic framework consists of: (i) a weather generator for simulating regional impacts of climate change; (ii) a new tailor-made evolutionary optimization algorithm for optimal irrigation scheduling with limited water supply; and (iii) the above mentioned models for simulating water transport and crop growth in a sound manner. The results present stochastic crop water production functions (SCWPF) for different crops which can be used as basic tools for assessing the impact of climate variability on the risk for the potential yield. Case studies from India, Oman, Malawi, and France are presented to assess the differences in modeling water stress and yield response for the different crop models.

Crop identification for the delineation of irrigated regions under scarce data conditions: a new approach based on chaos theory

NASA Astrophysics Data System (ADS)

Mangiarotti, S.; Muddu, S.; Sharma, A. K.; Corgne, S.; Ruiz, L.; Hubert-Moy, L.

2015-12-01

Groundwater is one of the main water reservoirs used for irrigation in regions of scarce water resources. For this reason, crop irrigation is expected to have a direct influence on this reservoir. To understand the time evolution of the groundwater table and its storage changes, it is important to delineate irrigated crops, whose evaporative demand is relatively higher. Such delineation may be performed based on classical classification approaches using optical remote sensing. However, it remains a difficult problem in regions where plots do not exceed a few hectares and exhibit a very heterogeneous pattern with multiple crops. This difficulty is emphasized in South India where two or three months of cloudy conditions during the monsoon period can hide crop growth during the year. An alternative approach is introduced here that takes advantage of such scarce signal. Ten different crops are considered in the present study. A bank of crop models is first established based on the global modeling technique [1]. These models are then tested using original time series (from which models were obtained) in order to evaluate the information that can be deduced from these models in an inverse approach. The approach is then tested on an independent data set and is finally applied to a large ensemble of 10,000 time series of plot data extracted from the Berambadi catchment (AMBHAS site) part of the Kabini River basin CZO, South India. Results show that despite the important two-month gap in satellite observations in the visible band, interpolated vegetation index remains an interesting indicator for identification of crops in South India. [1] S. Mangiarotti, R. Coudret, L. Drapeau, & L. Jarlan, Polynomial search and global modeling: Two algorithms for modeling chaos, Phys. Rev. E, 86(4), 046205 (2012).

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 .

Crop Yield Simulations Using Multiple Regional Climate Models in the Southwestern United States

NASA Astrophysics Data System (ADS)

Stack, D.; Kafatos, M.; Kim, S.; Kim, J.; Walko, R. L.

2013-12-01

Agricultural productivity (described by crop yield) is strongly dependent on climate conditions determined by meteorological parameters (e.g., temperature, rainfall, and solar radiation). California is the largest producer of agricultural products in the United States, but crops in associated arid and semi-arid regions live near their physiological limits (e.g., in hot summer conditions with little precipitation). Thus, accurate climate data are essential in assessing the impact of climate variability on agricultural productivity in the Southwestern United States and other arid regions. To address this issue, we produced simulated climate datasets and used them as input for the crop production model. For climate data, we employed two different regional climate models (WRF and OLAM) using a fine-resolution (8km) grid. Performances of the two different models are evaluated in a fine-resolution regional climate hindcast experiment for 10 years from 2001 to 2010 by comparing them to the North American Regional Reanalysis (NARR) dataset. Based on this comparison, multi-model ensembles with variable weighting are used to alleviate model bias and improve the accuracy of crop model productivity over large geographic regions (county and state). Finally, by using a specific crop-yield simulation model (APSIM) in conjunction with meteorological forcings from the multi-regional climate model ensemble, we demonstrate the degree to which maize yields are sensitive to the regional climate in the Southwestern United States.

Generic Engineering Competencies: A Review and Modelling Approach

ERIC Educational Resources Information Center

Male, Sally A.

2010-01-01

This paper puts forward the view that engineering educators have a responsibility to prepare graduates for engineering work and careers. The current literature reveals gaps between the competencies required for engineering work and those developed in engineering education. Generic competencies feature in these competency gaps. Literature suggests…

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.

Using statistical model to simulate the impact of climate change on maize yield with climate and crop uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Yi; Zhao, Yanxia; Wang, Chunyi; Chen, Sining

2017-11-01

Assessment of the impact of climate change on crop productions with considering uncertainties is essential for properly identifying and decision-making agricultural practices that are sustainable. In this study, we employed 24 climate projections consisting of the combinations of eight GCMs and three emission scenarios representing the climate projections uncertainty, and two crop statistical models with 100 sets of parameters in each model representing parameter uncertainty within the crop models. The goal of this study was to evaluate the impact of climate change on maize ( Zea mays L.) yield at three locations (Benxi, Changling, and Hailun) across Northeast China (NEC) in periods 2010-2039 and 2040-2069, taking 1976-2005 as the baseline period. The multi-models ensembles method is an effective way to deal with the uncertainties. The results of ensemble simulations showed that maize yield reductions were less than 5 % in both future periods relative to the baseline. To further understand the contributions of individual sources of uncertainty, such as climate projections and crop model parameters, in ensemble yield simulations, variance decomposition was performed. The results indicated that the uncertainty from climate projections was much larger than that contributed by crop model parameters. Increased ensemble yield variance revealed the increasing uncertainty in the yield simulation in the future periods.

Assessment of future crop yield and agricultural sustainable water use in north china plain using multiple crop models

NASA Astrophysics Data System (ADS)

Huang, G.

2016-12-01

Currently, studying crop-water response mechanism has become an important part in the development of new irrigation technology and optimal water allocation in water-scarce regions, which is of great significance to crop growth guidance, sustainable utilization of agricultural water, as well as the sustainable development of regional agriculture. Using multiple crop models(AquaCrop,SWAP,DNDC), this paper presents the results of simulating crop growth and agricultural water consumption of the winter-wheat and maize cropping system in north china plain. These areas are short of water resources, but generates about 23% of grain production for China. By analyzing the crop yields and the water consumption of the traditional flooding irrigation, the paper demonstrates quantitative evaluation of the potential amount of water use that can be reduced by using high-efficient irrigation approaches, such as drip irrigation. To maintain food supply and conserve water resources, the research concludes sustainable irrigation methods for the three provinces for sustainable utilization of agricultural water.

Crop diversity loss as primary cause of grey partridge and common pheasant decline in Lower Saxony, Germany.

PubMed

Ronnenberg, Katrin; Strauß, Egbert; Siebert, Ursula

2016-09-09

The grey partridge (Perdix perdix) and the common pheasant (Phasianus colchicus) are galliform birds typical of arable lands in Central Europe and exhibit a partly dramatic negative population trend. In order to understand general habitat preferences we modelled grey partridge and common pheasant densities over the entire range of Lower Saxony. Spatially explicit developments in bird densities were modelled using spatially explicit trends of crop cultivation. Pheasant and grey partridge densities counted annually by over 8000 hunting district holders over 10 years in a range of 3.7 Mio ha constitute a unique dataset (wildlife survey of Lower Saxony). Data on main landscape groups, functional groups of agricultural crops (consisting of 9.5 million fields compiled by the Integrated Administration and Control System) and landscape features were aggregated to 420 municipalities. To model linear 8 or 10 year population trends (for common pheasant and grey partridge respectively) we use rho correlation coefficients of densities, but also rho coefficients of agricultural crops. All models confirm a dramatic decline in population densities. The habitat model for the grey partridge shows avoidance of municipalities with a high proportion of woodland and water areas, but a preference for areas with a high proportion of winter grains and high crop diversity. The trend model confirms these findings with a linear positive effect of diversity on grey partridge population development. Similarly, the pheasant avoids wooded areas but showed some preference for municipalities with open water. The effect of maize was found to be positive at medium densities, but negative at very high proportions. Winter grains, landscape features and high crop diversity are favorable. The positive effect of winter grains and higher crop diversity is also supported by the trend model. The results show the strong importance of diverse crop cultivation. Most incentives favor the cultivation of specific crops, which results in large areas of monocultures. The results confirm the importance of sustainable agricultural policies.

Analytical steady-state solutions for water-limited cropping systems using saline irrigation water

NASA Astrophysics Data System (ADS)

Skaggs, T. H.; Anderson, R. G.; Corwin, D. L.; Suarez, D. L.

2014-12-01

Due to the diminishing availability of good quality water for irrigation, it is increasingly important that irrigation and salinity management tools be able to target submaximal crop yields and support the use of marginal quality waters. In this work, we present a steady-state irrigated systems modeling framework that accounts for reduced plant water uptake due to root zone salinity. Two explicit, closed-form analytical solutions for the root zone solute concentration profile are obtained, corresponding to two alternative functional forms of the uptake reduction function. The solutions express a general relationship between irrigation water salinity, irrigation rate, crop salt tolerance, crop transpiration, and (using standard approximations) crop yield. Example applications are illustrated, including the calculation of irrigation requirements for obtaining targeted submaximal yields, and the generation of crop-water production functions for varying irrigation waters, irrigation rates, and crops. Model predictions are shown to be mostly consistent with existing models and available experimental data. Yet the new solutions possess advantages over available alternatives, including: (i) the solutions were derived from a complete physical-mathematical description of the system, rather than based on an ad hoc formulation; (ii) the analytical solutions are explicit and can be evaluated without iterative techniques; (iii) the solutions permit consideration of two common functional forms of salinity induced reductions in crop water uptake, rather than being tied to one particular representation; and (iv) the utilized modeling framework is compatible with leading transient-state numerical models.

Future Climate Impacts on Crop Water Demand and Groundwater Longevity in Agricultural Regions

NASA Astrophysics Data System (ADS)

Russo, T. A.; Sahoo, S.; Elliott, J. W.; Foster, I.

2016-12-01

Improving groundwater management practices under future drought conditions in agricultural regions requires three steps: 1) estimating the impacts of climate and drought on crop water demand, 2) projecting groundwater availability given climate and demand forcing, and 3) using this information to develop climate-smart policy and water use practices. We present an innovative combination of models to address the first two steps, and inform the third. Crop water demand was simulated using biophysical crop models forced by multiple climate models and climate scenarios, with one case simulating climate adaptation (e.g. modify planting or harvest time) and another without adaptation. These scenarios were intended to represent a range of drought projections and farm management responses. Nexty, we used projected climate conditions and simulated water demand across the United States as inputs to a novel machine learning-based groundwater model. The model was applied to major agricultural regions relying on the High Plains and Mississippi Alluvial aquifer systems in the US. The groundwater model integrates input data preprocessed using single spectrum analysis, mutual information, and a genetic algorithm, with an artificial neural network model. Model calibration and test results indicate low errors over the 33 year model run, and strong correlations to groundwater levels in hundreds of wells across each aquifer. Model results include a range of projected groundwater level changes from the present to 2050, and in some regions, identification and timeframe of aquifer depletion. These results quantify aquifer longevity under climate and crop scenarios, and provide decision makers with the data needed to compare scenarios of crop water demand, crop yield, and groundwater response, as they aim to balance water sustainability with food security.

Projected irrigation requirements for upland crops using soil moisture model under climate change in South Korea

USDA-ARS?s Scientific Manuscript database

An increase in abnormal climate change patterns and unsustainable irrigation in uplands cause drought and affect agricultural water security, crop productivity, and price fluctuations. In this study, we developed a soil moisture model to project irrigation requirements (IR) for upland crops under cl...

Crop parameters for modeling sugarcane under rainfed conditions in Mexico

USDA-ARS?s Scientific Manuscript database

Crop models with well-tested parameters can improve sugarcane productivity for food and biofuel generation. This study aimed to (i) calibrate the light extinction coefficient (k) and other crop parameters for the sugarcane cultivar CP 72-2086, an early-maturing cultivar grown in Mexico and many oth...

A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration

USDA-ARS?s Scientific Manuscript database

Ensembles of process-based crop models are now commonly used to simulate crop growth and development for climate scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of de...

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.;

An overview of available crop growth and yield models for studies and assessments in agriculture.

PubMed

Di Paola, Arianna; Valentini, Riccardo; Santini, Monia

2016-02-01

The scientific community offers numerous crop models with different levels of sophistication. In such a wide range of crop models, users should have the possibility to choose the most suitable, in terms of detail, scale and representativeness, to their objectives. However, even when an appropriate choice is made, model limitations should be clarified such that modelling studies are put in the proper perspective and robust applications are achieved. This work is an overview of available models to simulate crop growth and yield. A summary matrix with more than 70 crop models is provided, storing the main model characteristics that can help users to choose the proper tool according to their purposes. Overall, we found that two main aspects of models, despite their importance, are not always clear from the published references, i.e. the versatility of the models, in terms of reliable transferability to different conditions, and the degree of complexity. Hence, the developers of models should be encouraged to pay more attention to clarifying the model limitations and limits of applicability, and users should make an effort in proper model selection, to save time often devoted to iteration of tuning steps to force an inappropriate model to be adapted to their own purpose. © 2015 Society of Chemical Industry.

Regional crop yield forecasting: a probabilistic approach

NASA Astrophysics Data System (ADS)

de Wit, A.; van Diepen, K.; Boogaard, H.

2009-04-01

Information on the outlook on yield and production of crops over large regions is essential for government services dealing with import and export of food crops, for agencies with a role in food relief, for international organizations with a mandate in monitoring the world food production and trade, and for commodity traders. Process-based mechanistic crop models are an important tool for providing such information, because they can integrate the effect of crop management, weather and soil on crop growth. When properly integrated in a yield forecasting system, the aggregated model output can be used to predict crop yield and production at regional, national and continental scales. Nevertheless, given the scales at which these models operate, the results are subject to large uncertainties due to poorly known weather conditions and crop management. Current yield forecasting systems are generally deterministic in nature and provide no information about the uncertainty bounds on their output. To improve on this situation we present an ensemble-based approach where uncertainty bounds can be derived from the dispersion of results in the ensemble. The probabilistic information provided by this ensemble-based system can be used to quantify uncertainties (risk) on regional crop yield forecasts and can therefore be an important support to quantitative risk analysis in a decision making process.

Crop classification modelling using remote sensing and environmental data in the Greater Platte River Basin, USA

USGS Publications Warehouse

Howard, Daniel M.; Wylie, Bruce K.; Tieszen, Larry L.

2012-01-01

With an ever expanding population, potential climate variability and an increasing demand for agriculture-based alternative fuels, accurate agricultural land-cover classification for specific crops and their spatial distributions are becoming critical to researchers, policymakers, land managers and farmers. It is important to ensure the sustainability of these and other land uses and to quantify the net impacts that certain management practices have on the environment. Although other quality crop classification products are often available, temporal and spatial coverage gaps can create complications for certain regional or time-specific applications. Our goal was to develop a model capable of classifying major crops in the Greater Platte River Basin (GPRB) for the post-2000 era to supplement existing crop classification products. This study identifies annual spatial distributions and area totals of corn, soybeans, wheat and other crops across the GPRB from 2000 to 2009. We developed a regression tree classification model based on 2.5 million training data points derived from the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) in relation to a variety of other relevant input environmental variables. The primary input variables included the weekly 250 m US Geological Survey Earth Observing System Moderate Resolution Imaging Spectroradiometer normalized differential vegetation index, average long-term growing season temperature, average long-term growing season precipitation and yearly start of growing season. An overall model accuracy rating of 78% was achieved for a test sample of roughly 215 000 independent points that were withheld from model training. Ten 250 m resolution annual crop classification maps were produced and evaluated for the GPRB region, one for each year from 2000 to 2009. In addition to the model accuracy assessment, our validation focused on spatial distribution and county-level crop area totals in comparison with the NASS CDL and county statistics from the US Department of Agriculture (USDA) Census of Agriculture. The results showed that our model produced crop classification maps that closely resembled the spatial distribution trends observed in the NASS CDL and exhibited a close linear agreement with county-by-county crop area totals from USDA census data (R 2 = 0.90).

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.

An integrated crop and hydrologic modeling system to estimate hydrologic impacts of crop irrigation demands

Treesearch

R.T. McNider; C. Handyside; K. Doty; W.L. Ellenburg; J.F. Cruise; J.R. Christy; D. Moss; V. Sharda; G. Hoogenboom; Peter Caldwell

2015-01-01

The present paper discusses a coupled gridded crop modeling and hydrologic modeling system that can examine the benefits of irrigation and costs of irrigation and the coincident impact of the irrigation water withdrawals on surface water hydrology. The system is applied to the Southeastern U.S. The system tools to be discussed include a gridded version (GriDSSAT) of...

The AgMIP GRIDded Crop Modeling Initiative (AgGRID) and the Global Gridded Crop Model Intercomparison (GGCMI)

NASA Technical Reports Server (NTRS)

Elliott, Joshua; Muller, Christoff

2015-01-01

Climate change is a significant risk for agricultural production. Even under optimistic scenarios for climate mitigation action, present-day agricultural areas are likely to face significant increases in temperatures in the coming decades, in addition to changes in precipitation, cloud cover, and the frequency and duration of extreme heat, drought, and flood events (IPCC, 2013). These factors will affect the agricultural system at the global scale by impacting cultivation regimes, prices, trade, and food security (Nelson et al., 2014a). Global-scale evaluation of crop productivity is a major challenge for climate impact and adaptation assessment. Rigorous global assessments that are able to inform planning and policy will benefit from consistent use of models, input data, and assumptions across regions and time that use mutually agreed protocols designed by the modeling community. To ensure this consistency, large-scale assessments are typically performed on uniform spatial grids, with spatial resolution of typically 10 to 50 km, over specified time-periods. Many distinct crop models and model types have been applied on the global scale to assess productivity and climate impacts, often with very different results (Rosenzweig et al., 2014). These models are based to a large extent on field-scale crop process or ecosystems models and they typically require resolved data on weather, environmental, and farm management conditions that are lacking in many regions (Bondeau et al., 2007; Drewniak et al., 2013; Elliott et al., 2014b; Gueneau et al., 2012; Jones et al., 2003; Liu et al., 2007; M¨uller and Robertson, 2014; Van den Hoof et al., 2011;Waha et al., 2012; Xiong et al., 2014). Due to data limitations, the requirements of consistency, and the computational and practical limitations of running models on a large scale, a variety of simplifying assumptions must generally be made regarding prevailing management strategies on the grid scale in both the baseline and future periods. Implementation differences in these and other modeling choices contribute to significant variation among global-scale crop model assessments in addition to differences in crop model implementations that also cause large differences in site-specific crop modeling (Asseng et al., 2013; Bassu et al., 2014).

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments.

PubMed

Tao, Fulu; Rötter, Reimund P; Palosuo, Taru; Gregorio Hernández Díaz-Ambrona, Carlos; Mínguez, M Inés; Semenov, Mikhail A; Kersebaum, Kurt Christian; Nendel, Claas; Specka, Xenia; Hoffmann, Holger; Ewert, Frank; Dambreville, Anaelle; Martre, Pierre; Rodríguez, Lucía; Ruiz-Ramos, Margarita; Gaiser, Thomas; Höhn, Jukka G; Salo, Tapio; Ferrise, Roberto; Bindi, Marco; Cammarano, Davide; Schulman, Alan H

2018-03-01

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources. © 2017 John Wiley & Sons Ltd.

Modeling Requirements for Cohort and Register IT.

PubMed

Stäubert, Sebastian; Weber, Ulrike; Michalik, Claudia; Dress, Jochen; Ngouongo, Sylvie; Stausberg, Jürgen; Winter, Alfred

2016-01-01

The project KoRegIT (funded by TMF e.V.) aimed to develop a generic catalog of requirements for research networks like cohort studies and registers (KoReg). The catalog supports such kind of research networks to build up and to manage their organizational and IT infrastructure. To make transparent the complex relationships between requirements, which are described in use cases from a given text catalog. By analyzing and modeling the requirements a better understanding and optimizations of the catalog are intended. There are two subgoals: a) to investigate one cohort study and two registers and to model the current state of their IT infrastructure; b) to analyze the current state models and to find simplifications within the generic catalog. Processing the generic catalog was performed by means of text extraction, conceptualization and concept mapping. Then methods of enterprise architecture planning (EAP) are used to model the extracted information. To work on objective a) questionnaires are developed by utilizing the model. They are used for semi-structured interviews, whose results are evaluated via qualitative content analysis. Afterwards the current state was modeled. Objective b) was done by model analysis. A given generic text catalog of requirements was transferred into a model. As result of objective a) current state models of one existing cohort study and two registers are created and analyzed. An optimized model called KoReg-reference-model is the result of objective b). It is possible to use methods of EAP to model requirements. This enables a better overview of the partly connected requirements by means of visualization. The model based approach also enables the analysis and comparison of the empirical data from the current state models. Information managers could reduce the effort of planning the IT infrastructure utilizing the KoReg-reference-model. Modeling the current state and the generation of reports from the model, which could be used as requirements specification for bids, is supported, too.

Enhancing Adoption of Irrigation Scheduling to Sustain the Viability of Fruit and Nut Crops in California

NASA Astrophysics Data System (ADS)

Fulton, A.; Snyder, R.; Hillyer, C.; English, M.; Sanden, B.; Munk, D.

2012-04-01

Enhancing Adoption of Irrigation Scheduling to Sustain the Viability of Fruit and Nut Crops in California Allan Fulton, Richard Snyder, Charles Hillyer, Marshall English, Blake Sanden, and Dan Munk Adoption of scientific methods to decide when to irrigate and how much water to apply to a crop has increased over the last three decades in California. In 1988, less than 4.3 percent of US farmers employed some type of science-based technique to assist in making irrigation scheduling decisions (USDA, 1995). An ongoing survey in California, representing an industry irrigating nearly 0.4 million planted almond hectares, indicates adoption rates ranging from 38 to 55 percent of either crop evapotranspiration (ETc), soil moisture monitoring, plant water status, or some combination of these irrigation scheduling techniques to assist with making irrigation management decisions (California Almond Board, 2011). High capital investment to establish fruit and nut crops, sensitivity to over and under-irrigation on crop performance and longevity, and increasing costs and competition for water have all contributed to increased adoption of scientific irrigation scheduling methods. These trends in adoption are encouraging and more opportunities exist to develop improved irrigation scheduling tools, especially computer decision-making models. In 2009 and 2010, an "On-line Irrigation Scheduling Advisory Service" (OISO, 2012), also referred to as Online Irrigation Management (IMO), was used and evaluated in commercial walnut, almond, and French prune orchards in the northern Sacramento Valley of California. This specific model has many features described as the "Next Generation of Irrigation Schedulers" (Hillyer, 2010). While conventional irrigation management involves simply irrigating as needed to avoid crop stress, this IMO is designed to control crop stress, which requires: (i) precise control of crop water availability (rather than controlling applied water); (ii) quantifying crop stress in order to manage it in heterogeneous fields; and (iii) predicting crop responses to water stress. The capacities of this IMO include: 1. Modeling of the disposition of applied water in spatially variable fields; 2. Conjunctive scheduling for multiple fields, rather than scheduling each field independently; 3. Long range forecasting of crop water requirements to better utilize limited water or limited delivery system capacity: and 4. Explicit modeling of the uncertainties of water use and crop yield. This was one of the first efforts to employ a "Next Generation" type computer irrigation scheduling advisory model or IMO in orchard crops. This paper discusses experiences with introducing this model to fruit and nut growers of various size and scale in the northern Sacramento Valley of California and the accuracy of its forecasts of irrigation needs in fruit and nut crops. Strengths and opportunities to forge ahead in the development of a "Next Generation" irrigation scheduler were identified from this on-farm evaluation.

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.

The use of seasonal forecasts in a crop failure early warning system for West Africa

NASA Astrophysics Data System (ADS)

Nicklin, K. J.; Challinor, A.; Tompkins, A.

2011-12-01

Seasonal rainfall in semi-arid West Africa is highly variable. Farming systems in the region are heavily dependent on the monsoon rains leading to large variability in crop yields and a population that is vulnerable to drought. The existing crop yield forecasting system uses observed weather to calculate a water satisfaction index, which is then related to expected crop yield (Traore et al, 2006). Seasonal climate forecasts may be able to increase the lead-time of yield forecasts and reduce the humanitarian impact of drought. This study assesses the potential for a crop failure early warning system, which uses dynamic seasonal forecasts and a process-based crop model. Two sets of simulations are presented. In the first, the crop model is driven with observed weather as a control run. Observed rainfall is provided by the GPCP 1DD data set, whilst observed temperature and solar radiation data are given by the ERA-Interim reanalysis. The crop model used is the groundnut version of the General Large Area Model for annual crops (GLAM), which has been designed to operate on the grids used by seasonal weather forecasts (Challinor et al, 2004). GLAM is modified for use in West Africa by allowing multiple planting dates each season, replanting failed crops and producing parameter sets for Spanish- and Virginia- type West African groundnut. Crop yields are simulated for three different assumptions concerning the distribution and relative abundance of Spanish- and Virginia- type groundnut. Model performance varies with location, but overall shows positive skill in reproducing observed crop failure. The results for the three assumptions are similar, suggesting that the performance of the system is limited by something other than information on the type of groundnut grown. In the second set of simulations the crop model is driven with observed weather up to the forecast date, followed by ECMWF system 3 seasonal forecasts until harvest. The variation of skill with forecast date is assessed along with the extent to which forecasts can be improved by bias correction of the rainfall data. Two forms of bias correction are applied: a novel method of spatially bias correcting daily data, and statistical bias correction of the frequency and intensity distribution. Results are presented using both observed yields and the control run as the reference for verification. The potential for current dynamic seasonal forecasts to form part of an operational system giving timely and accurate warnings of crop failure is discussed. Traore S.B. et al., 2006. A Review of Agrometeorological Monitoring Tools and Methods Used in the West African Sahel. In: Motha R.P. et al., Strengthening Operational Agrometeorological Services at the National Level. Technical Bulletin WAOB-2006-1 and AGM-9, WMO/TD No. 1277. Pages 209-220. www.wamis.org/agm/pubs/agm9/WMO-TD1277.pdf Challinor A.J. et al., 2004. Design and optimisation of a large-area process based model for annual crops. Agric. For. Meteorol. 124, 99-120.

Pressurization System Modeling for a Generic Bimese Two- Stage-to-Orbit Reusable Launch Vehicle

NASA Technical Reports Server (NTRS)

Mazurkivich, Pete; Chandler, Frank; Nguyen, Han

2005-01-01

A pressurization system model was developed for a generic bimese Two-Stage-to-orbit Reusable Launch Vehicle using a cross-feed system and operating with densified propellants. The model was based on the pressurization system model for a crossfeed subscale water test article and was validated with test data obtained from the test article. The model consists of the liquid oxygen and liquid hydrogen pressurization models, each made up of two submodels, Booster and Orbiter tank pressurization models. The tanks are controlled within a 0.2-psi band and pressurized on the ground with ambient helium and autogenously in flight with gaseous oxygen and gaseous hydrogen. A 15-psi pressure difference is maintained between the Booster and Orbiter tanks to ensure crossfeed check valve closure before Booster separation. The analysis uses an ascent trajectory generated for a generic bimese vehicle and a tank configuration based on the Space Shuttle External Tank. It determines the flow rates required to pressurize the tanks on the ground and in flight, and demonstrates the model's capability to analyze the pressurization system performance of a full-scale bimese vehicle with densified propellants.

Quantifying the indirect impacts of climate on agriculture: an inter-method comparison

DOE PAGES

Calvin, Kate; Fisher-Vanden, Karen

2017-10-27

Climate change and increases in CO2 concentration affect the productivity of land, with implications for land use, land cover, and agricultural production. Much of the literature on the effect of climate on agriculture has focused on linking projections of changes in climate to process-based or statistical crop models. However, the changes in productivity have broader economic implications that cannot be quantified in crop models alone. How important are these socio-economic feedbacks to a comprehensive assessment of the impacts of climate change on agriculture? In this paper, we attempt to measure the importance of these interaction effects through an inter-method comparisonmore » between process models, statistical models, and integrated assessment model (IAMs). We find the impacts on crop yields vary widely between these three modeling approaches. Yield impacts generated by the IAMs are 20%-40% higher than the yield impacts generated by process-based or statistical crop models, with indirect climate effects adjusting yields by between - 12% and + 15% (e.g. input substitution and crop switching). The remaining effects are due to technological change.« less

Quantifying the indirect impacts of climate on agriculture: an inter-method comparison

NASA Astrophysics Data System (ADS)

Calvin, Kate; Fisher-Vanden, Karen

2017-11-01

Climate change and increases in CO2 concentration affect the productivity of land, with implications for land use, land cover, and agricultural production. Much of the literature on the effect of climate on agriculture has focused on linking projections of changes in climate to process-based or statistical crop models. However, the changes in productivity have broader economic implications that cannot be quantified in crop models alone. How important are these socio-economic feedbacks to a comprehensive assessment of the impacts of climate change on agriculture? In this paper, we attempt to measure the importance of these interaction effects through an inter-method comparison between process models, statistical models, and integrated assessment model (IAMs). We find the impacts on crop yields vary widely between these three modeling approaches. Yield impacts generated by the IAMs are 20%-40% higher than the yield impacts generated by process-based or statistical crop models, with indirect climate effects adjusting yields by between -12% and +15% (e.g. input substitution and crop switching). The remaining effects are due to technological change.

Quantifying the indirect impacts of climate on agriculture: an inter-method comparison

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

Calvin, Kate; Fisher-Vanden, Karen

Climate change and increases in CO2 concentration affect the productivity of land, with implications for land use, land cover, and agricultural production. Much of the literature on the effect of climate on agriculture has focused on linking projections of changes in climate to process-based or statistical crop models. However, the changes in productivity have broader economic implications that cannot be quantified in crop models alone. How important are these socio-economic feedbacks to a comprehensive assessment of the impacts of climate change on agriculture? In this paper, we attempt to measure the importance of these interaction effects through an inter-method comparisonmore » between process models, statistical models, and integrated assessment model (IAMs). We find the impacts on crop yields vary widely between these three modeling approaches. Yield impacts generated by the IAMs are 20%-40% higher than the yield impacts generated by process-based or statistical crop models, with indirect climate effects adjusting yields by between - 12% and + 15% (e.g. input substitution and crop switching). The remaining effects are due to technological change.« less

Controlled Ecological Life Support System (CELSS) modeling

NASA Technical Reports Server (NTRS)

Drysdale, Alan; Thomas, Mark; Fresa, Mark; Wheeler, Ray

1992-01-01

Attention is given to CELSS, a critical technology for the Space Exploration Initiative. OCAM (object-oriented CELSS analysis and modeling) models carbon, hydrogen, and oxygen recycling. Multiple crops and plant types can be simulated. Resource recovery options from inedible biomass include leaching, enzyme treatment, aerobic digestion, and mushroom and fish growth. The benefit of using many small crops overlapping in time, instead of a single large crop, is demonstrated. Unanticipated results include startup transients which reduce the benefit of multiple small crops. The relative contributions of mass, energy, and manpower to system cost are analyzed in order to determine appropriate research directions.

Impact of the introduction of mandatory generic substitution in South Africa: private sector sales of generic and originator medicines for chronic diseases.

PubMed

Gray, Andrew Lofts; Santa-Ana-Tellez, Yared; J Wirtz, Veronika

2016-12-01

To assess the impact of mandatory offer of generic substitution, introduced in South Africa in May 2003, on private sector sales of generic and originator medicines for chronic diseases. Private sector sales data (June 2001 to May 2005) were obtained from IMS Health for proton pump inhibitors (PPIs; ATC code A02BC), HMG-CoA reductase inhibitors (statins; C10AA), dihydropyridine calcium antagonists (C08CA), angiotensin-converting enzyme inhibitors (ACE-I; C09AA) and selective serotonin reuptake inhibitors (SSRIs; N06AB). Monthly sales were expressed as defined daily doses per 1000 insured population per month (DDD/TIM). Interrupted time-series models were used to estimate the changes in slope and level of medicines use after the policy change. ARIMA models were used to correct for autocorrelation and stationarity. Only the SSRIs saw a significant rise in level of generic utilisation (0.2 DDD/TIM; P < 0.001) and a fall in originator usage (-0.1 DDD/TIM; P < 0.001) after the policy change. Utilisation of generic PPIs fell (level 0.06 DDD/TIM, P = 0.048; slope 0.01 DDD/TIM, P = 0.043), but utilisation of originator products also grew (level 0.05 DDD/TIM, P < 0.001; slope 0.003, P = 0.001). Generic calcium antagonists and ACE-I showed an increase in slope (0.01 DDD/TIM, P = 0.016; 0.02 DDD/TIM, P < 0.001), while the originators showed a decrease in slope (-0.003 DDD/TIM, P = 0.046; -0.01 DDD/TIM, P < 0.001). There were insufficient data on generic statin use before the policy change to allow for analysis. The mandatory offer of generic substitution appeared to have had a quantifiable effect on utilisation patterns in the 2 years after May 2003. Managed care interventions that were already in place before the intervention may have blunted the extent of the changes seen in this period. Generic policies are an important enabling provision for cost-containment efforts. However, decisions taken outside of official policy may anticipate or differ from that policy, with important consequences. © 2016 John Wiley & Sons Ltd.

Emission factors of atmospheric and climatic pollutants from crop residues burning.

PubMed

Santiago-De La Rosa, Naxieli; González-Cardoso, Griselda; Figueroa-Lara, José de Jesús; Gutiérrez-Arzaluz, Mirella; Octaviano-Villasana, Claudia; Ramírez-Hernández, Irma Fabiola; Mugica-Álvarez, Violeta

2018-04-13

Biomass burning is a common agricultural practice, because it allows elimination of postharvesting residues; nevertheless, it involves an inefficient combustion process that generates atmospheric pollutants emission, which has implications on health and climate change. This work focuses on the estimation of emission factors (EFs) of PM 2.5 , PM 10 , organic carbon (OC), elemental carbon (EC), carbon monoxide (CO), carbon dioxide (CO 2 ), and methane (CH 4 ) of residues from burning alfalfa, barley, beans, cotton, maize, rice, sorghum, and wheat in Mexico. Chemical characteristics of the residues were determined to establish their relationship with EFs, as well as with the modified combustion efficiency (MCE). Essays were carried out in an open combustion chamber with isokinetic sampling, following modified EPA 201-A method. EFs did not present statistical differences among different varieties of the same crop, but were statistically different among different crops, showing that generic values of EFs for all the agricultural residues can introduce significant uncertainties when used for climatic and atmospheric pollutant inventories. EFs of PM 2.5 ranged from 1.19 to 11.30 g kg -1 , and of PM 10 from 1.77 to 21.56 g kg -1 . EFs of EC correlated with lignin content, whereas EFs of OC correlated inversely with carbon content. EFs of EC and OC in PM 2.5 ranged from 0.15 to 0.41 g kg -1 and from 0.33 to 5.29 g kg -1 , respectively, and in PM 10 , from 0.17 to 0.43 g kg -1 and from 0.54 to 11.06 g kg -1 . CO 2 represented the largest gaseous emissions volume with 1053.35-1850.82 g kg -1 , whereas the lowest was CH 4 with 1.61-5.59 g kg -1 . CO ranged from 28.85 to 155.71 g kg -1 , correlating inversely with carbon content and MCE. EFs were used to calculate emissions from eight agricultural residues burning in the country during 2016, to know the potential mitigation of climatic and atmospheric pollutants, provided this practice was banned. The emission factors of particles, short-lived climatic pollutants, and atmospheric pollutants from the crop residues burning of eight agricultural wastes crops, determined in this study using a standardized method, provides better knowledge of the emissions of those species in Latin America and other developing countries, and can be used as inputs in air quality models and climatic studies. The EFs will allow the development of more accurate inventories of aerosols and gaseous pollutants, which will lead to the design of effective mitigation strategies and planning processes for sustainable agriculture.

Predicting of biomass in Brazilian tropical dry forest: a statistical evaluation of generic equations.

PubMed

Lima, Robson B DE; Alves, Francisco T; Oliveira, Cinthia P DE; Silva, José A A DA; Ferreira, Rinaldo L C

2017-01-01

Dry tropical forests are a key component in the global carbon cycle and their biomass estimates depend almost exclusively of fitted equations for multi-species or individual species data. Therefore, a systematic evaluation of statistical models through validation of estimates of aboveground biomass stocks is justifiable. In this study was analyzed the capacity of generic and specific equations obtained from different locations in Mexico and Brazil, to estimate aboveground biomass at multi-species levels and for four different species. Generic equations developed in Mexico and Brazil performed better in estimating tree biomass for multi-species data. For Poincianella bracteosa and Mimosa ophthalmocentra, only the Sampaio and Silva (2005) generic equation was the most recommended. These equations indicate lower tendency and lower bias, and biomass estimates for these equations are similar. For the species Mimosa tenuiflora, Aspidosperma pyrifolium and for the genus Croton the specific regional equations are more recommended, although the generic equation of Sampaio and Silva (2005) is not discarded for biomass estimates. Models considering gender, families, successional groups, climatic variables and wood specific gravity should be adjusted, tested and the resulting equations should be validated at both local and regional levels as well as on the scales of tropics with dry forest dominance.

Generics license 30-month-olds’ inferences about the atypical properties of novel kinds

PubMed Central

Graham, Susan A.; Gelman, Susan A.; Clarke, Jessica

2016-01-01

We examined whether the distinction between generic and nongeneric language provides toddlers with a rapid and efficient means to learn about kinds. In Experiment 1, we examined 30-month-olds’ willingness to extend atypical properties to members of an unfamiliar category when the properties were introduced in one of three ways: a) using a generic noun phrase (“Blicks drink ketchup”); b) using a nongeneric noun phrase (“These blicks drink ketchup”); and c) using an attentional phrase (“Look at this”). Hearing a generic noun phrase boosted toddlers’ extension of properties to both the model exemplars and to novel members of the same category, relative to when a property had been introduced with a nongeneric noun phrase or an attentional phrase. In Experiment 2, properties were introduced with a generic noun phrase and toddlers extended novel properties to members of the same-category, but not to an out-of-category object. Taken together, these findings demonstrate that generics highlight the stability of a feature and foster generalization of the property to novel within-category exemplars. PMID:27505699

Assessing variable rate nitrogen fertilizer strategies within an extensively instrument field site using the MicroBasin model

NASA Astrophysics Data System (ADS)

Ward, N. K.; Maureira, F.; Yourek, M. A.; Brooks, E. S.; Stockle, C. O.

2014-12-01

The current use of synthetic nitrogen fertilizers in agriculture has many negative environmental and economic costs, necessitating improved nitrogen management. In the highly heterogeneous landscape of the Palouse region in eastern Washington and northern Idaho, crop nitrogen needs vary widely within a field. Site-specific nitrogen management is a promising strategy to reduce excess nitrogen lost to the environment while maintaining current yields by matching crop needs with inputs. This study used in-situ hydrologic, nutrient, and crop yield data from a heavily instrumented field site in the high precipitation zone of the wheat-producing Palouse region to assess the performance of the MicroBasin model. MicroBasin is a high-resolution watershed-scale ecohydrologic model with nutrient cycling and cropping algorithms based on the CropSyst model. Detailed soil mapping conducted at the site was used to parameterize the model and the model outputs were evaluated with observed measurements. The calibrated MicroBasin model was then used to evaluate the impact of various nitrogen management strategies on crop yield and nitrate losses. The strategies include uniform application as well as delineating the field into multiple zones of varying nitrogen fertilizer rates to optimize nitrogen use efficiency. We present how coupled modeling and in-situ data sets can inform agricultural management and policy to encourage improved nitrogen management.

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2 O emissions.

PubMed

Ehrhardt, Fiona; Soussana, Jean-François; Bellocchi, Gianni; Grace, Peter; McAuliffe, Russel; Recous, Sylvie; Sándor, Renáta; Smith, Pete; Snow, Val; de Antoni Migliorati, Massimiliano; Basso, Bruno; Bhatia, Arti; Brilli, Lorenzo; Doltra, Jordi; Dorich, Christopher D; Doro, Luca; Fitton, Nuala; Giacomini, Sandro J; Grant, Brian; Harrison, Matthew T; Jones, Stephanie K; Kirschbaum, Miko U F; Klumpp, Katja; Laville, Patricia; Léonard, Joël; Liebig, Mark; Lieffering, Mark; Martin, Raphaël; Massad, Raia S; Meier, Elizabeth; Merbold, Lutz; Moore, Andrew D; Myrgiotis, Vasileios; Newton, Paul; Pattey, Elizabeth; Rolinski, Susanne; Sharp, Joanna; Smith, Ward N; Wu, Lianhai; Zhang, Qing

2018-02-01

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N 2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N 2 O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N 2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N 2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N 2 O emissions. Yield-scaled N 2 O emissions (N 2 O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N 2 O emissions at field scale is discussed. © 2017 John Wiley & Sons Ltd.

Large area application of a corn hazard model. [Soviet Union

NASA Technical Reports Server (NTRS)

Ashburn, P.; Taylor, T. W. (Principal Investigator)

1981-01-01

An application test of the crop calendar portion of a corn (maize) stress indicator model developed by the early warning, crop condition assessment component of AgRISTARS was performed over the corn for grain producing regions of the U.S.S.R. during the 1980 crop year using real data. Performance of the crop calendar submodel was favorable; efficiency gains in meteorological data analysis time were on a magnitude of 85 to 90 percent.

Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity

NASA Astrophysics Data System (ADS)

Deryng, Delphine; Elliott, Joshua; Folberth, Christian; Müller, Christoph; Pugh, Thomas A. M.; Boote, Kenneth J.; Conway, Declan; Ruane, Alex C.; Gerten, Dieter; Jones, James W.; Khabarov, Nikolay; Olin, Stefan; Schaphoff, Sibyll; Schmid, Erwin; Yang, Hong; Rosenzweig, Cynthia

2016-08-01

Rising atmospheric CO2 concentrations ([CO2]) are expected to enhance photosynthesis and reduce crop water use. However, there is high uncertainty about the global implications of these effects for future crop production and agricultural water requirements under climate change. Here we combine results from networks of field experiments and global crop models to present a spatially explicit global perspective on crop water productivity (CWP, the ratio of crop yield to evapotranspiration) for wheat, maize, rice and soybean under elevated [CO2] and associated climate change projected for a high-end greenhouse gas emissions scenario. We find CO2 effects increase global CWP by 10[047]%-27[737]% (median[interquartile range] across the model ensemble) by the 2080s depending on crop types, with particularly large increases in arid regions (by up to 48[25;56]% for rainfed wheat). If realized in the fields, the effects of elevated [CO2] could considerably mitigate global yield losses whilst reducing agricultural consumptive water use (4-17%). We identify regional disparities driven by differences in growing conditions across agro-ecosystems that could have implications for increasing food production without compromising water security. Finally, our results demonstrate the need to expand field experiments and encourage greater consistency in modelling the effects of rising [CO2] across crop and hydrological modelling communities.

Regional Disparities in the Beneficial Effects of Rising CO2 Emissions on Crop Water Productivity

NASA Technical Reports Server (NTRS)

Deryng, Delphine; Elliott, Joshua; Folberth, Christian; Meuller, Christoph; Pugh, Thomas A. M.; Boote, Kenneth J.; Conway, Declan; Ruane, Alex C.; Gerten, Dieter; Jones, James W.;

Climate Change Impacts for the Conterminous USA: An Integrated Assessment Part 7. Economic Analysis of Field Crops and Land Use with Climate Change

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

Sands, Ronald D.; Edmonds, James A.

PNNL's Agriculture and Land Use (AgLU) model is used to demonstrate the impact of potential changes in climate on agricultural production and land use in the United States. AgLU simulates production of four crop types in several world regions, in 15-year time steps from 1990 to 2095. Changes in yield of major field crops in the United States, for 12 climate scenarios, are obtained from simulations of the EPIC crop growth model. Results from the HUMUS model are used to constrain crop irrigation, and the BIOME3 model is used to simulate productivity of unmanaged ecosystems. Assumptions about changes in agriculturalmore » productivity outside the United States are treated on a scenario basis, either responding in the same way as in the United States, or not responding to climate.« less

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.

A New Strategy in Observer Modeling for Greenhouse Cucumber Seedling Growth

PubMed Central

Qiu, Quan; Zheng, Chenfei; Wang, Wenping; Qiao, Xiaojun; Bai, He; Yu, Jingquan; Shi, Kai

2017-01-01

State observer is an essential component in computerized control loops for greenhouse-crop systems. However, the current accomplishments of observer modeling for greenhouse-crop systems mainly focus on mass/energy balance, ignoring physiological responses of crops. As a result, state observers for crop physiological responses are rarely developed, and control operations are typically made based on experience rather than actual crop requirements. In addition, existing observer models require a large number of parameters, leading to heavy computational load and poor application feasibility. To address these problems, we present a new state observer modeling strategy that takes both environmental information and crop physiological responses into consideration during the observer modeling process. Using greenhouse cucumber seedlings as an instance, we sample 10 physiological parameters of cucumber seedlings at different time point during the exponential growth stage, and employ them to build growth state observers together with 8 environmental parameters. Support vector machine (SVM) acts as the mathematical tool for observer modeling. Canonical correlation analysis (CCA) is used to select the dominant environmental and physiological parameters in the modeling process. With the dominant parameters, simplified observer models are built and tested. We conduct contrast experiments with different input parameter combinations on simplified and un-simplified observers. Experimental results indicate that physiological information can improve the prediction accuracies of the growth state observers. Furthermore, the simplified observer models can give equivalent or even better performance than the un-simplified ones, which verifies the feasibility of CCA. The current study can enable state observers to reflect crop requirements and make them feasible for applications with simplified shapes, which is significant for developing intelligent greenhouse control systems for modern greenhouse production. PMID:28848565

A New Strategy in Observer Modeling for Greenhouse Cucumber Seedling Growth.

PubMed

Qiu, Quan; Zheng, Chenfei; Wang, Wenping; Qiao, Xiaojun; Bai, He; Yu, Jingquan; Shi, Kai

2017-01-01

State observer is an essential component in computerized control loops for greenhouse-crop systems. However, the current accomplishments of observer modeling for greenhouse-crop systems mainly focus on mass/energy balance, ignoring physiological responses of crops. As a result, state observers for crop physiological responses are rarely developed, and control operations are typically made based on experience rather than actual crop requirements. In addition, existing observer models require a large number of parameters, leading to heavy computational load and poor application feasibility. To address these problems, we present a new state observer modeling strategy that takes both environmental information and crop physiological responses into consideration during the observer modeling process. Using greenhouse cucumber seedlings as an instance, we sample 10 physiological parameters of cucumber seedlings at different time point during the exponential growth stage, and employ them to build growth state observers together with 8 environmental parameters. Support vector machine (SVM) acts as the mathematical tool for observer modeling. Canonical correlation analysis (CCA) is used to select the dominant environmental and physiological parameters in the modeling process. With the dominant parameters, simplified observer models are built and tested. We conduct contrast experiments with different input parameter combinations on simplified and un-simplified observers. Experimental results indicate that physiological information can improve the prediction accuracies of the growth state observers. Furthermore, the simplified observer models can give equivalent or even better performance than the un-simplified ones, which verifies the feasibility of CCA. The current study can enable state observers to reflect crop requirements and make them feasible for applications with simplified shapes, which is significant for developing intelligent greenhouse control systems for modern greenhouse production.

Rising temperatures reduce global wheat production

NASA Astrophysics Data System (ADS)

Asseng, S.; Ewert, F.; Martre, P.; Rötter, R. P.; Lobell, D. B.; Cammarano, D.; Kimball, B. A.; Ottman, M. J.; Wall, G. W.; White, J. W.; Reynolds, M. P.; Alderman, P. D.; Prasad, P. V. V.; Aggarwal, P. K.; Anothai, J.; Basso, B.; Biernath, C.; Challinor, A. J.; de Sanctis, G.; Doltra, J.; Fereres, E.; Garcia-Vila, M.; Gayler, S.; Hoogenboom, G.; Hunt, L. A.; Izaurralde, R. C.; Jabloun, M.; Jones, C. D.; Kersebaum, K. C.; Koehler, A.-K.; Müller, C.; Naresh Kumar, S.; Nendel, C.; O'Leary, G.; Olesen, J. E.; Palosuo, T.; Priesack, E.; Eyshi Rezaei, E.; Ruane, A. C.; Semenov, M. A.; Shcherbak, I.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Thorburn, P. J.; Waha, K.; Wang, E.; Wallach, D.; Wolf, J.; Zhao, Z.; Zhu, Y.

2015-02-01

Crop models are essential tools for assessing the threat of climate change to local and global food production. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 °C to 32 °C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each °C of further temperature increase and become more variable over space and time.

Rising Temperatures Reduce Global Wheat Production

NASA Technical Reports Server (NTRS)

Asseng, S.; Ewert, F.; Martre, P.; Rötter, R. P.; Lobell, D. B.; Cammarano, D.; Kimball, B. A.; Ottman, M. J.; Wall, G. W.; White, J. W.;

A sub-canopy structure for simulating oil palm in the Community Land Model (CLM-Palm): 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.

2015-11-01

In order to quantify the effects of forests to oil palm conversion occurring in the tropics on land-atmosphere carbon, water and energy fluxes, we develop a new perennial crop sub-model CLM-Palm for simulating a palm plant functional type (PFT) within the framework of the Community Land Model (CLM4.5). CLM-Palm is tested here on oil palm only but is meant of generic interest for other palm crops (e.g., coconut). The oil palm has monopodial morphology and sequential phenology of around 40 stacked phytomers, each carrying a large leaf and a fruit bunch, forming a multilayer canopy. A sub-canopy phenological and physiological parameterization is thus introduced so that each phytomer has its own prognostic leaf growth and fruit yield capacity but with shared stem and root components. Phenology and carbon and nitrogen allocation operate on the different phytomers in parallel but at unsynchronized steps, separated by a thermal period. An important phenological phase is identified for the oil palm - 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 oil palm were calibrated and validated with field measurements of leaf area index (LAI), yield and net primary production (NPP) from Sumatra, Indonesia. In calibration with a mature oil palm plantation, the cumulative yields from 2005 to 2014 matched notably well between simulation and observation (mean percentage error = 3 %). 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 and sufficiently represent the significant nitrogen- and age-related site-to-site variability in NPP and yield. Results also indicate that seasonal dynamics of yield and remaining small-scale site-to-site variability of NPP are driven by processes not yet implemented in the model or reflected in the input data. The new sub-canopy structure and phenology and allocation functions in CLM-Palm 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.

Uncertainty Analysis of Coupled Socioeconomic-Cropping Models: Building Confidence in Climate Change Decision-Support Tools for Local Stakeholders

NASA Astrophysics Data System (ADS)

Malard, J. J.; Rojas, M.; Adamowski, J. F.; Gálvez, J.; Tuy, H. A.; Melgar-Quiñonez, H.

2015-12-01

While cropping models represent the biophysical aspects of agricultural systems, system dynamics modelling offers the possibility of representing the socioeconomic (including social and cultural) aspects of these systems. The two types of models can then be coupled in order to include the socioeconomic dimensions of climate change adaptation in the predictions of cropping models.We develop a dynamically coupled socioeconomic-biophysical model of agricultural production and its repercussions on food security in two case studies from Guatemala (a market-based, intensive agricultural system and a low-input, subsistence crop-based system). Through the specification of the climate inputs to the cropping model, the impacts of climate change on the entire system can be analysed, and the participatory nature of the system dynamics model-building process, in which stakeholders from NGOs to local governmental extension workers were included, helps ensure local trust in and use of the model.However, the analysis of climate variability's impacts on agroecosystems includes uncertainty, especially in the case of joint physical-socioeconomic modelling, and the explicit representation of this uncertainty in the participatory development of the models is important to ensure appropriate use of the models by the end users. In addition, standard model calibration, validation, and uncertainty interval estimation techniques used for physically-based models are impractical in the case of socioeconomic modelling. We present a methodology for the calibration and uncertainty analysis of coupled biophysical (cropping) and system dynamics (socioeconomic) agricultural models, using survey data and expert input to calibrate and evaluate the uncertainty of the system dynamics as well as of the overall coupled model. This approach offers an important tool for local decision makers to evaluate the potential impacts of climate change and their feedbacks through the associated socioeconomic system.

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.

Disclosure of industry payments to prescribers: industry payments might be a factor impacting generic drug prescribing.

PubMed

Qian, Jingjing; Hansen, Richard A; Surry, Daniel; Howard, Jennifer; Kiptanui, Zippora; Harris, Ilene

2017-07-01

Pharmaceutical companies paid at least $3.91bn to prescribers in 2013, yet evidence indicating whether industry payments shift prescribing away from generics is limited. This study examined the association between amount of industry payments to prescribers and generic drug prescribing rates among Medicare Part D prescribers. A cross-sectional analysis was conducted among 770 095 Medicare Part D prescribers after linking the 2013 national Open Payments data with 2013 Medicare Provider Utilization and Payment data. The exposure variable was the categorized amount of total industry payments to prescribers (i.e., meals, travel, research, and ownership). The outcome was prescriber's annual generic drug prescribing rate. Multivariable generalized linear regression models were used to examine the association between the amount of industry payments and prescriber's annual generic drug prescribing rates, controlling for prescriber's demographic and practice characteristics. In this sample, over one-third (38.0%) of Medicare Part D prescribers received industry payments in 2013. The mean annual generic drug prescribing rate was highest among prescribers receiving no payments and lowest among those receiving more than $500 of industry payments (77.5% vs. 71.3%, respectively; p < 0.001). The receipt of industry payments was independently associated with prescribers' generic drug prescribing rate; higher payments corresponded with lower generic drug prescribing rates. Other prescriber characteristics associated with higher annual generic drug prescribing rate included male sex, non-northeast region, specialty, and patient volume. Receipt of industry payments was associated with a decreased rate of generic drug prescribing. How this affects patient care and total medical costs warrants further study. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Analyzing generic and branded substitution patterns in the Netherlands using prescription data.

PubMed

Pechlivanoglou, Petros; van der Veen, Willem Jan; Bos, Jens H; Postma, Maarten J

2011-04-27

As in other societies, pharmaceutical expenditures in the Netherlands are rising every year. As a consequence, needs for cost control are often expressed. One possible solution for cost control could come through increasing generic substitution by pharmacists. We aim to analyse the extent and nature of substitution in recent years and estimate the likelihood of generic or branded substitution in Dutch pharmacies in relation to various characteristics. We utilized a linked prescription dataset originating from a general practitioner (GP) and a pharmacy database, both from the northern Netherlands. We selected specific drugs of interest, containing about 55,000 prescriptions from 15 different classes. We used a crossed generalized linear mixed model to estimate the effects that certain patient and pharmacy characteristics as well as timing have on the likelihood that a prescription will eventually be substituted by the pharmacist. Generic substitution occurred at 25% of the branded prescriptions. Generic substitution was more likely to occur earlier in time after patent expiry and to patients that were older and more experienced in their drug use. Individually owned pharmacies had a lower probability of generic substitution compared to chain pharmacies. Oppositely, branded substitution occurred in 10% of generic prescriptions and was positively related to the patients' experience in branded use. Individually owned pharmacies were more likely to substitute a generic drug to a branded compared to other pharmacies. Antidepressant and PPI prescriptions were less prone to generic and more prone to branded substitution. Analysis of prescription substitution by the pharmacist revealed strong relations between substitution and patient experience on drug use, pharmacy status and timing. These findings can be utilised to design further strategies to enhance generic substitution.

Lessons from Climate Modeling on the Design and Use of Ensembles for Crop Modeling

NASA Technical Reports Server (NTRS)

Wallach, Daniel; Mearns, Linda O.; Ruane, Alexander C.; Roetter, Reimund P.; Asseng, Senthold

2016-01-01

Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

Global Agriculture Yields and Conflict under Future Climate

NASA Astrophysics Data System (ADS)

Rising, J.; Cane, M. A.

2013-12-01

Aspects of climate have been shown to correlate significantly with conflict. We investigate a possible pathway for these effects through changes in agriculture yields, as predicted by field crop models (FAO's AquaCrop and DSSAT). Using satellite and station weather data, and surveyed data for soil and management, we simulate major crop yields across all countries between 1961 and 2008, and compare these to FAO and USDA reported yields. Correlations vary by country and by crop, from approximately .8 to -.5. Some of this range in crop model performance is explained by crop varieties, data quality, and other natural, economic, and political features. We also quantify the ability of AquaCrop and DSSAT to simulate yields under past cycles of ENSO as a proxy for their performance under changes in climate. We then describe two statistical models which relate crop yields to conflict events from the UCDP/PRIO Armed Conflict dataset. The first relates several preceding years of predicted yields of the major grain in each country to any conflict involving that country. The second uses the GREG ethnic group maps to identify differences in predicted yields between neighboring regions. By using variation in predicted yields to explain conflict, rather than actual yields, we can identify the exogenous effects of weather on conflict. Finally, we apply precipitation and temperature time-series under IPCC's A1B scenario to the statistical models. This allows us to estimate the scale of the impact of future yields on future conflict. Centroids of the major growing regions for each country's primary crop, based on USDA FAS consumption. Correlations between simulated yields and reported yields, for AquaCrop and DSSAT, under the assumption that no irrigation, fertilization, or pest control is used. Reported yields are the average of FAO yields and USDA FAS yields, where both are available.

Evapotranspiration and crop coefficients for a super intensive olive orchard. An application of SIMDualKc and METRIC models using ground and satellite observations

NASA Astrophysics Data System (ADS)

Paço, Teresa A.; Pôças, Isabel; Cunha, Mário; Silvestre, José C.; Santos, Francisco L.; Paredes, Paula; Pereira, Luís S.

2014-11-01

The estimation of crop evapotranspiration (ETc) from the reference evapotranspiration (ETo) and a standard crop coefficient (Kc) in olive orchards requires that the latter be adjusted to planting density and height. The use of the dual Kc approach may be the best solution because the basal crop coefficient Kcb represents plant transpiration and the evaporation coefficient reproduces the soil coverage conditions and the frequency of wettings. To support related computations for a super intensive olive orchard, the model SIMDualKc was adopted because it uses the dual Kc approach. Alternatively, to consider the physical characteristics of the vegetation, the satellite-based surface energy balance model METRIC™ - Mapping EvapoTranspiration at high Resolution using Internalized Calibration - was used to estimate ETc and to derive crop coefficients. Both approaches were compared in this study. SIMDualKc model was calibrated and validated using sap-flow measurements of the transpiration for 2011 and 2012. In addition, eddy covariance estimation of ETc was also used. In the current study, METRIC™ was applied to Landsat images from 2011 to 2012. Adaptations for incomplete cover woody crops were required to parameterize METRIC. It was observed that ETc obtained from both approaches was similar and that crop coefficients derived from both models showed similar patterns throughout the year. Although the two models use distinct approaches, their results are comparable and they are complementary in spatial and temporal scales.

Potato Genome Sequencing: A Short Glimpse of the Non-Repetitive.

USDA-ARS?s Scientific Manuscript database

Potato is the world’s number one vegetable crop. The potato is a member of the Solanaceae family that contains other crops such tomato pepper and eggplant as well as model species tobacco and petunia. Tomato is both an important crop as well as a model species for genetic and physical genomic info...

Evaluation of orthomosics and digital surface models derived from aerial imagery for crop mapping

USDA-ARS?s Scientific Manuscript database

Orthomosics derived from aerial imagery acquired by consumer-grade cameras have been used for crop mapping. However, digital surface models (DSM) derived from aerial imagery have not been evaluated for this application. In this study, a novel method was proposed to extract crop height from DSM and t...

Using eddy covariance and flux partitioning to assess basal, soil, and stress coefficients for crop evapotranspiration models

USDA-ARS?s Scientific Manuscript database

Current approaches to scheduling crop irrigation using reference evapotranspiration (ET0) recommend using a dual-coefficient approach using basal (Kcb) and soil (Ke) coefficients along with a stress coefficient (Ks) to model crop evapotranspiration (ETc), [e.g. ETc=(Ks*Kcb+Ke)*ET0]. However, indepe...

Coupled socioeconomic-crop modelling for the participatory local analysis of climate change impacts on smallholder farmers in Guatemala

NASA Astrophysics Data System (ADS)

Malard, J. J.; Adamowski, J. F.; Wang, L. Y.; Rojas, M.; Carrera, J.; Gálvez, J.; Tuy, H. A.; Melgar-Quiñonez, H.

2015-12-01

The modelling of the impacts of climate change on agriculture requires the inclusion of socio-economic factors. However, while cropping models and economic models of agricultural systems are common, dynamically coupled socio-economic-biophysical models have not received as much success. A promising methodology for modelling the socioeconomic aspects of coupled natural-human systems is participatory system dynamics modelling, in which stakeholders develop mental maps of the socio-economic system that are then turned into quantified simulation models. This methodology has been successful in the water resources management field. However, while the stocks and flows of water resources have also been represented within the system dynamics modelling framework and thus coupled to the socioeconomic portion of the model, cropping models are ill-suited for such reformulation. In addition, most of these system dynamics models were developed without stakeholder input, limiting the scope for the adoption and implementation of their results. We therefore propose a new methodology for the analysis of climate change variability on agroecosystems which uses dynamically coupled system dynamics (socio-economic) and biophysical (cropping) models to represent both physical and socioeconomic aspects of the agricultural system, using two case studies (intensive market-based agricultural development versus subsistence crop-based development) from rural Guatemala. The system dynamics model component is developed with relevant governmental and NGO stakeholders from rural and agricultural development in the case study regions and includes such processes as education, poverty and food security. Common variables with the cropping models (yield and agricultural management choices) are then used to dynamically couple the two models together, allowing for the analysis of the agroeconomic system's response to and resilience against various climatic and socioeconomic shocks.

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.

Crop effect to soil moisture retrieval at different microwave frequencies

NASA Astrophysics Data System (ADS)

Zhang, Zhongjun; Luan, Jinzhe

2006-12-01

In soil moisture retrieval by microwave remote sensing technology, vegetation effect is important, due to its emission upward as well as masking the soil surface contribution. Because of good penetration characteristics through crop at low frequencies, L-band is often used, where crop is treated as a uniform layer, and 0 th-order Brightness Temperature model is used. Higher frequencies upper than L-band, the frequencies both on NASA AQUA AMSR-E and FY-3 to be launched next year in CHINA, may be more informative in SM retrieval. The multiple-scattering effects inside crop and that between crop layer and soil surface will be increasing when frequencies go higher from L-band. In this paper, a Matrix-Doubling model that account for multiple-scattering based on ray tracing technique is used to simulate the microwave emission of vegetated-surface at C- and X-band. The orientation and size of crop element such as leaves and cylinders are accounted for in crop layer, and AIEM is used for calculation of ground surface scattering. Simulation results from this model for corn and SGP99 experiment data are in good agreement. Since complicated theoretical model as used in this paper involves too many parameters, to make SM retrieval more directly, corresponding terms from the developed model are matched with 0 th-order,so as to derive effective single scattering albedo and vegetation opacity at C- and X-band.

Multimodel ensembles of wheat growth: many models are better than one.

PubMed

Martre, Pierre; Wallach, Daniel; Asseng, Senthold; Ewert, Frank; Jones, James W; Rötter, Reimund P; Boote, Kenneth J; Ruane, Alex C; Thorburn, Peter J; Cammarano, Davide; Hatfield, Jerry L; Rosenzweig, Cynthia; Aggarwal, Pramod K; Angulo, Carlos; Basso, Bruno; Bertuzzi, Patrick; Biernath, Christian; Brisson, Nadine; Challinor, Andrew J; Doltra, Jordi; Gayler, Sebastian; Goldberg, Richie; Grant, Robert F; Heng, Lee; Hooker, Josh; Hunt, Leslie A; Ingwersen, Joachim; Izaurralde, Roberto C; Kersebaum, Kurt Christian; Müller, Christoph; Kumar, Soora Naresh; Nendel, Claas; O'leary, Garry; Olesen, Jørgen E; Osborne, Tom M; Palosuo, Taru; Priesack, Eckart; Ripoche, Dominique; Semenov, Mikhail A; Shcherbak, Iurii; Steduto, Pasquale; Stöckle, Claudio O; Stratonovitch, Pierre; Streck, Thilo; Supit, Iwan; Tao, Fulu; Travasso, Maria; Waha, Katharina; White, Jeffrey W; Wolf, Joost

2015-02-01

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models. © 2014 John Wiley & Sons Ltd.

Multimodel Ensembles of Wheat Growth: More Models are Better than One

NASA Technical Reports Server (NTRS)

Martre, Pierre; Wallach, Daniel; Asseng, Senthold; Ewert, Frank; Jones, James W.; Rotter, Reimund P.; Boote, Kenneth J.; Ruane, Alex C.; Thorburn, Peter J.; Cammarano, Davide;

Multimodel Ensembles of Wheat Growth: Many Models are Better than One

NASA Technical Reports Server (NTRS)

Martre, Pierre; Wallach, Daniel; Asseng, Senthold; Ewert, Frank; Jones, James W.; Rotter, Reimund P.; Boote, Kenneth J.; Ruane, Alexander C.; Thorburn, Peter J.; Cammarano, Davide;

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.

High Resolution Modelling of Crop Response to Climate Change

NASA Astrophysics Data System (ADS)

Mirmasoudi, S. S.; Byrne, J. M.; MacDonald, R. J.; Lewis, D.

2014-12-01

Crop production is one of the most vulnerable sectors to climatic variability and change. Increasing atmospheric CO2 concentration and other greenhouse gases are causing increases in global temperature. In western North America, water supply is largely derived from mountain snowmelt. Climate change will have a significant impact on mountain snowpack and subsequently, the snow-derived water supply. This will strain water supplies and increase water demand in areas with substantial irrigation agriculture. Increasing temperatures may create heat stress for some crops regardless of soil water supply, and increasing surface O3 and other pollutants may damage crops and ecosystems. CO2 fertilization may or may not be an advantage in future. This work is part of a larger study that will address a series of questions based on a range of future climate scenarios for several watersheds in western North America. The key questions are: (1) how will snowmelt and rainfall runoff vary in future; (2) how will seasonal and inter-annual soil water supply vary, and how might that impacts food supplies; (3) how might heat stress impact (some) crops even with adequate soil water; (4) will CO2 fertilization alter crop yields; and (5) will pollution loads, particularly O3, cause meaningful changes to crop yields? The Generate Earth Systems Science (GENESYS) Spatial Hydrometeorological Model is an innovative, efficient, high-resolution model designed to assess climate driven changes in mountain snowpack derived water supplies. We will link GENESYS to the CROPWAT crop model system to assess climate driven changes in water requirement and associated crop productivity for a range of future climate scenarios. Literature bases studies will be utilised to develop approximate crop response functions for heat stress, CO2 fertilization and for O3 damages. The overall objective is to create modeling systems that allows meaningful assessment of agricultural productivity at a watershed scale under a range of climate scenarios.

Survival and distribution of Escherichia coli on diverse fresh-cut baby leafy greens under preharvest through postharvest conditions.

PubMed

Tomás-Callejas, Alejandro; López-Velasco, Gabriela; Camacho, Alex B; Artés, Francisco; Artés-Hernández, Francisco; Suslow, Trevor V

2011-12-02

Escherichia coli O157:H7 has been associated in multiple outbreaks linked to the consumption of whole produce and fresh-cut leafy vegetables. However, plant-based foods had not been traditionally recognized as a host for enteric pathogens until the elevated incidence of produce-related outbreaks became apparent. The survival dynamics of two cocktails of generic E. coli (environmental water, plant and soil isolates) and E. coli O157:H7 within the phyllosphere of Mizuna, Red Chard and Tatsoi during their production, harvest, minimal processing, packaging and storage over two greenhouse production cycles were studied. Genotyping of applied generic E. coli strains to evaluate their comparative survival and relative abundance in the phyllosphere by REP-PCR is also reported. The Mizuna, Red Chard and Tatsoi shoots were grown under standard greenhouse conditions and fertility management. Both E. coli cocktails were spray-inoculated separately and determined to result in an initial mean population density of log 4.2 CFU/cm². Leaves were harvested as mini-greens approximating commercial maturity, minimally processed in a model washing system treated with 3 mg/L of ClO₂ and stored for 7 days at 5 °C. Rapid decline of generic E. coli and E. coli O157:H7 populations was observed for all plant types regardless of the leaf age at the time of inoculation and the irrigation type across both seasonal growth cycle trials. The decline rate of the surviving populations for the fall season was slower than for the summer season. The minimal processing with 3 mg/L of ClO₂ was not sufficient to fully disinfect the inoculated leaves prior to packaging and refrigerated storage. Viable populations of E. coli and E. coli O157:H7 were confirmed throughout storage, including the final time point at the end of acceptable visual leaf quality. In this study, the ability of low populations of E. coli to survive during production and postharvest operations in selected mini-greens has been demonstrated. However, further field-based trials are needed to expand understanding of the post-contamination fate of enteric bacterial pathogens on leafy vegetables. In summary, this research work provides baseline data upon which to develop food safety preventive control guidance during the production and minimal processing of these crops. Copyright © 2011 Elsevier B.V. All rights reserved.

Old Dog New Tricks: Use of Point-based Crop Models in Grid-based Regional Assessment of Crop Management Technologies Impact on Future Food Security

NASA Astrophysics Data System (ADS)

Koo, J.; Wood, S.; Cenacchi, N.; Fisher, M.; Cox, C.

2012-12-01

HarvestChoice (harvestchoice.org) generates knowledge products to guide strategic investments to improve the productivity and profitability of smallholder farming systems in sub-Saharan Africa (SSA). A keynote component of the HarvestChoice analytical framework is a grid-based overlay of SSA - a cropping simulation platform powered by process-based, crop models. Calibrated around the best available representation of cropping production systems in SSA, the simulation platform engages the DSSAT Crop Systems Model with the CENTURY Soil Organic Matter model (DSSAT-CENTURY) and provides a virtual experimentation module with which to explore the impact of a range of technological, managerial and environmental metrics on future crop productivity and profitability, as well as input use. For each of 5 (or 30) arc-minute grid cells in SSA, a stack of model input underlies it: datasets that cover soil properties and fertility, historic and future climate scenarios and farmers' management practices; all compiled from analyses of existing global and regional databases and consultations with other CGIAR centers. Running a simulation model is not always straightforward, especially when certain cropping systems or management practices are not even practiced by resource-poor farmers yet (e.g., precision agriculture) or they were never included in the existing simulation framework (e.g., water harvesting). In such cases, we used DSSAT-CENTURY as a function to iteratively estimate relative responses of cropping systems to technology-driven changes in water and nutrient balances compared to zero-adoption by farmers, while adjusting model input parameters to best mimic farmers' implementation of technologies in the field. We then fed the results of the simulation into to the economic and food trade model framework, IMPACT, to assess the potential implications on future food security. The outputs of the overall simulation analyses are packaged as a web-accessible database and published on the web with an interface that allows users to explore the simulation results in each country with user-defined baseline and what-if scenarios. The results are dynamically presented on maps, charts, and tables. This paper discusses the development of the simulation platform and its underlying data layers, a case study that assessed the role of potential crop management technology development, and the development of a web-based application that visualizes the simulation results.

A Generic Authentication LoA Derivation Model

NASA Astrophysics Data System (ADS)

Yao, Li; Zhang, Ning

One way of achieving a more fine-grained access control is to link an authentication level of assurance (LoA) derived from a requester’s authentication instance to the authorisation decision made to the requester. To realise this vision, there is a need for designing a LoA derivation model that supports the use and quantification of multiple LoA-effecting attributes, and analyse their composite effect on a given authentication instance. This paper reports the design of such a model, namely a generic LoA derivation model (GEA- LoADM). GEA-LoADM takes into account of multiple authentication attributes along with their relationships, abstracts the composite effect by the multiple attributes into a generic value, authentication LoA, and provides algorithms for the run-time derivation of LoA. The algorithms are tailored to reflect the relationships among the attributes involved in an authentication instance. The model has a number of valuable properties, including flexibility and extensibility; it can be applied to different application contexts and support easy addition of new attributes and removal of obsolete ones.

Economic Models of Preventive Dentistry for Australian Children and Adolescents: A Systematic Review.

PubMed

Tonmukayakul, Utsana; Sia, Kah-Ling; Gold, Lisa; Hegde, Shalika; de Silva, Andrea M; Moodie, Marj

2015-01-01

To identify economic evaluation models and parameters that could be replicated or adapted to construct a generic model to assess cost-effectiveness of and prioritise a wide range of community-based oral disease prevention programmes in an Australian context. The literature search was conducted using MEDLINE, ERIC, PsycINFO, CINHAL (EBSCOhost), EMBASE (Ovid), CRD, DARE, NHSEED, HTA, all databases in the Cochrane library, Scopus and ScienceDirect databases from their inception to November 2012. Thirty-three articles met the criteria for inclusion in this review (7 were Australian studies, 26 articles were international). Existing models focused primarily on dental caries. Periodontal disease, another common oral health problem, was lacking. Among caries prevention studies, there was an absence of clear evidence showing continuous benefits from primary through to permanent dentition and the long-term effects of oral health promotion. No generic model was identified from previous studies that could be immediately adopted or adapted for our purposes of simulating and prioritising a diverse range of oral health interventions for Australian children and adolescents. Nevertheless, data sources specified in the existing Australian-based models will be useful for developing a generic model for such purposes.

Changes in yields and their variability at different levels of global warming

NASA Astrophysics Data System (ADS)

Childers, Katelin

2015-04-01

An assessment of climate change impacts at different levels of global warming is crucial to inform the political discussion about mitigation targets as well as for the inclusion of climate change impacts in Integrated Assessment Models (IAMs) that generally only provide global mean temperature change as an indicator of climate change. While there is a well-established framework for the scalability of regional temperature and precipitation changes with global mean temperature change we provide an assessment of the extent to which impacts such as crop yield changes can also be described in terms of global mean temperature changes without accounting for the specific underlying emissions scenario. Based on multi-crop-model simulations of the four major cereal crops (maize, rice, soy, and wheat) on a 0.5 x 0.5 degree global grid generated within ISI-MIP, we show the average spatial patterns of projected crop yield changes at one half degree warming steps. We find that emissions scenario dependence is a minor component of the overall variance of projected yield changes at different levels of global warming. Furthermore, scenario dependence can be reduced by accounting for the direct effects of CO2 fertilization in each global climate model (GCM)/impact model combination through an inclusion of the global atmospheric CO2 concentration as a second predictor. The choice of GCM output used to force the crop model simulations accounts for a slightly larger portion of the total yield variance, but the greatest contributor to variance in both global and regional crop yields and at all levels of warming, is the inter-crop-model spread. The unique multi impact model ensemble available with ISI-MIP data also indicates that the overall variability of crop yields is projected to increase in conjunction with increasing global mean temperature. This result is consistent throughout the ensemble of impact models and across many world regions. Such a hike in yield volatility could have significant policy implications by affecting food prices and supplies.

A Prototype Performance Assessment Model for Generic Deep Borehole Repository for High-Level Nuclear Waste - 12132

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

Lee, Joon H.; Arnold, Bill W.; Swift, Peter N.

2012-07-01

A deep borehole repository is one of the four geologic disposal system options currently under study by the U.S. DOE to support the development of a long-term strategy for geologic disposal of commercial used nuclear fuel (UNF) and high-level radioactive waste (HLW). The immediate goal of the generic deep borehole repository study is to develop the necessary modeling tools to evaluate and improve the understanding of the repository system response and processes relevant to long-term disposal of UNF and HLW in a deep borehole. A prototype performance assessment model for a generic deep borehole repository has been developed using themore » approach for a mined geological repository. The preliminary results from the simplified deep borehole generic repository performance assessment indicate that soluble, non-sorbing (or weakly sorbing) fission product radionuclides, such as I-129, Se-79 and Cl-36, are the likely major dose contributors, and that the annual radiation doses to hypothetical future humans associated with those releases may be extremely small. While much work needs to be done to validate the model assumptions and parameters, these preliminary results highlight the importance of a robust seal design in assuring long-term isolation, and suggest that deep boreholes may be a viable alternative to mined repositories for disposal of both HLW and UNF. (authors)« less

Modeling Gas Exchange in a Closed Plant Growth Chamber

NASA Technical Reports Server (NTRS)

Cornett, J. D.; Hendrix, J. E.; Wheeler, R. M.; Ross, C. W.; Sadeh, W. Z.

1994-01-01

Fluid transport models for fluxes of water vapor and CO2 have been developed for one crop of wheat and three crops of soybean grown in a closed plant a growth chamber. Correspondence among these fluxes is discussed. Maximum fluxes of gases are provided for engineering design requirements of fluid recycling equipment in growth chambers. Furthermore, to investigate the feasibility of generalized crop models, dimensionless representations of water vapor fluxes are presented. The feasibility of such generalized models and the need for additional data are discussed.

Modeling gas exchange in a closed plant growth chamber

NASA Technical Reports Server (NTRS)

Cornett, J. D.; Hendrix, J. E.; Wheeler, R. M.; Ross, C. W.; Sadeh, W. Z.

1994-01-01

Fluid transport models for fluxes of water vapor and CO2 have been developed for one crop of wheat and three crops of soybean grown in a closed plant growth chamber. Correspondence among these fluxes is discussed. Maximum fluxes of gases are provided for engineering design requirements of fluid recycling equipment in growth chambers. Furthermore, to investigate the feasibility of generalized crop models, dimensionless representations of water vapor fluxes are presented. The feasibility of such generalized models and the need for additional data are discussed.

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.

Optimization on Paddy Crops in Central Java (with Solver, SVD on Least Square and ACO (Ant Colony Algorithm))

NASA Astrophysics Data System (ADS)

Parhusip, H. A.; Trihandaru, S.; Susanto, B.; Prasetyo, S. Y. J.; Agus, Y. H.; Simanjuntak, B. H.

2017-03-01

Several algorithms and objective functions on paddy crops have been studied to get optimal paddy crops in Central Java based on the data given from Surakarta and Boyolali. The algorithms are linear solver, least square and Ant Colony Algorithms (ACO) to develop optimization procedures on paddy crops modelled with Modified GSTAR (Generalized Space-Time Autoregressive) and nonlinear models where the nonlinear models are quadratic and power functions. The studied data contain paddy crops from Surakarta and Boyolali determining the best period of planting in the year 1992-2012 for Surakarta where 3 periods for planting are known and the optimal amount of paddy crops in Boyolali in the year 2008-2013. Having these analyses may guide the local agriculture government to give a decision on rice sustainability in its region. The best period for planting in Surakarta is observed, i.e. the best period is in September-December based on the data 1992-2012 by considering the planting area, the cropping area, and the paddy crops are the most important factors to be taken into account. As a result, we can refer the paddy crops in this best period (about 60.4 thousand tons per year) as the optimal results in 1992-2012 where the used objective function is quadratic. According to the research, the optimal paddy crops in Boyolali about 280 thousand tons per year where the studied factors are the amount of rainfalls, the harvested area and the paddy crops in 2008-2013. In this case, linear and power functions are studied to be the objective functions. Compared to all studied algorithms, the linear solver is still recommended to be an optimization tool for a local agriculture government to predict paddy crops in future.

Soil organic carbon dynamics and crop yield for different crop rotations in a degraded ferruginous tropical soil in a semi-arid region: a simulation approach.

PubMed

Soler, C M Tojo; Bado, V B; Traore, K; Bostick, W McNair; Jones, J W; Hoogenboom, G

2011-10-01

In recent years, simulation models have been used as a complementary tool for research and for quantifying soil carbon sequestration under widely varying conditions. This has improved the understanding and prediction of soil organic carbon (SOC) dynamics and crop yield responses to soil and climate conditions and crop management scenarios. The goal of the present study was to estimate the changes in SOC for different cropping systems in West Africa using a simulation model. A crop rotation experiment conducted in Farakô-Ba, Burkina Faso was used to evaluate the performance of the cropping system model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT) for simulating yield of different crops. Eight crop rotations that included cotton, sorghum, peanut, maize and fallow, and three different management scenarios, one without N (control), one with chemical fertilizer (N) and one with manure applications, were studied. The CSM was able to simulate the yield trends of various crops, with inconsistencies for a few years. The simulated SOC increased slightly across the years for the sorghum-fallow rotation with manure application. However, SOC decreased for all other rotations except for the continuous fallow (native grassland), in which the SOC remained stable. The model simulated SOC for the continuous fallow system with a high degree of accuracy normalized root mean square error (RMSE)=0·001, while for the other crop rotations the simulated SOC values were generally within the standard deviation (s.d.) range of the observed data. The crop rotations that included a supplemental N-fertilizer or manure application showed an increase in the average simulated aboveground biomass for all crops. The incorporation of this biomass into the soil after harvest reduced the loss of SOC. In the present study, the observed SOC data were used for characterization of production systems with different SOC dynamics. Following careful evaluation of the CSM with observed soil organic matter (SOM) data similar to the study presented here, there are many opportunities for the application of the CSM for carbon sequestration and resource management in Sub-Saharan Africa.

Overview of the status and global strategy for neonicotinoids.

PubMed

Jeschke, Peter; Nauen, Ralf; Schindler, Michael; Elbert, Alfred

2011-04-13

In recent years, neonicotinoid insecticides have been the fastest growing class of insecticides in modern crop protection, with widespread use against a broad spectrum of sucking and certain chewing pests. As potent agonists, they act selectively on insect nicotinic acetylcholine receptors (nAChRs), their molecular target site. The discovery of neonicotinoids can be considered as a milestone in insecticide research and greatly facilitates the understanding of functional properties of the insect nAChRs. In this context, the crystal structure of the acetylcholine-binding proteins provides the theoretical foundation for designing homology models of the corresponding receptor ligand binding domains within the nAChRs, a useful basis for virtual screening of chemical libraries and rational design of novel insecticides acting on these practically relevant channels. Because of the relatively low risk for nontarget organisms and the environment, the high target specificity of neonicotinoid insecticides, and their versatility in application methods, this important class has to be maintained globally for integrated pest management strategies and insect resistance management programs. Innovative concepts for life-cycle management, jointly with the introduction of generic products, have made neonicotinoids the most important chemical class for the insecticide market.

The Generic Resolution Advisor and Conflict Evaluator (GRACE) for Unmanned Aircraft Detect-And-Avoid Systems

NASA Technical Reports Server (NTRS)

Abramson, Michael; Refai, Mohamad; Santiago, Confesor

2017-01-01

The paper describes the Generic Resolution Advisor and Conflict Evaluator (GRACE), a novel alerting and guidance algorithm that combines flexibility, robustness, and computational efficiency. GRACE is generic since it was designed without any assumptions regarding temporal or spatial scales, aircraft performance, or its sensor and communication systems. Therefore, GRACE was adopted as a core component of the Java Architecture for Detect-And-Avoid (DAA) Extensibility and Modeling, developed by NASA as a research and modeling tool for Unmanned Aerial Systems Integration in the National Airspace System (NAS). GRACE has been used in a number of real-time and fast-time experiments supporting evolving requirements of DAA research, including parametric studies, NAS-wide simulations, human-in-the-loop experiments, and live flight tests.

Generic icing effects on forward flight performance of a model helicopter rotor

NASA Technical Reports Server (NTRS)

Tinetti, Ana F.; Korkan, Kenneth D.

1989-01-01

An experimental program using a commercially available model helicopter has been conducted in the TAMU 7 ft x 10 ft Subsonic Wind Tunnel to investigate main rotor performance degradation due to generic ice adhesion. Base and iced performance data were gathered as functions of fuselage incidence, blade collective pitch, main rotor rotational velocity, and freestream velocity. The experimental values have shown that, in general, the presence of generic ice introduces decrements in performance caused by leading edge separation regions and increased surface roughness. In addition to the expected changes in aerodynamic forces caused by variations in test Reynolds number, forward flight data seemed to be influenced by changes in freestream and rotational velocity. The dependence of the data upon such velocity variations was apparently enhanced by increases in blade chord.

Knowledge Acquisition of Generic Queries for Information Retrieval

PubMed Central

Seol, Yoon-Ho; Johnson, Stephen B.; Cimino, James J.

2002-01-01

Several studies have identified clinical questions posed by health care professionals to understand the nature of information needs during clinical practice. To support access to digital information sources, it is necessary to integrate the information needs with a computer system. We have developed a conceptual guidance approach in information retrieval, based on a knowledge base that contains the patterns of information needs. The knowledge base uses a formal representation of clinical questions based on the UMLS knowledge sources, called the Generic Query model. To improve the coverage of the knowledge base, we investigated a method for extracting plausible clinical questions from the medical literature. This poster presents the Generic Query model, shows how it is used to represent the patterns of clinical questions, and describes the framework used to extract knowledge from the medical literature.

Branded versus generic clopidogrel in cardiovascular diseases: a systematic review.

PubMed

Caldeira, Daniel; Fernandes, Ricardo M; Costa, João; David, Cláudio; Sampaio, Cristina; Ferreira, Joaquim J

2013-04-01

In the United States, patent for branded Plavix has recently expired. Some studies have compared branded and generic clopidogrel in terms of pharmacokinetic parameters in healthy volunteers, but data on patients and clinical outcomes are scarce. We aimed to review efficacy and safety data from studies comparing Plavix with generic clopidogrel in patients with cardiovascular disease. Electronic databases were searched (from inception to May 2012) for prospective studies evaluating branded versus generic clopidogrel in patients with cardiovascular diseases. Studies' characteristics and data estimates were retrieved. Pooled risk ratio (RR) and 95% confidence intervals (95% CIs) were estimated through a random-effects model. Three studies evaluating 760 patients were included: 2 randomized controlled trials and 1 cohort study. The RR for major cardiovascular events was 1.01 (95% CI, 0.67-1.52). Incidence of adverse events was similar between Plavix and generic (RR 0.85; 95% CI, 0.49-1.48). The risks of mortality, bleeding, and drug discontinuation were also not different between groups. There are a limited number of studies comparing Plavix and generic clopidogrel in patients with cardiovascular diseases and reporting hard clinical end points. The available evidence is therefore limited and does not support the existence of differences in efficacy or safety between branded and generic clopidogrel.

Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya.

PubMed

Richard, Kyalo; Abdel-Rahman, Elfatih M; Subramanian, Sevgan; Nyasani, Johnson O; Thiel, Michael; Jozani, Hosein; Borgemeister, Christian; Landmann, Tobias

2017-11-03

Cropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a study site. Within the study site, field data was collected during the satellite acquisition period on general land use/land cover (LULC) and the two cropping systems. Firstly, non-cropland areas were masked out from other land use/land cover using the LULC mapping result. Subsequently an optimized RF model was applied to the cropland layer to map the two cropping systems (2nd classification step). An overall accuracy of 93% was attained for the LULC classification, while the class accuracies (PA: producer's accuracy and UA: user's accuracy) for the two cropping systems were consistently above 85%. We concluded that explicit mapping of different cropping systems is feasible in complex and highly fragmented agro-ecological landscapes if high resolution and multi-temporal satellite data such as 5 m RapidEye data is employed. Further research is needed on the feasibility of using freely available 10-20 m Sentinel-2 data for wide-area assessment of cropping systems as an important variable in numerous crop productivity models.

CFD Simulation of Aerial Crop Spraying

NASA Astrophysics Data System (ADS)

Omar, Zamri; Qiang, Kua Yong; Mohd, Sofian; Rosly, Nurhayati

2016-11-01

Aerial crop spraying, also known as crop dusting, is made for aerial application of pesticides or fertilizer. An agricultural aircraft which is converted from an aircraft has been built to combine with the aerial crop spraying for the purpose. In recent years, many studies on the aerial crop spraying were conducted because aerial application is the most economical, large and rapid treatment for the crops. The main objective of this research is to study the airflow of aerial crop spraying system using Computational Fluid Dynamics. This paper is focus on the effect of aircraft speed and nozzle orientation on the distribution of spray droplet at a certain height. Successful and accurate of CFD simulation will improve the quality of spray during the real situation and reduce the spray drift. The spray characteristics and efficiency are determined from the calculated results of CFD. Turbulence Model (k-ɛ Model) is used for the airflow in the fluid domain to achieve a more accurate simulation. Furthermore, spray simulation is done by setting the Flat-fan Atomizer Model of Discrete Phase Model (DPM) at the nozzle exit. The interaction of spray from each flat-fan atomizer can also be observed from the simulation. The evaluation of this study is validation and grid dependency study using field data from industry.

Assessing wild bees in perennial bioenergy landscapes: effects of bioenergy crop composition, landscape configuration, and bioenergy crop area

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

Graham, John B.; Nassauer, Joan I.; Currie, William S.

Wild bee populations are currently under threat, which has led to recent efforts to increase pollinator habitat in North America. Simultaneously, U.S. federal energy policies are beginning to encourage perennial bioenergy cropping (PBC) systems, which have the potential to support native bees. Our objective was to explore the potentially interactive effects of crop composition, total PBC area, and PBC patches in different landscape configurations. Using a spatially-explicit modeling approach, the Lonsdorf model, we simulated the impacts of three perennial bioenergy crops (PBC: willow, switchgrass, and prairie), three scenarios with different total PBC area (11.7%, 23.5% and 28.8% of agricultural landmore » converted to PBC) and two types of landscape configurations (PBC in clustered landscape patterns that represent realistic future configurations or in dispersed neutral landscape models) on a nest abundance index in an Illinois landscape. Our modeling results suggest that crop composition and PBC area are particularly important for bee nest abundance, whereas landscape configuration is associated with bee nest abundance at the local scale but less so at the regional scale. Moreover, strategies to enhance wild bee habitat should therefore emphasize the crop composition and amount of PBC.« less

Assessing wild bees in perennial bioenergy landscapes: effects of bioenergy crop composition, landscape configuration, and bioenergy crop area

DOE PAGES

Graham, John B.; Nassauer, Joan I.; Currie, William S.; ...

2017-03-25

Wild bee populations are currently under threat, which has led to recent efforts to increase pollinator habitat in North America. Simultaneously, U.S. federal energy policies are beginning to encourage perennial bioenergy cropping (PBC) systems, which have the potential to support native bees. Our objective was to explore the potentially interactive effects of crop composition, total PBC area, and PBC patches in different landscape configurations. Using a spatially-explicit modeling approach, the Lonsdorf model, we simulated the impacts of three perennial bioenergy crops (PBC: willow, switchgrass, and prairie), three scenarios with different total PBC area (11.7%, 23.5% and 28.8% of agricultural landmore » converted to PBC) and two types of landscape configurations (PBC in clustered landscape patterns that represent realistic future configurations or in dispersed neutral landscape models) on a nest abundance index in an Illinois landscape. Our modeling results suggest that crop composition and PBC area are particularly important for bee nest abundance, whereas landscape configuration is associated with bee nest abundance at the local scale but less so at the regional scale. Moreover, strategies to enhance wild bee habitat should therefore emphasize the crop composition and amount of PBC.« less

Potato Production as Affected by Crop Parameters and Meteoro Logical Elements

NASA Astrophysics Data System (ADS)

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

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

Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA

USGS Publications Warehouse

Groff, Shannon C.; Loftin, Cynthia S.; Drummond, Frank; Bushmann, Sara; McGill, Brian J.

2016-01-01

Non-native honeybees historically have been managed for crop pollination, however, recent population declines draw attention to pollination services provided by native bees. We applied the InVEST Crop Pollination model, developed to predict native bee abundance from habitat resources, in Maine's wild blueberry crop landscape. We evaluated model performance with parameters informed by four approaches: 1) expert opinion; 2) sensitivity analysis; 3) sensitivity analysis informed model optimization; and, 4) simulated annealing (uninformed) model optimization. Uninformed optimization improved model performance by 29% compared to expert opinion-informed model, while sensitivity-analysis informed optimization improved model performance by 54%. This suggests that expert opinion may not result in the best parameter values for the InVEST model. The proportion of deciduous/mixed forest within 2000 m of a blueberry field also reliably predicted native bee abundance in blueberry fields, however, the InVEST model provides an efficient tool to estimate bee abundance beyond the field perimeter.

A Better Representation of European Croplands into a Global Biosphere Model

NASA Astrophysics Data System (ADS)

Gervois, S.; de Noblet, N.; Viovy, N.; Ciais, P.; Brisson, N.; Seguin, B.

2002-12-01

Croplands cover a quarter of Europe's surface (about an hundred million hectares), their impact on carbon and water fluxes must therefore be estimated. Global biosphere models such as ORCHIDEE (http://www.ipsl.jussieu.fr/~ssipsl/) were conceived to simulate natural ecosystems only, so croplands are often described as grasslands. Not only cropland productivity depends on climate and soil conditions but also on irrigations, fertilisers impact, date of sowing... In addition crop species are usually selected genetically to shorten and accelerate their growth. Agronomic models such as STICS (Brisson et al. 1998) give a more realistic picture of croplands as they are especially designed to account for this human forcing. On the other hand they can be used at the local scale only. First we evaluate the ability of the two models to reproduce the seasonal behaviour the leaf area index (LAI), the aerial biomass, and the exchanges of water vapour and CO2 with the atmosphere. For that we compare the model outputs with measurements performed at five sites that are representative of most common European crops (wheat, corn, soybean). As expected the agronomic STICS better behaves than the generic model ORCHIDEE in representing the seasonal cycle of the above variables. In order to get a realistic representation of croplands areas at the regional scale, we decided to couple ORCHIDEE with STICS. First we present the main steps of the coupling procedure. The principle consists in forcing ORCHIDEE with five more realistic outputs of STICS: LAI, date of harvest, nitrogen stress, root profile, and vegetation height. On the other hand, ORCHIDEE computes its own carbon and water balance. The allocation scheme was also modified in ORCHIDEE in order to conserve the coherence between LAI and leaf biomass, and we added a harvest module into ORCHIDEE. The coupled model was validated against carbon and water fluxes observed respectively at two fields (wheat and corn) in the US. We also conducted at European scale two experiments where all arable lands are covered by corn for the first one, and by natural grasslands for the second one. We compared the fluxes between these two simulations. In the case of corn cover, the vegetative period is reduced and the absorption of carbon is more enhanced (until 15 per cent) during the maximum extension of vegetation. This work shows that croplands can be integrated into global biosphere models to simulate CO2 and water vapour regional fluxes, which should allow a better representation of those ecosystems for climate studies.

Preliminary Analysis of Assessment Instrument Design to Reveal Science Generic Skill and Chemistry Literacy

ERIC Educational Resources Information Center

Sumarni, Woro; Sudarmin; Supartono, Wiyanto

2016-01-01

The purpose of this research is to design assessment instrument to evaluate science generic skill (SGS) achievement and chemistry literacy in ethnoscience-integrated chemistry learning. The steps of tool designing refers to Plomp models including 1) Investigation Phase (Prelimenary Investigation); 2) Designing Phase (Design); 3)…

Generic tripartite Bell nonlocality sudden death under local phase noise

NASA Astrophysics Data System (ADS)

Ann, Kevin; Jaeger, Gregg

2008-11-01

We definitively show, using an explicit and broadly applicable model, that local phase noise that is capable of eliminating state coherence only in the infinite-time limit is capable of eliminating nonlocality in finite time in three two-level systems prepared in the Bell-nonlocal tripartite states of the generic entanglement class.