Journal articles: 'Evaluation of crop model'

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Nagarajan, Kumar, R. J. O'Neil, J. Lowenberg-DeBoer, and C. R. Edwards. "Indiana Soybean System Model (ISSM): I. Crop model evaluation." Agricultural Systems 43, no. 4 (January 1993): 357–79. http://dx.doi.org/10.1016/0308-521x(93)90029-2.

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Wu, X., N. Vuichard, P. Ciais, N. Viovy, N. de Noblet-Ducoudré, X. Wang, V. Magliulo, et al. "ORCHIDEE-CROP (v0), a new process-based agro-land surface model: model description and evaluation over Europe." Geoscientific Model Development 9, no. 2 (March 1, 2016): 857–73. http://dx.doi.org/10.5194/gmd-9-857-2016.

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Abstract. The response of crops to changing climate and atmospheric CO2 concentration ([CO2]) could have large effects on food production, and impact carbon, water, and energy fluxes, causing feedbacks to the climate. To simulate the response of temperate crops to changing climate and [CO2], which accounts for the specific phenology of crops mediated by management practice, we describe here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module, and a very simple parameterization of nitrogen fertilization, into the land surface model (LSM) ORCHIDEEv196, in order to simulate biophysical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but is tested here using maize and winter wheat, with the phenological parameterizations of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0) model against eddy covariance and biometric measurements at seven winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (net ecosystem exchange, NEE), latent heat, and sensible heat fluxes. Additional measurements of leaf area index (LAI) and aboveground biomass and yield are used as well. Evaluation results revealed that ORCHIDEE-CROP (v0) reproduced the observed timing of crop development stages and the amplitude of the LAI changes. This is in contrast to ORCHIDEEv196 where, by default, crops have the same phenology as grass. A halving of the root mean square error for LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m2 m−2 was obtained when ORCHIDEEv196 and ORCHIDEE-CROP (v0) were compared across the seven study sites. Improved crop phenology and carbon allocation led to a good match between modeled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0–54.2 %), crop yield, daily carbon and energy fluxes (with a NRMSE of ∼ 9.0–20.1 and ∼ 9.4–22.3 % for NEE), and sensible and latent heat fluxes. The simulated yields for winter wheat and maize from ORCHIDEE-CROP (v0) showed a good match with the simulated results from STICS for three sites with available crop yield observations, where the average NRMSE was ∼ 8.8 %. The model data misfit for energy fluxes were within the uncertainties of the measurements, which themselves showed an incomplete energy balance closure within the range 80.6–86.3 %. The remaining discrepancies between the modeled and observed LAI and other variables at specific sites were partly attributable to unrealistic representations of management events by the model. ORCHIDEE-CROP (v0) has the ability to capture the spatial gradients of carbon and energy-related variables, such as gross primary productivity, NEE, and sensible and latent heat fluxes across the sites in Europe, which is an important requirement for future spatially explicit simulations. Further improvement of the model, with an explicit parameterization of nutritional dynamics and management, is expected to improve its predictive ability to simulate croplands in an Earth system model.

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Wu, X., N. Vuichard, P. Ciais, N. Viovy, N. de Noblet-Ducoudré, X. Wang, V. Magliulo, et al. "ORCHIDEE-CROP (v0), a new process based Agro-Land Surface Model: model description and evaluation over Europe." Geoscientific Model Development Discussions 8, no. 6 (June 22, 2015): 4653–96. http://dx.doi.org/10.5194/gmdd-8-4653-2015.

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Abstract. The responses of crop functioning to changing climate and atmospheric CO2 concentration ([CO2]) could have large effects on food production, and impact carbon, water and energy fluxes, causing feedbacks to climate. To simulate the responses of temperate crops to changing climate and [CO2], accounting for the specific phenology of crops mediated by management practice, we present here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module and a very simple parameterization of nitrogen fertilization, into the land surface model (LSM) ORCHIDEEv196, in order to simulate biophysical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but it is tested here for maize and winter wheat, with the phenological parameterizations of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0) model against eddy covariance and biometric measurements at 7 winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (NEE), latent heat and sensible heat fluxes. Additional measurements of leaf area index (LAI), aboveground biomass and yield are used as well. Evaluation results reveal that ORCHIDEE-CROP (v0) reproduces the observed timing of crop development stages and the amplitude of pertaining LAI changes in contrast to ORCHIDEEv196 in which by default crops have the same phenology than grass. A near-halving of the root mean square error of LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m2 m−2 is obtained between ORCHIDEEv196 and ORCHIDEE-CROP (v0) across the 7 study sites. Improved crop phenology and carbon allocation lead to a general good match between modelled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0–54.2 %), crop yield, as well as of the daily carbon and energy fluxes with NRMSE of ~9.0–20.1 and ~9.4–22.3 % for NEE, and sensible and latent heat fluxes, respectively. The model data mistfit for energy fluxes are within uncertainties of the measurements, which themselves show an incomplete energy balance closure within the range 80.6–86.3 %. The remaining discrepancies between modelled and observed LAI and other variables at specific sites are partly attributable to unrealistic representation of management events. In addition, ORCHIDEE-CROP (v0) is shown to have the ability to capture the spatial gradients of carbon and energy-related variables, such as gross primary productivity, NEE, sensible heat fluxes and latent heat fluxes, across the sites in Europe, an important requirement for future spatially explicit simulations. Further improvement of the model with an explicit parameterization of nutrition dynamics and of management, is expected to improve its predictive ability to simulate croplands in an Earth System Model.

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Jahanshiri, Ebrahim, Nur Marahaini Mohd Nizar, Tengku Adhwa Syaherah Tengku Mohd Suhairi, Peter J. Gregory, Ayman Salama Mohamed, Eranga M. Wimalasiri, and Sayed N. Azam-Ali. "A Land Evaluation Framework for Agricultural Diversification." Sustainability 12, no. 8 (April 13, 2020): 3110. http://dx.doi.org/10.3390/su12083110.

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Shortlisting ecologically adaptable plant species can be a starting point for agricultural diversification projects. We propose a rapid assessment framework based on an ecological model that can accelerate the evaluation of options for sustainable crop diversification. To test the new model, expert-defined and widely available crop requirement data were combined with more than 100,000 occurrence data for 40 crops of different types (cereals, legumes, vegetables, fruits, and tubers/roots). Soil pH, texture, and depth to bedrock data were obtained and harmonised based on the optimal rooting depths of each crop. Global baseline temperature and rainfall data were used to extract averages at each location. To evaluate the ability of the method to capture intraspecies variation, a test was performed using more than 1000 accession records of bambara groundnut (Vigna subterranea (L.) Verdc.) as an exemplar underutilised crop. Results showed that a suitability index based on soil pH and an index that combines the thermal suitability moderated by the soil pH, texture, and depth suitability have the potential to predict crop adaptability. We show that the proposed method can be combined with traditional land use and crop models to evaluate diversification options for sustainable land and agrobiodiversity resources management.

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Müller, Christoph, Joshua Elliott, James Chryssanthacopoulos, Almut Arneth, Juraj Balkovic, Philippe Ciais, Delphine Deryng, et al. "Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications." Geoscientific Model Development 10, no. 4 (April 4, 2017): 1403–22. http://dx.doi.org/10.5194/gmd-10-1403-2017.

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Abstract. Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.

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Khafajeh, H., A. Banakar, S. Minaei, and M. Delavar. "Evaluation of AquaCrop model of cucumber under greenhouse cultivation." Journal of Agricultural Science 158, no. 10 (December 2020): 845–54. http://dx.doi.org/10.1017/s0021859621000472.

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AbstractWater consumption in agriculture is impossible without considering relations between water, soil and plant. In this regard, there are various models and developed software in order to evaluate relation between soil, water and crop growth stages. These models can be used for irrigation planning if properly optimized and applied. AquaCrop is one of the known crop models, which was developed by the Food and Agriculture Organization of the United Nations. In order to optimize this model for crop production and irrigation management, an experiment was developed in a hydroponic cucumber greenhouse. Various parameters including water consumption volume, crop yield and leaf area index were measured during a season. A fuzzy control system was utilized for controlling temperature, relative humidity, planting bed moisture, light intensity and carbon dioxide values. The main purpose of designing a control system in the greenhouse is to achieve the desired values of temperature and relative humidity. In this model, evapotranspiration, irrigation requirements and crop yield were simulated. The results show that the AquaCrop model can estimate evapotranspiration with the least error in the greenhouse environment, which is controlled by a fuzzy controller. Also the system has estimated the crop yield and biomass of the product with a good degree of precision and it may support crop production in a greenhouse, including crop management and environmental control.

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Colaizzi, P. D., R. C. Schwartz, S. R. Evett, T. A. Howell, P. H. Gowda, and J. A. Tolk. "Radiation Model for Row Crops: II. Model Evaluation." Agronomy Journal 104, no. 2 (March 2012): 241–55. http://dx.doi.org/10.2134/agronj2011.0083.

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Singh, Rajwinder, Rahul Rana, and Sunil Kr Singh. "Performance Evaluation of VGG models in Detection of Wheat Rust." Asian Journal of Computer Science and Technology 7, no. 3 (November 5, 2018): 76–81. http://dx.doi.org/10.51983/ajcst-2018.7.3.1892.

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The agricultural sector is the backbone of Indian economy and social development but due to lack of awareness towards crop management, a large number of crops get wasted each year. Automated Systems are required for this purpose. This paper tries to highlight the efficiency of two existing models of deep learning, VGG16 and VGG19 for proper detection of wheat rust disease in the infected wheat crop. These two models use convolutional neural networks for image classification and which can be used to design an intelligent system which can easily detect wheat rust in crop images. This paper basically presents the comparative analysis of the accuracy and efficiency along with usability to select the best model for systems that can be used for crop safety.

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Gebiso Challa, Tamrat. "Economic Evaluation of Asella Model-III Multi-crop Thresher." International Journal of Agricultural Economics 3, no. 3 (2018): 45. http://dx.doi.org/10.11648/j.ijae.20180303.12.

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Williams, Karina, Jemma Gornall, Anna Harper, Andy Wiltshire, Debbie Hemming, Tristan Quaife, Tim Arkebauer, and David Scoby. "Evaluation of JULES-crop performance against site observations of irrigated maize from Mead, Nebraska." Geoscientific Model Development 10, no. 3 (March 27, 2017): 1291–320. http://dx.doi.org/10.5194/gmd-10-1291-2017.

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Abstract. The JULES-crop model (Osborne et al., 2015) is a parametrisation of crops within the Joint UK Land Environment Simulator (JULES), which aims to simulate both the impact of weather and climate on crop productivity and the impact of croplands on weather and climate. In this evaluation paper, observations of maize at three FLUXNET sites in Nebraska (US-Ne1, US-Ne2 and US-Ne3) are used to test model assumptions and make appropriate input parameter choices. JULES runs are performed for the irrigated sites (US-Ne1 and US-Ne2) both with the crop model switched off (prescribing leaf area index (LAI) and canopy height) and with the crop model switched on. These are compared against GPP and carbon pool FLUXNET observations. We use the results to point to future priorities for model development and describe how our methodology can be adapted to set up model runs for other sites and crop varieties.

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Adhikari, Pradip, Nina Omani, Srinivasulu Ale, Paul B. DeLaune, Kelly R. Thorp, Edward M. Barnes, and Gerrit Hoogenboom. "Simulated Effects of Winter Wheat Cover Crop on Cotton Production Systems of the Texas Rolling Plains." Transactions of the ASABE 60, no. 6 (2017): 2083–96. http://dx.doi.org/10.13031/trans.12272.

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Abstract. Interest in cover crops has been increasing in the Texas Rolling Plains (TRP) region, mainly to improve soil health. However, there are concerns that cover crops could potentially reduce soil water and thereby affect the yield of subsequent cash crops. Previous field studies from this region have demonstrated mixed results, with some showing a reduction in cash crop yield due to cover crops and others indicating no significant impact of cover crops on subsequent cotton fiber yield. The objectives of this study were to (1) evaluate the CROPGRO-Cotton and CERES-Wheat modules within the cropping system model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT) for the TRP region, and (2) use the evaluated model to assess the long-term effects of growing winter wheat as a cover crop on water balances and seed cotton yield under irrigated and dryland conditions. The two DSSAT crop modules were calibrated using measured data on soil water and crop yield from four treatments: (1) irrigated cotton without a cover crop (CwoC-I), (2) irrigated cotton with winter wheat as a cover crop (CwC-I), (3) dryland cotton without a cover crop (CwoC-D), and (4) dryland cotton with a winter wheat cover crop (CwC-D) at the Texas A&M AgriLife Research Station at Chillicothe from 2011 to 2015. The average percent error (PE) between the CSM-CROPGRO-Cotton simulated and measured seed cotton yield was -10.1% and -1.0% during the calibration and evaluation periods, respectively, and the percent root mean square error (%RMSE) was 11.9% during calibration and 27.6% during evaluation. For simulation of aboveground biomass by the CSM-CERES-Wheat model, the PE and %RMSE were 8.9% and 9.1%, respectively, during calibration and -0.9% and 21.8%, respectively, during evaluation. Results from the long-term (2001-2015) simulations indicated that there was no substantial reduction in average seed cotton yield and soil water due to growing winter wheat as a cover crop. Keywords: CERES-Wheat, Cover crop, Crop simulation model, CROPGRO-Cotton, DSSAT, Seed cotton yield, Soil water.

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Ioslovich, Ilya, and Per-Olof Gutman. "Evaluation of Experiments for Estimation of Dynamical Crop Model Parameters." Bulletin of Mathematical Biology 69, no. 5 (February 15, 2007): 1603–14. http://dx.doi.org/10.1007/s11538-006-9181-x.

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Hoogenboom, Gerrit, Jeffrey W. White, Jorge Acosta‐Gallegos, Refe G. Gaudiel, James R. Myers, and Matt J. Silbernagel. "Evaluation of a Crop Simulation Model that Incorporates Gene Action." Agronomy Journal 89, no. 4 (July 1997): 613–20. http://dx.doi.org/10.2134/agronj1997.00021962008900040013x.

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Ioslovich, Ilya, and Per-Olof Gutman. "EVALUATION OF EXPERIMENTS FOR ESTIMATION OF DYNAMICAL CROP MODEL PARAMETERS." IFAC Proceedings Volumes 38, no. 1 (2005): 89–94. http://dx.doi.org/10.3182/20050703-6-cz-1902.02129.

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VAN GAELEN, H., A. TSEGAY, N. DELBECQUE, N. SHRESTHA, M. GARCIA, H. FAJARDO, R. MIRANDA, et al. "A semi-quantitative approach for modelling crop response to soil fertility: evaluation of the AquaCrop procedure." Journal of Agricultural Science 153, no. 7 (October 16, 2014): 1218–33. http://dx.doi.org/10.1017/s0021859614000872.

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SUMMARYMost crop models make use of a nutrient-balance approach for modelling crop response to soil fertility. To counter the vast input data requirements that are typical of these models, the crop water productivity model AquaCrop adopts a semi-quantitative approach. Instead of providing nutrient levels, users of the model provide the soil fertility level as a model input. This level is expressed in terms of the expected impact on crop biomass production, which can be observed in the field or obtained from statistics of agricultural production. The present study is the first to describe extensively, and to calibrate and evaluate, the semi-quantitative approach of the AquaCrop model, which simulates the effect of soil fertility stress on crop production as a combination of slower canopy expansion, reduced maximum canopy cover, early decline in canopy cover and lower biomass water productivity. AquaCrop's fertility response algorithms are evaluated here against field experiments with tef (Eragrostis tef (Zucc.) Trotter) in Ethiopia, with maize (Zea mays L.) and wheat (Triticum aestivum L.) in Nepal, and with quinoa (Chenopodium quinoa Willd.) in Bolivia. It is demonstrated that AquaCrop is able to simulate the soil water content in the root zone, and the crop's canopy development, dry above-ground biomass development, final biomass and grain yield, under different soil fertility levels, for all four crops. Under combined soil water stress and soil fertility stress, the model predicts final grain yield with a relative root-mean-square error of only 11–13% for maize, wheat and quinoa, and 34% for tef. The present study shows that the semi-quantitative soil fertility approach of the AquaCrop model performs well and that the model can be applied, after case-specific calibration, to the simulation of crop production under different levels of soil fertility stress for various environmental conditions, without requiring detailed field observations on soil nutrient content.

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Osborne, T., J. Gornall, J. Hooker, K. Williams, A. Wiltshire, R. Betts, and T. Wheeler. "JULES-crop: a parametrisation of crops in the Joint UK Land Environment Simulator." Geoscientific Model Development 8, no. 4 (April 22, 2015): 1139–55. http://dx.doi.org/10.5194/gmd-8-1139-2015.

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

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Osborne, T., J. Gornall, J. Hooker, K. Williams, A. Wiltshire, R. Betts, and T. Wheeler. "JULES-crop: a parametrisation of crops in the Joint UK Land Environment Simulator." Geoscientific Model Development Discussions 7, no. 5 (October 14, 2014): 6773–809. http://dx.doi.org/10.5194/gmdd-7-6773-2014.

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

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Shaykewich, C. F., G. H. B. Ash, R. L. Raddatz, and D. J. Tomasiewicz. "Field evaluation of a water use model for potatoes." Canadian Journal of Soil Science 78, no. 3 (August 1, 1998): 441–48. http://dx.doi.org/10.4141/s97-088.

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A water use model for potatoes (Solanum tuberosum L.) was calibrated and tested. The model requires phenological relationships for estimating emergence, degree of crop cover and rooting depth. These weather-driven crop growth functions were previously calibrated using field data from 1994 and 1995. In this paper, the model was tested using field data from the 1996 growing season at two locations. The 1996 crop growth parameters were estimated fairly accurately. This contributed to reasonably accurate (average bias <3 mm, root mean square error <15 mm) root zone available soil water estimates by the model. Thus, the model could be used in irrigation scheduling. Key words: Evapotranspiration, rooting depth, ground cover

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Canner, Stephen R., L. J. Wiles, Robert H. Erskine, Gregory S. McMaster, Gale H. Dunn, and James C. Ascough. "Modeling With Limited Data: The Influence of Crop Rotation and Management on Weed Communities and Crop Yield Loss." Weed Science 57, no. 2 (April 2009): 175–86. http://dx.doi.org/10.1614/ws-08-036.1.

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Theory and models of crop yield loss from weed competition have led to decision models to help growers choose cost-effective weed management. These models are available for multiple-species weed communities in a single season of several crops. Growers also rely on crop rotation for weed control, yet theory and models of weed population dynamics have not led to similar tools for planning of crop rotations for cost-effective weed management. Obstacles have been the complexity of modeling the dynamics of multiple populations of weed species compared to a single species and lack of data. We developed a method to use limited, readily observed data to simulate population dynamics and crop yield loss of multiple-species weed communities in response to crop rotation, tillage system, and specific weed management tactics. Our method is based on the general theory of density dependence of plant productivity and extensive use of rectangular hyperbolic equations for describing crop yield loss as a function of weed density. Only two density-independent parameters are required for each species to represent differences in seed bank mortality, emergence, and maximum seed production. One equation is used to model crop yield loss and density-dependent weed seed production as a function of crop and weed density, relative time of weed and crop emergence, and differences among species in competitive ability. The model has been parameterized for six crops and 15 weeds, and limited evaluation indicates predictions are accurate enough to highlight potential weed problems and solutions when comparing alternative crop rotations for a field. The model has been incorporated into a decision support tool for whole-farm management so growers in the Central Great Plains of the United States can compare alternative crop rotations and how their choice influences farm income, herbicide use, and control of weeds in their fields.

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Bourd?t, G. W., P. D. Jamieson, and G. A. Hurrell. "Evaluation of a mechanistic model of wheat and weed growth." Proceedings of the New Zealand Plant Protection Conference 52 (August 1, 1999): 203–8. http://dx.doi.org/10.30843/nzpp.1999.52.11566.

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A mechanistic crop growth model, where daily growth is the product of light use efficiency and intercepted radiation, was tested for its ability to simulate biomass growth in wheat and weeds. Wheat was sown at six densities (including 0), in September 1994 in Canterbury and the naturally occurring weed community was either left intact or removed by herbicide. Weed biomass growth was accurately simulated in the presence and absence of the crop, but late-season wheat growth was underestimated, particularly at low sowing densities. Herbicide treatment reduced early-season crop growth, but a grain yield loss of 6.8% due to weed competition, was prevented. Weed biomass accumulation was reduced with increasing wheat density.

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Brisson, N., D. King, B. Nicoullaud, F. Ruget, D. Ripoche, and R. Darthout. "A crop model for land suitability evaluation a case study of the maize crop in France." European Journal of Agronomy 1, no. 3 (1992): 163–75. http://dx.doi.org/10.1016/s1161-0301(14)80066-x.

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Ustuner, Mustafa, and Fusun Balik Sanli. "Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation." ISPRS International Journal of Geo-Information 8, no. 2 (February 21, 2019): 97. http://dx.doi.org/10.3390/ijgi8020097.

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In terms of providing various scattering mechanisms, polarimetric target decompositions provide certain benefits for the interpretation of PolSAR images. This paper tested the capabilities of different polarimetric target decompositions in crop classification, while using a recently launched ensemble learning algorithm—namely Light Gradient Boosting Machine (LightGBM). For the classification of different crops (maize, potato, wheat, sunflower, and alfalfa) in the test site, multi-temporal polarimetric C-band RADARSAT-2 images were acquired over an agricultural area near Konya, Turkey. Four different decomposition models (Cloude–Pottier, Freeman–Durden, Van Zyl, and Yamaguchi) were employed to evaluate polarimetric target decomposition for crop classification. Besides the polarimetric target decomposed parameters, the original polarimetric features (linear backscatter coefficients, coherency, and covariance matrices) were also incorporated for crop classification. The experimental results demonstrated that polarimetric target decompositions, with the exception of Cloude–Pottier, were found to be superior to the original features in terms of overall classification accuracy. The highest classification accuracy (92.07%) was achieved by Yamaguchi, whereas the lowest (75.99%) was achieved by the covariance matrix. Model-based decompositions achieved higher performance with respect to eigenvector-based decompositions in terms of class-based accuracies. Furthermore, the results emphasize the added benefits of model-based decompositions for crop classification using PolSAR data.

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Zhichkin, K., V. Nosov, L. Zhichkina, Zh Dibrova, and T. Cherepova. "Development of evaluation model effectiveness of modern technologies in crop production." IOP Conference Series: Earth and Environmental Science 315 (August 23, 2019): 022023. http://dx.doi.org/10.1088/1755-1315/315/2/022023.

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Sarangi, A., K. K. Bandyopadhyay, A. Samal, and A. Pathan. "Evaluation of FAOAqua Crop model for wheat under different irrigation regimes." Journal of Applied and Natural Science 8, no. 1 (March 1, 2016): 473–80. http://dx.doi.org/10.31018/jans.v8i1.820.

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The experiment was conducted at the research farm of the Water Technology Centre, IARI, New Delhi during rabi seasons of 2010-11and 2011-12. Irrigation treatments include irrigation applied at 50% deficit (W1) and 25 % deficit (W2) and full irrigation (W3) under recommended fertilization levels with split doses of N-fertilizer. Fullirrigation treatment was based on irrigations to meet the soil moisture deficit up to the field capacity (FC) level and deficit irrigation treatments of 25% and 50% were imposed with respect to the full irrigation.The model was calibrated with experiment generated data sets of rabi 2010-11 and validated using the data set of rabi 2011-12. It was observed that the validated model performed well for grain yield prediction with absolute prediction error of 2.9%, 0.91% and 7.85% for full, 25% deficit and 50% deficit irrigation levels, respectively. Also, for prediction of biomass yield the prediction error ranged from 11.81% to 28.96% for all three irrigation treatments. Moreover, the validated model was observed to predict the water productivity with absolute prediction errors of 43.57%, 13.87% and 12.8% for full, 25% deficit and 50% deficit irrigation treatment levels, respectively. Nonetheless, it was observed from this study that the AquaCrop model can be used to simulate the grain and biomass yield for wheat crop with acceptable accuracy under different irrigation regimes in a semi-arid enviroment.

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Vanclooster, M., P. Viaene, J. Diels, and J. Feyen. "A deterministic evaluation analysis applied to an integrated soil-crop model." Ecological Modelling 81, no. 1-3 (August 1995): 183–95. http://dx.doi.org/10.1016/0304-3800(94)00170-m.

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Grenz, J. H., A. M. Manschadi, P. deVoil, H. Meinke, and J. Sauerborn. "Simulating crop–parasitic weed interactions using APSIM: Model evaluation and application." European Journal of Agronomy 24, no. 3 (April 2006): 257–67. http://dx.doi.org/10.1016/j.eja.2005.10.002.

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Casadebaig, Pierre, Emmanuelle Mestries, and Philippe Debaeke. "A model-based approach to assist variety evaluation in sunflower crop." European Journal of Agronomy 81 (November 2016): 92–105. http://dx.doi.org/10.1016/j.eja.2016.09.001.

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Elliott, J., C. Müller, D. Deryng, J. Chryssanthacopoulos, K. J. Boote, M. Büchner, I. Foster, et al. "The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0)." Geoscientific Model Development Discussions 7, no. 4 (July 15, 2014): 4383–427. http://dx.doi.org/10.5194/gmdd-7-4383-2014.

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Abstract. We present protocols and input data for Phase 1 of the Global Gridded Crop Model Intercomparison, a project of the Agricultural Model Intercomparison and Improvement Project's (AgMIP's) Gridded Crop Modeling Initiative (AgGRID). The project includes global simulations of yields, phenologies, and many land-surface fluxes by 12–15 modeling groups for many crops, climate forcing datasets, and scenarios over the historical period from 1948–2012. The primary outcomes of the project include (1) a detailed comparison of the major differences and similarities among global models commonly used for large-scale climate impact assessment, (2) an evaluation of model and ensemble hindcasting skill, (3) quantification of key uncertainties from climate input data, model choice, and other sources, and (4) a multi-model analysis of the impacts to agriculture of large-scale climate extremes from the historical record.

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Zhu, Mingbang, Shanshan Liu, Ziqing Xia, Guangxing Wang, Yueming Hu, and Zhenhua Liu. "Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN." Agriculture 10, no. 8 (August 1, 2020): 318. http://dx.doi.org/10.3390/agriculture10080318.

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Rapid and accurate evaluation of cultivated land quality (CLQ) using remotely sensed images plays an important role for national food security and social stability. Current approaches for evaluating CLQ do not consider spectral response relationships between CLQ and spectral indicators based on crop growth stages. This study aimed to propose an accurate spectral model to evaluate CLQ based on late rice phenology. In order to increase the accuracy of evaluation, the Empirical Bayes Kriging (EBK) interpolation was first performed to scale down gross primary production (GPP) products from a 500 m spatial resolution to 30 m. As an indicator, the ability of MODIS-GPPs from critical growth stages (tillering, jointing, heading, and maturity stages) was then investigated by combining Pearson correlation analysis and variance inflation factor (VIF) to select the phases of CLQ evaluation. Finally, a linear Partial Least Squares Regression (PLSR) and two nonlinear models, including Support Vector Regression (SVR) and Genetic Algorithm-Based Back Propagation Neural Network (GA-BPNN), were driven to develop an accurate spectral model of evaluating CLQ based on MODIS-GPPs. The models were tested and compared in the Conghua and Zengcheng districts of Guangzhou City, Guangdong, China. The results showed that based on field measured GPP data, the validation accuracy of 30 m spatial resolution MODIS GPP products with a root mean square error (RMSE) of 7.43 and normalized RMSE (NRMSE) of 1.59% was higher than that of the 500 m MODIS GPP products, indicating that the downscaled 30 m MODIS GPP products by EBK were more appropriate than the 500 m products. Compared with PLSR (R2 = 0.38 and RMSE = 87.97) and SVR (R2 = 0.64 and RMSE = 64.38), the GA-BPNN model (R2 = 0.69 and RMSE = 60.12) was more accurate to evaluate CLQ, implying a non-linear relationship of CLQ with the GPP spectral indicator. This is the first study to improve the accuracy of estimating CLQ using the rice growth stage GPP-driven spectral model by GA-BPNN and can thus advance the literature in this field.

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Williams, Jeffrey R., Orlan H. Buller, Gary J. Dvorak, and Harry L. Manges. "A Microcomputer Model for Irrigation System Evaluation." Journal of Agricultural and Applied Economics 20, no. 1 (July 1988): 145–51. http://dx.doi.org/10.1017/s0081305200025735.

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AbstractICEASE (Irrigation Cost Estimator and System Evaluator) is a microcomputer model designed and developed to meet the need for conducting economic evaluation of adjustments to irrigation systems and management techniques to improve the use of irrigated water. ICEASE can calculate the annual operating costs for irrigation systems and has five options that can be used to economically evaluate improvements in the pumping plant or the way the irrigation system is used for crop production.

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Šťastná, M., M. Trnka, J. Křen, M. Dubrovský, and Z. Žalud. "Evaluation of the CERES models in different production regions of the Czech Republic." Plant, Soil and Environment 48, No. 3 (December 11, 2011): 125–32. http://dx.doi.org/10.17221/4209-pse.

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The main goal of this work was to calibrate and evaluate the CERES-Barley and CERES-Wheat crop models. The experimental fields used for the model evaluation are situated in three different production regions (maize, sugar beet and potato main growing regions, respectively) with altitudes of 179, 204 and 560 meters above the sea level. Grain yield and date of anthesis together with maturity dates served as reference for the model evaluation. Two evaluation approaches were tested in this study. The first one uses historical data series and it is based on long-term field experiments with capability to reflect interannual weather variability. The second approach uses results of one-year multiple treatment experiment. The model evaluation is then based on a set of treatments differing e.g. in sowing date or an amount of used nitrogen fertilizer. Grain yields simulated by both models are acceptable when compared with experimental results: the coefficient of determination for historical series varied from 0.69 to 0.86 for evaluation of CERES-Barley at the three examined sites and reached values of 0.60 and 0.86 for the CERES-Wheat model at two experimental sites. The lower coefficient of determination of the wheat model was recorded at the locality with the highest altitude and coldest winter conditions. There, also the worst reliability of simulated phenological development was noted. At the second locality where the CERES-Wheat model was tested and at all three localities where CERES-Barley was applied, the simulated duration of vegetation period and anthesis dates were relatively accurate and yielded strong statistical correlation. The one-year multiple treatment experiment proved to be useful to determine the models sensitivity to differences in crop management. The combination of both approaches seems to be the best solution for evaluation of similar crop models if the detail long term experimental data are not available.

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Zhu, Yongbin, Yajuan Shi, Changxin Liu, Bing Lyu, and Zhenbo Wang. "Reinspecting the Climate-Crop Yields Relationship at a Finer Scale and the Climate Damage Evaluation: Evidence from China." Complexity 2020 (September 17, 2020): 1–8. http://dx.doi.org/10.1155/2020/9424327.

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This paper reinvestigated the climate-crop yield relationship with the statistical model at crops’ growing stage scale. Compared to previous studies, our model introduced monthly climate variables in the production function of crops, which enables separating the yield changes induced by climate change and those caused by inputs variation and technique progress, as well as examining different climate effects during each growing stage of crops. By applying the fixed effect regression model with province-level panel data of crop yields, agricultural inputs, and the monthly climate variables of temperature and precipitation from 1985 to 2015, we found that the effects of temperature generally are negative and those of precipitation generally are positive, but they vary among different growth stages for each crop. Specifically, GDDs (i.e., growing degree days) have negative effects on spring maize’s yield except for the sowing and ripening stages; the effects of precipitation are negative in September for summer maize. Precipitation in December and the next April is significantly harmful to the yield of winter wheat; while, for the spring wheat, GDDs have positive effects during April and May, and precipitation has negative effects during the ripening period. In addition, we computed climate-induced losses based on the climate-crop yield relationship, which demonstrated a strong tendency for increasing yield losses for all crops, with large interannual fluctuations. Comparatively, the long-term climate effects on yields of spring maize, summer maize, and spring wheat are more noticeable than those of winter wheat.

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Garcia Millan, Virginia E., Cassidy Rankine, and G. Arturo Sanchez-Azofeifa. "Crop Loss Evaluation Using Digital Surface Models from Unmanned Aerial Vehicles Data." Remote Sensing 12, no. 6 (March 18, 2020): 981. http://dx.doi.org/10.3390/rs12060981.

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Precision agriculture and Unmanned Aerial Vehicles (UAV) are revolutionizing agriculture management methods. Remote sensing data, image analysis and Digital Surface Models derived from Structure from Motion and Multi-View Stereopsis offer new and fast methods to detect the needs of crops, greatly improving crops efficiency. In this study, we present a tool to detect and estimate crop damage after a disturbance (i.e., weather event, wildlife attacks or fires). The types of damage that are addressed in this study affect crop structure (i.e., plants are bent or gone), in the shape of depressions in the crop canopy. The aim of this study was to evaluate the performance of four unsupervised methods based on terrain analyses, for the detection of damaged crops in UAV 3D models: slope detection, variance analysis, geomorphology classification and cloth simulation filter. A full workflow was designed and described in this article that involves the postprocessing of the raw results from the terrain analyses, for a refinement in the detection of damages. Our results show that all four methods performed similarly well after postprocessing––reaching an accuracy above to 90%––in the detection of severe crop damage, without the need of training data. The results of this study suggest that the used methods are effective and independent of the crop type, crop damage and growth stage. However, only severe damages were detected with this workflow. Other factors such as data volume, processing time, number of processing steps and spatial distribution of targets and errors are discussed in this article for the selection of the most appropriate method. Among the four tested methods, slope analysis involves less processing steps, generates the smallest data volume, is the fastest of methods and resulted in best spatial distribution of matches. Thus, it was selected as the most efficient method for crop damage detection.

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Moeller, Carina, Mustafa Pala, Ahmad M. Manschadi, Holger Meinke, and Joachim Sauerborn. "Assessing the sustainability of wheat-based cropping systems using APSIM: model parameterisation and evaluation." Australian Journal of Agricultural Research 58, no. 1 (2007): 75. http://dx.doi.org/10.1071/ar06186.

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Assessing the sustainability of crop and soil management practices in wheat-based rotations requires a well-tested model with the demonstrated ability to sensibly predict crop productivity and changes in the soil resource. The Agricultural Production Systems Simulator (APSIM) suite of models was parameterised and subsequently used to predict biomass production, yield, crop water and nitrogen (N) use, as well as long-term soil water and organic matter dynamics in wheat/chickpea systems at Tel Hadya, north-western Syria. The model satisfactorily simulated the productivity and water and N use of wheat and chickpea crops grown under different N and/or water supply levels in the 1998–99 and 1999–2000 experimental seasons. Analysis of soil-water dynamics showed that the 2-stage soil evaporation model in APSIM’s cascading water-balance module did not sufficiently explain the actual soil drying following crop harvest under conditions where unused water remained in the soil profile. This might have been related to evaporation from soil cracks in the montmorillonitic clay soil, a process not explicitly simulated by APSIM. Soil-water dynamics in wheat–fallow and wheat–chickpea rotations (1987–98) were nevertheless well simulated when the soil water content in 0–0.45 m soil depth was set to ‘air dry’ at the end of the growing season each year. The model satisfactorily simulated the amounts of NO3-N in the soil, whereas it underestimated the amounts of NH4-N. Ammonium fixation might be part of the soil mineral-N dynamics at the study site because montmorillonite is the major clay mineral. This process is not simulated by APSIM’s nitrogen module. APSIM was capable of predicting long-term trends (1985–98) in soil organic matter in wheat–fallow and wheat–chickpea rotations at Tel Hadya as reported in literature. Overall, results showed that the model is generic and mature enough to be extended to this set of environmental conditions and can therefore be applied to assess the sustainability of wheat–chickpea rotations at Tel Hadya.

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Mereu, Gallo, and Spano. "Optimizing Genetic Parameters of CSM-CERES Wheat and CSM-CERES Maize for Durum Wheat, Common Wheat, and Maize in Italy." Agronomy 9, no. 10 (October 22, 2019): 665. http://dx.doi.org/10.3390/agronomy9100665.

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The expected increase in population and the pressure posed by climate change on agricultural production require the assessment of future yield levels and the evaluation of the most suitable management options to minimize climate risk and promote sustainable agricultural production. Crop simulation models are widely applied tools to predict crop development and production under different management practices and environmental conditions. The aim of this study was to parameterize CSM-CERES-Wheat and CSM-CERES-Maize models, implemented in the Decision Support System for Agrotechnology Transfer (DSSAT) software, to predict phenology and grain yield of durum wheat, common wheat, and maize in different Italian environments. A 10-year (2001–2010) dataset was used to optimize the genetic parameters for selected varieties of each species and to evaluate the models considering several statistical indexes. The generalized likelihood uncertainty estimation method, and trial and error approach were used to optimize the cultivar-specific parameters of these models. Results show good model performances in reproducing crop phenology and yield for the analyzed crops, especially with the parameters optimized with the trial and error procedure. Highly significant (p ≤ 0.001) correlations between observed and simulated data were found for both anthesis and yield in model calibration and evaluation (p ≤ 0.01 for grain yield of maize in model evaluation). Root mean square error (RMSE) values range from six to nine days for anthesis and from 1.1 to 1.7 t ha-1 for crop yield and index of agreement (d-index) from 0.96 to 0.98 for anthesis and from 0.8 to 0.87 for crop yield. The set of genetic parameters obtained for durum wheat, common wheat, and maize may be applied in further analyses at field, regional, and national scales to guide operational (farmers), strategic, and tactical (policy makers) decisions.

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Saldaña Villota, Tatiana María, and José Miguel Cotes Torres. "Comparison of statistical indices for the evaluation of crop models performance." Revista Facultad Nacional de Agronomía Medellín 74, no. 3 (September 1, 2021): 9675–84. http://dx.doi.org/10.15446/rfnam.v74n3.93562.

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This study presents a comparison of the usual statistical methods used for crop model assessment. A case study was conducted using a data set from observations of the total dry weight in diploid potato crop, and six simulated data sets derived from the observationsaimed to predict the measured data. Statistical indices such as the coefficient of determination, the root mean squared error, the relative root mean squared error, mean error, index of agreement, modified index of agreement, revised index of agreement, modeling efficiency, and revised modeling efficiency were compared. The results showed that the coefficient of determination is not a useful statistical index for model evaluation. The root mean squared error together with the relative root mean squared error offer an excellent notion of how deviated the simulations are in the same unit of the variable and percentage terms, and they leave no doubt when evaluating the quality of the simulations of a model.

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Vieth, Gary R., and Pramote Suppapanya. "An Evaluation of Selected Decision Models: A Case of Crop Choice in Northern Thailand." Journal of Agricultural and Applied Economics 28, no. 2 (December 1996): 381–91. http://dx.doi.org/10.1017/s1074070800007380.

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AbstractThis research examines the predictability of a profit maximization model, an expected value-variance utility maximization (E-V) model, and two versions of the target-MOTAD model for modeling risky agricultural production decisions. Model solutions were translated into expected value and variance of farm income for analysis. Direct comparison and chi-square analysis of actual and predicted expected income distributions were used in the analyses. It was concluded that the utility maximization and cash-cost target-MOTAD models predicted distributions of farm income better than the variable-cost target-MOTAD and profit maximization models.

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Gordon, R., D. M. Brown, and M. A. Dixon. "Evaluation of a cultivar-sensitive soil water model for the potato crop." Canadian Journal of Soil Science 76, no. 3 (August 1, 1996): 275–83. http://dx.doi.org/10.4141/cjss96-034.

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A modified version of the irrigation scheduling model SimISP (Simulating Irrigation Scheduling in Potatoes) was evaluated in terms of its ability to simulate the potato crop root zone soil water content through the growing season. The model uses daily radiation, temperature, humidity, windspeed, precipitation and seasonal crop and soil parameter inputs to estimate evaporation, transpiration, canopy expansion and dry matter accumulation. Recent cultivar-specific characteristics incorporated into the model allow for more precise simulations between cultivars.Simulations were conducted for rainfed, irrigated and artificial shelter-imposed stress conditions during four growing seasons on two soil types in Colchester County, Nova Scotia. Generally strong agreement between simulations and field measured (TDR) available soil water content in the top 15 cm of the soil profile was achieved with an average error within ± 4.7 mm and a relative error within ± 0.26. Larger errors were obtained for the 15- to 30-cm soil layer with average errors within ± 7.3 mm and the relative error within ± 0.52. Differences between model simulations and field measurements indicate the need for an improved root growth sub-model that is soil water sensitive and more precise estimates of soil water recharge after rain. Key words:Solanum tuberosum L., SimISP, simulation model

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Silva, L. L., F. J. Baptista, J. F. Meneses, and R. Ragab. "Evaluation of the SALTMED model for tomato crop production in unheated greenhouses." Acta Horticulturae, no. 1170 (July 2017): 441–46. http://dx.doi.org/10.17660/actahortic.2017.1170.54.

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L. C. Guerra, G. Hoogenboom, V. K. Boken, J. E. Hook, D. L. Thomas, and K. A. Harrison. "EVALUATION OF THE EPIC MODEL FOR SIMULATING CROP YIELD AND IRRIGATION DEMAND." Transactions of the ASAE 47, no. 6 (2004): 2091–100. http://dx.doi.org/10.13031/2013.17794.

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SHIM, Kyomoon, Maengki KIM, Jeongtaek LEE, Yangsoo LEE, Gunyeob KIM, and Wooseop LEE. "Crop Model-Based Evaluation of Rice Yield under Climate Scenario in Korea." Journal of Agricultural Meteorology 60, no. 5 (2005): 609–12. http://dx.doi.org/10.2480/agrmet.609.

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42

Seidel, Sabine J., Stefan Werisch, Klemens Barfus, Michael Wagner, Niels Schütze, and Hermann Laber. "Field Evaluation of Irrigation Scheduling Strategies using a Mechanistic Crop Growth Model." Irrigation and Drainage 65, no. 2 (February 5, 2016): 214–23. http://dx.doi.org/10.1002/ird.1942.

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Puchkov, Mikhail Yu, Diana Sh Smirnova, Elena G. Loktionova, and Shamas A. Yakubov. "Economic and Ecological Evaluation of Living Standards Based on Crop Production Model." European Geographical Studies 3, no. 3 (September 15, 2014): 116–25. http://dx.doi.org/10.13187/egs.2014.3.116.

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Montoya, F., D. Camargo, J. F. Ortega, J. I. Córcoles, and A. Domínguez. "Evaluation of Aquacrop model for a potato crop under different irrigation conditions." Agricultural Water Management 164 (January 2016): 267–80. http://dx.doi.org/10.1016/j.agwat.2015.10.019.

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Liben, F. M., C. S. Wortmann, H. Yang, J. L. Lindquist, T. Tadesse, and D. Wegary. "Crop model and weather data generation evaluation for conservation agriculture in Ethiopia." Field Crops Research 228 (November 2018): 122–34. http://dx.doi.org/10.1016/j.fcr.2018.09.001.

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HEUVELINK, E. "Evaluation of a Dynamic Simulation Model for Tomato Crop Growth and Development." Annals of Botany 83, no. 4 (April 1999): 413–22. http://dx.doi.org/10.1006/anbo.1998.0832.

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Bom, M., and G. J. Boland. "Evaluation of disease forecasting variables for sclerotinia stem rot (Sclerotinia sclerotiorum) of canola." Canadian Journal of Plant Science 80, no. 4 (October 1, 2000): 889–98. http://dx.doi.org/10.4141/p99-071.

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Selected environmental, crop and pathogen variables were sampled weekly from winter and spring canola crops before and during flowering and evaluated for the ability to predict sclerotinia stem rot, caused by Sclertinia sclerotirum. Linear and nonlinear relationships were examined among variables but, because no strong correlations were observed between final disease incidence and any of the variables tested, a categorical approach (e.g., disease severity) was used instead. Disease severity in individual crops was categorized as low (< 20% diseased plants) or high (> 20% disease), and differences in weekly rainfall, soil moisture, crop height, percentage of petal infestation, and number of apothecia m−2 and clumps of apothecia m−2 were significantly associated with differences in disease severity within or between years. Two disease prediction models were compared for the ability to predict low or high disease severities using petal infestation alone, or petal infestation in combination with soil moisture. The model that included petal infestation and soil moisture predicted more fields correctly than the model using petal infestation alone, but the accuracy of both was affected by the timing of soil moisture measurements in relation to petal infestation, and threshold values used in discriminating categories of soil moisture and petal infestation. Key words: Brassica rapa, Brassica napus, Sclerotinia sclerotiorum, disease prediction

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Nyawira, Sylvia S., Julia E. M. S. Nabel, Axel Don, Victor Brovkin, and Julia Pongratz. "Soil carbon response to land-use change: evaluation of a global vegetation model using observational meta-analyses." Biogeosciences 13, no. 19 (October 13, 2016): 5661–75. http://dx.doi.org/10.5194/bg-13-5661-2016.

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Abstract. Global model estimates of soil carbon changes from past land-use changes remain uncertain. We develop an approach for evaluating dynamic global vegetation models (DGVMs) against existing observational meta-analyses of soil carbon changes following land-use change. Using the DGVM JSBACH, we perform idealized simulations where the entire globe is covered by one vegetation type, which then undergoes a land-use change to another vegetation type. We select the grid cells that represent the climatic conditions of the meta-analyses and compare the mean simulated soil carbon changes to the meta-analyses. Our simulated results show model agreement with the observational data on the direction of changes in soil carbon for some land-use changes, although the model simulated a generally smaller magnitude of changes. The conversion of crop to forest resulted in soil carbon gain of 10 % compared to a gain of 42 % in the data, whereas the forest-to-crop change resulted in a simulated loss of −15 % compared to −40 %. The model and the observational data disagreed for the conversion of crop to grasslands. The model estimated a small soil carbon loss (−4 %), while observational data indicate a 38 % gain in soil carbon for the same land-use change. These model deviations from the observations are substantially reduced by explicitly accounting for crop harvesting and ignoring burning in grasslands in the model. We conclude that our idealized simulation approach provides an appropriate framework for evaluating DGVMs against meta-analyses and that this evaluation helps to identify the causes of deviation of simulated soil carbon changes from the meta-analyses.

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Präger, Achim, Kenneth J. Boote, Sebastian Munz, and Simone Graeff-Hönninger. "Simulating Growth and Development Processes of Quinoa (Chenopodium quinoa Willd.): Adaptation and Evaluation of the CSM-CROPGRO Model." Agronomy 9, no. 12 (December 2, 2019): 832. http://dx.doi.org/10.3390/agronomy9120832.

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In recent years, the intra-annual yield variability of traditional food crops grown in Europe increased due to extreme weather events driven by climate change. The Andean crop quinoa (Chenopodium quinoa Willd.), being well adapted to drought, salinity, and frost, is considered to be a promising new crop for Europe to cope with unfavorable environmental conditions. However, cultivation guidelines and cropping experiences are missing on a long-term scale. The adaptation of a mechanistic crop growth model will support the long-term evaluation of quinoa if grown under the diverse environmental conditions of Europe. The objective of this study was to adapt the process-based cropping system model (CSM) CROPGRO, which is included in the Decision Support System for Agrotechnology Transfer (DSSAT). Therefore, species and genetic coefficients were calibrated using literature values and growth analysis data, including crop life cycle, leaf area index (LAI), specific leaf area (SLA), dry matter partitioning and nitrogen concentrations in different plant tissues, aboveground biomass, and yield components, of a sowing date experiment (covering two cultivars and four sowing dates) conducted in southwestern Germany in 2016. Model evaluation was performed on the crop life cycle, final aboveground biomass, and final grain yield for different sowing dates using an independent data set collected at the same site in 2017. The resulting base temperatures regarding photosynthetic, vegetative, and reproductive processes ranged between 1 and 10 °C, while the corresponding optimum temperatures were between 15 and 36 °C. On average, the crop life cycle was predicted with a root mean square error (RMSE) of 4.7 and 3.0 days in 2016 and 2017, respectively. In 2016, the mean predicted aboveground biomass during the growth cycle showed a d-index of 0.98 (RMSE = 858 kg ha−1). Furthermore, the LAI, SLA, and leaf nitrogen concentrations were simulated with a high accuracy, showing a mean RMSE of 0.29 (d-index = 0.94), 25 cm2 g−1 (d-index = 0.88), and 0.51% (d-index = 0.95). Evaluations on the grain yield and aboveground biomass across four sowing dates in 2017 suggested a good robustness of the new quinoa model. The mean predicted aboveground biomass and grain yield at harvest maturity were 6479 kg ha−1 (RMSE = 898.9 kg ha−1) and 3843 kg ha−1 (RMSE = 450.3 kg ha−1), respectively. Thus, the CSM-CROPGRO model can be used to evaluate the long-term suitability, as well as different management strategies of quinoa under European conditions. However, further development on the simulation of small seed sizes and under water or nitrogen-limited environments are needed.

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Sulyok, Dénes, and Tamás Rátonyi. "The Role of the 4M-ECO Agrieconomical Modell in the Crop Cultivation." Acta Agraria Debreceniensis, no. 13 (May 4, 2004): 170–73. http://dx.doi.org/10.34101/actaagrar/13/3406.

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Today, c for agricultural use are of ever increasing significance. These provide an opportunity for more accurate planning, and favourably influence the efficiency and economic performance of given enterprise. The relevant literature divides models according to various criteria. The most common is the division between optimising and non-optimising models. Non-optimising models generally endeavour to make the best use of technological lines, machine capacity, while optimising models are used to optimise revenue returns from sales or, occasionally, production costs. In our case, revenue and returns from sales were optimised. The models examined consist of several modules. Which include the following: plant cultivation modules, evaluations (assessment of situation, conception plan, complex corporate evaluation), supplementary sheets (sheets and charts for ancillary plant production, general costs of operation, summary and crops structure optimisation). With the help of the model, annual a particular can be made for an optimal crop structure the resources of the enterprise. This it becomes possible to define the largest net revenue on a corporate level.

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Journal articles on the topic 'Evaluation of crop model'

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