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Crop simulation model
About: Crop simulation model is a research topic. Over the lifetime, 938 publications have been published within this topic receiving 37813 citations.
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TL;DR: The benefits of the new, re-designed DSSAT-CSM will provide considerable opportunities to its developers and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems at field, farm, and higher levels.
3,339 citations
TL;DR: In this article, a review of methods to estimate yield gaps, with a focus on the local-to-global relevance of outcomes, is presented, based on which the authors recommend key components for a yield gap assessment that can be applied at local to global scales.
1,196 citations
University of Florida1, University of Bonn2, Goddard Institute for Space Studies3, Commonwealth Scientific and Industrial Research Organisation4, Institut national de la recherche agronomique5, Agro ParisTech6, Michigan State University7, Blaise Pascal University8, International Water Management Institute9, CGIAR10, University of Leeds11, Counterintelligence Field Activity12, University of Tübingen13, University of Alberta14, International Atomic Energy Agency15, University of Reading16, University of Guelph17, University of Hohenheim18, Joint Global Change Research Institute19, Potsdam Institute for Climate Impact Research20, Indian Agricultural Research Institute21, Aarhus University22, Rothamsted Research23, Washington State University24, Wageningen University and Research Centre25, Chinese Academy of Sciences26, International Trademark Association27, Texas A&M University28
TL;DR: In this article, the authors present the largest standardized model intercomparison for climate change impacts so far, finding 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.
Abstract: Projections of climate change impacts on crop yields are inherently uncertain(1). Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate(2). However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models(1,3) are difficult(4). 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 and policymaking.
1,049 citations
01 Jan 1998
TL;DR: In this paper, the use of a Crop Simulation Model for Planning Wheat Irrigation in Zimbabwe J.T. Bowen, W.J. Boote, and W.W. Wilkens.
Abstract: Preface. Acronyms. 1. Overview of IBSNAT G. Uehara, G.Y. Tsuji. 2. Data for Model Operation, Calibration, and Evaluation L.A. Hunt, K.J. Boote. 3. Soil Water Balance and Plant Water Stress J.T. Ritchie. 4. Nitrogen Balance and Crop Response to Nitrogen in Upland and Lowland Cropping Systems D.C. Godwin, U. Singh. 5. Cereal Growth, Development and Yield J.T. Ritchie, et al. 6. The CROPGRO Model for Grain Legumes K.J. Boote, et al. 7. Modeling Growth and Development of Root and Tuber Crops U. Singh, et al. 8. Decision Support System for Agrotechnology Transfer: DSSAT v3 J.W. Jones, et al. 9. Modeling and Crop Improvement J.W. White. 10. Simulation as a Tool for Improving Nitrogen Management W.T. Bowen, W.E. Baethgen. 11. The Use of a Crop Simulation Model for Planning Wheat Irrigation in Zimbabwe J.F. MacRobert, M.J. Savage. 12. Simulation of Pest Effects on Crops Using Coupled Pest-Crop Models: The Potential for Decision Support P.S. Teng, et al. 13. The Use of Crop Models for International Climate Change Impact Assessment C. Rosenzweig, A. Iglesias. 14. Evaluation of Land Resources Using Crop Models and a GIS F.H. Beinroth, et al. 15. The Simulation of Cropping Sequences Using DSSAT W.T. Bowen, et al. 16. Risk Assessment and Food Security P.K. Thornton, P.W. Wilkens. 17. Incorporating Farm Household Decision-Making within Whole Farm Models G. Edwards-Jones, et al. 18. Network Management and Information Dissemination for Agrotechnology Transfer G.Y. Tsuji. 19. Crop Simulation Models as an Educational Tool R.A. Ortiz. 20. Synthesis G. Uehara.
719 citations
TL;DR: In this article, the authors present evidence for the importance of variability in temperature for annual crop yields, and consider how the impacts of these events may be predicted, and three research needs are identified in order to provide a framework for predicting the impact of episodes of hot temperatures on the yields of annual crops.
620 citations