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It is crucial to enhance models' ability to estimate the impact of soil waterlogging on plant processes. These developments advance our ability to understand, predict and, thereby, mitigate yield loss as increases in climatic volatility lead to more frequent and intense flooding events in the future. When used to project soybean response to future climate scenarios, the model showed that intense rain events had a greater negative effect on yield than a 25% increase in rainfall distributed over 1 or 3 month(s). Extensive model testing found that the improved model accurately simulates plant responses to flooding including how these responses change with flood timing and duration. The relative root mean square error (RRMSE) for yield predictions improved by 26% and the RRMSE predictions of biomass improved by 40%. Improvements in prediction accuracy were quantified by comparing model performance before and after the implementation of new stage-dependent excess water functions for phenology, photosynthesis and N-fixation.
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Five datasets were used for model parameterization of new functions and three datasets were used for testing. that investigated the impact of flood timing and duration on soybean. Literature data included greenhouse and field experiments from across the U.S.
#Apsim confrence modeling soil water 2020 software
In light of this, we synthesized literature data and used the APSIM software to enhance the modeling capacity to simulate plant growth, development, and N fixation response to flooding. Department of Agronomy, Iowa State University, Ames, IA, Unites StatesÄespite the detrimental impact that excess moisture can have on soybean ( Glycine max Merr) yields, most of today's crop models do not capture soybean's dynamic responses to waterlogged conditions.