Modeling Flood-Induced Stress in Soybeans

A new paper has been published on the modelling of flood-induced stress in soybeans.

Despite the detrimental impact that excess moisture can have on soybean (Glycine max
[L.] Merr) yields, most of today’s crop models do not capture soybean’s dynamic
responses to waterlogged conditions. 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. Literature data included greenhouse
and field experiments from across the U.S. that investigated the impact of flood timing and
duration on soybean. Five datasets were used for model parameterization of new
functions and three datasets were used for testing. 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. The relative root mean square error (RRMSE) for yield
predictions improved by 26% and the RRMSE predictions of biomass improved by 40%.
Extensive model testing found that the improved model accurately simulates plant
responses to flooding including how these responses change with flood timing and
duration. 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). 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.

The full paper can be accessed here:  doi: 10.3389/fpls.2020.00062