Modeling Flood-Induced Stress in Soybeans

Abstract

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.

Chilling stress tolerance of two soya bean cultivars: Phenotypic and molecular responses

“…Nowadays, climate change is among the principal factors causing a decline in agricultural productivity. Drought, salinity, low/high temperatures, flooding, acidic conditions and nutrient starvation are the world's most dominant abiotic stresses, also affecting soya bean yield (Arora & Tewari, 2016;Carrera & Dardanelli, 2016;Jumrani & Bhatia, 2018;Pasley, Huber, Castellano, & Archontoulis, 2020;Pathan et al, 2014). Low temperatures are one of the most important limitations of crop productivity, as it has been shown that temperature and the duration of chilling stress affect the mode of action and intensity of plant responses (Ercoli, Mariotti, Masoni, & Arduini, 2004).…”

Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt

“…It includes many crop models along with soil water, C, N, crop residue modules, which all interact on a daily time step. In this project, we used the APSIM maize version 7.9 and in particular the calibrated model version for US Corn Belt environments as outlined by Archontoulis et al 1 that includes simulation of shallow water tables and inhibition of root growth due to excess water stress 38 and waterlogging functions 39 . Within APSIM we used the following modules: maize 40 , SWIM soil water 41 , soil N and carbon 42 , surface residue 42 , 43 , soil temperature 44 and various management rules to account for tillage and other management operations.…”

Corn stover harvest reduces soil CO2 fluxes but increases overall C losses

“…We configured APSIM version 7.9 with a series of modules, including maize (Keating et al., 2003), surfaceOM (Probert, Dimes, Keating, Dalal, & Strong, 1998; Thorburn, Meier, & Probert, 2005; Thorburn, Probert, & Robertson, 2001), manager (Keating et al., 2003), biochar (Archontoulis et al., 2016), soiltemperature2 (Campbell, 1985; Dietzel et al., 2016), and SoilWat (Probert et al., 1998). The model accounted for shallow water table fluctuations in this field (Archontoulis et al, 2020) and included recent waterlogging additions made to the model (Ebrahimi‐Mollabashi et al., 2019; Pasley, Huber, Castellano, & Archontoulis, 2020). We used soil profile information from SSURGO (USDA‐NRCS, 2019), which we updated with measurements of soil C in the 0–5 and 5–15 cm depths.…”