Simulating the effects of global warming on soybean growth and yield in the U.S. Mississippi Delta

Authors: SUN, WENGUANG - University Of Nebraska; Fleisher, David; Timlin, Dennis; LI, SANAI - Us Forest Service (FS); WANG, ZHUANGJI - University Of Maryland; BEEGUM, SAHILA - University Of Nebraska; Reddy, Vangimalla

Submitted to: European Journal of Agronomy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/8/2022
Publication Date: 8/24/2022
Citation: Sun, W., Fleisher, D.H., Timlin, D.J., Li, S., Wang, Z., Beegum, S., Reddy, V. 2022. Simulating the effects of global warming on soybean growth and yield in the U.S. Mississippi Delta. European Journal of Agronomy. 140. https://doi.org/10.1016/j.eja.2022.126610.
DOI: https://doi.org/10.1016/j.eja.2022.126610

Interpretive Summary: Soybean is an important global commodity and the Mississippi Delta region is one of the largest contiguous soybean planting areas in the United States. Global atmospheric air temperature and CO2 concentrations are expected to rise significantly by 2050 and beyond. Tools such as crop models are needed to understand these climate impacts on U.S. food security and identify possible adaptation, management, and future policy strategies. This study investigated three soybean models, GLYCIM, SoySim, and CROPGRO, varying in representation of different soil-water-plant components, for their ability to simulate crop growth, development, as well as seed yield. Sensitivity analysis with respect climate change scenarios involving increasing air temperature and CO2 using the average estimate from three models indicated that yield increased by 8.8 % per 100 ppm CO2 but will decline 4.8 % per °C. These results suggested that rising temperature will offset positive increase in yield due to CO2 and thus further adaptation measures need to be identified. This multi-model ensemble approach will establish the confidence in predication of soybean development and growth over a range of environment, and therefore is recommended to assess global crop production under future climate conditions for policy planners, scientists, and crop consultants.

Technical Abstract: Crop simulation models are indispensable tools that facilitate studies involving yield impacts, adaptation and management strategies, and policy analysis under projected climate change. Three soybean models, GLYCIM, SoySim, and CROPGRO, varying in representation of different soil -water-plant components, were evaluated with regards of accuracy of soybean growth, development, and seed yield predictions. Experimental data from the United States Mississippi Delta region including 156 site-year-cultivar-irrigation combinations were used. Statistical criteria for estimating the goodness-of-fit of the models to the observed data were Wilmott's index of agreement (IA) and root mean square error (RMSE). The simulation of seed yield across all validated datasets had RMSE of 0.92 Mg ha-1 or lower, with GLYCIM and CROPGRO exhibiting more accurate metrics. A similar pattern was observed for IA, which ranged between 0.82 and 0.65. Sensitivity analysis with respect climate change scenarios involving increasing air temperature and CO2 on yield were evaluated using mean model ensemble values. Average yield increased 8.8 % per 100 ppm CO2 and declined 4.8 % per °C. Although both GLYCIM and CROPGRO models simulated yield by reductions between 0.4 to 7.2%, the SoySim model estimated positive impacts between 3.7% and 12.1% under two climate scenarios with rising CO2 and air temperature. Given the difference in model structure and predictions, a multi-model ensemble approach is recommended to assess global crop production under future climate conditions.