Simulated climate change effects on soybean production in Southeastern United States with two crop modules in RZWQM2

Authors: Ma, Liwang; FANG, Q - Qingdao University; SIMA, M - Princeton University; Burkey, Kent; Harmel, Daren

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/25/2020
Publication Date: 2/17/2021
Citation: Ma, L., Fang, Q.X., Sima, M.W., Burkey, K.O., Harmel, R.D. 2021. Simulated climate change effects on soybean production in Southeastern United States with two crop modules in RZWQM2. Agronomy Journal. 113(2):1349-1365. https://doi.org/10.1002/agj2.20548.
DOI: https://doi.org/10.1002/agj2.20548

Interpretive Summary: Understanding climate change effects on crop yield through field experiments and crop modeling is essential for developing mitigation strategies. The objective of this study was to compare two different soybean (Glycine max L. Merrill) modules (CROPGRO and HERMES) in the Root Zone Water Quality Model 2 (RZWQM2) for their ability to predict climate change effects on soybean production. The modules were calibrated for measured temperature responses using data from a four-year air exclusion experiment (2015-2018) in North Carolina, USA. Both crop modules simulated similar climate change effects in terms of yield and biomass by the end of year 2100 using 40 general circulation models (GCMs) and two Representative Concentration Pathways (RCP4.5 and RCP8.5), compared with the simulations using current baseline (2002-2018). For both modules, much greater reductions in biomass and seed yield were simulated under RCP8.5 than under RCP4.5 due to higher temperature, which could be partially offset by elevated CO2 under both Pathways. CROPGRO predicted a greater positive climate change effect in response to the projected higher precipitation and increased CO2 (compared with baseline conditions) than HERMES. In terms of practical impacts, it seems that soybean production will likely benefit more from the projected high precipitation and CO2 under rainfed conditions than under irrigated conditions. Due to much higher simulated yield under irrigation, supplementary irrigation may be an effective mitigation strategy to maintain soybean yield; however, adjusting sowing dates appear to have little effect on soybean production.

Technical Abstract: The ability to predict climate change effects on crop yield through field experiments and crop modeling is essential for developing mitigation strategies. The objective of this study was to compare two different soybean (Glycine max L. Merrill) modules (CROPGRO and HERMES) in the Root Zone Water Quality Model 2 (RZWQM2) for their predicted climate change effects on soybean production. The modules were calibrated for measured temperature responses using data from a four-year air exclusion experiment (2015-2018) in North Carolina, USA. Both crop modules simulated similar climate change effects in terms of yield and biomass by the end of year 2100 using 40 general circulation models (GCMs) and two Representative Concentration Pathways (RCP4.5 and RCP8.5), compared with the simulations using current baseline (2002-2018). For both modules, much greater reductions in biomass and seed yield were simulated under RCP8.5 than under RCP4.5 due to higher temperature, which could be partially offset by elevated CO2 under both Pathways. In addition, both modules predicted lower variability of biomass and seed yield across these GCMs under irrigated than under rainfed conditions, which is expected given the differences in projected precipitation from year to year among GCMs. CROPGRO predicted a greater positive climate change effect in response to the projected higher precipitation and increased CO2 (compared with baseline conditions) than HERMES. In terms of practical impacts, it seems that soybean production will likely benefit more from the projected high precipitation and CO2 under rainfed conditions than under irrigated conditions. Due to much higher simulated yield under irrigation, supplementary irrigation may be an effective mitigation strategy to maintain soybean yield; however, adjusting sowing dates appear to have little effect on soybean production.