[A-CG39] Biogeochemical cycles in Land Ecosystem

[ACG39-P05] Development of the process-based soybean growth model MATCRO-Soybean

*Astrid Yusara¹, Tomomichi Kato², Yuji Masutomi³, Elizabeth A. Ainsworth⁴, Rafael Battisti⁵, Yushan Wu⁶ (1.Graduate School of Agriculture, Hokkaido University, Japan, 2.Research Faculty of Agriculture, Hokkaido University, Japan, 3.National Institute of Environmental Studies, Japan, 4.USDA ARS Global Change and Photosynthesis Research Unit, Urbana, IL, 5.Escola de Agronomia, Universidade Federal de Goiás, Brazil, 6.College of Agronomy, Sichuan Agricultural University, China)

Keywords:soybean model, nitrogen supply, yield variability

Soybean is one of the world's most important food crops providing a good source of protein and oil. Approximately 77% of global soybean production is used for animal feed, while the remaining is for industry 3.8% and direct human consumption 19.2% (Ritchie and Roser, 2021). It is expected that total demand of livestock will grow higher in the future along with the soybean consumption. Hence, accurate estimation of soybean production is necessary in terms of providing good data for food supply chains. Current crop simulation models have been widely used to understand the effects of environmental factors on soybean growth and development. Simulations of soybean yields at the global scale will provide important information about the limiting factors in varied conditions. As increasing temperature and rising CO₂ as the result of climate change will affect soybean yield in near future, it may lead to insufficient sources for increasing soybean demand worldwide.
This study applied a process-based crop growth model MATCRO (Masutomi et al., 2016) with the present climate data to globally simulate soybean production (MATCRO-Soybean). While MATCRO was initially developed for rice crop, this study introduced a nitrogen fixation process to estimate the soybean yields and calibrated parameters with field-scale data in three sites for some years with different varieties in Brazil (2013-2014), China (2014-2016), and the United States (2002-2007). The varieties used were Pioneer 93B15, Jiuyuehang, BRS 284, Nandou12, and Texuan13 which classified into different relative maturity groups (3, 5, 6.5, 6.5, and 7 respectively). A variety is classified to a specific relative maturity group based on the length of time from planting to maturity that is determined by photoperiod and temperature in a particular geographic location. Different maturity groups and environmental conditions used in this study are expected to represent model capability in capturing varied phenological conditions.
The objectives of this study are to 1) develop a soybean growth version of MATCRO, 2) validate observational soybean yields, aboveground biomass, and LAI measured at three sites, and 3) represent global soybean production. It is expected that further analysis regarding effects of environmental changes (e.g. excessed water, drought, and nitrogen dynamics) on the soybean yields will be conducted to provide comprehensive study in global soybean production estimation.