Soybean-BioCro: a semi-mechanistic model of soybean growth

Authors: MATTHEWS, MEGAN - University Of Illinois; MARSHALL-COLON, AMY - University Of Illinois; McGrath, Justin; LOCHOCKI, EDWARD - University Of Illinois; LONG, STEPHEN - University Of Illinois

Submitted to: in silico Plants
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/12/2021
Publication Date: 12/5/2021
Citation: Matthews, M.L., Marshall-Colon, A., McGrath, J.M., Lochocki, E.B., Long, S.P. 2021. Soybean-BioCro: a semi-mechanistic model of soybean growth. in silico Plants. 4(1). Article diab032.
DOI: https://doi.org/10.1093/insilicoplants/diab032

Interpretive Summary: Crop models provide a means to examine how crops will respond to future climate and to investigate ways to improve crop yield. The BioCro model has been used previously to examine feasibility of crops for biofuel, and here the model has been extended to simulate growth of soybean. The model successfully predicts soybean growth in current and future carbon dioxide concentrations and provides a foundation to examine ways to improve soybean growth in a changing climate.

Technical Abstract: Soybean is a major global source of protein and oil. Understanding how soybean crops will respond to the changing climate and identifying the responsible molecular machinery, are important for facilitating bioengineering and breeding to meet the growing global food demand. The BioCro family of crop models are semi-mechanistic models scaling from biochemistry to whole crop growth and yield. BioCro was previously parameterized and proved effective for the biomass crops miscanthus, coppice willow, and Brazilian sugarcane. Here, we present Soybean-BioCro, the first food crop to be parameterized for BioCro. Two new module sets were incorporated into the BioCro framework describing the rate of soybean development and carbon partitioning and senescence. The model was parameterized using field measurements collected over the 2002 and 2005 growing seasons at the open air [CO2] enrichment (SoyFACE) facility under ambient atmospheric [CO2]. We demonstrate that Soybean-BioCro successfully predicted how elevated [CO2] impacted field-grown soybean growth without a need for re-parameterization, by predicting soybean growth under elevated atmospheric [CO2] during the 2002 and 2005 growing seasons, and under both ambient and elevated [CO2] for the 2004 and 2006 growing seasons. Soybean-BioCro provides a useful foundational framework for incorporating additional primary and secondary metabolic processes or gene regulatory mechanisms that can further aid our understanding of how future soybean growth will be impacted by climate change.