Agricultural Model Intercomparison and Improvement Project (AgMIP) established valuable tools for assessing climate change effects on crops. It is used to develop adaptation strategies and address the environmental consequences of agricultural products. While soybeans are important protein sources globally for human and animal feed, understanding their response to changing environments is a global challenge. However, a process-based eco-physiological model can provide advantages in predicting global change effects. These models are vital for optimizing agriculture yield, sustainability, and resource management to comprehend and understand crop response toward environmental conditions.

Further studies on agro-climatic conditions and their effects on crop growth are to develop more resilient and efficient agricultural practices. This study aimed to enhance the accuracy of global soybean yield prediction using a process-based model MATCRO, aligning with the aim of developing a global crop model for climate change studies. MATCRO structures have crop-specific parameters that can be dynamically adjusted to a specific crop. While the photosynthesis response is limited by nitrogen and water, this model considers the soybean response to those environmental factors, assuming the value for crop-specific parameters does not differ between soybean cultivars. MATCRO incorporates leaf-level photosynthesis mechanism to quantify the interaction between stomata and leaf-boundary layer involving the exchange of CO2 gas during photosynthesis. The estimation of glucose produced is partitioned to plant’s organ. MATCRO-Soybean is calibrated using site-specific experimental datasets from previous studies, and the simulated yield is assessed with reference data at global, country, and
grid cell levels.

Evaluation of the time series correlation in global yield shows significancy between MATCRO-Soybean and reference about 0.452 after removing the trend. This study provides a tool to understand the response of global soybean yield production and MATCRO as a coupled climate model opens the opportunity for further study of the impact of climate change on crop production.

This poster was presented during the 10th APN Early Career Professional Poster and Networking Session, held alongside the APN 26th Joint Intergovernmental and Scientific Planning Group Meetings on 13 June 2024 at the National Research and Innovation Agency (BRIN) in Jakarta, Indonesia.