Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields

Abstract

Soybean is one of the most important agricultural commodities in the world, thus making it important for global food security. However, widely used process-based crop models, such as the GIS-based Environmental Policy Integrated Climate (GEPIC) model, tend to underestimate the impacts of extreme climate events on soybean, which brings large uncertainties. This study proposed an approach of hybrid models to constrain such uncertainties by coupling the GEPIC model and extreme climate indicators using machine learning. Subsequently, the key extreme climate indicators for the globe and main soybean producing countries are explored, and future soybean yield changes and variability are analyzed using the proposed hybrid model. The results show the coupled GEPIC and Random Forest (GEPIC+RF) model (R: 0.812, RMSD: 0.716 t/ha and rRMSD: 36.62%) significantly eliminated uncertainties and underestimation of climate extremes from the GEPIC model (R: 0.138, RMSD: 1.401 t/ha and rRMSD: 71.57%) compared to the other five hybrid models (R: 0.365–0.612, RMSD: 0.928–1.021 and rRMSD: 47.48–52.24%) during the historical period. For global soybean yield and those in Brazil and Argentina, low-temperature-related indices are the main restriction factors, whereas drought is the constraining factor in the USA and China, and combined drought–heat disaster in India. The GEPIC model would overestimate soybean yields by 13.40–27.23%. The GEPIC+RF model reduced uncertainty by 28.45–41.83% for the period of 2040–2099. Our results imply that extreme climate events will possibly cause more losses in soybean in the future than we have expected, which would help policymakers prepare for future agriculture risk and food security under climate change.

Incorporating Meteorological Data and Pesticide Information to Forecast Crop Yields Using Machine Learning

“…The researchers have integrated various machine learning methods with crop modeling and analyzed complex meteorological factors to offer precise predictions. The intricate interaction among climate change, agricultural practices, and machine learning methodologies presents opportunities for novel approaches to address food security and agricultural sustainability issues [41], [42]. South Asia and India, in particular, need more research in agricultural forecasts.…”

Training Machine Learning Algorithms Using Remote Sensing and Topographic Indices for Corn Yield Prediction

“…An alternative method to integrate the process-based crop model and ML (random forest algorithm) was proposed by [37] to reduce uncertainties of global soybean yield prediction. They suggest that this integration of the GIS-based Environmental Policy Integrated Climate (GEPIC) process-based model and extreme climate indicators using ML reduced uncertainty by 28.45-41.83% for the future scenario of 2040-2099.…”

Applying the SIMPLE Crop Model to Assess Soybean (Glicine max. (L.) Merr.) Biomass and Yield in Tropical Climate Variation

“…In addition, EPIC-IIASA is applicable for studying the effects of CO 2 on yields in Argentina, Brazil, and the USA [12]. Process-based models can be coupled with artificial intelligence for crop assessment, i.e., machine learning links GEPIC to assess the effects of climate change on soybean in Argentina, Brazil, China, and the USA [13], and the application of artificial neural networks for the yield estimation in Maryland, USA [14]. The models are different from each other but geographically dependent, and thus their simulation performance levels are various.…”