Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change

The rapid expansion of soybean-growing areas across Europe raises questions about the suitability of agroclimatic conditions for soybean production. Here, using data-driven relationships between climate and soybean yield derived from machine-learning, we made yield projections under current and future climate with moderate (Representative Concentration Pathway (RCP) 4.5) to intense (RCP 8.5) warming, up to the 2050s and 2090s time horizons. The selected model showed high R-2 (>0.9) and low root-mean-squared error (0.35t ha(-1)) between observed and predicted yields based on cross-validation. Our results suggest that a self-sufficiency level of 50% (100%) would be achievable in Europe under historical and future climate if 4-5% (9-11%) of the current European cropland were dedicated to soybean production. The findings could help farmers, extension services, policymakers and agribusiness to reorganize the production area distribution. The environmental benefits and side effects, and the impacts of soybean expansion on land-use change, would need further research.

Automated summary

This study used data-driven relationships and machine learning to predict future soybean production in Europe, suggesting that achieving 50% self-sufficiency level would be possible by dedicating 4-5% of current European cropland to soybean production. These findings have important implications for farmers, policymakers, and agribusiness.