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

Nicolas Guilpart (1) , Toshichika Iizumi (2) , David Makowski (3)

1 Agronomie
2 NIAES - National Institute of Agro-Environmental Sciences
3 MIA Paris-Saclay - Mathématiques et Informatique Appliquées

The rapid expansion of soybean-growing areas across Europe raises questions about the suitability of agro-climatic 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 (RCP 4.5) to intense (RCP 8.5) warming, up to the 2050s and 2090s time horizons. The selected model showed high R² (higher than 0.9) and low RMSE (0.35 t 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 is 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, as well as the impacts of soybean expansion on land-use change, would need further research.

Domains: Environmental Sciences; Life Sciences [q-bio]

Nicolas Guilpart, Toshichika Iizumi, David Makowski. Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change. Nature Food, In press, ⟨10.1038/s43016-022-00481-3⟩. ⟨hal-03626485⟩