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

Abstract

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 R2 (>0.9) and low root-mean-squared error (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 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.

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Fig. 1: Projected soybean yield in Europe under historical and future climate.
Fig. 2: Effect of climate change on projected soybean yield in Europe.
Fig. 3: Analysis of climate drivers of projected yield changes by the 2050s under RCP 4.5 relative to historical climate.
Fig. 4: Area requirements for soybean self-sufficiency in Europe.
Fig. 5: Production area required for soybean self-sufficiency, with associated yields and fertilizer savings.

Data availability

The soybean yield projections generated during this study have been deposited in the Zenodo repository (https://doi.org/10.5281/zenodo.6136215) (ref. 81)

Code availability

The R code to reproduce key results of this paper is available at: https://github.com/nguilpart/soybean_yield_projections_europe

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Acknowledgements

This work was supported by the CLAND convergence institute (16-CONV-0003) funded by the French National Research Agency (ANR), by the ACCAF INRA meta-programme (COMPROMISE project, COMPROMISE_MP-P10177) and by the LegValue project funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement number 727672. T.I. was partly supported by the Environment Research and Technology Development Fund (S-14) of the Environmental Restoration and Conservation Agency of Japan and Grant-in-Aid for Scientific Research (16KT0036, 17K07984 and 18H02317) of JSPS.

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N.G and D.M. designed research and performed the analysis. T.I. supplied yield and cliamte data. N.G. wrote the manuscript, with substantial contributions from all co-authors. D.M. initiated research.

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Correspondence to Nicolas Guilpart.

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Nature Food thanks Zvi Hochman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Guilpart, N., Iizumi, T. & Makowski, D. Data-driven projections suggest large opportunities to improve Europe’s soybean self-sufficiency under climate change. Nat Food 3, 255–265 (2022). https://doi.org/10.1038/s43016-022-00481-3

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