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

Nature Food volume 3, pages 255–265 (2022)Cite this article

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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.

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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

References

  1. FAOSTAT Statistics Database (Food and Agriculture Organization of the United Nations, 2019); http://www.fao.org/faostat/en/#data

  2. Magrini, M. B. et al. Why are grain-legumes rarely present in cropping systems despite their environmental and nutritional benefits? Analyzing lock-in in the French agrifood system. Ecol. Econ. 126, 152–162 (2016).

    Article  Google Scholar 

  3. Zander, P. et al. Grain legume decline and potential recovery in European agriculture: a review. Agron. Sustain. Dev. 36, 26 (2016).

    Article  Google Scholar 

  4. Cernay, C., Makowski, D. & Pelzer, E. Preceding cultivation of grain legumes increases cereal yields under low nitrogen input conditions. Environ. Chem. Lett. 16, 631–636 (2018).

    Article  CAS  Google Scholar 

  5. Nemecek, T. et al. Environmental impacts of introducing grain legumes into European crop rotations. Eur. J. Agron. 28, 380–393 (2008).

    Article  Google Scholar 

  6. Gaba, S. et al. Multiple cropping systems as drivers for providing multiple ecosystem services: from concepts to design. Agron. Sustain. Dev. 35, 607–623 (2014).

    Article  Google Scholar 

  7. Jensen, E. S. et al. Legumes for mitigation of climate change and the provision of feedstock for biofuels and biorefineries. A review. Agron. Sustain. Dev. 32, 329–364 (2012).

    Article  CAS  Google Scholar 

  8. Foyer, C. H. et al. Neglecting legumes has compromised human health and sustainable food production. Nat. Plants 2, 16112 (2016).

    Article  PubMed  Google Scholar 

  9. Messina, M., Rogero, M. M., Fisberg, M. & Waitzberg, D. Health impact of childhood and adolescent soy consumption. Nutr. Rev. 75, 500–515 (2017).

    Article  PubMed  Google Scholar 

  10. Jayachandran, M. & Xu, B. An insight into the health benefits of fermented soy products. Food Chem. 271, 362–371 (2019).

    Article  CAS  PubMed  Google Scholar 

  11. Dold, C. et al. Long-term carbon uptake of agro-ecosystems in the Midwest. Agric. For. Meteorol. 232, 128–140 (2017).

    Article  ADS  Google Scholar 

  12. Gilmanov, T. G. et al. Productivity and carbon dioxide exchange of leguminous crops: estimates from flux tower measurements. Agron. Journa 106, 545–559 (2014).

    Article  CAS  Google Scholar 

  13. Urruty, N., Deveaud, T., Guyomard, H. & Boiffin, J. Impacts of agricultural land use changes on pesticide use in French agriculture. Eur. J. Agron. 80, 113–123 (2016).

    Article  Google Scholar 

  14. Rüdelsheim, P. L. J. & Smets, G. Baseline information on agricultural practices in the EU soybean (Glycine max (L.) Merr.). Perseus BVBA (2012).

  15. Martin, N. Domestic soybean to compensate the European protein deficit: illusion or real market opportunity? Oilseeds Fats Crops Lipids 22, D502 (2015).

    Google Scholar 

  16. Krön, M. & Bittner, U. Danube soya—Improving European GM-free soya supply for food and feed. Oilseeds Fats Crops Lipids 22, D509 (2015).

    Google Scholar 

  17. OECD‑FAO Agricultural Outlook 2019–2028 (OECD/FAO, 2019).

  18. Iizumi, T. et al. Historical changes in global yields: major cereal and legume crops from 1982 to 2006. Glob. Ecol. Biogeogr. 23, 346–357 (2014).

    Article  Google Scholar 

  19. Iizumi, T. et al. Uncertainties of potentials and recent changes in global yields of major crops resulting from census- and satellite-based yield datasets at multiple resolutions. PLoS ONE 13, e0203809 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Iizumi, T., Okada, M. & Yokozawza, M. A meteorological forcing data set for global crop modeling: development, evaluation, and intercomparison. J. Geophys. Res. Atmos. Res. 119, 363–384 (2014).

    Article  ADS  Google Scholar 

  21. Roberts, D. R. et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929 (2017).

    Article  Google Scholar 

  22. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and experiment design. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  ADS  Google Scholar 

  23. Van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).

    Article  ADS  Google Scholar 

  24. Koutroulis, A. G. et al. Freshwater vulnerability under high end climate change. A pan-European assessment. Sci. Total Environ. 614, 271–286 (2018).

    Article  ADS  Google Scholar 

  25. Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  26. Müller, C. et al. The Global Gridded Crop Model Intercomparison phase 1 simulation dataset. Sci. Data 6, 1–22 (2019).

    Article  Google Scholar 

  27. Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc. Natl Acad. Sci. 106, 15594–15598 (2009).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. Mourtzinis, S. et al. Climate-induced reduction in US-wide soybean yields underpinned by region- and in-season specific responses. Nat. Plants 1, 14026 (2015).

    Article  PubMed  Google Scholar 

  29. Ramankutty, N., Evan, A. T., Monfreda, C. & Foley, J. A. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochem. Cycles 22, 1–19 (2008).

    Article  Google Scholar 

  30. Sustainable Land Use (Greening) (European Commission, accessed March 2022); https://ec.europa.eu/info/food-farming-fisheries/key-policies/common-agricultural-policy/income-support/greening_en

  31. Đorđević, V., Malidža, G., Vidić, M., Milovac, Ž. & Šeremešić, S. Best Practice Manual for Soya Bean Cultivation in the Danube Region (Danube Soya, 2016).

  32. Hartman, G. L., West, E. D. & Herman, T. K. Crops that feed the world 2. Soybean-worldwide production, use, and constraints caused by pathogens and pests. Food Secur. 3, 5–17 (2011).

    Article  Google Scholar 

  33. Pannecoucque, J. et al. Screening for soybean varieties suited to Belgian growing conditions based on maturity, yield components and resistance to Sclerotinia sclerotiorum and Rhizoctonia solani anastomosis group 2-2IIIB. J. Agric. Sci. https://doi.org/10.1017/S0021859618000333 (2018).

  34. Grandes cultures biologiques—Les clés de la réussite (Chambres d’agriculture de France, 2017).

  35. Grassini, P., Specht, J. E., Tollenaar, M., Ciampitti, I. & Cassman, K. G. High-yield maize–soybean cropping systems in the US Corn Belt, in Crop Physiology: Applications for Genetic Improvement and Agronomy, (eds Sadras, V. O. & Calderini, D. F.) 17-41 (Academic Press, 2015).

  36. Salembier, C., Elverdin, J. H. & Meynard, J. Tracking on-farm innovations to unearth alternatives to the dominant soybean-based system in the Argentinean pampa. Agron. Sustain. Dev. https://doi.org/10.1007/s13593-015-0343-9 (2016).

  37. Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).

    Article  ADS  CAS  PubMed  Google Scholar 

  38. Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochem. Cycles 22, 1–19 (2008).

    Article  Google Scholar 

  39. Kurasch, A. K. et al. Identification of mega-environments in Europe and effect of allelic variation at maturity E loci on adaptation of European soybean. Plant, Cell Environ. 40, 765–778 (2017).

    Article  CAS  Google Scholar 

  40. Houlton, B. Z. et al. A world of cobenefits: solving the global nitrogen challenge. Earths Future 7, 865–872 (2019).

    Article  ADS  Google Scholar 

  41. Weisberger, D., Nichols, V. & Liebman, M. Does diversifying crop rotations suppress weeds? A meta-analysis. PLoS ONE 14, 1–12 (2019).

    Article  Google Scholar 

  42. Gibson, K. E. B., Gibson, J. P. & Grassini, P. Benchmarking irrigation water use in producer fields in the US central great plains. Environ. Res. Lett. 14, 054009 (2019).

    Article  ADS  Google Scholar 

  43. Nori, J. et al. Protected areas and spatial conservation priorities for endemic vertebrates of the Gran Chaco, one of the most threatened ecoregions of the world. Divers. Distrib. 22, 1212–1219 (2016).

    Article  Google Scholar 

  44. Fehlenberg, V. et al. The role of soybean production as an underlying driver of deforestation in. Glob. Environ. Chang. 45, 24–34 (2017).

    Article  Google Scholar 

  45. Meyfroidt, P. et al. Middle-range theories of land system change. Glob. Environ. Chang. 53, 52–67 (2018).

    Article  Google Scholar 

  46. Delerce, S. et al. Assessing weather-yield relationships in rice at local scale using data mining approaches. PLoS ONE 11, e0161620 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Everingham, Y., Sexton, J., Skocaj, D. & Inman-Bamber, G. Accurate prediction of sugarcane yield using a random forest algorithm. Agron. Sustain. Dev. 36, 27 (2016).

    Article  Google Scholar 

  48. Jeong, J. H. et al. Random forests for global and regional crop yield predictions. PLoS ONE 11, e0156571 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Partridge, T. F. et al. Mid-20th century warming hole boosts US maize yields. Environ. Res. Lett. 14, 114008 (2019).

    Article  ADS  Google Scholar 

  50. Cernay, C., Pelzer, E. & Makowski, D. A global experimental dataset for assessing grain legume production. Sci. Data 3, 160084 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Setiyono, T. D. et al. Understanding and modeling the effect of temperature and daylength on soybean phenology under high-yield conditions. Field Crop. Res. 100, 257–271 (2007).

    Article  Google Scholar 

  52. Hafner, S. Trends in maize, rice, and wheat yields for 188 nations over the past 40 years: a prevalence of linear growth. Agric. Ecosyst. Environ. 97, 275–283 (2003).

    Article  Google Scholar 

  53. Schoving, C. et al. Combining simple phenotyping and photothermal algorithm for the prediction of soybean phenology: application to a range of common cultivars grown in Europe. Front. Plant Sci. 10, 1–14 (2020).

    Article  Google Scholar 

  54. Folberth, C. et al. Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nat. Commun. 7, 1–13 (2016).

    Article  Google Scholar 

  55. Guilpart, N. et al. Rooting for food security in sub-Saharan Africa. Environ. Res. Lett. 12, 114036 (2017).

    Article  ADS  Google Scholar 

  56. Shangguan, W., Hengl, T., Mendes de Jesus, J., Yuan, H. & Da, Y. Mapping the global depth to bedrock for land surface modeling. J. Adv. Model. Earth Syst. 9, 65–88 (2017).

    Article  ADS  Google Scholar 

  57. Jones, A., Montanarella, L. & Jones, R. Soil Atlas of Europe (European Commission, 2005).

  58. Cober, E. R. & Morrison, M. J. Soybean yield and seed composition changes in response to increasing atmospheric CO2 concentration in short-season Canada. Plants 8, 250 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  59. Thomey, M. L., Slattery, R. A., Bernacchi, C. J., Köhler, I. H. & Ort, D. R. Yield response of field‐grown soybean exposed to heat waves under current and elevated [CO2]. Glob. Chang. Biol. 25, 4352–4368 (2019).

    Article  ADS  PubMed  Google Scholar 

  60. Vera, U. M. R., Bernacchi, C. J., Siebers, M. H. & Ort, D. R. Canopy warming accelerates development in soybean and maize, offsetting the delay in soybean reproductive development by elevated CO2 concentrations. Plant Cell Environ. 41, 2806–2820 (2018).

    Article  Google Scholar 

  61. Li, Y. et al. Elevated CO2 increases nitrogen fixation at the reproductive phase contributing to various yield responses of soybean cultivars. Front. Plant Sci. 8, 1546 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Gray, S. B. et al. Intensifying drought eliminates the expected benefits of elevated carbon dioxide for soybean. Nat. Plants 2, 16132 (2016).

    Article  CAS  PubMed  Google Scholar 

  63. Xu, G. et al. Soybean grown under elevated CO2 benefits more under low temperature than high temperature stress: varying response of photosynthetic limitations, leaf metabolites, growth, and seed yield. J. Plant Physiol. 205, 20–32 (2016).

    Article  CAS  PubMed  Google Scholar 

  64. Makowski, D., Marajo-Petitzon, E., Durand, J. L. & Ben-Ari, T. Quantitative synthesis of temperature, CO2, rainfall, and adaptation effects on global crop yields. Eur. J. Agron. 115, 126041 (2020).

    Article  CAS  Google Scholar 

  65. Sacks, W. J., Deryng, D., Foley, J. A. & Ramankutty, N. Crop planting dates: an analysis of global patterns. Glob. Ecol. Biogeogr. 19, 607–620 (2010).

    Google Scholar 

  66. Ruane, A. C., Goldberg, R. & Chryssanthacopoulos, J. Climate forcing datasets for agricultural modeling: merged products for gap-filling and historical climate series estimation. Agric. For. Meteorol. 200, 233–248 (2015).

    Article  ADS  Google Scholar 

  67. You, L., Wood, S., Wood-Sichra, U. & Wu, W. Generating global crop distribution maps: from census to grid. Agric. Syst. 127, 53–60 (2014).

    Article  Google Scholar 

  68. Grassini, P. et al. Soybean yield gaps and water productivity in the western US Corn Belt. Field Crops Res. 179, 150–163 (2015).

    Article  Google Scholar 

  69. Merlos, F. A. et al. Potential for crop production increase in Argentina through closure of existing yield gaps. Field Crops Res. 184, 145–154 (2015).

    Article  Google Scholar 

  70. Sentelhas, P. C. et al. The soybean yield gap in Brazil—magnitude, causes and possible solutions for sustainable production. J. Agric. Sci. 153, 1394–1411 (2015).

    Article  Google Scholar 

  71. Grassini, P., Eskridge, K. M. & Cassman, K. G. Distinguishing between yield advances and yield plateaus in historical crop production trends. Nat. Commun. 4, 2918 (2013).

    Article  ADS  PubMed  Google Scholar 

  72. Rubel, F., Brugger, K., Haslinger, K. & Auer, I. The climate of the European Alps: shift of very high resolution Köppen–Geiger climate zones 1800-2100. Meteorol. Zeitschrift 26, 115–125 (2017).

    Article  ADS  Google Scholar 

  73. Dupin, M. et al. Effects of the training dataset characteristics on the performance of nine species distribution models: application to Diabrotica virgifera virgifera. PLoS ONE 6, e20957 (2011).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  74. Günther, F. & Fritsch, S. neuralnet: training of neural networks. R J 2, 30–38 (2010).

    Article  Google Scholar 

  75. Wright, M. N. & Ziegler, A. ranger: a fast implementation of random forests for high dimensional data in C++ and R. J. Stat. Softw. 77, 1–17 (2017).

    Article  Google Scholar 

  76. Hastie, T. gam: Generalized Additive Models, R package, version 0.98 (R Foundation for Statistical Computing, 2013).

  77. Minamikawa, K., Fumoto, T., Iizumi, T., Cha-un, N. & Pimple, U. Prediction of future methane emission from irrigated rice paddies in central Thailand under different water management practices. Sci. Total Environ. 566–567, 641–651 (2016).

    Article  ADS  PubMed  Google Scholar 

  78. Rosenzweig, C. et al. The Agricultural Model Intercomparison and Improvement Project (AgMIP): protocols and pilot studies. Agric. For. Meteorol. 170, 166–182 (2013).

    Article  ADS  Google Scholar 

  79. Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  80. Villoria, N. B. et al. Rapid aggregation of global gridded crop model outputs to facilitate cross-disciplinary analysis of climate change impacts in agriculture. Environ. Model. Softw. 75, 193–201 (2016).

    Article  Google Scholar 

  81. Guilpart, N., Iizumi, T. & Makowski, D. Soybean yield projections in Europe under historical (1981–2010) and future climate (2050–2059 and 2090–2099 for RCP4.5 and RCP8.5) [Dataset] (Zenodo, 2022); https://doi.org/10.5281/zenodo.6136216

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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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Authors and Affiliations

  1. Université Paris-Saclay, AgroParisTech, INRAE, UMR Agronomie, Thiverval-Grignon, France

    Nicolas Guilpart

  2. Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan

    Toshichika Iizumi

  3. Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA-Paris, Paris, France

    David Makowski

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  1. Nicolas Guilpart
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  2. Toshichika Iizumi
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  3. David Makowski
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Contributions

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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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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