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Link to original content: https://pubmed.ncbi.nlm.nih.gov/38556523
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. 2024 Mar 31;14(1):7612.
doi: 10.1038/s41598-024-57522-z.

European soybean to benefit people and the environment

Jose L Rotundo  1   2 , Rachel Marshall  3 , Ryan McCormick  4 , Sandra K Truong  5 , David Styles  6 , Jose A Gerde  7 , Emmanuel Gonzalez-Escobar  3 , Elizabete Carmo-Silva  3 , Victoria Janes-Bassett  8 , Jennifer Logue  9 , Paolo Annicchiarico  10 , Chris de Visser  11 , Alice Dind  12 , Ian C Dodd  3 , Louise Dye  13 , Stephen P Long  3   14 , Marta S Lopes  15 , Joke Pannecoucque  16 , Moritz Reckling  17   18 , Jonathan Rushton  19 , Nathaniel Schmid  12 , Ian Shield  20 , Marco Signor  21 , Carlos D Messina  22 , Mariana C Rufino  3   23
Affiliations

European soybean to benefit people and the environment

Jose L Rotundo et al. Sci Rep. .

Abstract

Europe imports large amounts of soybean that are predominantly used for livestock feed, mainly sourced from Brazil, USA and Argentina. In addition, the demand for GM-free soybean for human consumption is project to increase. Soybean has higher protein quality and digestibility than other legumes, along with high concentrations of isoflavones, phytosterols and minerals that enhance the nutritional value as a human food ingredient. Here, we examine the potential to increase soybean production across Europe for livestock feed and direct human consumption, and review possible effects on the environment and human health. Simulations and field data indicate rainfed soybean yields of 3.1 ± 1.2 t ha-1 from southern UK through to southern Europe (compared to a 3.5 t ha-1 average from North America). Drought-prone southern regions and cooler northern regions require breeding to incorporate stress-tolerance traits. Literature synthesized in this work evidenced soybean properties important to human nutrition, health, and traits related to food processing compared to alternative protein sources. While acknowledging the uncertainties inherent in any modelling exercise, our findings suggest that further integrating soybean into European agriculture could reduce GHG emissions by 37-291 Mt CO2e year-1 and fertiliser N use by 0.6-1.2 Mt year-1, concurrently improving human health and nutrition.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
European soy production, imports, cultivated area and yield gaps. (A) Soybean seed production and import of soybean seed equivalents in millions of tons (Mt) for 2020 in 39 countries (EU-27 + UK plus 12 neighbouring European countries). Vertical and horizontal red dashed lines indicate mean imports and production. Countries above these averages are labelled. (B) Change in soy cultivated area (1992–2020) of 12 European countries that cultivate > 20,000 hectares since 2017. Numbers in parenthesis on the legend indicate the slope in thousand hectares per year for the period 2010–2020 (all linear models p < 0.05) (C) Change in soybean yield from 1992 to 2020 (grey dots). Red lines show the linear trend with the increase in yield as percentage per year (*p < 0.05, **p < 0.01, ***p < 0.001). Blue lines indicate simulated water-limited yield (mean of 20-year weather) and percent yield gap calculated as: (water-limited yield − observed yield in 2020)/water-limited yield. Green dashed lines indicate irrigated potential yield. All data except simulations are from FAOSTAT.
Figure 2
Figure 2
Soybean crop performance across Europe and its potential growing regions (16 clusters with similar outcomes for maturity groups 0000 to III). Clusters L to P were shaded dark grey because soy cannot complete its growth cycle in these regions. (A) Median and interquartile ranges of potential (y-axis) and rainfed yields (x-axis) for the maturity group with the largest yield within each region. Interquartile ranges for yields originate from temporal and spatial variation in weather, site-to-site variation in soil properties, and uncertainty associated with satellite-derived soil water initial conditions. Regions were assigned to crop improvement strategies: breeding for cold-tolerance in cool climates (blue shade), agronomic optimisation (green shade), and improving drought tolerance (brown shade); dashed line is the 1:1 regression line. (B) Geographical extent of each cluster; a bar plot inset is labelled with maturity group with the largest median rainfed and/or potential yield, and the bar is coloured cyan according to the proportion of years the maturity group successfully reached R7. For insets with two bars, the upper bar represents the maturity group with the largest median rainfed yield, and the lower bar represents the maturity group with the largest median yield potential. Insets with one bar indicate the same maturity group had the largest median potential and rainfed yields. The proportion of years failing to achieve R7 is coloured red, and the proportion of years with crop harvest success are coloured in light blue. The map was created using Python 3.10.0 (https://www.python.org/downloads/).
Figure 3
Figure 3
Soybean and soy-product traits, qualities, and breeding aims: (A) Crop traits for soy recommended to increase (green plus symbol) or to decrease (red minus symbol). The grey box indicates negative selection for anti-nutritional compounds and the orange boxes indicate positive selection of traits (protein, sugars, fatty acids and minerals). (B) Soy products from fresh edible and mature grain and target traits for crop improvement contributing to better food and feed quality.
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