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Grain legume yields are as stable as other spring crops in long-term experiments across northern Europe.

Moritz RecklingThomas F DöringGöran BergkvistFrederick L StoddardChristine A WatsonSylvia SeddigFrank-M ChmielewskiJohann Bachinger
Published in: Agronomy for sustainable development (2018)
Grain legumes produce high-quality protein for food and feed, and potentially contribute to sustainable cropping systems, but they are grown on only 1.5% of European arable land. Low temporal yield stability is one of the reasons held responsible for the low proportion of grain legumes, without sufficient quantitative evidence. The objective of this study was to compare the yield stability of grain legumes with other crop species in a northern European context and accounting for the effects of scale in the analysis and the data. To avoid aggregation biases in the yield data, we used data from long-term field experiments. The experiments included grain legumes (lupin, field pea, and faba bean), other broad-leaved crops, spring, and winter cereals. Experiments were conducted in the UK, Sweden, and Germany. To compare yield stability between grain legumes and other crops, we used a scale-adjusted yield stability indicator that accounts for the yield differences between crops following Taylor's Power Law. Here, we show that temporal yield instability of grain legumes (30%) was higher than that of autumn-sown cereals (19%), but lower than that of other spring-sown broad-leaved crops (35%), and only slightly greater than spring-sown cereals (27%). With the scale-adjusted yield stability indicator, we estimated 21% higher yield stability for grain legumes compared to a standard stability measure. These novel findings demonstrate that grain legume yields are as reliable as those of other spring-sown crops in major production systems of northern Europe, which could influence the current negative perception on grain legume cultivation. Initiatives are still needed to improve the crops agronomy to provide higher and more stable yields in future.
Keyphrases
  • climate change
  • big data
  • small molecule
  • risk assessment
  • current status
  • data analysis
  • cross sectional