Crop asynchrony stabilizes food production

Access through your institution Buy or subscribe arising from D. Renard & D. Tilman _Nature_ https://doi.org/10.1038/s41586-019-1316-y (2019) Stable agricultural systems are fundamental

for the reliability of agricultural production and food security. Recently, Renard and Tilman1 reported that crop diversity, calculated as the exponential value of the Shannon diversity

index of harvested areas of 176 crops, stabilizes national food production. Here we show that crop asynchrony—that is, asynchronous production trends between different crops2—is an even

better predictor of agricultural production stability than is crop diversity. Our finding suggests that asynchrony is one important property that can explain why a higher crop diversity

supports the stability of national food production, and that it should be considered in strategies to stabilize agricultural production through crop diversification. This is a preview of

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* Log in * Learn about institutional subscriptions * Read our FAQs * Contact customer support DATA AVAILABILITY All datasets used and generated during this study are provided in a public

repository: https://github.com/legli/AgriculturalStability. CODE AVAILABILITY The codes used for data preparation and analyses are provided in a public repository:

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Google Scholar  Download references ACKNOWLEDGEMENTS L.E. acknowledges funding from the Helmholtz Association (Research School ESCALATE, VH-KO-613). We thank V. Grimm for discussions; M. Wu

for statistical support and D. Renard for discussions and the exchange of code to make our analysis clearer and more consistent. The FAOSTAT database is maintained and regularly updated by

FAO with regular support from its Member States. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * UFZ - Helmholtz Centre for Environmental Research, Leipzig, Germany Lukas Egli, Matthias

Schröter & Ralf Seppelt * University of Potsdam, Institute of Biochemistry and Biology, Potsdam, Germany Lukas Egli * University of Münster, Institute of Landscape Ecology, Münster,

Germany Christoph Scherber * Centre for Biodiversity Monitoring, Zoological Research Museum Alexander Koenig, Bonn, Germany Christoph Scherber * University of Göttingen, Agroecology,

Department of Crop Sciences, Göttingen, Germany Teja Tscharntke * University of Göttingen, Centre of Biodiversity and Sustainable Land Use (CBL), Göttingen, Germany Teja Tscharntke *

Institute of Geoscience and Geography, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany Ralf Seppelt Authors * Lukas Egli View author publications You can also search for

this author inPubMed Google Scholar * Matthias Schröter View author publications You can also search for this author inPubMed Google Scholar * Christoph Scherber View author publications You

can also search for this author inPubMed Google Scholar * Teja Tscharntke View author publications You can also search for this author inPubMed Google Scholar * Ralf Seppelt View author

publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS L.E., M.S., T.T. and R.S. designed the study. L.E. and C.S. performed the analysis. All authors wrote

the manuscript. CORRESPONDING AUTHOR Correspondence to Lukas Egli. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. EXTENDED DATA FIGURES AND TABLES EXTENDED DATA FIG. 1 MAIN

DETERMINANTS OF NATIONAL CALORIC PRODUCTION STABILITY. A–H, Effects of crop diversity (A), crop asynchrony (B), irrigation (C), nitrogen use intensity (D), temperature instability (E),

precipitation instability (F), warfare (G) and time (H) on caloric production stability. Results are shown for the linear regression models including crop diversity (green), crop asynchrony

(blue) and both (orange) (_n_ = 590). Irrigation and nitrogen use intensity were back-transformed from square-root-transformation, predicted values were back-transformed from

log-transformation. Predictions were calculated using the observed range of the focal predictor, while keeping all the other predictors at their mean values. Shaded areas represent 95%

confidence intervals. The figure was created with the statistical software package R 3.6.110. SUPPLEMENTARY INFORMATION SUPPLEMENTARY METHODS. REPORTING SUMMARY RIGHTS AND PERMISSIONS

Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Egli, L., Schröter, M., Scherber, C. _et al._ Crop asynchrony stabilizes food production. _Nature_ 588, E7–E12 (2020).

https://doi.org/10.1038/s41586-020-2965-6 Download citation * Received: 12 February 2020 * Accepted: 30 September 2020 * Published: 09 December 2020 * Issue Date: 10 December 2020 * DOI:

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