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Plant pathogen infection risk tracks global crop yields under climate change


Global food security is strongly determined by crop production. Climate change-induced losses to production can occur directly or indirectly, including via the distributions and impacts of plant pathogens. However, the likely changes in pathogen pressure in relation to global crop production are poorly understood. Here we show that temperature-dependent infection risk, r(T), for 80 fungal and oomycete crop pathogens will track projected yield changes in 12 crops over the twenty-first century. For most crops, both yields and r(T) are likely to increase at high latitudes. In contrast, the tropics will see little or no productivity gains, and r(T) is likely to decline. In addition, the United States, Europe and China may experience major changes in pathogen assemblages. The benefits of yield gains may therefore be tempered by the greater burden of crop protection due to increased disease and unfamiliar pathogens.

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Fig. 1: Summary of infection cardinal temperatures for 80 plant pathogens included in this study.
Fig. 2: Average change in Rr and pathogen turnover under RCP 6.0 across all months.
Fig. 3: Effect of RCP and pathogen restriction method on change in Rr and pathogen turnover.
Fig. 4: Effect of RCP on average change in Rr and pathogen turnover across all months.
Fig. 5: Changes in crop yield and r(T) under RCP 6.0 by latitude.

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Data availability

The fungal and oomycete cardinal temperature data are available in Dryad42 ( and from ref. 16. The data on annual crop yield projections used in this study are from the Inter-Sectoral Model Intercomparison Project ( The fungal and oomycete host plant data and geographical distributions (the Plantwise database) were used under license for the current study and are available with permission from CABI. The FAOSTAT commodity list is available from The global gridded climate data and climate projections are available from WorldClim ( The global gridded crop distribution data used in this study are available from EarthStat ( and MIRCA2000 ( The fungal and oomycete names and name disambiguation data were obtained from Species Fungorum ( and MycoBank ( The annual per capita GDP (PPP) data were obtained from the World Bank ( Coupled Model Intercomparison Project 5 single-level monthly near-surface RH data were obtained from the Climate Data Store ( Administrative boundaries for the maps were obtained from GADM ( The coastal outlines were obtained from package rworldmap version 1.3–6 for R version 4.0.1.

Code availability

All analyses were conducted using existing functions for R version 4.0.1. No substantial custom code was used. R code used for data manipulation is available from the corresponding author on reasonable request.


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T.M.C. is supported by BBSRC SWBio DTP studentship no. BB/M009122/1. D.P.B. and S.J.G. are supported by BBSRC grant no. BB/N020847/1 and the Global Burden of Crop Loss project (Bill and Melinda Gates Foundation). S.J.G. is supported by a CIFAR Fellowship, ‘The Fungal Kingdom: Threats and Opportunities’.

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Authors and Affiliations



D.P.B. and T.M.C. developed the concept, collated the data, conducted the analyses and prepared the figures. D.P.B. wrote the manuscript with contributions from T.M.C. and S.J.G.

Corresponding author

Correspondence to Daniel P. Bebber.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–16, Tables 1–8 and Appendix.

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Source data

Source Data Fig. 1

Numerical data used to generate graphs.

Source Data Fig. 2a

Numeric data for maps presented in Geotiff raster file format.

Source Data Fig. 2b,d

Numeric data used to generate maps and graphs.

Source Data Fig. 2c

Numeric data for maps presented in Geotiff raster file format.

Source Data Fig. 3

Numerical data used to generate heat maps. Note that Rr and pathogen turnover values are not directly comparable between Fig. 3a and Fig. 3b excel sheets. This is because Rr and pathogen turnover values in Fig. 3a are scaled to enable colour saturation comparison between the RCPs in Fig. 3a (see scale bars in Fig. 3a).

Source Data Fig. 4

Numerical data used to generate graphs.

Source Data Fig. 5

Numerical data used to generate graphs.

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Chaloner, T.M., Gurr, S.J. & Bebber, D.P. Plant pathogen infection risk tracks global crop yields under climate change. Nat. Clim. Chang. 11, 710–715 (2021).

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