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Anthropogenic climate change has slowed global agricultural productivity growth

Nature Climate Change volume 11, pages 306–312 (2021)Cite this article

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Abstract

Agricultural research has fostered productivity growth, but the historical influence of anthropogenic climate change (ACC) on that growth has not been quantified. We develop a robust econometric model of weather effects on global agricultural total factor productivity (TFP) and combine this model with counterfactual climate scenarios to evaluate impacts of past climate trends on TFP. Our baseline model indicates that ACC has reduced global agricultural TFP by about 21% since 1961, a slowdown that is equivalent to losing the last 7 years of productivity growth. The effect is substantially more severe (a reduction of ~26–34%) in warmer regions such as Africa and Latin America and the Caribbean. We also find that global agriculture has grown more vulnerable to ongoing climate change.

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Fig. 1: Recent trends in agricultural productivity and climate.
Fig. 2: Response of agricultural productivity to weather.
Fig. 3: Global impact of ACC on productivity.
Fig. 4: Global impact of ACC for multiple econometric models.
Fig. 5: Global, regional and country-level impacts of ACC.

Data availability

Data and code necessary to fully reproduce results in this study are deposited in a permanent online repository at the Cornell Institute for Social and Economic Research (CISER): https://doi.org/10.6077/pfsd-0v93.

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Acknowledgements

The authors thank C.B. Barrett and participants at the AERE and EAAE summer meetings, the Southern Economic Association meeting, the AGU Fall meeting, Giannini Foundation’s Big Ag Data Conference and seminars at Cornell University, Arizona State University, University of Arizona, North Carolina State University, Duke University, Michigan State University, University of Connecticut, Virginia Tech, UC Berkeley and Oregon State University and three anonymous referees for useful comments. A.O.B. was partially supported by the USDA National Institute of Food and Agriculture, Hatch/Multi State project 1011555. T.R.A. and C.M.C. were partially supported by NSF grants 1602564 and 1751535, as well as the Cornell Atkinson Center for Sustainability, the Cornell Initiative for Digital Agriculture and the Braudy Foundation.

Author information

Authors and Affiliations

  1. Charles H. Dyson School of Applied Economics and Management, Cornell University, Ithaca, NY, USA

    Ariel Ortiz-Bobea

  2. Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA

    Toby R. Ault & Carlos M. Carrillo

  3. Department of Agricultural and Resource Economics, University of Maryland – College Park, College Park, MD, USA

    Robert G. Chambers

  4. Department of Earth System Science and Center on Food Security and the Environment, Stanford University, Stanford, CA, USA

    David B. Lobell

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  1. Ariel Ortiz-Bobea
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Contributions

A.O.B. conceived the study and conducted and led research and the writing of the manuscript. C.M.C. obtained and downscaled modelled climate data. T.R.A., R.G.C. and D.B.L. provided detailed guidance and advice throughout the project. All authors contributed to writing the manuscript.

Corresponding author

Correspondence to Ariel Ortiz-Bobea.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks Keith Fuglie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Composition of global agricultural production.

Share of net production value of cereal crops, non-cereal crops and livestock. Source: FAOSTAT (http://www.fao.org/faostat/en/#data/QV, accessed 6/29/2020).

Extended Data Fig. 2 The response of agricultural productivity to weather without 10% of coldest countries.

a, Response function of changes in country-level TFP to changes in green-season average T. Response functions are centered vertically so that the exposure-weighted marginal effect is zero. The baseline response function with all countries is shown in dashed lines. The coloured bands represent 90 and 95% confidence bands based on 500 year-by-region block bootstraps. The blue bars represent the country-level distribution of green-season average T over the sample period. The average green-season T is indicated for a select number of large countries. b, Panel shows the result of a placebo check whereby TFP and weather data are randomly mismatched or reshuffled by years. The distribution represents the linear and quadratic T coefficients based on 10,000 reshuffled datasets. c, Same as previous panel but based on datasets reshuffled by country. d, Response function of changes in country-level TFP to changes in green-season total P. e, Same as panel B but for P coefficients. f, Same as panel c but for P coefficients.

Extended Data Fig. 3 The response of agricultural productivity to weather without 10% of hottest countries.

a, Response function of changes in country-level TFP to changes in green-season average T. Response functions are centered vertically so that the exposure-weighted marginal effect is zero. The baseline response function with all countries is shown in dashed lines. The coloured bands represent 90 and 95% confidence bands based on 500 year-by-region block bootstraps. The blue bars represent the country-level distribution of green-season average T over the sample period. The average green-season T is indicated for a select number of large countries. b, Panel shows the result of a placebo check whereby TFP and weather data are randomly mismatched or reshuffled by years. The distribution represents the linear and quadratic T coefficients based on 10,000 reshuffled datasets. c, Same as previous panel but based on datasets reshuffled by country. d, Response function of changes in country-level TFP to changes in green-season total P. e, Same as panel B but for P coefficients. f, Same as panel c but for P coefficients.

Extended Data Fig. 4 The response of agricultural productivity to weather for 1962–1988.

a, Response function of changes in country-level TFP to changes in green-season average T. Response functions are centered vertically so that the exposure-weighted marginal effect is zero. The baseline response function for 1962–2015 is shown in dashed lines. The coloured bands represent 90 and 95% confidence bands based on 500 year-by-region block bootstraps. The blue bars represent the country-level distribution of green-season average T over the sample period. The average green-season T is indicated for a select number of large countries. b, Panel shows the result of a placebo check whereby TFP and weather data are randomly mismatched or reshuffled by years. The distribution represents the linear and quadratic T coefficients based on 10,000 reshuffled datasets. c, Same as previous panel but based on datasets reshuffled by country. d, Response function of changes in country-level TFP to changes in green-season total P. e, Same as panel B but for P coefficients. f, Same as panel c but for P coefficients.

Extended Data Fig. 5 The response of agricultural productivity to weather for 1989–2015.

a, Response function of changes in country-level TFP to changes in green-season average T. Response functions are centered vertically so that the exposure-weighted marginal effect is zero. The baseline response function for 1962–2015 is shown in dashed lines. The coloured bands represent 90 and 95% confidence bands based on 500 year-by-region block bootstraps. The blue bars represent the country-level distribution of green-season average T over the sample period. The average green-season T is indicated for a select number of large countries. b, Panel shows the result of a placebo check whereby TFP and weather data are randomly mismatched or reshuffled by years. The distribution represents the linear and quadratic T coefficients based on 10,000 reshuffled datasets. c, Same as previous panel but based on datasets reshuffled by country. d, Response function of chanfcentges in country-level TFP to changes in green-season total P. e, Same as panel B but for P coefficients. f, Same as panel c but for P coefficients.

Extended Data Fig. 6 The response of agricultural productivity to weather for 1962–1988 without 10% of coldest countries.

a, Response function of changes in country-level TFP to changes in green-season average T. Response functions are centered vertically so that the exposure-weighted marginal effect is zero. The baseline response function with all countries and years is shown in dashed lines. The coloured bands represent 90 and 95% confidence bands based on 500 year-by-region block bootstraps. The blue bars represent the country-level distribution of green-season average T over the sample period. The average green-season T is indicated for a select number of large countries. b, Panel shows the result of a placebo check whereby TFP and weather data are randomly mismatched or reshuffled by years. The distribution represents the linear and quadratic T coefficients based on 10,000 reshuffled datasets. c, Same as previous panel but based on datasets reshuffled by country. d, Response function of changes in country-level TFP to changes in green-season total P. e, Same as panel B but for P coefficients. f, Same as panel c but for P coefficients.

Extended Data Fig. 7 The response of agricultural productivity to weather for 1989–2015 without 10% of coldest countries.

a, Response function of changes in country-level TFP to changes in green-season average T. Response functions are centered vertically so that the exposure-weighted marginal effect is zero. The baseline response function with all countries and years is shown in dashed lines. The coloured bands represent 90 and 95% confidence bands based on 500 year-by-region block bootstraps. The blue bars represent the country-level distribution of green-season average T over the sample period. The average green-season T is indicated for a select number of large countries. b, Panel shows the result of a placebo check whereby TFP and weather data are randomly mismatched or reshuffled by years. The distribution represents the linear and quadratic T coefficients based on 10,000 reshuffled datasets. c, Same as previous panel but based on datasets reshuffled by country. d, Response function of changes in country-level TFP to changes in green-season total P. e, Same as panel B but for P coefficients. f, Same as panel c but for P coefficients.

Extended Data Fig. 8 The response of agricultural productivity to weather for 1962–1988 without 10% of hottest countries.

a, Response function of changes in country-level TFP to changes in green-season average T. Response functions are centered vertically so that the exposure-weighted marginal effect is zero. The baseline response function with all countries and years is shown in dashed lines. The coloured bands represent 90 and 95% confidence bands based on 500 year-by-region block bootstraps. The blue bars represent the country-level distribution of green-season average T over the sample period. The average green-season T is indicated for a select number of large countries. b, Panel shows the result of a placebo check whereby TFP and weather data are randomly mismatched or reshuffled by years. The distribution represents the linear and quadratic T coefficients based on 10,000 reshuffled datasets. c, Same as previous panel but based on datasets reshuffled by country. d, Response function of changes in country-level TFP to changes in green-season total P. e, Same as panel B but for P coefficients. f, Same as panel c but for P coefficients.

Extended Data Fig. 9 The response of agricultural productivity to weather for 1989–2015 without 10% of hottest countries.

a, Response function of changes in country-level TFP to changes in green-season average T. Response functions are centered vertically so that the exposure-weighted marginal effect is zero. The baseline response function with all countries and years is shown in dashed lines. The coloured bands represent 90 and 95% confidence bands based on 500 year-by-region block bootstraps. The blue bars represent the country-level distribution of green-season average T over the sample period. The average green-season T is indicated for a select number of large countries. b, Panel shows the result of a placebo check whereby TFP and weather data are randomly mismatched or reshuffled by years. The distribution represents the linear and quadratic T coefficients based on 10,000 reshuffled datasets. c, Same as previous panel but based on datasets reshuffled by country. d, Response function of changes in country-level TFP to changes in green-season total P. e, Same as panel B but for P coefficients. f, Same as panel c but for P coefficients.

Extended Data Fig. 10 Global impact of anthropogenic climate change under a wide range of econometric models.

The upper part of the figure shows the impact estimates for 298 model variations. The vertical lines around each estimate represent the 90 and 95% confidence intervals (in light and dark colour, respectively) around the ensemble mean estimate for a particular model. ACC impacts for the baseline model, also shown in Extended Data Fig. 3a, is highlighted in blue whereas alternative models are shown in grey. The red horizontal line and band represent the average mean impact of the 288 models out of the 298 that do not exclude observations, plus and minus a standard deviation (−16.9 ± 5.9%). The vertical bars directly below the impact estimates represent the reduction in out-of-sample MSE of a 10-fold cross-validation (whereby years of data are sampled together) relative to a model that excludes weather variables. Thus, higher bars indicate better model fit. The dotted table on the bottom part of the figure provides information about the characteristics of each econometric model shown in the upper part of the figure.

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Ortiz-Bobea, A., Ault, T.R., Carrillo, C.M. et al. Anthropogenic climate change has slowed global agricultural productivity growth. Nat. Clim. Chang. 11, 306–312 (2021). https://doi.org/10.1038/s41558-021-01000-1

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