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Brazilian maize yields negatively affected by climate after land clearing

Nature Sustainability volume 3, pages 845–852 (2020)Cite this article

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Abstract

Over 50% of the Brazilian Cerrado has been cleared, predominantly for agropastoral purposes. Here, we use the Weather Research and Forecasting model to run 15-year climate simulations across Brazil with six land-cover scenarios: (1) before extensive land clearing, (2) observed in 2016, (3) Cerrado replaced with single-cropped (soy) agriculture, (4) Cerrado replaced with double-cropped (soy–maize) agriculture, (5) eastern Amazon replaced with single-cropped agriculture and (6) eastern Amazon replaced with double-cropped agriculture. All land-clearing scenarios (2–6) contain significantly more growing season days with temperatures that exceed critical temperature thresholds for maize. Evaporative fraction significantly decreases across all land-clearing scenarios. Altered weather reduces maize yields between 6% and 8% compared with the before-extensive-land-clearing scenario; however, soy yields were not significantly affected. Our findings provide evidence that land clearing has degraded weather in the Brazilian Cerrado, undermining one of the main reasons for land clearing: rain-fed crop production.

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Fig. 1: WRF Model run domain and the six land-cover scenarios.
Fig. 2: Hydrologic seasonal cycles and increases in maize warm nights.
Fig. 3: Number of days above critical temperature threshold across WRF scenarios.
Fig. 4: Decreases in corn yields and increases in maize hot days.

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Data availability

The crop-cover dataset is available at https://doi.org/10.7910/DVN/ZFHCTI.

Code availability

NCAR’s WRF Model is freely available for download at http://www2.mmm.ucar.edu/wrf/users/downloads.html.

All modifications made to the WRF Model code are detailed in the main text and Supplementary Information. Code to train and run the crop models can be found at: https://github.com/tpartrid/BrazilCropModel.

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Acknowledgements

This study was funded by the Neukom Institute for Computational Science at Dartmouth College, United States Department of Agriculture National Institute of Food and Agriculture (2015‐68007‐23133 and 2018-67003-27406), National Science Foundation (BCS 184018) and Nelson A. Rockefeller Center at Dartmouth College. We thank Research Computing at Dartmouth College for their assistance with compiling and running WRF.

Author information

Authors and Affiliations

  1. Department of Geography and Environment, University of Richmond, Richmond, VA, USA

    Stephanie A. Spera

  2. Neukom Institute for Computational Science, Dartmouth College, Hanover, NH, USA

    Stephanie A. Spera

  3. Department of Geography, Dartmouth College, Hanover, NH, USA

    Stephanie A. Spera & Jonathan M. Winter

  4. Department of Earth Science, Dartmouth College, Hanover, NH, USA

    Jonathan M. Winter & Trevor F. Partridge

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Contributions

S.A.S., J.M.W. and T.F.P. conceived and designed the experiments. S.A.S. performed the climate modelling experiments, and T.F.P. performed yield analyses. S.A.S, J.M.W. and T.F.P. analysed the data. S.A.S. wrote the manuscript with contributions from J.M.W. and T.F.P.

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Correspondence to Stephanie A. Spera.

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Extended data

Extended Data Fig. 1 Monthly average minimum and maximum temperatures.

Seasonal cycles of a, minimum temperature and b, maximum temperature spatially averaged over the whole region of interest (white box in Fig. 1a). The solid lines represent mean monthly values, and the shaded area represents bootstrapped 95% confidence intervals.

Extended Data Fig. 2 Differences in annual and September-October-November evapotranspiration across scenarios.

Estimation plots of a, annual evapotranspiration and b, September, October, and November evapotranspiration. Each point in the scatter plot represents the spatial average over the whole region of interest for the 15 (2001 – 2015) harvest years (top), with bootstrapped 95% confidence intervals of the effect size (bottom). Note CeAzOg = BzBLC in main text. ‘Mean difference’ refers to a difference (mm) in distribution means.

Extended Data Fig. 3 Differences in number of days above critical growing season temperature maize thresholds across the Mato Grosso sub-region.

Estimation plots of the number of days in the corn growing season (Jan – Aug) with minimum temperatures above 24 °C (left) and maximum temperatures above 35 °C (right). Each point in the scatter plot represents the spatial average over the Mato Grosso Amazon-boundary sub-region for the 15 (2001 – 2015) harvest years (top), with bootstrapped 95% confidence intervals of the effect size (bottom). Note CeAzOg = BzBLC in main text. ‘Mean difference’ refers to a difference (number of days) in distribution means.

Extended Data Fig. 4 Differences in start and end of rainy season across scenarios.

Estimation plots of the start of the rainy season (left) and end of the rainy season (right), both defined as the number days after Aug 1. Each point in the scatter plot represents the spatial average over the whole region of interest for the 15 (2001 – 2015) harvest years (top), with bootstrapped 95% confidence intervals of the effect size (bottom). Note CeAzOg = BzBLC in main text. ‘Mean difference’ refers to a difference (number of days after Aug 1) in distribution means.

Extended Data Fig. 5 Seasonal maps of average precipitation change between BzBLC and AzSc and AzDC scenarios.

Seasonal maps of the average precipitation change, in percent, between AzSC and BzBLC scenarios (left) and AzDC and BzBLC scenarios (right) between 2001-2015 harvest years in each grid cell. Stippled areas highlight where the percent change is greater than 95% of the variance of BzBLC precipitation between 2001-2015 in each grid cell.

Extended Data Fig. 6 Difference in September-October precipitation across scenarios.

Estimation plots of September-October precipitation (mm). Scatterplots of each land-cover scenarios for each year (top) and bootstrapped 95% confidence intervals of the effect size (bottom). Each point in the scatter plots represents the spatial average over the whole region for the 15 (2001-2015) harvest years (top), with bootstrapped 95% confidence intervals of effect size (bottom). Note CeAzOg = BzBLC in main text. ‘Mean difference’ refers to a difference (mm) in distribution means.

Extended Data Fig. 7 Difference in annual and September-October precipitation across scenarios across the Tocantins sub-region.

Estimation plots of a, annual precipitation and b, September-October precipitation in the Tocantins sub-region. Each point in the scatter plot represents the spatial average over the Tocantins sub-region for the 15 (2001 – 2015) harvest years (top), with bootstrapped 95% confidence intervals of the effect size (bottom). ‘Mean difference’ refers to a difference (mm) in distribution means.

Extended Data Fig. 8

Left: Violin plots of the percent difference between predicted maize yield the original land use scenario (BzBLC) and each of the counterfactual climate scenarios. Right: Estimation plot of corn yields (kg/ha). Scatterplots of each land-cover scenarios (top) and bootstrapped 95% confidence intervals of the effect size (bottom).

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Supplementary Methods, Tables 1–4 and Figs. 1–49.

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Spera, S.A., Winter, J.M. & Partridge, T.F. Brazilian maize yields negatively affected by climate after land clearing. Nat Sustain 3, 845–852 (2020). https://doi.org/10.1038/s41893-020-0560-3

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