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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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.
References
O’Connell, C. S. et al. Balancing tradeoffs: reconciling multiple environmental goals when ecosystem services vary regionally. Environ. Res. Lett. 13, 064008 (2018).
Klink, C. A. & Machado, R. B. Conservation of the Brazilian Cerrado. Conserv. Biol. 19, 707–713 (2005).
Françoso, R. D. et al. Habitat loss and the effectiveness of protected areas in the Cerrado Biodiversity Hotspot. Nat. Conserv. 13, 35–40 (2015).
Oliveira, P. T. S. et al. Trends in water balance components across the Brazilian Cerrado. Water Resour. Res. 50, 7100–7114 (2014).
Spera, S. A., Galford, G. L., Coe, M. T., Macedo, M. N. & Mustard, J. F. Land-use change affects water recycling in Brazil’s last agricultural frontier. Glob. Change Biol. 22, 3405–3413 (2016).
Nóbrega, R. L. B. et al. Effects of conversion of native Cerrado vegetation to pasture on soil hydro-physical properties, evapotranspiration and streamflow on the Amazonian agricultural frontier. PLoS ONE 12, e0179414 (2017).
Bustamente, M. M. C., Corbeels, M., Scopel, E. & Roscoe, R. Soil Carbon Storage and Sequestration Potential in the Cerrado Region of Brazil (FAO, 2006).
Silvério, D. V. et al. Agricultural expansion dominates climate changes in southeastern Amazonia: the overlooked non-GHG forcing. Environ. Res. Lett. 10, 104015 (2015).
Prevedello, J. A., Winck, G. R., Weber, M. M., Nichols, E. & Sinervo, B. Impacts of forestation and deforestation on local temperature across the globe. PLoS ONE 14, e0213368 (2019).
Butt, N., de Oliveira, P. A. & Costa, M. H. Evidence that deforestation affects the onset of the rainy season in Rondonia, Brazil. J. Geophys. Res. Atmos. 116, D1120 (2011).
Wright, J. S. et al. Rainforest-initiated wet season onset over the southern Amazon. Proc. Natl Acad. Sci. USA 114, 8481–8486 (2017).
Leite‐Filho, A. T., Pontes, V. YdeS. & Costa, M. H. Effects of deforestation on the onset of the rainy season and the duration of dry spells in southern Amazonia. J. Geophys. Res. Atmos. 124, 5268–5281 (2019).
Leite‐Filho, A. T., Costa, M. H. & Fu, R. The southern Amazon rainy season: the role of deforestation and its interactions with large-scale mechanisms. Int. J. Climatol. 40, 2328–2341 (2019).
Riskin, S. H. et al. Solute and sediment export from Amazon forest and soybean headwater streams. Ecol. Appl. 27, 193–207 (2017).
Dias, L. C. P., Macedo, M. N., Costa, M. H., Coe, M. T. & Neill, C. Effects of land cover change on evapotranspiration and streamflow of small catchments in the Upper Xingu River Basin, Central Brazil. J. Hydrol. Reg. Stud. 4, 108–122 (2015).
Panday, P. K., Coe, M. T., Macedo, M. N., Lefebvre, P. & Castanho, A. DdeA. Deforestation offsets water balance changes due to climate variability in the Xingu River in eastern Amazonia. J. Hydrol. 523, 822–829 (2015).
Aragão, L. E. O. C. et al. Interactions between rainfall, deforestation and fires during recent years in the Brazilian Amazonia. Phil. Trans. R. Soc. B 363, 1779–1785 (2008).
Houghton, R. in Tropical Deforesation and Climate Change (eds Moutinho, P. & Schwartzman, S.) 13–21 (IPAM, 2005).
Karstensen, J., Peters, G. P. & Andrew, R. M. Attribution of CO2 emissions from Brazilian deforestation to consumers between 1990 and 2010. Environ. Res. Lett. 8, 024005 (2013).
Lima, L. S. et al. Feedbacks between deforestation, climate, and hydrology in the Southwestern Amazon: implications for the provision of ecosystem services. Landsc. Ecol. 29, 261–274 (2014).
World Agricultural Production (USDA, 2019).
Brazil: Grain and Feed Annual (USDA FAS, 2019).
2017 Censo Agropecuario Tabela 6764 (Instituto Brasileiro de Geographia e Estatistica, 2017).
Lee, J.-E. et al. Reduction of tropical land region precipitation variability via transpiration. Geophys. Res. Lett. 39, L19704 (2012).
Arima, E. Y., Walker, R. T., Perz, S. & Souza, C. Jr. Explaining the fragmentation in the Brazilian Amazonian forest. J. Land Use Sci. 11, 257–277 (2016).
Knox, R., Bisht, G., Wang, J. & Bras, R. Precipitation variability over the forest-to-nonforest transition in southwestern Amazonia. J. Clim. 24, 2368–2377 (2010).
Khanna, J., Medvigy, D., Fueglistaler, S. & Walko, R. Regional dry-season climate changes due to three decades of Amazonian deforestation. Nat. Clim. Change 7, 200–204 (2017).
Oliveira, L. J. C., Costa, M. H., Soares-Filho, B. S. & Coe, M. T. Large-scale expansion of agriculture in Amazonia may be a no-win scenario. Environ. Res. Lett. 8, 024021 (2013).
Coe, M. et al. The forests of the Amazon and Cerrado moderate regional climate and are the key to the future. Trop. Conserv. Sci. 10, 1–6 (2017).
Lawrence, D. & Vandecar, K. Effects of tropical deforestation on climate and agriculture. Nat. Clim. Change 5, 27–36 (2015).
Bagley, J. E., Desai, A. R., Harding, K. J., Snyder, P. K. & Foley, J. A. Drought and deforestation: has land cover change influenced recent precipitation extremes in the Amazon? J. Clim. 27, 345–361 (2013).
Costa, M. H. & Pires, G. F. Effects of Amazon and Central Brazil deforestation scenarios on the duration of the dry season in the arc of deforestation. Int. J. Climatol. 30, 1970–1979 (2010).
Alves, L. M., Marengo, J. A., Fu, R. & Bombardi, R. J. Sensitivity of Amazon regional climate to deforestation. Am. J. Clim. Change 6, 75–98 (2017).
Le Page, Y. et al. Synergy between land use and climate change increases future fire risk in Amazon forests. Earth Syst. Dynam. 8, 1237–1246 (2017).
Wright, J. S., Fu, R. & Heymsfield, A. J. A statistical analysis of the influence of deep convection on water vapor variability in the tropical upper troposphere. Atmos. Chem. Phys. 9, 5847–5864 (2009).
Malhado, A. C. M., Pires, G. F. & Costa, M. H. Cerrado conservation is essential to protect the Amazon rainforest. Ambio 39, 580–584 (2010).
Sampaio, G. et al. Regional Climate Change over eastern Amazonia caused by pasture and soybean cropland expansion. Geophys. Res. Lett. 34, L17709 (2007).
Spangler, K. R., Lynch, A. H. & Spera, S. A. Precipitation drivers of cropping frequency in the Brazilian Cerrado: evidence and implications for decision-making. Weather Clim. Soc. 9, 201–213 (2017).
Spera, S. A., Winter, J. M. & Chipman, J. W. Evaluation of agricultural land cover representations on regional climate model simulations in the Brazilian Cerrado. J. Geophys. Res. Atmos. 123, 5163–5176 (2018).
CONAB. Acompanhamento da safra Brasileira de graos. Cia. Nac. Abast. 6, 1–113 (2019).
de Araújo, M. L. S. et al. Spatiotemporal dynamics of soybean crop in the Matopiba region, Brazil (1990–2015). Land Use Policy 80, 57–67 (2019).
Pires, G. F. & Costa, M. H. Deforestation causes different subregional effects on the Amazon bioclimatic equilibrium. Geophys. Res. Lett. 40, 3618–3623 (2013).
Swann, A. L. S., Longo, M., Knox, R. G., Lee, E. & Moorcroft, P. R. Future deforestation in the Amazon and consequences for South American climate. Agric. For. Meteorol. 214–215, 12–24 (2015).
Apley, D. W. & Zhu, J. Visualizing the effects of predictor variables in black box supervised learning models. Preprint at https://arxiv.org/abs/1612.08468 (2016).
Lobell, D. B. & Burke, M. B. On the use of statistical models to predict crop yield responses to climate change. Agric. For. Meteorol. 150, 1443–1452 (2010).
Partridge, T. F. et al. Mid-20th century warming hole boosts US maize yields. Environ. Res. Lett. 14, 114008 (2019).
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).
Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA 114, 9326–9331 (2017).
Casado, L. & Londoño, E. Under Brazil’s far-right leader, Amazon protections slashed and forests fall. The New York Times (28 July 2019).
Skamarock, C. et al. A Description of the Advanced Research WRF Version 3 Technical Note 475+STR (NCAR, 2008).
Niu, G.-Y. et al. The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res. Atmos. 116, D12109 (2011).
Yang, Z.-L. et al. The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins. J. Geophys. Res. Atmos. 116, D12110 (2011).
Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).
Georgescu, M., Lobell, D. B., Field, C. B. & Mahalov, A. Simulated hydroclimatic impacts of projected Brazilian sugarcane expansion. Geophys. Res. Lett. 40, 972–977 (2013).
Mu, Q., Zhao, M. & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800 (2011).
Pei, L. et al. WRF model sensitivity to land surface model and cumulus parameterization under short-term climate extremes over the southern Great Plains of the United States. J. Clim. 27, 7703–7724 (2014).
Massey, J. D., Steenburgh, W. J., Knievel, J. C. & Cheng, W. Y. Y. Regional soil moisture biases and their influence on WRF model temperature forecasts over the Intermountain West. Weather Forecast. 31, 197–216 (2015).
Cuntz, M. et al. The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model. J. Geophys. Res. Atmos. 121, 10676–10700 (2016).
MapBiomas Project—Collection 3.1 of the Annual Land Use Land Cover Maps of Brazil (MapBiomas, accessed 18 January 2017).
PRODES—Projeto de Monitoramento do Desmatamento na Amazônia Brasileira por Satélite [Monitoring Deforestation in the Brazilian Amazon by Satelite Project] (INPE, 2019).
Monitoramento Sistemático dos Desmatamentos no Bioma Cerrado (SIAD-Cerrado) (LAPIG, 2019).
Spera, S. Agricultural Intensification can preserve the Brazilian Cerrado: applying lessons from Mato Grosso and Goiás to Brazil’s last agricultural frontier. Trop. Conserv. Sci. 10, 1–7 (2017).
Rausch, L. L. et al. Soy expansion in Brazil’s Cerrado. Conserv. Lett. 12, e12671 (2019).
Morton, D. C. et al. Reevaluating suitability estimates based on dynamics of cropland expansion in the Brazilian Amazon. Glob. Environ. Change 37, 92–101 (2016).
Garrett, R. D. & Rausch, L. L. Green for gold: social and ecological tradeoffs influencing the sustainability of the Brazilian soy industry. J. Peasant Stud. 43, 461–493 (2016).
Cohn, A. S. et al. Forest loss in Brazil increases maximum temperatures within 50 km. Environ. Res. Lett. 14, 084047 (2019).
Sauer, S. Soy expansion into the agricultural frontiers of the Brazilian Amazon: the agribusiness economy and its social and environmental conflicts. Land Use Policy 79, 326–338 (2018).
Jepson, W., Brannstrom, C. & Filippi, A. Access regimes and regional land change in the Brazilian Cerrado, 1972–2002. Ann. Assoc. Am. Geogr. 100, 87–111 (2010).
Ho, J., Tumkaya, T., Aryal, S., Choi, H. & Claridge-Chang, A. Moving beyond P values: data analysis with estimation graphics. Nat. Methods 16, 565–566 (2019).
Calendário de Plantio e Colheita de Grãos no Brasil 2019 (Companhia Nacional de Abastecimento, 2019).
Filho, I. A. P. in Embrapa Milho e Sorgo 9th edn (Embrapa, 2015).
Tecnologias de Produção de Soja—Região Central do Brasil 2014 (Embrapa Soja, 2014).
Sibaldelli, R. N. R. & Farias, J. R. B. Boletim Agrometeorológico da Embrapa Soja (Embrapa Soja, 2016).
Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).
Ferreira, D. B. & Rao, V. B. Recent climate variability and its impacts on soybean yields in southern Brazil. Theor. Appl. Climatol. 105, 83–97 (2011).
Deryng, D., Sacks, W. J., Barford, C. C. & Ramankutty, N. Simulating the effects of climate and agricultural management practices on global crop yield. Glob. Biogeochem. Cycles 25, 2006 (2011).
Viana, J. S., Gonçalves, E. P., Silva, A. C. & Matos, V. P. in A Comprehensive Survey of International Soybean Researach—Genetics, Physiology, Agronomy and Nitrogen Relationships (ed. Board, J.) Ch. 18 (IntechOpen, 2013).
Caratti, F. C., Lamego, F. P., Silva, J. D. G., Garcia, J. R. & Agostinetto, D. Partição da competição por recursos entre soja e milho como planta competidora. Planta Daninha 34, 657–666 (2016).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Butler, E., Mueller, N. & Huybers, P. Peculiarly pleasant weather for US maize. Proc. Natl Acad. Sci. USA 115, 201808035 (2018).
Everingham, Y., Sexton, J., Skocaj, D. & Inman-Bamber, G. Accurate prediction of sugarcane yield using a random forest algorithm. Agron. Sustain. Dev. 36, 27 (2016).
2018 Producao Agricola Municpal Tabela 839 (Instituto Brasileiro de Geographia e Estatistica, 2019).
Deryng, D., Conway, D., Ramankutty, N., Price, J. & Warren, R. Global crop yield response to extreme heat stress under multiple climate change futures. Environ. Res. Lett. 9, 034011 (2014).
Teixeira, E. I., Fischer, G., van Velthuizen, H., Walter, C. & Ewert, F. Global hot-spots of heat stress on agricultural crops due to climate change. Agric. For. Meteorol. 170, 206–215 (2013).
Salem, M. A., Kakani, V. G., Koti, S. & Reddy, K. R. Pollen-based screening of soybean genotypes for high temperatures. Crop Sci. 47, 219–231 (2007).
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.
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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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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).
Supplementary information
Supplementary Information
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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DOI: https://doi.org/10.1038/s41893-020-0560-3
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