Abstract
Brazil’s leadership in soybean and maize production depends on predictable rainfall in the Amazon-Cerrado agricultural frontier. Here we assess whether agricultural expansion and intensification in the region are approaching a climatic limit to rainfed production. We show that yields decline in years with unusually low rainfall or high aridity during the early stages of crop development—a pattern observed in rainfed and irrigated areas alike. Although agricultural expansion and intensification have increased over time, dry–hot weather during drought events has slowed their rate of growth. Recent regional warming and drying already have pushed 28% of current agricultural lands out of their optimum climate space. We project that 51% of the region’s agriculture will move out of that climate space by 2030 and 74% by 2060. Although agronomic adaptation strategies may relieve some of these impacts, maintaining native vegetation is a critical part of the solution for stabilizing the regional climate.
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Data availability
All the raw climate datasets analysed in this study are available in the Google Earth Engine repository56. Raw land-use transition data that support the findings of this section are from published sources8,21. These data were used under license for the current study and are available from the corresponding author upon reasonable request and with permission of S.A.S. (sspera@richmond.edu).
Code availability
Processed and extracted variables used directly in the analyses are available at GitHub (https://github.com/ludmilarattis/effect-of-climate-on--agriculture/tree/Agriculture_Climate). The scripts and datasets used to analyse the effects of climate on agricultural production, land-use transitions and climate space are also available on Zenodo at https://doi.org/10.5281/zenodo.5363671.
References
Brazil. USDA Foreign Agricultural Service https://www.fas.usda.gov/regions/brazil (2019).
Planilha do PIB do Agronegócio Brasileiro de 1996 a 2018 (Centro de Estudos Avançados em Economia Aplicada, 2018); https://www.cepea.esalq.usp.br/br/pib-do-agronegocio-brasileiro.aspx
Boletim da Safra de Grãos. Companhia Nacional de Abastecimento https://www.conab.gov.br/info-agro/safras/graos/boletim-da-safra-de-graos (2020).
Projeções do Agronegócio: Brasil 2017/18 a 2027/28 Projeções de Longo Prazo (Ministério da Agricultura, Pecuária e Abastecimento, 2018).
Atlas Irrigação: Uso da Água na Agricultura Irrigada (Agência Nacional de Águas, 2017).
Costa, M. H. et al. Climate risks to Amazon agriculture suggest a rationale to conserve local ecosystems. Front. Ecol. Environ. 17, 584–590 (2019).
Fu, R. et al. Increased dry-season length over southern Amazonia in recent decades and its implication for future climate projection. Proc. Natl Acad. Sci. USA 110, 18110–18115 (2013).
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).
Abrahão, G. M. & Costa, M. H. Evolution of rain and photoperiod limitations on the soybean growing season in Brazil: the rise (and possible fall) of double-cropping systems. Agric. Meteorol. 256–257, 32–45 (2018).
Silvério, D. V. et al. Agricultural expansion dominates climate changes in southeastern Amazonia: the overlooked non-GHG forcing. Environ. Res. Lett. 10, 104015 (2015).
Barkhordarian, A., Saatchi, S. S., Behrangi, A., Loikith, P. C. & Mechoso, C. R. A recent systematic increase in vapor pressure deficit over tropical South America. Sci. Rep. 9, 15331 (2019).
Barkhordarian, A., von Storch, H., Zorita, E., Loikith, P. C. & Mechoso, C. R. Observed warming over northern South America has an anthropogenic origin. Clim. Dyn. 51, 1901–1914 (2018).
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 (2020).
FAOSTAT (Food and Agriculture Organization of the United Nations, 2020); http://www.fao.org/faostat/en/#data/QC
Presidência da República Secretaria-Geral Subchefia para Assuntos Jurídicos (Ministério da Agricultura, 2015).
Rashid, M. A. et al. Impact of heat-wave at high and low VPD on photosynthetic components of wheat and their recovery. Environ. Exp. Bot. 147, 138–146 (2018).
Lobell, D. B. et al. Greater sensitivity to drought accompanies maize yield increase in the U.S. Midwest. Science 344, 516–519 (2014).
Fletcher, A. L., Sinclair, T. R. & Allen, L. H. Transpiration responses to vapor pressure deficit in well watered ‘slow-wilting’ and commercial soybean. Environ. Exp. Bot. 61, 145–151 (2007).
Bunce, J. A. Comparative responses of leaf conductance to humidity in single attached leaves. J. Exp. Bot. 32, 629–634 (1981).
Kiniry, J. et al. Radiation-use efficiency response to vapor pressure deficit for maize and sorghum. Field Crops Res. 56, 265–270 (1998).
Spera, S. A. et al. Recent cropping frequency, expansion, and abandonment in Mato Grosso, Brazil had selective land characteristics. Environ. Res. Lett. 9, 064010 (2014).
Dias, L. C. P., Pimenta, F. M., Santos, A. B., Costa, M. H. & Ladle, R. J. Patterns of land use, extensification, and intensification of Brazilian agriculture. Glob. Change Biol. 22, 2887–2903 (2016).
Cohn, A. S., Vanwey, L. K., Spera, S. A. & Mustard, J. F. Cropping frequency and area response to climate variability can exceed yield response. Nat. Clim. Change 6, 601–604 (2016).
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).
Duursma, R. A. et al. The peaked response of transpiration rate to vapour pressure deficit in field conditions can be explained by the temperature optimum of photosynthesis. Agric. Meteorol. 189–190, 2–10 (2014).
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).
Cirino, P. H., Féres, J. G., Braga, M. J. & Reis, E. Assessing the impacts of ENSO-related weather effects on the Brazilian agriculture. Proc. Econ. Financ. 24, 146–155 (2015).
Pereira, P. A. A., Martha, G. B., Santana, C. A. & Alves, E. The development of Brazilian agriculture: future technological challenges and opportunities. Agric. Food Secur. 1, 4 (2012).
Marengo, J. A. & Bernasconi, M. Regional differences in aridity/drought conditions over Northeast Brazil: present state and future projections. Climatic Change 129, 103–115 (2015).
Naylor, R. L. Energy and resource constraints on intensive agricultural production. Annu. Rev. Energy Environ. 21, 99–123 (1996).
Getirana, A. Extreme water deficit in Brazil detected from space. J. Hydrometeorol. 17, 591–599 (2016).
Lathuillière, M. J., Coe, M. T. & Johnson, M. S. A review of green- and blue-water resources and their trade-offs for future agricultural production in the Amazon Basin: what could irrigated agriculture mean for Amazonia? Hydrol. Earth Syst. Sci. 20, 2179–2194 (2016).
Dobrovolski, R. & Rattis, L. Water collapse in Brazil: the danger of relying on what you neglect. Nat. Conserv. 13, 80–83 (2015).
da Silva, A. L. et al. Water appropriation on the agricultural frontier in western Bahia and its contribution to streamflow reduction: revisiting the debate in the Brazilian Cerrado. Water 13, 1054 (2021).
Pousa, R. et al. Climate change and intense irrigation growth in western Bahia, Brazil: the urgent need for hydroclimatic monitoring. Water 11, 933 (2019).
Ort, D. R. & Long, S. P. Limits on yields in the corn belt. Science 344, 484–485 (2014).
de Bossoreille de Ribou, S., Douam, F., Hamant, O., Frohlich, M. W. & Negrutiu, I. Plant science and agricultural productivity: why are we hitting the yield ceiling? Plant Sci. 210, 159–176 (2013).
Long, S. P. & Ort, D. R. More than taking the heat: crops and global change. Curr. Opin. Plant Biol. 13, 240–247 (2010).
Pommer, C. V. & Barbosa, W. The impact of breeding on fruit production in warm climates of Brazil. Rev. Bras. Frutic. 31, 612–634 (2009).
Lenka, N. K. et al. Carbon dioxide and temperature elevation effects on crop evapotranspiration and water use efficiency in soybean as affected by different nitrogen levels. Agric. Water Manag. 230, 105936 (2020).
Soares, W. R., Marengo, J. A. & Nobre, C. A. Assessment of warming projections and probabilities for Brazil in Climate Change Risks in Brazil (eds Nobre, C. et al.) 7–30 (Springer, 2019); https://doi.org/10.1007/978-3-319-92881-4_2
Schwalm, C. R., Glendon, S. & Duffy, P. B. RCP8.5 tracks cumulative CO2 emissions. Proc. Natl Acad. Sci. USA 117, 19656–19657 (2020).
Schwalm, C. R., Glendon, S. & Duffy, P. B. Reply to Hausfather and Peters: RCP8.5 is neither problematic nor misleading. Proc. Natl Acad. Sci. USA 117, 27793–27794 (2020).
Sistematização das Informações sobre Recursos Naturais—Mapa de Biomas do Brasil (Instituto Brasileiro de Geografia e Estatística, 2006); https://www.ibge.gov.br/geociencias/cartas-e-mapas/informacoes-ambientais/15842-biomas.html?=&t=downloads
Base Cartográfica Continua Do Brasil, Escala 1:250.000—BC250 (Instituto Brasileiro de Geografia e Estatística, 2019); https://geoftp.ibge.gov.br/cartas_e_mapas/bases_cartograficas_continuas/bc250/versao2019/informacoes_tecnicas/Documentacao_bc250_v2019.pdf
Campos, J., de, O. & Chaves, H. M. L. Tendências e variabilidades nas séries históricas de precipitação mensal e anual no bioma Cerrado no período 1977–2010. Rev. Bras. Meteorol. 35, 157–169 (2020).
Debortoli, N. S. et al. Rainfall patterns in the southern Amazon: a chronological perspective (1971–2010). Climatic Change 132, 251–264 (2015).
Oliveira, P. T. S. et al. Trends in water balance components across the Brazilian Cerrado. Water Resour. Res. 50, 7100–7114 (2014).
Panisset, J. S. et al. Contrasting patterns of the extreme drought episodes of 2005, 2010 and 2015 in the Amazon Basin. Int. J. Climatol. 38, 1096–1104 (2018).
Cai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Clim. Change 4, 111–116 (2014).
Jiménez-Muñoz, J. C. et al. Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Niño 2015–2016. Sci. Rep. 6, 33130 (2016).
Marengo, J. A. & Espinoza, J. C. Extreme seasonal droughts and floods in Amazonia: causes, trends and impacts. Int. J. Climatol. 36, 1033–1050 (2016).
Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).
Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Challinor, A. J. & Wheeler, T. R. Crop yield reduction in the tropics under climate change: processes and uncertainties. Agric. Meteorol. 148, 343–356 (2008).
Bates, D. et al. lme4. R package version (2012).
Barton, K. MuMIn: Multi-model inference. R package version 1.0.0 (2009).
Arvor, D., Dubreuil, V., Ronchail, J., Simões, M. & Funatsu, B. M. Spatial patterns of rainfall regimes related to levels of double cropping agriculture systems in Mato Grosso (Brazil). Int. J. Climatol. 34, 2622–2633 (2014).
Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).
Brill, F., Passuni Pineda, S., Espichán Cuya, B. & Kreibich, H. A data-mining approach towards damage modelling for El Niño events in Peru. Geomat. Nat. Hazards Risk 11, 1966–1990 (2020).
Rattis, L. ludmilarattis/effect-of-climate-on--agriculture: Rattis_etal_NCC_2021. Zenodo https://zenodo.org/badge/latestdoi/271879616 (2021).
Malhi, Y. et al. Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proc. Natl Acad. Sci. USA 106, 20610–20615 (2009).
Castanho, A. D. A. et al. Potential shifts in the aboveground biomass and physiognomy of a seasonally dry tropical forest in a changing climate. Environ. Res. Lett. 15, 034053 (2020).
Allen, R. G. et al. The ASCE Standardized Reference Evapotranspiration Equation (American Society of Civil Engineers, 2005).
IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer, L. A.) (IPCC, 2014).
Koutroulis, A. G., Grillakis, M. G., Tsanis, I. K. & Papadimitriou, L. Evaluation of precipitation and temperature simulation performance of the CMIP3 and CMIP5 historical experiments. Clim. Dyn. 47, 1881–1898 (2016).
Análise Territorial para o Desenvolvimento da Agricultura Irrigada no Brasil (Ministério da Integração Nacional, 2014).
Acknowledgements
This work was supported through funding from the NSF INFEWS/T1 (#1739724), CNPq/ANA (#446412/2015-5), MCTIC/CNPq – NEXUS 19/2017 (#441463/2017-7) and CNPq/PELD (#441703/2016-0). We thank B. Rebelatto, A. Ribeiro, N. Muller and S. Davis for discussions and P. Lefebvre and C. Churchill for advice in mapping techniques. We also acknowledge that the study area encompasses the traditional land of Indigenous people of more than 80 different ethnicities.
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L.R., P.M.B., M.N.M. and M.T.C. conceived of the presented ideas and wrote the manuscript; S.A.S. investigated the land-use transition patterns and helped to revise the findings of this work. L.R. performed the analytic calculations with support from A.D.A.C., N.Q.C., E.Q.M. and D.V.S. All authors contributed in the final version of the manuscript.
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Peer review information Nature Climate Change thanks Marcelo Galdos, Guiling Wang, Anita Wreford 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 Location of the study area relative to Brazilian biomes.
The study region location relative to the Legal Amazon and Cerrado biome. Approximately 68% of the study region falls within the Legal Amazon. The region spans the Cerrado (67.2%), Amazon (~27%) (the Legal Amazon and the Cerrado biome have a 761 km2 overlap), Pantanal (3.2%), Caatinga (1.7%), and Atlantic Forest (1.3%) biomes.
Extended Data Fig. 2 Predicting crop yields as a function of monthly weather conditions.
Soybean and maize second crop yields as a function of precipitation and vapor pressure deficit at early stages of development. We predicted soybean (A B) and maize second crop yields as a function of VPD and precipitation in Mato Grosso (A and C) and Cerrado (B and D). The effects of monthly weather were tested in drought (on the left of each panel) and in non-drought (on the right of each panel) conditions.
Extended Data Fig. 3 Edaphoclimatic conditions in areas where the agriculture has either intensified and de-intensified.
Edaphoclimatic conditions in areas where the agriculture has either intensified and de-intensified. Precipitation (A), VPD (B) and Sand Content (c) of the growing season. In red, double-cropping to fallowing; in brown, double- to single-cropping and in orange,from signle- to double-cropping. Only transitions with a consistency of two years were considered. We have tested if the groups presented differences among their means using Kruskal-Wallis test and present it for each year.
Extended Data Fig. 4 Predicting double-cropping occurrence as a function of monthly weather conditions.
Predicted values of changes in double-cropping occurrence as a function of A) the observed precipitation and VPD, B) using year as a random term in the agricultural plots from 2002 to 2016. For each 100 mm decrease in the total precipitation the chances of double-cropping decreased by 2%. For each 1 KPa increase in VPD, the chances of double cropping decreased by 30%.
Extended Data Fig. 5 Climate envelope for the Amazon Cerrado region according to RCP 4.5 W/m.
The climate envelope for the last 50 years in the Amazon Cerrado Region based on past observed data: 1970–1979 (solid line in black); 2000:2009 (solid line in pink); and on future modeled data (CMIP5—RCP 4.5 W m−2): 2020–2029 (dotted line in salmon) and 2060–2069 (dotted line in purple). Each pixel on the maps (on the left) correspond to one point in the scatterplot (on the right). The colors on map are the same as the point falls on the background of the scatter plot. The convex hulls delimit the climate conditions in the represented decade.
Extended Data Fig. 6 Magnitude of change of climatic conditions of each agricultural plot in the Amazon Cerrado Region.
Quantifying the magnitude of change of climatic conditions of each agricultural plot in the Amazon Cerrado Region. Panels A, C and E show the distribution of distance in mm each agricultural plot had changed from 1970 to 2010 (A), from 1970 to 2030 (C) and from 1970 to 2060 (E). The direction of those changes are shown in panels B (1970–2010), D (1970–2030) and F (1970–2060). All agricultural plots became warmer. In yellow those moving to warmer and wetter conditions. In red those moving towards warmer and drier conditions. one point in the scatterplot (on the right).
Supplementary information
Supplementary Information
Supplementary information on study region, Description of agriculture expansion into extreme conditions, Tables 1–5, Figs. 1–4 and Discussion.
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Rattis, L., Brando, P.M., Macedo, M.N. et al. Climatic limit for agriculture in Brazil. Nat. Clim. Chang. 11, 1098–1104 (2021). https://doi.org/10.1038/s41558-021-01214-3
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DOI: https://doi.org/10.1038/s41558-021-01214-3
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