Thank you for visiting Nature. The browser version you are using has limited support for CSS. For the best experience, we recommend that you use a newer version of the browser (or turn off the compatibility mode in Internet Explorer). At the same time, to ensure continued support, we will display sites without styles and JavaScript.
Natural Climate Change (2021) Cite this article
Brazil's leading position in soybean and corn production depends on the predictable rainfall of the Amazon-Cerrado agricultural frontier. Here, we assess whether the expansion and intensification of agriculture in the region is approaching the climatic limit of rainfed production. We show that in the early stages of crop development, yields decline in years with abnormally low rainfall or high drought levels-this pattern has been observed in both rain-fed and irrigated areas. Although agricultural expansion and intensification have increased over time, the dry and hot weather during the drought event slowed their growth. Recent regional warming and dryness have pushed 28% of the existing agricultural land outside the optimal climate space. We predict that by 2030, 51% of the region's agriculture will move out of this climate space, and by 2060 it will reach 74%. Although agronomic adaptation strategies may mitigate some of these effects, maintaining native vegetation is a key part of the solution to stabilize the regional climate.
Get full access to the journal for 1 year
All prices are net prices. VAT will be added at checkout later. Tax calculation will be done at checkout.
Get time-limited or full article access on ReadCube.
All prices are net prices.
All the original climate data sets analyzed in this study can be found in the Google Earth Engine repository. The original land-use conversion data supporting the findings of this section are from published sources8,21. These data are used under the permission of the current research and can be obtained from the corresponding author upon reasonable request and with the permission of SAS (sspera@richmond.edu).
The processed and extracted variables used directly in the analysis are available on GitHub (https://github.com/ludmilarattis/effect-of-climate-on--agriculture/tree/Agriculture_Climate). Zenodo also provides scripts and data sets for analyzing the impact of climate on agricultural production, land use transition, and climate space at https://doi.org/10.5281/zenodo.5363671.
Brazil. U.S. Department of Agriculture 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 Graos. Companhia Nacional de Abastecimento https://www.conab.gov.br/info-agro/safras/graos/boletim-da-safra-de-graos (2020).
Projeções do Agronegócio: Brazil 2017/18 and 2027/28 Projeções de Longo Prazo (Ministry of Agriculture, Pecuária e Abastecimento, 2018).
Atlas Irrigação: Uso da Água na Agricultura Irrigada (Agência Nacional de Águas, 2017).
Costa, MH etc. The climate risks faced by Amazon agriculture show the reasons for protecting local ecosystems. front. Ecology. environment. 17, 584–590 (2019).
Fu, R. etc. The length of the dry season in southern Amazonia has increased in recent decades and its impact on future climate predictions. Process National Academy of Sciences. science. United States 110, 18110–18115 (2013).
Spera, SA, Galford, GL, Coe, MT, Macedo, MN & Mustard, JF Land use change affects water recycling in Brazil's last agricultural frontier. Sphere. Change biology. 22, 3405–3413 (2016).
Abrahão, GM & Costa, MH The evolution of rainfall and photoperiod constraints during the soybean growing season in Brazil: the rise (and possible decline) of the dual crop system. agriculture. meteorological. 256-257, 32-45 (2018).
Silverio, DV, etc. Agricultural expansion dominates climate change in the southeastern Amazon: neglected non-greenhouse gas forcing. environment. Res. Wright. 10, 104015 (2015).
Barkhordarian, A., Saatchi, SS, Behrangi, A., Loikith, PC & Mechoso, CR The recent systematic increase in vapor pressure difference in tropical South America. science. Representative 9, 15331 (2019).
Barkhordarian, A., von Storch, H., Zorita, E., Loikith, PC & Mechoso, CR have observed man-made causes of warming in northern South America. climate. Dyne. 51, 1901-1914 (2018).
Leite-Filho, AT, Costa, MH & Fu, R. The rainy season in the southern Amazon: the role of deforestation and its interaction with large-scale mechanisms. internationality. J. Climatology. 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 (Ministry of Agriculture, 2015).
Rashid, Ma et al. The effect of high and low VPD heat waves on wheat photosynthesis and its recovery. Environment. Experience. robot. 147, 138–146 (2018).
Lobel, DB, etc. With the increase in corn production in the Midwestern United States, the sensitivity to drought has increased. Science 344, 516–519 (2014).
Fletcher, AL, Sinclair, TR & Allen, LH Transpiration response to well watered "slow wilting" soybeans and insufficient vapor pressure in commercial soybeans. environment. Experience. robot. 61, 145–151 (2007).
Bunce, JA The comparative response of leaf electrical conductivity to the humidity of a single attached leaf. J. Experience. robot. 32, 629–634 (1981).
Kiniry, J. et al. The radiation use efficiency response to insufficient vapor pressure of corn and sorghum. Field crop research. 56, 265–270 (1998).
Spella, SA etc. The recent planting frequency, expansion, and abandoned land in Mato Grosso State, Brazil have selective land characteristics. environment. Res. Wright. 9, 064010 (2014).
Dias, LCP, Pimenta, FM, Santos, AB, Costa, MH & Ladle, RJ The land use, expansion and intensification model of Brazilian agriculture. Sphere. Change biology. 22, 2887–2903 (2016).
Cohn, AS, Vanwey, LK, Spera, SA & Mustard, JF The response of crop frequency and area to climate variability may exceed the yield response. Nat. climate. Change 6, 601–604 (2016).
Morton, DC, etc. Re-evaluate the suitability estimate based on the dynamics of farmland expansion in the Brazilian Amazon. Sphere. environment. Change 37, 92–101 (2016).
Duursma, RA etc. The peak response of transpiration rate to insufficient vapor pressure under field conditions can be explained by the optimal temperature for photosynthesis. agriculture. meteorological. 189–190, 2–10 (2014).
After reclaiming the land in Spera, SA, Winter, JM & Partridge, TF, the corn production in Brazil was negatively affected by the climate. Nat. maintain. 3, 845–852 (2020).
Cirino, PH, Féres, JG, Braga, MJ & Reis, E. Assess the impact of ENSO-related weather on Brazilian agriculture. Process economy. finance. 24, 146–155 (2015).
Pereira, PAA, Martha, GB, Santana, CA & Alves, E. The development of Brazilian agriculture: future technological challenges and opportunities. agriculture. food safety. 1, 4 (2012).
Marengo, JA and Bernasconi, M. Regional differences in drought/drought conditions in northeastern Brazil: current status and future predictions. Climate change 129, 103–115 (2015).
Naylor, RL Energy and resource constraints of intensive agricultural production. Anu. Priest energy environment. 21, 99–123 (1996).
Getirana, A. Detected extreme water shortage in Brazil from space. J. Hydrometeorol. 17, 591–599 (2016).
Lathuillière, MJ, Coe, MT & Johnson, MS Review of green and blue water resources and their trade-offs for future agricultural production in the Amazon Basin: What does irrigated agriculture mean for the Amazon River? Hydraulic. Earth system. science. 20, 2179–2194 (2016).
Dobrovolski, R. and Rattis, L. Floods in Brazil: the dangers that depend on what you ignore. Nat. keep. 13, 80–83 (2015).
Da Silva, AL etc. Water consumption at the agricultural frontier in western Bahia and its contribution to flow reduction: revisiting the Brazilian Cerrado debate. Water 13, 1054 (2021).
Pousa, R. etc. Climate change and intense irrigation growth in western Bahia, Brazil: an urgent need for hydroclimatic monitoring. Water 11, 933 (2019).
Ort, DR & Long, SP corn belt yield restrictions. Science 344, 484–485 (2014).
de Bossoreille de Ribou, S., Douam, F., Hamant, O., Frohlich, MW & Negrutiu, I. Plant science and agricultural productivity: Why do we reach the production limit? Plant science. 210, 159–176 (2013).
Long, SP & Ort, DR is more than just heating: crops and global change. curry. opinion. Plant Biology 13, 240–247 (2010).
Pommer, CV and Barbosa, W. The impact of breeding on fruit production in Brazil's warm climate. Priest bra. decisive. 31, 612–634 (2009).
Lenka, NK etc. The effects of carbon dioxide and temperature increase on evapotranspiration and water use efficiency of soybean crops are affected by different nitrogen levels. agriculture. Water management. 230, 105936 (2020).
Soares, WR, Marengo, JA and Nobre, CA Brazil's climate change risk assessment and probability assessment of Brazilian warming (Nobre, C. ed.) 7-30 (Springer, 2019); https://doi.org /10.1007/978-3-319-92881-4_2
Schwalm, CR, Glendon, S. & Duffy, PB RCP8.5 Tracks cumulative carbon dioxide emissions. Process National Academy of Sciences. science. United States 117, 19656–19657 (2020).
Schwalm, CR, Glendon, S. & Duffy, PB responded to Hausfather and Peters: RCP8.5 is neither problematic nor misleading. Process National Academy of Sciences. science. United States 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-ambientaimass/15842-bioentais .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/inform_acoes_tec250
Campos, J., de, O. & Chaves, HML Tendências e variabilidades nas séries históricas de precipitação mensal e anual no bioma Cerrado no período 1977-2010. Priest bra. meteorological. 35, 157–169 (2020).
Debortoli, NS etc. Rainfall patterns in the southern Amazon: chronological order (1971-2010). Climate Change 132, 251–264 (2015).
Oliveira, PTS etc. Trends in the composition of water balance in the Cerrado region, Brazil. Water resources. Reservoir 50, 7100-7114 (2014).
Panisett, JS etc. A comparative model of extreme drought events in the Amazon basin in 2005, 2010, and 2015. internationality. J. Climatology. 38, 1096–1104 (2018).
Cai, W. etc. Due to the warming of the greenhouse, the frequency of extreme El Niño events has increased. Nat. climate. Amendment 4, 111–116 (2014).
Jiménez-Muñoz, JC, etc. The Amazon rainforest experienced record-breaking warming and extreme drought during the 2015-2016 El Niño phenomenon. science. Representative 6, 33130 (2016).
Marengo, JA and Espinoza, JC Extreme seasonal droughts and floods in the Amazon: causes, trends, and effects. internationality. J. Climatology. 36, 1033–1050 (2016).
Funk, C. etc. Climate hazards with stations—infrared precipitation—a new environmental record for monitoring extreme events. science. Data 2, 150066 (2015).
Abatzoglou, JT, Dobrowski, SZ, Parks, SA and Hegewisch, KC TerraClimate, a high-resolution global dataset of monthly climate and climate water balance from 1958 to 2015. science. Data 5, 170191 (2018).
Harris, I., Osborn, TJ, Jones, P. & Lister, D. CRU TS The fourth edition of the monthly high-resolution grid multivariate climate dataset. science. Data 7, 109 (2020).
Gorelick, N. etc. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing environment. 202, 18-27 (2017).
R Core Team R: The Language and Environment of Statistical Computing (R Statistical Computing Foundation, 2019).
Challinor, AJ & Wheeler, TR Tropical crop yield reduction under climate change: process and uncertainty. agriculture. meteorological. 148, 343–356 (2008).
Bates, D. et al. lme4. R package version (2012).
Barton, K. MuMIn: Multi-model reasoning. R package version 1.0.0 (2009).
Arvor, D., Dubreuil, V., Ronchail, J., Simões, M. & Funatsu, BM The spatial pattern of rainfall conditions related to the level of the dual crop agricultural system in Mato Grosso State (Brazil). internationality. J. Climatology. 34, 2622–2633 (2014).
Hengl, T. etc. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).
Brill, F., Passuni Pineda, S., Espichán Cuya, B. and Kreibich, H. Data mining methods for damage modeling of El Niño events in Peru. carpet. Nat. Hazard Risk 11, 1966–1990 (2020).
Rattis, L. ludmilarattis/The impact of climate on agriculture: Rattis_etal_NCC_2021. Zenodo https://zenodo.org/badge/latestdoi/271879616 (2021).
Malhi, Y. et al. Explore the possibility and mechanism of climate change leading to the death of the Amazon rainforest. Process National Academy of Sciences. science. United States 106, 20610–20615 (2009).
Castagno, ADA, etc. Potential changes in above-ground biomass and landforms in seasonal dry tropical forests during climate change. environment. Res. Wright. 15, 034053 (2020).
Allen, RG, etc. ASCE standardized reference evapotranspiration equation (American Society of Civil Engineers, 2005).
IPCC Climate Change 2014: Comprehensive Report (eds Core Writing Team, Pachauri, RK & Meyer, LA) (IPCC, 2014).
Koutroulis, AG, Grillakis, MG, Tsanis, IK & Papadimitriou, L. CMIP3 and CMIP5 historical experiments precipitation and temperature simulation performance evaluation. climate. Dyne. 47, 1881–1898 (2016).
Análise Territorial para o Desenvolvimento da Agricultura Irrigada no Brasil (Ministério da Integração Nacional, 2014).
This work was supported by 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 their discussions, and P. Lefebvre and C. Churchill for their suggestions on drawing techniques. We also acknowledge that the study area covers the traditional lands of more than 80 indigenous peoples of different races.
Woodwell Climate Research Center, Falmouth, Massachusetts, USA
Ludmila Rattis, Paulo M. Brando, Marcia N. Macedo, Andrea DA Castanho and Michael T. Coe
Instituto de Pesquisa Ambiental da Amazônia, Canarana, Brazil
Ludmila Rattis, Paulo M. Brando, Marcia N. Macedo, Nathane Q. Costa, and Michael T. Coe
Department of Earth System Science, University of California, Irvine, Irvine, California, USA
Department of Geography and Environment, University of Richmond, Richmond, Virginia, USA
Universidade do Estado do Mato Grosso, Nova Savantina, Brazil
Universidade Federal Rural da Amazônia, Capitão Poço, Brazil
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
LR, PMB, MNM, and MTC conceived the proposed ideas and wrote manuscripts; SAS investigated land use transition patterns and helped revise the results of this work. LR carried out analysis and calculation with the support of ADAC, NQC, EQM and DVS. All authors contributed to the final version of the manuscript.
The author declares no competing interests.
Peer review information Nature Climate Change thanks Marcelo Galdos, Guiling Wang, Anita Wreford and other anonymous reviewers for their contributions to the peer review of this work.
The publisher states that Springer Nature remains neutral on the jurisdiction claims of published maps and agency affiliates.
The location of the study area relative to the legal Amazon and Cerrado biomes. Approximately 68% of the study area is legal Amazon. The area spans the Cerrado (67.2%), Amazon (~27%) (the legal Amazon and Cerrado biomes have an overlap of 761 square kilometers), Pantanal (3.2%), Catinga (1.7%) ) And the Atlantic Forest (1.3%) biome.
Soybean and corn production in the second season is a function of precipitation and insufficient vapor pressure in the early stages of development. We forecast soybean (AB) and corn production in the second season as a function of the VPD and precipitation for Mato Grosso (A and C) and Cerrado (B and D). The impact of monthly weather was tested under drought (left side of each panel) and non-arid (right side of each panel) conditions.
Soil environmental conditions in intensive and non-intensive agricultural areas. Rainfall (A), VPD (B) and sand content (c) during the growing season. Red, double-season fallow; brown, double-season to single-season and orange, from single-season to double-season. Only consider transitions with two-year consistency. We have used the Kruskal-Wallis test to test whether the groups show the difference between their averages, and show it every year.
The predicted value of the double-season occurrence rate change in the farmland from 2002 to 2016 is used as A) Observed precipitation and VPD, and B) Using the year as a function of the random term. For every 100 mm reduction in total precipitation, a reduction of 2% in both seasons. For every 1 KPa increase in VPD, the probability of double harvesting will decrease by 30%.
The climate envelope of the Amazon Cerrado region in the past 50 years based on past observation data: 1970-1979 (solid black line); 2000:2009 (solid pink line); and future modeling data (CMIP5-RCP 4.5 W m-2): 2020-2029 (salmon dashed line) and 2060-2069 (purple dashed line). Each pixel on the map (left side) corresponds to a point in the scatter plot (right side). The colors on the map are the same as the points that fall on the background of the scatter chart. The convex hull delineates the climatic conditions representing ten years.
Quantify the change in climatic conditions of each farmland in the Amazonian Cerrado region. Figures A, C and E show the distance distribution (in millimeters) of each farmland from 1970 to 2010 (A), from 1970 to 2030 (C), and from 1970 to 2060 (E) . The directions of these changes are shown in panels B (1970-2010), D (1970-2030), and F (1970-2060). All the farmland has become warmer. The yellow ones are those who move to warm and humid conditions. The red ones are those who are moving towards warmer and drier conditions. One point in the scatter chart (right side).
Supplementary information about the study area, descriptions of agricultural expansion to extreme conditions, table 1-5, figure. 1-4 and discussion.
Rattis, L., Brando, PM, Macedo, MN, etc. The climatic limits of Brazilian agriculture. Nat. climate. open. (2021). https://doi.org/10.1038/s41558-021-01214-3
DOI: https://doi.org/10.1038/s41558-021-01214-3
Anyone you share the following link with can read this content:
Sorry, there is currently no shareable link in this article.
Provided by Springer Nature SharedIt content sharing program
Nature Climate Change (Nat. Clim. Chang.) ISSN 1758-6798 (online) ISSN 1758-678X (print)