Regional Environmental Change

, Volume 18, Issue 3, pp 873–883 | Cite as

Climate change impact on the potential yield of Arabica coffee in southeast Brazil

  • Priscila da Silva Tavares
  • Angélica Giarolla
  • Sin Chan Chou
  • Adan Juliano de Paula Silva
  • André de Arruda Lyra
Original Article


The Intergovernmental Panel on Climate Change (IPCC) projections of global mean temperature rises are worrisome for coffee crop due to the intolerance of the Arabica species to high air temperature variations. The crop has a large participation in the Brazilian trade balance; therefore, in this study, the impacts of climate change on the potential yield of Arabica coffee (Coffea arabica L.) were assessed in the areas of Southeast Brazil in future climate change scenarios. Simulations of the Eta Regional Climate Model at 5-km resolution used in this study were generated from a second dynamic downscaling of the HadGEM2-ES model runs. The projections adopted two scenarios of greenhouse gas concentration, the RCP4.5 and RCP8.5, and considered the period 2011–2100. The projections indicated a large reduction of about 20 to 60% of the areas currently suitable for coffee cultivation in Southeast Brazil. In the RCP8.5 scenario, at the end of century, coffee cultivation is suitable only in elevated mountain areas, which would pose difficulties to farming management due to the operation of agricultural machinery in mountain areas. In addition, coffee cultivation in these regions could produce environmental impacts in the remnant Brazilian Atlantic Forest. Areas of high climatic risk increase due to temperature increase. The projections showed that the potential yield could be reduced by about 25% by the end of the twenty-first century. These results of potential coffee yield in the future climate indicate a need for adaptation studies of Arabica coffee cultivation.


Climate scenarios Agroclimatic zoning Arabica coffee Eta model Brazil 



The authors thank the São Paulo Research Foundation (FAPESP) for the grant 2014/00192-0, Brazilian National Council for Scientific and Technological Development (CNPq) for the grants 457874/2014-7 and 308035/2013-5, the MCTI/UNDP for the grant BRA/10/G32, and the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES) the project INCT for Climate Change (MCTI/CNPq).


  1. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration—guidelines for computing crop water requirements. FAO irrigation and drainage paper 56. FAO. Accessed 31 March 2015
  2. Assad ED, Pinto HS, Junior JZ, Ávila AD (2004) Impacto das mudanças climáticas no zoneamento agroclimático do café no Brasil. Pesq Agrop Brasileira 39(11):1057–1064. CrossRefGoogle Scholar
  3. Baliza DP, Oliveira AL, Dias RAA, Guimarães RJ, Barbosa CR (2013) Antecipação da produção e desenvolvimento da lavoura cafeeira implantada com diferentes tipos de mudas. Coffee Sci 8(1):61–68Google Scholar
  4. Betts AK, Miller MJ (1986) A new convective adjustment scheme. Part II: single column tests using GATE wave, BOMEX, ATEX and arctic air-mass data sets. Quarterly J R Meteorol Soc 112(473):693–709. Google Scholar
  5. Bragança R, dos Santos AR, de Souza EF, de Carvalho AJC, Luppi ASL, da Silva RG (2016) Impactos das mudanças climáticas no zoneamento agroclimatológico do café arábica no Espírito Santo. Revista Agro@mbiente On-line 10(1):77–82.  10.18227/1982-8470ragro.v10i1.2809 CrossRefGoogle Scholar
  6. Bunn C (2015) Modeling the climate change impacts on global coffee production. Doctoral dissertation, Humboldt-Universität zu Berlin, Lebenswissenschaftliche FakultätGoogle Scholar
  7. Bunn C, Läderach P, Rivera OO, Kirschke D (2015) A bitter cup: climate change profile of global production of Arabica and Robusta coffee. Clim Chang 129(1–2):89–101. CrossRefGoogle Scholar
  8. Camargo, AP (1977) Zoneamento de aptidão climática para a cafeicultura de arábica e robusta no Brasil. In: Fundação IBGE, recursos, meio ambiente e poluição. p.68–76Google Scholar
  9. Carvalho LG, Rios GFA, Miranda WL, Neto PC (2011) Evapotranspiração de referência: uma abordagem atual de diferentes métodos de estimativa. Pesquisa Agropecuária Tropical 41(3):456–465. Google Scholar
  10. Chou SC, Marengo JA, Lyra AA, Sueiro G, Pesquero JF, Alves LM, Kay G, Betts R, Chagas DJ, Gomes JL, Bustamante JF, Tavares P (2012) Downscaling of South America present climate driven by 4-member HadCM3 runs. Clim Dyn 38(3–4):635–653. CrossRefGoogle Scholar
  11. Chou SC, Lyra A, Mourão C, Dereczynski C, Pilotto I, Gomes J, Bustamante J, Tavares P, Silva A, Rodrigues D, Campos D, Chagas D, Sueiro G, Siqueira G, Marengo J (2014a) Assessment of climate change over South America under RCP 4.5 and 8.5 downscaling scenarios. Am J Clim Chang 3(5):512–525. CrossRefGoogle Scholar
  12. Chou SC, Lyra A, Mourão C, Dereczynski C, Pilotto I, Gomes J, Bustamante J, Tavares P, Silva A, Rodrigues D, Campos D, Chagas D, Sueiro G, Siqueira G, Nobre P, Marengo J (2014b) Evaluation of the eta simulations nested in three global climate models. Am J Clim Chang 3(5):438–454. CrossRefGoogle Scholar
  13. Companhia Nacional de Abastecimento (CONAB) (2015) Acompanhamento da safra brasileira: café. Accessed 22 April 2015
  14. DaMatta FM, Ronchi CP, Maestri M, Barros RS (2007) Ecophysiology of coffee growth and production. Braz J Plant Physiol 19(4):485–510. CrossRefGoogle Scholar
  15. Ek MB, Mitchell KE, Lin Y, Rogers E, Grummann P, Koren V, Gayno G, Tarpley JD (2003) Implementation of Noah land-surface model advances in the NCEP operational mesoscale eta model. J Geophys Res 108(D22):8851–8867. CrossRefGoogle Scholar
  16. Eugenio FC, Peluzio TMO, Pereira AAB, dos Santos AR, Peluzio JBE, Bragança R, Fiedler NC, Paula EDSO (2014) Zoning agroclimatological Coffea canephora for Espírito Santo by spatial interpolation. Coffee Sci 9(3):319–328Google Scholar
  17. Fels SB, Schwarzkopf MD (1975) The simplified exchange approximation: a new method for radiative transfer calculations. J Atmos Sci 32(7):1475–1488.<1475:TSEAAN>2.0.CO;2 CrossRefGoogle Scholar
  18. Instituto Brasileiro de Geografia e Estatística (IBGE) (2016). PAM: Produção Agrícola Municipal. Accessed 10 May 2016
  19. Intergovernmental Panel on Climate Change (IPCC) (2013) Twelfth session of working group I. Summary for Policymakers. Accessed 30 October 2013
  20. International Coffee Organization (ICO) (2015) Catalog coffee.é. Accessed 10 May 2015
  21. International Coffee Organization (ICO) (2017) Statistical catalog—historical data. Accessed 05 January 2017
  22. Janjić ZI (1984) Nonlinear advection schemes and energy cascade on semi-staggered grids. Mon Weather Rev 112(6):1234–1245.<1234:NASAEC>2.0.CO;2 CrossRefGoogle Scholar
  23. Janjić ZI (1994) The step-mountain eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon Weather Rev 122(5):927–945.<0927:TSMECM>2.0.CO;2 CrossRefGoogle Scholar
  24. Karlsson KG, Riihelä A, Müller R, Meirink JF, Sedlar J, Stengel M, Lockhoff M, Trentmann J, Kaspar F, Hollmann R, Wolters E (2012) CLARA-A1: CM SAF CLouds, albedo and RAdiation dataset from AVHRR data–edition 1–monthly means/daily means/pentad means/monthly histograms. Satell Appl Facil Climate Monit.
  25. Kobayashi ES (2007) Consumo de Água e Produtividade de Cafeeiros Arábica na Região de Mococa, SP. Dissertation (MSc in tropical and subtropical agriculture), Instituto Agronômico de CampinasGoogle Scholar
  26. Lacis AA, Hansen JE (1974) A parameterization for the absorption of solar radiation in the earth’s atmosphere. J Atmos Sci 31(1):118–133.<0118:APFTAO>2.0.CO;2 CrossRefGoogle Scholar
  27. Luppi ASL, Santos AR, Eugênio FC, Bragança R, Pelúzio JBE, Dalfi RL, Silva RG (2014) Metodologia para Classificação de Zoneamento Agroclimatológico. Revista Brasileira de Climatologia 15.
  28. Lyra AA, Tavares PS, Chou SC, Sueiro G, Dereczynski C, Sonderman M, Silva A, Marengo J, Giarolla A (2017). Climate change projections over three metropolitan regions in Southeast Brazil using the non-hydrostatic eta regional climate model at 5-km resolution. Theor Appl Climatology Accepted doi:
  29. Marengo JA, Chou SC, Kay G, Alves LM, Pesquero JF, Soares WR, Santos DC, Lyra AA, Sueiro G, Betts R, Chagas DJ, Gomes JL, Bustamante JF, Tavares P (2012) Development of regional future climate change scenarios in South America using the eta CPTEC/HadCM3 climate change projections: climatology and regional analyses for the Amazon, São Francisco and the Paraná River basins. Clim Dyn 38(9–10):1829–1848. CrossRefGoogle Scholar
  30. Matiello JB (1991) O café: do cultivo ao consumo. Editora Globo. Farmer’s Collection: Grain. Globo Rural Publications, São PauloGoogle Scholar
  31. MCTI (2016) Third National Communication of Brazil to the United Nations framework convention on climate change—volume II/Ministry of Science, Technology and Innovation. Brasília: Ministério da Ciência, Tecnologiae Inovação, 2016. 229 p. ISBN: 978-85-88063-24-2Google Scholar
  32. Meireles EJL, Volpato MML, Alves HMR, Vieira TGC (2007) Zoneamento agroclimático: um estudo de caso para o café. Informe Agropecuário Belo Horizonte 28(241):50–57Google Scholar
  33. Meireles EJL, Camargo M, Pezzopane JRM, Thomaziello R, Fahl JI, Bardin L, Santos JCF, Japiassú LB, Garcia AWR, Miguel AE, Ferreira RA (2009) Fenologia do cafeeiro: condições agrometeorológicas e balanço hídrico do ano agrícola 2004–2005. Embrapa Informação Tecnológica, Embrapa Café, MAPA, Brasília (Document 5). Accessed 10 Feb 2012
  34. Mellor GL, Yamada T (1982) Development of a turbulence closure model for geophysical fluid problems. Rev Geophys 20(4):851–875. CrossRefGoogle Scholar
  35. Mendonça PV (1958) Sobre o novo método de balanço hidrológico do solo de Thornthwaite-Mather. In Congresso Luso-espanhol para o progresso das ciências 24 (Proceedings): Madrid. p. 271–282Google Scholar
  36. Mesinger F, Chou SC, Gomes JL, Jovic D, Lyra AA, Bustamante JF, Bastos PR, Lazic L, Morelli S, Ristic I (2012) An upgraded version of the eta model. Meteorog Atmos Phys 116(3–4):63–79. CrossRefGoogle Scholar
  37. Ministério da Agricultura, Pecuária e Abastecimento (MAPA) (2013). Crop catalog/coffee. Accessed 22 April 2013
  38. Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, Van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovic N, Riahi K, Smith SJ, Stouffer RJ, Thomson AM, Weyant JP, Wilbanks TJ (2010) The next generation of scenarios for climate change research and assessment. Nature 463(7282):747–756. CrossRefGoogle Scholar
  39. Paulson CA (1970) The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J Appl Meteorol 9(6):857–861.<0857:TMROWS>2.0.CO;2 CrossRefGoogle Scholar
  40. Pereira AR, Camargo AP, Camargo MBP (2008) Agrometeorologia de cafezais no Brasil. Instituto Agronômico de Campinas (IAC). 127 p. ISBN: 978-85-85564-19-3Google Scholar
  41. Pesquero JF, Chou SC, Nobre CA, Marengo JA (2010) Climate downscaling over South America for 1961–1970 using the eta model. Theor Appl Climatol 99(1–2):75–93. CrossRefGoogle Scholar
  42. Pinto HS, Assad ED, Zullo Junio RJ, Evangelista SRM, Otavian AF, Ávila AMH, Evangelista BA, Marin F, Macedo Junior C, Pellegrino G, Coltri PP, Coral G (2008) Aquecimento global e a nova geografia da produção agrícola no Brasil. Embrapa, São Paulo Accessed 09 February 2012Google Scholar
  43. Ranjitkar S, Sujakhu NM, Merz J, Kindt R, Xu J, Matin MA, Ali M, Zomer RJ (2016) Suitability analysis and projected climate change impact on banana and coffee production zones in Nepal. PloS One 11(9):e0163916. CrossRefGoogle Scholar
  44. Saha S, Moorthi S, Pan HL, Wu X, Wang J, Nadiga S, Tripp P, Kistler R, Woollen J, Behringer D, Liu H, Stokes D, Grumbine R, Gayno G, Wang J, Chuang YH, Juang HM, Sela J, Iredell M, Treadon R, Kleist D, Delst PV, Keyser D, Derber J, Ek M, Wei JM, Yang R, Lord S, Dool HVD, Kumar A, Wang W, Long C, Chelliah M, Xue Y, Huang B, Schemm JK, Ebisuzaki W, Lin R, Xie P, Chen M, Zhou S, Higgins W, Zou CZ, Liu Q, Chen Y, Han Y, Cucurull L, Reynolds WR, Rutledge G, Goldberg M (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91(8):1015–1057. CrossRefGoogle Scholar
  45. Sediyama GC (1996) Estimativa da evapotranspiração: histórico, evolução e análise crítica. Revista Brasileira de Agrometeorologia 4(1):1–12 ISSN 0104-1347Google Scholar
  46. Souza VCO, Vieira TGC, Volpato MML, Alves HMR (2012) Espacialização e dinâmica da cafeicultura mineira entre 1990 e 2008, utilizando técnicas de geoprocessamento. Coffee Sci 7(2):122–134Google Scholar
  47. Thomaziello RA, Fazuoli LC, Pezzopane, JRM, Fahl JI, Carelli MLC (2000) Café arábica: cultura e técnicas de produção. 1ed. Campinas: Instituto Agronômico. 82p. Boletim Técnico n.187Google Scholar
  48. Thornthwaite CW, Mather JR (1957). Instructions and tables for computing potential evapotranspiration and the water balance. Drexel Institute of Technology, Laboratory of Climatology, Centerton, New Jersey. Publications in climatology, vol. 10, no. 3Google Scholar
  49. Wintgens JN (2004) Coffee: growing, processing, sustainable production. A guidebook for growers, processors, traders, and researchers. 2nd edn. WILEY-VCH. 1040 p. ISBN: 978-3-527-33253-3Google Scholar
  50. Zhao Q, Black TL, Baldwin ME (1997) Implementation of the cloud prediction scheme in the eta model at NCEP. Weather Forecast 12(3):697–712.<0697:IOTCPS>2.0.CO;2 CrossRefGoogle Scholar
  51. Zullo J, Pinto HS, Assad ED (2006) Impact assessment study of climate change on agricultural zoning. Meteorol Appl 13(S1):69–80. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Priscila da Silva Tavares
    • 1
  • Angélica Giarolla
    • 2
  • Sin Chan Chou
    • 1
  • Adan Juliano de Paula Silva
    • 1
  • André de Arruda Lyra
    • 1
  1. 1.Center for Weather Forecasts and Climate Studies (CPTEC-INPE)Cachoeira PaulistaBrazil
  2. 2.Earth System Science Centre (CCST), National Institute for Space Research (INPE)São José dos CamposBrazil

Personalised recommendations