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A bitter cup: climate change profile of global production of Arabica and Robusta coffee


  • Christian Bunn


  • Peter Läderach
  • Oriana Ovalle Rivera
  • Dieter Kirschke


Coffee has proven to be highly sensitive to climate change. Because coffee plantations have a lifespan of about thirty years, the likely effects of future climates are already a concern. Forward-looking research on adaptation is therefore in high demand across the entire supply chain. In this paper we seek to project current and future climate suitability for coffee production (Coffea arabica and Coffea canephora) on a global scale. We used machine learning algorithms to derive functions of climatic suitability from a database of geo-referenced production locations. Use of several parameter combinations enhances the robustness of our analysis. The resulting multi-model ensemble suggests that higher temperatures may reduce yields of C. arabica, while C. canephora could suffer from increasing variability of intra-seasonal temperatures. Climate change will reduce the global area suitable for coffee by about 50 % across emission scenarios. Impacts are highest at low latitudes and low altitudes. Impacts at higher altitudes and higher latitudes are still negative but less pronounced. The world’s dominant production regions in Brazil and Vietnam may experience substantial reductions in area available for coffee. Some regions in East Africa and Asia may become more suitable, but these are partially in forested areas, which could pose a challenge to mitigation efforts. Copyright The Author(s) 2015

Suggested Citation

  • Christian Bunn & Peter Läderach & Oriana Ovalle Rivera & Dieter Kirschke, 2015. "A bitter cup: climate change profile of global production of Arabica and Robusta coffee," Climatic Change, Springer, vol. 129(1), pages 89-101, March.
  • Handle: RePEc:spr:climat:v:129:y:2015:i:1:p:89-101
    DOI: 10.1007/s10584-014-1306-x

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    References listed on IDEAS

    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    2. Karatzoglou, Alexandros & Meyer, David & Hornik, Kurt, 2006. "Support Vector Machines in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i09).
    3. VanDerWal, Jeremy & Shoo, Luke P. & Graham, Catherine & Williams, Stephen E., 2009. "Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?," Ecological Modelling, Elsevier, vol. 220(4), pages 589-594.
    4. Jurandir Zullo & Hilton Pinto & Eduardo Assad & Ana Ávila, 2011. "Potential for growing Arabica coffee in the extreme south of Brazil in a warmer world," Climatic Change, Springer, vol. 109(3), pages 535-548, December.
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    Cited by:

    1. Anika Reetsch & Kai Schwärzel & Christina Dornack & Shadrack Stephene & Karl-Heinz Feger, 2020. "Optimising Nutrient Cycles to Improve Food Security in Smallholder Farming Families—A Case Study from Banana-Coffee-Based Farming in the Kagera Region, NW Tanzania," Sustainability, MDPI, Open Access Journal, vol. 12(21), pages 1-34, November.
    2. Götz Schroth & Peter Läderach & Armando Isaac Martinez-Valle & Christian Bunn, 2017. "From site-level to regional adaptation planning for tropical commodities: cocoa in West Africa," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 22(6), pages 903-927, August.
    3. Rahn, Eric & Vaast, Philippe & Läderach, Peter & van Asten, Piet & Jassogne, Laurence & Ghazoul, Jaboury, 2018. "Exploring adaptation strategies of coffee production to climate change using a process-based model," Ecological Modelling, Elsevier, vol. 371(C), pages 76-89.
    4. Finlay MacGregor & Vasna Ramasar & Kimberly A. Nicholas, 2017. "Problems with Firm-Led Voluntary Sustainability Schemes: The Case of Direct Trade Coffee," Sustainability, MDPI, Open Access Journal, vol. 9(4), pages 1-25, April.
    5. Clay, Daniel C. & Bizoza, Alfred, 2018. "The Challenge To Sustainable Growth In Rwanda’S Coffee Sector," Feed the Future Innovation Lab for Food Security Policy Research Papers 275678, Michigan State University, Department of Agricultural, Food, and Resource Economics, Feed the Future Innovation Lab for Food Security (FSP).
    6. Stephen J. Fain & Maya Quiñones & Nora L. Álvarez-Berríos & Isabel K. Parés-Ramos & William A. Gould, 2018. "Climate change and coffee: assessing vulnerability by modeling future climate suitability in the Caribbean island of Puerto Rico," Climatic Change, Springer, vol. 146(1), pages 175-186, January.
    7. Quan Vu Le & Grace Jovanovic & Don-Thuan Le & Sanya Cowal, 2020. "Understanding the Perceptions of Sustainable Coffee Production: A Case Study of the K’Ho Ethnic Minority in a Small Village in Lâm Đồng Province of Vietnam," Sustainability, MDPI, Open Access Journal, vol. 12(3), pages 1-16, January.
    8. Celia Ruiz-de-Oña & Patricia Rivera-Castañeda & Yair Merlín-Uribe, 2019. "Coffee, Migration and Climatic Changes: Challenging Adaptation Dichotomic Narratives in a Transborder Region," Social Sciences, MDPI, Open Access Journal, vol. 8(12), pages 1-26, November.
    9. Lee Hannah & Camila I. Donatti & Celia A. Harvey & Eric Alfaro & Daniel Andres Rodriguez & Claudia Bouroncle & Edwin Castellanos & Freddy Diaz & Emily Fung & Hugo G. Hidalgo & Pablo Imbach & Peter Läd, 2017. "Regional modeling of climate change impacts on smallholder agriculture and ecosystems in Central America," Climatic Change, Springer, vol. 141(1), pages 29-45, March.
    10. Sigrun Wagner & Laurence Jassogne & Elizabeth Price & Martin Jones & Richard Preziosi, 2021. "Impact of Climate Change on the Production of Coffea arabica at Mt. Kilimanjaro, Tanzania," Agriculture, MDPI, Open Access Journal, vol. 11(1), pages 1-15, January.
    11. Rachmat Mulia & Duong Dinh Nguyen & Mai Phuong Nguyen & Peter Steward & Van Thanh Pham & Hoang Anh Le & Todd Rosenstock & Elisabeth Simelton, 2020. "Enhancing Vietnam’s Nationally Determined Contribution with Mitigation Targets for Agroforestry: A Technical and Economic Estimate," Land, MDPI, Open Access Journal, vol. 9(12), pages 1-25, December.
    12. Kouadio, Louis & Tixier, Philippe & Byrareddy, Vivekananda & Marcussen, Torben & Mushtaq, Shahbaz & Rapidel, Bruno & Stone, Roger, 2021. "Performance of a process-based model for predicting robusta coffee yield at the regional scale in Vietnam," Ecological Modelling, Elsevier, vol. 443(C).
    13. Pablo Imbach & Megan Beardsley & Claudia Bouroncle & Claudia Medellin & Peter Läderach & Hugo Hidalgo & Eric Alfaro & Jacob Etten & Robert Allan & Debbie Hemming & Roger Stone & Lee Hannah & Camila I., 2017. "Climate change, ecosystems and smallholder agriculture in Central America: an introduction to the special issue," Climatic Change, Springer, vol. 141(1), pages 1-12, March.
    14. Guido, Zack & Knudson, Chris & Finan, Tim & Madajewicz, Malgosia & Rhiney, Kevon, 2020. "Shocks and cherries: The production of vulnerability among smallholder coffee farmers in Jamaica," World Development, Elsevier, vol. 132(C).
    15. Luca Di Corato & Tsegaye Ginbo, 2020. "Climate change and coffee farm relocation in Ethiopia: a real-options approach," Working Papers 2020:02, Department of Economics, University of Venice "Ca' Foscari".
    16. Venturin, Afonso Zucolotto & Guimarães, Claudinei Martins & Sousa, Elias Fernandes de & Machado Filho, José Altino & Rodrigues, Weverton Pereira & Serrazine, Ícaro de Araujo & Bressan-Smith, Ricardo &, 2020. "Using a crop water stress index based on a sap flow method to estimate water status in conilon coffee plants," Agricultural Water Management, Elsevier, vol. 241(C).
    17. Margaret Buck Holland & Sierra Zaid Shamer & Pablo Imbach & Juan Carlos Zamora & Claudia Medellin Moreno & Efraín J. Leguía Hidalgo & Camila I. Donatti & M. Ruth Martínez-Rodríguez & Celia A. Harvey, 2017. "Mapping adaptive capacity and smallholder agriculture: applying expert knowledge at the landscape scale," Climatic Change, Springer, vol. 141(1), pages 139-153, March.
    18. Fabian Y. F. Verhage & Niels P. R. Anten & Paulo C. Sentelhas, 2017. "Carbon dioxide fertilization offsets negative impacts of climate change on Arabica coffee yield in Brazil," Climatic Change, Springer, vol. 144(4), pages 671-685, October.

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