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

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  • Christian Bunn
  • Peter Läderach
  • Oriana Ovalle Rivera
  • Dieter Kirschke

Abstract

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

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    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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