Multiclass classification of agro-ecological zones for arabica Coffee: an improved understanding of the impacts of climate change

10(10): e0140490

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2015-10-27

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en

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Peer Review

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Open Access Open Access

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CC-BY-4.0

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Bunn, Christian; Läderach, Peter; Pérez Jimenez, Juan Guillermo; Montagnon, Christophe; Schilling, Timothy. 2015. Multiclass classification of agro-ecological zones for arabica Coffee: an improved understanding of the impacts of climate change. PLoS One 10(10): e0140490.

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Abstract/Description

Cultivation of Coffea arabica is highly sensitive to and has been shown to be negatively impacted by progressive climatic changes. Previous research contributed little to support forward-looking adaptation. Agro-ecological zoning is a common tool to identify homologous environments and prioritize research. We demonstrate here a pragmatic approach to describe spatial changes in agro-climatic zones suitable for coffee under current and future climates. We defined agro-ecological zones suitable to produce arabica coffee by clustering geo-referenced coffee occurrence locations based on bio-climatic variables. We used random forest classification of climate data layers to model the spatial distribution of these agro-ecological zones. We used these zones to identify spatially explicit impact scenarios and to choose locations for the long-term evaluation of adaptation measures as climate changes. We found that in zones currently classified as hot and dry, climate change will impact arabica more than those that are better suited to it. Research in these zones should therefore focus on expanding arabica's environmental limits. Zones that currently have climates better suited for arabica will migrate upwards by about 500m in elevation. In these zones the up-slope migration will be gradual, but will likely have negative ecosystem impacts. Additionally, we identified locations that with high probability will not change their climatic characteristics and are suitable to evaluate C. arabica germplasm in the face of climate change. These locations should be used to investigate long term adaptation strategies to production systems.

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