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Climate change has become a reality that affects coffee producers across the world. The prioritization of adaptation tools for coffee is not a trivial task: available means are limited, climate change is not uniform and each coffee stakeholder group has a different decision environment Therefore, climate change adaptation is crop-/site- and actor-specific.
To support climate change adaptation strategies we developed a gradient of climate change impacts for coffee production in Central America. First, we differentiated different types of climates suitable for coffee production by clustering occurrence locations on 20 bioclimatic variables. The climate zones were described and ranked using their dry season characteristics, including its length, mean temperature and precipitation. The spatial distribution of these climate types was modeled using the machine learning algorithm Random Forest for current conditions
and future projections from 19 global climate models. The difference between current and the
most likely future distribution resulted in the gradient of impacts
We found that about a third of currently potentially suitable area will become unsuitable for
coffee production without adaptation. Another third will require substantial adaptation efforts to
production systems. The remainder will be less affected and will only require incremental
adaptation to improve the resilience of the system. We found a clear relationship of these
impacts with altitude. The lowest regions were found 200m higher in altitude than under current
conditions. We could not find a clear relationship between impacts and dry season
characteristics,probably caused by the high modeling uncertainty of global climate model projections.