In addition to its socio-economic importance, the agronomy of coffee production justifies research on the crop’s adaptation to climate change.The average lifespan of a coffee plantation is about 30 years (Wintgens ) but can be more than 50 years.Using these data, we trained three popular machine-learning algorithms; Support Vector Machines (Karatzoglou et al. We used distinct parameter combinations as outlined below, to give a total of 135 models.We evaluated the model performance against the performance of a trivial inverse-distance model.These GCMs are representative of projected changes of global mean temperature and precipitation (Warszawski et al.
But in the absence of reliable data to compare inter-temporal climate and species distribution changes, there is no clear guidance for parameter values that allow reliable extrapolation (Elith and Graham ) used them to generate risk maps of dengue fever.
The objective of this paper is to predict current and future climate suitability for coffee (Arabica and Robusta) production on a global scale.
The ensemble approach we chose improves the robustness of the analysis compared with previous studies.
The resulting multi-model ensemble suggests that higher temperatures may reduce yields of 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.