Coffee, the second most traded commodity in the world, has had a long relationship with mankind. As a beverage, it dates back to the 11th century, if not earlier, and has become even more widespread today as people find ways to lengthen their days. It’s hard to imagine a world without the aromatic beverage; however, a recent study published in the Public Library of Science (PLOS One) suggests that coffee could be global warming’s next victim. As temperatures undoubtedly continue to rise, the effects on coffee bean growth around the world could potentially be devastating.
The Coffea arabica L. plant species, commonly known as Arabica coffee, provides 70% of the worlds coffee beans and is cultivated primarily in Ethiopia. Studies have shown that the optimal temperatures for Arabica productivity range from 18° to 21° Celsius, with temperatures outside this range causing decreased productivity. A climate change of about 1.8° to 4° is projected by the end of the 21st century, demanding immediate mitigation measures in order to sustain Arabica yields.
Aaron P. Davis (University of Reading) and colleagues at the Royal Botanic Gardens studied the indigenous Arabica plant found in areas of Southwest Ethiopia, Southern Sudan, and Northern Kenya. The indigenous population provides a greater potential for conservation due to its high genetic diversity and resistance to disease and pests. Davis created models for current and future populations under the influence of climate change from three sources: field survey data for populations in Ethiopia collected between 2000-2006, records of wild population from literature, and herbarium specimens. Although a total of 713 localities of Arabica were observed for the study, localities were sorted into 197 clusters. Using these 197 data points, MaxEnt software was used to generate future maps of population distribution by analyzing current environmental conditions and species niche to predict the suitability of the habitat. Simultaneously, climate data from 1941 to 2006 and the Hadley Centre Coupled Model (HadCM3) climate model were used to predict future climate change. Predicted climate change and population distribution were utilized in creating future species distribution maps. Lastly, the species distribution models were assessed for accuracy using Area Under the Curve (AUC) and resulted in a value of 0.99 for all data points used, suggesting that the models were very accurate.
The species distribution maps predicted that by the year 2080, climate change would cause a reduction of 65% in environmentally suitable localities of Arabica in the best-case scenario. Alarmingly, worst-case scenario models predicted a reduction of 100%, essentially suggesting that no viable localities would survive. Moreover, these models do not take into account habitat destruction by deforestation or other human interference, so predictions are modest at most. These maps of Arabica coffee distribution serve to provide data on localities in need of attention as the climate continues to change, as well as an assessment for in situ and ex situ conservation efforts. Localities that are predicted to become endangered soon are considered good candidates for ex situ conservation, while localities seemingly capable of surviving for the long-term under climate change would benefit from in situ conservation.
Since models for Arabica coffee are relatively new, data for species distribution maps are limited. In order to ensure that more accurate models of climate change influence arise in the future, data should continue to be collected on a large scale across localities in order to plan conservation efforts efficiently.
Climate change has undoubtedly had dire consequences on several environments and is sure to be detrimental to the Arabica coffee plantations in Ethiopia. As the rate of climate change continues to increase, mitigation measures must be planned to avoid devastating one of the world’s most valued crops.
Davis AP, Gole TW, Baena S, Moat J (2012) The Impact of Climate Change on Indigenous Arabica Coffee (Coffea arabica): Predicting Future Trends and Identifying Priorities. PLoS ONE 7(11): e47981. doi:10.1371/journal.pone.0047981