Modeling the responses of Coffee (Coffea arabica L.) distribution to current and future climate change in Jimma Zone, Ethiopia

Coffee arabica species have already been affected by climate change, with socioeconomic implications. Smallholder farmers have encountered and will continue to confront issues in maintaining their coffee plants' productivity. This study aimed to determine which bio-climatic characteristics are most beneficial to coffee production in current and future climate change scenarios. The responses of coffee distribution to climatic conditions were studied under the current, moderate representative concentration (RCP4.5), and worst representative concentration (RCP8.5) pathways using a bioclimatic modelling approach or the Maxent model. Multiple regression models (path and response optimizers) were used to parameterize and optimize the logistic outputs of plant distribution. Results showed that climatic factors such as total precipitation, precipitation seasonality, and mean temperature are the most important climatic factors in determining the success of C. arabica farming. Under the current conditions, total precipitation significantly benefits C. arabica whereas precipitation seasonality significantly affects it (P < 0.001). In the current condition, coffee responded neither negatively nor positively to the mean temperature, but positively in RCP4.5 and RCP8.5. It would also respond positively to increased total precipitation under RCP4.5 but negatively to rising precipitation under the RCP8.5. The average five top-optimal multiple responses of C. arabica were 75.8, 77, and 70% for the present, RCP4.5, and RCP8.5, respectively. The positive response of C. arabica to bioclimatic variables in the RCP4.5 scenario is projected to be much bigger than in the present and RCP4.5 scenarios (P < 0.001). As precipitation and temperature-related variables increase, the cultivation of C. arabica will increase by 1.2% under RCP4.5 but decrease by 5.6% under RCP8.5. A limited number of models and environmental factors were used in this study, suggesting that intensive research into other environmental aspects is needed using different models.