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Local-scale analysis and understanding of long-term spatio-temporal climatic patterns are crucial for designing site-specific climate change adaptation strategies in the Ethiopian Arabica coffee context. We conducted a comprehensive examination of long-term spatio-temporal trends and variability of rainfall and temperature during different phenological stages of Arabica coffee growth across elevation zones in five major coffee-growing districts of southwestern Ethiopia. Employing Mann–Kendall tests, Sen's slopes, coefficients of variation, and anomalies, we identified a significant increasing trend (P≤0.05) in mean annual and seasonal rainfall at a rate of 5.09, 5.43, 6.44, 6.49, and 6.26 mm/year for Ale, Yayu, Gera, Goma, and Limu Seka respectively, accompanied by year-to-year variability. Maximum and minimum temperatures exhibited a similar increasing trend and year-to-year variability across all study sites and altitude zones. Minimum temperature increased at a similar rate of 0.2°C per decade in all districts, while maximum temperature increased at a rate of 0.2°C/decade for Ale and 0.3°C /decade in Gera, Goma, Limu Seka, and Yayu. High- and mid-altitude areas of three of our study sites (Ale, Gera, and Goma) are already experiencing conditions outside optimal coffee production ranges, as rainfall has exceeded ideal conditions. Should the current trends persist, however, other areas are at risk, as rising variability in the mean amount of rainfall and temperature can disrupt coffee phenological stages, reducing yield and quality. The increasing trend of maximum and minimum temperatures has already been identified as a threat to lowland and midland coffee-producing areas, with some hoping that the highlands might be a refuge for coffee production in the future. Unfortunately, changing rainfall patterns also threaten coffee production in the highlands in our study areas. Our results suggest the importance of local-scale analysis and a clear understanding of specific contexts using fine-resolution gridded climate datasets in areas where weather stations are scant and sparsely distributed.
Mamuye et al. (Mon,) studied this question.