The direct and indirect impacts of climate change may jeopardize livelihoods of millions of poor coffee-growing farmers, unless the sector manages to deploy adequate adaptation measures. This study provides valuable insight into smallholder coffee farmers’ adaptation strategies to climate change impacts and factors determining their choices based on a survey of 240 smallholder farmers in Jimma zone, southwest Ethiopia. The assessment indicates that the most common adaptation measures used by the producers include adjustment of planting date, change of the crop type and variety, tree plantation, and mixed farming. Access to weather forecasts and formal extension services, level of education, age of the household head, farm size and income from coffee and agro-ecological setting affect the coffee growers’ decision to adapt to climate change. Enhancing smallholder coffee farmers’ adaptation to climate change requires further efforts from the government to improve farmers’ access to accurate and timely agro-meteorological forecasts, capacity building and technical support for income diversification through improved provision of agricultural extension services.

This paper reports an evaluation of eleven oat genotypes in four environments for two consecutive years to identify high-biomass-yielding, stable, and broadly adapted genotypes in selected parts of Ethiopia. Genotypes were planted and evaluated with a randomized complete block design, which was repeated three times. The additive main effect and multiplicative interaction analysis of variances revealed that the environment, genotype, and genotype–environment interaction had a significant (p ≤ 0.001) influence on the biomass yield in the dry matter base (t ha⁻¹). The interaction of the first and second principal component analysis accounted for 73.43% and 14.97% of the genotype according to the environment interaction sum of squares, respectively. G6 and G5 were the most stable and widely adapted genotypes and were selected as superior genotypes. The genotype-by-environment interaction showed a 49.46% contribution to the total treatment of sum-of-squares variation, while genotype and environment effects explained 34.94% and 15.60%, respectively. The highest mean yield was obtained from G6 (12.52 kg/ha), and the lowest mean yield was obtained from G7 (8.65 kg/ha). According to the additive main effect and multiplicative interaction biplot, G6 and G5 were high-yielding genotypes, whereas G7 was a low-yielding genotype. Furthermore, according to the genotype and genotype–environment interaction biplot, G6 was the winning genotype in all environments. However, G7 was a low-yielding genotype in all environments. Finally, G6 was an ideal genotype with a higher mean yield and relatively good stability. However, G7 was a poor-yielding and unstable genotype. The genotype, environment, and genotype x environment interaction had extremely important effects on the biomass yield of oats. The findings of the graphic stability methods (additive main effect and multiplicative interaction and the genotype and genotype–environment interaction) for identifying high-yielding and stable oat genotypes were very similar.

This study assesses the perceptions and vulnerability of the farming communities to climate change in the southwestern parts of Ethiopia. Climate change vulnerability assessment is a prerequisite to designing climate change adaptation strategies. A multistage cluster sampling technique was used to select four of the six zones from the southwestern parts of Oromia. Close-ended and open-ended questionnaires were used to assess household perceptions of climate change and the degree of vulnerability to climate change by using five household capitals: natural, social, financial, physical, and human capital. Data were collected from 442 households in 4 districts: Jimma Arjo, Bako Tibe, Chewaka, and Sekoru. The vulnerability of the farming communities was assessed using the households’ livelihood vulnerability index. A total of forty indicators from five capitals were applied to calculate household livelihood vulnerability to climate change. Household perceptions of climate change had a statistically significant relationship with changes in rainfall pattern (75.6%, p < 0.001), temperature pattern (69.7%, p < 0.001), drought (41.6%, p = 0.016), flood (44.1%, p = 0.000), and occurrence of early (53.2%, p < 0.001) and late rain (55.9%, p < 0.001). The results show that households in the Sekoru district were the most vulnerable (0.61), while households in the Jimma Arjo district were less vulnerable (0.47) to the effect of climate change. Household vulnerability to climate change is mainly related to the occurrence of drought, lack of much-needed infrastructure facilities, and weak institutional support. Links with financial organizations are also lacking in the household. The findings of this study will help policymakers to address the impact of climate change. To support disaster risk management on the one hand and increase the resilience of vulnerable societies to climate change on the other, we recommend a detailed assessment of the remaining districts of the region.

From 2013 to 2015, a study was conducted on 29 coffee plots to assess the influence of landscape context and plot features on the epidemics of coffee leaf rust (CLR) descriptors in Gumay and Setema districts, Southwest Ethiopia. Maximum CLR severity and number of sporulating lesion density per leaf was significantly correlated with different land use types (tree density at 100 m, 200 m, pasture and crop lands at 200 m circular radius), and plot features (altitude, North-South aspect, East-West aspect and leaf area index) over different years of observation. High tree density in the surrounding coffee plots significantly increased the severity of the disease. There was a general decreasing trend of CLR severity, sporulating lesion density and proportion of leaf fall over the altitudinal gradient. The interaction of landscape context with CLR descriptors found were so complex and need further detail analysis.

Projections of future climate change trends in four urban centers of southwest Ethiopia were examined under a high Representative Concentration Pathways (RCP8.5) scenario for near- (2030), mid- (2050), and long-term (2080) periods based on high-resolution (0.22⁰) Coordinated Regional Climate Downscaling Experiment (CORDEX) for Africa data. The multi-model ensemble projects annual maximum and minimum temperatures increasing by 0.047 °C per year (R² > 0.3) and 0.038 °C per year (R² > 0.7), respectively, with the rates increased by a factor of 10 for decadal projections between the 2030s and 2080s. The monthly maximum temperature increase is projected to be 1.41 °C and 2.82 °C by 2050 and 2080, respectively. In contrast, the monthly minimum temperature increase is projected to reach +3.2 °C in 2080. The overall seasonal multi-model ensemble average shows an increment in maximum temperature by +1.1 °C and +1.9 °C in 2050 and 2080, with the highest change in the winter, followed by spring, summer, and autumn. Similarly, the future minimum temperature is projected to increase across all seasons by 2080, with increases ranging from 0.4 °C (2030s) to 3.2 °C (2080s). All models consistently project increasing trends in maximum and minimum temperatures, while the majority of the models projected declining future precipitation compared to the base period of 1971–2005. A two-tailed T-test (alpha = 0.05) shows a significant change in future temperature patterns, but no significant changes in precipitation were identified. Changes in daily temperature extremes were found in spring, summer, and autumn, with the largest increases in extreme heat in winter. Therefore, our results support proactive urban planning that considers suitable adaptation and mitigation strategies against increasing air temperatures in urban centers in southwest Ethiopia. Future work will examine the likely changes in temperature and precipitation extremes.

Arabica coffee is an essential crop in the national economy of Ethiopia. There is little information available about the cup quality profile of Teppi coffee, despite several sites across the country having conducted research on the organoleptic properties of various coffee types. Therefore, an experiment was conducted to determine the quality attributes of the genotypes and correlate their inherited quality with morphological traits. The experiment was superimposed on five-year-old plants of 17 coffee genotypes during the 2018 cropping season at Teppi and Gemadro sites. Using the wet processing method, six kilograms of red coffee cherries were carefully harvested, processed, and sun dried to a moisture content of 10.5% to 11.0%. To obtain the necessary number of replications, each sample was divided into three, and three cups for each sample were prepared for the tasting sessions. Organoleptic quality attributes were scored using the standard established. The analysis of variance for aromatic quality, acidity, flavor, and overall cup quality showed a highly significant difference ( $P$ = 0.01) among the tested coffee genotypes. All of the organoleptic quality attributes evaluated showed a no significant correlation with morphological traits like body (r = −0.11) and hundred-bean weight and, flavor (r = −0.1) and bean thickness. However, there was a strong correlation between organoleptic quality attributes like flavor (r = 0.95 ${}^{\ast \ast }$ ) and acidity (r = 0.94 ${}^{\ast \ast }$ ), demonstrating that any organoleptic characteristic is an important element in beverage quality. It is challenging to simultaneously choose for cup quality and bean physical features because there were no significant correlations between organoleptic quality and these traits. In this study, genotype 3/8 showed outstanding performance across locations for all organoleptic quality attributes, which can be used in the Arabica coffee-breeding program.