This study was aimed at establishing allometric models for estimating LA (Leaf Area) of eight Coffea arabica genotypes in Mana district of Jimma Zone Oromia Regional State, South Western Ethiopia (7˚46'N, 36˚0'E). Many Methodologies and instruments have been devised to facilitate measurement of leaf area. However, these methods are destructive, laborious and expensive. For modeling leaf area, leaf width, leaf length and leaf area of 1200 leaves (50 leaves for each genotype) was measured for model calibration and the respective measurements on 960 leaves were used for model validation. Linear measurement was taken from leaves and branch diameters of eight genotypes of C. arabica, cultivated in field following a randomized complete blocks design at three altitudes (High, Medium and Low) were evaluated to identify best option for input in the models, and to validate the method to estimate the leaf area. Linear and non-linear models were tested for their accuracy to predict leaf area of the eight C. arabica genotypes. The use of linear model resulted in high accuracy for all of the eight C. arabica genotypes. No significant effect of growing altitude and genotype was obtained among the slopes of the models. Therefore, one single model was fitted to the combined data of all genotypes at all altitudes (LA = 0.6434LW). Comparison between observed and predicted leaf area was made using this model in another independent dataset, conducted for model validation, exhibited a high degree of correlation (r = 0.98 -0.99, P < 0. 01). The over or under estimation of the leaf area using this model ranges between 0.02% to 1.7% and this model is adequate to estimate the leaf area for the eight C. arabica genotypes. Hence, this model can be proposed to be reliably used and with this developed model, researchers can estimate the leaf area of newly released eight genotypes of C. arabica at different altitudes accurately.

Wageningen Centre for Development Innovation supports value creation by strengthening capacities for sustainable development. As the international expertise and capacity building institute of Wageningen University & Research we bring knowledge into action, with the aim to explore the potential of nature to improve the quality of life. With approximately 30 locations, 6,500 members (5,500 fte) of staff and 12,500 students, Wageningen University & Research is a world leader in its domain. An integral way of working, and cooperation between the exact sciences and the technological and social disciplines are key to its approach.

Coffea arabica L. is one of the major economically important crops grown in the southwestern part of Ethiopia. However, the productivity of coffee is very low in the region. The main aim of the study was to assess the determinants of arabica coffee yield and farmers’ preference to shade tree species in Jimma zone, Southwestern Ethiopia. Two districts (Chora Botor and Limu Kossa) were selected purposively out of nine coffee-growing districts in Jimma zone. Three kebeles from Chora Botor and five kebeles from Limmu Kossa were randomly selected to run the household survey (399 randomly selected coffee growers). The collected data were analyzed using SPSS and STATA software. Ordinary Least square (OLS) regression models were implemented to resolve the determinant of coffee yield. The results indicated that socioeconomic and biophysical factors such as gender, education level, family size, coffee-growing experience, coffee farm size, weed management practices, disease, and insect pests were found to be determinants of coffee yield. Although the scale of shade tree preferences varies, farmers prefer coffee shade trees that have long heights, wider crown shapes, and evergreen leaves throughout the growing season. Accordingly, the ranking analysis in both districts indicated that Albizia gummifera, Acacia abyssinica, and Millettia ferruginea were the greatest preferred coffee shade tree species by farmers of the study area. Hence, improving the capacity of farmers about coffee farm management skills and promoting the use of preferred coffee shade trees should be strengthened to improve the coffee yield and quality in the region.

Wageningen Centre for Development Innovation supports value creation by strengthening capacities for sustainable development. As the international expertise and capacity building institute of Wageningen University & Research we bring knowledge into action, with the aim to explore the potential of nature to improve the quality of life. With approximately 30 locations, 6,500 members (5,500 fte) of staff and 12,500 students, Wageningen University & Research is a world leader in its domain. An integral way of working, and cooperation between the exact sciences and the technological and social disciplines are key to its approach.

Wageningen Centre for Development Innovation supports value creation by strengthening capacities for sustainable development. As the international expertise and capacity building institute of Wageningen University & Research we bring knowledge into action, with the aim to explore the potential of nature to improve the quality of life. With approximately 30 locations, 6,500 members (5,500 fte) of staff and 12,500 students, Wageningen University & Research is a world leader in its domain. An integral way of working, and cooperation between the exact sciences and the technological and social disciplines are key to its approach.