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Major crop producing regions in Ethiopia viz., Tigray, Amhara,Oromia and SNNP were included in the study. Three models of production function: linear, exponential and Cobb Douglas were considered. Statistical model diagnostics suggested that production function for each of the regions was found to be appropriately represented by the Cobb- Douglas function. Since parameter estimates using OLS were misleading due to the incidence of several outlying cases,robust regression was taken as viable alternative. After robust regression, many of the parameter estimates took expected signs,R2 values were…mehr

Produktbeschreibung
Major crop producing regions in Ethiopia viz., Tigray, Amhara,Oromia and SNNP were included in the study. Three models of production function: linear, exponential and Cobb Douglas were considered. Statistical model diagnostics suggested that production function for each of the regions was found to be appropriately represented by the Cobb- Douglas function. Since parameter estimates using OLS were misleading due to the incidence of several outlying cases,robust regression was taken as viable alternative. After robust regression, many of the parameter estimates took expected signs,R2 values were increased and estimated standard errors were decreased. In general, farm size, fertilizer,seed, oxen power and human labor were playing central role for maximization of yield. Yet, the contribution of education for crop yield was found to be insignificant in Tigray and Amhara. Production elasticities for the inputs at each region except farm size in Tigray, Amhara and Oromia suggested that the relation between inputs and output was inelastic. Additionally, crop production functions revealed that returns to scale were greater than unity in each of the regions.
Autorenporträt
Born in a small village, Merawi, Ethiopia, Taddesse studied Statistics at Addis Ababa University and was awarded B.Sc. Degree in July 2005 and M.Sc. Degree in July 2009. He is currently serving as lecturer in Statistics Department, University of Gondar. His research interests are robustness, multivariate analysis and biostatistics.