Assessment of Factors That Affect the Performance of Agricultural Production, in the Case of Amhara Region, Ethiopia

Abstract

The agricultural sector is the basis of livelihood for a large proportion of society in Ethiopia. In the three political regimes in modern Ethiopia, the Imperial, the military and the Ethiopian people revolutionary democratic front (EPRDF), agriculture has been regarded as a critical sector. The Agricultural Development Led Industrialization (ADLI) is the national policy of the country. Regardless of the government policy attention and investment, there is a long way to go for smallholders to ensure food self-sufficiency. Agriculture is the base of our food, transformation to industrialization, climatic change control system. Agriculture is the soul of our sovereignty for development as well as poverty reduction for individuals and country level. In Ethiopia, population density is high and has been increasing and agricultural land has been decreasing because of fragmenting or converting it into residential plots. To meet the domestic food requirements, use of improved production technologies developed by research is come out to be important. Therefore, the goal of this study was to analyze factors that affect the performance of agricultural products in Amahara region national state and to determine the highly significant input factors for producing high and qualified agricultural outputs. Data regarding total agricultural outputs and its input factors in study area of Amahara region from 2010 to 2018 was obtained from Amhara national state branch of the Ethiopian institute of agricultural sector. Correlation analyses were used to examine the strength of the relationship between each of the determinant factors with total agricultural output, while multiple regression analysis was employed to examine the simultaneous effects of several independent variables on the dependent variable, total agricultural outputs. These analyses were employed through the packages R and Stata to achieve the main objectives of the study. All of the independent variables were highly correlated with the total agricultural output. The overall regression model was highly significant (p-value < 0.01) with F = 45.532. The R-squared value implies that 93.8% percent of the changes in average total agricultural outputs are successfully explained by the variables used in the model of this study. If we take model size into account, 91.8% percent of the variation in average total agricultural output was explained by the values of the independent variables. Specifically, among the independent variables irrigated land, fertilizer, improved seed and pesticides are the most significant factors for total products (p-value < 0.05).