MULTIVARIATE TIME SERIES ANALYSIS OF CEREAL CROPS PRODUCTION IN ETHIOPIA

Abstract

In Ethiopia’s agricultural sector, of the major crop production is very exposed to extreme weather events that lead to an influence the annual quantity of agricultural production supply. This study was focused on two important crops specifically wheat, and maize and number of factors including rainfall variability, acreage, and fertilizer. The dynamic relationships among the variables considered for observing the impact of rainfall variability, acreage, and fertilizer on crop productivity in Ethiopia by using the vector error correction (VEC) model based on data for the time period of 1993 – 2020. The results of the study show that the unit root tests of all the series are non-stationary at level and stationary after first difference. The results show existence of the short run adjustments and the long run relationships between rainfall, acreage and crop productivity. Then, the vector error correction (VEC) model was employed to evaluate the annual rainfall, have positive and statistically significant on the crop productivity. So, the one mm increase in rainfall will increase crop productivity by 68.4% and implies that the long-run relationship is attainable in productivity. The inter relationship between annual rainfall variability and the crop production is highly variable, so crop productivity was dependence on annual rainfall and also the one hectare increase in acreage under cultivation will be increase crops productivity by 56.89%, thus area of land cultivated for harvest is, one of sustainable for increased productivity. The results of granger causality test is additionally conducted to identify the overall possible for long-run causal effects among the variables. The effects of an exogenous shock to one or more the innovations on current and future values of the endogenous variables are determined by the impulse response evaluation and variance decomposition. Finally the VEC model has been checked to its accuracy of fitted model for makes out of sample forecasting is formed select of crops productivity in Ethiopia. The rainfall variability has timely effects on crop production. So, the provision of timely information on expected changes on these rainfall variability is critical in improving awareness and crops cultivation methods shall be adjusted according to the changing pattern of rainfall.