Browsing by Author "Tsegaye, Diribsa"

Now showing 1 - 1 of 1
  • No Thumbnail Available
    Item
    Analysis of Climatic Variability and Its Effects on Production of Selected Crop in Ada'a District: Multivariate Time Series Approach
    (Addis Abeba university, 2016-06) Tsegaye, Diribsa; Gotu, Butte(PhD)
    Climate change affects all economic sectors to some degree, but the agricultural sector is perhaps the most sensitive and vulnerable. In the last three decades, Ethiopia has been affected by climate-related hazards. Agriculture, the most dominant sector in the national economy, has been most at risk because of its dependence on seasonal rainfall. Anticipated climate change has negatively impacted the agricultural sector due to increased temperatures and decreased or greater variability in precipitation, leading to increased food insecurity. This study was carried out with the general objective of examining climate variability and its effects on selected crop production for the last three decades in Ada'a district of East Showa Zone of Oromia Regional State. The relevant data were obtained from the National Meteorological Agency (NMA) and district’s agricultural office. Data on selected crop production, total rainfall and average temperature for the period of 1985 to 2015 were used. The vector autoregressive (VAR) model is employed for modeling. The cointegration relations among the variables were identified by applying Johansen’s cointegration tests, while potential causal relations were examined by employing Granger’s causality tests. Moreover, the short run interactions among the variables were determined through the application of impulse response analysis. The results of the research imply the existence of short term adjustments and long-term dynamics in crop productivity, total rainfall, minimum and maximum temperature. The result of Johansen test indicates the existence of one cointegration relation between the variables. The final result shows that a Vector Error Correction (VEC) model of lag two with one cointegration equations best fits the data