Abstract:
Sesame is an important annual oil crop of the world, which is cultivated almost in all tropical
and sub-tropical Asia and African countries for its highly nutritious and edible seeds. The crop
also is known as the queen of oil seeds due to the high oil content of its seed. Sesame is the major
oilseeds crop in the country (Ethiopia) in terms of exports next to coffee. Anticipated climate
change has impacted negatively the agricultural sector due to increased temperatures and
decreased or greater variability in precipitation, leading to increased food insecurity. The
general objective of this study is to study the effects of rainfall and temperature on sesame
production using Appropriate Time Series analysis in central and west Gondar, North Ethiopia.
The result of this study helps to understand sesame yield trained and could be of interest to
further studies relation of sesame yield value and with temperature and rainfall. In this study
differencing is used to transform non-stationary time series data to stationary time series data.
There is a long-run relationship based on the Co-integration test and a short-run relationship
based on the ARDL bound test among the production of sesame, rainfall, and temperature, and
the ARDL long-run and short-run estimation are appropriate than any other economic model.
The appropriate lagged order of the selected model is selected at lagged one and the
appropriately selected model is ARDL (1,1,1) selected by Akaike information criteria. In the
long-run and short-run bound test, the F-bound test shows there is a strong association between
the response variable yields of sesame and the explanatory variable (rainfall and temperature)
beyond a certain limit. The long-run bound tests showed that the rainfall and temperature value
of the response variables have a highly significant effect on sesame productivity. On the other
hand, the short-run test of association by error correction term has a negative and significant
value, so the coefficient of error terms implies that the deviation from the long-run
equilibrium level of yields of sesame in the current period is corrected by 83.41% in the next
period to bring back equilibrium when there is a shock to the productivity of sesame and its
determinants relationship. The appropriate structural analysis is explained by Impulse Response
Functions and Variance Decomposition analysis. The Impulse response function indicates the
effects of an exogenous shock on the whole process over time and Variance decomposition tell us
the proportion of movements due to its shocking innovations versus shocks to other variables
