RISK ASSESSMENT OF CLIMATE VARIABILITY ON RICE PRODUCTIVITY IN SINDH PROVINCE OF PAKISTAN
Department of Agricultural Economics, Sindh Agriculture University, Tandojam, Pakistan
The focus of the present research was to assess the dependent variable i.e., Vector Autoregression (VAR) study model estimation with lag 2 indicated that Akaike AIC and Schwarz Sc for data using lag 2 is smaller than lag 3, lag 4 and lag 5, so the lower values Akaike AIC 17.11070 and Schwarz Sc 19.23172 for lag 2 make the model more fitted. For that reason, VAR model lag 2 in the study is more prefer-able as compared to other lag values. The ADF test showed the variables of model to be non- stationary at straight levels of significance and indicated that the first difference variables are completely stationary, which showed that all the variables are integrated of order-1, whereas water availability data is already in the stationary form. Area under rice crop is not expected to be different from the 2014-15 and the productivity is forecasted as 42.6 mds per hectare, which is a good yield but not good enough, and most probably due to the climatic uncertainty. Furthermore, the econometric results illustrated that the increase in temperatures and a decrease in precipitation tendency would cause less or more negative impact on rice productivity in up-coming years in the province of Sindh, where estimated productivity will get decreased by 7.32 percent in the short-run and 13.31 percent in the long-run with an increase in temperature by 1°C and 10 percent decrease in precipitation. Results showed decline in the productivity of the crop under study area. This might be an alarming situation and the big threats to agricultural productivity associated with rapid climate changes. A well-defined planning and sagacious policies will play sustainable productivity of the rice crop. New hybrid and climatic change resistance crop varieties may be introduced to secure the staple food crops including rice for food security.