Application of the cost-transfer technique for estimating the wheat demand demand function of the Sistan region

  Water shortage in the Sistan region has become a water crisis in the region due to its full dependence on the Hirmand River and the droughts of the last two decades. On the other hand, the vast majority of people in the region have been affected by the agricultural sector, and the limited availability of water has pushed this sector into food supply with a production challenge. Due to the fluctuation and high risk of water supply in this region, because water resources in the Sistan area are limited and scarce, water demand management is of particular importance to address this problem. On the other hand, wheat production in Sistan region is one of the most important and strategic products that plays a major role in the agricultural economy of the region. Therefore, in the present study, the cost function of the Sistan wheat demand function has been estimated from the translog cost function. The data needed to estimate the translog cost function, including quantities and input prices, and the production of 150 wheat labor, were collected using cross-sectional data of 2016-2017. The method used in this study is a seemingly unrelated duplicate regression (SURE). The results of the model estimation show that the price of labor and family and land use and land use have a positive effect on the share of water costs, while the price of water and the amount of production have a negative effect on the share of water costs and the intersection stretches It shows that the input of water has a strong succession with other inputs. The water has a substitute relationship with the inputs of the leased and family labor force, fertilizer, and sub-cultivation area, and it has the highest degree of succession with fertilizer. All coefficients of the variables in the water cost share model, with the exception of the leased labor force, are meaningful. Due to the low coefficient of determination in the cross-sectional data, the coefficient of determination in the estimated model for wheat yield is %0.61 Represents the good fit of the model, which explains independent variables as well as dependent variables (total cost change). The Watson camera statistics show that there is no self-correlation phenomenon in the model. The absolute magnitude of the self-priced stretch of water demand for wheat is greater than one, which indicates that it is possible to control water demand by adopting pricing policies through influencing production inputs other than water