Examining Factors Affecting the Sub-vector Water Efficiency in Wheat Production: A Radial Basis Function Artificial Neural Network and the Tobit Model

In the present study, by using information of 150 wheat beneficiaries in Zarghan, Fars region collected in the 2010-2011 crop year, the most important factors affecting the sub-vector water efficiency in wheat production were analyzed. In order to measure the water use efficiency, data envelopment analysis was used. Afterward, the most important factors affecting sub-vector water efficiency were identified by using radial basis function (RBF) artificial neural network. Then, the most important factors were analyzed by applying the Tobit model. The results of neural network model showed that variables yield cultivated area, gross income per hectare and time interval between each two subsequent irrigations have been the most important factors affecting sub-vector water use efficiency. In addition, results from Tobit model suggested the positive impact of variables yield cultivated area and gross income per hectare and negative impact of time interval between each two subsequent irrigations on water use efficiency. Finally, paying more attention to the land integration for increasing sub-vector water efficiency was proposed.