Production System Sustainability of Summer Crop Farmers: Application of Bayesian Analysis of Spatial Probit Models

The purpose of this study is to identify factors influencing the production system sustainability by summer crop farmers. In this regard, the spatial Probit models and Bayesian approach was used. The data of 250 summer crop farmers in Khuzestan province based on cluster random sampling were collected in winter 2015. To calculate Bayesian coefficients the Gibbs sampling and Metropolis–Hastings algorithm were used. A Lagrange Multiplier test for spatial error dependence [LM(err)] and a Lagrange Multiplier test for spatial lag dependence [LM(lag)] to extract the appropriate model were used. According to the results, both models were statistically significant with 99% probability. Thus, both models could be used in interpreting the results. Results of the estimation of both spatial models, respectively, showed that the variables of income (0.812, 0.659), sustainability knowledge (0398, 0.465), crop yield (0.457, 0.765), participation in extension class (0.427, 0.486), educational level (0.562, 0.454), exploitation system (0.786, 0.576) and the spatial autoregressive coefficient (0.829, 0.739) had significant role on production system sustainability