Simulation of Chickpea Production in Khorasan Provinces Using a Simple Crop Model and Markov Chain Monte Carlo technique

One of the main constraints in predicting a crop yield mostly under rainfed conditions is the final yield temporal and spatial variability. This is mainly due to considering a constant value for a parameter within a model while in reality it usually changes at both time and location. In this study we used the Markov Chain Monte Carlo (MCMC) approach together with a simple crop model for chickpea yield prediction under both rainfed and irrigated conditions to see if there is any possibility to simulate the chickpea production when there are sparse and not enough available data. The crop model was run for the great Khorasan province and simulation results were compared with historical observed crop yield data (21 years) obtained by Ministry of Agriculture and various field experiments on chickpea within Khorasan. For model results verification Root Mean Square Difference (RMSD) was employed. Model results were acceptable at both irrigated and rainfed conditions which in turn indicated the high capability of the very simple crop model when linked with MCMC technique. Such a package would be able to analyze the chickpea production throughout the Khorasan chickpea production systems.