A Bayesian decision model for drought management in rainfed wheat farms of North East Iran

Drought is a feature of climate that can occur in virtually all climates. Therefore, it is an inevitable global but site-specific phenomenon which requires tools to predict and strategies and options to cope with it. In this research, the ability and effectiveness of the Bayesian Decision Networks (BDNs) approach in decision-making and evaluating drought management options for rainfed wheat production in the eastern region of Golestan Province, Iran are demonstrated. The results revealed that during drought conditions, the Koohdasht cultivar had higher yield than other cultivars of wheat. Two management scenarios have been specified for the forecasted period on the basis of wheat cultivars adopted in the region. The results of scenario analysis with a BDN model indicate that the probability of low, medium and high yield levels in scenario 2 (Koohdasht 70%, Zagros 20% and the other cultivars 10%) has a better status compared with scenario 1 (current condition). The paired t- test indicates that there is a significant difference between the two scenarios for wheat yield in low and medium states (P