Agriculture has been identified as one of the most vulnerable sectors affected by climate change. In the present study, we investigate the impact of climatic change on dryland wheat yield in the northwest of Iran for the future time horizon of 2041–2070. The Just and Pope production function is applied to assess the impact of climate change on dryland wheat yield and yield risk for the period of 1991–2016. The Statistical Downscaling Model (SDSM) is used to generate climate parameters from General Circulation Model (GCM) outputs. The results show that minimum temperature is negatively related to average yield in the linear model while the relationship is positive in the non-linear model. An increase in precipitation increases the mean yield in either model. The maximum temperature has a positive effect on the mean yield in the linear model, while this impact is negative in the non-linear model. Drought has an adverse impact on yield levels in both models. The results also indicate that maximum temperature, precipitation, and drought are positively related to yield variability, but minimum temperature is negatively associated with yield variability. The findings also reveal that yield variability is expected to increase in response to future climate scenarios. Given these impacts of temperature on rain-fed wheat crop and its increasing vulnerability to climatic change, policy-makers should support research into and development of wheat varieties that are resistant to temperature variations.

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Climate change is one of the most pressing global issues of the twenty-first century. This phenomenon has an increasingly severe impact on water resources and crop production. The main purpose of this study is to evaluate the impact of climate change on water resources, crop production, and agricultural sustainability in an arid environment in Iran. To this end, the study constructs a new integrated climate-hydrological-economic model to assess the impact of future climate change on water resources and crop production. Furthermore, the agricultural sustainability is evaluated using the multicriteria decision making (MCDM) technique in the context of climate change. The findings regarding the prediction of climate variables show that the minimum and maximum temperatures are expected to increase by about 5.88% and 6.05%, respectively, while precipitation would decrease by approximately 30.68%. The results of the research reveal that water availability will decrease by about 13.79–15.45% under different climate scenarios. Additionally, the findings show that in the majority of cases crop production will reduce in response to climate scenarios so that rainfed wheat will experience the greatest decline (approximately 59.95%). The results of the MCDM model show that climate change can have adverse effects on economic and environmental aspects and, consequently, on the sustainability of the agricultural system of the study area. Our findings can inform policymakers on effective strategies for mitigating the consequences of climate change on water resources and agricultural production in dry regions.

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Optimization models are widely used in agricultural water resources management programs. However, optimization models in most applications use data that is subject to uncertainty. Recently, robust optimization has been used as an optimization model that incorporates uncertainty. This paper proposes a linear programming model with the objective of maximizing the total gross margin for the delivery of water to agricultural areas that cover an irrigation network over a planning horizon. The writers apply uncertain data in this system in the form of a robust optimization approach. The writers also consider analysis of the sensitivity of the total gross margin in accordance with the variations of the degree of conservatism (reliability), irrigation efficiency, and price of irrigation water. The results of changes to these parameters in a wide range of variations caused large changes in the optimal total gross margin of the planning horizon. Application of the proposed model to the case study of the Nekooabad irrigation network in the province of Isfahan, Iran, over a 3-year planning horizon (2012-2014) demonstrates the reliability and flexibility of the model.