The Economic Impacts of Climate Change and Water Resources Management Scenarios on Agronomy Subsector: Using Risk-Based Hydro-Economic Model

Document Type : Research Article

Authors

• F. Sani ¹
• Gh. Dashti ²
• A. Majnooni ³
• J. Hosseinzad ²

¹ Agricultural Economics, University of Tabriz.

² Agricultural Economics, University of Tabriz

³ University of Tabriz

https://doi.org/10.22067/jead.2021.17813.0

Abstract

Introduction: Ajichay basin is one of the largest agricultural areas and water consumption in Urmia Lake basin. During the recent years, the impact of climate change on one hand, and human factors on the other hand, have changed Ajichay basin to a center of crisis as it has lost its efficiency in supplying water for Urmia Lake. Having the main branches of Ajichay, Sarab county has a great role in crop production and therefore agricultural water consumption compared to other counties around the basin. Therefore, managing water consumption in Sarab County is suggested to resolve the decreased quality and quantity of water in Ajichay basin. Therefore, the aim of the current study is to investigate the impact of climate and water management scenarios on water resources, cropping pattern, yield, and profits of farmers in Sarab County.
Material and Methods: To achieve the study aims, the hydro-economic model was used. In the economic section, quadratic risk programming model and in the hydrological section, the WEAP-MABIA model was used. The purpose in quadratic risk programming model is to maximize the expected farmers’ utility to some technical and structural restrictions. Maximum expected utility of farmers which is calculated by subtracting the risk element from the net income for each crop. MABIA uses a two-part crop coefficient. In the dual crop coefficient approach, the effects of crop transpiration and soil evaporation are determined separately. Two coefficients are used: the basal crop coefficient (Kcb) to describe plant transpiration, and the soil water evaporation coefficient (Ke) to describe evaporation from the soil surface. The current study applied HadCM3, as the general circulation model. LARS-WG was used for downscaling climatic generator and producing rainfall, radiation, and minimum and maximum temperature in a station under A2, B1, and A1B emission scenarios. The period 1987-2018 was used as the base and the future considered period was 2018-2050. All required variables such as information about input values, production quantities, and economic information were collected from 210 questionnaires filled by farmers during 2018 which were selected through stratified random sampling.
Results and Discussion: The results showed that the average rainfall decreased in the range of 21-38% under the emission scenarios of A2, B1, and AIB during 2018-2050 period. In the next period of 2018-2050, the average annual temperature will also increase by 2.5 °C compared to the baseline period under A2 scenario. The results of simulations revealed that the crop yields would undergo a decrease after climate change scenarios. The most considerable yield reduction belongs to A2 scenario in which potato will have the highest yield reduction of 17%. The crop yields of barley and wheat shows a slight reduction. Thus, these two products have larger cropping area in the climatic scenarios. The results of climate change indicate a diminishing trend in available water and water supply reliability for agricultural purposes. The available water for irrigation areas had 21.92% decrease after applying climate change scenario. The mean for water supply reliability in the sub-basin decrease from 84.93% to 62.35% if the future years continue to have a decrease in rainfall and increase in temperature. By applying the scenario of agricultural water reduction along with climate change scenario, the profit in each region will decrease compared to the reference scenario. The highest reduction rate belongs to Asbforushan 2 area with 28% compared to the reference scenario.The profit in all the sub-bases had a rise after increasing irrigation efficiency scenario. Thus, applying increasing efficiency scenario, in addition to more useful and efficient use of allocated water, will also increase farmers' profits which offer a better situation than the scenario of reducing the share of agricultural water.Among the studied crops, bean had the highest reduction in cultivation, which stemmed from its high-water requirement. However, potato also had a high water requirement compared to bean but maintained a high cropping area due to higher gross profits. Findings of the current study revealed that wheat and barley had more resistance against the effects of climate change and shifting the patterns of cropping was an adaptive strategy for coping with the effects of climate change.Climate change reduces the labor employment. As Implementing A2 scenario results in a 14.48% decrease in the average of agricultural employment in the area. The agricultural water reduction scenario alone results in 5.9% decrease in labor, whereas the increasing irrigation efficiency scenario has an 8.9% decrease. Applying the agricultural water reduction scenario along with climate change reduces the employment by 17.2% in the region by reducing the area under cultivation of crops that require a lot of labor. The increasing irrigation efficiency along with climate change scenario also results in a 20.9% reduction in labor employment.
Conclusion: Overall, the findings of the current study revealed that without changing the management strategies there would be a considerable reduction in crop yield in near future. Optimizing management methods, selection of right time for crop cultivation, optimized harvest, studying the feasibility of cultivating crops with shorter growth period and using cultivars with higher yield are the effective ways to confront the effects of climate change. The analysis of scenarios revealed that policies alone cannot compensate for water problems and there is a need for plenty of scenario for optimum results.

Keywords

• Ajichay Basin
• Hydro-Economic Model
• Quadratic Risk Programming
• WEAP-MABIA

20.1001.1.20084722.1399.34.4.7.6

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