Introduction: Poverty is recognized as one of the main human problems, especially in developing countries. Eradicating poverty in all its forms and everywhere by 2030 is the first goal among the 17 Sustainable Development Goals (SDGs) set by the United Nations. Therefore, one of the goals of agriculture is to reduce poverty, especially in developing countries. In addition to poverty, food insecurity has also become a global problem in rural areas over recent decades. Given the importance of the agricultural sector in the economy of developing countries, it is crucial to maintain agriculture as a source of income and to produce and supply the required food. Emphasis should be placed on the resources available to farmers and the factors affecting decision-making in resource allocation. Increasing agricultural production necessitates enhancing the productivity of scarce resources. The strong relationship between poverty and income links the issues of poverty and the optimal use of agricultural production factors. Official statistics show that poverty in Iran has increased in recent years. Therefore, this study aimed mainly at providing a Multi-objective Mathematical Programming (MMP) model to improve the poverty index in line with sustainable development and food security of the lands downstream of the irrigation and drainage networks of the Karun-e-Bozorg (Great Karun).
Materials and Methods: This study aimed to develop a mathematical programming model to improve the FGT poverty index and address objectives related to food security and water resource sustainability. The multi-objective mathematical programming method was used. Before explaining the proposed model, the FGT poverty index was reviewed and introduced. This index measures the distributional and income effects on poverty and indicates that poverty rates from different population subgroups can be aggregated to achieve a single poverty rate for the entire population. So, different objectives could be considered depending on the agricultural and livelihood situation in the lands under irrigation networks, including: 1) to minimize the FGT poverty index, 2) to maximize energy production from food consumption (food security), and 3) to minimize irrigation water consumption (environmental). Multi-objective programming methods require harmonizing the measurement criteria in different objectives. A multi-objective fuzzy nonlinear programming model derived from Jones & Barnes (2000) was used to harmonize the objectives. The required data was collected through interviews with experts from the Agriculture-Jahad Center of Ahwaz County of Iran in 2024 and from the Statistical Center of Iran (SCI). The areas studied included North East Ahvaz, Gotvand, Miyan-Ab, East Shoaybiyeh, which had different crops and horticultural crops depending on the climate and soil. The solution of the proposed models was carried out using GAMS software and the CONOPT algorithm, which uses the generalized simplex method to solve linear and nonlinear problems.
Results and Discussion: The study results showed that the poverty index minimization goal had the greatest impact on changing the optimal crop area of the study areas compared to current conditions. The irrigation water minimization scenario had the least contribution in this regard. The amount of water consumed in the multi-objective model was greater in winter and less in summer compared to the current conditions. Considering the triple objectives, the profits of all studied regions would increase compared to the current conditions. The highest profit growth would be related to the Gotvand region, and the lowest to the East Shoaybiyeh region. Under the goal of minimizing irrigation water, the poverty index for the four regions would not change. Considering other goals, the poverty index would decrease compared to the current conditions. By applying optimal conditions in the form of a multi-objective model, the poverty index in the Miyan-Ab region would decrease by 51 percent. Under the objective of minimizing irrigation water consumption, the amount of calorie intake resulting from crop production would not change compared to the current conditions. However, under multi-objective conditions, considering the objective of maximizing calorie production, the volume of calories produced would exceed 57 million calories. The data showed that the total calorie intake would increase by 39 percent under the optimal conditions compared to the current conditions.
Conclusion and Suggestions: The study findings indicated that the allocation of agricultural inputs in the studied areas was not optimal and the current cultivated area was very different from the optimal values. Under the optimal conditions, the cultivated area of beans, cucumbers, alfalfa, and rice in all four studied regions will decrease compared to the current conditions. However, the cultivated area of corn and wheat for all regions will increase under the optimal conditions. This study confirms that the optimal use of agricultural production resources will lead to increased profits, improved poverty, reduced water consumption, and increased calorie intake in the four regions. The proposed mathematical programming model will contribute to achieving sustainable development and improving food security. Encouraging farmers to adopt an optimal cropping pattern requires a combination of support policies and economic incentives. It is recommended to provide financial and credit facilities to farmers to purchase agricultural inputs and equipment needed to implement the optimal cropping pattern, hold training and extension courses for farmers on the benefits of the cropping pattern and optimal cultivation methods, and guarantee the purchase of farmers’ products at reasonable prices to create strong incentives for adopting the cropping pattern.