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Bank credit is one of the main sources of spending on productivity and economic services. However, because of the limitations in its amount, accurate planning is essential to optimize its allocation to applicants. Despite the total volume of credits allocated to the agricultural sector, as well as the large number of applicants and sub-sectors applying for these facilities, there is still no clear pattern for the optimal allocation of agricultural bank credits in Iran. It is bank managers who must decide on the distribution of financial capital in a competitive environment. Based on this fact, the paper investigates the optimum portfolio composition of the Agricultural Bank credits in accordance with optimistic, pessimistic, and collaborative strategies by using an interval non-linear multi-objective programming model and considering three different states in determining the rate of return using a genetic algorithm. The results showed that the current pattern of the distribution of bank credits is estimated as different from the optimal one. In the optimum patterns estimated in all states, the agriculture, agricultural services, animal husbandry, aviculture and greenhouses sections were assigned the largest shares in their optimum portfolio combination. Managers can choose their desired model according to three studied strategies and depending on the importance, different estimates of return, and risk of each of them.

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The present study estimates households' resilience against food insecurity in a selected village of Qalandar Abad district in Iran. The Resilience Index Measurement and Analysis (RIMA) of the Food and Agriculture Organization of the United Nations (FAO) was used for the first time in Iran, to achieve this goal. The samples included 149 farmers randomly selected and the Data were collected through interviews. The factor analysis method was used to estimate the components of resilience, and the MIMIC method was used to estimate the latent variable of resilience. The results showed that the components of asset and adaptive capacity had a significant role in increasing the resilience of rural households in the study area. Variables such as the land area, water availability, and the yield of crops had a significant positive role in improving the asset pillar. The households head and other members' education also had a significant positive effect in improving the adaptive capacity pillar. Therefore, due to time and budget constraints in the execution of macro-policies, adopting and implementing policies that increase the above components will improve the resilience of rural households