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Problem statement:The purpose of the study was to find the optimal cropping pattern, in Taybad, which maximizes the net return per water cubic meter and per fertilizer kilogram. Approach: A linear programming model and a fuzzy multi-objective fractional programming model were applied and then these models were compared. Results: Result of study showed ratio of net return into consumption of inputs and Ratio of consumption of inputs into area under cultivation are improved with applying of FMOLFP. Conclusion: FMOLFP models can be used as an effective tool for optimal cropping pattern when in addition to economical goals, environmental goals are noticed. Managers and decision makers can apply these models for optimization of ratio of objectives.

Barley is one of the main crops after wheat and rice. The importance of this product increases because it is an essential input in the livestock and poultry industries. The prices of input, which used in the livestock and poultry industries, faced fluctuations in recent years. Thus, in this study, the price fluctuation of barley in the Iran Mercantile Exchange, Iran domestic free market and World Market compared by applying the GARCH model. This model applied to monthly prices of barley from March 2009 to February 2017. Also, the volatility and shock transmission of barley price between these three markets analyzed by the BEKK model. The results showed that the price fluctuations of the domestic market are more than the global market. In addition, the shocks and volatilities of the world and Iran free market transmitted to the Iran Mercantile Exchange. Thus, the use of new financial instruments in the domestic free market is necessary.

Different regions of Iran are influenced by dust storms since they are located in the arid and semi-arid zone and due to their plain structure, the penetration of different synoptic systems, and adjacency with desert and important dust centers. This research investigates the effect of dust sedimentation on the yield of irrigated and rainfed wheat in four neighbouring provinces and on wheat production in Iran (Khuzestan, Ilam, Kermanshah, and Lorestan). Data are for the crop years from 2011–2012 to 2018–2019. For higher accuracy, the research studies irrigated and rainfed wheat systems separately with the panel model of the Just–Pope function. In the Just–Pope stochastic function, the effects of inputs on two indices of mean yield and yield variance are studied simultaneously. Based on the results, the risk function of irrigated wheat is significantly influenced by the variables of machinery, labour, and dust at the 1% level and by the variables of water and precipitation at the 10% level. The most influential variable in this function is dust (3.03%). In the risk function of rainfed wheat, all variables have a significant impact on production fluctuations at the 1% level. The most influential factor is the temperature (0.86%). Furthermore, the results reveal that the variable of the number of dusty days is significant in the production of irrigated and rainfed wheat at the 15% and 1% levels, respectively. The effect of this variable on rainfed wheat is negative and 0.101%. In this regard, it is necessary to develop dust-specific projects in the agricultural sector and estimate the cost imposed by this phenomenon.

This study tries to evaluate economic resilience and sustainability in industrial dairy cattle farms. Resilience explains how well production systems withstand or rebound from aberration. This includes preserving, and restoring agricultural systems under threats that impact national economic development. A dairy farm is a type of agricultural system, production quality and profitability of them are a dominant feature of the economic resilience. These features are used in a multi-indicator modeling approach to assess the economic resilience of 30 dairy farms in Khorasan Razavi Province (Iran). Total revenue and total cost across all farms are 5.18×10 09 IRR, and 2×10 09 IRR per day, respectively. The average milk quality is 0.52, which indicates the low-level milk quality. To evaluate the economic resilience indicator (ERI) for dairy farms, we developed an economic resilience indicator using profitability and milk quality which are normalized between zero and one. The economic resilience indicator was obtained using the arithmetic mean. The proposed indicator across all farms is about 0.49, meaning that dairy farms are at the low-level. Hence, to increase the economic resilience of dairy farms, it is essential to improve the revenue and production quality of these units while reducing the total cost.