Increasing population and food demand, disproportionate cultivation and annual production of various agricultural products with market needs and low productivity of the agricultural sector and the loss of water and soil resources have made it necessary to determine and implement the country's optimal cropping pattern. In this study, due to the limitations and problems of classical methods in order to reduce processing time and improve the quality of solutions, the Multi-Objective Chaotic Particle Swarm Optimization was used to determine the optimal cultivation pattern of Sistan plain in optimal conditions and deficit irrigation. The results of the Multi-Objective Chaotic Particle Swarm Optimization for the dominant cultures in the region showed that the current cropping pattern of the region is not optimal and with the implementation of the proposed model, the profit per unit area under cultivation will increase. The results of application of deficit irrigation during different growing periods of wheat, barley, alfalfa, sorghum, watermelon and grapes showed that applying deficit irrigation in this plain is not a good strategy and therefore only a full irrigation strategy is recommended. The results of sensitivity analysis of the model showed that at low prices, farmers reaction is less and at higher prices more reaction to price changes and with increasing prices, the program efficiency is lower.

In this study, the optimum cropping pattern in Rey County of Iran was determined using Linear Programming model (conventional), considering various degrees of risk using risk models of MOTAD, Target MOTAD and Advanced MOTAD. The required data were collected through field study and filling out 149 questionnaires from beneficiaries of the county as well as the concerned Agricultural-Jehad Management and subordinate organizations and offices in five farming years of 2010-15. The results from linear programming model showed that compared to the present status, applying the optimized cropping pattern made 6. 69 percent increase in programmed output. Also, the results from estimation of risky models suggested that there was a positive relationship between risk and the farm’ s programmed output. Again, risky models in highest possible level of risk indicated results similar to those of linear programming, and the pattern presented by linear model was placed at maximum possible level of risk.

Introduction The optimized cropping pattern can not only sustainably preserve water resources but bring more income as well. Therefore, since no research has been done on optimizing cropping pattern in the Shahdad county, this study identifies the optimized cropping pattern in this region with the goal of both maximizing farmers’ gross profit and decreasing water consumption. Materials and Method Since more than 90 percent of the current cropping pattern in Shahdad is cultivated with the four following crops: irrigated barley and wheat, garlic, and Alfalfa, the needed data were collected during 2016-17 crop year, from 450 farmers who cultivate these four crops simultaneously. 106 farmers were selected for face-to-face interview by using questionnaire and based on simple random sampling. The ant colony meta-heuristic model based on binary knapsack problem to achieve the optimized cropping pattern was used. Results and Discussion The ACO algorithm showed that the cultivated area of irrigated barley, irrigated wheat, garlic, and Alfalfa changed from 509, 408, 617, and 1124 Ha in the observed cropping pattern to 421, 588, 998 and 651 Ha in the optimized cropping pattern, respectively. Therefore, the gross profit, by 282. 96%, has increased from 201. 59 billion Rials in the observed cropping pattern to 772 billion Rials in the optimal cropping pattern. Suggestion Results showed that optimized cropping pattern in addition to saving 5% of water consumption, will increase gross profit to 282. 96%. Therefore, it is suggested to change cropping pattern based on results of this study.

Selection of suitable crops for cultivation in an uncertain environment is considered as an important management topic in the agricultural sector. When faced with uncertainty, the only solution is to use subjective judgmental of persons in domain field rather than historical data. Based on the provided evidence, the quantification of subjective judgmental in the framework of probability theory and risk programming is not true and should be carried out in another theory called the uncertainty theory and uncertain programming method. With perception these conditions and considering that the agricultural sector is always faced with uncertain variables such as price of crops and weather conditions such as rainfall, in this study the optimal cropping pattern of in Goharbaran region of Sari was determined using uncertain programming in terms of uncertainty in rainfall and crops price. To elicitation the uncertainty distribution of these variables based on the subjective judgments of the farmers, 42 farmers were questioned randomly through cluster sampling in 2017. Subsequently, by calculating a causal relationship between rainfall and crops yield, uncertainty distribution of yield was also extracted and thus expected profit were calculated based on uncertainty theory. In order to calculate and minimize the uncertainty of the model, a Tail Value at Risk index was used. The results showed farmers that predict much uncertainty for prices and rainfall, it is advisable to growth the Tarom rice and tomatoes and to prevent Shiroodi rice and watermelon in order to deal with uncertainty and achieve a certain expected profit.

Agricultural development depends on the optimal use of resources and the optimal use of resources depends on the optimal allocation of agricultural land to different crops. Every farmer cultivates different crops during a growing season, but there is no certainty that this cropping pattern used by the farmer is the optimal cropping pattern. Considering the importance and role of optimal cultivation pattern in the sustainable production of agricultural products, the purpose of this study is to present the optimal cultivation pattern of crops in Gorgan under three scenarios: Optimization of economic efficiency, production maximization and simultaneous maximization of economic efficiency and production with emphasis on Water consumption management. In this regard, in order to achieve the optimal cultivation pattern with the desired goals, the genetic algorithm was used and to obtain the need for irrigation, CROPWAT software was used. The studied crops include wheat, barley, and rice, soybeans, sunflower, beans, and cotton and fodder corn. The results showed that the current cultivation pattern of the region was not optimal. Based on the results of Optimization with a genetic algorithm, forage corn crop has been removed from the crop pattern and the area under rice cultivation has decreased compared to the current crop pattern in the region, in all Optimization scenarios. The area under sunflower and soybean crops increased in all three scenarios and replaced rice and fodder corn crops. Wheat and soybean crops have the highest area under cultivation in Optimization scenarios and the lowest area under cultivation in all three scenarios is related to cotton. In the current situation, the total water consumption is 213.626 × 106 m3, which decreases to 234.277 × 106 m3 after Optimization.

Agricultural sector is the largest water consumer and uncertainty is an inevitable aspect of water management in this sector. In this study, fully fuzzy linear programming using two different solutions were applied for multi-objective optimizing of cropping pattern and net benefit in the uncertain conditions for the Zarrinehroud Basin. The uncertainties within the optimized cropping pattern were considered using a fuzzy method. Moreover, in order to consider the uncertainties in the available water limitation, three different hydrological conditions were applied to determine maximum, average, and minimum of fuzzy bond. The results showed an increase of %2.53 in net benefit comparing to the crisp Optimization and an increase of %36.34 comparing to the present cropping pattern through decreasing low income crops substituted by high income crops. It is also shown that applying fully fuzzy linear programming instead of crisp linear programming leads to a greater saving in water consumption with the amount of %88.22. Considering %10 and %20 uncertainty bands for Optimization parameters caused more water saving and net benefits from optimal cropping pattern. Therefore, Optimization of cropping pattern through considering uncertainties based on fuzzy method leads to a more optimal planning for agricultural water.

This paper focuses on the theory and application of fuzzy linear goal programming model in Optimization cropping pattern. In this relation, sum total membership degrees for all fuzzy goals were maximized in the model. This, versus crisp goal programming model, allows the decision makers to choose importance and access degree of membership function. Also this method is capable of creating consistent membership function under decision maker's expectations. Technical coefficient in this model is not crisp, and changes in the limited area. This paper, beside an analysis of the theory of fuzzy linear goal programming, applied an Optimization cropping pattern for irrigated lands located in Haraz plain in north of Iran. Results indicated that imposing flexibility at technical coefficients and RHS in model caused improvement in resource allocation and cropping land developed.

Introduction: The occurrence of successive droughts, along with increasing water needs and lack of proper management of water resources has caused a water crisis that has various environmental and economic consequences. In addition to the drought, the change in the cropping pattern towards water crops has also made the water crisis the first critical phenomenon in recent years in the community, which has a direct impact on the agricultural sector as the largest consumer of water. Therefore, optimizing the cropping pattern is one of the most important factors in managing water resources and coping with water shortages. In this study, to determine the optimal cropping pattern of major crops in Silakhor plain in the next three years using two approaches using Linear Programming and Meta-Heuristic Algorithms. Materials and Methods: In the first step, in order to determine the optimal cropping pattern with the aim of maximizing farmers' incomes in the next three years and the limited water and land available, the amount of rainfall recharge is used as a criterion to determine the water exploitation interval and determine the minimum and maximum exploitation each year. In order to forecast rainfall, SARIMA time series models and Genetic Programming were used considering the data of the last 10 years in both seasonal and monthly modes, and according to RMSE and D. C. criteria, a better model was selected. Then, for each crop year, 100 exploitation scenarios were determined according to the amount of groundwater recharge caused by rainfall and the amount of exploitation in previous years. In the second step, Linear Programming was used to determine the optimal cropping pattern with the aim of maximizing farmers' incomes and limitations of exploitable water in each scenario and arable land. The price of each product is projected according to the average long-term inflation of the country, i. e., 20%, and the profit from the cultivation of each product was calculated as a proportion of the price of the product in each year by examining the previous years. Finally, the performance of three types of Static, Dynamic, and Classified Dynamics Penalty Functions into two algorithms, Differential Evolution and PSO was investigated to achieve the results obtained from Linear Programming. Static penalty functions use a constant value during the Optimization process, whereas in dynamic penalty functions, the fines are modified during the process and depend on the number of generations. In the classified dynamics penalty, groups of violations are also determined, and the penalty of each response is determined according to the amount of violation of the restrictions and the generation number. Results and Discussion: The results show that with increasing groundwater exploitation, farmers' incomes also increase,However, in the exploitation of more than 223. 5, 222. 2, and 225. 1 million cubic meters for the cropping years 2020-2021, 2021-2022, and 2022-2023, respectively, the limitation of the total arable land has prevented the increase of the area under cultivation, and by increasing exploitation, farmers' incomes remain stable. Also, in order to cultivate four crops of wheat, barley, rice, and corn with the current area under cultivation in Silakhor plain, 142 million cubic meters of water is harvested annually from underground sources. By optimizing the cropping pattern for the four crops studied, with the current water exploitation, the income of farmers in the region will increase by 18%. In general, the PSO algorithm answers this problem much faster. The average number of iterations of the PSO algorithm to solve each scenario in this problem is 38% of the number of iterations of the Differential Evolution algorithm. Overall, in solving this problem, the PSO algorithm has performed better in 84% of the scenarios. In penalty functions, the best performance in both algorithms belongs to the classified dynamics, dynamic, and static penalty functions, respectively. By changing the penalty function from static to classified dynamics penalty function, the number of iterations of the Differential Evolution algorithm to achieve the Linear Programming solution is reduced by an average of 11%,In contrast, the PSO algorithm did not react significantly to the change in the penalty function, and its repetitions decreased by an average of only 3%. Conclusion: The results show that the cropping pattern of the region is not optimal, and with the increase of water exploitation, it will move towards the cultivation of water products. Also, by optimizing the cultivation pattern of the region, farmers' incomes can be increased. Examination of Differential Evolution and PSO algorithms with three types of penalty functions also show that using the classified dynamics penalty function in the PSO algorithm can have good results.

Optimization of cropping pattern is one of the most important methods to increase water productivity and protect the limited water resources throughout the country. The objective of this study was Optimization of cropping pattern in Dehloran plain based on spatial variations of water quality, water availability, chemical and physical characteristics of soil, and groundwater level. To this end, the Dehloran plain was divided into four zones: area covered by the Meymeh networks, Doyraj, Tropical systems and lands covered by wells. Then, AquaCrop-GIS software was calibrated and validated by filed data. Finally, the production functions were extracted and the cropping pattern was optimized using the linear programming method and the objective function of maximum net benefit. The results showed that AquaCrop-GIS are a robust tool for analyzing spatial variation of parameters affecting yield and cropping pattern in a plain. Moreover, crop pattern Optimization related to water quality and quantity features beside soil physical and chemical properties could be influential on the net benefit and water productivity to be increased by 30 to 120 percent in different regions of Dehloran plain.

Increasing the resources productivity using cropping pattern Optimization is a proper way for agricultural development. In this study, the ideals’ realization possibility of maximizing the gross margin in compromising with reducing the water consumption, minimizing the fertilizers uses, minimizing the production risk, and maximizing the social benefits of cropping pattern were analyzed using a multi-objectives fuzzy non-linear programming model in Marvdasht City of Fars Province. In this approach, the crop area is optimized to maximize the weighted sum of fuzzy objective in the range of their given bearing bounds. Results show that in many cases the possibility of complete ideals realization in the multiple goals model in comparison with single goal patterns was not observed. Considering the outcomes and the relevant weight assigned to each of the goals by the decision maker consisting of the Fuzzy Composite Distance Function reveals that the cropping patterns base on multiple goals are superior to current patterns and even single goal pattern in supply of multiple compromised ideals. Implementation of these models in study areas has significant influence on reducing water use as well as increasing the gross margin and reducing fertilizer use and risk.