Agricultural Economics
Mahsa Ghasabi; Mahmood Haji Rahimi; Hamed Ghaderzadeh; Razieh Shankayi
Abstract
IntroductionAgriculture has always involved various degrees of risk, with farmers contending with numerous potential threats that can disrupt their livelihoods and productivity. These risks are compounded by agriculture's high dependency on climatic conditions, rendering it particularly vulnerable to ...
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IntroductionAgriculture has always involved various degrees of risk, with farmers contending with numerous potential threats that can disrupt their livelihoods and productivity. These risks are compounded by agriculture's high dependency on climatic conditions, rendering it particularly vulnerable to the adverse effects of climate change. Shifting weather patterns, extreme temperatures, and unpredictable rainfall can drastically affect crop yields, posing a serious threat to both farmers’ income and food security. Additionally, the agricultural sector faces ongoing challenges from pests, diseases, and fluctuations in market prices, further destabilizing the livelihoods of farmers who rely on steady production for economic sustainability.Beyond environmental factors, agricultural activities are influenced by a host of political, social, and economic risks. Environmental challenges, production limitations, legal constraints, financial hardships, and marketing uncertainties all represent significant risk categories that can impact farming operations. Natural disasters, such as floods, droughts, and storms, often lead to reduced productivity, directly diminishing farmers’ incomes. Furthermore, health and well-being concerns for farmers add another layer of risk, as labor-intensive farming activities require both physical resilience and long-term health, which can be compromised by lack of access to medical services and health risks associated with exposure to pesticides and other chemicals. As the global population grows, demand for food continues to rise, but the essential resources needed for agriculture, such as arable land and water, are finite. This increasing demand coupled with limited resources has placed substantial pressure on farmers, who must navigate these compounding risks while striving to meet production needs. These challenges underscore the need for effective risk management tools that can support farmers in making informed decisions regarding crop selection, resource allocation, and overall farm management strategies. One of the most versatile risk management tools available is the cropping pattern, or the arrangement of crops within a given agricultural area over a specific time period. Determining an optimal cropping pattern that takes risk factors into account is crucial for enhancing profitability and resilience in farming. Therefore, this study aims to determine the optimal cropping pattern for farms in the Dehgolan Plain, located in Kurdistan Province, under both risk-free and risk-sensitive conditions. By focusing on maximizing farmers' gross income while accounting for factors such as water availability and market conditions, this research seeks to provide practical recommendations for sustainable agricultural practices.Materials and MethodsThis study examines the cropping patterns of major crops cultivated in the Dehgolan Plain, Kurdistan Province, using data spanning the agricultural years from 2014 to 2023. To establish an optimal cropping pattern, the analysis considers various production constraints and resource limitations specific to this region. The goal is to develop a cropping model that maximizes gross income for farmers under both risk and no-risk scenarios. Several mathematical programming techniques were employed to achieve this objective. The Linear Programming (LP) model, widely used in agricultural studies for determining optimal cropping patterns under conditions of certainty, served as the foundation for this analysis. The LP model optimizes crop selection to maximize gross income while adhering to constraints such as water availability, land area, and resource limitations specific to the Dehgolan Plain. In addition to the LP model, nonlinear programming approaches, specifically Quadratic Programming and the Minimization of Total Absolute Deviation (MOTAD) model, were implemented to assess the cropping pattern under conditions of risk. These models allow for the incorporation of income volatility and risk into decision-making, helping to capture the trade-offs between risk and income potential. The Quadratic Programming model, which can handle non-linear relationships, is suitable for cases where increasing returns or diminishing marginal gains are present. Meanwhile, the MOTAD model assists in achieving minimum income variability, thus offering farmers a more stable income flow in unpredictable economic and climatic conditions.Results and DiscussionThe analysis revealed notable differences in the cropping patterns under risk and no-risk scenarios. Under no-risk conditions, as optimized by the Linear Programming model, the cropping pattern favored crops with higher gross incomes per hectare. This led to a significant reduction in the cultivation of wheat, barley, and potatoes, as these crops did not yield the highest economic returns. However, despite their relatively low profitability, wheat and barley remain essential for their lower water requirements and the security provided by government-guaranteed purchase programs. As a result, farmers may be reluctant to reduce the acreage of these crops due to their inherent risk-mitigation benefits. In the risk-sensitive scenario, modeled through Quadratic Programming and MOTAD, a positive correlation was found between risk levels and gross income. As farmers sought to maximize their income, the cropping pattern initially reflected a concentration of higher-income crops. However, as income maximization goals became tempered by risk considerations, crop diversity increased, indicating a clear trend toward diversification as a viable risk management strategy. This finding aligns with previous studies that emphasize crop diversification as a way to stabilize income and mitigate yield risks in agricultural systems prone to volatility. The MOTAD model, in particular, highlighted the trade-offs between risk and expected income. For example, a modest income increase of approximately 0.59% (from 1,700,000 million tomans to 1,710,000 million tomans) resulted in a substantial increase in risk, with the standard deviation, a measure of income variability, rising by 17.65%. This illustrates that achieving marginal income gains in agriculture often comes at a steep cost in terms of heightened risk. Furthermore, the cropping pattern varied significantly at different risk levels. At the highest risk threshold, which yielded an expected income of 1,780,133 million tomans, the cropping pattern included high-value crops such as cucumber, alfalfa, and canola, while reducing lower-value crops. Conversely, as the income expectation decreased and risk levels were minimized to 1,060,285 million tomans, these higher-income crops were scaled back, favoring more stable, government-backed crops like wheat, barley, and potatoes. This shift suggests that farmers, when presented with risk-reducing incentives, may gravitate toward crops with guaranteed purchase agreements and lower input costs, prioritizing stability over potential profit. Both risk models underscore the importance of balancing income maximization with risk minimization, as farmers seek to secure stable returns in an environment where crop failures or price declines could have significant impacts on household livelihoods.ConclusionRisk is an inevitable aspect of agriculture, and the findings of this study suggest that risk-sensitive models, such as MOTAD, enhance cropping pattern decision-making by incorporating income variability. The results demonstrate that under high-risk scenarios, increasing the cultivation area of crops like wheat aligns with government policies aimed at food security, as these crops provide a stable, government-supported income stream. This study also recommends adopting multi-cropping systems and crop rotation as effective strategies for reducing income variability and enhancing resilience. By diversifying cropping patterns, farmers can manage risk more effectively and contribute to the long-term sustainability of agricultural systems in regions like the Dehgolan Plain, where climate, market, and resource limitations impose unique challenges. Future studies could build upon these findings by examining the impact of additional risk factors, such as climate projections and market trends, to further refine optimal cropping strategies for vulnerable agricultural regions.
Agricultural Economics
H. Fouladi; H. Amirnejad; S. Shirzadi Laskookalayeh
Abstract
IntroductionIn recent decades, the issue of climate change has become one of the global issues and has affected the agricultural sector. The continuation of agriculture regardless of the water shortage crisis has had an inappropriate effect on the sustainability and growth of this sector. On the other ...
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IntroductionIn recent decades, the issue of climate change has become one of the global issues and has affected the agricultural sector. The continuation of agriculture regardless of the water shortage crisis has had an inappropriate effect on the sustainability and growth of this sector. On the other hand, the destructive effect of excessive use of chemical fertilizers and pesticides on water, soil, health of ecosystems, humans and other living beings is undeniable. For this reason, the void of using an efficient model that can provide all economic and environmental aspects at the same time was completely felt. The aim of this study was to provide an optimal cropping pattern using the integrated method of Goal and Grey planning. For this purpose, the farmers of the agronomy sub-sector of Tajan Basin were selected as the statistical population. In this regard, time series information was collected from the aggregation of the average data of 401 settlements located in this area during the years 2017-2021 from the annual reports of experts. Materials and MethodsThe Linear Programming (LP) Model quantifies an optimal way of integrating constraints to satisfy the objective function to optimize crop production and profits for irrigation farmers. To use LP, one must convert the problem into a mathematical model. To do this, an objective such as maximizing profit or minimizing losses is required. The model must also include decision variables that affect those objectives, and constraints that limit what user can do. Therefore, the LP Model is a single-objective method. Goal Programming (GP) is an extension of LP in which targets are specified for a set of constraints. GP is used to perform three types of analysis: Determining the required resources to achieve a desired set of objectives. Determining the degree of attainment of the goals with the available resources. Providing the best satisfying solution under a varying amount of resources and priorities of the goals. Thus, the GP model is a multi-objective method. The Grey system theory is identified as an effective methodology that can be used to solve uncertain problems with partially known information. Grey modelling approach uses accident data for estimating the model parameters. The model can reflect the dynamics, balance the conflicting the multidimensional targets of cropping patterns, and promoting the sustainable use of cultivated land. For achieving different goals in unstable economic and environmental conditions, we used a Goal-Grey model that was obtained from the integration of Goal programing and Grey Programing. The Goal-Grey model, by considering the uncertainty in the data, leads to overlap between the economic and environmental goals and provides the scope of cultivation for the selected products. Results and DiscussionBy estimating the Linear Programming (LP) Model, crops like wheat and canola are removed from the cropping pattern, while the cultivation areas for barley and high-yielding long-grain rice increase by 644% and 31%, respectively. In contrast, the cultivation areas for high-quality long-grain rice and maize decrease by 89% and 10%, respectively. Implementing this model boosts the gross profit of farmers in the Tajan region by 14% solely through adjusting the crop composition, without altering the current input levels. Additionally, the findings show that applying the LP Model results in fertilizer savings of 5%, 13%, and 10% for phosphate, nitrogen, and potash, respectively. The amount of herbicide and fungicide consumption in the LP Model is exactly equal to the current model of the region. However, the implementation of this model will lead to a 5% increase in the consumption of insecticides poison. The amount of irrigation water consumption in the LP Model was calculated to be 2% less than the current model of the region. In addition, the results indicate that by estimating the Goal-Grey Model, only canola is removed from the cropping pattern. Also, in order to achieve the defined goals in this study, the cultivation area of wheat and maize should be equal to 208 and 7356 hectares respectively. However, the flexibility of input usage enables adjustments to other crop cultivation areas, facilitating high-quality long-grain rice production on 970 to 18,157 hectares. Plus, the cultivation area of long-grain rice can vary from 7654 to 9995 hectares. In this model, barley can be removed from the crop composition like the linear pattern or cultivated on a maximum of 2553 hectares. The implementation of the Goal-Grey model will lead to a maximum 2% increase in the gross profit of the farmers of Tajan region compared to the current model of this region. Also, by implementing the Goal-Grey Model, on average, phosphate, nitrogen, and potash fertilizer consumption is saved by 16, 27, and 20 percent, respectively. In addition, with the implementation of the Goal-Grey Model, the consumption of agricultural pesticides will decrease from 733 to 355 thousand liters on average. ConclusionThe LP Model is designed based on current regional conditions; however, as a single-objective model with fixed parameters, it lacks the flexibility to offer an adaptable program for farmers during drought or wet periods or when inputs are limited. Findings indicate that under current conditions, there is excessive use of chemical inputs and irrigation water. By accounting for data uncertainty, the Goal-Gray model addresses these limitations, balancing economic and environmental objectives and defining a cultivation range for selected crops. Acknowledgement We are grateful to the experts of agronomy management and plant conservation management of Mazandaran Province Agricultural Jihad Organization and Sari City Agricultural Jihad Management who cooperated in data collection. This article is taken from the preliminary results of a doctoral dissertation with material and intellectual rights related to Sari Agricultural Sciences and Natural Resources University, which is gratefully acknowledged.
Agricultural Economics
D. Jahangirpour; M. Zibaei
Abstract
Modern irrigation systems are considered as a way to both respond to the effects of climate changes and improve the water security. Applying such systems, save the water used in farming activities and consequently made some environmental challenges in terms of increasing energy consumption and greenhouse ...
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Modern irrigation systems are considered as a way to both respond to the effects of climate changes and improve the water security. Applying such systems, save the water used in farming activities and consequently made some environmental challenges in terms of increasing energy consumption and greenhouse gas emissions. Although some recent studies analyzed the relationship between water and energy in the agricultural irrigation systems, considering the objectives on productivity, adaptation, and mitigation in a cropping pattern optimization problem is necessary. Climate-Smart agriculture as a strong programming concept, addresses these three objectives and has created the potential for a "triple-win" solution. This study is an effort to fill the study gap on triple-win solution in modern irrigation by developing an integrated economic-hydrological-environmental model called WECSAM at the basin level using a hydrological model called WEAP. For this purpose, a multi-objective optimization model has been developed with the concepts of water footprint, energy footprint, and the greenhouse gas emissions in the context of CSA. We applied the model to the northern region of Bakhtegan basin called Doroodzan irrigation network located in Iran. The result of the WECSAM model indicated that by simultaneously optimizing the conflicting objectives of maximizing profit and minimizing water footprint, energy footprint, and CO2 emissions, as compared to the single-objective model of maximizing economic profit, the water footprint decreases by 8.2%, Energy footprint decreases by 21.2%, CO2 emissions decreases by 6.9% and profit decreases by 7.4%. The share of each system in irrigating the water-smart, energy-smart, and climate-smart cropping pattern is as follow: 54% for drip system, 26% for semi-permanent sprinkler system, 11% for surface systems, 8% for center-pivot, and <1% for classic permanent sprinkler system.
F. Baradaran Sirjani; M.R. Kohansal; M. Sabouhi
Abstract
Optimal allocation of water resources is an essential service in agriculture that must be considered by farmers. One of the most significant factors in optimal allocation of water resources in agriculture is to define optimal farm cropping pattern. In this study, in order to determine optimal cropping ...
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Optimal allocation of water resources is an essential service in agriculture that must be considered by farmers. One of the most significant factors in optimal allocation of water resources in agriculture is to define optimal farm cropping pattern. In this study, in order to determine optimal cropping pattern and water resources allocation in central district of Mashhad city (Toos village), the two-stages multi-objective fuzzy linear programming was used. The required data was collected through interviews with farmers of the study area and filling in 116 questionnaire using simple random sampling during the years 2012-2013.The results indicated that, optimal values in the two-stage multi-objective fuzzy linear programming model for maximizing gross margin is 239420100 Rials, for utilizing organic fertilizers is 3867.19 Kg, and for minimizing the consumption of irrigation water is 53645.62 square meters, which were modified in the second phase. The objective amount of chemical fertilizer was 817.80 kg., having no change in the second phase. The cropping pattern will be optimized, if the most area under cultivation being allocated to potato, then to barley, wheat, t, onion and sugar beet, while tomato and corn cultivation being removed. Results illustrate that, two-stage multi-objective fuzzy linear programming model in comparison with multi-objective fuzzy linear model yield better results in defining optimal cropping pattern and allocation of irrigation water to the study area.
M. Bahrami Nasab; A. Dourandish; M.R. Kohansal
Abstract
Determining the optimal pattern for growing crops in accordance with the availability of the resources and risk factors as well as the uncertainty of agriculture sector would help the farmers, managers and economic planners in selecting the type of the product and the level of cultivation. Fuzzy programming ...
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Determining the optimal pattern for growing crops in accordance with the availability of the resources and risk factors as well as the uncertainty of agriculture sector would help the farmers, managers and economic planners in selecting the type of the product and the level of cultivation. Fuzzy programming model with Interval programming approach is used in this study to determine the optimal cultivation pattern in the Esfarayen county while taking terms of uncertainty into consideration. The required data were gathered via 128 questionnaire and interviews with the farmers of the region as simple random sampling in 2013. Using different levels of Alpha cut in the model made the whole parameter-related fuzzy data to access the optimization process fractionally. Models assessments results with perspective to optimistic and pessimistic conditions in accordance with various Alpha cuts show the benefit increases in optimistic conditions while decreasing in conservative ones due to the increase in the level of uncertainty and risk or expansion of fluctuations periphery due to smaller Alpha cuts. Forage maize, Red beans and wheat in most case scenarios are economical as well as optimum crops for cultivation. In order to improve the farmers’ money-making situation along with optimal usage of production sources, encouraging and supportive policies to be performed by Agriculture- Jehad (Ministry of Agriculture) of Northern Khorasan or the Esfarayen county is recommended.
Gh. Dashti; P. Ghaderinejad
Abstract
Abstract
With consideration to the resource limitation, it seems necessary to design cropping pattern scientifically in order to increase productivity of production factors and to decrease production average costs. This studyaimed at identifying the optimum cropping pattern based on comparative advantage ...
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Abstract
With consideration to the resource limitation, it seems necessary to design cropping pattern scientifically in order to increase productivity of production factors and to decrease production average costs. This studyaimed at identifying the optimum cropping pattern based on comparative advantage in three counties of Ilam Province including Darreh Shahr, Ivan, and Shirvan chardaval. To calculate comparative advantage indexes, we used the dataset associated with the year 2011 . Moreover, linear programming approach was used to identify optimum cropping pattern. The results showed that the study counties do not have comparitive advategies in producing some of their products. . Comparing the linear programming results with the current situation, it is concluded that averagely 50 percent of the current products are acceptable based on the comparitive advantage index. Consequently, to economically adjust the current crop pattern, it is recommended to consider products with comparative advantege for each county.