نوع مقاله : مقالات پژوهشی به زبان انگلیسی
نویسندگان
1 گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه کردستان، سنندج، ایران
2 گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه تهران، تهران، ایران.
چکیده
وجود ریسک در فعالیت‌های کشاورزی واقعیتی انکارناپذیر است. لذا، نادیده گرفتن این عامل منجر به عدم تخصیص بهینه منابع در این بخش می‌شود. تئوری‌ها و الگوهای برنامه‌ریزی ریاضی مختلفی برای تصمیم‌گیری در مورد مدیریت الگوی کشت محصولات، در شرایط وجود ریسک، وجود دارد. در این پژوهش، الگوی کشت بهینه محصولات زراعی دشت دهگلان در استان کردستان، براساس آمار و اطلاعات سال‌های زراعی 1393 الی 1402، با استفاده از مدل برنامه‌ریزی خطی با هدف حداکثرسازی درآمد ناخالص کشاورزان این منطقه برآورد گردید و سپس با الگوی حاصل از مدل برنامه‌ریزی درجه دوم و مدل موتاد با لحاظ حداقل سازی ریسک مقایسه گردید. نتایج نشان داد که در صورت نادیده گرفتن عامل ریسک، الگوی کشت به طور قابل توجهی تغییر می‌کند. در سطح ریسک 1780133/7 میلیون تومان، که بالاترین سطح ریسک می‌باشد، الگوی کشت تنها شامل خیار، یونجه و کلزا می‌باشد، در این شرایط، تمایل به سمت کشت محصولات با درآمد ناخالص بالاتر افزایش می‌یابد، هر چند که این محصولات به آب بیشتری نیاز دارند. همچنین رابطه بین ریسک و درآمد ناخالص مزرعه در دو مدل ریسکی تخمین زده شد. پس از تخمین مدل‌های ریسکی و با استفاده از تغییر درآمد مورد انتظار، مدل‌های بهینه مختلف تحت شرایط ریسک‌های متفاوت تعیین گردید. با وارد کردن ریسک، سطح زیرکشت گندم و جو نسبت به حالت عدم توجه به ریسک افزایش یافت. به عبارت دیگر، وجود ریسک منجر به گرایش به سمت محصولات با نیاز آبی پایین‌تر، به طبع موجب کاهش درآمد ناخالص می‌شود. بنابراین نتایج حاصل از این مطالعه لزوم بازنگری در سیاست‌های دولت به منظور حداکثرسازی درآمد کشاورزان و پایداری تولید می‌باشد
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
Determining the Optimal Cropping Pattern with Emphasis on the Interaction Between Risk and Profitability: Farmlands of Dehgolan Plain, Kurdistan Province, Iran
نویسندگان [English]
- M. Ghasabi 1
- M. Haji Rahimi 1
- H. Ghaderzadeh 1
- R. Shankayi 2
1 Department of Agricultural Economics, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran.
2 Department of Agricultural Economics, Faculty of Agriculture, Tehran University, Tehran, Iran
چکیده [English]
Introduction
Agriculture 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 Methods
This 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 Discussion
The 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.
Conclusion
Risk 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.
کلیدواژه‌ها [English]
- Cropping pattern
- Risk model
- Linear Programming model
- Quadratic Programming model
- MOTAD model
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