اقتصاد کشاورزی و توسعه

اقتصاد کشاورزی و توسعه

بهینه‌سازی چندمعیاره الگوی کشت محصولات زراعی دشت قزوین مبتنی بر توان تناسب اراضی

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی دکتری مدیریت و توسعة کشاورزی، دانشکدة اقتصاد و توسعة کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.
2 استاد گروه مدیریت و توسعة کشاورزی، دانشکدة اقتصاد و توسعة کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.
3 عضو هیئت علمی گروه تحقیقات اقتصادی، اجتماعی و ترویجی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی اصفهان، سازمان تحقیقات، آموزش و ترویج کشاورزی، اصفهان، ایران.
10.30490/aead.2026.367520.1692
چکیده
خاک از مهم‌ترین منابع تولید کشاورزی است که توان (پتانسیل) و توانایی آن نقشی تعیین‌کننده در تولید محصولات دارد. ارزیابی تناسب اراضی فرآیندی کلیدی در برنامه‏ریزی کاربری زمین و رویکردی مؤثر برای اطمینان از مناسب بودن خاک برای تولید محصولات مختلف است. مطالعه حاضر، در سال 1403، به‏ منظور بهینه‌‏سازی الگوی کشت محصولات زراعی دشت قزوین با اهداف اقتصادی، اجتماعی، زیست‌‏محیطی و امنیت غذایی، بر مبنای نتایج ارزیابی تناسب اراضی و با بهره‏‌گیری از یک مدل برنامه‌‏ریزی غیرخطی فازی چندهدفه (MOFNLP) انجام گرفت. داده‌‏های مطالعه از طریق ترکیب مطالعات اسنادی (داده‌های رسمی دستگاه‏‌های مختلف) و پیمایش میدانی (تکمیل 379 پرسشنامه) جمع‏‌آوری شد. نتایج حاصل از اجرای مدل و تحلیل مصالحه بین اهداف نشان داد که گروه‌‏های غلات و نباتات علوفه‌‏ای (که 93 درصد از الگوی کشت محصولات دشت را به خود اختصاص داده‌‏اند)، به‏‌ترتیب، با کاهش و افزایش سطح زیر کشت مواجه‌‏اند؛ در بین سایر محصولات نیز کاهش سطح زیر کشت برنج و افزایش سطح زیر کشت سورگوم و گوجه‌‏فرنگی قابل توجه بوده و همچنین، دستاوردهای الگوی پیشنهادی شامل افزایش 18/1 درصدی سود خالص اقتصادی، کاهش دو درصدی مصرف آب، افزایش 7/3 درصدی انرژی تولیدی (شاخص امنیت غذایی)، افزایش 0/5 درصدی اشتغال و کاهش 0/6 درصدی اثرات زیست‏‌محیطی است. از آنجا که اهداف و متغیرهای مد نظر مطالعة حاضر بر پایه نتایج ارزیابی تناسب اراضی و منطبق با اصول کشاورزی پایدار است، می‌توان انتظار داشت که اجرای الگوی پیشنهادی، نسبت به وضعیت فعلی، پایداری بیشتری در ابعاد اقتصادی، اجتماعی و زیست‌محیطی به ‏همراه داشته باشد. افزون بر این، با بهره‌گیری از نقشه‌ها و مکان‌های مناسب تولید محصولات مختلف بر اساس ارزیابی تناسب اراضی، امکان تسهیل و دقت بیشتر در پیاده‌سازی و نظارت بر اجرای الگو در دشت قزوین فراهم می‌شود.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Multi-Criteria Optimization of Cropping Pattern in Qazvin Plain Based on Land Suitability Potential

نویسندگان English

Esmaeil Nasr Esfahani 1
Khalil Kalantari 2
Ali Asadi 2
Alireza Nikooei 3
1 Ph.D Student in Agricultural Management and Development, Faculty of Agricultural Economics and Development, Campus of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
2 Professor, Department of Agricultural Management and Development, Faculty of Agricultural Economics and Development, Campus of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
3 Faculty Member, Department of Economic, Social and Extension Research, Agricultural and Natural Resources Research and Education Center of Isfahan, Agricultural Research, Education and Extension Organization (AREEO), Isfahan
چکیده English

Introduction: Land suitability evaluation plays a crucial role in land use planning by ensuring the proper use of soil for different crops. Cropping patterns are shaped by natural factors such as climate, water, and soil resources as well as economic, social, environmental, and policy considerations. Therefore, developing a comprehensive cropping pattern model is essential. Despite the long-standing importance and fertility of Qazvin Plain of Iran, previous studies in this region have mainly focused on specific aspects and have not integrated economic, social, and environmental goals. Moreover, land suitability evaluation has not been utilized as a basis for regional cropping pattern design. This study addressed these gaps by optimizing the cropping pattern of field crops in the Qazvin Plain through an integrated approach, considering the economic, social, environmental, and food security objectives based on the land suitability evaluation.
Materials and Methods: To determine the cropping pattern based on land capabilities and potentials, land suitability maps were used from Soil and Water Research Institute of Ministry of Agriculture-Jahad (MAJ). Additional data were collected through surveys and documentary sources and processed for programming model. Corp production costs and revenues data were obtained via questionnaires completed by farmers and local experts in 2024, while complementary data were gathered from agricultural yearbooks and reports of MAJ, the Qazvin Agricultural Jihad Organization, Agricultural Planning, Economics and Rural Development Research Institute (APERDRI), and Qazvin Regional Water Organization. A multi-objective fuzzy nonlinear programming model was applied to develop the cropping pattern, with linear programming used in other cases. The weights for solving the multi-criteria programming model were estimated from expert opinions in the agricultural sector using the Analytic Hierarchy Process (AHP) technique.
Results and Discussion: The implementation of the model and the trade-off analysis among objectives revealed modest changes in the distribution of crop groups relative to the current cropping pattern. The shares of cereals, forages, and vegetable-summer crops increased, while those of industrial crops and legumes declined. However, the model led to more substantial adjustments within the composition of the 22 crops cultivated in the plain. Notably, the cultivation area of rice decreased considerably, whereas the cultivation areas of sorghum and tomato increased relative to the current cropping pattern. The proposed model also generated several advantages compared to the current cropping pattern, including: net economic profit increased by 18.1 percent, water consumption decreased by 2 percent, energy production (as a proxy for the food security index) rose by 7.3 percent, employment improved by 0.5 percent, and environmental impacts were reduced by 0.6 percent. These results highlight the potential of the optimized cropping pattern to achieve higher levels of economic, social, and environmental sustainability in the Qazvin Plain.
Conclusion and Suggestions: Both cropping pattern optimization and land suitability evaluation aim to identify the most efficient use of land resources. Since the objectives and variables of this study were developed based on land suitability evaluation and aligned with the principles of sustainable agriculture, the proposed cropping pattern is expected to deliver greater sustainability than that of the current system in economic, social, and environmental dimensions. Moreover, incorporating land suitability results into cropping pattern optimization facilitates more accurate implementation and monitoring for planners and decision-makers. Accordingly, it is recommended that crop pattern design in different regions be carried out in full alignment with the land suitability evaluation. Such an integrated approach improves the accuracy of crop selection according to environmental capacities, ensures optimal use of resources, enhances production, and promotes more scientific and efficient management of the agricultural sector; thereby, fostering agricultural sustainability. In practice, this approach will be most effective when combined with cadastral studies of farming units in the Qazvin Plain. Therefore, for future research, it is suggested that the cropping pattern design be developed based on both land potential and cadastral information of agricultural lands in the plain. This can contribute to production planning, improvement of agricultural product distribution systems, and optimal water and soil management within the framework of integrated regional governance.

کلیدواژه‌ها English

Cropping Pattern
Multi-Objective Planning
Land Suitability
Qazvin Plain.
1.      Abdelbaki, A. M., & Alzahrani, A. S. (2024). Gradually optimization of cropping pattern in Saudi Arabia for sustainable agricultural development until 2030. Ain Shams Engineering Journal, 15(4), 102624.
2.      Ahmadi, A. (2022). The effect of increasing water use efficiency on improving the status of groundwater resources using WEAP model in Qazvin Plain. Water and Soil Management and Modeling, 2(1), 53-62. [In Persian]
3.      Akbari, M., Najafi Alamdarloo, H., & Mousavi, S. H. (2019). Impacts of climate change and drought on income risk and crop pattern in Qazvin Plain irrigation network. Water Research in Agriculture, 33(2), 265-281. DOI: 10.22092/jwra.2019.119742. [In Persian]
4.      Akinsunmade, A., & Ejieji, C. (2021). Land suitability and crop pattern model using integrated pollination intelligence algorithm and remote sensing. Earthline Journal of Mathematical Sciences, 5(1), 1-15.
5.      Aliaga, M. A., & Chaves-Dos-Santos, S. M. (2014). Food and nutrition security public initiatives from a human and socioeconomic development perspective: mapping experiences within the 1996 World Food Summit signatories. Social Science and Medicine, 104, 74-79.
6.      Amini, A. (2015). Application of fuzzy multi-objective programming in optimization of crop production planning. Asian Journal of Agricultural Research, 9(5), 208-222.
7.      Asaadi , M., Khalilian, S., & Mousavi, S. H. (2019). Effects of deficit irrigation simultaneously with reduced usage of fertilizer and chemical pesticides on changing cropping pattern in Qazvin irrigation network. Water Research in Agriculture, 33(1), 121-136. [In Persian]
8.      Brosseau, A., Saito, K., van Oort, P. A., Diagne, M., Valbuena, D., & Groot, J. C. (2021). Exploring opportunities for diversification of smallholders’ rice-based farming systems in the Senegal River Valley. Agricultural Systems, 193, 103211.
9.      Bulukazari, S., Babazadeh, H., Ebrahimi Pak, N., Mousavi-Jahromi, S. H., & Ramezani Etedali, H. (2022). Optimization of water and land allocation in salinity and deficit-irrigation conditions at farm level in Qazvin Plain. PlosOne, 17(7), e0269663.
10.   Chandra, S., & Aggarwal, A. (2014). On solving fuzzy linear programming problems: a revisit to Zimmermann’s approach. Journal of Intelligent and Fuzzy Systems, 27(5), 2603-2610.
11.   Chen, Y., Zhou, Y., Fang, S., Li, M., Wang, Y., & Cao, K. (2022). Crop pattern optimization for the coordination between economy and environment considering hydrological uncertainty. Science of the Total Environment, 809, 151152.
12.   Ehsani Kolikand, S., Bijan Nazari, B., Ramezani Etedali, H., & Sotoodehnia, A. (2023). Optimization of cropping patterns in agricultural wells with monthly and anually water volume restrictions (case study: Qazvin Plain). Water Management in Agriculture, 9(2), 31-44. [In Persian]
13.   Elliot, T., Bertrand, A., Almenar, J. B., Petucco, C., Proença, V., & Rugani, B. (2019). Spatial optimisation of urban ecosystem services through integrated participatory and multi-objective integer linear programming. Ecological Modelling, 409, 108774. 
14.   FAO (2017). The future of food and agriculture: trends and challenges. Food andAgriculture Organization of the United Nations (FAO), Rome, Italy.
15.   Fehér, A., Gazdecki, M., Véha, M., Szakály, M., & Szakály, Z. (2020). A comprehensive review of the benefits of and the barriers to the switch to a plant-based diet. Sustainability, 12(10). 1-18.
16.   Fleming, K., Kouassi, A., Ondula, E., & Waweru, P. (2016). Toward farmer decision profiles to improve food security in Kenya. IBM Journal of Research and Development, 60(5/6), 1-6.
17.   Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., & Gibbs, H. K. (2005). Global consequences of land use. Science, 309(5734), 570-574.
18.   Gholami, Z., Ebrahimian, H., & Noory, H. (2018). Prioritization of major agricultural crops cultivation considering the energy and water costs in Qazvin Plain. Irrigation Sciences and Engineering, 41(1), 17-30. [In Persian]
19.   Hacısüleyman, V., & Özger, M. (2024). Multi-objective cropping pattern optimization and comparative assessment with the food-energy-water nexus. Water Supply, 24(12), 4077-4093.
20.   Hao, L., Su, X., & Singh, V. P. (2018). Cropping pattern optimization considering uncertainty of water availability and water saving potential. International Journal of Agricultural and Biological Engineering, 11(1), 178-186.
21.   Hou, Z., Liu, Y., Wang, J., Manevski, K., & Zeng, Z. (2026). Multi-objective optimization framework for cropping structure based on water-carbon-economy nexus: large-scale case study in Northeast China. Field Crops Research, 340, 110367.
22.   Hussain, M. A., Riley, W., & Bekhit, A. E.-D. A. (2022). Trends and motivations for novel protein sources and contribution towards food security. In: Alternative Proteins (pp. 1-16). CRC Press.
23.   Itoh, T., Ishii, H., & Nanseki, T. (2003). A model of crop planning under uncertainty in agricultural management. International Journal of Production Economics, 81, 555-558.
24.   IWRMC (2019). Forbidden plains of Iran. Iran Water Resources Management Company (IWRMC). Available at https://www.danab.ir/wp-content/uploads/2020/09/total97.pdf. [In Persian] 
25.   Jones, D., & Barnes, E. (2000). Fuzzy composite programming to combine remote sensing and crop models for decision support in precision crop management. Agricultural Systems, 65(3), 137-158.
26.   Joolaie, R., Abedi Sarvestani, A., Taheri, F., Van Passel, S., & Azadi, H. (2017). Sustainable cropping pattern in North Iran: application of fuzzy goal programming. Environment, Development and Sustainability, 19(6), 2199-2216.
27.   Joulaei, R., Azar, A., & Chizari, H. (2005). Several regional planning models and its application in agriculture, case study of Fars province. Agricultural Economics and Development 13, 87-125. [In Persian]
28.   Karami, E. (2006). Appropriateness of farmers’ adoption of irrigation methods: the application of the AHP model. Agricultural Systems, 87(1), 101-119. [In Persian]
29.   Kelley, H. W. (1983). Keeping the land alive: soil erosion--its causes and cures. Food and Agriculture Organization (FAO), Rome, Italy.
30.   Kihoro, J., Bosco, N. J., & Murage, H. (2013). Suitability analysis for rice growing sites using a multicriteria evaluation and GIS approach in great Mwea region, Kenya. SpringerPlus, 2(1), 1-9.
31.   Kousar, S., Sangi, M. N., Kausar, N., Pamučar, D., Ozbilge, E., & Cagin, T. (2023). Multi-objective optimization model for uncertain crop production under neutrosophic fuzzy environment: a case study. AIMS Mathematics, 8(3): 7584–7605. 
32.   Li, J., Qiao, Y., Lei, X., Kang, A., Wang, M., Liao, W., Wang, H., & Ma, Y. (2019). A two-stage water allocation strategy for developing regional economic-environment sustainability. Journal of Environmental Management, 244, 189-198.
33.   Li, M., Fu, Q., Singh, V. P., Liu, D., Li, T., & Zhou, Y. (2020). Managing agricultural water and land resources with tradeoff between economic, environmental, and social considerations: a multi-objective non-linear optimization model under uncertainty. Agricultural Systems, 178, 102685. 
34.   Li, X., Kang, S., Niu, J., Du, T., Tong, L., Li, S., & Ding, R. (2017). Applying uncertain programming model to improve regional farming economic benefits and water productivity. Agricultural Water Management, 179, 352-365.
35.   Liu, Y. (2018). Introduction to land use and rural sustainability in China. Land Use Policy, 74, 1-4.
36.   Liu, Y., Fang, F., & Li, Y. (2014). Key issues of land use in China and implications for policy making. Land Use Policy, 40, 6-12.
37.   Long, H., Li, Y., Liu, Y., Woods, M., & Zou, J. (2012). Accelerated restructuring in rural China fueled by ‘increasing vs. decreasing balance’ land-use policy for dealing with hollowed villages. Land Use Policy, 29(1), 11-22.
38.   Loni, R., & Sharifzadeh, M. (2022). A review of water, energy, and food nexus in Iran: necessity, challenges and suggested solutions. Sustainability, Development and Environment, 3(3), 29-49. [In Persian]
39.   Lyu, J., Jiang, Y., Xu, C., Liu, Y., Su, Z., Liu, J., & He, J. (2022). Multi-objective winter wheat irrigation strategies optimization based on coupling AquaCrop-OSPy and NSGA-III: a case study in Yangling, China. Science of the Total Environment, 843, 157104.
40.   Mahmoodi, M., & Parhizkari, A. (2016). Economic analysis of the climate change impacts on products yield, cropping pattern and farmer’s gross margin (case study: Qazvin Plain). Economic Growth and Development Research, 1(2), 25-40. [In Persian]
41.   MAJ (2015). Qualitative assessment of land suitability for important agricultural crops in the Qazvin Plain. Agricultural Research, Education and Extension Organization (AREEO), Soil and Water Research Institute, Karaj, Iran. [In Persian]
42.   Majid, H. (1996). Systematic: agricultural geography. Rawat Publications.
43.   Mandal, V. P., Rehman, S., Ahmed, R., Masroor, M., Kumar, P., & Sajjad, H. (2020). Land suitability assessment for optimal cropping sequences in Katihar district of Bihar, India using GIS and AHP. Spatial Information Research, 28(5), 589-599.
44.   Manikas, I., Ali, B. M., & Sundarakani, B. (2023). A systematic literature review of indicators measuring food security. Agriculture and Food Security, 12(10), 1-31.
45.   Manos, B., Papathanasiou, J., Bournaris, T., & Voudouris, K. (2010). A multicriteria model for planning agricultural regions within a context of groundwater rational management. Journal of Environmental Management, 91(7), 1593-1600.
46.   Mardani Najafabadi, M., & Mirzaei, A. (2019). Evaluating effect of policy programs to achieve water resources stability objective in Qazvin Plain. Journal of Agricultural Economics Research, 11(3), 155-176. [In Persian]
47.   Mardani Najafabadi, M., Nikouei, A., Ziaei, S., & Ahmadpour, M. (2016). Codifying regional cropping pattern of agricultural and horticultural products in Isfahan province: multi-objective structural planning approach. Agricultural Economics and Development, 30(3), 188-206.  [In Persian] 
48.   Mardani Najafabadi, M., Ziaei, S., Nikouei, A., & Borazjani, M. A. (2019). Mathematical Programming Model (MPM) for optimization of regional cropping patterns decisions: a case study. Agricultural Systems, 173, 218-232
49.   Marzban, Z., Asgharipour, M. R., Ghanbari, A., Ramroudi, M., & Seyedabadi, E. (2021). Determining cropping patterns with emphasis on optimal energy consumption using LCA and multi-objective planning: a case study in eastern Lorestan province, Iran. Energy, Ecology and Environment, 7, 489-507.
50.   Matori, A., & Chandio, I. A. (2011). Land suitability analysis using Geographic Information System (GIS) for hillside development: a case study of Penang Island. International Conference on Environmental and Computer Science (IPCBEE Vol. 19, pp. 1–6). IACSIT Press, Singapore
51.   Meenambal, T. (2019). Environmental science and engineering. MJP Publisher.
52.   Mirzaei Bafti, M., Rahmani, S., & Parhizkari, A. (2019). The economic value of irrigation water, cropping pattern, and farmer gross margin under drought conditions: the case of the Qazvin Plain. Journal of Hydrosciences and Environment, 3(6), 32-42.
53.   Mohammadi, H., Boustani, F., & Kafilzadeh, F. (2012). Optimal cropping pattern using a multi-objectives fuzzy non-linear optimization algorithm: a case study. Water and Wastewater, 23(4), 43-55. [In Persian]
54.   Nikouei, A., Asgharipour, M. R., & Marzban, Z. (2022). Modeling land allocation to produce crops under economic and environmental goals in Iran: a multi-objective programming approach. Ecological Modelling, 471, 110062.
55.   Nikouei, A., & Mardani Najafabadi, M. (2021). Application of agricultural land cadastre in compilation of comprehensive and operational cropping pattern of farms: a case study in Isfahan province. Agricultural Economics and Development, 29(1), 235-266. [In Persian]
56.   Nikouei, A., Zibaei, M., & Ward, F. A. (2012). Incentives to adopt irrigation water saving measures for wetlands preservation: an integrated basin scale analysis. Journal of Hydrology, 464, 216-232.
57.   Nouri, H., Stokvis, B., Borujeni, S. C., Galindo, A., Brugnach, M., Blatchford, M., Alaghmand, S., & Hoekstra, A. (2020). Reduce blue water scarcity and increase nutritional and economic water productivity through changing the cropping pattern in a catchment. Journal of Hydrology, 588, 125086.
58.   Ojaghi, S., Bayat, R., Fazli , S., Keshavarz Turk, E., & Taheri , A. F. (2024). Determining the key drivers affecting employment and social welfare in ]ran: the approach of analyzing mutual effects in future study. Majlis and Rahbord, 31(117), 335-377. DOI: 10.22034/mr.2022.5406.5161.
59.   Okola, I., Omulo, E. O., Ochieng, D. O., & Ouma, G. (2025). Multi‐objective optimization of the food‐energy‐water nexus problem: a review of the key concepts and emerging opportunities in objective functions, decision variables, and optimization techniques. Earths Future, 13(4), e2024EF004718.
60.   Parhizkari, A. (2021). Economic analysis of the effects of increase saffron acreage on cropping pattern, the consumption of inputs and farmer’s gross profit in Qazvin Plain. Agricultural Economics Research, 13(1), 1-24. [In Persian] 
61.   Porter, J., Xie, L., Challinor, A. J., Howden, M., Iqbal, M. M., Lobell, D. B., & Travasso, M. I. (2014). Food security and food production systems. (pp. 485–533). Cambridge University Press. 
62.      Pozza, L. E., & Field, D. J. (2020). The science of soil security and food security. Soil Security, 1(1),100002. https://doi.org/10.1016/j.soisec.2020.100002
63.   Raheli-Namin, S., Mortazavi, M., Mobinifar, M., & Adeli, M. (2016). Groundwater quality probability mapping and assessment for domestic and irrigation purposes in Ghara-Su Basin of Golestan province. Iran. J. Mater. Environ. Sci, 7(1), 259-271
64.   Rahman, M. N. (2020). Crops pattern change and agricultural diversification: a case study of Domar Upazila, Nilphamari. International Journal of Agricultural Science and Food Technology, 6(1), 022-029.
65.   Randall, M., Schiller, K., Lewis, A., Montgomery, J., & Alam, M. S. (2024). A systematic review of crop planning optimisation under climate change. Water Resources Management, 38(6), 1867-1881.
66.   Roser, M., Ritchie, H., & Rosado, P. (2013). Food supply. Available at http://www.OurWorldInData. org.
67.   Sengupta, A., Pal, T. K., & Chakraborty, D. (2001). Interpretation of inequality constraints involving interval coefficients and a solution to interval linear programming. Fuzzy Sets and Systems, 119(1), 129-138.
68.   Shabanzadeh Khoashrody, M., Peykani, G., Hosseini, S. S., & Yazdani, S. (2019). Change from the purchasing price policy to the guaranteed price policy and its effects on cropping pattern in Qazvin Plain. Agricultural Economics Research, 11(1), 101-129. [In Persian]
69.   Shirshahi, F., Babazadeh, H., Ebrahimi Pak, N., & Khaledian, M. R. (2021). Optimization of water allocation and optimal cropping pattern in irrigation and drainage network of Ghazvin Plain. Irrigation Sciences and Engineering, 44(3), 103-116. [In Persian]
70.   Shokoohi, A., Ramezani Etedali, H., Mojtabavi, S. A., & Singh, V. P. (2016). Using water footprint accounting for optimizing crop patterns respecting sustainable development (case study: Qazvin Plain). Iran- Water Resource Research, 12(3), 99-113. [In Persian]
71.   Song, M., Xie, Q., Shahbaz, M., & Yao, X. (2023). Economic growth and security from the perspective of natural resource assets. Resources Policy, 80, 10315.
72.   Tilman, D., Balzer, C., Hill, J., & Befort, B. L. (2011). Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences, 108(50), 20260-20264.
73.   Tilman, D., Socolow, R., Foley, J. A., Hill, J., Larson, E., Lynd, L., Pacala, S., Reilly, J., Searchinger, T., & Somerville, C. (2009). Beneficial biofuels: the food, energy, and environment trilemma. Science, 325(5938), 270-271.
74.   Tudi, M., Daniel Ruan, H., Wang, L., Lyu, J., Sadler, R., Connell, D., Chu, C., & Phung, D. T. (2021). Agriculture development, pesticide application and its impact on the environment. International Journal of Environmental Research and Public Health, 18(3), 1112
75.   Vafa, P., Barary, M., Alizade, Y., & Faramarzi, M. (2018). Agro-ecological zoning of wheat irrigation using geographic information systems and the analytical hierarchy process in Ilam province. Journal of Agroecology, 8(1), 61-74.
76.   Vafaeinejad, A. (2016). Cropping pattern optimization by using TOPSIS and genetic algorithm based on the capabilities of GIS. Eco Hydrology, 3(1), 69-86. [In Persian] 
77.   Varade, S., & Patel, J. N. (2018). Determination of optimum cropping pattern using advanced optimization algorithms. Journal of Hydrologic Engineering, 23(6), 05018010.
78.   Vivekanandan, N., & Viswanathan, K. (2007). Optimization of multi-objective cropping pattern using linear and goal programming approaches. Mausam, 58(3), 323-334.
79.   Wardlow, B. D., & Egbert, S. L. (2002). Discriminating cropping patterns in the US Central Great Plains region using time-series MODIS 250-meter NDVI data: preliminary results. Proceedings, Pecora 15 and Land Satellite Information IV Conference.
80.   Yang, G., Fu, C., Zuo, Q., Shi, J., Wu, X., Qiao, X., & Ben-Gal, A. (2025). Multi-objective optimized allocation of arid saline farmlands and irrigation water resources for sustainable agriculture. Agricultural Water Management, 321, 109929.
81.   Yazdani, S., Mahmoodi, M., Yavari, G., Nazari, M., & Mirzaei, M. (2016). Analysis of the economic effects of nonprice policy reduced water supply in Qazvin Plain. Economic Growth and Development Research, 6(23(2)), 89-98. [In Persian]
82.   Yu, H., Liu, K., Bai, Y., Luo, Y., Wang, T., Zhong, J., Liu, S., & Bai, Z. (2021). The agricultural planting structure adjustment based on water footprint and multi-objective optimisation models in China. Journal of Cleaner Production, 297, 126646.
83.   Yu, J., Chen, Y., Wu, J., & Khan, S. (2011). Cellular automata-based spatial multi-criteria land suitability simulation for irrigated agriculture. International Journal of Geographical Information Science, 25(1), 131-148.
84.   Zeng, X., Kang, S., Li, F., Zhang, L., & Guo, P. (2010). Fuzzy multi-objective linear programming applying to crop area planning. Agricultural Water Management, 98(1), 134-142.
85.   Zhang, K., Yu, Z., Li, X., Zhou, W., & Zhang, D. (2007). Land use change and land degradation in China from 1991 to 2001. Land Degradation & Development, 18(2), 209-219.