Negotiating the Soil: From Prescriptive to Participatory Land Allocation for Agricultural Sustainability (A Bargaining Approach in Harand County, Iran)

Articles in Press

Document Type : Original Article

Authors
Alireza Alipour
Nafiseh Ghodrati
Mohammad Sadegh Ebrahimi
Department of Rural Development, College of Agriculture, Isfahan University of Technology, Isfahan, Islamic Republic of Iran.
Abstract
Agricultural land allocation in Iran is usually prescribed top-down, which ignores conflicts among stakeholders. This study compares a prescriptive strategy, simulated with weighted goal programming (WGP), with a participatory strategy, simulated as a three-player bargaining game, for Harand County. The economic player maximizes profit, the environmental player minimizes chemical fertilizer use and the social player maximizes rural employment. Both models use farm-level technical coefficients from a survey of 150 farmers. The single-objective models show that profit conflicts with fertilizer reduction and employment, which are aligned with each other, and the WGP solution is unchanged over a wide range of weights. In the bargaining game, a bargaining equilibrium emerged after five rounds, in which the economic player conceded about 10% of its ideal profit; relative to equal-weight WGP, profit was 8.6% lower and fertilizer use 4.2% lower, with similar employment. The study contributes a framework that treats land allocation as an equilibrium of stakeholder negotiation rather than the outcome of analyst-chosen weights, and a four-year rotation scheme adapted to the county.
Keywords
Agricultural land allocation
Bargaining equilibrium
Harand County
Social participation
Sustainability
Weighted goal programming (WGP).
Subjects
1.            Agrawal, A. Bakshi, B. R. Kodamana, H. Ramteke, M. (2024). Multi-objective optimization of food-energy-water nexus via crops land allocation. Comput. Chem. Eng. 183: 108610. https://doi.org/10.1016/j.compchemeng.2024.108610.
2.            Anonymous. (2024). Rural and urban labour markets: Different challenges for promoting decent work. International Labour Organization. Available at: http://www.ilo.org. 
3.            Badri, M. (2024). A review of the process of soil erosion and destruction and its protection strategies in Iran. The 5th National Conference on Development and Promotion of Agricultural Engineering and Soil Sciences in Society. Tehran, Iran.
4.            Borowski, P. F. Patuk, L. (2021). Environmental, social and economic factors in sustainable development with food, energy and eco-space aspect security. Present Environ. 15(1): 153-169.  https://doi.org/10.15551/pesd2021151012.
5.            Charnes, A. and Cooper, W.W. (1961) Management Models and the Industrial Applications of Linear Programming. John Wiley, New York.
6.            Cheng, H. Jiang, X. Wang, M. Zhu, T. Wang, L. Miao, L. Chen, X. Qiu, J. Shu, J. Cheng, J. (2023). Optimal allocation of agricultural water and land resources integrated with virtual water trade: A perspective on spatial virtual water coordination. J. Environ. Manage. 347: 119189. https://doi.org/10.1016/j.jenvman.2023.119189.
7.            Cochran, W.G. (1977). Sampling Techniques, 3rd edn. John Wiley & Sons, New York.
8.            Ghadirian, O. Lotfi, A. Moradi, H. Shetab Boushehri, S. N. Yousefpour, R. (2023). Area-based scenario development in land-use change modeling: A system dynamics-assisted approach for mixed agricultural-residential landscapes. Ecol. Inform. 76: 102129. https://doi.org/10.1016/j.ecoinf.2023.102129.
9.            Gohar, A. A. Cashman, A. El-bardisy, H. (2021). Modeling the impacts of water-land allocation alternatives on food security and agricultural livelihoods in Egypt: Welfare analysis approach. Environ. Dev. 19: 100650. https://doi.org/10.1016/j.envdev.2021.100650.
10.        Kamaludin, N. M. Narmaditya, B. S. Wibowo, A. Febrianto, L. (2021). Agricultural land resource allocation to develop food crop commodities: lesson from Indonesia. Heliyon. 7(7): e07520. https://doi.org/10.1016/j.heliyon.2021.e07520.
11.        Ma, X. Zhao, X. (2015). Land use allocation based on a multi-objective artificial immune optimization model: An application in Anlu County, China. Sustainability. 7(11): 15632–15651. https://doi.org/10.3390/su71115632.
12.        McCarl, B. A. Spreen, T. H. (2002). Applied Mathematical Programming Using Algebraic System. Department of Agricultural Economics, Texas A&M University, College Station. http://agecon2.tamu.edu/people/faculty/mccarl-bruce/books.htm. 
13.        Mehregan, M. (2013). Decision-making models with multiple objectives. Department of Management, Tehran University Press. (In Persian).
14.        Memmah, M.M. Lescourret, F. Yao, X. Lavigne, C. (2015). Metaheuristics for agricultural land use optimization. A review. Agron. Sustain. Dev. 35: 975–998. https://doi.org/10.1007/s13593-015-0303-4.   
15.        Moradi, S. Mohammadi Limaei, S. (2018). Multi-objective game theory model and fuzzy programing approach for sustainable watershed management. Land Use Policy. 71: 363-371. https://doi.org/10.1016/j.landusepol.2017.12.008. 
16.        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. Ecol. Model. 471: 110062. https://doi.org/10.1016/j.ecolmodel.2022.110062.   
17.        Porta, J. Parapar, J. Doallo, R. Rivera, F.F. Santé, I. Crecente, R. (2013). High performance genetic algorithm for land use planning. Comput. Environ. Urban Syst. 37(1): 45–58. https://doi.org/10.1016/j.compenvurbsys.2012.05.003.
18.        Wang, S. Wu, B. Li, H-X. Zhao, M-m. Yuan, L. Wu, X. Ma, T. Li, F-c. Han, Sh-b. (2024). Optimization of water-urban-agricultural-ecological land use pattern: A case study of Guanzhong Basin in the southern Loess Plateau of Shaanxi Province, China. China Geol. 7(3): 480-493. https://doi.org/10.31035/cg2022068.
19.        Wang, Y. Wang, H. Sun, J. Qi, P. Zhang, W. Zhang, G. (2025). Spatially optimized allocation of water and land resources based on multi-dimensional coupling of water quantity, quality, efficiency, carbon, food, and ecology. Agric. Water Manage. 309: 109340. https://doi.org/10.1016/j.agwat.2025.109340.
20.        Wu, H. Guo, S. Guo, P. Shan, B. Zhang, Y. (2022). Agricultural water and land resources allocation considering carbon sink/source and water scarcity/degradation footprint. Sci. Total Environ. 819: 152058. https://doi.org/10.1016/j.scitotenv.2021.152058.
21.        Yang, L. Zhu, A. Shao, J. Chi, T. (2018). A knowledge-informed and Pareto-based artificial bee colony optimization algorithm for multi-objective land-use allocation. ISPRS Int. J. Geo-Inf. 7(2): 63. https://doi.org/10.3390/ijgi7020063.
22.        Zhang, Z. Guan, Q. Zhao, B. Xiao, X. Zhang, J. Yang, X. Luo, H. (2025). Multi-objective optimal allocation of agricultural water and land resources in the Heihe River Basin: Coupling of climate and land use change. J. Hydrol. 653: 132783. https://doi.org/10.1016/j.jhydrol.2025.132783.
Journal of Agricultural Science and Technology

Articles in Press, Accepted Manuscript
Available Online from 08 November 2026