Introduction: In many agricultural regions of Iran, fragmentation and dispersion of farmland parcels have become a serious obstacle to the productivity and efficiency of irrigation networks and rural infrastructure. Various reports indicate that over 99% of farms in some countries consist of parcels smaller than 5 ha, and in Iran the average size of arable land is estimated at about 4.9 ha. This study, inspired by land consolidation principles and utilizing game theory, presents an innovative model for redistribution of agricultural parcels that improves the agricultural land reallocation process while preserving landowners' rights and preferences. In the proposed method, landowners select their desired blocks for reallocation. The challenge of heightened demand for a particular block during the consolidation process is addressed by modeling the competition among landowners as players in a game.

Materials and Methods: The study area—approximately 43 ha in size—is located in Seraj-Mahalleh village in the Galugah County of Mazandaran Province. This village is situated in the Eastern Kolbad Rural District, and according to the 2011 census by the Statistical Center of Iran, its population was 1,220 individuals in approximately 312 households. Spatial and attribute data related to 135 landowners and 33 agricultural parcels were collected. The land reallocation modeling process was developed in six main stages: (a) Defining players: Each landowner was introduced as a player with a set of strategies (choosing target blocks). (b) Determining strategies: Owners selected blocks for land reallocation requests based on their parcel area and preference ranking. (c) Scoring criteria: Owners with only one parcel did not enter the game; their lands remained in their originally assigned blocks. The remaining owners were grouped based on the condition of their parcels within blocks. For the first group, four criteria were defined: parcel geometry, distance to main road, percentage overlap with the target block, and parcel area percentage relative to competing parcels in the same block. For the second group, only the first two criteria (geometry and distance) were considered. (d) Payoff Matrix Calculation: The payoff for each pairwise strategy combination in each block was determined by summing the criterion scores. (e) Nash Equilibrium Identification: Using maximin and minimax principles, the two-player Nash equilibrium was computed for each pair of owners. (f) Final arrangement: After determining the Nash equilibria and ranking strategies, an algorithm was proposed and implemented to continuously reallocated parcels within blocks.

Results and Discussion: Implementation of the model on the study area data showed that the number of agricultural parcels decreased from 176 to 145; this means that approximately 17.62% of land fragmentation was reduced. Twenty-eight owners with multiple parcels entered the reallocation process: 23 landowners achieved their first priority, four landowners reached their second priority, and one landowner reached their third priority. This means about 82.15% of owners received their first choice, 14.28% their second, and 3.57% their third. The results demonstrate that applying game theory in the agricultural land reallocation process can effectively manage competition among owners and lead to a fairer distribution of resources. These findings highlight the efficacy of a game-based approach in resolving owner conflicts and achieving land consolidation objectives.

Conclusion: The success of land consolidation projects has a direct relationship with landowner satisfaction. This model, using game theory and assigning the majority of landowners' parcels in their first priority, has been able to take steps toward attracting landowner satisfaction, increasing the success of these projects, and improving the land reallocation process. However, given regional differences, future studies are recommended to employ a broader set of criteria and incorporate methods for ranking and weighting those criteria. In practical applications, researchers and policymakers can add criteria such as soil quality and fertility or land value as secondary criteria to the owners' prioritization matrix without changing the core of the decision-making algorithm.