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In the developing country, the primary inquiry is, “How can the limited land resources for various crops be managed to achieve the minimum required consumption goals based on irrigated agriculture?” Therefore, to feed the growing population while using limited resources, it is imperative to discover a scientific method to model and solve the multi-objective cropland allocation problem (MOCLAP). Existing literature indicates that classical optimization methods are commonly employed for solving MOCLAPs. However, the deterministic nature of crop yield, production costs, and profits in classical optimization methods presents limitations in real-world scenarios due to the influence of various uncontrollable factors. Furthermore, managing uncertain boundary values poses a significant challenge for existing optimization techniques. Thus, this paper not only developed a novel scientific method to model and solve a MOCLAP by addressing these challenges but also analyzed its optimal results with farmer experiences in small-scale irrigation-based crop production. By incorporating uncertainties inherent in the crop production planning process through extended-membership functions, the study employs intuitionistic fuzzy compensatory techniques for multi-objective optimization. The results demonstrate significant improvements in net profit and effective utilization of irrigation land, with increases of 22% and 65%, respectively, compared to farmers’ previous experiences in the Sidama region, Ethiopia. Moreover, a comparative analysis of the obtained optimal results with those derived from existing optimization methods highlights the non-dominated nature of the proposed method. Sensitivity analysis has also been done on the compensation parameter.
Moges et al. (Mon,) studied this question.