Comparative Evaluation of Crop Production under Uncertainty Using Two-stage FM-TOPSIS.

Agriculture production is inherently affected by uncertainty arising from climate variability, input availability, market fluctuations, and management practices. Conventional statistical and time series forecasting models often fail to effectively capture the vagueness and imprecision associated with such agriculture data. To address this issue, the present study proposes a fuzzy multi-criteria decision-making framework based on the Fuzzy Multi-Attribute Technique for Order Preference by Similarity to Ideal Solution (FM-TOPSIS) for the comparative evaluation of crop production performance using year-wise agricultural data. In the proposed approach, each agricultural year is treated as an independent alternative, while multiple production-related factors such as yield, rainfall adequacy, cost cultivation, pest incidence, soil fertility status, and market stability are considered as evaluation criteria under a fuzzy environment. Linguistic assessments provided by domain experts and farmers are converted into triangular fuzzy numbers to construct the fuzzy decision matrix. FM TOPSIS is then applied to determine the relative closeness of each year to the ideal agriculture performance scenario. Furthermore, a two-stage hybrid evaluation strategy is employed, wherein yearwise rankings obtained through FM-TOPSIS are aggregated to derive an overall performance assessment across multiple years. This hybrid framework enhances robustness and enables consistent comparative analysis of agricultural performance under uncertainty. The results demonstrate that the proposed FM-TOPSIS-based methodology provides a reliable and practical decision support tool for evaluating crop production trends and supporting sustainable agricultural planning.