A complex circular intuitionistic fuzzy decision framework for evaluating sustainable agriculture strategies under uncertainty

Agricultural productivity is accompanied by complex multi-factor problems that are economic, environmental, and social. Decision-making practice in this area requires a methodology that can handle uncertainty, regular growth, circular patterns, and conflicting evaluation requirements. A new multi-criteria decision-making (MCDM) model based on complex circular intuitionistic fuzzy sets (CCIRIFSs) is presented in this study. This model combines the circular intuitionistic fuzzy set and complex numbers synergistically to address directional periodicity and two-dimensional uncertainties. On the other hand, to improve aggregation performance, two new operators are introduced, namely complex circular intuitionistic fuzzy dombi weighted averaging (CCIRIFDWA) and geometric (CCIRIFDWG). These operators, which have, of necessity, mathematical properties including idempotency, boundedness, and monotonicity, are built up of parameterized Dombi t-norms and t-conorms and thus meet the analytical requirements of rigour and flexibility in the processing of imprecise data. The case study presented in this study is a detailed analysis of sustainable agriculture, demonstrating that the given framework can be effective and useful for evaluating sustainability approaches across different criteria. The results established that greenhouse farming under a controlled environment was the best strategy, with scores of 0.874 for CCIRIFDWA and 0.861 for CCIRIFDWG, indicating the model's strong and flexible nature. Altogether, the given framework can be regarded as a novice, mathematically complete, and context-specific decision-making approach to cyclic, uncertain agricultural systems.