Sustainable Agriculture Method Selection Using Spherical Fuzzy Bonferroni Mean-Based Approach

The escalating global population, rapid urbanization, and changing climate conditions demand the development of sustainable agricultural techniques that not only increase food production but also preserve the natural ecosystem. These challenges highlight the necessity for innovative methods to ensure environmental sustainability in agriculture. This study aims to develop a spherical fuzzy Bonferroni mean-based approach for selecting sustainable agricultural techniques, addressing the critical need for robust decision-making frameworks in environmentally friendly farming practices. An intelligent decision-making algorithm for the multi-attribute group decision-making (MAGDM) problem is established to integrate the opinions of a group of experts and rank preferences under conflicting criteria. An experimental case study was conducted to evaluate a realistic agriculture approach under the derived strategies. The influence study confirmed the high feasibility, robustness, and consistency of the proposed aggregation models. A comparative analysis demonstrated the superiority of our algorithm, providing clear evidence that the proposed approach effectively supports decision-making in sustainable agriculture. This research presents a comprehensive and innovative decision-support framework for selecting sustainable agricultural methods. It provides a practical solution to enhance crop output and preserve soil health in the face of environmental challenges, thereby advancing the global pursuit of ecological sustainability.