Z-cloud Rough Fuzzy-Based PIPRECIA and CoCoSo Integration to Assess Agriculture Decision Support Tools

The livestock sector has exacerbated the problems of ensuring global food safety and greenhouse gas emissions. The rapid increase in livestock production has called to shed light on decision-support tools that develop sustainable production strategies. In this context, this study aims to expand the application of multiple-criteria decision analysis (MCDM) methods to assign weights to criteria and classify decision support tools for livestock with a high degree of certainty. In order to begin serious steps to address the global sustainability problem, this study extended the PIPRECIA method with a high-certainty fuzzy environment called Z-cloud rough numbers (ZCRNs) to record the weight of 19 criteria for decision support tools in livestock farming. An innovative and advanced method called CoCoSo has been utilized to rank decision-support tools for livestock farming. The methodology included two stages. The first phase involved developing the decision matrix. The second phase encompassed developing MCDM methods by clarifying the steps of the PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) method for assigning weight to criteria, in addition to highlighting the steps of the CoCoSo method for classifying decision support tools in the livestock industry. The results of the PIPRECIA method extended to the fuzzy environment of ZCRNs confirmed that visualization and herd characteristics received the highest weight compared to the rest of the criteria of decision support tools. The CoCoSo results provided insight into ranking alternatives for livestock decision support tools. AgRECalc has the highest ranking, and FCFC has the lowest ranking. This study conducted an evaluation test to increase the chances of generalizing the results of ranking decision-support tools of the livestock industry.