Prioritizing dairy cattle dystocia risk factors using a comparative fuzzy multi-criteria decision-making algorithms

Dystocia in dairy cattle presents significant challenges to productivity and animal welfare, leading to increased stillbirths, calf mortality, and reduced fertility. This study employs Fuzzy Decision-Making Trial and Evaluation Laboratory-Analytic Network Process (FDANP) and Fuzzy Analytic Hierarchy Process (FAHP) methodologies to analyze and prioritize the multifactorial causes of dystocia, integrating expert judgments and addressing uncertainties through fuzzy logic. Following the collection of data from experts through a questionnaire, the findings indicated that the FDANP identified calf weight, the body condition score of the dam, and the calving interval as the most significant factors influencing dystocia. In contrast, parity, calf sex, and multiple births were determined to have the least impact on this condition. Furthermore, the FAHP method, which does not measure the criteria of influence and susceptibility, highlighted milk period, calf weight, and gender status as the most important factors. This divergence is expected, as FDANP captures the systemic causal structure-where factors like body condition score and calving interval have broad influence-while FAHP reflects direct, hierarchical judgments of importance, often placing greater weight on intrinsic attributes like parity. The study introduces a novel approach by combining breeder expertise with advanced analytical methods, providing a comprehensive framework for understanding and managing dystocia in dairy cattle. The integration of fuzzy logic accommodates the inherent uncertainties and variabilities in expert opinions, enhancing the robustness of the analysis. This research underscores the importance of expert ideas and comprehensive management strategies in mitigating dystocia risks, ultimately aiming to improve animal welfare and economic outcomes in dairy operations. Also, this comparative study represents a significant advancement in the field of dairy cattle production as, to the best of our knowledge, it provides the first framework for conducting more effective strategies within this domain using a comparative fuzzy multi-criteria decision making (MCDM), approach. Furthermore, the study outlines recommendations for future research, aimed at enhancing the applicability of these methods in dairy cattle production systems.