A combinatorial model for effective estrus detection in Murrah buffalo

Abstract

Background: Buffaloes are silent heat animals and lacunae in their estrus detection results a substantial economic loss in developing countries. Many advanced tools to aid heat detection have been developed but are neither affordable nor easily interpretable by marginal farmers. Aim: The present investigation was made to develop a cost-effective estrus detection model by combining several known estrus predicting parameters. Materials and Methods: Various signs of estrus were classified under major parameters such as visual, cow behavioral, bull behavioral, biochemical, and gyneco-clinical. Expression of those parameters was observed in buffaloes, and the percentage of positive estrus detection was calculated for each combination of estrus prediction parameters. Results: The present result concludes that the model comprises of five parameters group with several signals with twentysix different combinations. It was observed that the expression of individual combinations and their corresponding estrus detection efficiency varies significantly, i.e., detection efficiency rises as the number of combination increases. Conclusion: Combination of three parameters would provide an estrus detection efficiency > 70% and suggested for an easy estrus detection. This would be a cost-effective model for farmers and benefits in enhancing buffalo population/reproduction.