(untitled) (Indian Journal of Animal Production and Management, 2024)
Hoof lesions and lameness in cattle is currently a serious problem in the dairy industry which significantly affects the health and welfare of animals. Predictive modelling offers a promising solution by leveraging data analytics to anticipate when an animal may require hoof trimming, thereby enhancing proactive lameness management strategies. The aim of this study was to develop models on hoof trimming intervention on lameness in dairy cattle before and after hoof trimming. In this study twenty lame cows were selected and lameness index score was given to them (0-5 score). Lameness index scores were recorded before week of hoof trimming (HT) and after hoof trimming intervention it was recorded on 0thday, 3rdday, 6thday, 8thday,11thday and 14thday. To predict the lameness, best fit models were constructed using different independent variables which were significantly influencing lameness. The reliable model was applied to draw the significant interference based on the coefficient of determination (R²). Only those independent variables which were significantly influencing lameness were used to construct prediction models. Among all the variables used for multiple regression analysis, age, parity, milk yield, milk pH, stage of lactation and dung pH was attributing significantly to the prediction of lameness in all studied lame dairy cows..