Advancing Dairy Herd Health: Predictive Models for Hoof Trimming in Lameness Management

Abstract

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..