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Lameness is one of the most urgent health and welfare problem of dairy cowsand has significant effect on the economy of dairy
industry.Increased reported incidence of lameness over last decade is due to an improved ability to identify abnormalities in
cow gaits. In this study load cell platform, a kind of Force Plate that has capability to detect lameness from change in dynamic
leg loads exerted by lame as well as healthy cow were used. Recorded data on load cell platformwere used to develop predictive
models. Development of predictive model is a step forward in the direction of automated detection of the lameness at farm level.
Since the lameness prediction is a complex and non?linear problem therefore artificial neural network, decision tree and logistic
regression like advanced computing tools were used in thestudy using SAS Enterprise Minor 6.1. Results shown that, Percent
body weight distributed on the left front legs of both lame and healthy cows was higher as compared right front legs of both lame
and healthy cows.In lame cows left hind legsbear less weight as compare to right hind legs. This revealed that left hind leg of lame
cow was mostly affected as compared to right hind leg. Artificial neural network was found to be the best among decision tree
and logistic regression to predict more accurately lame and healthy cow.
Ravi Kant Gupta is currently working as an Assistant Professor at Shaheed Gundadhoor College of Agriculture & Research Station, Jagdalpur under
Indira Gandhi Agriculture University, Raipur Chhattisgarh (India). Dr. Gupta did his master?s in Livestock Production & Management (Animal Science)
from Indian Veterinary Research Institute, Izatnagar (
) and Ph.D. in same discipline from National Dairy Research Institute, Karnal (
research interests are in thearea of lameness in animals that includes lameness prevention and early detection of lameness. Dr. Gupta has published
popular articles, research articles and book chapters in his research area.
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