Shende, T. C.Kapane Prasad Devidas2024-11-042024-11-042024-04-10https://krishikosh.egranth.ac.in/handle/1/5810216011Pandharpuri buffalo is considered as important buffalo breed in Maharashtra with speciality of multiple milking character. The milk production and fat % of this breed is 1790 kg and 8 % respectively. Body weight is crucial to judge growth and overall development of animals to facilitate their selection for breeding purpose. To overcome various constraints faced by the farmers to weigh the animals by weighing balance in the present study to develop a prediction equation to predict live body weight (LBW) of Pandharpuri buffaloes (N=207) by measurement of various body dimensions. A total of 15 body dimensions were used viz. body length (BL), heart girth (HG), abdominal girth (AG), height at withers (HAW), height at the hook (HH), the distance between pins (DPN), the distance between hook (DHK) and distance between pin and hook (DPH), horn length (HL), rump height (RH), muzzle width (MW), neck girth (NG), scapioischial length (SIL), tail length (TL), and head width (HW). Initial statistical analysis revealed highest correlation of LBW with NG followed by DHK. In this study, different mathematical equations were tested for LBW prediction, the Aggarwal's formula exhibited the lowest standard error of estimate. Assumptions for multiple linear regression were tested, and simple regression identified NG as the most influential predictor. Multiple regression analysis through enter and stepwise methods identified significant independent variables affecting LBW. The final stepwise model consisted of neck girth (NG), distance between hook (DHK), scapio-ischial length (SIL), height at withers (HAW), and abdominal girth (AG), achieving a high coefficient of determination (r2 = 0.865) and low standard error of estimate (26.92 kg). The formula of the model was y = a + b1 X1 + b2 X2 + b3 X3 + b4 X4 + b5 X5 + e, where y = LBW, a = intercept of the model (-259.190), b1 = regression coefficient of NG (4.612), b2 = regression coefficient of DHK (2.47), b3 = regression coefficient of SIL (1.529), b4 = regression coefficient of HAW (-1.79), b5 = regression coefficient of AG (0.799) and e = error of the model. Validation of the final model on field data demonstrated its reliability in predicting LBW. This study provides a cost-effective and accessible method for estimating LBW in Pandharpuri buffaloes, aiding farmers in monitoring animal performance and enhancing dairy management practices.EnglishThesis