Prediction of Live Body Weight Using Linear Body Measurements in Pandharpuri Buffalo
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Date
2024-04-10
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MAFSU, Nagpur
Abstract
Pandharpuri 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.