Body
Measurements and Body Condition Scoring as Basis for Estimation of
Live Weight in Nili-Ravi Buffaloes
Muhammad Tariq1, Muhammad Younas2,
Abdul Basit Khan2 and Eva Schlecht1*
1Animal
Husbandry in the Tropics and Subtropics, University of Kassel and Georg-August-UniversitätGöttingen,
Steinstrasse 19, 37213 Witzenhausen, Germany; 2Department
of Livestock Management, University of Agriculture Faisalabad,
Pakistan *Corresponding author: tropanimals@uni-kassel.de
Abstract
Implementation of management recommendations for
the Nili-Ravi buffalo in small- and medium scale commercial dairy production
systems in Pakistan is hampered by difficulties to determine body weight (BW) of
the animal. A workable and reliable method of predicting BW of this breed by
using body measurements and body condition scoring (BCS) was therefore
explored.Nili-Ravi buffaloes (n=211)were divided into three age groups (1-3
years = G1; >3-8 years = G2; >8 years = G3). Animals were weighed on a
mechanical scale and their heart girth (HG), body length (BL) and shoulder
height (SH) was measured. In addition, BCS was performed using a 5 point scale.
Recorded data were subjected to simple and multiple linear regression
analysis.The overall mean values of BW, HG, BL, SH and BCS were 359±160.9 kg,
170±30.1 cm, 130±19.2 cm, 125±14.5 cm and 3.8±0.77. With correlation
coefficients (r) of 0.97 (HG), 0.94 (BL), 0.93 (SH) and 0.43 (BCS), the
relationship between the individual independent variable with BW were
significant (P<0.01) in all cases. The multiple linear regression between BW and
HG, BL and BCS was highly significant (P<0.001) for each of the three groups
(G1: r²=0.95, G2: r²=0.86, G3: r²=0.83).Buffalo farmers who lack mechanical or
electronic scales to regularly determine BW of their animals can thus combine
simple morphometric body measurements (HG, BL) with BCS or just rely on HG in
order to calculate feed requirements, monitor growth, determine breeding age,
marketing weight and estimate the animals’ cash value.
Key words:
Morphometric measurements,
Multiple linear regression analysis, Water buffalo