An attempt was made in this study to estimate the relationship between body weight and body measurements of Madras Red sheep. The samples of the study consist of about 138 Madras red sheep under farm condition. Data pertaining to body weight and body measurements such as Withers height (WH), Body length (BL), Heart girth or chest girth (CG), and Paunch girth (PG) were taken. The results recorded from the body weight and body measurements were positively correlated and significant with each other except in 12 months. Chest girth had the highest correlation value between the body weight variable which was significant at all age groups and it will be the best to predict the body weight in correlations followed by Withers height. In the age of 6 months and 9 months only all the parameters were highly significant and highest R2 values among the equation. Paunch girth and body length which had been contribute low in body weight. From the regression equation 3 months age groups had the highest R2value of 74 per cent and 6 months age group which had the highest R2 value 92 per cent, whereas the 9 months of age R2 values was 87 per cent which were significant. For the 12 months age groups which R2 value was low value of 48 percent. Among the four equations best predictor of body weight using body measurements can be between 6-9 months age and chest girth will be the best parameter to prediction of body weight accurately followed by withers height which can be inclusion with chest girth for best prediction body weight more accurately.
Sheep rearing was one of the most important farming activities among the landless farmers. Madras Red sheep is a most popular native breed in Tamil Nadu distributed in Kancheepuram, Tiruvallur and northern parts of Villupuram and eastern parts of Vellore and Thiruvannamalai districts in Tamil Nadu (Ganesakale and Rathnasabapathy, 1973, Acharya, 1982). It is medium-sized breed reared mainly for meat production, hairy type with tucked lip bellies, light and long feet with clean bony limbs and pouncing quick gait, flat sides and short tail (Balasubramanyam et al., 2010, Raman et al., 2003). The most economic value for the farmers is body weight. Body composition and growth performance are important to assess the potential of development in animals. Weight, growth rate and conformation are important components influencing the profitability of sheep and are essential objectives in selection strategies. The ability of the producers and buyers to relate the live animal’s measurement based on growth characteristics. This ability will also adequately reward livestock farmers rather than the middlemen that tend to gain more profit in livestock production business, especially in the developing countries (Afolayan et al., 2006).
The relationship between live weight and economically important yield is well known in farm animals and live weight estimations using the body measurements is a matter of concern for sheep industry (Pesmen and Yardmci, 2008). The biological relationship between body weight and body measurements is used to predict the body weight of animals. Body measurements could be considerable to predict the live weight fairly when the weighing balance is not available. The objective of the study to estimate relationship between body weights based on a linear measurements and prediction of the growth in Madras red sheep.
Materials and Methods
The data for this study was obtained from Madras red sheep (138) animals from Post Graduate Research institute in Animal sciences under farm condition. Body dimensional measurements were taken for all ages except at birth. Linear body measurements were taken by a measuring tape and body weight was taken using a digital weighing balance, while the animals were motionless.
Animals were divided into four age groups such as 3 months, 6 months, 9 months and 12 months age groups among the animals. Relationships between the body measurements were calculated by Pearson correlations and regression equations were established. The multiple linear regression model was fitted to obtain prediction equations of body weight from body measurement variables. The multiple linear regression model adopted was:
Y = b0 + b1X1 + b2X2 + b3X3 + b4X4 + µ
Y = body weight
b0 = intercept
b1, b2, b3, b4 = regression coefficients
X1, X2, X3, X4 = predictor variables (CG, PG, BL and WH)
µ = error term
Similarly, raw correlations between all body measurements under considerations were computed. Regression analysis has been carried out by including different body measurement variables individually and collectively. To determine the best fitted regression equation the criterion viz. estimated by coefficient multiple determination (R2), residual mean squares (MSE) were used as described by Snedecor and Cochran (1989). Statistical analyses were done in IBM® SPSS® 20.0 for windows.
Results and Discussion
The results of the phenotypic correlations between body weight and body measurements are given in Table 1.
Table 1: Phenotypic Correlations between body weight and body measurements
** Significant at (P<0.01), *Significant (P<0.05), Body weight (BW), Chest girth (CG), Paunch girth (PG), Body length (BL) and Withers height (WH)
From the Table 1 results the body weight and body measurements noted that highly significant and positively correlated with each other except in 12 months. Pearson correlations values of chest girth were 0.819, 0.921, 0.859, 0.553 in 3 months, 6 months , 9 months, 12 months age groups all age groups. Among the measurements chest girth had the highest correlation between the body weights followed by withers at height. Pearson correlations values of withers height were 0.746, 0.829, 0.825 and 0.501 in 3 months, 6 months, 9 months, 12 months age groups. As the age increases the body measurements value was increased among the measurements reported (Ravimurugan et al., 2012). Highest correlations values were seen in during 6 months and 9 months age group among the all body measurements and they were significant to each other. Chest girth and withers height was significant at all age groups and it would used for estimation of body weight.
Table 2: Multiple linear regression equations for estimating body weight from body measurements
** Significant at (P<0.01), *Significant at (P<0.05)
The Table 2 revealed that body weight of sheep as dependent variable and body measurements as independent variable to predict the body weight in all age groups. The coefficient of determination of (R2) indicated the variation in body measurements and body weight in sheep. The value of R2 increased as more independent variables were added to the regression the higher R2 value indicated that all the linear body measurements to determine the body weight. In all age groups the chest girth had highly significant values among the predictors of body weight except in 12 months of age groups. The higher association of chest girth in body weight which have large contribution due to (viscera bones) circumference it will contribute more body weight. In the next predictor of body weight could be withers height which would be highly significant values among the variables. Paunch girth and body length which would contributed low in body weight when compared to other variables. In the age of 6 months and 9 months only all the parameters were highly significant and highest R2 values among the equation. As the age advances the body measurements characteristics had been decreased. As the best parameter to predict the body weight more accurately could be chest girth had highly significant among the predicted equations similar results reported by (Pesmen and Yardmci, 2008, Thiruvenkadam et al., 2005, Topal and Mehmet, 2003). On inclusion of all the variables R2value was 74 percent at 3 months of age groups. For age 6 months group which had highest R2 value 92 percent, whereas the 9 months of age R2 values is 87 percent and which were significant. For the 12 months age groups which R2 value were 48 percent which had low values when compared to other age groups. The results concluded that age group at 6 months and 9 months would be best prediction of body weight using body measurements. Among the variables chest girth will be best prediction of body weight accurately followed by withers at height which can be combination with other measurements for accurate prediction of body weight.
The relationship between body weight and body measurements were significantly correlated with each other. The coefficients between body weight and morphometric traits were highest during 6 months and 9 months age group when compared with weaning and 12 months age groups. Chest girth had the higher association with the body weight. From the measurements chest girth will be best to predict the body weight followed by withers height, paunch girth and body length. The combinations of all the measurements will be the accurate to predict the body weight.
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