Morphological Indices and Simple Regression to Predict Live Body Weight from Morphological Traits of Indigenous Sheep

Data was collected from various livestock farms of southern Punjab. The measurements about fourteen morphological traits (body dimensions) were taken by inch tape and long scale. These measurements were taken from 291 indigenous sheep up to 2 years of age. The measurement accuracy was achieved by controlling the movement of sheep by making strong grip on them with the help of farm workers. The following morphological traits were measured: barrel depth, body length, body weight, ear length, ear width, head length, head width, heart girth, neck length, neck width, rump length, rump width, tail length and withers height. Many of these traits were also used in calculation of body indices by McManus et al. (2008), Pesmen et al. (2008), Chacon et al. (2011), Banerjee (2016), Moutchou et al. (2017), Dauda (2018) and Olaniyi et al. (2018).

The following ten body indices were used on the basis of above morphological traits:

The descriptive statistical measures were used for morphological traits as well as indices to observe the variation pattern. Pearson correlations were used to check the nature and strength of relationship between body weight and other measurements. On the basis of Pearson correlations, the simple regression models were obtained to predict the body weight from morphological traits individually. To find the results two software i.e. Excel and SPSS (2008) were used.

Table I shows relatively small variation in ear length, neck length, ear width, neck width and tail length as compared to other traits and relatively greater variation was present in barrel depth, body weight, head length, head width, rump length and rump width. The smaller value of coefficient of variation showed that there is homogeneity in the traits while larger value of coefficient of variation showed that sheep differ greatly in respect of those morphological traits. Our results showed that there is greater variation is those traits that apparently seem different and smaller variation in those traits that apparently seem similar. Similarly, descriptive measures of body indices are given in Table II. The values of coefficient of variation showed that the body index, thoracic development and beron crevit are more consistent indices as their values of coefficient of variations are relatively small than other indices. While the compact index-1 and area index had large values of coefficient of variation relatively and are less consistent.

Table III.- Correlations and regression coefficients, their significance and confidence intervals of regression coefficients in simple regression.

Pearson correlations between body weight and other morphological traits are shown in Table III. We see that almost all the correlations of morphological traits with body weight are strong except ear width and neck length. Even these two correlation values are small but highly significant. The highest correlation was reported of barrel depth, head length, head width, heart girth, rump length and rump width with body weight having all correlations values greater than 0.90. All significant correlations showed that all morphological traits are significant component of body weight. The highest correlations suggested the simple linear regression model.

The simple linear regressions for each morphological trait were fitted and results are shown in Table III. The significance and 95% confidence interval regression coefficients (β’s) were also reported in the table. To check the significance of simple linear regression, model the values of F-statistic along with its significance values were reported in the table. The values goodness of fit, for simple linear regression model, statistic i.e. coefficient of determination (R2) were also reported in the table. Also, the Pearson correlations were reported along with simple linear regression results for comparison of Pearson correlations and coefficient of determination. The results showed that all the regression coefficients (β’s) were significant and all regressions were significant. The values of coefficient of determination are high except ear length, ear width, neck length, neck width and tail length. The small values of R2 showed these morphological traits are not good predictors as compared to those having high values of R2. The values are Pearson correlations and R2 having same pattern. Both were highest and lowest for same trait. The simple linear regression results showed that body weight could be predicted from all morphological traits.

The analysis finding showed that all morphological traits were consistent having smaller values of coefficient of variation except body weight, head length, head width and rump width. Similarly, all body indices were found to be consistent having smaller values of coefficient of variations except compact index-1 and area index. All the morphological traits were highly correlated with body weight as their correlations were highly significant but the barrel depth was highly correlated with body weight having correlation value 0.968. Simple regressions of all morphological traits were best fit as all regressions R2 were high but for the barrel depth R2 is highest which showed that the barrel depth was significant predictor of body weight as compared to other morphological traits.

Special thanks to the management of Government Livestock Experiment Stations, Livestock and Dairy Development Department, Govt. of Punjab.

The authors declare no conflict of interest.