Least squares means along with the standard deviation (S.D.) and percent coefficient of variation (CV%) for different traits under study are given in Table 1. The least squares means for various traits were BW = 32.45±0.09 kg; 3BW= 66.68±0.31 kg; 6BW =110.76±0.52kg; 9BW=152.15±0.71kg; 12BW=200.23±1.01kg; 18BW=277.14±1.23kg; 24BW=360.40±1.13kg; 30BW=404.35±1.31kg; 36BW= 458.71±1.73kg. The sex of the calf significantly affected all the growth traits except 3BW and 6BW (p<0.01, p<0.05) as male calves are heavier than female calves. Season of calving also had significant effect on all the traits except 3BW where calves born during winter season were heavier compared to those born in other seasons. This may be due to appropriate temperature and good feeding regime during winter season. Furthermore, period of calving had significantly affected all the growth traits. The effect of period of calving reflects year to year variability with respect to climate change, management practises and feeding. Moreover, in the current study, the effect of dam’s parity was significant on early rearing phase i.e. BW, 3BW and 6BW. Birth weight was lower for primiparous dam than those of multiparous. Also with increase in parity number higher birth weight was observed. The effect of dam’s parity on BW and other growth traits may be attributed to the maturity status of dams in their advanced parity, they have adequate body capacity leading to better development of foetus.
(Co)variance components and genetic parameters estimated by most appropriate model in univariate analysis for various traits of Murrah buffalo are presented in Table 2. As per LRT, the best model for birth weight (BW) was Model-5, which included direct additive and permanent environmental effects of the dam. For 12BW, Model 1 (a simple animal model) was the best that included only direct additive effect. For rest of the traits (3BW,6BW,9BW,18BW,24BW,30BW and 36BW), Model 3 was the best that includes direct additive effect of dam and covariance between direct genetic and dam’s direct additive effect.
Model-1 gave substantially higher estimate of heritability for birth weight than other models i.e. 0.34±0.03, which reduced to 0.18±0.02 on addition of maternal genetic effect in Model-2. Also fitting maternal genetic effect resulted in improvement of Log L over Model-1. Direct heritability estimate was increased to 0.24±0.04 when direct additive and maternal permanent environmental effect(c2) were included in the Model-4, however did not increase the likelihood. Model-5 provided a much better fit to data for birth weight with significantly high likelihood value over all other models for birth weight in Model 5 with the direct additive estimate of 0.19±0.03, maternal additive effect ratio (m2) 0.13±0.04 and maternal permanent environmental effect(c2) 0.05±0.03. In this study low value of maternal permanent environmental effect(c2) was observed. The buffalo is monotochous animal. The litter size is one and in the farm, where data is collected, the calf does not remain for much period with dam. These reasons apart from difficulty in actual partitioning of maternal variance into additive and permanent environment might have reduced the estimate. Moderate estimate of heritability in the present data set indicates further scope of selection for the Murrah buffaloes, however, it must be cautioned that the selection on the birth weight basis is not effective due to significant maternal influence. Lower h2 estimate of birth weight 0.10 with maternal additive (m2)0.11 and maternal permanent environmental effect(c2) 0.04 were reported by Thiruvenkadan et al.(2009) in Murrah buffaloes. It was observed that in literature available, higher estimate of heritability for birth weight in Murrah buffalo were found as compared to our results. The possible reason of inflation may lie with the method of estimation difference with paternal half sib method. Pandya et al.(2015) reported similar h2 estimate (0.18) in Surti buffalo using paternal half-sib method. Higher h2 estimates were reported by Yadav et al.(2001), Gupta et al.(2015) and Shahin (2010) in Murrah buffalo and Soh et al. (2020) in swamp buffalo using paternal half sib method.
Three month body weight
Model 3 was found to be best for the 3-month body weight with estimates for direct additive h2 0.14±0.05 and maternal additive effect (m2) 0.18±0.03. There was negative covariance between direct genetic and maternal effects. Direct additive h2 estimate of 0.07 in Model1 was fairly consistent across models. estimate were 0.06 (h2) and 0.02 (m2) in Model 2 and 0.06 (h2) and 0.04 (c2) in Model 4, respectively. However, Model 5 had the highest likelihood. The estimates from the inclusive model 5 were h2 =0.06, m2 =0.00 and c2 =0.04. This indicated that the trait had maternal permanent environment alone as the maternal effect. However, we understand that partitioning the maternal effect in to its genetic and environmental component is not easy. Model 6 gave higher estimate of h2 (0.14), m2 (0.07) and c2 (0.04), however these estimates were inflated to adjust the negative covariance between direct and maternal additive effect. Our results were in line with those of Neyser et al.(2012) in Brangus cattle and Akhtar et al. (2012) in Niliravi buffalo for 3-month body weight. Similar estimate in Murrah buffalo where direct h2 estimate of 0.19, m2 as 0.09 and c2 as 0.03 were reported by Thiruvenkadan et al.(2009).
Six month and Nine month body weight
Among six animal models, Model-3 resulted in highest likelihood for both 6 and 9-month body weights. High direct heritability estimate were found for both the traits with h2 as 0.37±0.08 and 0.43±0.11 for 6BW and 9BW, respectively. 18% of total variance was explained by the maternal direct effects for both the traits. However, as the direct additive and maternal covariance was high and negative, the estimates for variance ratios were probably inflated. Very high negative covariance (ram) between direct genetic and maternal effect was found which indicate antagonism between the direct effects of dam and animal for these traits. To account for this negative ram and hence inflated variance ratios, we estimated total heritability (Wilham 1972). The estimate of h2T for 6BW and 9BW were 0.14 and 0.16 respectively. Similar results were reported by Shahin (2010) and Kharzai et al.(2020) using paternal half sib method. The h2 estimate of 0.22 and 0.18, m2 as 0.08 and 0.05 and c2 as 0.03 and 0.03 were reported for 6BW and 9BW, respectively by Thiruvenkadan et al. (2009) in Murrah buffalo.
Twelve month body weight
For 12BW Model-1 was found to be superior amongst all animal models based on LRT. As animal became older, the maternal effect lost its impact on the growth of animals. Heritability estimate for yearling weight was 0.10±0.03. Low additive variability may be attributed to the environment effect over 12 BW. Adding maternal genetic effect (Model 2) also gave the same estimate of h2 0.10 with negligible m2. Model 3 partitioned total variance into h2 0.17and m2 0.04 with negative high ram, however, the inflated variance ratios were mostly due to negative ram. Permanent environmental effect was also not significant in Model 4. No effect of dam’s additive and permanent environment effect across all the advanced models was seen on 12BW, indicating independent expression of the direct effect of animal for growth in later stages. Results of our study were in agreement with those of Pandya et al. (2015) and Gupta et al. (2015) but our method of estimation was more appropriate. There was no evidence of maternal effects after 6 month of age indicating animals own genotype is more important and should be considered for selection.
Genetic parameters for higher age live weights
Higher estimate of direct heritability 0.30±0.08 was found for both the traits 18BW and 24BW with m2 estimate of 0.10±0.04 and 0.02±0.03, respectively. The estimate of h2 for 30BW and 36BW were 0.45±0.10 and 0.27±0.08, respectively, which were higher. However, lower m2 estimate 0.10 and 0.05 were obtained for 30BW and 36BW (Table2). The estimate of ram was significantly higher and negative for all these 4 traits. This estimate has actually inflated the variance ratios and hence the higher estimates of the heritability was observed. To account for the maternal effect, we have estimated the total heritability using Willham (1972) as h2T1and Eaglen and Bijma (2009) as h2T2. The estimate for total heritability h2T1 after correctly accounting for the maternal effects were ,0.15, 0.21, 0.24 and 0.23 for 18BW, 24BW, 30BW and 36BW respectively and h2T2 0.13 ,0.17, 0.21, 0.24 for 18BW, 24BW, 30BW and 36BW respectively.
Higher heritability estimates in buffaloes of Murrah breed augurs the scope for selection for higher weights, if selection is implemented in this direction. In accordance with our study, the higher estimate of h2 for 18BW were reported by Tien and Tripathi (1990) and Gurang and Johar, (1983). Similarly, for 36-month body weight, nearly similar estimate (0.23) using animal model was obtained by Akhtar et al. (2012) in Niliravi buffaloes.
The bivariate analysis for estimation of correlation between different economic traits of Murrah buffaloes was done using the most appropriate models from the single-trait analyses. The correlation estimates obtained between different economic traits were positive and moderate to high suggesting selection of any of the trait will have its positive consequence over the other correlated trait (Table 3). Our results of genetic correlation between body weight traits falls within the range of estimates reported by Pandya et al.(2015) in Niliravi buffaloes.
Estimates for direct genetic correlation (rg) between birth weight and body weight at different ages were low to moderate except very low of 0.03 between BW to 9BW. It ranged from 0.29 between BW and 6BW to 0.61 between BW and 36BW. Our findings were consistent with positive and medium to high genetic correlation estimates of BW with 3BW (0.72), 6BW (0.70), 9BW (0.68) and 12BW (0.52) by Thiruvenkadan et al.(2009). Neyser et al.(2012) also reported high genetic correlation of birth weight with weaning and yearling weight. However, low correlation estimates of birth weight with different ages were reported by Gupta et al.(2015) in Murrah buffaloes and Shahin et al.(2010) in Egyptian buffaloes. We observed very high rg estimate between 3BW and body weights at later stages (0.92 between 3BW and 6BW, 0.99 between 3BW and 9BW, 0.52 between 3BW and 12 BW, 0.63 between 3BW and 18BW, 0.99 between 3BW and 24BW, 0.75 between 3BW and 30BW and 0.71 between 3BW and 36BW). These results were in agreement with correlation estimates reported by Thiruvenkadan (2009), Neyser (2012) and Pandya (2015). Shahin (2010) reported low correlation estimates between 3BW and other growth traits. Genetic correlation between 6BW and other ages were also high ranging from 0.50 to 0.91 indicating the scope of indirect selection for post-6BW on the basis of 6BW, suggesting that animals with above average 6BW would tend to be above average in genetic merit for 9BW, 12BW, 18BW, 24BW, 30BW and 36BW too. Similar high genetic correlation was observed between other traits 9BW,12BW,18BW,24BW,30BW and 36BW (Table 3).Estimates of maternal genetic correlation were high for BW and 6BW (0.89). For other traits, non-reliable maternal correlation estimates were obtained with high standard error. Most of the phenotypic correlation estimates were lesser than their genetic counterparts (Table 3). Phenotypic correlation between all the body weight traits were positive and medium to large. Selecting animals for their live weights is a difficult task, as the optimum live weight gained is expressed at later ages. However, owing to high genetic correlation estimates of 6BW with later ages live weight, we can suggest use of 6BW as the criteria for indirect selection for achieving optimum weight at later ages.
Genetic trend of growth trait
The estimates of annual rates of genetic progress for all the growth traits were positive but low (Table 4). For birth weight and 3-month body weight, statistically significant (p<0.05) genetic gain of 9.7 gm/year and 11gm/year was obtained respectively. For other growth traits, the genetic trend was found to be non-significant for all the body weight traits with gain of 8gm/year,19gm/year, 24gm/year, 53gm/year, 74gm/year, 73gm/year and 110gm/year for 6BW,9BW,12BW,18BW,24BW,30BW and 36BW respectively. The sluggish genetic gain in all growth traits indicates the negligence of these traits while formulation of the breeding strategies. Therefore, the present result shows that we necessarily need to include these economic traits in our breeding goals.