The results of model effect with phenotypic means and standard errors were presented in Table 1. Fishers scoring optimisation technique and wald chi-square statistics criteria were used to assess the goodness of fit. The ratio between Chi-square statistics and degree of freedom was estimated as close to one for all fertility indicator traits and it indicates that the variability in the data has been properly modelled.
Non Return Rate at 90 days (NRR90F)
Table 1: Season and period wise least squares means and standard errors of success to insemination traits (fertility) of Gir cattle.
The number of individual for NRR90F was estimated as0.42 on data scale. The intercept of NRR90F along with standard error were estimated as (-) 1.14±0.20 on log it scale. Negative intercept value indicated that proportion of non-return rate at 90 days was lower than the alternative outcome i.e
heat return rate after insemination (0.58).The present findings showed lower success rate of insemination than the failure rate to conception. It may be due to that success of insemination is reflected by large number of environmental factors compared to alternative outcome.
Subjecting the data to statistical analysis, NRR90 was significantly influenced (P<0.001) by period of AI on indicating that the NRR 90 is not equally successful for conception during the period. The cattle inseminated in period of 2020 had low estimates for non-return rate at 90 days compare to the year of 2018 and 2019. The odds ratio NRR90 was estimated as 2.751 for in the year of 2019 compare to the year of 2020. The LS estimates have been converted back to the data scale by the inverse link function and results have been presented in Table 1. NRR90 during the period showed that success rate of non-return at 90 days was reduced by 22 per cent in the year of 2020. The effect of insemination period on NRR90 may be expected due to no uniform AI practices during the period. It led to the conclusion that effect of period on non-return rate may be mediated through the management practices.
Success rate of AI was assessed among the seasons using odds ratio and results of odds ratio and 95% confidence intervals (CIs) were presented Table 2. In relation to season of AI, it was found to be non-significant for NRR90F. In general, insemination during the summer season showed higher success rate for NRR90 and it was estimated as 1.101 times higher to the autumn season. Lowest success rate of NRR90 was observed in the rainy season. In agreement with our findings,
Table 2: Estimates of oddsratio and confidence limitfor conception in Gir cattle with frozen thawed semen.
It is possible that decreasing day light during autumn season negatively influences the hormonal secretion responsible for the reproductive function.
Conception rate (CR)
The overall means of CR was estimated as 0.43 on data scale. Intercept on logit scale was recorded as -0.85±0.19 for conception rate in Gir cattle (Table 1). Statistical analysis for fixed effects showed significant difference among CR during the period. The conception rate was lower by 22 percent in the year of 2020 as compared to the year of 2019.In order to predict predict the success rate of AI, odds ratio was estimated among the period and seasons. It was estimated as 1.547 for the year of 2019 compared to the year of 2020. Proportion of CR observed in present study was lower than the reports of NDRI, (2015)
who found that the overall conception rate (CR) as 0.43 in herd maintained at NDRI, Karnal. Highest estimates for conception rate was observed in summer season (0.42) followed by (0.40). It can be inferred that summer season of AI is appropriate for higher CR and effect of season may be variable due to the inter-annual random change of climatic factors. CR may be decreased under stress of rainy. It necessitated the need to take effective measures for standardised AI practices for achieving optimum fertility.
Similarly, the present study was similar to that have reported non-significant effect of season of artificial insemination on conception rate was observed in field animals by Bhagat and Gokhale (2016)
. The period of AI had exerted a significant effect (P<0.001) on NRR90 and it showed decreasing trend over the period. The present findings may be observed due to the inter-annual random change of climatic factors. Incidence of NRR90 may be decreased under stress of heat and cold. It necessitated the need to take effective measures for standardised AI practices for achieving optimum fertility.
Number of insemination to conception (INS)
The wald chi- square estimates and least squares means along with standard error for period and season have presented in Table1. The data from 1074 conception was analysed and average cumulative proportion to conception was estimated as 0.71, 0.91 and 0.97 from first, second and third insemination cycle, respectively. Present studies was estimated as 91 percent conception from 2 insemination and rest 9 percent conception was from3 or more insemination i.e.
incidence of repeat breeding in Gir population. Intercept was estimated as positive 0.55±0.27, 2.05±0.28 and 3.28±0.32 on cumulative log it scale for one, second and third insemination cycle indicating that maximum conception was achieved from first AI cycle compared to other categories. However, variation to conception is marked in ordered insemination and it might be due to environmental and genetic factors of inseminations. Subjecting to the statistical analysis, ordered insemination to conception were significantly influenced (P<0.002) by season and (P<0.04) period of AI. Higher proportion of conception was estimated in winter season and lowest was found in rainy season (Table 1).
The 95% confidence interval (CI) was used to estimate the precision of the OR and results are presented in Table 2. High estimates of OR for INS traits indicates a low level of precision of the OR, whereas a small CI indicates a higher precision of the OR.
In the absence of reproductive disorders such as cystic ovaries, anoestrus and chronic endometritis, overall success to conception from two AI was estimated as 0.90. The present study was in agreement with the findings of Gustafsson et al., (2002).
It led to the conclusion that in female cattle with normal fertility the incidence of fertilisation failure is approximately 10% and early embryonic death within 3 weeks following fertilisation accounts for approximately 30% leading to a total early pregnancy loss of close to 40% during the first 21 days post AI. This means that on average 40% females will return to oestrus after each AI or mating. Several environmental factors e.g. nutrition, climate, as well as intrinsic animal factors have been suggested to be the cause behind this early embryonic loss in cattle. It has also been proposed that early embryonic loss should be regarded as “normal” due to an early elimination of unfit genotypes.
Estimation of heritability for NRR90, CR and number of insemination to conception (INS)
Estimated variance component on the underlying scales based on sire model were presented in Table 3.
Table 3: Estimation of Variance components for non-return rate at 90 days, conception rate and Number of insemination to conception.
Variance component of service sire for all indicator traits showed very low estimate. Heritability using logit sire model were estimated as 0.02, 0.03 and 0.09 for NRR90F, CR and INS, respectively. The heritability estimates for INS washigher than others. However, multinomial trait INS showed higher additive genetic variance related to NRR90 F and CR as binary traits. Additive genetic variance for NRR90F was the least among all indicator traits. The present finding was more or less in agreement with those of workers who have estimated heritability values in the Holstein breed for fertility traits (Averill et al. 2004; Rahbarr, 2016)
. In the case of fertility traits, the range of heritability was reported as 0.02 to 0.07 (Rahbar, 2016)
. As shown in Table 3, estimates of additive genetic correlation varied from 0.26 for CR and INS to 0.92 for NRR90F and CR. Sun et al., (2009)
estimated very low heritability for non-return rate and number of insemination to conception in Holstein cows. Wall et al., (2003)
also reported very low estimates for non-return rate after 56 days and number of inseminations to conception in dairy cows using maternal grandsire model. Heritability estimates of heifer conception rate on first service were 0.005 from the linear model and 0.01 from the threshold model in Holstein cattle (Kuhn et al., 2006).
The low heritability in this study suggested that improvement in success rate to conception could be achieved by improving the reproductive management. The high and positive correlation among NRR90F, CR and INS in this study suggests that these traits are genetically equivalent and influenced by the same genes. Integration of information related to these traits may be more effective for efficient selection strategies.
Multi-trait sire evaluation
The breeding values using best linear unbiased predictor method (BLUP) formulti-traits (NRR90F, CR and INS) were obtained duriring GLIMMIX analysis. Breeding valuesofeach traitand corresponding ranking are presentedin Fig 1.
Fig 1: BreedingValue of siresforNRR90F, CR and INS.
Its howed wider range of variation in breeding value for all indicator traits. The percent bullabove mean BV were found as 22.22% for NRR90F, 55.55% for CR and INS in Gir cattle population. Theranking of BV among Girbulls for NRR 90F, CR and INS traits was very high and aproaching to unity. It can be inferred from the present study that earlier observed trait NRR90 F could be used for evaluation of sire for successful insemination traits and this could in turn saves the cost of production.