Fertility of farm animals mainly depends upon genetic potential and environmental combination including nutrition, health and overall management adopted by farmers, and to assess the same per cent pregnancy rate is the accepted indicator. Increased age at first calving, service period, calving interval which ultimately results into lowering, overall lifetime productivity of animals is repercussion of low pregnancy rate either due to non-expression of heat, reproductive problems or increased number of services per pregnancy. Lower heritability of the trait is indicative of greater environment and management influence suggesting scope for improvement in management of animals by farmers. Available literature on relation of animal breed, season of artificial insemination (AI), sire used for AI, animal lactation order, animal age at the time of AI and sequence of AI etc. with pregnancy of animals is inadequate to explain the role of these factors for deciding fertility strategy at village level. An attempt in the present investigation was made to study these factors affecting pregnancy rate as an indicator of fertility in animals under field conditions of Uttar Pradesh state.

Materials and Methods

The field progeny testing of Holstein Friesian crossbred bulls operated under National Dairy Plan (NDP) phase-I in Uttar Pradesh during February 2014 to March 2019 and coordinated through National Dairy Development Board (NDDB) Anand, Gujarat, in India. BAIF, Pune contributed the bulls for test inseminations. Total 1,43,065 AI’s were performed on 80,244 animals during the period of five years and out of that 1,25,612 AI’s were followed till December 2018 for pregnancy confirmation and this forms data for present investigation. These animals were maintained by 55,416 farmers spread over jurisdiction of 100 cattle development centres distributed in five districts namely Azamgarth, Gorakhpur, Jaunpur, Pratapgarh and Sultanpur of Eastern Uttar Pradesh state. All animals were maintained and reared by the farmers’ individually. The housing ranged from open to permanent constructed sheds. Animals were stall fed with dry and green fodder along with concentrate. The calls for artificial insemination received through mobile phones and animals were inseminated with frozen semen at doorstep of farmers. Cows not repeated within 60 to 70 days post insemination were examined for pregnancy confirmation by rectal palpation. The pregnancy rate calculated by formula as suggested Qureshi et al. (2008)

The information on districts (Azamgarh, Gorakhpur, Jaunpur, Pratapgarh Sultanpur), animal breed (HF cross of Gir, ND, Sahiwal), animal age at the time of AI (below 30, 31 to 42, 43 to 55, above 55 months), year of AI (2014, 2015, 2016, 2017, 2018), season of AI (Rainy-June to September, Winter-October to January, Summer-February to May), sequence of AI (1^st, 2^nd, 3^rd, 4^th& above) lactation order of animal (heifer, first, second, third, fourth, fifth & above), and sire used for AI (HF50%, HF75%) was compiled for studying effect on pregnancy rate.

Logistic Regression

Generally identifying genetic and non-genetic factors influencing various functional traits in dairy animals’ is difficult using conventional analysis wherein the normality of residual error assumed. Under such conditions of binary or discontinuous nature of response, variable, logistic regression can facilitate effectively in exploring the relationship between the dependent and independent variables. Logistic regressions work with odds rather than proportions. The odds are simply the ratio of the proportions for the two possible mutually exclusive outcomes. If p is the proportion for one outcome, then (1−p) is the proportion for the second mutually exclusive outcome-

ODDS=p/(1−p)

The logistic regression model relates the log of the odds to the explanatory variable in the form of a linear function. In case of multiple logistic regression, more than one continuous/discrete explanatory variable can be incorporated in the model to study their simultaneous effect on the categorical response variable. The Akaike Information Criterion (AIC) was used to assess the fitness of the model (Manoj et al., 2015). The data was analyzed using R project for statistical computing software (version 3.6.1).

Results and Discussion

The overall mean pregnancy rate was recorded as 47.47±0.14 per cent which was remarkably higher than finding of Anzar et al. (2003) in Pakistan (29.00%), Nordin et al. (2004) in Malaysia (35.50%), Bhagat et al. (2008 & 2009) in field crossbred cattle (45.16±0.46 & 43.58±0.40%) in Maharashtra state, however lower to that of reported by Woldu et al. (2011) in Ethiopian cattle (48.30%), Shindey et al. (2014) in animals from Wardha district of Maharashtra state (46.40±0.19%), Razi et al. (2010) in Bangladeshi cattle, Gokhale and Bhagat (2015) and Bansal et al. (2019) under field conditions of Maharashtra (55.96±0.31%) and Bihar state (52.16%), respectively.

District

District significantly affected the pregnancy rate of animals (Table 1). The reports of Bansal et al. (2019) supported the present findings however, Pandey et al. (2016) reported non-significant effect of districts on pregnancy rate under Jharkhand state conditions.

Table 1: District affecting pregnancy rate in HF crossbred cattle with multivariate regression model

** (p<0.01)

Highest per centage of animals covered (34.95%) under AI’s were recorded from Sultanpur district followed by Gorakhpur district (19.91%) and lowest from Pratapgarh district (14.63%). The coverage of AI from Azamgarh and Jaunpur districts was at par (15.20 & 15.31%, resp.). The AI coverage and pregnancy rate found to be negatively correlated. The probability of getting highest animals pregnant was noticed at par in Gorakhpur and Jaunpur districts (51.80±0.32 & 51.78±0.36%) as well as Azamgarh and Pratapgarh districts (49.65±0.36 & 49.12±0.37%) and lowest from Sultanpur district (41.49±0.24%). These results were in agreement with the findings of Bansal et al. (2019) under Bihar state field conditions. The individual farmers’ management and agro-climatic conditions of respective district might be attributed to the significant differences in pregnancy rate of animals.

Animal Breed

Although 95.58% portion of cattle population was comprised of HF Non-Descript (ND) crossbred, HF Gir and HF Sahiwal crosses (0.83 & 3.59%, respectively) also recorded under study (Table 2). The pregnancy rate among breeds non-significantly differed, which disagree with the results of Miah et al. (2004) in Bangladeshi crossbred cows and Bansal et al. (2019) in Bihar state field animals noticed significant effect of animal breed on conception rate. Highest chances of getting pregnancies was noticed in HF Sahiwal cross animals (49.79±0.75%) followed by HF ND cross (47.40±0.14%) and lowest in HF Gir cross (46.11±1.55%) animals. Similar findings of significant higher pregnancy rate in local animals and ND crosses were also reported by Bhagat et al. (2009), Anzar et al. (2003), Razi et al. (2010), Pandey et al. (2016), Potdar et al. (2016) and Bansal et al. (2019) in field animals, however Qureshi et al. (2008) reported higher pregnancy rate in Holstein crosses in Jammu region.

Table 2: Animal breed affecting pregnancy rate in HF crossbred cattle with multivariate regression model

NS-Non-Significant

Age at AI (Months)

With the advancement of animal’s age the pregnancy rate goes down is established fact and this proved in this Uttar Pradesh state field conditions. With advancement of animal age, the pregnancy rate significantly reduced from 51.51±0.31 per cent for animals having age below 30 months at the time AI to 46.13±0.21 per cent for animals having age more than 55 months (Table 3).

Table 3: Age at AI affecting pregnancy rate in HF crossbred cattle with multivariate regression model

** (p<0.01)

This might be because of in old age animals’ feed and fodder intake reduces, digestion affects and ultimately hormonal changes leads to un-time heat, weak heat symptoms, delayed ovulation, unnoticed pre-mature abortions etc. which results into increased number of AI’s and servicer period.

Year of AI

The scientist Gwazdauskas (1985) reported that extremes in climate could affect deleteriously reproduction in animals. Seasonal variation of environment, nutrition, and management alters estrous activity and duration of estrus. Conception rates are reduced under stress of heat and cold. Endocrine functions altered by climatic extremes. In present investigation pregnancy rate noticed to be significantly reduced from 51.36±0.40 per cent in the year 2014 to 43.23±0.28 per cent in the year 2018, but coverage of AI increased almost double (12.69 to 24.14%) during the period of five years from 2014 to 2018 (Table 4).

Table 4: Year of AI affecting pregnancy rate in HF crossbred cattle with multivariate regression model

** (p<0.01)

Season of AI

From Table 5, it was revealed that the distribution of per cent inseminations performed was 39.43% in rainy season and during summer and winter, AI’s were similar (30.32 & 30.24%).

Table 5: Season of AI affecting pregnancy rate in HF crossbred cattle with multivariate regression model

** (p<0.01)

Bansal et al. (2019) recorded maximum AI in summer (34.96%) season compared with rainy (34.85%) and winter season (30.19%). Significantly higher pregnancies were recorded in summer season (52.09±0.26%) followed by winter season (46.44±0.26%) and rainy season (44.71±0.22%) these results were resembled with the findings of Shindey et al. (2014), Pandey et al. (2016) and Potdar et al. (2016) who noticed that animals inseminated during summer season had higher pregnancy rate. However, Bhagat and Gokhale (2013, 2016) and Bansal et al. (2019) reported higher pregnancies in winter season. Higher pregnancies in summer season might be attributed to spill over better effect of winter season on overall animal health.

Sequence of AI

The endeavors to get the animal conceived in minimum AI is beneficial in many folds to farmer like, it saves expenditure incurred on breeding, reduces age at first calving, service period, inter-calving period of animals as well as increases overall productive life of animals. In present study from Table 6, it was seen that almost two third inseminations (63.27%) were performed in first attempt followed by second (22.36%), third (7.91%) and very few in fourth and above attempt (6.48%). The pregnancies recorded in first attempt were significantly highest (50.11±0.18%) compared to inseminations performed in second (47.60±0.30%), third (40.60±0.49%) and forth and more attempts (29.64±0.51%). The results obtained in present investigation corroborated with the findings of Shindey et al. (2014) and Bhagat and Gokhale (2016) who recorded significantly highest conception rate in first attempt in field animals at Maharashtra state conditions.

Table 6: Sequence of AI affecting pregnancy rate in HF crossbred cattle with multivariate regression model

** (p<0.01)

Lactation Order

Animal lactation order significantly affected pregnancy rate (Table 7). Shindey et al. (2014), Bhagat and Gokhale (2016), Potdar et al. (2016) and Bansal et al. (2019) also recorded similar results, however, Bhagat and Gokhale (2013) and Pandey et al. (2016) recorded non-significant effect of parity on pregnancy rate. Highest pregnancy rate was observed in heifers (49.22±0.24%) compared with multiparous animals, which agree with the results of Potdar et al. (2016) whereas, the findings of Gunasekaran et al. (2008), Razi et al. (2010), Bhagat and Gokhale (2016), Pandey et al. (2016) and Bansal et al. (2019) differed the present investigation as they noticed lowest pregnancy rate in heifers. The higher pregnancy in heifers might be due more attention of farmers to young stock being a future generation to reap maximum returns. Bansal et al. (2019) in their study reported that in multiparous animals, the likelihood of getting pregnancies increased with progress of lactation order and reached highest in third lactation (52.93%) and decreased thereafter however, in present investigation, no such trend noticed but highest pregnancies were recorded in animals having second (47.27±0.31%) and fourth lactation (47.19%) compared with other lactation animals.

Table 7: Lactation order affecting pregnancy rate in HF crossbred cattle with multivariate regression model

** (p<0.01)

Breed of Bull

Bull (whose semen used for AI) breed had significant influence on pregnancy rate (Table 8). Present results supported the findings of Bhagat and Gokhale (2016), Pandey et al. (2016), Potdar et al. (2016) and Bansal et al. (2019), however, Miah et al. (2004) reported that genotype of bulls used for AI did not affect the pregnancy rate. Being a HF crossbred progeny testing program use of HF crossbred bulls semen was mandatory and the results indicated that more than three fourth inseminations (83.02%) were performed by using HF75% bulls semen, while remaining 16.98% AI’s done by using HF50% bulls semen. The probability of attaining highest pregnancy rate (45.11%) was recorded in animals inseminated with HF75% bulls’ semen.

Table 8: Breed of bull affecting pregnancy rate in HF crossbred cattle with multivariate regression model

** (p<0.01)

Conclusion

The study indicated that pregnancy rate significantly affected due to district, animal age at AI, year, season and sequence of AI, lactation order of animal and breed of bull used for inseminating the field animals. These factors need to be emphasized for having better pregnancy in AI bred cattle under Uttar Pradesh field conditions.

Acknowledgment

The financial assistance provided by National Dairy Plan phase-I, through National Dairy Development Board (NDDB), Anand is gratefully acknowledged. The authors are indebted to president of BAIF, for his inspiration and ceaseless support to undertake the research work.

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