Factors Affecting Conception Rate in AI Bred Cattle under Field Conditions of Maharashtra

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Factors Affecting Conception Rate in AI Bred Cattle under Field Conditions of Maharashtra
Vinod V. Potdar*, Jayant R Khadse, Sachin Joshi, Marimuthu Swaminathan IntroductIon G etting cows pregnant in a timely manner is important in maintaining a profitable dairy business. Artificial insemination (AI) was introduced as an effective breeding program in the beginning of 1960s with the objective of upgrading indigenous local cows. AI program always demands to keep records of non-return rate, conception rate, service per conception and calving rate in order to properly evaluate the reproductive efficiency of cows, skillness of the inseminators, fertility and semen quality of bulls. However, an effective reproductive recording system must provide the cattle owner with the key information required to make reproductive management decision. Conception rate is directly associated with the production attribute and responsible for monitoring life time productivity of the individual animal. Conception is the first pre-requisite of an animal entering into the productive life. Conception rate determines directly to the total profitability of farm enterprises. Thus, to achieve the maximum profitability, it is very important to increase the conception rate up to maximum level. An attempt was made to study the factors affecting conception rate as an indicator of fertility in animals under field conditions of Maharashtra.

MAterIAls A n d MethIds
In Maharashtra state, a total of 98336 artificial inseminations performed on 56037 animals during January 2010 to November 2015 maintained by 29097 farmers' from 44 cattle development centres spread across 2 districts of the Maharashtra state were collected and analyzed. The animals were individually maintained and reared by the farmers. The housing ranged from open to permanent constructed sheds. Animals were stall fed with dry and green fodder along with concentrate. The calls for AIs were received through data logger device on windows based platform 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 and conception rate was calculated as per the formula given by Qureshi et al. (2008). The information on factors like districts (Jalgaon and Beed), economic status of farmers (APL, BPL), animal breed (HF cross, Indigenous, Jersey cross, Nondescript), parity of animal (heifer, first, second, third, fourth, fifth and above calvers), animal body condition score (no ribs exposed, one rib exposed, two ribs exposed, three ribs exposed), heat stage (early, mid, late), season of AI (rainy-June to September, winter-October to January, summer-February to May) and sire used for AI (Indigenous, HF, HF crossbreed, Jersey, Jersey crossbreed), AI sequence number (1,2,3), and AI Year (2010 to 2015) were compiled to study the effect on conception rate. Least Square Means analysis of the data was done using 'R' software (version 3.5.1).

results A n d dIscussIon
The ANOVA shows district, parity of animal, artificial insemination sequence number, body condition of animal, heat stage during insemination, and year of AI had significant effect over conception rate (Table 1).
District significantly (p<0.05) affected the conception rate. Conception rate of Jalgaon district was 45.8 ± 0.46% while that of Beed district was 46.6 ± 0.56%. Management of animals at different agroclimatic condition with different locations have major role in differentiating conception rate ( Table 2).
The economic status of farmers' did not affect significantly (p>0.05) the conception rate. The animals owned by below poverty line (BPL) group of farmers however recorded apparently higher conception rate (46.5 ± 0.54%) compared to above poverty line (APL) category of farmers (46.1 ± 0.49%; Table 3). Bhagat and Gokhale (2016) and Pandey et al. (2016) also noticed higher conception rate in animals owned by BPL category farmers. More caring of animals at BPL families and lesser number of animals may be major factor for higher conception rate. Higher coverage of AI and lower conceptions are negatively correlated (Ricord et al., 2004).
Animal parity also significantly (p<0.001) affected the conception rate as has been reported by Shindey et al. (2014), Bhagat and Gokhale (2016) and Potdar et al. (2016). The highest conception rate of 47.5 ± 0.61% was observed in animals of 1 st parity, while the lowest conception rate was observed in heifers (45.20 ± 0.53%, Table 5). This might be due to greater attention by the farmers towards productive     (2016) and Pandey et al. (2016) supported the present investigation as they also noticed lowest conception rate in heifers. Other parity wise detail of conception rate is given in Table 5.
Body condition score of animal shows how it is managed and fed, an important tool to judge condition of animal. All animals under study were divided into 4 subgroups as per appearance of ribs to study its effect on conception rate. Significantly (p<0.001) higher pregnancies (48.6 ± 0.58%) was recorded in animals showing one rib exposed and lowest in no rib exposed (44.8 ± 0.66%, Table 6). The results obtained differed with report of Bhave et al. (2016), who noticed highest conception rate in field buffaloes having three ribs exposed, whereas Bhagat et al. (2009) noticed highest conception rate in field animals having no rib exposed. Potdar et al. (2016) indicated insignificantly highest conception rate in field animals having no rib exposed. Balanced diet feeding, vitamin and minerals can overcome these issues of infertility (Balakrishnan, 2003).
Heat stage of animal, during which AI is done, is one of the most important factors that contribute to conception rate in dairy animals. It has significant (p<0.001) effect on conception rate. In present study, animals having early heat stage showed the highest conception rate (48.01 ± 0.73%) followed by mid heat (47.7 ± 0.37%) and lowest in late heat stage (43.0 ± 0.88%, Table 7). Gunasekaran et al. (2008), Pandey et al. (2016) and Potdar et al. (2016) noticed higher conceptions in animals exhibiting early heat.
Since the present analysis of AI work is for year 2010 to 2015, it showed significant (p<0.001) effect of year over conception rate. The highest conception rate was noted in year 2011 (47.5 ± 0.64% ), while the lowest conception rate was observed in year 2010 (43.0 ± 0.90%). Other year wise details of conception rate are given below in Table 9.
Bull breed whose semen is used for AI work had significant (p<0.001) influence on conception rate as has been reported by Bhagat and Gokhale (2016), Pandey et al. (2016) and Potdar et al. (2016). Highest conception rate was observed in sire of Indigenous origin (51.1 ± 0.61%) followed by HF cross (47.1 ± 0.56%), HF pure (46.80 ± 0.55%), Jersey     (2016), Pandey et al. (2016) and Potdar et al. (2016), who noticed highest conception rate in indigenous breeds used for inseminating the field animals. However, Miah et al. (2004) reported that genotype of bulls used for AI did not affect the conception rate. Artificial insemination sequence number had significant (p<0.001) effect over conception rate, the highest conception rate was observed with first AI (49.7 ± 0.45%) followed by second AI (47.8 ± 0.56%) and lowest in third AI sequence number (41.2 ± 0.67%, Table 11). Potdar et al. (2016) and Bhagat and Gokhale (2016) also reported the same results.