Abstract

    The main objective of this study was to identify the factors that affect the success of artificial insemination (A.I) services under field conditions. The data from a total of 459 inseminations were analyzed. The effects of farm, animal, semen/bull and A.I. technician on conception rate were studied.  Milk progesterone concentration was used as an indicator of conception. Milk samples were collected from animals at day 0, 11 and 22 post-insemination and analysed for progesterone (P4) concentrations using radioimmunoassay (RIA) kits.  Results indicated that the overall conception rate through A.I. under field condition was 29%.  Among the farm-related factors, only region had a significant effect on conception rate (P<0.01). Nutrition had a significant effect on conception (P<0.01).  Housing system and the time interval from first heat signs to A.I. had no effect on the conception rate (P>0.05). Animals inseminated within 24 hours after the onset of estrus had a higher, though statistically insignificant, conception rate than those inseminated after 24 hours. Among the animal-related factors, species, milk production, body condition score (BCS), lactation state, heat signs and uterine tone had a significant effect on conception rate. The conception rate in buffaloes was higher than in cattle (P<0.05). Animals with the higher BCS had a better conception rate than those having lower condition. Conception rate in the milking animals was more than the dry ones (P<0.05). Animals showing the passage of mucus from external genitalia (P<0.05) and marked uterine tone (P<0.01) showed better conception rate. Among the semen/bull related factors, bull breed, semen type, quality and source had a marked effect on conception rate. The conception rate was higher with the semen of cross bred and buffalo bulls (P<0.05).  The conception rate with liquid semen was high and low with frozen semen (P<0.01). Good quality semen yielded higher conception rate than poor quality semen (P<0.01). The conception rate varied significantly due to A.I. technician (P<0.01). In conclusion, there is a tremendous scope to improve the existing A.I. technology in field through improved management of both animal and farm, supply of high quality frozen semen and enhancement in insemination skill of A.I. technicians.