Comparative efficacy of oestrus synchronization protocols in buffalo (Bubalus bubalis)

Effect of Different Synchronization Regimens on Reproductive Variables of Crossbred (Swamp × Riverine) Nulliparous and Multiparous Buffaloes during Peak and Low Breeding Seasons

Simple Summary

Parity and season are two prominent variants which influence buffalo reproductive performance and synchronization outcome. The present study compared modified new Ovsynch synchronization (GPGMH) protocol with conventional Ovsynch (OVS) in reproductive parameters of buffaloes with different parity (nulliparous and multiparous) in different seasons (peak and low breeding seasons). Compared to the conventional Ovsynch, the modified GPGMH protocol was more effective to enhance the estrus rate, ovulation rate and pregnancy rate in multiparous crossbred buffaloes. In addition, the application of GPGMH protocol improve the reproductive performance during the peak breeding season than low breeding season.

Abstract

The present study was conducted to examine the effect of conventional the Ovsynch protocol (OVS) and a modified Ovsynch synchronization (GPGMH) protocol on the follicular dynamics, estrus, ovulation, and pregnancy in nulliparous and multiparous crossbred (swamp × riverine) buffaloes during different seasons. GPGMH or OVS protocols were used to synchronize nulliparous (n = 128; GPGMH = 94, OVS = 34) and multiparous (n = 154; GPGMH = 122, OVS = 32) buffaloes during the peak (n = 186; GPGMH = 143, OVS = 43) and low breeding (n = 96; GPGMH = 73, OVS = 23) seasons. Buffaloes were monitored for follicular dynamics, estrus response, ovulation, and pregnancy rates. The results showed that protocol, parity, and season had significant effects on estrus, ovulation, and pregnancy variables, and interactions among parity and protocol, season and protocol, and season and parity were observed for few of reproductive indices in the crossbred buffaloes. There were no significant (p > 0.05) interaction for protocol, parity and season. In multiparous buffaloes, the application of the GPGMH protocol significantly (p < 0.05) increased the interaction to the interval to estrus onset after the second GnRH, estrus response, ovulation rate, and pregnancy rate, and lowered (p < 0.05) the silent estrus when compared with the conventional OVS protocol. During the peak breeding season, the application of the GPGMH protocol significantly (p < 0.05) improved the interaction to the estrus response, ovulation rate, and pregnancy rate, while it lowered (p < 0.05) the silent estrus incidence when compared to the conventional OVS protocol. In conclusion, the GPGMH protocol, in comparison to the OVS protocol, improves the follicular dynamics, estrus response, ovulation, and pregnancy rates in crossbred multiparous buffaloes during the peak breeding seasons.

Evaluation of Ovsynch versus modified Ovsynch program on pregnancy rate in water buffaloes: a meta-analysis

Ovsynch is a widely accepted estrus synchronization protocol for improving the reproductive performance of water buffaloes who manifest low reproductive efficiency. Recently, some modified protocols based on Ovsynch such as 2 injections of prostaglandin 14 days apart following the Ovsynch are also introduced to enhance the reproductive potential of this species. In the present study, a meta-analytical assessment was performed with the objective to evaluate the reproductive performance of water buffaloes synchronized with Ovsynch or modified Ovsynch programs. Meta-analysis of the fixed or random effects model was determined by the heterogeneity among the studies. Reproductive outcome of interest was pregnancy per artificial insemination (P/AI) measured on day 25 (25-100). A total of 32 articles including 4003 buffaloes using either Ovsynch or modified Ovsynch protocol were reviewed. In the random effects model for buffaloes, the overall proportion of P/AI was 42.55% [95% confidence interval (CI): 37.48-47.70; n = 3,089] and 46.44% (95% CI: 39.63-53.31; n = 914) on day 25 after AI for Ovsynch and modified Ovsynch, respectively. Results for P/AI were then categorized by ovarian activity, where P/AI was available for 3575 cyclic buffaloes and 320 non-cyclic buffaloes. For cyclic buffaloes, the overall proportion of P/AI was 47.54% (95% CI: 42.72-52.38; n = 2911) and 57.97% (95% CI: 54.12-61.77; n = 664) on day 25 after AI for Ovsynch and modified Ovsynch, respectively. In the fixed effects model for non-cyclic buffaloes, the overall proportion of P/AI was 19.68% (95% CI: 13.48-26.58; n = 167) and 33.01% (95% CI: 25.50-40.94; n = 153) on day 25 after AI for Ovsynch and modified Ovsynch, respectively. In conclusion, a benefit for P/AI is detected in buffaloes with the modified Ovsynch protocol. Besides, whichever estrus synchronization protocols (Ovsynch or modified Ovsynch), cyclic buffaloes have higher P/AI compared with non-cyclic buffaloes.

Reproductive management in buffalo by artificial insemination

Artificial insemination (AI) is important for genetic improvement and to control the period of breeding in buffalo and has increased significantly over the past 20 years. AI is more difficult in buffalo compared with cattle due to variable estrous cycles, reduced estrous behavior, and reproductive seasonality. The latter is associated with a higher incidence of anestrus and increased embryonic mortality during the nonbreeding season. Protocols to control the stage of the estrous cycle have undergone recent development in buffalo. These protocols are based on the control of both the luteal phase of the cycle, mainly by prostaglandins and progesterone, and follicle development and ovulation, by prostaglandins, progesterone, GnRH, hCG, eCG and estradiol. Protocols that synchronize the time of ovulation enable fixed timed AI, avoiding estrous detection. Factors to consider when selecting an AI protocol include animal category (heifers, primiparous or pluriparous), reproductive status (cyclic or anestrus), and season. This review looks at the current status of estrus synchronization and AI in buffalo and provides some practical suggestions for application of AI in the field.