Reproductive and Endocrine Impact of Deep versus Conventional Artificial Insemination in Dairy Cows Synchronized with Hormonal Protocols for Fixed-Time Insemination
Year: 2025
Type: article
Abstract: : The objective of this study was to evaluate the reproductive performance of dairy cows and heifers subjected to two artificial insemination (AI) techniques deep intrauterine AI and conventional rectovaginal AI under fixed-time artificial insemination (FTAI) protocols using distinct hormonal regimens. Twenty Holstein females (10 primiparous and 10 multiparous) were assigned to four treatment groups combining AI method (deep or conventional) with parity status. Synchronization of estrus was achieved through Ov-synch in primiparous cows and J-synch in multiparous cows, both involving exogenous administration of gonadotropin-releasing hormone (GnRH), prostaglandin F2α (PGF2α), and progesterone (P4). Pregnancy diagnosis was performed 45 days post-insemination via transrectal ultrasonography. The results revealed a superior pregnancy rate in cows subjected to deep AI (80%) compared to conventional AI (60%), regardless of parity. Ovarian activity was higher in heifers than in multiparous cows, where a 20% incidence of postpartum anestrus was attributed to negative energy balance and suboptimal body condition. Additionally, the synchronization protocols achieved a 90% estrus response across all animals. These findings suggest that deep AI, by depositing semen closer to the uterotubal junction, increases the probability of fertilization, especially when paired with precise hormonal control of ovulation. This approach could improve reproductive efficiency and reduce open days in dairy herds. Moreover, the study underscores the importance of integrating hormonal synchronization and insemination technique optimization as a strategy for enhancing fertility outcomes in intensive dairy systems.
Author: Francisco Echeverria, Riveliño Ramón-Curay, Jagger Segura Ochoa, Joao Mazón-Vélez, Dagnny Mazón-Vélez, Katherin Beltrán Moso
Language: English
Subfield: Agronomy and Crop Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W6968449076
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