PSXIII-22 Modeling the optimal time point at which to perform timed artificial insemination

Abstract

Abstract Timing of artificial insemination (AI) critically impacts likelihood of pregnancy success. In modern timed AI protocols for cattle, AI is performed at a predetermined time point following estrus synchronization. As a result, there may be significant variation among cows or heifers in estrous status and in timing of insemination relative to estrus onset. Recommendations as to when timed AI should be performed are formulated based on research trials evaluating alternative time points. However, available data are limited, as such trials are costly and require large numbers to detect significant differences. As an alternative approach, a mathematical model was developed to predict the anticipated aggregate pregnancy rate to timed AI based on the hour at which timed AI is performed. Previously published distributions of estrus onset were compiled separately for two long-term progestin-based protocols for beef heifers (MGA-PG and 14-d CIDR-PG). Probability of pregnancy was modeled using a regression equation for fertility based on timing of AI in relation to timing of estrus, with heifers grouped in one hour time blocks. Based on a previously published meta-analysis that compiled pregnancy rates when timed AI was performed prior to onset of estrus, a reduced pregnancy rate was assigned to heifers for which timed AI would be performed prior to anticipated estrus expression. Modeled pregnancy rates based on timing of AI were plotted for each protocol. Additionally, use of a split-time AI versus fixed-time AI approach was modeled, as well as predicted impact of varying intervals between the two time points used for split-time AI. Modeled cumulative estrous response and aggregate pregnancy rates were compared to published data for the MGA-PG and 14-d CIDR-PG protocols. Results suggest a mathematical model may be useful to determine the optimal time point at which to perform timed AI following estrus synchronization.