|dc.description.abstract||The installation of precision farming technology includes the utilization of image biometrics to calculate body condition scores (BCS) in Holstein cattle. Body condition scores help dairy operations to individually estimate energy reserves for each animal, based on subcutaneous fat found throughout the body but more specifically along the spine and the pelvis. Body condition scoring was originally a visual task performed by trained personnel that required specialized training and was often subjective. With the installation of a new automated system that has been validated (DeLaval Body Condition Scoring BCS™), BCS has become more accessible and flexible as a herd management tool. The hypothesis of this study was that low BCS, or a loss in BCS, during early lactation would reduce the rate of conception at multiple artificial inseminations (AI) increasing the number of days to pregnancy. Therefore, the overall objective of this research was to evaluate the association between BCS dynamics and the probability of conception at multiple AI. In chapter 1, a brief literature review about the challenges during the transition period, fertility, and BCS is presented. Chapter 2 is focused on the association between BCS and BCS changes (∆BCS) at multiple time points post-parturition and conception at first AI, while chapter 3 analyzed subsequent breedings up to fourth AI. This prospective observational study was performed on a single dairy operation in Windsor, Colorado, USA with a population of 2,885 Holstein cows including 1,460 primiparous and 1,425 multiparous cows. Study cows were housed in a free stall, cross-ventilated barn and milked three times per day. For study 1, automatic BCS was recorded using the DeLaval Body Condition Scoring BCS™. The records of BCS were gathered at 7, 21, 35, 49, and 60 d in milk (DIM) and on the d of first AI (dAI1). A 5-point scale was used to record BCS with 0.1 intervals. The categorization of BCS was defined as low (L; < mean - 1 SD), intermediate (M; mean ± 1 SD) and high (H; > mean + 1 SD). Changes in BCS were also categorized as no loss (NL; ΔBCS ≥ 0 points) and loss (Los; ΔBCS < 0 points). Multivariate logistic regression models were used to estimate the effect of explanatory variables on conception as a binary outcome. Additionally, a cox regression analysis with hazard ratios were used along with frequency analysis to further visualize the data. The overall conception rate at first AI was 30.1% (34.6 and 25.5% in primiparous and multiparous cows, respectively). Low BCS was associated with lower conception rate to first AI, while loss of BCS resulted in greater days to conception. The same study design was applied in chapter three; however, BCS records were gathered at 7, 30, and 60 DIM, and at dAI1, on the day of second AI (dAI2), third AI (dAI3), and fourth AI (dAI4). Low BCS during early lactation resulted in lower odds of pregnancy at multiple AI. Logistic regression analyses of ∆BCS also showed cows that lost BCS had greater odds of pregnancy at different inseminations. The likelihood that cows will conceive concurrent with a loss in BCS was greater across multiple AI compared to cows that did not lose BCS. The overall success of pregnancy was 27.8% at second AI (pAI2), 21.4% at third AI (pAI3), and 16.0% at fourth AI (pAI4). In conclusion, low BCS were associated with lower conception rates at AI. Furthermore, a loss in BCS were associated with greater number of days from parturition to conception to first AI However, a loss in BCS was associated with greater conception at second, third, or fourth AI. Monitoring daily automatic BCS provides potential for assessing future fertility of dairy cows.