The studies’ objectives were to identify cow-level and environmental factors associated with metritis cure in animals receiving ceftiofur treatment and metritis spontaneous cure in animals remaining untreated for metritis, to predict metritis cure using traditional statistics and machine learning algorithms. Additionally, to characterize the changes in the uterine metabolome in cows with metritis development and cure for cows treated with antimicrobials due to metritis. The dataset used for study one is from a previous study comparing the efficacy of different therapies and self-cure for metritis. Metritis was defined as fetid, watery, reddish-brownish discharge, with or without fever. Cure was defined as an absence of metritis signs 12 days after diagnosis. Cows were randomly allocated to receive ceftiofur treatment or (CEF, n = 275) remain untreated (CON, n = 275). For study two focusing on spontaneous metritis cure study, a subset of 438 primiparous and multiparous lactating Holstein cows having metritis and remaining untreated was used. Environmental and cow-related variables were offered for univariate logistic regression and selected for the multivariable logistic regression model according to their P-value (P < 0.05). In the first study, variables were offered to the model to assess their association with metritis cure, and in the second study to assess their association with spontaneous metritis cure. Additionally, machine learning algorithms analysis was performed in both studies. Multivariable logistic regression and ROC analysis indicate that cows developing metritis after the first week postpartum, having an increase in milk production starting on the day before metritis diagnosis day, and no fever at the time of metritis diagnosis have an increased odds of cure of metritis. Additionally, the machine learning algorithms achieved satisfactory accuracy for the prediction of metritis cure. For the metabolome study, vaginal discharge samples were collected from 86 cows within 6 hours after parturition, at 4 and 7 DIM, at metritis diagnosis, and at 4 and 7 days after metritis diagnosis. Cows with metritis (MET; n = 17) were paired with counterparts without metritis (NoMET) of similar DIM and parity (n = 49). Metabolomic data were analyzed using the MetaboAnalyst software. Untargeted GC-TOF-MS metabolomic analysis highlighted changes in the uterine metabolome in the first week postpartum in cows developing metritis compared to healthy animals. For the metritic group there are significant changes in the uterine metabolome associated to cure. In all scenarios, the metabolites lignoceric, malic, and maleic acids, ornithine, and hypotaurine, which are associated with arginine/aminoacyl-tRNA biosynthesis and taurine/purine metabolism, were upregulated in NoMET group and in cows curing from metritis. Also, cows not curing from metritis had significant changes in the uterus metabolome independent of receiving ceftiofur or remaining untreated. Improving spontaneous metritis cure prediction is an important way to contribute toward antimicrobial stewardship due to promoting selective treatment for metritis, reducing the use of antibiotics, potentially reducing the dissemination of antimicrobial resistance, reducing the cost of the disease, and improving animal welfare. Metabolome analysis may be an important tool to understand changes in the uterus during the postpartum and the dynamic of metritis development.

Recent advancements in the reproductive management of dairy cattle highlight the integration of multiple disciplines, including physiology, management, nutrition, genetics, economics, veterinary herd health, and production medicine, to improve reproductive performance. Generation of timely and cost-effective pregnancies is a key economic driver in dairy herds, influencing milk yield, income over feed cost, and culling decisions. Artificial insemination (AI) accelerates genetic progress, controls venereal diseases, and ensures safety. Estrous cycle synchronization programs, particularly timed AI (TAI), have emerged as cost-effective solution to improve pregnancy rates and reducing estrus detection needs. Despite the development of numerous controlled breeding programs, a thorough understanding of estrous cycle physiology and follicular wave dynamics is essential. Different synchronization programs enhance insemination and pregnancy rates in dairy cows and heifers. Genetic selection for reproductive traits has improved breeding strategies by emphasizing both productive and reproductive traits. Improved understanding of hormonal influences, estrous synchronization, and genetic selection has significantly enhanced reproductive performance. The goals of this thesis are to: 1) review the literature on the current status of knowledge of factors impacting effective reproductive management strategies and hormonal manipulations in dairy cattle; 2) assess the impact of increasing the dose of GnRH at the beginning of a CIDR Synch to improve ovulation and pregnancy in dairy heifers, and 3) to compare key reproductive outcomes in two reproductive programs with variable estrus detection length followed by OvSynch and to assess their relationship with the fertility traits genomic prediction for daughter pregnancy rate (GDPR) and the genomic prediction for cow conception rate (GCCR) in lactating Holstein dairy cows.

The present thesis consists of two chapters. The first chapter focuses on reviewing previously conducted research pertaining to feed efficiency, nitrogen metabolism, and fatty acids utilization in dairy cows to underscore the key points investigated in the research conducted during my master’s. The second chapter describes a study aimed to identify biomarkers associated with residual feed intake (RFI) and improve predictive models for feed efficiency. The study used a subset of 24 lactating Holstein cows representing extremes of least feed-efficient (LFE, n = 12, RFI = 2.44) and most feed-efficient (MFE, n = 12, RFI = -2.69) that had no difference in 3 energy sinks (body weight change, metabolic body weight, and energy secreted in milk) from a population of 454 genotyped cows with full phenotype for feed efficiency. Rumen fluid and serum samples were collected between 60 and 90 DIM. Rumen fluid samples were collected using an oroesophageal tubing procedure. Serum samples were used to measure fatty acids using a two-step assay. The fatty acid methyl ester was performed using solid phase extraction and quantified using chromatographic peak area and internal standard-based calculations. Ruminal ammonia nitrogen was performed using a phenol-hypochlorite assay, while serum urea was measured using a commercial ELISA test validate for the bovine species. Cows in the MFE group had higher ruminal ammonia nitrogen concentrations than cows in the LFE group. There were no differences in serum urea concentration between MFE and LFE cows. Serum fatty acids concentration differed between groups, with myristic acid, palmitic acid, cis-heptadecenoic acid, stearic acid, and total saturated fatty acids in greater concentrations in the MFE group than in the LFE group. Total polyunsaturated fatty acids concentration was lower in the MFE group than in the LFE group. Myristic acid, palmitic acid, total polyunsaturated fatty acids (PUFA), and saturated fatty acids (SFA) were correlated with RFI. A model incorporating myristic acid, palmitic acid, palmitoleic acid, ante iso heptadecanoic acid + palmitoleic acid, oleic acid, cis-vaccenic acid, petroselinic acid, stearic acid, linoleic acid, dihomo-γ-linolenic acid (DGLA), cis-monounsaturated fatty acids, omega 6 PUFA, total PUFA, total SFA, other and unknown fatty acids resulted in a R2 = 0.74. When rumen ammonia nitrogen was added to the previous model, an improvement was observed (Full R2 = 0.84). The findings of the current study provide evidence that ruminal ammonia and fatty acids in serum are correlated with RFI, and the integration of these metabolites may improve the prediction of RFI.