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Smallholder beef cattle farms in South Africa have had low reproductive performance, which has been associated with management practices. Considering current farm management practices, a multi-stage selection study was conducted to assess reproductive performance as defined by pregnancy rate, fetal and calf losses, calving interval and days open to benchmark reproductive performance. Data were collected twice, in autumn (March–May) for pregnancy diagnosis and in spring (September–November) for monitoring of confirmed pregnancies. Overall, 3694 cow records from 40 smallholder herds were collected during 2018 and 2019 breeding seasons from five provinces. The preferred 25th quartile described target performance and GLIMMIX procedure determined associations between management practices and performance. Smallholder farms on average recorded 50% pregnancy rate and 12% fetal and calf losses with 304 and 608 days open and calving interval, respectively. The derived target benchmarks for pregnancy rate, fetal and calf losses, days open and calving intervals in smallholder farms were 54%, 1.4%, 152 and 425 days, respectively. Reproductive performance was associated with no knowledge of body condition scoring before breeding, culling of old and non-productive cows, record keeping and low bull to cow ratio (p < 0.05). The performance benchmarks implied that industry averages may be improved if sustainable management services are provided through extension and advisory services.

Reproductive failure is still a challenge for beef producers and a significant cause of economic loss. The increased availability of transcriptomic data has shed light on the mechanisms modulating pregnancy success. Furthermore, new analytical tools, such as machine learning (ML), provide opportunities for data mining and uncovering new biological events that explain or predict reproductive outcomes. Herein, we identified potential biomarkers underlying pregnancy status and fertility-related networks by integrating gene expression profiles through ML and gene network modeling. We used public transcriptomic data from uterine luminal epithelial cells of cows retrospectively classified as pregnant (P, n = 25) and non-pregnant (NP, n = 18). First, we used a feature selection function from BioDiscML and identified SERPINE3, PDCD1, FNDC1, MRTFA, ARHGEF7, MEF2B, NAA16, ENSBTAG00000019474, and ENSBTAG00000054585 as candidate biomarker predictors of pregnancy status. Then, based on co-expression networks, we identified seven genes significantly rewired (gaining or losing connections) between the P and NP networks. These biomarkers were co-expressed with genes critical for uterine receptivity, including endometrial tissue remodeling, focal adhesion, and embryo development. We provided insights into the regulatory networks of fertility-related processes and demonstrated the potential of combining different analytical tools to prioritize candidate genes.

After parturition, cows undergo negative energy balance leading to fat mobilization, predisposing them to fatty liver syndrome and ketosis with major consequences for health and reproduction. Supplementation of rumen-protected choline (RPC) has attracted major research efforts during the last decade, assuming that choline improves liver function by increasing very low-density lipoprotein exportation from the liver, thereby improving metabolic profiles, milk production, and reproduction. However, the effects of RPC on production, health, and reproduction have been inconsistent. Therefore, the aim of this meta-analysis was to evaluate the effects of RPC supplementation, starting from d 20 (± 12.2) ante partum to d 53 (± 31.0) postpartum, on feed intake, milk production performance and metabolic profiles of dairy cows early postpartum. Data analyses from 27 published studies showed an increase in postpartal dry matter intake (from on average 19.1 to 19.9 kg/d; p < 0.01) and milk yield (from on average 31.8 to 32.9 kg/d; p = 0.03) in cows receiving RPC. Milk fat yield and milk protein yield were also increased (p ≤ 0.05), without changing milk protein and fat contents. However, no interactive effects between cow’s milk yield level and RPC-supplementation as well as no dose-dependent effects of RPC supplementation were observed. Supplementing the diet with RPC showed no effects on blood metabolites (non-esterified fatty acids, beta-hydroxybutyrate, glucose, and cholesterol), independent of the milk yield level of the cows. An effect on liver triacylglycerol contents, incidence of ketosis, and mastitis could not be confirmed across all studies included in this meta-analysis. Also, the positive effects of RPC supplementation on reproductive performance were not consistent findings. In conclusion, supplementing RPC in lactating dairy cows showed positive effects on dry matter intake which likely caused the improved milk yield. However, RPC supplementation did not improve the metabolic health status of the cows. As several factors might be related to the responses to RPC, further research is needed to explore the precise mechanisms of RPC action in lactating cows, especially with regards to feed intake improvement and its related metabolic health-promoting potential in early lactating dairy cows.