Álvaro García
The body condition of cows at calving and its fluctuations throughout lactation significantly impact the health and fertility of high-producing dairy cows. Body condition scoring (BCS) is a valuable tool for estimating body fat reserves and plays a crucial role in farm management. While traditional BCS assessment is labor-intensive, recent technological advancements have simplified the process, providing essential support for efficient BCS estimation. Measurable parameters like plasma β-hydroxybutyrate and BCS play a pivotal role in diagnosing subclinical ketosis in dairy herds. β-hydroxybutyrate has proven to be a reliable health indicator, displaying a strong positive correlation with the average milk yield in primiparous cows. However, its applicability in commercial farm scenarios remains dubious, especially for multiparous and primiparous cows. The combined use of blood biomarkers can provide critical information, especially for detecting subclinical stages of diseases that might otherwise go unnoticed in outwardly healthy cows. However, an alternative, more commercially viable avenue for biomarker analysis, without the need to draw blood from each individual cow, had remained largely unexplored until recently.
A study by Antanaitis et al. (2021) aimed to explore the connections between BCS, milk yield, and biomarkers such as β-hydroxybutyrate (BHB), milk lactate dehydrogenase (LDH), and milk progesterone (mP4). The primary objective of this study was to determine if these biomarkers exhibited differences between pregnant and non-pregnant cows, offering valuable insights into their potential roles in ensuring successful pregnancies, overall cow health, and fertility. Data on these biomarkers were collected from the automated milking system for each cow from the day of estrus to 7 days post-estrus. The cows consistently received a balanced diet meeting their nutritional requirements and were grouped based on their BCS, lactation, and pregnancy status. Body condition was assessed using a 3D camera system, while the milk biomarkers were collected through the Herd Navigator™ system. The Herd Navigator™ is an automated system developed by Lattec I/S, a Danish company based in Hillerød, Denmark. The system serves multiple functions, including tracking reproductive status, detecting health issues like mastitis, monitoring milk yield, integrating various data sources, automating processes, and providing decision support to enhance overall farm efficiency. This system was combined with a DeLaval milking robot which sampled milk from each cow during milking for a fully automated inline LDH, mP4, and BHB analyses. Elevated BHB levels are closely linked to reduced milk production, impaired reproductive performance, and a higher risk of metabolic disorders. Thus, the early detection of elevated BHB levels is vital for dairy cow well-being. Measuring inline lactate dehydrogenase (LDH) activity in milk offers a cost-effective and dependable method with high sensitivity and specificity for detecting subclinical mastitis. Additionally, LDH’s efficacy as an inflammatory indicator of mastitis rivals that of acute-phase proteins and somatic cell counts. The Herd Navigator™ system thus has precise diagnostic capabilities, shedding light on the factors influencing the reproductive physiology of dairy cows. It not only supports reproductive management but also enables continuous evaluation of luteal activity and its impact on fertility.
The results of this study revealed that pregnant cows had 0.49-point higher BCS, produced 4.36 kg less milk, and had 6.11 ng/mL higher mP4 levels compared to non-pregnant cows. Body condition score was significantly associated with pregnancy status, and it also correlated with other biomarkers like milk yield, BHB, LDH, and mP4. Cows with BCS greater than 3.2 were 22 times more likely to achieve reproductive success than cows with BCS less than or equal to 3.2. Moreover, a 0.5-point increase in BCS was linked to an eight-fold increase in reproductive success.
These findings emphasize the importance of monitoring milk biomarkers, particularly BCS, mP4, and milk yield, for assessing reproductive status and overall cow health. These biomarkers offer crucial insights into the factors affecting reproductive success, providing opportunities to enhance dairy herd management. More importantly, this study underscored the economic importance of integrating routine BCS monitoring through 3D cameras into dairy farm management. These cameras enable precise body condition scoring, a key factor in estimating body fat reserves, which in turn significantly influences the health and fertility of high-yield dairy cows. While traditional BCS assessment is labor-intensive, the application of 3D cameras streamlines this process, resulting in more efficient BCS estimation. This technological innovation not only saves valuable time but also enhances the accuracy of assessments. Analyzing milk biomarkers in conjunction with BCS is critical for diagnosing subclinical conditions. By efficiently assessing the health and reproductive status of cows, farmers can optimize feeding management and reduce the risks of reduced milk production, impaired reproductive performance, and metabolic disorders. Ultimately, the use of 3D cameras in conjunction with advanced automated systems is an economically sound approach to dairy farm management, as it improves cow well-being, improves fertility, and increases milk yield, thus maximizing overall profitability.
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