Álvaro García
Body condition scoring (BCS) serves as a rapid assessment tool for evaluating a cow’s fat reserves, providing crucial insights into its energy balance. Traditionally, BCS assessment relied on visual inspection by trained personnel, introducing subjectivity influenced by the scorer’s experience and training. However, even among trained individuals, variations in scoring reliability exist, both between different scorers and within the same scorer over short periods. Therefore, consistent training and experience are essential to minimize subjective influences.
BCS fluctuation during the lactation
The period surrounding parturition significantly affects the BCS, with cows experiencing a reduction in fat reserves ranging from 30% to 40%. This period often leads to a negative energy balance, where the cow’s nutritional demands exceed intake, observed in over 80% of dairy cows during lactation. Initially, cows undergo a loss of condition, followed by a gradual regain as energy demands decrease throughout lactation. Studies indicate that most cows reach a positive energy balance by 45 days in milk (DIM), with 90% achieving this balance by 63 DIM. Cows prioritize resource allocation towards milk production until the next pregnancy, allowing for the replenishment of body reserves. The greatest body condition loss typically occurs in the first 30 DIM, followed by a maintenance period until around 90 DIM, after which cows begin regaining body condition. However, variations in the timing of the lowest point of BCS exist, ranging from 60 to 100 DIM. Parity, DIM, and previous BCS influence body condition, with primiparous cows generally maintaining higher scores compared to older cows. Additionally, the rate of body condition loss differs between parity groups, with second lactation cows experiencing less loss post-calving compared to older cows.
Selective breeding for milk production has been associated with increased mobilization of body reserves. Low BCS has been linked to higher milk production, although a non-linear relationship exists between calving BCS and milk yield. Cows at approximately 3.5 BCS produce the highest 60-day yield, contradicting earlier findings suggesting no correlation between BCS and milk yield. The condition of cows at the start of lactation affects their progression and body condition curve, with higher-conditioned cows exhibiting decreased dry matter intake initially, leading to greater body condition loss over lactation. Additionally, cows with underlying issues like rumen acidosis may experience delayed BCS recovery, and over-conditioned cows tend to lose condition faster than under-conditioned ones. Both over- and under-conditioned cows tend to maintain their respective conditions post-calving. Despite efforts to improve BCS objectivity and accuracy using technologies like automated scoring systems, most remain impractical for commercial farm use.
Recent study
A study conducted at a commercial dairy farm with a herd size of 3000 cows aimed to collect data over one entire lactation period of 2343 Holstein cows, both dry and lactating. The cows had an average parity of 2.1 ± 1.1, days in milk (DIM) of 186.1 ± 111.1, measured BCS using a 3D camera of 3.42 ± 0.24, and predicted milk yield of 28,046 ± 4471 pounds of milk. Cows were fed a total mixed ration according to their requirements and based on desired BCS at different lactation stages.
The farm was equipped with two Body Condition Scoring (BCS) cameras, each mounted above the sort-gate at the two existing parlor exits. An individual radio frequency identification sensor (RFID) was affixed to each sort-gate to identify cows as they exited the parlor using their corresponding RFID ear tags. The camera created a unique 3D image of each cow, highlighting the contours created by muscle and body fat across the cow’s back. The camera software processed the 3D image to derive an automated BCS based on key anatomical features on the cow’s back, with scores reported in 0.1 increments on a 1 to 5-point scale.
The results
The results of the study revealed that the mean BCS recorded during calving and throughout the lactation period was 3.42 ± 0.22 and 3.29 ± 0.25, respectively, with scores ranging from 2.2 to 4.0. Primiparous cows reached lowest point of at 38 days post-calving, while multiparous cows reached lowest point of at 54 days post-calving, with an average BCS loss of 0.24 points (±0.25) post-calving. The average time for cows to regain lost BCS post-calving was 256 days (3.42 ± 0.23). By the end of the study period at 300 DIM, the average BCS was 3.47 ± 0.22. Stratifying by lactation number revealed a consistent BCS trajectory across all lactations, with first-lactation cows exhibiting numerically less BCS loss and maintaining higher scores across lactation. Multiparous cows experienced more than double the calving body condition loss compared to primiparous cows but reached lowest point of 16 days later. Plotting BCS against milk yield revealed a negative energy balance associated with BCS mobilization around 16 DIM, with a positive energy balance achieved around day 230 DIM.
The trajectory of automated body condition scoring throughout lactation aligned with recent modeling of BCS curves based on human visual scoring methods. Notably, the mean calving automated BCS in this study was higher compared to studies involving cows housed indoors. While visual BCS scoring may introduce bias due to human factors such as experience and training, automated BCS offers daily scoring precision, potentially enhancing condition status assessment. The modeled BCS curves provided insights into factors influencing BCS progression throughout lactation, offering opportunities for further investigation and incorporation into predictive models or on-farm management strategies. It is crucial to recognize that genetic predisposition may affect BCS trajectories, while environmental factors can influence a cow’s ability to adhere to its genetic path. Early lactation monitoring is crucial to optimize the cows’ genetic potential, while late lactation presents opportunities for them to re-establish their genetic capacity but may pose risks of over-conditioning. Genetic merit also impacts BCS loss post-calving, with high-merit cows experiencing greater loss and reaching lowest point of later.
Implications
Although the study provided insights into factors predicting BCS, incorporating a predictive BCS function into farm management requires further investigation. Future studies should explore additional factors influencing BCS, such as genetics, to improve model accuracy and applicability on-farm. Utilizing BCS systems for frequent data collection and integrating them into management protocols may enhance understanding of BCS changes in dairy cows, potentially improving animal well-being and management practices in commercial dairy operations. While previous studies examined BCS progression across the lactation, this study aimed to explore these effects using a novel automated body condition scoring system. The continuous monitoring enabled by BCS offers advantages over visual scoring methods, allowing for more insights into BCS dynamics. Significant factors were identified for predicting BCS curves during lactation, which could inform monitoring practices or establish benchmarks for lactating dairy cows. Integrating automated BCS into future studies and on-farm protocols may aid in refining energy requirement equations to mitigate negative energy balance during the transition period and improve overall understanding of body condition changes in dairy cows.
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