Álvaro García
Effective management of body composition is crucial for beef well-being and performance. Understanding their chemical composition, including water, lipids, proteins, minerals, and energy, is essential to optimize feed efficiency and meet nutrient requirements. Traditional methods, like postmortem carcass grinding and chemical analyses, are expensive and impractical for continuous monitoring. Traditional visual or palpation-based methods, such as body condition scoring, are common but manual and subjective. The emergence of 3D imaging technology (3DIT) presents promising opportunities to overcome the limitations associated with traditional methods. Innovations in 3DIT have attracted interest for accurately and non-invasively determining body weight and composition of cattle and other livestock species. Unlike conventional methods, 3DIT allows safe and contactless measurement, facilitating high-throughput phenotyping of morphological traits. Notably, 3DIT has been successfully applied in growing or finishing cattle to estimate parameters such as average daily gain, carcass grading, and body composition.
Current research
A recent study by Xavier et al. (2023) sought to calibrate and assess the precision of in vivo 3DIT for estimating empty body and carcass composition in common beef-on-dairy crossbreds. The study included 100 crossbred male offspring from Brown Swiss dams and Angus, Limousin, or Simmental sires. The calves, averaging 31.5 days old and 73.6 kg, were fed milk, hay, concentrate, and maize silage until reaching 160 kg. They were then given one of two total mixed rations until slaughter. The data were divided into two sets based on how body composition was assessed: Set 1 used direct postmortem analysis, while Set 2 used direct or indirect estimation via the 11th rib dissection method.
- Set 1: Comprised 48 calves (16 from each crossbreed) slaughtered at various body weights along the growth trajectory, including 18 slaughtered at 517 kg body weight, for which direct postmortem measurements of body composition were available.
- Set 2: Included 70 calves (23 with Angus sire, 24 with Limousin sire, and 23 with Simmental sire) slaughtered at 517 kg body weight. For this set, body composition was estimated either directly (n = 18) or indirectly through the 11th rib dissection method (n = 52).
Hot carcass weight varied widely from 34 kg to 306 kg, with an average yield of 56 ± 3%. Despite this variation, water, protein, and mineral proportions in the empty body remained stable, while lipid and energy proportions showed greater variability. A strong negative correlation between water and lipid proportions was observed in both empty body (r ≤ -0.99) and carcass (r ≤ -0.99), indicating a strong inverse relationship. Fat-free empty body composition remained consistent, comprising approximately 75.0 ± 1.1% water, 20.8 ± 1.0% protein, and 4.2 ± 0.2% minerals, mirroring the fat-free carcass composition. At the terminal point (Set 2), where bulls reached commercial slaughter weight (517 ± 10 kg BW), hot carcass weight averaged 289.3 ± 10.0 kg, with a carcass yield of 56 ± 2%. Once again, lipid proportions in both empty body and carcass exhibited the highest variability (CVs: 17% and 19%, respectively). Angus crossbred bulls showed higher lipid content and energy storage compared to Limousin or Simmental crossbreeds, with lower water proportions.
Comparative analysis
At the final slaughter weight, differences in chemical composition were noticeable among sire breeds. Angus crossbred bulls exhibited higher lipid and energy content, but lower water proportion compared to Limousin or Simmental crossbreds. Distinct body traits among sire breeds were revealed through 3DIT, with Limousin crossbred bulls displaying larger dimensions compared to Angus and Simmental crossbreds along both the growth trajectory and at the terminal point. Key 3DIT variables for empty body chemical mass estimation included thigh length and hip height, with additional variables enhancing predictions, particularly during the growth trajectory stage. Additionally, six supplementary variables and two ratios consistently contributed to empty body proportion estimation in most Partial Least Squares models. The six supplementary variables were shoulder width, heart girth, chest depth, hip width, thigh girth, and body length—and the two ratios that consistently contributed were body length to heart girth and shoulder width to hip width.
This study revealed notable differences in the chemical composition of crossbred bulls among different sire breeds. Angus crossbreds exhibited higher lipid and energy content, but lower water proportion compared to Limousin or Simmental crossbreds, indicating distinct carcass composition variations based on breed. Using 3DIT researchers were able to identify unique body traits among different sire breeds, critical to estimate both empty body weight and carcass composition across various growth stages. The inclusion of 3DIT variables significantly enhanced the precision of estimations, especially for lipid mass and energy content, compared to models using only body weight or conformation grades. Body weight estimation from 3DIT variables proved reliable, with the precision in BW estimation was comparable to previous research.
Placing the cameras
Proximoty to the water troughs seem the ideal spot to place 3D cameras to monitor feedlot cattle. It’s crucial, however, to protect the camera from the elements which could involve installing protective enclosures or shades. To monitor individual animals effectively, the camera should be installed at a heigh (2 m) and angle (45 degrees) that allows clear images of individual animals as they approach to drink, minimizing interference from other animals. Typically, a waterer measuring 6.5″W x 12″H x 26.5″L is adequate for 50 steers, so a pen for 100 steers would require two such waterers and two cameras.
A recent experiment conducted at the University of Wisconsin, Madison (Padilla et al. 2021) evaluated computer vision to monitor growth in beef cattle. The steers were fitted with RFID ear tags and a 3DIT camera was installed above the water trough in each pen. When an animal approached the water tank, an RFID antenna triggered the camera to capture a top-down view image, which was then transmitted to a central computer and later to the cloud. An algorithm was developed to filter and process the images, extracting relevant biological features such as body length, width, height, area, and volume. The system successfully processed usable images, demonstrating its potential for real-time growth monitoring.
Incorporating 3D cameras atop the water troughs offers a novel approach to continuously monitor cattle body composition. This placement takes advantage of the frequent visits animals make to the water source, providing ample data collection opportunities without disturbing their normal behavior. This setup aligns with precision livestock farming principles, allowing for non-invasive, real-time assessment of body traits crucial for estimating body composition. If successfully implemented, this method could revolutionize livestock management practices by seamlessly integrating body composition monitoring into daily routines, enhancing both animal welfare and production efficiency.
Implications
The addition of 3DIT variables to BW enhanced precision for estimating body composition, particularly lipid mass. Imaging technology offers technical simplicity, non-invasiveness, and safety, with potential for broader implementation in precision livestock farming. Using 3DIT for precise estimation of body and carcass composition in beef-on-dairy crossbred bulls represents a significant advancement. Continued research and refinement hold promise for enhancing precision livestock farming practices, contributing to improved management, cattle wellbeing, and economic outcomes for livestock producers.
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