Álvaro García
Beef production in the United States is not only a significant economic driver but also a deeply entrenched aspect of American agriculture. According to data from the US Department of Agriculture (USDA), the industry contributed over $66 billion in cash receipts in 2019, making it one of the largest sectors within the agricultural landscape. A vital component of the beef industry, the cow-calf production segment, accounted for nearly 41% of the total US beef cattle inventory in 2019, as reported by the US National Agricultural Statistics Service. This segment comprises a staggering 41 million head of cows and heifers that have calved, underscoring its pivotal role in sustaining the nation’s beef supply chain.
Furthermore, most cattle and calf operations in the United States are comprised of cow-calf and stocker/backgrounder cattle farms, which collectively represent approximately 91% of the total. It’s noteworthy that 96% of these operations are family-owned and operated, highlighting the familial and often intergenerational nature of beef production in the US, as indicated by data from the National Cattlemen’s Beef Association.
Challenges in today’s cow/calf operations
Efficiency is paramount in beef cattle production, particularly within the expansive cow-calf operations, where producers must navigate various challenges, including rising production costs, market volatility, and environmental factors, all while striving to maintain profitability. Accurate measurement of body weight (BW) in cows is fundamental for effective production management, enabling producers to make informed decisions regarding feeding, breeding, and marketing strategies. However, traditional methods of weighing cattle using scales can be cumbersome and labor-intensive, posing logistical challenges and potentially inducing stress in the animals. Manual handling of cattle for weighing purposes also carries inherent risks for both the animals and operators, such as accidents or injuries during the weighing process.
In response to these challenges, precision livestock management has emerged as a promising approach to optimize production processes and enhance overall efficiency in beef cattle operations. This field leverages advanced technologies, such as imaging techniques and machine learning algorithms, to provide producers with precise, real-time insights into the health, welfare, and productivity of their livestock.
Imaging technologies, particularly depth cameras, offer efficient data collection and individual animal monitoring capabilities. While these technologies have seen widespread adoption in dairy production systems, their application in the US beef cattle sector remains relatively nascent. Nonetheless, studies conducted in other regions have demonstrated the efficacy of imaging technology in predicting BW and body condition scores (BCS) for beef cattle, highlighting its potential for enhancing production management practices.
What does the research say?
In a recent study conducted by Xiong et al. (2023) at the University of Nebraska, Lincoln, researchers aimed to bridge this gap by evaluating the feasibility of using depth imaging techniques to estimate body volume, quantify BW and metabolic weight (MBW), and classify BCS in mature beef cows. The study involved a comprehensive data collection process, wherein mature Red Angus × Simmental cows were selected based on specific criteria regarding age, weight, and environmental conditions.
During the data collection phase, depth imagery data were obtained in a controlled environment, with minimal contact with the animals to minimize stress. Machine learning algorithms were then employed to analyze the data and derive predictive metrics, including BW, BCS, and metabolic weight, based on the image-obtained measurements.
The results of the study demonstrated a strong correlation between image-projected body volume and measured BW, validating the efficacy of depth imaging technology in estimating BW and BCS of beef cattle. However, challenges such as data collection methods, camera placement, and data diversity remain to be addressed to fully realize the potential of this technology in livestock management practices.
In a recent 2023 study, researchers evaluated the efficacy of 3D imaging technology in predicting the body weight (BW) of beef heifers. The study focused on Red Angus/Simmental yearlings weighing between 282 to 440 kg. Employing a 3D camera positioned approximately 3 to 4 meters above the floor, the researchers estimated dorsal projected volumes of the heifers, excluding the head region. Subsequently, regression models were employed to forecast BW based on these volumes, with metabolic BW calculated through a 0.75 power adjustment of BW. The evaluation compared the estimated weights derived from 3D images with the actual weights measured on scales using strong statistical methods.
The results of the analysis underscored the accuracy of estimated weights based on 3D images, as evidenced by a coefficient of determination (R2) of 0.89 and a Pearson correlation coefficient (r) of 0.94. The R2 and r values assess the relationship between variables. An R2 of 0.89 indicates that 89% of the variability in estimated weights is explained by actual weights. The r value of 0.94 indicates a strong positive linear relationship between estimated and actual weights, suggesting that as one variable increases, the other tends to increase as well. These metrics indicate a strong relationship between the estimated and actual weights of the heifers. Despite an average estimated error of approximately 3.3 kilograms, the 3D imaging method demonstrated remarkable accuracy in estimating the weights of the yearling heifers.
The findings of this study underscore the precision of predicting BW utilizing depth images of yearling heifers and highlight the promising potential of 3D imaging technology in precisely estimating BW in livestock. Furthermore, beyond its utility in BW prediction, 3D cameras have demonstrated the ability to accurately assess the animals’ body condition. When integrated with weight assessment, these cameras provide a comprehensive depiction of the animals’ nutritional status, offering a holistic approach to livestock management that encompasses both weight and body condition evaluation. Such comprehensive insights can significantly enhance the effectiveness of livestock management practices, aiding in informed decision-making and optimizing productivity in beef production systems.
BCS results require effective feed management
A study by Cooke et al. (2021) investigated the reproductive and productive outcomes of Bos indicus and B. taurus beef cows, focusing on their BCS at the onset of the breeding season. Cows with a BCS of 5 or higher displayed enhanced calving rates, earlier parturition, and produced heavier calves compared to those with a BCS below 5. Sustaining cows at a BCS of 5 or above during breeding is advisable, often necessitating supplementary feed for cows with a BCS lower than 5 at calving to enhance their condition, despite the potential cost implications, especially while nursing calves. A decline in body condition, particularly among cows scoring 5 or less, from calving to breeding, can negatively impact pregnancy rates. However, cows with BCS scores of 7 or 8 can tolerate some degree of condition loss without compromising breeding success, provided their BCS remains above 5. Effective BCS management entails segregating cows 90 to 100 days before calving and assigning to them feeds to achieve BCS scores of 5 to 7 at calving, optimizing reproductive performance while minimizing supplemental feed expenses. Supplemental feeding strategies guided by BCS assessments facilitate the evaluation of cattle’s body composition. Cows with BCS scores of 5 or higher experiencing reproductive challenges may indicate underlying deficiencies, diseases, genetic predispositions, or issues related to bull fertility. Conversely, cows with BCS scores below 5 might encounter inadequacies in energy and protein intake, potentially linked to factors such as phosphorus levels and internal parasite burdens.
In conclusion, depth sensing technology holds great promise for revolutionizing beef cattle production by offering accurate and non-invasive methods for estimating BW and BCS. By overcoming existing challenges and leveraging advancements in machine learning and data analytics, producers can harness the full potential of imaging technologies to enhance decision-making, improve productivity, and ensure the welfare of their livestock.
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