Álvaro García
According to the National Agricultural Statistics Service, the combined count of beef cows and calved heifers in the US in 2022 totaled 39.5 million, with beef cows alone representing 30 million. About 90% of these cows typically graze extensively on pasture or range, a natural and crucial part of their diet and behavior, particularly during breeding and calf-rearing periods. Though specific figures may fluctuate due to varying practices and regional differences, the overwhelming majority rely heavily on grazing during pivotal seasons like breeding and calving. While some may be confined for monitoring during pregnancy, the primary nutrition source for these cows is grazing. Over 283 days, they receive essential nutrients vital for the healthy development of their calves, marking the beginning of comprehensive ranch work. As calving approaches, cows move to specific pastures, with ranchers on standby, aiding calving and ensuring the well-being of both cows and calves. Post-calving, cow-calf pairs typically stay on pasture for about six months, the average weaning period for calves.
Post-Weaning and Grazing Management
After weaning, calves weighing over 500 pounds head directly to feedlots, while others go to backgrounding or stocker operations to gain weight. Grazing management extends beyond stocking rates, encompassing a careful balance involving vigilant monitoring of cows’ body weight and condition, along with heifers’ growth during their initial pregnancy. Fostering an environment where grazing optimally supports the herd’s nutritional needs is critical for their thriving through crucial reproductive cycles. Maintaining appropriate stocking rates aligns with the land’s carrying capacity, ensuring it sustains grazing pressure without ecological damage. Simultaneously, tracking cows’ weight and condition during pregnancy is vital, ensuring they get sufficient nutrition for their health and the calves’ development.
Similarly, heifers’ growth during their first pregnancy requires careful observation, setting the stage for their future roles as productive cows. Overseeing these aspects of grazing management fosters an environment conducive to the herd’s well-being, ensuring reproductive success and long-term sustainability in cow-calf operations. Within these operations, precise body weight measurement plays a pivotal role in nutritional and management decisions. However, accurately and frequently measuring body weight presents challenges for beef producers. Traditional methods relying on physical scales might be impractical in vast grazing environments, compounded by cattle behavior during weighing processes and the cost involved in setting up scale facilities. Innovative solutions are essential for informed decision-making related to animal health, nutrition, breeding, and overall herd management.
Accurate body weight measurement in grazing conditions is vital for assessing nutrition programs and pasture management strategies, guiding interventions for optimized herd health, and understanding pasture utilization. These metrics inform breeding decisions and aid in managing reproductive performance effectively. Yet, despite advancements in other livestock sectors, implementing instrumentation systems for outdoor environments and accounting for diverse body structures among cattle breeds remain challenges. The emerging technology of 3D imaging via depth cameras shows promise as an indirect method to predict body weight without conventional weighing. However, its application in grazing systems in the US requires further exploration to offer a feasible alternative for body weight measurement in cattle operations.
Recent experiment with grazed cows
Anderson et al. (2022) conducted an experiment at the University of Nebraska to determine the efficacy of implementing 3D imaging technology as a method to predict BW of beef heifers managed grazing upland native range. A group of 69 Red Angus × Simmental crossbred yearling beef heifers, aged approximately 12 months and weighing between 282 to 440 kg, were involved in the video collection process. These heifers underwent a 24-hour feed and water restriction period to standardize gut fill variations before data collection.
The study employed a Time-of-Flight depth camera for 3D image and video capture due to its widespread availability, cost-effectiveness, and user-friendly nature. The camera, positioned about 3 meters above the floor level, facilitated the capture of complete animal outlines within the frame, enabling accurate video collection for analysis. The collection of dorsal 3D depth videos of the heifers was conducted with the camera positioned approximately 3 meters beyond the chute exit. These videos captured biometric measurements (e.g., body length, shoulder width) required to estimate body volume for the heifers. Video recordings were strategically managed to not impede employee workflow pace while ensuring appropriate treatment of the cattle. Specific criteria were applied during manual video analysis to select frames suitable for further data processing, ensuring all four feet of the heifer were on the ground, no obstructions from other elements were present, and the head and neck alignment was consistent with the body posture. Corresponding scale-measured BW for each animal was recorded before video capture in the working chute. Utilizing a custom program, height pixel values forming the dorsal area of the heifers were generated. The summation of these height pixel values determined the heifers’ dorsal volume, excluding the head region to minimize variations related to head positions during analysis.
Following the extraction of dorsal body volumes from images, linear regression equations were developed, regressing scale-measured BW against dorsal volumes to calculate heifer BW (Predicted BW = b × dorsal volumes + a; where b and a represent linear regression coefficients). Predicted shrunk BW was then estimated using these equations. Accuracy assessment involved regressing predicted BW against scale-measured BW and transforming scale-measured shrunk BW into metabolic BW (MBW) for further evaluation. Metabolic boy weight (MBW), determined by taking BW to the 0.75 power, indicates nutritional requirements for maintenance energy. Linear regression models were formulated to evaluate correlations between 1) body volume and scale-measured BW, 2) Scale-measured BW and predicted BW, and 3) calculated MBW versus predicted MBW.
Results
The regression analysis between scale-measured shrunk BW and predicted body volume generated an R2 value of 0.89. This regression equation was utilized to forecast BW. Upon regressing predicted BW against scale-measured shrunk BW an R2 of 0.89 was achieved. The average discrepancy between scale-measured shrunk BW and predicted BW was 7.40 kg. Correlation analysis between scale-measured shrunk BW and predicted BW revealed an r of 0.9437 (P < 0.0001). When comparing predicted and actual MBW, an average difference of 1.3 kg was observed. The coefficient of determination R2 was 0.8877 when evaluating predicted and actual MBW.
Regular BW measurements offer critical insights for cattle producers, aiding decisions to enhance animal profitability, reproductive performance, and health monitoring. Accurate BW prediction using image analysis produced an R2 of 0.99 with minimal error in some crossbred cattle studies. However, cattle predictions display variability due to body composition differences, especially concerning rumen fill which can affect gastrointestinal content significantly.
Conclusions and future directions
This study aligned well with other cattle research, yielding strong correlations (R2 = 0.89) for both BW and MBW predictions. The potential of image-based BW prediction in cattle has gained traction since the 1990s, evolving with technology. Accurate BW estimates hold immense value for livestock management, yet challenges remain, especially in diverse cattle settings. This recent University of Nebraska study demonstrated the promise of accurately forecasting BW in yearling beef heifers using 3D imaging. Further validation across different breeds, ages, and management systems is crucial for practical implementation. Automation of data analysis is pivotal for commercial adoption, urging further technological advancements. Continued research will solidify the applicability of this model across diverse cattle demographics.
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