This study aimed to determine the factors affecting the body weight (BW) of Hanwoo steers by collecting a large number of BW measurements using an automated weighing system (AWS). The BW of 12 Hanwoo steers was measured automatically using an AWS for seven days each month over three months. On the fourth day of the BW measurement each month, an additional BW measurement was conducted manually. After removing the outliers of BW records, the deviations between the AWS records (a) and manual weighing records (b) were analyzed. BW measurement deviations (a − b) were significantly (p < 0.05) affected by month, day and the time within a day as well as the individual animal factor; however, unexplained random variations had the greatest impact (70.4%). Excluding unexplained random variations, the difference between individual steers was the most influential (80.1%). During the day, the BW of Hanwoo steers increased before feed offerings and significantly decreased immediately after (p < 0.05), despite the constant availability of feeds in the feed bunk. These results suggest that there is a need to develop pattern recognition algorithms that consider variations in individual animals and their feeding patterns for the analysis of BW changes in animals.

This study aimed to investigate the effect of varying levels of dietary crude protein (CP) on growth performance, rumen characteristics, blood metabolites, and methane emissions in fattening Hanwoo steers. Twenty-four steers, weighing 504 ± 33.0 kg (16 months old), were assigned to four dietary treatments with different CP concentrations (15, 18, 19, and 21% of CP on a dry matter (DM) basis). A linear increasing trend in the average daily gain (ADG) was observed (p = 0.066). With increased dietary CP levels, the rumen ammonia concentration significantly increased (p < 0.001), while the propionate proportion linearly decreased (p = 0.004) and the proportions of butyrate and valerate linearly increased (p ≤ 0.003). The blood urea exhibited a linear increase (p < 0.001), whereas the blood non-esterified fatty acids and cholesterol showed a linear decrease (p ≤ 0.003) with increasing dietary CP. The methane concentration from eructation per intake (ppm/kg), forage neutral detergent fiber (NDF) intake, total NDF intake, and ADG exhibited linear decreases (p ≤ 0.014) across the treatments. In conclusion, increasing the dietary CP up to 21% in concentrates demonstrated a tendency to linearly increase the ADG and significantly decrease the propionate while increasing the butyrate. The methane concentration from eructation exhibited a tendency to linearly decrease with increasing dietary CP.

This study investigated the effect of different dietary fat levels in concentrate mixes on the growth performance, rumen characteristics, digestibility, blood metabolites, and methane emissions in growing Hanwoo steers. Thirty steers (386 ± 24.6 kg of body weight [BW]; 12 months old), blocked by BW, were randomly assigned to three dietary treatments with varying fat concentrations in concentrate mix (48, 74, and 99 g of ether extract per kg dry matte [DM]). The fat intake of the low-fat treatment represented 4.15% of the total dry matter intake (DMI), while the medium- and high-fat treatments accounted for 5.77% and 7.23% of total DMI, respectively. Concentrate mix DMI decreased with increasing fat level (p < 0.01). The growth rate and digestibility did not significantly differ based on the fat level (p > 0.05). As the fat level increased, propionate in the total ruminal volatile fatty acids increased, and butyrate and acetate-to-propionate decreased (p < 0.01). Cholesterol in blood serum increased significantly with increasing dietary fat levels (p < 0.01). Methane emissions exhibited a linear decrease with increasing fat level (p < 0.05). In conclusion, elevating fat content in the concentrates up to 100 g/kg DM reduced methane emissions without compromising the growth performance of growing Hanwoo steers.

An automatic concentrate feeding system (ACFS) divides the day into several intervals, allowing cattle to consume a predetermined amount of concentrate mix per interval. This study investigated the impact of changing these intervals (four vs. six) in an ACFS on its precision in monitoring the feed intake and feeding behavior of fattening Hanwoo steers. The experiment, involving 29 fattening Hanwoo steers (688 ± 43.3 kg of body weight, 24 months old), employed a switchback design with two interval settings: four and six per day. Both individual forage and concentrate intakes and feeding behaviors were automatically recorded; however, the ACFS measured feed supply, not actual intake. The precision of the ACFS’s intake recordings was tested by manually assessing feed residuals per visit using video recordings. Although no difference was observed in the concentrate intake (p > 0.05), the six-interval setting reduced concentrate residuals by 0.2 kg per visit (p < 0.05). The increased interval setting also resulted in fewer visits for forage consumption and decreased forage and total dry matter intakes (p < 0.05). In conclusion, the increased interval setting for the ACFS reduced the visit frequency for forage consumption and actual forage consumption while improving the precision of the ACFS’s intake recordings.

Mitigating enteric methane emissions through diet formulation remains a significant challenge in cattle nutrition. This study developed a system to evaluate the methane production potential of feeds, expressed as the effective ruminal methane production rate (eRMR, mL/g dry matter [DM]), using a discontinuous in vitro ruminal fermentation system using rumen fluid. Sixteen concentrate feeds and two forages were tested, with a reference diet (ryegrass straw:corn:corn gluten feed = 1:1:1) included in each batch to standardize conditions and account for associative effects among feeds. Test feeds were incubated with the reference diet in closed bottles under strictly anaerobic conditions. Methane and total gas production were measured at 2, 4, 6, and 24 h, and true dry matter digestibility was calculated after 6 and 24 h. For each batch, sample feed values were corrected and standardized using those of the reference diet. The eRMR value was calculated by integrating a differential equation with parameters incorporating ruminal digestion and passage dynamics. The test feed eRMR values ranged from 1.2 mL/g DM (soybean meal) to 56.7 mL/g DM (soybean hull), with the reference diet at 14.8 mL/g DM. Evaluation of feed eRMR using data from two in vivo studies demonstrated strong correlations between predicted diet-specific eRMR values and measured methane emissions from Hanwoo steers (r = 0.93 and 0.85). This system, incorporating rumen dynamics with a reduced sampling schedule, provides a precise and practical tool for predicting in vivo enteric methane production and optimizing diet formulations to mitigate methane emissions from cattle.

Weight is the most basic and important indicator in cattle management, and automation of its measurement serves as a fundamental step toward modern smart livestock farming. Automated weighing systems (AWS) capable of continuously measuring cattle weight, even during movement, have been explored as key monitoring components in smart livestock farming. However, owing to the high measurement variability caused by environmental factors, the accuracy of AWSs has been questioned. These factors include real-time fluctuations due to animal activities (e.g., feeding and locomotion), as well as measurement errors caused by residual feed or excreta within the AWS. Therefore, this study aimed to develop an algorithm to enhance the reliability of steer weight measurements using an AWS, ensuring close alignment with actual cattle body weight. Accordingly, daily weight data from 36 Hanwoo steers were processed using a three-stage approach consisting of outlier detection and removal, weight estimation, and post-processing for weight adjustment. The best-performing algorithm that combined Tukey’s fences for outlier detection, mean-based estimation, and post-processing based on daily weight gain recommended by the National Institute of Animal Science achieved a root mean square error of 12.35 kg, along with an error margin of less than 10% for individual steers. Overall, the study concluded that the AWS measured steer weight with high reliability through the developed algorithm, thereby contributing to data-driven intelligent precision feeding.