Dairy cattle farming contributes significantly to greenhouse gas (GHG) emissions through methane (CH₄) from enteric fermentation. To complement global efforts to mitigate climate change, there is a need for accurate estimations of GHG emissions using country-specific emission factors (EFs). The objective of this study was to develop national EFs for the estimation of CH₄ emissions from enteric fermentation in South Korean dairy cattle. Information on dairy cattle herd characteristics, diet, and management practices specific to South Korean dairy cattle farming was obtained. Enteric CH₄ EFs were estimated according to the 2019 refinement of the 2006 Intergovernmental Panel on Climate Change (IPCC) using the Tier 2 approach. Three animal subcategories were considered according to age: milking cows >2 years, 650 kg body weight (BW); heifers 1–2 years, 473 kg BW; and growing animals <1 year, 167 kg BW. The estimated enteric CH₄ EFs for milking cows, heifers, and growing animals, were 139, 83 and 33 kg/head/year, respectively. Currently, the Republic of Korea adopts the Tier 1 default enteric CH₄ EFs from the North America region for GHG inventory reporting. Compared with the generic Tier 1 default EF of 138 (kg CH₄/head/year) proposed by the 2019 refinement to the 2006 IPCC guidelines for high-milking cows, our suggested value for milking cows was very similar (139 kg CH₄ /head/year) and different to heifers and growing animals EFs. In addition, enteric CH₄ EFs were strongly correlated with the feed digestibility, level of milk production, and CH₄ conversion rate. The adoption of the newly developed EFs for dairy cattle in the next national GHG inventory would lead to a potential total GHG reduction from the South Korean dairy sector of 97,000 tons of carbon dioxide-equivalent per year (8%). The outcome of this study underscores the importance of obtaining country-specific EFs to estimate national enteric CH₄ emissions, which can further support the assessment of mitigation actions.

Indiscriminate use of antibiotics can result in antibiotic residues in animal products; thus, plant compounds may be better alternative sources for mitigating methane (CH₄) production. An in vitro screening experiment was conducted to evaluate the potential application of 152 dry methanolic or ethanolic extracts from 137 plant species distributed in East Asian countries as anti-methanogenic additives in ruminant feed. The experimental material consisted of 200 mg total mixed ration, 20 mg plant extract, and 30 mL diluted ruminal fluid-buffer mixture in 60 mL serum bottles that were sealed with rubber stoppers and incubated at 39 °C for 24 h. Among the tested extracts, eight extracts decreased CH₄ production by >20%, compared to the corresponding controls: stems of Vitex negundo var. incisa, stems of Amelanchier asiatica, fruit of Reynoutria sachalinensis, seeds of Tribulus terrestris, seeds of Pharbitis nil, leaves of Alnus japonica, stem and bark of Carpinus tschonoskii, and stems of Acer truncatum. A confirmation assay of the eight plant extracts at a dosage of 10 mg with four replications repeated on 3 different days revealed that the extracts decreased CH₄ concentration in the total gas (7–15%) and total CH₄ production (17–37%), compared to the control. This is the first report to identify the anti-methanogenic activities of eight potential plant extracts. All extracts decreased ammonia (NH₃-N) concentrations. Negative effects on total gas and volatile fatty acid (VFA) production were also noted for all extracts that were rich in hydrolysable tannins and total saponins or fatty acids. The underlying modes of action differed among plants: extracts from P. nil, V. negundo var. incisa, A. asiatica, and R. sachalinensis resulted in a decrease in total methanogen or the protozoan population (p < 0.05) but extracts from other plants did not. Furthermore, extracts from P. nil decreased the population of total protozoa and increased the proportion of propionate among VFAs (p < 0.05). Identifying bioactive compounds in seeds of P. nil by gas chromatography-mass spectrometry analysis revealed enrichment of linoleic acid (18:2). Overall, seeds of P. nil could be a possible alternative to ionophores or oil seeds to mitigate ruminal CH₄ production.

The present study evaluated the influence of dietary protein level on growth performance, fatty acid composition, and the expression of lipid metabolic genes in intramuscular adipose tissues from 18- to 23-month-old Hanwoo steers, representing the switching point of the lean-to-fat ratio. Forty steers with an initial live weight of 486 ± 37 kg were assigned to one of two treatment groups fed either a concentrate diet with 14.5% CP and or with 17% CP for 6 months. Biopsy samples of intramuscular tissue were collected to analyze the fatty acid composition and gene expression at 23 months of age. Throughout the entire experimental period, all steers were restrained twice daily to allow individual feeding. Growth performance, blood metabolites, and carcass traits, according to ultrasonic measurements, were not affected by the experimental diets. The high-protein diet significantly increased the expression of intramuscular PPARα (p < 0.1) and LPL (p < 0.05) but did not affect genes involved in fatty acid uptake (CD36 and FABP4) nor lipogenesis (ACACA, FASN, and SCD). In addition, it downregulated intramuscular VLCAD (p < 0.01) related to lipogenesis but also GPAT1 (p = 0.001), DGAT2 (p = 0.016), and SNAP23 (p = 0.057), which are involved in fatty acid esterification and adipocyte size. Hanwoo steers fed a high-protein diet at 18–23 months of age resulted in a relatively lower lipid turnover rate than steers fed a low-protein diet, which could be responsible for shortening the feeding period.

This study was performed to update and generate prediction equations for converting digestible energy (DE) to metabolizable energy (ME) for Korean Hanwoo beef cattle, taking into consideration the gender (male and female) and body weights (BW above and below 350 kg) of the animals. The data consisted of 141 measurements from respiratory chambers with a wide range of diets and energy intake levels. A simple linear regression of the overall unadjusted data suggested a strong relationship between the DE and ME (Mcal/kg DM): ME = 0.8722 × DE + 0.0016 (coefficient of determination (R²) = 0.946, root mean square error (RMSE) = 0.107, p < 0.001 for intercept and slope). Mixed-model regression analyses to adjust for the effects of the experiment from which the data were obtained similarly showed a strong linear relationship between the DE and ME (Mcal/kg of DM): ME = 0.9215 × DE − 0.1434 (R² = 0.999, RMSE = 0.004, p < 0.001 for the intercept and slope). The DE was strongly related to the ME for both genders: ME = 0.8621 × DE + 0.0808 (R² = 0.9600, RMSE = 0.083, p < 0.001 for the intercept and slope) and ME = 0.7785 × DE + 0.1546 (R² = 0.971, RMSE = 0.070, p < 0.001 for the intercept and slope) for male and female Hanwoo cattle, respectively. By BW, the simple linear regression similarly showed a strong relationship between the DE and ME for Hanwoo above and below 350 kg BW: ME = 0.9833 × DE − 0.2760 (R² = 0.991, RMSE = 0.055, p < 0.001 for the intercept and slope) and ME = 0.72975 × DE + 0.38744 (R² = 0.913, RMSE = 0.100, p < 0.001 for the intercept and slope), respectively. A multiple regression using the DE and dietary factors as independent variables did not improve the accuracy of the ME prediction (ME = 1.149 × DE − 0.045 × crude protein + 0.011 × neutral detergent fibre − 0.027 × acid detergent fibre + 0.683).

The monitoring of pre-weaned calf behavior is crucial for ensuring health, welfare, and optimal growth. This study aimed to develop and validate a machine learning-based technique for the simultaneous monitoring of multiple behaviors in pre-weaned beef calves within a cow–calf contact (CCC) system using collar-mounted sensors integrating accelerometers and gyroscopes. Three complementary models were developed to classify feeding-related behaviors (natural suckling, feeding, rumination, and others), postural states (lying and standing), and coughing events. Sensor data, including tri-axial acceleration and tri-axial angular velocity, along with video recordings, were collected from 78 beef calves across two farms. The LightGBM algorithm was employed for behavior classification, and model performance was evaluated using a confusion matrix, the area under the receiver operating characteristic curve (AUC-ROC), and Pearson’s correlation coefficient (r). Model 1 achieved a high performance in recognizing natural suckling (accuracy: 99.10%; F1 score: 96.88%; AUC-ROC: 0.999; r: 0.997), rumination (accuracy: 97.36%; F1 score: 95.07%; AUC-ROC: 0.995; r: 0.990), and feeding (accuracy: 95.76%; F1 score: 91.89%; AUC-ROC: 0.990; r: 0.987). Model 2 exhibited an excellent classification of lying (accuracy: 97.98%; F1 score: 98.45%; AUC-ROC: 0.989; r: 0.982) and standing (accuracy: 97.98%; F1 score: 97.11%; AUC-ROC: 0.989; r: 0.983). Model 3 achieved a reasonable performance in recognizing coughing events (accuracy: 88.88%; F1 score: 78.61%; AUC-ROC: 0.942; r: 0.969). This study demonstrates the potential of machine learning and collar-mounted sensors for monitoring multiple behaviors in calves, providing a valuable tool for optimizing production management and early disease detection in the CCC system