Metabolic and Physiological Predictors of Enteric Methane Emissions in Early Lactation Dairy Cows: A Prospective...

This study aimed to investigate the relationship between enteric methane (CH₄) emissions and metabolic, physiological, and behavioural factors in early lactation Holstein cows. Forty-two cows were observed over a span of five consecutive weeks (0-100 days in lactation). CH₄ concentration (ppm) was quantified with a portable laser detector, whereas rumination duration, temperature, and water consumption were documented using intraruminal boluses. Weekly blood samples were examined for beta-hydroxybutyrate (BHB), C-reactive protein (CRP), urea (UREA), lactate dehydrogenase (LDH), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT) levels. The evaluation of milk yield and composition was conducted utilising in-line infrared sensors. Cows were classified against clinical reference intervals, and associations were tested via group comparisons, correlations, multiple linear regression, linear mixed models (cow ID random effect), ROC analysis, and by relating CH₄ to dry matter intake (DMI). Cows with elevated BHB (≥1.2 mmol/L) emitted 87.8% more CH₄ than cows within range and showed higher CH₄ yield per kg DMI; elevated GGT was likewise associated with higher CH₄ (+25.2%). CH₄ correlated positively with BHB (r = 0.54, p < 0.01), and negatively with rumination (r = -0.38, p < 0.05). Regression explained 30.2% of CH₄ variance (adjusted R² = 0.302): BHB was a positive predictor (β = 0.55, p = 0.047), whereas LDH was negative (β = -0.21, p = 0.033). A three-way interaction (BHB group × AST × GGT) was significant in the mixed model (F = 6.91, p = 0.002). For discrimination of high emitters, BHB achieved AUC = 0.889; among on-farm traits, milk yield (AUC = 0.823) and lactose (AUC = 0.701) performed best. DMI related inversely to CH₄ yield (r = -0.69, p = 0.058). The findings indicate that enteric methane production during early lactation is not exclusively influenced by diet but is significantly associated with systemic metabolic health. Integrating physiological and production characteristics may improve precision-driven methane monitoring and mitigation strategies in dairy systems.