Predicting Enteric Methane Emissions from Crossbred Growing Bulls (Montbéliarde × Borgou) Under Semi-Intensive Production Systems

Accurate estimation of enteric methane emissions is important for improving greenhouse gas inventories and supporting mitigation strategies in tropical cattle production systems. This study aimed to develop empirical equations for predicting daily enteric methane emissions from growing bulls managed under semi-intensive conditions in Benin. The database comprised 507 valid observations obtained from 39 crossbred growing bulls across 13 nutrition trials conducted at the Okpara Breeding Farm. Enteric methane emissions were measured individually using the GreenFeed® system, while dry matter intake (DMI), body weight (BW), and average daily gain (ADG) were obtained from the experimental datasets. Nine candidate linear mixed-effects models incorporating these variables individually, additively, and through selected interactions were evaluated. Model performance was compared using the Akaike information criterion (AIC), Bayesian information criterion (BIC), marginal R2, root mean square error (RMSE), and mean absolute error (MAE). The model combining DMI and BW (M5: CH4 = 170.04 + 4.09 × DMI − 0.22 × BW) provided the best balance between model fit and parsimony, with the lowest AIC (−8143.03) and a marginal R2 of 0.671. DMI was positively associated with daily methane production, whereas BW showed a negative conditional association after adjustment for DMI. More complex models including ADG or interaction terms did not provide sufficient improvement in model fit to justify their additional complexity. The resulting equation provides a preliminary locally derived approach for estimating methane emissions under the production conditions represented in the present dataset. However, because model development and evaluation were based on the same database, independent validation using larger and more diverse cattle populations is required before broader application.