Configuration-dependent predictors of methane variation in forage systems

Predicting enteric methane (CH₄) emissions from forage-based ruminant systems is challenging because nutrient-methane relationships may vary across forage and experimental contexts. This study evaluated whether forage nutritional characteristics provide transferable predictive information across heterogeneous multi-study datasets. We analyzed 244 observations from 22 independent studies using eight nutritional predictors: dry matter digestibility (DMD), acid detergent lignin (ADL), acid detergent fibre (ADF), crude protein (CP), neutral detergent fibre (NDF), CP: NDF ratio, lignification index, and fibre: protein ratio. Six machine-learning approaches were evaluated using leave-one-study-out cross-validation. Feature importance, pure-versus-mixture comparisons, nutrient-methane correlations, and sensitivity analyses were used to examine configuration-dependent relationships. A Proportion-Weighted Study Ensemble (PWSE) analysis was additionally conducted as a secondary evaluation of configuration-aware representation. Cross-study prediction remained challenging across all models. Random Forest provided the strongest performance (R² = -0.375; RMSE = 29.88 mL/g DM; MAE = 23.34 mL/g DM), followed by Gradient Boosting (R² = -0.389; RMSE = 30.02 mL/g DM). DMD was the dominant predictive feature, with a mean importance of 0.626 across the tree-based models. However, its relationship with methane was strongly configuration-dependent: DMD was positively correlated with methane in pure forage systems ( r = 0.684, p < 0.001) but negatively correlated in mixture systems ( r = -0.809, p < 0.001), with the difference between correlations statistically distinguishable by Fisher’s r-to-z analysis ( p < 0.001). Mixture systems had higher crude protein and lower ADL than pure systems, and methane emissions were also higher in mixture systems (51.39 vs. 43.61 mL/g DM; p = 0.033). PWSE showed weak predictive performance and substantial sensitivity to study concentration. Methane variation is better characterized by configuration-dependent nutritional relationships than by isolated nutrient effects. The contrasting DMD-CH 4 relationships demonstrate that the predictive meaning of a nutritional trait can change across forage contexts. These findings support context-aware machine learning, study-level validation, and improved evidence representation in future methane modelling.