"The effects of climate change on humans and other living ecosystems is an area of on-going research. The ruminant livestock sector is considered to be one of the most significant contributors to the existing greenhouse gas (GHG) pool. However, there are opportunities to combat climate change by reducing the emission of GHGs from ruminants. Methane (CH4) and nitrous oxide (N2O) are emitted by ruminants via anaerobic digestion of organic matter in the rumen and manure, and by denitrification and nitrification processes which occur in manure. The quantification of these emissions by experimental methods is difficult and takes considerable time for analysis of the implications of the outputs from empirical studies, and for adaptation and mitigation strategies to be developed. To overcome these problems, computer simulation models offer substantial scope for predicting GHG emissions. These models often include all farm activities while accurately predicting the GHG emissions, including both direct as well as indirect sources. The models are fast and efficient in predicting emissions and provide valuable information on implementing the appropriate GHG mitigation strategies on farms. Further, these models help in testing the efficacy of various mitigation strategies that are employed to reduce GHG emissions. These models can be used to determine future adaptation and mitigation strategies, to reduce GHG emissions thereby combating livestock induced climate change."
"Methane ( CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animalrelated data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/ animal·d, ANIM-B models) and CH4 yield (g CH4/ kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin’s concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies."