Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy

• Published in: Scientific reports Vol. 13; no. 1; pp. 21305 - 11
• Main Authors: Zhang, Boyang, Lin, Shili, Moraes, Luis, Firkins, Jeffrey, Hristov, Alexander N., Kebreab, Ermias, Janssen, Peter H., Bannink, André, Bayat, Alireza R., Crompton, Les A., Dijkstra, Jan, Eugène, Maguy A., Kreuzer, Michael, McGee, Mark, Reynolds, Christopher K., Schwarm, Angela, Yáñez-Ruiz, David R., Yu, Zhongtang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.12.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/326; 631/326/171; 631/326/2565; Animal Feed - analysis; Animal models; Animals; Bacteria; Bacteria - genetics; Bayes Theorem; Bayesian analysis; Correlation coefficient; Diet - veterinary; Dry matter; Emission inventories; Emissions; Environmental perception; Female; Food and Nutrition; Humanities and Social Sciences; Lactation; Life Sciences; Mathematical models; Methane; Microbiota; multidisciplinary; Prediction models; Rumen; Ruminants; Science; Science (multidisciplinary); Sheep

Methane (CH 4 ) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH 4 . To address thi...