Integrating heterogeneous across-country data for proxy-based random forest prediction of enteric methane in dairy cattle
(2022)
Journal Article
Negussie, E., González-Recio, O., Battagin, M., Bayat, A., Boland, T., de Haas, Y., …Biscarini, F. (2022). Integrating heterogeneous across-country data for proxy-based random forest prediction of enteric methane in dairy cattle. Journal of Dairy Science, 105(6), 5124-5140. https://doi.org/10.3168/jds.2021-20158
Direct measurements of methane (CH4) from individual animals are difficult and expensive. Predictions based on proxies for CH4 are a viable alternative. Most prediction models are based on multiple linear regressions (MLR) and predictor variables tha... Read More about Integrating heterogeneous across-country data for proxy-based random forest prediction of enteric methane in dairy cattle.