Development of Prediction Models of Methane Production by Sheep and Cows Using Rumen Microbiota Data
Year: 2018
Type: article
Abstract: Methane emission from the rumen leads to approximately 10 % loss of the ingested energy, at the conversion of digestible to metabolizable energy.Besides research on mitigation of methane emission, much research interests have also been gravitated towards development of prediction models of methane emissions from livestock because the global warming is reducing agriculture productivity (Johnson and Johnson, 1995).An accurate prediction of enteric methane production from cattle and sheep can assist in balancing the increased livestock production with subsequent environmental impacts.Methane is an inevitable byproduct of the microbial fermentation processes in the rumen, and certain ruminal microbes have direct impacts to methane production (Morgavi et al., 2010).Thus, we hypothesized that the inclusion of individual microbial groups as predictor variables could improve the robustness and accuracy of prediction models.However, inclusion of microbial variables into prediction models can result in overfitting.Machine-learning algorithms can automatically select the key explanatory predictors, and Linear Mixed Models can provide a framework to predict random effects describing between animal variations.We proposed three novel frameworks for subset selections of microbial variables (MV) and one framework for generalized linear mixed models (GLMM) using L1-penalization (GLMMLASSO) selection with cross-validations (CV) iii to address the overfitting problems and to develop parsimonious prediction models of methane production.Methane emission can be expressed as unit methane per animal per d, per unit of dry matter intake (DMI), or per unit of metabolic body weight (MBW) per d.Thus, we developed prediction models based on g CH4/per animal/d (Animal-based models), g CH4/kg DMI (DMI-based models), and g CH4/kg metabolic bodyweight/d (MBW-based models).Two datasets were used: one dataset from a study that compared the rumen microbiota and methane production among sheep in New Zealand, while the other dataset that was collated from two datasets (one generated using dairy cows in Finland, and the other generated using steers in Australia).The cattle datasets were generated from studies that used different anti-methane feed additives to mitigate methane emission.Each dataset contained both animal data and relative sequence abundance (RSA) of genera of rumen microbes including bacteria, methanogens, protozoa, and fungi.Relative abundance of a genus was expressed as % of the sequences assigned to that genus over the total sequences of a marker gene, 16S rRNA gene for bacteria and methanogens, 18S rRNA gene for protozoa, and ITS1 for fungi).Subset and GLMMLASSO selections of MV combined with CV were based on minimal Bayesian information criterion (BIC).Linear mixed effects models were built based on the MV selected.The cross-validation was used to identify the best subset of MV that resulted in the lowest mean square prediction error (MSPE) to include in the prediction models.We also compared our new models with traditional models that only contained DMI, acetate: propionate ratio, and BW.
Author: Zhang, Boyang
Language: English
Subfield: Animal Science and Zoology
Domain: Life Sciences
SDGs: Climate action, Zero hunger
Open Access status: green
OpenAlex ID: W6981078957