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Development of Prediction Models of Methane Production by Sheep and Cows Using Rumen Microbiota Data

Zhang, Boyang
http://rave.ohiolink.edu/etdc/view?acc_num=osu1534524559001648

Abstract Details

2018, Master of Science, Ohio State University, Animal Sciences.
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) 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. From the sheep dataset, we developed 3 parsimonious models when the size of the pool of potential variables was limited to =132, but parsimonious models were not developed when the size of the pool of potential variables was over 300. Most importantly, GLMMLASSO successfully converged when the penalty parameter that controls the shrinkage, Lambda, was set between 0 to 1,000, selecting about 10 variables for all the models (animal-, DMI-, or MBW-based) when the size of pool of potential predictor variables was limited to be between 132 and 310. The GLMMLASSO approach combined with CV identified the important variables to explain the effects of the 8 feed additives in the combined cow dataset. The random effect associated with the between-animal variance component was small and of similar magnitude to the random error variance component. The linear mixed effects models based on GLMMLASSO selection of MV have root mean square error as the percentage of the mean of methane emission (RMSPE %) reduced by 2.3 percent points and performed better than those based on forward-stepwise MV selection and the traditional models based only on animal variables. Log transformation of the microbial data generally improved model performance, probably due to a better monotonicity of the MV. This thesis research indicates that individual groups of rumen microbes can be included in methane prediction models to improve prediction of methane production from sheep and cows. In conclusion, we built frameworks for model selections using MV, and GLMMLASSO combined with CV and forward-stepwise selection allowed identification of significant MV that can be included in methane prediction models that solved the overfitting problem and improved the prediction accuracy. GLMMLASSO selection coupled with CV is a useful method to extract a parsimonious and significant subset of MV from hundreds of MV that can improve the accuracy of methane prediction models. This is the first research to develop methane prediction models that contain rumen microbes as predictor variables. Future research can focus on the exploration of the common microbial predictor variables in the cow and the sheep datasets to understand the contribution of the microbes to methane production in the rumen and to develop models with a more mechanistic nature.
Jeffrey Firkins (Committee Member)
Zhongtang Yu (Advisor)
Luis Moraes (Committee Member)
119 p.
Animal Sciences

Recommended Citations

Citations

  • Zhang, B. (2018). Development of Prediction Models of Methane Production by Sheep and Cows Using Rumen Microbiota Data [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534524559001648

    APA Style (7th edition)

  • Zhang, Boyang. Development of Prediction Models of Methane Production by Sheep and Cows Using Rumen Microbiota Data . 2018. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1534524559001648.

    MLA Style (8th edition)

  • Zhang, Boyang. "Development of Prediction Models of Methane Production by Sheep and Cows Using Rumen Microbiota Data ." Master's thesis, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534524559001648

    Chicago Manual of Style (17th edition)

osu1534524559001648
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© 2018, some rights reserved.Creative Commons License Development of Prediction Models of Methane Production by Sheep and Cows Using Rumen Microbiota Data by Boyang Zhang is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.