Application of Meta-Analysis and Machine Learning Methods to the Prediction of Methane Production from In Vitro Mixed Ruminal Micro-Organism Fermentation
Author
Ellis, Jennifer L.Alaiz-Moretón, Héctor
Navarro-Villa, Alberto
McGeough, Emma J.
Purcell, Peter
Powell, Christopher D.
O’Kiely, Padraig
France, James
López, Secundino
Date
2020-04-21
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Ellis, J.L.; Alaiz-Moretón, H.; Navarro-Villa, A.; McGeough, E.J.; Purcell, P.; Powell, C.D.; O’Kiely, P.; France, J.; López, S. Application of Meta-Analysis and Machine Learning Methods to the Prediction of Methane Production from In Vitro Mixed Ruminal Micro-Organism Fermentation. Animals 2020, 10, 720. https://doi.org/10.3390/ani10040720Abstract
In vitro gas production systems are utilized to screen feed ingredients for inclusion in ruminant diets. However, not all in vitro systems are set up to measure methane (CH4) production, nor do all publications report in vitro CH4. Therefore, the objective of this study was to develop models to predict in vitro CH4 production from total gas and volatile fatty acid (VFA) production data and to identify the major drivers of CH4 production in these systems. Meta-analysis and machine learning (ML) methodologies were applied to a database of 354 data points from 11 studies to predict CH4 production from total gas production, apparent DM digestibility (DMD), final pH, feed type (forage or concentrate), and acetate, propionate, butyrate and valerate production. Model evaluation was performed on an internal dataset of 107 data points. Meta-analysis results indicate that equations containing DMD, total VFA production, propionate, feed type and valerate resulted in best predictability of CH4 on the internal evaluation dataset. The ML models far exceeded the predictability achieved using meta-analysis, but further evaluation on an external database would be required to assess generalization ability on unrelated data. Between the ML methodologies assessed, artificial neural networks and support vector regression resulted in very similar predictability, but differed in fitting, as assessed by behaviour analysis. The models developed can be utilized to estimate CH4 emissions in vitro.Funder
Department of Agriculture, Food and the MarineGrant Number
RSF 05 224; RSF 06 361; RSF 07 517ae974a485f413a2113503eed53cd6c53
https://doi.org/10.3390/ani10040720
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