Application of Meta-Analysis and Machine Learning Methods to the Prediction of Methane Production from In Vitro Mixed Ruminal Micro-Organism Fermentation.

Secundino López,Peter Purcell,Jennifer L Ellis,Padraig O’Kiely,Alberto Navarro-Villa,James France,Christopher D Powell,Héctor Alaiz-Moretón,Emma J Mcgeough

doi:10.3390/ani10040720

Abstract

Simple SummaryIn vitro gas production systems are regularly 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 papers report in vitro CH4. Therefore, the objective of this study was to develop models to predict in vitro production of CH4, a greenhouse gas produced by ruminants, from in vitro 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 predict CH4 production from in vitro gas parameters. Meta-analysis results indicate that equations containing apparent dry matter (DM) digestibility, total VFA production, propionate, valerate and feed type (forage vs. concentrate) resulted in best prediction of CH4. The ML models far exceeded the predictability achieved using meta-analysis, but further evaluation on an external database would be required to assess their generalization capacity. The models developed can be utilized to estimate CH4 emissions in vitro.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.

Highlights

Greenhouse gas (GHG) emissions from the agriculture, forestry and other land use (AFOL) sector account for ~23% of the global anthropogenic greenhouse gas (GHG) total emissions [1], with enteric methane (CH4 ) from fermentation in the forestomach of ruminants representing 32%–40% of that total [1]
The database compiled for this study consisted of 397 in vitro rumen fermentation bottle means, taken after 24 h of incubation, from 13 experiments reported in 10 publications [10,11,12,13,14,15,16,17,18,19], plus 1 unpublished study [20]
Studies evaluated the in vitro gas and CH4 production from oven-dried feedstuffs, including ryegrass, forbs, grass silages, clover, maize silage and other whole-crop cereal silages and concentrate feeds

Summary

Introduction

Greenhouse gas (GHG) emissions from the agriculture, forestry and other land use (AFOL) sector account for ~23% of the global anthropogenic GHG total emissions [1], with enteric methane (CH4 ) from fermentation in the forestomach of ruminants representing 32%–40% of that total [1] (thereby 7.4%–9.2% of the global anthropogenic total). CH4 is produced as a byproduct of anaerobic fermentation in the rumen and hindgut of ruminants, whereby methanogens utilize H2 to obtain ATP by reducing CO2 to CH4 [6]. Other pathways must be promoted to utilize H2 or otherwise fermentation, digestibility and intake may be negatively affected [6] The removal of H2 through methanogenesis, the main H-sink in the rumen [6], prevents the inhibitory effect of H2 on ruminal fermentation and allows for the degradation and fermentation of feed to proceed.

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