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Modelling to understand and predict ruminant methane emissions

Researchers from the Joint Research Unit for Systemic Modelling Applied to Ruminants (MoSAR) are working to develop a mathematical modelling approach to improve understanding of the metabolism of ruminal microbiota and estimate how much methane they produce. This type of model will make it possible to create nutritional strategies to support a microbial ecosystem that is compatible with more sustainable livestock farming.

Modelling to understand and predict ruminant methane emissions
By Sylvie André, translated by Teri Jones-Villeneuve
Updated on 05/23/2019
Published on 04/10/2019

Reducing methane emissions from ruminants is a major objective for more sustainable livestock farming. Enteric methane is a byproduct of the fermentation of food by microbes in the rumen. Creating strategies to reduce methane requires a two-part approach: first, a better understanding of ruminal microbe metabolism (especially the methane-producing archaea) to determine how the ruminal microbiota can be modified; and second, non-invasive and inexpensive techniques to estimate individual methane emissions on farms. Researchers from the MoSAR unit are working to solve this challenge using a mathematical modelling approach to develop strategies to support a microbial ecosystem that is more favourable to sustainable agriculture.

1) To better understand methanogenic metabolism, the researchers used an approach combining microbiology, thermodynamics and mathematical modelling. In vitro experiments were conducted on three strains of methanogenic archaea to characterise the metabolic reactions and heat flux dynamics. Based on a ruminal fermentation model previously developed by the team, the researchers designed a dynamic model with an energetic-based function to describe the methanogenesis. This model efficiently reproduced the experimental data of the methanogenesis dynamics.
Together, the experimental data and the model enabled the kinetic and energetic differences between the methanogenic strains studied (archaeal group) to be quantified and to produce new knowledge on the thermodynamics and kinetics of methanogens. This research was conducted in cooperation with colleagues from the DINAMIC team from the Herbivore Joint Research Unit and the Université Clermont Auvergne.

2) To estimate ruminant methane emissions on a large scale, the researchers developed a dynamic parsimonious model* that uses a single predictor variable to estimate individual methane emission: information on food intake, assessed either by dry matter intake (DMI) or intake time (IT). Because IT measurements are easier to obtain than DMI, using a real-time sensor integrated into this in silico model can provide accurate IT measurements, making this a viable solution for predicting methane output on a large scale. This research was conducted in collaboration with colleagues from the Universidad de Antioquia (Medellín, Colombia), Scotland’s Rural College–SRUC (Edinburgh, United Kingdom) and Biomathematics & Statistics Scotland–BioSS (Edinburgh, United Kingdom).

A recent review paper by a number of international experts in rumen microbiology (Huws et al., 2018) showed that to guide new nutritional strategies, predictive mathematical models of the rumen environment should integrate microbial genomics knowledge (omics data). They should also take into account modulating factors related to lipid metabolism, fermentation, diet and phenotypic data (a synchronised measurement of the methane production and food and water intake kinetics of cattle). Integrating all of these data will make the models more reliable and efficient.

*Parsimony is a principle that consists in using only the minimum number of basic causes to describe a phenomenon.

References

Pre-print:

Hydrogenotrophic methanogens of the mammalian gut: functionally similar, thermodynamically different. A modelling approach. 2018. Muñoz-Tamayo R, Popova M, Tillier M, Morgavi DP, Graviou D, Morel JP, Fonty G and Morel-Desrosiers N. BioRxiv: https://doi.org/10.1101/445171 . The manuscript is under submission to The Isme Journal.

Article:

A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle. 2018. Muñoz-Tamayo R, Ramírez Agudelo JF, Dewhurst RJ, Miller G, Vernon T, and Kettle H.  Animal. https://doi.org/10.1017/S1751731118002550

Other article cited:

Huws et al., 2018. Addressing Global Ruminant Agricultural Challenges Through Understanding the Rumen Microbiome: Past, Present, and Future. Frontiers in Microbiology, 9: 33.