Reducing enteric methane production from ruminants is a key priority for the grazing industries to meet their Net Zero goals. It is well established that the composition and digestibility of the fibrous component of forages interacts with secondary compounds such as tannin and saponins to impact on the amount of methane produced by livestock. This interaction is complex and an improved understanding of the concentration and composition of these components across species and seasons is essential to enable better predictions of differences in methanogenesis between the species in the diet and in turn match the forage component of the diet with methane mitigating supplements.

Our traditional understanding of these traits is based on destructive assessment of herbage at fixed time points throughout the life of a pasture. These methods involve harvesting, drying and grinding samples before submitting samples for chemical or in vitro analysis. These techniques are expensive, time consuming and are not well-suited to understanding the detailed interactions between these compounds and enteric methane production. Furthermore, this approach does not readily assist in diet formulation and supplement addition or the development of digital proxies to allow the breeding of forages with high yield and reduced methane production. Recent advances in the use of hyperspectral technologies and machine learning models have allowed the development of rapid methods for the assessment of forage composition.

This PhD project would involve the development of data visualisation and analysis tools to allow the rapid measurement of methanogenesis potential in pastures and pasture species across seasons and species taking into consideration both fibre characteristics and plant secondary compounds using both laboratory and in situ measurement. The project would utilise techniques such as laboratory and canopy-based hyperspectral sensors to link with mass spectrometry and in vitro gas production to develop techniques that enable the rapid and accurate visualization and analysis of this data.

Techniques and technologies supporting this PhD include, but are not limited to:

In-field non-destructive measurement of plant nutritive characteristics
In vitro methane measurement techniques
Chemical analysis of plant fibre components and plant secondary compounds
Sensor-based analysis of species and temporal variation in methanogenic potential
Computerised data pipelines for data visualization and analysis

Prof

Kevin Smith

Honorary Professorial Fellow

School of Agriculture, Food and Ecosystem Sciences

Prof

Kevin Smith

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