Mallory Embree, Co-Founder/Chief Science Officer
Barrier to Climate-Smart Agriculture: Accurate Methane Measurement
Dairy industry partners have been ramping up their requests for dairy producers to reduce their greenhouse gas emissions. The big problem with their ask? It’s hard to measure whether interventions are working. Common measurement systems, likerespiration chambers or head-boxes, carry a hefty price tag and are limited in the number of animals they can measure in on-farm settings (Eve et al., 2014). Additionally, these methods can disrupt cows' natural behaviors, leading to skewed measurements that don’t reflect true on-farm emissions. On a broader scale, micrometeorology techniques attempt to measure methane emissions over entire farms but fall short in capturing the effect of specific interventions on individual animals. Alternative proxy methods, such as correlating EME with diet and milk fat, can also be inaccurate as they assume methane production in the rumen remains constant.
Given the urgent need to reduce greenhouse gas emissions in agriculture, a reliable, high-throughput, and cost-effective method of quantifying EME directly on the farm is essential.
Methanogens and Their Role in Methane Production
More than 90% of enteric methane is produced by methanogens, a group of rumen-dwelling archaea. Studies have consistently shown that the diversity and population of methanogens in the rumen are closely linked to the amount of methane emitted by cows (Tapio et al., 2017; Auffret et al., 2018; Wallace, et al., 2019; Martínez-Álvaro et al., 2022). While measuring these populations directly would provide invaluable insights, current methods are invasive and time-consuming. Rumen fluid sampling requires oral stomach tubing or even surgical cannulation, posing risks to animal welfare and making large-scale implementation unfeasible.
This is where our innovation comes in.
A New Non-Invasive Method: Saliva-Based EME Estimation
Cows naturally regurgitate feed as part of the cud-chewing process, which means their saliva contains a wealth of information about their rumen microbiome. Recent studies have shown that the microbiomes of the oral cavity and rumen are highly correlated (Kittlemann et al., 2015, Tapio et al., 2016; Young et al., 2020), presenting an exciting opportunity: the oral microbiome could serve as a proxy for methane emissions.
By using non-invasive oral swabs, we can gather samples of the cow's saliva and sequence the microbiome using Next-Generation Sequencing (NGS). This approach offers a more accurate, affordable, and scalable alternative to existing on-farm EME measurement methods.
The Power of Machine Learning
Once we’ve collected saliva samples from cows, the microbial data can be analyzed to predict EME. A machine learning model has been developed using 2,500 rumen and saliva samples collected from 142 cows to correlate the diversity and composition of the oral microbiome with methane emissions measured by established methane measurement equipment likeGreenFeed. This approach allows for the identification of key microbial markers that serve as predictors of methane production.
These microbial markers then form the basis of a simple ordinary least-squares regression model that incorporates milk production (at the individual or pen level), animal intake (at the individual or pen level), and the microbial markers to predict EME.
On-Farm Trials and the Road to Validation
Our team is conducting 20 on-farm trials as part of a USDA NRCS CIG grant program. These trials are comparing the feed efficiency and productivity impacts of Galaxis Frontier-fed cows to controls across dairies in the Western United States. In addition to performance metrics, EME levels will be compared between treatment and control animals using both the saliva methodology as well as conventional EME measurementequipment. The goal is to build a robust, commercially-relevantdataset to further refine our machine learning model, ensuring it can accurately predict methane emissions based on saliva samples from Jersey, Holstein, and crossbreed cows on various diets. Additional cross validation will be performed using data from respiration chambers in a controlled setting.
If successful, this saliva-based methodology will drastically reduce the cost and complexity of EME measurements on-farm, allowing dairy producers to easily monitor and optimize their methane mitigation strategies.
Transforming the Industry
This novel method holds the potential to revolutionize how the dairy industry approaches methane mitigation. By providing a low-cost, non-invasive way to measure EME, producers can implement climate-smart solutions more efficiently, making it easier to validate the effectiveness of interventions across farms of all sizes.
With this innovation, we are paving the way for a future where every farm can measure, manage, and mitigate enteric methane emissions, contributing to a more sustainable dairy industry.
References:
1. Auffret, M. D., R. Stewart, R. J. Dewhurst, C. A. Duthie, J. A. Rooke, R. J. Wallace, T. C. Freeman, T. J. Snelling, M. Watson, and R. Roehe. 2018. Identification, comparison, and validation of robust rumen micro bial biomarkers for methane emissions using diverse Bos Taurus breeds and basal diets. Front Microbiol. 8:2642.
2. Eve, M., D. Pape, M. Flugge, R. Steele, D. Man, M. Riley-Gilbert, and S. Biggar. 2014. Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry: Methods for Entity-Scale Inventory.
3. Kittelmann, S., M. R. Kirk, A. Jonker, A. McCulloch, and P. H. Janssen. 2015. Buccal swabbing as a noninvasive method to determine bacterial, archaeal, and eukaryotic microbial community structures in the rumen. Appl Environ Microbiol. 81:7470–7483.
4. Martínez-Álvaro, M., M. D. Auffret, C. A. Duthie, R. J. Dewhurst, M. A. Cleveland, M. Watson, and R. Roehe. 2022. Bovine host genome acts on rumen microbiome function linked to methane emissions. Commun Biol. 5.
5. Tapio, I., K. J. Shingfield, N. McKain, A. Bonin, D. Fischer, A. R. Bayat, J. Vilkki, P. Taberlet, T. J. Snelling, and R. J. Wallace. 2016. Oral Samples as Non-Invasive Proxies for Assessing the Composition of the Rumen Microbial Community. PLoS One. 11:e0151220.
6. Wallace, R. J., G. Sasson, P. C. Garnsworthy, I. Tapio, E. Gregson, P. Bani, P. Huhtanen, A. R. Bayat, F. Strozzi, F. Biscarini, T. J. Snelling, N. Saunders, S. L. Potterton, J. Craigon, A. Minuti, E. Trevisi, M. L. Callegari, F. P. Cappelli, E. H. Cabezas-Garcia, J. Vilkki, C. Pinares-Patino, K. O. Fliegerová, J. Mrázek, H. Sechovcová, J. Kopečný, A. Bonin, F. Boyer, P. Taberlet, F. Kokou, E. Halperin, J. L. Williams, K. J. Shingfield, and I. Mizrahi. 2019. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci Adv. 5:8391–8394.
7. Young, J., J. H. Skarlupka, M. S. Cox, R. T. Resende, A. Fischer, K. F. Kalscheur, J. C. McClure, J. B. Cole, G. Suen, and D. M. Bickhart. 2020. Validating the use of bovine buccal sampling as a proxy for the rumen microbiota by using a time course and random forest classification approach. Appl Environ Microbiol. 86.
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