Method: Comparing averaging methods for gas flux data generated by automated head chamber systems

Authors: BECK, MATTHEW - Texas A&M University; THOMPSON, LOGAN - Kansas State University; Moffet, Corey; REUTER, RICHARD - Oklahoma State University; Gunter, Stacey

Submitted to: Animal - Open Space
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/28/2025
Publication Date: 9/8/2025
Citation: Beck, M.R., Thompson, L.R., Moffet, C., Reuter, R.R., Gunter, S.A. 2025. Method: Comparing averaging methods for gas flux data generated by automated head chamber systems. Animal - Open Space. 4. Article 100106. https://doi.org/10.1016/j.anopes.2025.100106.
DOI: https://doi.org/10.1016/j.anopes.2025.100106

Interpretive Summary: Currently, automated head chamber systems measure rates of respiratory and gastrointestinal gas exchange by cattle for short, intermittent measurements made over several days. The data from these devices are used to estimate daily emission rates for important greenhouse gases like carbon dioxide and methane and consumption rates for gases like oxygen. Animals are typically allowed to use the device voluntarily and often the timing of use is uneven across time. When measurement time has an effect on gas exchange rates then uneven use may bias the daily estimate if the measurements are simply averaged and temporal effects are not properly accounted. In this paper scientists from USDA-ARS, Kansas State University, Oklahoma State University, and Texas A&M University aimed to evaluated existing approaches, discuss the apparent inadequacy of these current approaches, and propose a standardized pre-processing method. To achieve this, we used 5 previously published datasets for cattle fed a variety of diets as the basis for our evaluation and discussion and we compare these with the recommended approach that uses statistical models to process spot-measurement data and provide reliable daily gas exchange estimates. The improvement in the estimates will increase the efficiency of research being done to understand animal gas exchange using these automated head chamber systems.

Technical Abstract: Researchers are increasingly using automated head chamber systems (AHCS; GreenFeed; C-Lock inc., Rapid City, SD) for estimating gaseous emissions, such as carbon dioxide (CO2) and methane (CH4), and consumption, such as oxygen (O2). Despite several laboratory groups employing this technology, there is a lack of standardization in preprocessing methods for data collected from the AHCS. Standardization is needed to ensure that data collected are repeatable across laboratory groups. Accordingly, our objective was to explore different data preprocessing methods to ultimately make a recommendation for a standardized procedure. For this investigation, we collated data from 5, previously published manuscripts – 3 from grazing studies and 2 from studies utilizing finishing beef steers. We initially explored if removing outliers is necessary, utilizing the third quantile plus 3 times the interquartile range as the threshold for extreme values. This removed 0-0.5% of visit observations across the 5 experiments. We concluded that estimates derived when extreme values were removed only slightly reduced (= 0.2%, = 0.1%, and = 2.4% for CO2, O2, and CH4, respectively) gas estimates compared to when they were not removed. Accordingly, we do not find removing extreme values necessary. Next, we compared simple arithmetic or time-bin (8, 3-h intervals) averaging and least-squares means (LSMEANS) methodologies to arrive at a single estimate for each animal from gas estimates for each visit. For the LSMEANS approach, a mixed effects model was fit for each gas as the dependent variable, animal ID as fixed effects, visit duration and average airflow as covariates, and date and hour of day by animal ID as random effects. If duration and average airflow are not significant, they can be removed from the model. After fitting the model, LSMEANS were generated for each animal with a standard error of the mean (SEm) for each animal estimate. We then analyzed the data for each experiment according to the model presented in its respective manuscript, to obtain residual standard deviation (RSD) and to calculate the coefficient of variation (CV). Ultimately, time-bin averaging increased unexplained error relative to arithmetic averaging and the LSMEANS approach. We conclude that the proposed LSMEANS approach controls for any potential diurnal variation in gas flux, without increasing unexplained error as seen by time-bin averaging. This analysis provides valuable insights for future research to standardize AHCS data preprocessing across experiments and laboratory groups.