Characterization of the number of spot samples required for quantification of gas fluxes and metabolic heat production from grazing beef cows using a GreenFeed

Elizabeth A Dressler; Jennifer M Bormann; Robert L Weaber; Megan M Rolf

doi:10.1093/jas/skad176

. 2023 May 29;101:skad176. doi: 10.1093/jas/skad176

Characterization of the number of spot samples required for quantification of gas fluxes and metabolic heat production from grazing beef cows using a GreenFeed

Elizabeth A Dressler ¹, Jennifer M Bormann ², Robert L Weaber ³, Megan M Rolf ^4,^✉

PMCID: PMC10263114 PMID: 37246780

Abstract

Enteric fermentation from cattle results in greenhouse gas production that is an environmental concern and also an energetic loss. Several methods exist to quantify gas fluxes; however, an open circuit gas quantification system (OCGQS) allows for unencumbered quantification of methane (CH₄), carbon dioxide (CO₂), and oxygen (O₂) from grazing cattle. While previous literature has proven the accuracy of an OCGQS, little work has been done to establish the minimum number of spot samples required to best evaluate an individual grazing animal’s gas fluxes and metabolic heat production. A GreenFeed system (C-Lock Inc.) was used to collect at least 100 spot samples each from 17 grazing cows. The mean gas fluxes and metabolic heat production were computed starting from the first 10 visits (forward) and increasing by increments of 10 until an animal had 100 visits. Mean gas fluxes and metabolic heat production were also computed starting from visit 100 (reverse) in increments of 10 using the same approach. Pearson and Spearman correlations were computed between the full 100 visits and each shortened visit interval. A large increase in correlations were seen between 30 and 40 visits. Thus, mean forward and reverse gas fluxes and metabolic heat production were also computed starting at 30 visits and increasing by 2 until 40 visits. The minimum number of spot samples was determined when correlations with the full 100 visits were greater than 0.95. The results indicated that the minimum numbers of spot samples needed for accurate quantification of CH₄, CO2, and O2 gas fluxes are 38, 40, and 40, respectively. Metabolic heat production can be calculated using gas fluxes collected by the OCGQS with 36 spot samples. Practically, calculation of metabolic heat production will require 40 spot samples because the component gases for metabolic heat calculation require up to 40 spot samples. Published literature from nongrazing (confined) environments recommended a similar number of total spot samples. Large variation existed around the average number of spot samples for an animal per day, therefore a wide range of test durations may be needed to meet the same number of spot samples in different populations. For this reason, protocols for the OCGQS should be based on the total number of spot samples, rather than a test duration.

Keywords: grazing, GreenFeed, greenhouse gas, methane, spot samples

Environmentally important gases (methane, carbon dioxide, and oxygen) were quantified for grazing beef cows using a GreenFeed (C-Lock Inc.). Individual animal metabolic heat production was calculated using these gas fluxes. The minimum number of spot samples were determined for quantification of methane, carbon dioxide, oxygen, and metabolic heat production for grazing beef cows.

Introduction

In the United States, 27% of methane (CH₄) emissions are produced from enteric fermentation of livestock species (U.S. EPA, 2021). CH₄ is produced as a byproduct of enteric fermentation and is expelled from the animal primarily by eructation (Murray et al., 1975; Muñoz et al., 2012). It is important that in the process of reducing CH₄ production, animal productivity is not sacrificed, or that sacrifices in performance are utilized to achieve an optimum balance between CH₄ emissions and productivity to maximize profitability and environmental sustainability. To achieve these goals, it is critical to measure CH₄ phenotypes in cattle to quantify the current state of performance as well as provide data to generate and evaluate mitigation methodologies, such as genetic selection.

An open-circuit gas quantification system (OCGQS) is a method to measure individual animal CH₄, carbon dioxide (CO₂), and oxygen (O₂) gas fluxes. There are several protocol parameters that need to be established prior to use of the OCGQS, including the total number of emissions records for an animal from the trial period. Most of the protocols that exist in literature are from animals in confinement (feedlot and dairy). Substantial research has been done to validate and assess the accuracy of emission spot-measurements from the OCGQS; however, little work has been done to evaluate the total number of emission records necessary to best evaluate an individual grazing animal’s gas emissions and metabolic heat production for the purpose of genetic evaluation.

The objective of this study was to determine the minimum number of gas flux records required to accurately estimate CH₄, CO₂, and O₂ gas fluxes and metabolic heat production from an individual grazing beef cow.

Materials and Methods

Ethics statement

All animal procedures were approved by the Institutional Animal Care and Use Committee at Kansas State University (protocol 4463) in accordance with Federation of Animal Science Societies (FASS, 2010) guidelines.

Study design

CH₄, CO₂ and O₂ fluxes were collected using an OCGQS (GreenFeed, C-Lock Inc.). Collection of daily CH₄, CO₂, was performed on grazing mature Angus beef cows (n = 23) from the Kansas State Purebred Unit near Manhattan, KS, from May 23, 2021 to September 9, 2021. Three animals refused to use the system, giving a refusal rate of 13%. Seventeen of the twenty animals actively using the system achieved at least 100 visits during the trial and were used in this analysis.

The OCGQS system used was equipped with two units mounted side-by-side on a bumper-pull trailer. The system has two alleyways, one leading to the feed dish of each unit to ensure that only one animal has access to a unit at a time. Wind barriers were mounted on either side of the feed dish as well as high-density polyethylene boards fastened to both sides of the alleyways to minimize wind interference. Cattle panels (3.048 m × 1.524 m) surrounded the trailer to limit animal access to the technical equipment on the trailer. The batteries that power the units were charged by a generator. The OCGQS was equipped with a gas auto-calibration system and CO₂ recovery tests were conducted monthly and at the beginning and end of the trial.

Before using the OCGQS, each animal received a half-duplex radio frequency electronic ID (RFID; Allflex USA Inc.). The OCGQS was placed in a location in the pasture where animals were already grazing, near a mineral feeder. Prior to gas flux collection, animals were exposed to the OCGQS during an acclimation period of approximately 2 wk. Animals freely grazed the pasture during acclimation and were not penned with the equipment for any period of time. During the acclimation period, alleyways were stowed upright so animals had easier access to the feed bins. The acclimation period was considered complete when approximately 75% of the cows used the OCGQS frequently. After the acclimation period was complete, alleyways were lowered to ensure only one animal had access to a unit at a time.

During collection, cows freely grazed the native grass pasture and were provided a mineral supplement. Animals were enticed to visit the OCGQS using alfalfa pellets approximately 7 mm in diameter. Other studies have dropped 50 to 55 g of feed at 45 s intervals up to five times in one feeding (Dorich et al., 2015; Hammond et al., 2015; Rischewski et al., 2017). However, in an effort to extend the total visit duration to better capture an eructation event, the protocol can be altered by decreasing the amount of feed dropped, shortening the drop interval, and increasing the number of feed drops. The system in this study was programmed to drop approximately 25 g of feed every 30 s up to eight times during one visit. In this study, animals were allowed to visit the system up to 5 times per day with a minimum of 2 h between visits, which encourages animals to visit during different times of the day to better capture the diurnal pattern of CH₄ emissions (Gregorini, 2012).

Raw collection data were validated by C-Lock Inc., which included checking that head proximity is higher than the low head proximity cutoff, the CO₂ response must be higher than the low CO₂ response cutoff, and the visit must be at least 2 min in duration. This process formed the preliminary dataset. During the final review process, there are several checks on standard gas calibrations, CO₂ recovery tests, airflow correction, and wind correction.

Phenotypic data

The OCGQS calculates the emission rates of gases (Q_c) in order to calculate the gas flux (Q_m;g/d). The Q_c were calculated using the following equation (Huhtanen et al., 2015):

\begin{array}{l} Q_{c} = [C_{p} * (C o n c - B C o n c) * Q_{a i r}] / 10^{6} \end{array}

where C_p is the fractional capture rate of air, Conc is the concentration of captured gas measured by the OCGQS gas sensor, BConc is the background concentration of gas measured by the OCGQS gas sensor, and Q_air is the volumetric airflow measured by the air velocity transmitter.

Once Q_c is known, the Q_m, is determined as follows (McLean and Tobin, 1987; Huhtanen et al., 2015):

\begin{array}{l} Q_{m} = Q_{c} * 273.15 / (273.15 + T_{a i r}) * p_{c} \end{array}

where T_air is the air temperature and p_c is the density of gas at 1 atm and 273.15 K.

Only the first 100 visits were used in this analysis, therefore visits that exceeded 100 for an animal were truncated. Using the daily gas fluxes calculated as described above, average CH₄, CO₂, and O₂ for each animal were computed for increasing visit intervals in 10 visit increments starting with the first 10 visits and increasing until the full 100 visit dataset (forward) was utilized. Average CH₄, CO₂, and O₂ were also calculated starting from visit number 100 (reverse) using the same approach to determine if there were substantial differences due to collection time.

Metabolic heat production was calculated for each animal for each interval using the following equation (Brouwer, 1965):

\begin{array}{l} H P (M c a l / d a y) = & 3.866 * O_{2} + 1.2 * C O_{2} \\ - 0.518 * C H_{4} - 1.431 * N \end{array}

where O₂, CO₂, and CH₄ were the average values for each animal by interval in liters. In the current study, information on nitrogen (N) was unavailable and was omitted from the calculation. A single metabolic heat production value was calculated for each animal and each interval using the corresponding average gas fluxes for that animal and interval, both forward and reverse. Rather than calculating a metabolic heat production value for each spot sample, a single value was found using the average gas fluxes for each animal and interval, which is why no standard deviations for metabolic heat production presented in Tables 2 and 4.

Table 2.

Means (line 1), standard deviations (line 2), and phenotypic variances (line 3) across all animals for CH₄, CO₂, O₂, and metabolic heat production (kcal/d) across all visit intervals

Gas	Direction	10 visits	20 visits	30 visits	40 visits	50 visits	60 visits	70 visits	80 visits	90 visits	100 visits
CH ₄ , g/d	Forward	358.2^a 41.9 1,658.9	362.3^a 46.0 1,995.0	358.0^a 48.6 2,232.0	358.3^a 49.4 2,304.9	357.7^a 50.8 2,430.0	358.1^a 51.0 2,454.3	355.9^a 51.0 2,456.6	354.3^a 50.0 2,355.7	353.7^a 49.9 2,345.8	352.9^a 50.0 2,361.4
CH ₄ , g/d	Reverse	346.0^a 59.8 3,375.4	347.0^a 52.0 2,551.7	345.0^a 48.7 2,237.1	345.3^a 49.3 2,288.5	348.0^a 50.6 2,414.8	349.6^a 52.1 2,560.9	351.3^a 52.4 2,591.2	350.9^a 52.7 2,615.6	352.4^a 52.5 2,595.1	352.9^a 50.0 2,361.4
CO ₂ , g/d	Forward	10,493.4^a 1,339 1,6888,418	10,618.2^a 1,164 1,276,015	10,634.1^a 1,221 1,403,901	10,623.6^a 1,256 1,485,665	10,622.4^a 1,242 1,452,722	10,580.9^a 1,227 1,418,761	10,510.9^a 1,193 1,341,212	10,456.9^a 1,164 1,276,968	10,432.6^a 1,132 1,208,146	10,399.6^a 1,134 1,210,876
CO ₂ , g/d	Reverse	10,083.8^a 1,286 1,558,351	10,163.2^a 1,061 1,059,527	10,126.6^a 1,076 1,090,063	10,130^a 1,080 1,099,193	10,180.5^a 1,093 1,126,003	10,257.5^a 1,116 1,173,556	10,305.5^a 1,144 1,232,387	10,352.3^a 1,181 1,314,619	10,390.6^a 1,162 1,272,180	10,399.6^a 1,134 1,210,876
O ₂ , g/d	Forward	7,815.1^a 946 843,395	7,855.0^a 893 751,093	7,845.9^a 951 852,799	7,858.2^a 996 934,983	7,863.9^a 976 898,316	7,836.4^a 957 862,381	7,779.5^a 927 809,918	7,737.2^a 908 777,401	7,715.2^a 889 744,727	7,693.6^a 887 742,131
O ₂ , g/d	Reverse	7482.4^a 969 885,489	7,511.8^a 855 689,431	7,481.1^a 854 686,723	7,484.8^a 843 670,339	7,530.0^a 844 671,134	7,590.2^a 862 700,410	7,630.1^a 896 755,765	7,656.4^a 919 795,191	7,680.0^a 911 782,122	7,693.6^a 887 742,131
Metabolic heat production, kcal	Forward	27,663.1^a 10,727,114	27,848.8^a 9,172,749	27,838.6^a 10,382,150	27,864.9^a 11,299,402	27,880.1^a 10,893,190	27,778.1^a 10,480,824	27,580.5^a 9,848,375	27,432.2^a 9,429,231	27,357.5^a 9,000,816	27,278.2^a 8,982,075
Metabolic heat production, kcal	Reverse	26,507.1^a 10,914,998	26,637.2^a 8,216,359	26,532.3^a 8,254,679	26,544.2^a 8,107,512	26,696.9^a 8,142,437	26,908.8^a 8,504,256	27,046.3^a 9,132,183	27,148.3^a 9,647,215	27,236.0^a 9,454,059	27,278.2^a 8,982,075

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^aIndicates no significant differences between forward or reverse interval means (P < 0.05).

Table 4.

Means (line 1), standard deviations (line 2), and phenotypic variances (line 3) for each shortened increment between 30 and 40 visits and the full 100 visits for CH₄, CO₂, O₂, and metabolic heat production (kcal/d)

Gas	Direction	30 visits	32 visits	34 visits	36 visits	38 visits	40 visits
CH₄, g/d	Forward	358.07^a 48.69 2,232.0	359.05^a 49.48 2,304.4	358.98^a 50.06 2,359.0	358.49^a 49.94 2,347.2	357.91^a 48.95 2,255.8	358.33^a 49.48 2,304.9
CH₄, g/d	Reverse	345.07^a 48.75 2,237.1	343.93^a 48.89 2,249.6	344.62^a 49.14 2,273.0	344.83^a 48.84 2,245.0	344.79^a 49.13 2,272.0	345.35^a 49.31 2,288.5
CO₂, g/d	Forward	10,634.1^a 1,221.3 1,403,901	10,653.5^a 1,225.3 1,413,144	10,640.0^a 1,225.7 1,414,053	10,629.1^a 1,234.1 1,433,540	10,619.1^a 1,242.6 1,453,437	10,623.6^a 1,256.3 1,485,665
CO₂, g/d	Reverse	10,126.6^a 1,076.1 1,090,063	10,134.4^a 1,077.7 1,093,258	10,113.9^a 1,068.6 1,074,826	10,114.9^a 1,069.9 1,077,369	10,122.4^a 1,079.5 1,096,802	10,130^a 1,080.6 1,099,193
O₂, g/d	Forward	7,845.9^a 951.8 852,799	7,861.8^a 952.8 854,560	7,852.3^a 962.7 872,383	7,854.9^a 971.3 888,021	7,852.3^a 984.5 912,264	7858.2^a 996.7 934,983
O₂, g/d	Reverse	7,481.1^a 854.1 686,723	7,490.4^a 846.3 674,207	7,470.8^a 835.7 657,453	7,471.6^a 835.6 657,262	7,477.4^a 840.4 664,828	7484.8^a 843.9 670,339
Metabolic heat production, kcal/d	Forward	27,838.6^a 10,382,150	27,893.4^a 10,411,648	27,858.9^a 10,577,131	27,859.3^a 10,763,610	27,846.4^a 11,033,407	27,864.9^a 11,299,402
Metabolic heat production, kcal/d	Reverse	26,532.3^a 8,254,679	26,563.5^a 8,141,997	26,496.7^a 7,952,487	26,499.2^a 7,953,584	26,526.7^a 8,057,674	26,544.2^a 8,107,512

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^aIndicates no significant differences between forward or reverse interval means (P < 0.05).

Statistical analysis

Means and standard deviations for CH₄, CO₂, O₂, and metabolic heat production were calculated for each shortened visit interval within animal using mean and standard deviation functions in R (v4.1.2; R Core Team, 2022). Pairwise differences between forward and reverse mean gas flux and metabolic heat production values for each interval were calculated using the PROC GLM procedure in SAS 9.4 with the LSMEANS statement. Phenotypic (Pearson and Spearman) correlations were also calculated for each visit interval compared to the full 100 visits with the correlation function in R (v4.1.2; R Core Team, 2022). In this study, the minimum recommended number of visits for CH₄, CO₂, O₂, and metabolic heat production was determined when the Pearson and Spearman correlation was greater than 0.95. This is the level used for the Beef Improvement Federation guidelines for feed intake and weight gain (BIF, 2021) and other traits such as water intake (Ahlberg et al., 2018). Spearman correlations were utilized to determine the extent of reranking of animals between visit intervals and the full 100 visits. The phenotypic variance of CH₄, CO₂, O₂ and metabolic heat production values were calculated for all intervals. A Bland–Altman plot analysis was conducted in SAS to quantify the agreement between each increasing 10 visit interval and the full 100 visits for CH₄, CO₂, O₂, and metabolic heat production.

After the initial correlation analysis between each interval and the full 100 visits, the 0.95 Pearson correlation was reached by 40 visits for each gas and for metabolic heat production, but the increase in correlation between 30 and 40 visits tended to be fairly large. Thus, the 30 to 40 visit interval was split further into increments of two visits for both forward and reverse in a second correlation analysis for CH₄, CO₂, O₂, and metabolic heat production. Means and standard deviations for CH₄, CO₂, O₂, and metabolic heat production values were calculated using the mean and standard deviation functions of R (v4.1.2; R Core Team, 2022) for each increment. Analysis for the increments between 30 and 40 spot samples was the same as described above for the 10 spot sample intervals.

Results

Summary statistics for CH₄, CO₂, O₂, and metabolic heat production are reported in Table 1. The interval means for CH₄, CO₂, O₂, and metabolic heat production followed the same general trend. The interval means that were calculated from the beginning of the study (forward) were numerically greater than the interval means that were calculated from the end (reverse), although were not significantly different (Table 2). The means gradually became closer in numerical value as the number of visits increased toward 100 visits.

Table 1.

Summary statistics for gas fluxes and metabolic heat production calculated using the first 100 visits to the OCGQS

	n	Mean	Minimum	Maximum	Standard deviation
CH ₄ , g/d	17	353.8	106.0	599.0	83.7
CO ₂ , g/d	17	10,428.1	5,585.0	14,996.0	1,754.7
O ₂ , g/d	17	7,713.2	3,913.0	11,629.0	1,325.1
Metabolic heat production, kcal/d	17	27,278.2	22,068.2	32,391.3	3,089.2

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The Spearman and Pearson correlations for CH₄ between the first 10 visits and 100 visits were 0.69 and 0.68, respectively, for the forward approach (Table 3). All CH₄ correlations reached 0.95 in the interval of 30 to 40 visits. There was a large increase in correlation between the 30 and 40 visit intervals. For the forward approach, the Spearman correlation increased from 0.87 to 0.98, while the Pearson correlation increased from 0.93 to 0.97. When the 30 to 40 visit interval was split into 2-visit increments, the correlation with the full 100 visits first reached 0.95 at 34 visits for the Pearson correlation with a forward approach (Table 5). All correlations were above 0.95 after 38 visits. Therefore, the recommended minimum number of visits for calculation of CH₄ emissions is 38. Animals utilized the OCGQS for an average of 29.5 ± 8.7 d to reach the recommended 38 visits for calculation of CH₄ emissions.

Table 3.

Spearman and Pearson correlations for each shortened number of visits interval and the full 100 visits for CH₄, CO₂, O₂ and metabolic heat production (kcal/d)

Gas	Direction	Analysis	10 visits	20 visits	30 visits	40 visits	50 visits	60 visits	70 visits	80 visits	90 visits	100 visits
CH ₄ , g/d	Forward	Spearman	0.69	0.82	0.87	0.98	0.99	0.99	0.99	0.98	0.99	1
	Forward	Pearson	0.68	0.86	0.93	0.97	0.98	0.99	0.99	0.99	0.99	1
	Reverse	Spearman	0.83	0.90	0.89	0.96	0.96	0.96	0.96	0.97	0.98	1
	Reverse	Pearson	0.89	0.95	0.96	0.98	0.98	0.98	0.99	0.99	0.99	1
CO ₂ , g/d	Forward	Spearman	0.70	0.86	0.93	0.95	0.95	0.98	0.98	0.99	0.99	1
	Forward	Pearson	0.67	0.82	0.93	0.96	0.97	0.98	0.99	0.99	0.99	1
	Reverse	Spearman	0.89	0.97	0.88	0.93	0.92	0.95	0.95	0.97	0.98	1
	Reverse	Pearson	0.92	0.95	0.94	0.95	0.96	0.97	0.98	0.99	0.99	1
O ₂ , g/d	Forward	Spearman	0.65	0.81	0.94	0.97	0.98	0.98	0.99	0.99	0.99	1
	Forward	Pearson	0.72	0.86	0.94	0.96	0.97	0.98	0.99	0.99	0.99	1
	Reverse	Spearman	0.88	0.91	0.90	0.95	0.96	0.98	0.98	0.99	1	1
	Reverse	Pearson	0.93	0.95	0.94	0.95	0.97	0.98	0.98	0.99	0.99	1
Metabolic heat production, kcal/d	Forward	Spearman	0.67	0.81	0.90	0.95	0.97	0.99	0.99	0.99	1	1
	Forward	Pearson	0.71	0.86	0.94	0.96	0.97	0.98	0.99	0.99	0.99	1
	Reverse	Spearman	0.85	0.91	0.91	0.94	0.96	0.98	0.99	0.99	0.99	1
	Reverse	Pearson	0.93	0.95	0.94	0.95	0.97	0.97	0.98	0.99	0.99	1

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Table 5.

Spearman and Pearson correlations for each shortened number of visits interval between 30 and 40 visits and the full 100 visits for CH₄, CO₂, O₂, and metabolic heat production (kcal/d)

Gas	Direction	Analysis	30 visits	32 visits	34 visits	36 visits	38 visits	40 visits	100 visits
CH ₄ , g/d	Forward	Spearman	0.87	0.90	0.89	0.92	0.95	0.98	1
	Forward	Pearson	0.93	0.94	0.95	0.96	0.96	0.97	1
	Reverse	Spearman	0.89	0.90	0.94	0.97	0.96	0.96	1
	Reverse	Pearson	0.96	0.97	0.97	0.98	0.98	0.98	1
CO ₂ , g/d	Forward	Spearman	0.93	0.93	0.95	0.95	0.95	0.95	1
	Forward	Pearson	0.93	0.94	0.94	0.94	0.95	0.96	1
	Reverse	Spearman	0.88	0.89	0.89	0.90	0.91	0.93	1
	Reverse	Pearson	0.94	0.94	0.94	0.94	0.94	0.95	1
O ₂ , g/d	Forward	Spearman	0.94	0.93	0.95	0.94	0.97	0.97	1
	Forward	Pearson	0.94	0.94	0.94	0.95	0.96	0.96	1
	Reverse	Spearman	0.90	0.92	0.92	0.94	0.94	0.95	1
	Reverse	Pearson	0.94	0.95	0.95	0.95	0.95	0.95	1
Metabolic heat production, kcal/d	Forward	Spearman	0.90	0.93	0.94	0.95	0.96	0.96	1
	Forward	Pearson	0.94	0.94	0.94	0.95	0.96	0.96	1
	Reverse	Spearman	0.91	0.93	0.93	0.94	0.94	0.94	1
	Reverse	Pearson	0.94	0.94	0.94	0.95	0.95	0.95	1

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Contribution number 23-148-J from the Kansas Agricultural Experiment Station.

For CO₂, the correlation between the first 10 visits and the full 100 visits ranged from 0.67 to 0.92 depending upon direction of analysis and type of correlation (Table 3). A 0.95 correlation with 100 visits was first achieved between 30 and 40 visits for all correlations except reverse Spearman. When the 30 to 40 visit interval was split into 2-visit increments, a 0.95 correlation with the full 100 visits was first accomplished at 34 visits with the forward Spearman correlation and remained the only correlation greater than 0.95 for 36 visits as well (Table 5). All correlations were above 0.95 at 40 visits except for the reverse Spearman correlation. Thus, the recommended number of visits for calculation of CO₂ emissions is 40 visits. An average of 31.8 ± 9.2 d were needed for animals to meet the recommended 40 visits to the OCGQS for quantification of CO₂ gas flux.

For O₂, 0.95 correlation with the full 100 visits was first achieved at 20 visits for the reverse Pearson correlation, while others still ranged from 0.81 to 0.91 at 20 visits (Table 3). The 0.95 correlation threshold was exceeded for all the correlations after 40 visits. When the 30 to 40 visit interval was split into 2-visit increments, the reverse Pearson correlation was the first correlation to reach 0.95 at 32 visits (Table 5). At 40 visits, all correlations with the full 100 visits had reached 0.95. Thus, the minimum recommended number of visits for calculation of O₂ consumption is 40 visits. Animals utilized the OCGQS for an average of 30.52 ± 9.1 d to achieve the recommended 40 spot samples for the quantification of O₂ gas flux.

A 0.95 correlation with the full 100 visits for metabolic heat production was first found at 20 visits for the reverse Pearson correlation (Table 3). Three correlations were above 0.95 at 40 visits, with a large increase in correlation with the full 100 visits found between 30 and 40 visits. When the 30 to 40 visit interval was split into 2-visit increments, a 0.95 correlation with the full 100 visits first was reached at 36 visits for all correlations except reverse Spearman (Table 5). Thus, the minimum recommended number of visits for the calculation of metabolic heat production using gas fluxes is 36 visits. Animals needed 29.5 ± 8.7 d to reach the recommended 36 spot samples for the calculation of metabolic heat production.

In the Bland–Altman analysis, the 95% confidence intervals for the 40 visit interval and 100 visits measurements were ± 18.2, 461.8, 360.2, and 1,258.7 g/d for CH₄, CO₂, O₂, and metabolic heat production, respectively. The Bland–Altman plot analysis indicated a strong agreement between 40 visits and 100 visits for all three gases and metabolic heat production.

Discussion

In a study of crossbred steers with a silage diet, McGeogh et al. (2010) reported a range of CH₄ emissions from 228 to 304 g/d. This average is lower than the current study (353.8 g/d); however, that could be due to the forage-based diet eaten by cows in this study, which is known to be associated with higher CH₄ emissions (Beauchemin and McGinn, 2005). Huhtanen et al. (2015) reported an average CH₄ emissions of 453 g/d from Swedish Red dairy cows using a GreenFeed. This is higher than the average of the current study, perhaps because lactating dairy cows typically produce more CH₄ than beef cows (Broucek, 2014) due to higher nutritional requirements (National Research Council, 2001). Lactating Holstein-Friesian cows grazing paddocks had CH₄ emissions ranging from 298 to 334 g/d (Waghorn et al., 2016). Meta-analyses from Jonker et al. (2020) and Huhtanen et al. (2019) reported average CH₄ emissions from dairy cows of 293 and 378 g/d, respectively.

Grazing Holstein-Friesian cows had average CO₂ emissions ranging from 9,360 to 11,500 g/d (Waghorn et al., 2016). The average CO₂ emissions from the current study (10,428.1 g/d) falls within that range. Huhtanen et al. (2015) reported an average CO₂ emission of 11,619 g/d for lactating dairy cows. Pinares-Patino et al. (2007) reported average CO₂ emissions of grazing dairy cows to be 9,363 or 10,496 g/d depending on stocking rate. Manafiazar et al. (2016) reported a range from 6,422 to 6,532 g/d for beef heifers and Arthur et al. (2018) reported average CO₂ emissions from heifers to be 5,760 g/d and steers to be 8,939 g/d. The CO₂ emissions reported from Manafiazar et al. (2016) and Arthur et al. (2018) are lower than the average CO₂ emissions from the current study, which could be because the study populations were housed in a dry lot setting whereas the current study used grazing cows which are expected to have higher CO₂ production due to additional energy expenditure from walking and grazing ((Agnew and Yan, 2000; Brosh et al., 2010).

The average O₂ consumption of young bulls in Guarnido-Lopez et al. (2022) ranged from 2,921.1 to 3,156.3 g/d. This could be lower than the current study’s average O₂ consumption (7,713.2 g/d) due to a difference in study population. A meta-analysis of respiration chamber gas flux data from dairy cows found an average O₂ consumption of 3,880.8 g/d (Aubry and Yan, 2015), which is near the low end of the range in the current study.

Kumar et al. (2016) reported a range of heat production for Sahiwal and Karan Fries heifers from 5,858.01 to 8,634.67 kcal/d, which is much lower than the average in the current study (27,278.2 kcal/d). However, this could be because heifers generate far less metabolic heat than cows (West, 2003). In addition, Kumar et al. (2016) performed their study in India with different management, feeding practices, and breeds. Nkrumah et al. (2006) reported a maximum heat production for feedlot steers of 18,072.77 kcal, which is lower than the current study’s average. However, Nkrumah et al. (2006) studied feedlot steers fed a concentrate diet in a confinement setting, whereas the current study used mature cows grazing forages on pasture. Herd et al. (2021) reported metabolic heat production for steers (24,856.59 kcal/d) and heifers (16,013.38 kcal/d), which is similar to the average metabolic heat production in the current study.

The numeric difference in mean values from forward and reverse approaches for all gases and metabolic heat production could have been due to a difference in collection time. This study took place from May to September, which was likely accompanied by decreasing forage quality and the maturation of warm-season grasses (George et al., 2001). Mature forages have a reduced soluble carbohydrate content and more lignified plant cell walls which promotes acetate production in the rumen and increases CH₄ production per unit of forage digested (Pinares-Patino et al. 2003, 2007; Beauchemin et al., 2009; Jonker et al., 2016). However, reduced forage quality is normally associated with reduced intake, so reduced forage quality may not increase the amount of CH₄ produced as a percentage of gross energy intake (Pinares-Patino et al., 2003). Therefore, the cows could have had lower CH₄ emissions toward the end of the trial due to reduced intake stemming from reduced forage quality, making the mean CH₄ emissions from the reverse approach lower than the forward approach. CO₂ production is influenced by feeding level and nutrient composition of the diet (Brouwer, 1965; Aguerre et al., 2011). Lower O₂ consumption is expected as feed intake decreases (Blaxter, 1962). Feed intake and muscular activity are two factors that influence metabolic heat production in domestic animals (Blaxter, 1989). Therefore, the reason that mean gas fluxes and metabolic heat production were numerically higher when calculated with the forward approach could potentially be attributed to reduced forage intake near the end of the trial.

For all gases, the correlation between 100 visits and a small number of visits, such as 10 or 20, is still reasonably high. Arthur et al. (2017) found that with only 20 visits the variance of CH₄ was reduced by 54% compared to the variance at 5 visits. Therefore, for genetic evaluation, there is still likely value even in a small number of visits. While a complete record may require more visits according to the recommendations made in the current study, methodologies to incorporate and properly weight “incomplete” records should be explored.

In a grazing cattle trial, it is difficult to control the exact number of visits from each animal or stop the trial at an exact number of visits, although it is important to establish a minimum number of visits that must be completed before the trial ends. Renand and Maupetit (2016) suggested that approximately 50 spot-measures would be sufficient for calculation of CH₄ emissions in a confinement study with heifers. Arthur et al. (2017) reported a 70% reduction in variance after 30 records relative to the initial variance of 5 records. Arthur et al. (2017) completed this study using both steers and heifers in a confined lot setting, which contrasts with the current study which included only cows in a grazing setting, which could be why the recommendation in the current study is slightly greater.

Gunter and Bradford (2017) reported that for grazing heifers, 12 to 15 visits are required to accurately calculate CH₄ flux. The animals in Gunter and Bradford (2017) visited the OCGQS 2.4 times a day, whereas animals in the current study visited less frequently (1.2 visits/d), which could be why this recommendation is much lower. Renand and Maupetit (2016) reported that CH₄ emissions calculated from 2 wk of testing had a 0.69 correlation with emissions calculated from 8 wk of testing. Arbre et al. (2016) reported that 17 d were required to achieve a repeatability of 0.70 for CH₄ emissions. Gunter and Beck (2018) reported that CH₄ emissions could be calculated accurately during a 14-d period when grazing animals visit the OCGQS 2.5 times per day. Animals that visit 2.5 times per day for 14 d would have a total of 35 visits, which is very close to the recommendation from the current study. However, animals in the current study took a greater number of days to reach the recommendation than animals from Gunter and Beck (2018). The discrepancy in the number of visits an animal makes per day is one reason that spot sample recommendations should be a total number of visits instead of a test duration.

The recommendation of 40 visits for quantifying CO₂ is similar to the recommendation of 36 to 38 visits for CH₄ in the current study. Carbon dioxide and CH₄ were found to have a strong linear relationship in dairy cattle (Aubry and Yan, 2015). Thus, it is reasonable that a similar number of visits would be necessary for both CH₄ and CO₂. Arthur et al. (2017) made a minimum recommendation of 30 records for calculation of CO₂ emissions. Gunter and Bradford (2017) reported that 3.4 to 3.8 d were required to quantify CO₂ emissions when animals visited the OCGQS 2.4 times per day. Gunter and Beck (2018) reported that CO₂ emissions could be accurately calculated during a 14-d period when grazing animals visited the OCGQS 2.5 times per day. If an animal visits the OCGQS 2.5 times per day for 14 d, the total number of visits is 35, which is very close to the recommendation from the current study.

The recommendation of 40 visits for the calculation of O₂ consumption is similar to the current study’s recommendation for CH₄ and CO₂. Aubry and Yan (2015) found that CO₂ and O₂ had a strong positive linear relationship (R² = 0.92) as did CH₄ and O₂ (R² = 0.86). Thus, it is logical that the recommended number of visits would be similar among gases. Gunter and Bradford (2017) reported that 3.7 to 4.1 d were required to calculate O₂ consumption when animals visited the OCGQS 2.4 times per day. Gunter and Beck (2018) reported that O₂ consumption can be calculated in 14 d when grazing animals visited the OCGQS 2.5 times per day for a total of 35 visits, which is similar to the recommendation in the current study.

The recommendation of 36 visits for the calculation of metabolic heat production is similar to the number of visit recommendations for other gases in the current study. This is expected as the CH₄, CO₂, and O₂ gas fluxes were used for the calculation of metabolic heat production. Herd et al. (2020) collected gas fluxes using an OCGQS on steers for 10 wk as a part of a larger feeding test and on heifers for 15 d following acclimation to calculate metabolic heat production. However, Herd et al. (2020) did not evaluate the accuracy of metabolic heat production calculated from these test durations. Currently, no other published literature is available on the recommended number of visits to an OCGQS required to calculate metabolic heat production for comparison.

Conclusion

The results from the current study suggest that the number of spot samples required for accurate calculation of CH₄, CO₂, and O₂ gas fluxes with an OCGQS are 38, 40, and 40 spot samples, respectively. The current study’s recommendations are slightly longer but similar to previous recommendations. This study also suggests that metabolic heat production can be calculated from gas fluxes collected with an OCGQS with 36 spot samples. There is an opportunity to only collect 38 spot samples if CH₄ is the only gas flux of interest in a study. However, if collecting all gases simultaneously, 40 spot samples are needed to meet the recommendation for CO₂ and O₂. Animals met the required number of visits for quantification of CH₄ emissions and metabolic heat production in 29.5 ± 8.7 d. It took animals 30.5 ± 9.1 and 31.8 ± 9.2 d to meet the required number of visits for calculation of O₂ and CO₂, respectively. The average number of visits per day can vary widely which is why protocols for the OCGQS should include the total number of spot samples for the test rather than a total test duration in days.

Acknowledgments

This project was supported by the Angus Foundation, an affiliate of the American Angus Association. Additionally, this work was supported by the National Association of Animal Breeders through funding provided by the NAAB Doak Graduate Fellowship. We would also like to thank Shane Werk and the staff at the Kansas State University Purebred Unit for their help in collecting data for this project. Thank you! to Countryside Feed, LLC in Seneca, KS for the donation of feed used for this project.

Abbreviations

CH₄: methane;
CO₂: carbon dioxide;
N: nitrogen;
OCGQS: open-circuit gas quantification system;
O₂: oxygen;
Q_c: gas emission rate;
Q_m: gas flux;
RFID: radio frequency identification

Contributor Information

Elizabeth A Dressler, Department of Animal Sciences and Industry, Kansas State University, Manhattan, KS 66506, USA.

Jennifer M Bormann, Department of Animal Sciences and Industry, Kansas State University, Manhattan, KS 66506, USA.

Robert L Weaber, Department of Animal Sciences and Industry, Kansas State University, Manhattan, KS 66506, USA.

Megan M Rolf, Department of Animal Sciences and Industry, Kansas State University, Manhattan, KS 66506, USA.

Conflict of interest statement

The authors declare no real or perceived conflicts of interest.

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[CIT0006] Beauchemin, K. A., and McGinn S. M.. . 2005. Methane emissions from feedlot cattle fed barley and corn diets. J. Anim. Sci. 83:653–661. doi: 10.2527/2005.833653x [DOI] [PubMed] [Google Scholar]

[CIT0042] Beauchemin, K. A., McAllister T. A., and McGinn S. M.. 2009. Dietary mitigation of enteric methane from cattle. CAB Rev.: Perspect. Agric. 4. doi: 10.1079/PAVSNNR20094035. [DOI] [Google Scholar]

[CIT0007] Beef Improvement Federation (BIF). 2021. Guidelines for uniform beef improvement program. BIF Guidelines Wiki. https://guidelines.beefimprovement.org/index.php/Guidelines_for_Uniform_Beef_Improvement_Programs.

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[CIT0009] Broucek, J. 2014. Production of methane emissions from ruminant husbandry: a review. J. Environ. Prot. Sci. 5:1482–1493. doi: 10.4236/jep.2014.515141 [DOI] [Google Scholar]

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[CIT0011] Dorich, C. D., Varner R. K., Pereira A. B. D., Martineau R., Soder K. J., and Brito A. F.. . 2015. Short communication: use of portable, automated, open-circuit gas quantification system and the sulfur hexafluoride tracer technique for measuring enteric methane emissions in Holstein cows fed ad libitum or restricted. J. Dairy Sci. 98:2676–2681. doi: 10.3168/jds.2014-8348 [DOI] [PubMed] [Google Scholar]

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PERMALINK

Characterization of the number of spot samples required for quantification of gas fluxes and metabolic heat production from grazing beef cows using a GreenFeed

Elizabeth A Dressler

Jennifer M Bormann

Robert L Weaber

Megan M Rolf

Abstract

Introduction

Materials and Methods

Ethics statement

Study design

Phenotypic data

Table 2.

Table 4.

Statistical analysis

Results

Table 1.

Table 3.

Table 5.

Discussion

Conclusion

Acknowledgments

Abbreviations

Contributor Information

Conflict of interest statement

Literature Cited

ACTIONS

PERMALINK

RESOURCES

Cite

Add to Collections

PERMALINK

Characterization of the number of spot samples required for quantification of gas fluxes and metabolic heat production from grazing beef cows using a GreenFeed

Elizabeth A Dressler

Jennifer M Bormann

Robert L Weaber

Megan M Rolf

Abstract

Introduction

Materials and Methods

Ethics statement

Study design

Phenotypic data

Table 2.

Table 4.

Statistical analysis

Results

Table 1.

Table 3.

Table 5.

Discussion

Conclusion

Acknowledgments

Abbreviations

Contributor Information

Conflict of interest statement

Literature Cited

ACTIONS

PERMALINK

RESOURCES

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