Skip to main content
NCBI home page
JDS Communications logoLink to JDS Communications
. 2022 May 5;3(4):255–259. doi: 10.3168/jdsc.2021-0184

Methane emissions in growing heifers while eating from a feed bin compared with 24-hour emissions and relationship with feeding behavior

Ashraf Biswas 1,2,*, Ajmal Khan 1, Dongwen Luo 1, Arjan Jonker 1,*
PMCID: PMC9623804  PMID: 36338017

Graphical Abstract

graphic file with name fx1.jpg

Open in a new tab

Summary: We determined the relationships between daily CH4 emissions estimated during mealtime and measured daily CH4 emissions, and that with feeding behavior in growing heifers (Hereford × Holstein-Friesian; n = 8) fed alfalfa silage in respiration chambers, which were linked to an analyzer that measured CH4 in each chamber every 3 min. Each 3-min measurement was expressed as grams per day and averaged per 24 h or per time during a meal. We observed a strong correlation (r = 0.88) between CH4 emissions (g/d) during mealtime and measured over 24 h, without apparent systematic bias. Feeding behavior parameters correlated with CH4 yield were negatively correlated with the number of visits to the feed bin (r = −0.45), average meal size (r = −0.57), and average daily eating rate (r = −0.48). In summary, CH4 measured during meals was similar to 24-h measured CH4 output in growing heifers fed ad libitum alfalfa silage in respiration chambers. Some feeding behavior parameters explained some of the variation in CH4 yield between animals.

Highlights

  • •

    Methane emissions during mealtime, converted to daily emissions, were compared with 24-h methane emissions from heifers fed ad libitum alfalfa silage in respiration chambers.

  • •

    Methane measured during meals was similar to 24-h measured CH4 output.

  • •

    Visits to the feed bin, average meal size, and average daily eating rate were natively related with CH4 per unit of intake.

Abstract

The objective of the current study was to determine the relationship of daily CH4 emissions estimated during mealtime compared with measured daily CH4 emissions, and determine the relationship with feeding behavior, in growing heifers fed alfalfa silage in respiration chambers. Data from 8 growing cattle (Hereford × Holstein-Friesian) individually housed in 4 respiration chambers and fed ad libitum alfalfa silage delivered in Insentec feed-bins to record feeding behavior and intake were used. The 4 chambers are linked to 1 analyzer, which measures CH4 in each chamber approximately every 3 min. Each 3-min measurement was expressed as grams per day and averaged per 24 h or per time during a meal. A strong correlation (r = 0.88; determined using Deming regression) was observed between CH4 emissions (g/d) during mealtime (276 ± 22.7 g/d) and measured over 24 h (262 ± 24.0 g/d), without apparent systematic bias. Feeding behavior parameters that were correlated with CH4 yield (g/kg dry matter intake) in the current study were a negative correlation with the number of visits to the feed bin (r = −0.45), average meal size (r = −0.57), and average daily eating rate (r = −0.48). In summary, CH4 measured during meals was similar to 24-h measured CH4 output in growing heifers fed ad libitum alfalfa silage in respiration chambers, and some feeding behavior parameters, based on feed bin visits, explained some of the variation in CH4 yield between animals.

Methane emitted by ruminants is a potent greenhouse gas (GHG) that constitutes approximately 15% of global GHG emissions (Gerber et al., 2013) and approximately 33% of total GHG emissions in New Zealand (MfE, 2017). Therefore, a large body of research is in progress to find mitigation options, which has sparked the development of cheaper and more practical methods with higher throughput to measure CH4 from ruminants. Many of these new methods estimate emissions based on multiple short-term (spot-samples) measurements from individual ruminant animals. One spot-sampling strategy developed is based on analyzing breath samples while the animal is visiting a feed bin with forage or TMR (Troy et al., 2016; Flay, 2018). However, the rate of CH4 emissions is not constant during the day and is affected by diet, feed allowance, and feeding pattern (Müller et al., 1980; Jonker et al., 2014), which might affect the predictive power of CH4 spot-sampling methods. Currently, little is known about the accuracy of methane emission estimates based on breath sample analysis during feeding.

The objective of the current study was to determine the relationship of daily CH4 emissions estimated during mealtimes compared with measured daily CH4 emissions and the relationship with feeding behavior in growing heifers fed alfalfa silage in respiration chambers. The hypothesis was that there would be a strong relationship between daily CH4 emissions estimated during mealtimes compared with measured daily CH4 emissions.

The animal experiment reported here was reviewed and approved by the AgResearch Grasslands Animal Ethics Committee (Palmerston North, New Zealand), and heifers were cared for according to the AgResearch Code of Ethical Conduct (AgResearch CEC, 2018). Data from 8 growing heifers (Hereford × Holstein-Friesian; BW = 487 ± 29 kg) were used for the current analysis (Jonker et al., 2014, 2016). Animals were fed ad libitum alfalfa silage, which contained 369 g/kg DM of NDF, 238 g/kg DM of CP, and 10.4 MJ/kg DM of ME calculated according to NRC (2000). During the measurement period, the animals were individually housed in 4 respiration chambers for 3 (first group of 4 over the weekend) or 2 (second group of 4 during the week) consecutive days. The silage was fed in Insentec feed-bins on loadcells (Hokofarm Group BV) inside the chambers with bins refilled at approximately 0800 and 1530 h. Airflow rate was 1.8 m3/min in each chamber and therefore the time required to exchange the chamber air was approximately 9 min. The 4 chambers were linked to a switching unit that directs the air stream of each chamber to one gas analyzer in sequence, which took approximate 3 min per cycle. Every 3-min measured CH4 value was expressed as grams per day as follows: CH4 (g) per measurement time interval/measurement interval (min) × 1,440 min in 24 h.

The Insentec feed bin system recorded entry and exit time and feed weight for each eating event during the day allowing the calculation of feeding time (min), intake (g), and intake rate (g/min) for each visit to the bin. However, during a meal, the animal sometimes takes the head out of the feed bin to chew and then goes back in, resulting in several consecutive recordings that are part of one meal. It was, therefore, necessary to define a meal criterion with start and end times. Here, we define meal criteria as described previously (Tolkamp et al., 2000; von Keyserlingk and Weary, 2010) based on the frequency distribution of intervals (feed bin exit time to next entry time) expressed on a log scale. The bimodal pattern was apparent with 20 min at the intersection between the 2 peaks. Therefore, for the current study, a 20-min interval was used as the threshold to define if a visit to the feed bin fell within a meal or if a new meal started. This interval for meal criteria was in a similar range of 17.9 to 29.8 min as previously found in growing heifers (DeVries and von Keyserlingk, 2009a,b).

Then, the start and end time of each meal was aligned with the respiration chamber data to identify the CH4 emissions measurements during each meal. The multiple CH4 values (which were already expressed as g/d as described above) within a meal were averaged to generate the CH4 production within a meal. The 24-h measured CH4 production (g/d) was calculated by averaging all ~3-min CH4 values.

Deming regression was performed to compare daily 24-h measured CH4 and daily CH4 calculated during mealtime (Linnet, 1993). Deming regression allows fitting a straight line to 2-dimensional data where both variables (X and Y) are measured with error. Bland-Altman (Bland and Altman, 1986) mean difference plot was generated to identify potential systematic bias and outliers in the data. Pearson correlations of feeding behavior parameters with 24-h CH4 production and yield were also performed. The data were analyzed using package ‘mcr' in R version 3.4.2 (R Core Team, 2018).

Average 24-h CH4 emissions for the 8 heifers were 319 ± 24.0 g/d compared with 313 ± 22.7 g/d when estimated from mealtime measurements (Table 1). The mealtime CH4 emissions were on average recorded during 13 meals/d, lasting 34.6 min/meal and 436 min/d (~7.3 h; Table 2). Mealtime CH4 had a strong correlation with 24-h measured CH4 production (r = 0.88) as determined using Deming regression (Figure 1). The 95% confidence interval of the slope between 24-h measured CH4 and mealtime CH4 estimated using Deming regression was 0.77 to 1.35, which indicates that the slope was not different from 1. There was no trend visible in the Bland-Altman plot, suggesting that there was no systematic bias in CH4 estimates based on simulated mealtime CH4 measurements.

Table 1.

Methane production (g/d) and yield (g/kg DMI) estimated from measurements during mealtime at the feed bin and from 24 CH4 measurements in growing heifers fed ad libitum alfalfa silage in respiration chambers

Item Mean SD Maximum Minimum CV
24-h CH41 (g/d) 262 24.0 319 226 9.2
24-h CH4 (g/kg of DMI) 25.3 2.15 29.0 20.8 8.5
Mealtime CH4 (g/d) 276 22.7 313 241 8.2
Mealtime CH4 (g/kg of DMI) 25.8 3.07 35.3 20.8 11.9
Open in a new tab
1

The 24-h measured CH4 production was calculated by averaging all ~3-min CH4 values recorded in a 24-h period by the chamber system.

Table 2.

Feed intake and feeding behavior parameters of 8 growing heifers fed ad libitum alfalfa silage and Pearson correlation (r) of feeding behavior parameters with daily methane production (CH4p) and yield (CH4y), all measured in respiration chambers

Item Mean SD Maximum Minimum CV Correlation (r) with:
CH4p1 CH4y
DMI (kg/d) 10.8 1.56 14.2 7.5 14.5 0.71* −0.77*
Feed bin visit frequency (/d) 97 34 169 47 35.1 0.06 −0.45*
Number of meals2 (/d) 13 2.9 22 9 21.7 0.78* −0.11
Eating time (min/meal) 14.7 3.74 22.3 7.8 25.4 −0.50* 0.13
Meal duration (min/meal) 34.7 13.4 71.4 19.4 38.8 −0.35 −0.16
Total eating time (min/d) 186 25.3 241 132 13.6 0.04 0.14
Total mealtime (min/d) 436 112.5 642 232 25.8 0.08 −0.29
Meal size (kg of DM/meal) 0.8 0.15 1.21 0.59 19.2 −0.25 −0.57*
Eating time eating rate (g of DM/min) 57.4 12.7 91.4 39.6 22.1 0.47* −0.48*
Mealtime eating rate (g of DM/min) 30.4 8.87 46.7 17.2 29.2 0.30 −0.10
Interval between meals (min/interval) 75.6 16.02 110.8 40.3 21.2 −0.26 −0.24
Open in a new tab
1

The 24-h measured CH4 production was calculated by averaging all ~3-min CH4 values recorded in a 24-h period by the chamber system.

2

Meal criteria was defined as described previously (Tolkamp et al., 2000; von Keyserlingk and Weary, 2010) based on the frequency distribution of intervals of the feed bin exit time to next entry time expressed on a log scale (Figure 1).

*

P < 0.05.

Figure 1.

Figure 1

Open in a new tab

Deming regression (A) and Bland-Altman plot (mean difference plot; B) of CH4 production during mealtime at the feed bin (expressed as g/d) with 24-h measured CH4 production (g/d) by 8 growing heifers fed ad libitum alfalfa silage in respiration chambers. The shaded area indicates the 95% confidence interval; this was −111.5 to 48.9 for the intercept (−31.32) and 0.77 to 1.35 for the slope (1.06). The 24-h measured CH4 production was calculated by averaging all ~3-min CH4 values recorded in a 24-h period by the chamber system.

These results are consistent with the findings of Troy et al. (2016), who compared CH4 emissions determined with a custom-built hood system over Insentec feed-bins, with one open side to allow access to the feed by the animal, followed by measurements in respiration chambers with growing beef cattle in 2 experiments fed a range of diets (R2 = 0.64 in experiment 1 and R2 = 0.24 in experiment 2). However, absolute values were much lower (~3×) with the feed bin system than in respiration chambers and including diet fed in the multiple-regression model greatly improved the prediction of daily CH4 using feed bin CH4 data (concordance correlation from 0.55 increased to 0.79). In contrast, Derno et al. (2013) concluded that short-term CH4 measurements during feeding at the feed bin did not reflect average daily CH4 production based on respiration chamber data with dairy cows. However, this conclusion was based on time series analysis cross-correlation (correlation between 2 times series at lags) between feed intake and CH4 emissions, which is different from an analysis where CH4 measured during multiple feeding events in a day is averaged and then compared with 24-h measured CH4 emissions, as in the current study and the study of Troy et al. (2016).

It is not very useful to compare CH4 production estimates (g/d) to findings of other studies because DMI is the main driver of CH4 production (Charmley et al., 2016; Jonker et al., 2017); however, CH4 emissions per unit of DMI (yield) can be compared when animals are fed similar diets and feeding level. The CH4 yield in the current study averaged 25.3 g/kg DMI (range, 20.8–29.0 g/kg DMI; Table 1), which is in a similar range as for growing, dry, and lactating cattle fed forage-based diets (18.5–25.8 g/kg DMI; Jonker et al., 2020). Other studies measuring CH4 during all feeding events at the feed bin, using GreenFeed systems, also found CH4 yields in a similar range for growing heifers fed alfalfa cubes (20.7–22.7 g/kg DMI; Flay et al., 2019) and growing beef cattle fed concentrate-based diets (21.1–23.7 g/kg DMI; Biswas et al., 2018). These suggest that CH4 yields based on measurement to the feed bin can provide similar estimates to those measured during 24-h periods. However, the number of diets and animal measurements tested using this system are currently limited and the conclusion of Derno et al. (2013) suggested that mealtime CH4 emissions could not be used to estimate 24-h CH4 emissions. Therefore, further studies using other cattle categories and feeding different diets should be carried out to come to more robust conclusions about the accuracy of measuring CH4 during all feeding events only compared with 24-h measured emissions.

The heifers ate their feed on average in 13 meals/d, consuming 800 g of DM/meal, lasting 34.6 min/meal, and at a rate of 30.4 g of DM/min in the current study (Table 2), which was a similar range as previously found in growing dairy heifers who ate their feed in 6.8 to 11.1 meal/d, consuming 520 to 980 g/meal, lasting 26.0 to 62.9 min/meal, and at a rate of 37.7 to 57 g of DM/min (DeVries and von Keyserlingk, 2009a,b). Feeding behavior parameters that were correlated with CH4 production in the current study were a positive correlation (r = 0.78) with the number of meals per day and a negative correlation (r = −0.50) with average eating time per meal (min/meal). Previously, intake time was found to correlate (concordance correlation) positively with CH4 production in 2 studies (Muñoz-Tamayo et al., 2019; Ramirez-Agudelo et al., 2019). In the current study, total daily mealtime and total daily eating time had very weak correlations with CH4 production. There was also only a very weak correlation of daily DMI with daily mealtime and daily eating time (data not shown), which likely explains why intake time was a poor predictor of CH4 production in the current study.

Feeding behavior parameters that were correlated with CH4 yield in the current study were a negative correlation with the number of visits to the feed bin (r = −0.45), average meal size (r = −0.57), and average daily eating rate (r = −0.48). Llonch et al. (2018) also found a negative association between the number of visits to the feed bin and CH4 yield in growing beef cattle, supporting the finding of the current study. Offering less frequent and larger meals to lactating dairy cows and sheep was previously found to result in lower CH4 yield (Müller et al., 1980; Swainson et al., 2011), suggesting that the relationships of feeding behavior with CH4 yield in the current study make sense from a biological point of view.

In summary, CH4 measured during meals was similar to 24-h measured CH4 output in growing heifers fed ad libitum alfalfa silage in respiration chambers. Some feeding behavior parameters, based on feed bin visits, explained some of the variation in CH4 production and yield.

Notes

A. Biswas was financially supported by the New Zealand government through the Global Research Alliance Livestock Emissions and Abatement Research Network (LEARN) awards program, and the animal trial was funded by the New Zealand government to support the objectives of the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases.

The authors have not stated any conflicts of interest.

Contributor Information

Ashraf Biswas, Email: biswas30669@gmail.com.

Arjan Jonker, Email: arjan.jonker@agresearch.co.nz.

References

  1. AgResearch CEC . AgResearch Limited; Hamilton, New Zealand: 2018. AgResearch Code of Ethical Conduct For the use of Animals for Research, Testing and Teaching. Version 3, 5 October 2018.https://www.agresearch.co.nz/assets/Uploads/agresearch-documents-code-of-ethical-conduct-approved.pdf [Google Scholar]
  2. Biswas A.A., Lee S.-S., Mamuad L.L., Kim S.-H., Choi Y.-J., Lee C., Lee K., Bae G.-S., Lee S.-S. Effects of illite supplementation on in vitro and in vivo rumen fermentation, microbial population and methane emission of Hanwoo steers fed high concentrate diets. Anim. Sci. J. 2018;89:114–121. doi: 10.1111/asj.12913. 28960611. [DOI] [PubMed] [Google Scholar]
  3. Bland M.J., Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;327:307–310. doi: 10.1016/S0140-6736(86)90837-8. 2868172. [DOI] [PubMed] [Google Scholar]
  4. Charmley E., Williams S.R.O., Moate P., Hegarty R., Herd R., Oddy H., Reyenga P., Staunton K., Anderson A., Hannah M. A universal equation to predict methane production of forage-fed cattle in Australia. Anim. Prod. Sci. 2016;56:169. doi: 10.1071/AN15365. [DOI] [Google Scholar]
  5. Derno M., Hammon H., Rontgen M., Metges C., Kuhla B. Periprandial methane production in late pregnant and early lactating German Holstein cows. Adv. Anim. Biosci. 2013;4:374. [Google Scholar]
  6. DeVries T.J., von Keyserlingk M.A.G. Competition for feed affects the feeding behavior of growing dairy heifers. J. Dairy Sci. 2009;92:3922–3929. doi: 10.3168/jds.2008-1934. 19620675. [DOI] [PubMed] [Google Scholar]
  7. DeVries T.J., von Keyserlingk M.A.G. Short communication: Feeding method affects the feeding behavior of growing dairy heifers. J. Dairy Sci. 2009;92:1161–1168. doi: 10.3168/jds.2008-1314. 19233808. [DOI] [PubMed] [Google Scholar]
  8. Flay H.E. Massey University; Palmerston North, New Zealand: 2018. Methane emissions from dairy heifers as affected by residual feed intake and breed. MS thesis. [Google Scholar]
  9. Flay H.E., Kuhn-Sherlock B., Macdonald K.A., Camara M., Lopez-Villalobos N., Donaghy D.J., Roche J.R. Hot topic: Selecting cattle for low residual feed intake did not affect daily methane production but increased methane yield. J. Dairy Sci. 2019;102:2708–2713. doi: 10.3168/jds.2018-15234. 30639015. [DOI] [PubMed] [Google Scholar]
  10. Gerber P.J., Hristov A.N., Henderson B., Makkar H., Oh J., Lee C., Meinen R., Montes F., Ott T., Firkins J., Rotz A., Dell C., Adesogan A.T., Yang W.Z., Tricarico J.M., Kebreab E., Waghorn G., Dijkstra J., Oosting S. Technical options for the mitigation of direct methane and nitrous oxide emissions from livestock: A review. Animal. 2013;7:220–234. doi: 10.1017/S1751731113000876. 23739465. [DOI] [PubMed] [Google Scholar]
  11. Jonker A., Green P., Waghorn G., van der Weerden T., Pacheco D., de Klein C. A meta-analysis comparing four measurement methods to determine the relationship between methane emissions and dry-matter intake in New Zealand dairy cattle. Anim. Prod. Sci. 2020;60:96–101. doi: 10.1071/AN18573. [DOI] [Google Scholar]
  12. Jonker A., Molano G., Antwi C., Waghorn G.C. Feeding lucerne silage to beef cattle at three allowances and four feeding frequencies affects circadian patterns of methane emissions, but not emissions per unit of intake. Anim. Prod. Sci. 2014;54:1350–1353. doi: 10.1071/AN14235. [DOI] [Google Scholar]
  13. Jonker A., Molano G., Koolaard J., Muetzel S. Methane emissions from lactating and non-lactating dairy cows and growing cattle fed fresh pasture. Anim. Prod. Sci. 2017;57:643–648. doi: 10.1071/AN15656. [DOI] [Google Scholar]
  14. Jonker A., Muetzel S., Molano G., Pacheco D. Effect of fresh pasture forage quality, feeding level and supplementation on methane emissions from growing beef cattle. Anim. Prod. Sci. 2016;56:1714–1721. doi: 10.1071/AN15022. [DOI] [Google Scholar]
  15. Linnet K. Evaluation of regression procedures for methods comparison studies. Clin. Chem. 1993;39:424–432. doi: 10.1093/clinchem/39.3.424. 8448852. [DOI] [PubMed] [Google Scholar]
  16. Llonch P., Somarriba M., Duthie C.A., Troy S., Roehe R., Rooke J., Haskell M.J., Turner S.P. Temperament and dominance relate to feeding behaviour and activity in beef cattle: Implications for performance and methane emissions. Animal. 2018;12:2639–2648. doi: 10.1017/S1751731118000617. 29606168. [DOI] [PubMed] [Google Scholar]
  17. MfE New Zealand's greenhouse gas inventory 1990–2015. 2017. http://www.mfe.govt.nz/node/23304/
  18. Müller H.L., Sax J., Kirchgessner M. Energieverluste über kot, harn und methan durch unterschiedliche häufigkeit der fütterung bei nichtlaktierenden und laktierenden kühen. Z. Tierphysiol. Tierernähr. Futtermittelkd. 1980;44:181–189. doi: 10.1111/j.1439-0396.1980.tb00653.x. 7210895. [DOI] [PubMed] [Google Scholar]
  19. Muñoz-Tamayo R., Ramírez Agudelo J.F., Dewhurst R.J., Miller G., Vernon T., Kettle H. A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle. Animal. 2019;13:1180–1187. doi: 10.1017/S1751731118002550. 30333069. [DOI] [PubMed] [Google Scholar]
  20. NRC . The National Academies Press; 2000. Nutrient Requirements of Beef Cattle: Seventh Revised Edition: Update 2000. [Google Scholar]
  21. Ramirez-Agudelo J.F., Rosero-Noguera J.R., Posada-Ochoa S.L., Escobar-Restrepo C.S., Munoz-Tamayo R. Proc. 75th Annual Conference of The British Society of Animal Science. Advances in Animal Biosciences. Cambridge University Press; 2019. A feeding behaviour-based system to estimate methane emissions in cattle; p. 155. [Google Scholar]
  22. R Core Team . R foundation for Statistical Computing; 2018. R: A language and environment for statistical computing.https://www.R-project.org [Google Scholar]
  23. Swainson N., Martin C., Muetzel S., Pinares-Patiño C.S. Hydrogen emissions from sheep: A spill-over for methanogenesis? Adv. Anim. Biosci. 2011;2:531. [Google Scholar]
  24. Tolkamp B.J., Schweitzer D.P.N., Kyriazakis I. The biologically relevant unit for the analysis of short-term feeding behavior of dairy cows. J. Dairy Sci. 2000;83:2057–2068. doi: 10.3168/jds.S0022-0302(00)75087-9. 11003239. [DOI] [PubMed] [Google Scholar]
  25. Troy S.M., Duthie C.-A., Ross D.W., Hyslop J.J., Roehe R., Waterhouse A., Rooke J.A. A comparison of methane emissions from beef cattle measured using methane hoods with those measured using respiration chambers. Anim. Feed Sci. Technol. 2016;211:227–240. doi: 10.1016/j.anifeedsci.2015.12.005. [DOI] [Google Scholar]
  26. von Keyserlingk M.A.G., Weary D.M. Review: Feeding behaviour of dairy cattle: Measures and applications. Can. J. Anim. Sci. 2010;90:303–309. doi: 10.4141/CJAS09127. [DOI] [Google Scholar]

Articles from JDS Communications are provided here courtesy of Elsevier

RESOURCES