Section 20: Pro's and con's of devices

From ICAR Wiki
This is the approved revision of this page, as well as being the most recent.

NOTE: This version of Section 20 has been approved by the working group's Chair. Please be aware that further revisions may occur before final review and approval by the Board and ICAR members per the Approval of Page Process.

Daily methane emission measures

Due to the large diurnal variation in enteric CH4 emission in relation with feeding pattern (Grainger et al., 2007[1]; Jonker et al. 2014[2]), the highest accuracy of daily CH4 production rate (DMPR) will be obtained with methods that encompass the whole day emissions. Two methods are available: Respiration Chambers (RC) and SF6 methods.

Alternative methods are based on short-term measures of CH4 production rate: Portable Accumulation Chambers (PAC) for sheep and GreenFeed Emission Monitoring (GEM) systems for cattle and sheep (Hegarty, 2013[3]).

DMPR with Respiration Chamber (RC)

It should be noted that CH4 emissions recorded in RC also include gases from flatulence in addition to eructed and expired CH4. Compared with mouth exhaled CH4, CH4 from flatulence is generally considered as limited.

Feed intake in the RC may not be representative of the normal animal feed intake (Bickell et al., 2014[4]; Llonch et al., 2016[5]; Troy et al., 2016[6]). As a consequence, the DMPR measured could be biased. Animals are usually not fed ad libitum when recorded in RC. It is therefore recommended to compare animal or diet effects on Methane Yield (MY) calculated as the ratio of the observed DMPR/DMI during the RC recording in order to take into account possible differences among animals in DMI bias. Animal effects can also be compared on the Residual Methane Production Rate (RMPR) the difference between the observed DMPR and the expected DMPR obtained by regression of observed DMPR on DMI recorded during RC test. Residual traits, however, require a large number of recorded animals for valid adjustment.

Repeatability coefficients between measures taken on consecutive days are very high, rep=0.85 [0.75 to 0.94] for MeY and RMPR of cattle and sheep (Grainger et al., 2007[7]; Donoghue et al., 2016[8]; Pinares-Patino et al., 2013[9]). It has been concluded that 1-day measurement duration could be recommended as it will have a limited impact, less than 5%, on the efficiency of selection of MeY as compared to a selection on a 2-day measurement duration.

When repeated measures of CH4 emission of sheep are taken few days to two weeks apart the repeatability coefficients of MeY and RMPR drops to rep=0.36 [0.26 to 0.41] on average (Pinares-Patino et al., 2013[9]; Robinson et al., 2014a[10]). Interestingly, repeatability maintains at a moderate level, rep=0.27 [0.23 to 0.53], when animals were measured several months or even years apart. Similar results were found in Angus cattle, rep=0.20, between MeY and RMPR measures taken more than 60 days apart (Donoghue et al., 2016[8]).

Conclusions and reccomendations

All these results show that animal effects exist on daily CH4 emissions and animal differences are partially under genetic determinism. This trait, as any other physiology trait, is subject to number of environmental effects and to evolution with time. Ranking animals on their CH4 emission requires standardization of the testing environment. Although highly precise, a single measure recorded in RC is not sufficient for characterizing an animals emission aptitude. In order to characterize a long term phenotype it is therefore recommended to record several 1-day measures, each a few weeks apart, instead of one single 2-day measure, keeping the testing environment as constant as possible.

DMPR with GEM

At each visit CH4 and CO2 fluxes are measured and animal emission rates are obtained by averaging the short-term flux measures recorded during the testing period. In a review of published results (Dorich et al., 2015[11]; Hammond et al., 2015[12]; Velazco et al., 2016[13]) Hammond et al. (2016A)[14] concluded that the GEM system provides similar DMPR values as the RC or SF6 methods. Similar accuracy was found by Arbre et al. (2016)[15] for CH4 yield measured with GEM as compared with RC and SF6 measures.

The spot measures are highly variable since they include, in addition to the animal and environment effects, an important within-animal and within-day variance. The latter is considered as an error term. Consequently, the precision of the animal estimates increase with the number of spot measures averaged per animal. From the results reported by Renand and Maupetit (2016)[16] with 124 beef heifers controlled indoors, it can be shown that the coefficient of variation of that error term (CVe) decreases exponentially with the number of spot measures: 13.7%, 10.8%, 7.9% and 4.9% with 5, 10, 25 and 100 measures respectively. Results reported by Arbre et al. (2016)[15] with 7 lactating dairy cows controlled indoors, also show that CVe decreases from 12.8% to 11.4%, 9.5% and 6.8% when the number of measures increases from 5 to 10, 25 and 100. With dairy cows at pasture, Waghorn et al. (2016)[17] showed that the coefficient of variation among 36 dairy cows at pasture was half (6.6 and 7.5%) when CH4 production rate was averaged over 16 days with approximately 18 to 26 measures per cow, as compared with 4 day averages with 4 to 6 measures per cow (13.0 and 17.2%). These authors concluded that at least 16 days are required to give confident estimates.

With 45 to 50 spot measures recorded during 2 weeks Arbre et al. (2016)[15] and Renand and Maupetit (2016)[16] obtained repeatabilityof 0.78 and 0.73 for DMPR estimates of 7 dairy cows and 124 beef heifers, respectively. A similar repeatability coefficient (0.74) was obtained by Huhtanen et al. (2015)[18] with 25 dairy cows recorded during 3 weeks, with 20 to 30 samples per cow. Interestingly, these latter authors fitted gas concentration, airflow and head position measurement equipments into two automatic milking systems that were used to measure CH4 emission of 59 dairy cows during two periods of 10 days. After filtering data for acceptable head-position, the repeatability of DMPR was 0.75.

Considering the need to average enough spot measures and the advantage of measuring DMPR over long periods to take into account the emission variability with time, the GEM system should be run over several weeks. Averaging 40 to 50 spot measures per animal should provide a precise measure of the animal DMPR. The minimum duration of CH4 recording will depend on the number of spot measures actually recorded per day.

The GEM system relies on animals that voluntarily visit the GEM unit when attracted with pellets dispensed by a feeder at a controlled rate. The visitation frequency appears to be highly variable among different studies reported up to now. While some experiments report a very high frequency of cattle visiting the GEM units (up to 96%), the proportion of not visiting animals may be very high in other studies (up to 60%) (Dorich et al., 2015[11]; Hammond et al, 2015A[12], Arbre et al., 2016[15]; Renand and Maupetit, 2016[16]; Velazco et al., 2016[13]; Waghorn et al., 2016[17]). The reason why some animals may not visit the unit is not obvious. That problem of no or low visiting frequency may jeopardize the precise ranking of animals on their DMRP. Training them is an important requisite for the success of DMPR recording with the GEM system (see recommendations on the C-Lock website). Palatability of the pellets used to attract the cattle should be high compared with the diet they receive in the trough or the grass they are grazing.

In addition to the effect on precision, the low visiting frequency may have an impact on accuracy if associated in some animals with specific time of visiting. Enteric CH4 emissions have a diurnal variation with a minimum at the end of night, before the first feeding, and a steady increase after each feeding. A weak diurnal pattern in CH4 emission was detected by Velazco et al. (2016)[13] using GEM systems. Renand et al. (2013)[16] observed significant differences between visit hours (CV=10%). If some animals visit the GEM at specific hours of the day, the rough average of spot measures will be biased. In order to get rid of this time effect on the DMPR measure, Dorich et al. (2015)[11] and Hristov et al. (2016)[19] came up with a protocol where the GEM units were moved sequentially from one cow to the next one over several days, so that all the cows were equally measured during different hours of the day. That protocol is possible only with tie stall cattle and is obviously not applicable for measuring large number of animals. However, with animals controlled in their production environment, the bias generated by potential specific visiting patterns can actually be removed if the measuring hour is taken into account in the linear model when estimating the animal effect.

As voluntary visiting of the GEM system may be a limiting factor under some conditions, measures of DMPR can be designed when animals are drinking or eating, i.e. several times per day. Velazco et al. (2016)[13] showed that a GEM water unit prototype designed and built by C-Lock Inc., displayed different eructation patterns as compared with a plain GEM unit. They concluded that further development appears necessary before any application. Troy et al. (2016)[6] tested a CH4 hood (MH) system placed above an automated feeding bin. That system includes an air extraction fan for each hood with continuously recorded airflow. Methane concentration was measured using 4 infrared analyzers, one for 8 hoods. In this system one CH4 concentration value was recorded every 6 min. With 9 to 12 feeding events per day on average and feeding visits averaging 8 min, there were between 12 to 16 CH4 concentration values recorded and CH4 production rates calculated per day. The measurements were recorded during 46 days and ranking of animals in relation with the test duration was studied. However no repeatability coefficient was given for comparison with other methods. That system was compared with respiratory chambers results in two experiments with 82 and 80 steers fed different diet-treatment combinations. Over the whole experimental design, a good concordance was found between MH and RC results as a consequence that both methods detected similar effects for the diet-treatment effects. However no correlation was given between both methods within diet-treatment samples that are the essential information needed to evaluate the ability of this new method to predict individual DMPR.

Conclusions and reccomendations

With only a single gas analyzer for 8 feed bins, the time when useful CH4 concentration is recorded is certainly too short for including several eructation peaks. Fitting one gas analyzer per feed bin will combine advantages of the measurement time during visits of the GEM system with the visiting frequency allowed by the MH system.

MPR with PAC

The delay between the measurement and the last feeding has to be carefully monitored and taken into account when calculating animal emission values. As individual DMI is difficult to record, direct measurement of CH4 yield (MY=MPR/DMI) turns out to be impossible. Although not representative of a whole day production rate, that method can be used to characterize individual CH4 emission rates if standardized protocols are applied. It was first validated with 40 ewes measured 1 hour in PAC after three 22-hour measures in RC: a correlation of 0.71 was found between the two measures of CH4 production rate over 1 or 22 hours (Goopy et al., 2011[20]). The 1-hour CH4 production measure in PAC has a moderate repeatability of rep=0.50 [0.37 to 0.60] when taken few days to seven weeks apart (Robinson et al., 2015[10]; Goopy et al., 2016[21]). Heritability coefficient of this 1-hour CH4 production measure is estimated to h²=0.12 in a population of 2,279 sheep (Robinson et al., 2014b[10]) with a repeatability coefficient rep=0.25.

Conclusions and reccomendations

The authors recommend using the mean of 3 PAC measurements in order to get accurate phenotype estimates.

  1. ↑ Grainger, C., Clarke, T., McGinn, S.M., Auldist, M.J., Beauchemin, K.A., Hannah, M.C., Waghorn, G.C., Clark, H., and Eckard, R J. 2007. Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. J. Dairy Sci. 90:2755-2766.
  2. ↑ Jonker, A., Molano, G., Antwi, C., Waghorn, G.. 2014. 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.54:1350-1353.
  3. ↑ Hegarty, R.S. 2013. Applicability of short term emission measurements for on-farm quantification of enteric methane. Animal 7, s2:401-408.
  4. ↑ Bickell, S.L., Revell, D.K., Toovey, A.F., and Vercoe, P. E. 2014. Feed intake of sheep when allowed ad libitum access to feed in methane respiration chambers. J. Anim. Sci. 92:2259-2264.
  5. ↑ Llonch, P.,  Somarriba, M.,. Duthie, C-A, Haskell, M.J., Rooke, J.A.,  Troy, S., Roehe, R., and . Turner, S.P. 2016 Association of temperament and acute stress responsiveness with productivity, feed efficiency, and methane emissions in beef cattle: an observational study. Front. Vet. Sci. 3: 43.
  6. ↑ 6.0 6.1 Troy, S.M., Duthie, C.A., Ross, D.W., Hyslop, J.J., Roehe, R., Waterhouse, A., and Rooke, J.A. 2016. A comparison of methane emissions from beef cattle measured using methane hoods with those measured using respiration chambers. Anim. Feed Sci. Technol. 211:227-240.
  7. ↑ Grainger, C., Clarke, T., McGinn, S.M., Auldist, M.J., Beauchemin, K.A., Hannah, M.C., Waghorn, G.C., Clark, H., and Eckard, R J. 2007. Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. J. Dairy Sci. 90:2755-2766.
  8. ↑ 8.0 8.1 Donoghue, K.A., Bird-Gardiner, T., Arthur, P.F., Herd, R.M., and Hegarty, R.F. 2016. Genetic and phenotypic variance and covariance components for methane emission and postweaning traits in Angus cattle. J. Anim. Sci. 94:1438–1445. doi:10.2527/jas2015-0065.
  9. ↑ 9.0 9.1 Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.
  10. ↑ 10.0 10.1 10.2 Robinson, D.L., Goopy, J.P., Donaldson, A.J., Woodgate, R.T., Oddy, V.H., and Hegarty, R.S. 2014. Sire and liveweight affect feed intake and methane emissions of sheep confined in respiration chambers. Anima, 8:1935-1944.
  11. ↑ 11.0 11.1 11.2 Dorich, C.D., Varner, R.K., Pereira, A.B.D., Martineau, R., Soder, K.J., and Brito, A.F. 2015. Use of a 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.
  12. ↑ 12.0 12.1 Hammond, K.J., Humphries, D.J., Crompton, L.A., Green, C., and Reynolds, C.K. 2015. Methane emissions from cattle: Estimates from short-term measurements using a GreenFeed system compared with measurements obtained using respiration chambers or sulphur hexafluoride tracer. Anim. Feed Sci. Technol. 203:41-52. doi:10.1016/j.anifeedsci.2015.02.008.
  13. ↑ 13.0 13.1 13.2 13.3 Velazco, J. I., Hegarty, R., Cottle, D., and Li, L. 2016. Quantifying daily methane production of beef cattle from multiple short-term measures using the GreenFeed system. https://rune.une.edu.au/web/handle/1959.11/23580.
  14. ↑ Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018
  15. ↑ 15.0 15.1 15.2 15.3 Arbre, M., Rochette, Y., Guyader, J., Lascoux, C., Gómez, L.M., Eugène, M., Morgavi, D.P., Renand, G., Doreau, M. and Martin, C. 2016. Repeatability of enteric methane determinations from cattle using either the SF6 tracer technique or the GreenFeed system. Anim. Prod. Sci. 56:238-243.
  16. ↑ 16.0 16.1 16.2 16.3 Renand, G., and Maupetit, D. 2016. Assessing individual differences in enteric methane emission among beef heifers using the GreenFeed Emission Monitoring system: effect of the length of testing period on precision. Anim. Prod. Sci. 56:218-223.
  17. ↑ 17.0 17.1 Waghorn, G.C., Jonker, A., and Macdonald, K A. (2016). Measuring methane from grazing dairy cows using GreenFeed. Anim. Prod. Sci. 56:252-257.
  18. ↑ Huhtanen, P., Cabezas-Garcia, E.H., Utsumi, S., and Zimmerman, S. 2015. Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 98:3394–3409. doi:10.3168/jds.2014-9118.
  19. ↑ Hristov,  A.N., O,h J., Giallongo, F., Frederick, T., Harper, M.T., Weeks, H., Branco, F., Price, W.J., Moate, P.J., Deighto,n M.H., Williams, S.R.O., Kindermann, M., and Duval, S. 2016. Short communication: Comparison of the GreenFeed system with the sulfur hexafluoride tracer technique for measuring enteric methane emissions from dairy cows. J. Dairy Sci. 5461–5465. doi:10.3168/jds.2016-10897.
  20. ↑ Goopy, J.P., Woodgate, R., Donaldson, A., Robinson, D.L., and Hegarty, R.S. 2011. Validation of a short term methane measurement using portable static chambers to estimate methane production in sheep. Anim. Feed Sci. Technol. 166-167;219-226.
  21. ↑ Goopy, J.P., Robinson, D.L., Woodgate, R.T., Donaldson, A.J., Oddy, V.H., Vercoe, P. E., and Hegarty, R.S. 2016. Estimates of repeatability and heritability of methane production in sheep using portable accumulation chambers. Anim. Prod. Sci. 56:116-122.
Retrieved from "http://wiki.icar.org/index.php?title=Section_20:_Pro%27s_and_con%27s_of_devices&oldid=4320"