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Estimating daily methane production in individual cattle with irregular feed intake patterns from short-term methane emission measurements

Published online by Cambridge University Press:  24 August 2015

D. J. Cottle ,
J. Velazco ,
R. S. Hegarty  and
D. G. Mayer
D. J. Cottle*
Affiliation:
School of Environmental and Rural Science, University of New England, Armidale NSW 2351, Australia
J. Velazco
Affiliation:
School of Environmental and Rural Science, University of New England, Armidale NSW 2351, Australia
R. S. Hegarty
Affiliation:
School of Environmental and Rural Science, University of New England, Armidale NSW 2351, Australia
D. G. Mayer
Affiliation:
Department of Agriculture and Fisheries, Ecosciences Precinct, Dutton Park Qld 4102, Australia
*
†E-mail: dcottle@une.edu.au
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Abstract

Spot measurements of methane emission rate (n = 18 700) by 24 Angus steers fed mixed rations from GrowSafe feeders were made over 3- to 6-min periods by a GreenFeed emission monitoring (GEM) unit. The data were analysed to estimate daily methane production (DMP; g/day) and derived methane yield (MY; g/kg dry matter intake (DMI)). A one-compartment dose model of spot emission rate v. time since the preceding meal was compared with the models of Wood (1967) and Dijkstra et al. (1997) and the average of spot measures. Fitted values for DMP were calculated from the area under the curves. Two methods of relating methane and feed intakes were then studied: the classical calculation of MY as DMP/DMI (kg/day); and a novel method of estimating DMP from time and size of preceding meals using either the data for only the two meals preceding a spot measurement, or all meals for 3 days prior. Two approaches were also used to estimate DMP from spot measurements: fitting of splines on a ‘per-animal per-day’ basis and an alternate approach of modelling DMP after each feed event by least squares (using Solver), summing (for each animal) the contributions from each feed event by best-fitting a one-compartment model. Time since the preceding meal was of limited value in estimating DMP. Even when the meal sizes and time intervals between a spot measurement and all feeding events in the previous 72 h were assessed, only 16.9% of the variance in spot emission rate measured by GEM was explained by this feeding information. While using the preceding meal alone gave a biased (underestimate) of DMP, allowing for a longer feed history removed this bias. A power analysis taking into account the sources of variation in DMP indicated that to obtain an estimate of DMP with a 95% confidence interval within 5% of the observed 64 days mean of spot measures would require 40 animals measured over 45 days (two spot measurements per day) or 30 animals measured over 55 days. These numbers suggest that spot measurements could be made in association with feed efficiency tests made over 70 days. Spot measurements of enteric emissions can be used to define DMP but the number of animals and samples are larger than are needed when day-long measures are made.

Keywords

modelssplinesGreenFeed
Type
Research Article
Information
animal , Volume 9 , Supplement 12 , December 2015 , pp. 1949 - 1957
Copyright
© The Animal Consortium 2015 

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References

Arthur, PF, Archer, JA, Herd, RM and Melville, GJ 2001. Response to selection for net feed intake in beef cattle. In Proceedings of the Association for the Advancement of Animal Breeding and Genetics, Queenstown, NZ, 13, pp. 135–138.Google Scholar
Arthur, PF, Herd, RM, Wright, JH, Xu, G, Dibley, KCP and Richardson, EC 1996. Net feed conversion efficiency and its relationship with other traits in beef cattle. In Proceedings of the Australian Society of Animal Production 21, pp. 107–110.Google Scholar
Benchaar, C, Rivest, J, Pomar, C and Chiquette, J 1998. Prediction of methane production from dairy cows using existing mechanistic models and regression equations. Journal of Animal Science 76, 617–627.Google Scholar
Bindon, BM 2001. Genesis of the Cooperative Research Centre for the Cattle and Beef Industry: integration of resources for beef quality research 1993–2000. Australian Journal of Experimental Agriculture 41, 843–853.Google Scholar
Blaxter, KL and Clapperton, JL 1965. Prediction of the amount of methane produced by ruminants. British Journal of Nutrition 19, 511–522.Google Scholar
Boadi, DA, Wittenberg, KM and Kennedy, AD 2002. Validation of the sulphur hexafluoride SF6. Tracer gas technique for measurement of methane and carbon dioxide production by cattle. Canadian Journal of Animal Science 82, 125–131.CrossRefGoogle Scholar
Chagunda, MGG, Ross, D and Roberts, DJ 2009. On the use of a laser methane detector in dairy cows. Computers and Electronics in Agriculture 68, 157–160.Google Scholar
Cottle, DJ 2013. The trials and tribulations of estimating the pasture intake of grazing animals. Animal Production Science 53, 1209–1220.Google Scholar
Cox, DR and Solomon, PJ 2003. Components of Variance. Chapman & Hall/CRC, Boca Raton, USA.Google Scholar
Crompton, LA, Mills, JAN, Reynolds, CK and France, J 2011. Fluctuations in methane emission in response to feeding pattern in lactating dairy cows. In Modelling nutrient digestion and utilisation in farm animals (ed. D Sauvant, J Milgen, P Faverdin and N Friggens), pp. 176–180. Wageningen Academic Publishers, Wageningen, The Netherlands.Google Scholar
Department of Environment 2014. Australian National Greenhouse Accounts. National Inventory Report 2012. Retrieved December 10, 2014, from http://www.environment.gov.au/node/35779 Google Scholar
Dijkstra, J, France, J, Dhanoa, MS, Maas, JA, Hanigan, MD, Rook, AJ and Beever, DE 1997. A model to describe growth patterns of the mammary gland during pregnancy and lactation. Journal of Dairy Science 80, 2340–2354.Google Scholar
DoE 2013. Guidance for on-farm measurement of agricultural greenhouse gas emissions and soil carbon. Retrieved February 10, 2015, from http://www.agriculture.gov.au/SiteCollectionDocuments/climate-change/aff/aotg/aotg-guidance-for-on-farm-measurement-final.doc Google Scholar
Ellis, JL, Kebreab, E, Odongo, NE, McBride, BW, Okine, EK and France, J 2007. Prediction of methane production from dairy and beef cattle. Journal of Dairy Science 90, 3456–3467.CrossRefGoogle ScholarPubMed
Garnsworthy, PC, Craigon, J, Hernandez-Medrano, JH and Saunders, N 2012. On-farm methane measurements during milking correlate with total methane production by individual dairy cows. Journal of Dairy Science 95, 3166–3180.Google Scholar
Harper, LA, Denmead, OT, Freney, JR and Byers, FM 1999. Direct measurements of methane emissions from grazing and feedlot cattle. Journal of Animal Science 77, 1392–1401.CrossRefGoogle ScholarPubMed
Hegarty, RS, Goopy, JP, Herd, RM and McCorkell, B 2007. Cattle selected for lower residual feed intake have reduced daily methane production. Journal of Animal Science 85, 1479–1486.Google Scholar
Hegarty, RS 2013. Applicability of short-term emission measurements for on-farm quantification of enteric methane. Animal 7, 401–408.Google Scholar
JMP 2014. Fit curve options. Retrieved December 10, 2014, from http://www.jmp.com/support/help/Fit_Curve_Options.shtml Google Scholar
Jones, FM, Phillips, FA, Naylor, T and Mercer, NB 2011. Methane emissions from grazing Angus beef cows selected for divergent residual feed intake. Animal Feed Science and Technology 166, 302–307.Google Scholar
Jonker, A, Molano, G, Antwi, C and 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. Animal Production Science 54, 1350–1353.Google Scholar
Kennedy, PM and Charmley, E 2012. Methane yields from Brahman cattle fed tropical grasses and legumes. Animal Production Science 52, 225–239.Google Scholar
Moe, PW and Tyrrell, HF 1979. Methane production in dairy cows. Journal of Dairy Science 62, 1580–1586.CrossRefGoogle Scholar
Münger, A and Kreuzer, M 2008. Absence of persistent methane emission differences in three breeds of dairy cows. Australian Journal of Experimental Agriculture 48, 77–82.Google Scholar
Pickering, NK, de Haas, Y, Basarab, J, Cammack, K, Hayes, B, Hegarty, RS, Lassen, J, McEwan, JC, Miller, S, Pinares-Patiño, CS, Shackell, G, Vercoe, P and Oddy, VH 2013. Consensus methods for breeding low methane emitting animals. Retrieved February 15, 2015, from http://www.asggn.org/publications,listing,95,mpwg-white-paper.html Google Scholar
Pinares-Patiño, CS, Baumont, R and Martin, C 2003. Methane emissions by Charolais cows grazing a monospecific pasture of timothy at four stages of maturity. Canadian Journal of Animal Science 83, 769–777.CrossRefGoogle Scholar
Ramin, M and Huhtanen, P 2013. Development of equations for predicting methane emissions from ruminants. Journal of Dairy Science 96, 2476–2493.Google Scholar
Robinson, DL, Bickell, SL, Toovey, AF, Revell, DK and Vercoe, PE 2011. Factors affecting variability in feed intake of sheep with ad-libitum access to feed and the relationship with daily methane production. In Proceeding of the Australasian Association for Advancement of Animal Breeding and Genetics, University of Western Australia, Perth, 19, pp. 159–162.Google Scholar
Russell, JB 1998. The importance of pH in the regulation of ruminal acetate to propionate ratio and methane production in vitro. Journal of Dairy Science 81, 3222–3230.CrossRefGoogle ScholarPubMed
Ulyatt, MJ, Lassey, KR, Shelton, ID and Walker, CF 2002. Seasonal variation in methane emission from dairy cows and breeding ewes grazing ryegrass/white clover pasture in New Zealand. New Zealand Journal of Agricultural Research 45, 217–226.Google Scholar
Velazco, J, Cottle, DJ and Hegarty, R 2014. Methane emissions and feeding behaviour of feedlot cattle supplemented with nitrate or urea. Animal Production Science 54, 1737–1740.Google Scholar
Vlaming, JB, Lopez-Villalobos, N, Brookes, IM, Hoskin, SO and Clark, H 2008. Within- and between-animal variance in methane emissions in non-lactating dairy cows. Australian Journal of Experimental Agriculture 48, 124–127.Google Scholar
Wood, PDP 1967. Algebraic model of the lactation curve in cattle. Nature 216, 164–165.Google Scholar
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