Updated predictions of enteric methane emissions from sheep suitable for use in the New Zealand national greenhouse gas inventory
Natasha Swainson A , Stefan Muetzel A and Harry Clark B CA AgResearch Ltd, Grasslands Research Centre, Tennent Drive, Private Bag 11008, Palmerston North, 4442, New Zealand.
B New Zealand Agricultural Greenhouse Gas Research Centre, Grasslands Research Centre, Tennent Drive, Private Bag 11008, Palmerston North, 4442, New Zealand.
C Corresponding author. Email: harry.clark@nzagrc.org.nz
Animal Production Science 58(6) 973-979 https://doi.org/10.1071/AN15766
Submitted: 2 November 2015 Accepted: 4 May 2016 Published: 8 June 2016
Journal Compilation © CSIRO 2018 Open Access CC BY-NC-ND
Abstract
Enteric methane (CH4) emissions make up approximately one-third of all New Zealand’s carbon dioxide equivalent greenhouse gas emissions. In current national inventory calculations, fixed values are used to estimate emissions from sheep; 20.9 g CH4 per kg dry matter intake (DMI) for sheep <1 year old and 16.8 g CH4 per kg DMI for sheep >1 year old. These values have been principally derived from trials where intake was estimated, and CH4 emissions were measured indirectly using the sulfur hexafluoride tracer technique. Using New Zealand sheep data collected between 2009 and 2015, where intake was accurately measured and CH4 emissions were measured for a minimum of 48 h in respiration chambers (n = 817), updated sheep methane prediction algorithms suitable for use in the national greenhouse gas inventory were derived. A single equation for all sheep based on daily DMI (kg) alone (ln(g CH4/day) = 0.763 × ln(DMI) + 3.039) explained 76% of the variation in CH4 emissions. Splitting the dataset into two age classes (sheep <1 year old and sheep >1 year old) provided two alternative equations; (sheep >1 year old), ln(g CH4/day) = 0.765 × ln(DMI) + 3.09 and (sheep <1 year old), ln(g CH4/day) = 0.734 × ln(DMI) + 0.05(metabolisable energy) + 2.46. An analysis of concordance suggests that a better fit to the data is obtained by using a two-algorithm approach. The use of these updated algorithms in the national inventory resulted in small changes to estimated emissions both within and between years.
Additional keywords: dry matter intake, metabolisable energy, respiration chambers.
References
Blaxter KL, Clapperton JL (1965) Prediction of the amount of methane produced by ruminants. British Journal of Nutrition 19, 511–522.| Prediction of the amount of methane produced by ruminants.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaF28XitFKktg%3D%3D&md5=6cde6902b771bbb1af70db2dca0760aaCAS | 5852118PubMed |
Charmley E, Williams SRO, Moate PJ, Hegarty RS, Herd RM, Oddy VH, Reyenga P, Staunton KM, Anderson A, Hannah MC (2016) A universal equation to predict methane production of forage-fed cattle in Australia. Animal Production Science 56, 169–180.
| A universal equation to predict methane production of forage-fed cattle in Australia.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC28Xis1amu70%3D&md5=d1eb2b764dd6396af5b37141ae7a2b3dCAS |
Clark H, Brookes I, Walcroft A (2003) Enteric methane emissions from New Zealand ruminants 1990–2001 calculated using an IPCC Tier 2 approach. Report prepared for the Ministry of Agriculture and Forestry (March 2003). Ministry of Agriculture and Forestry, Wellington, New Zealand.
Doreau M, van der Werf HMG, Micol D, Dubroeucq H, Agabriel J, Rochette Y, Martin C (2011) Enteric methane production and greenhouse gases balance of diets differing in concentrate in the fattening phase of a beef production system. Journal of Animal Science 89, 2518–2528.
| Enteric methane production and greenhouse gases balance of diets differing in concentrate in the fattening phase of a beef production system.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXps12jtrw%3D&md5=6abb9bf4c85a0c8e26af3786f3c1ab36CAS | 21383032PubMed |
Ellis JL, Kebreab E, Odongo NE, McBride BW, Okine EK, France J (2007) Prediction of methane production from dairy and beef cattle. Journal of Dairy Science 90, 3456–3466.
| Prediction of methane production from dairy and beef cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXntlCksLY%3D&md5=566e93a0ba92e8c0af29a08f3fa90fecCAS | 17582129PubMed |
Hammond KJ, Muetzel S, Waghorn GC, Pinares-Patino CS, Burke JL, Hoskin SO (2009) The variation in methane emissions from sheep and cattle is not explained by the chemical composition of ryegrass. Proceedings of the New Zealand Society of Animal Production 69, 174–178.
Hammond KJ, Burke JL, Koolaard JP, Muetzel S, Pinares-Patiño CS, Waghorn GC (2013) Effects of feed intake on enteric methane emissions from sheep fed fresh white clover (Trifolium repens) and perennial ryegrass (Lolium perenne) forages. Animal Feed Science and Technology 179, 121–132.
| Effects of feed intake on enteric methane emissions from sheep fed fresh white clover (Trifolium repens) and perennial ryegrass (Lolium perenne) forages.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhvVCktbnF&md5=dcc40fc0623a5c992b3c9ca2e77af35bCAS |
Hart KJ, Martin PG, Foley PA, Kenny DA, Boland TM (2009) Effect of sward dry matter digestibility on methane production, ruminal fermentation, and microbial populations of zero-grazed beef cattle. Journal of Animal Science 87, 3342–3350.
| Effect of sward dry matter digestibility on methane production, ruminal fermentation, and microbial populations of zero-grazed beef cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXht1WksLzF&md5=a4deae2d10e184f2077e3187c4a6c70eCAS | 19542500PubMed |
Hristov AN, Oh J, Lee C, Meinen R, Montes F, Ott T, Firkins J, Rotz A, Dell C, Adesogan A, Yang W, Tricarico J, Kebreab E, Waghorn G, Dijkstra J, Oosting S (2013) Mitigation of greenhouse gas emissions in livestock production – a review of technical options for non-CO2 emissions. (Eds PJ Gerber, B Henderson, HPS Makkar) FAO animal production and health paper no. 177. (FAO: Rome, Italy)
IPCC (2006) ‘2006 IPCC guidelines for national greenhouse gas inventories. Volume 4. Agriculture, forestry and other land use.’ Chapter 10. (Eds HS Eggleston, L Buendia, K Miwa, T Ngara, K Tanabe) pp. 1–68. (Institute for Global Environmental Strategies: Kanagawa, Japan)
Jonker A, Muetzel S, Molano G, Pacheco D (2015) Effect of fresh pasture forage quality, feeding level and supplementation on methane emissions from growing beef cattle. Animal Production Science
| Effect of fresh pasture forage quality, feeding level and supplementation on methane emissions from growing beef cattle.Crossref | GoogleScholarGoogle Scholar |
Lana RP, Russell JB, Vanamburgh ME (1998) The role of pH in regulating ruminal methane and ammonia production. Journal of Animal Science 76, 2190–2196.
MfE (2015) New Zealand’s greenhouse gas inventory 1990–2013. Available at http://www.mfe.govt.nz/publications/climate-change/new-zealands-greenhouse-gas-inventory-1990-2013 [Verified 19 May 2016]
Molano G, Clark H (2008) The effect of level of intake and forage quality on methane production by sheep. Australian Journal of Experimental Agriculture 48, 219–222.
| The effect of level of intake and forage quality on methane production by sheep.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXovVyn&md5=c9cac3c054bdeddd25f22b114357e04dCAS |
Muetzel S, Clark H (2015) Methane emissions from sheep fed fresh pasture. New Zealand Journal of Agricultural Research 58, 472–489.
| Methane emissions from sheep fed fresh pasture.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXhvFKks7bP&md5=7eda8b5b6a30966bf11cda68efc45988CAS |
Payne RW, Murray DA, Harding SA, Baird DB, Soutar DM (2009) ‘Genstat for Windows. Introduction.’ 13th edn. (VSN International: Hemel, Hempstead, UK)
Reynolds CK, Crompton LA, Mills JAN (2011) Improving the efficiency of energy utilisation in cattle. Animal Production Science 51, 6–12.
| Improving the efficiency of energy utilisation in cattle.Crossref | GoogleScholarGoogle Scholar |
Van Kessel JA, Russell JB (1996) The effect of pH on ruminal methanogenesis. FEMS Microbiology Ecology 20, 205–210.
| The effect of pH on ruminal methanogenesis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK28XkslGiur4%3D&md5=98d6165300bb1728873629b1d1b6bfb8CAS |
