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Interactions between enteric methane and nitrogen excretion in dairy cows

Published online by Cambridge University Press:  27 September 2013

A. Bannink ,
J. L. Ellis ,
N. Mach ,
J. W. Spek  and
J. Dijkstra
A. Bannink*
Affiliation:
Wageningen UR Livestock Research, Wageningen University Research Centre, PO Box 65, Lelystad 8200 AB, The Netherlands
J. L. Ellis
Affiliation:
Animal Nutrition Group, Wageningen University, Wageningen 6708 WD, The Netherlands
N. Mach
Affiliation:
INRA UMR 1313, Génétique Animale et Biologie Intégrative, 78352 Jouy en Josas, France
J. W. Spek
Affiliation:
Wageningen UR Livestock Research, Wageningen University Research Centre, PO Box 65, Lelystad 8200 AB, The Netherlands Animal Nutrition Group, Wageningen University, Wageningen 6708 WD, The Netherlands
J. Dijkstra
Affiliation:
Animal Nutrition Group, Wageningen University, Wageningen 6708 WD, The Netherlands
*
†E-mail: [email protected]
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Abstract

Next to dry matter (DM) intake, nutritional factors cause considerable variation in methane (CH4) emitted and nitrogen (N) excreted per kg of DM intake or per kg of milk. Rumen function in particular determines CH4 emission and concomitant (amount and site) of N excretion, including the trade-offs between them with changes in nutrition and cow characteristics. Quantification of the interaction between CH4 and N emission hence requires quantification of effects on rumen function in particular. The models available to quantify CH4 emission require the same types of input. The detail of questions posed determines the choice of model and the required level of detail of model inputs needed to investigate mitigation measures and the interaction between CH4 and N emission for a specific farming case. Simulation results with a mechanistic model of enteric fermentation confirmed a profound impact of nutritional measures on both CH4 and N emission, but also demonstrated that nutritional measures to mitigate N excretion can be associated with an increase in CH4 emission. This result demonstrates the need to consider details on the rumen level when the aim is to quantify accurately the net effect on greenhouse gas emission for a specific case studied, which contrasts with applying generic values. As an alternative to models of quantification, on-farm measurement of emission might be pursued by sampling of excreta and air. The principle problem is that concentrations are measured which not necessarily reflect daily rates. Milk production rate is recorded on-farm however, which makes indicators based on milk composition just as promising candidates to estimate CH4 (milk fat) or N (milk urea) emission, provided bias by variation in milk composition unrelated to CH4 and N emission rate can be prevented.

Keywords

enteric methanenitrogen excretionquantifying emissionsfarm indicators
Type
Full Paper
Information
Advances in Animal Biosciences , Volume 4 , Special Issue s1: Proceedings of the Dairy Solutions Symposium, 20th – 21st June 2012, University College Dublin, Dublin, Ireland , September 2013 , pp. 19 - 27
Copyright
Copyright © The Animal Consortium 2013 

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References

Bannink, A, Van Schijndel, MW, Dijkstra, J 2011. A model of enteric fermentation in dairy cows to estimate methane emission for the Dutch National Inventory Report using the IPCC Tier 3 approach. Animal Feed Science and Technology 166, 603–618.Google Scholar
Bannink, A, Kogut, J, Dijkstra, J, France, J, Kebreab, E, Van Vuuren, AM, Tamminga, S 2006. Estimation of the stoichiometry of volatile fatty acid production in the rumen of lactating dairy cows. Journal of Theoretical Biology 238, 36–51.Google Scholar
Bannink, A, France, J, Lopez, S, Gerrits, WJJ, Kebreab, E, Tamminga, S, Dijkstra, J 2008. Modelling the implications of feeding strategy on rumen fermentation and functioning of the rumen wall. Animal Feed Science and Technology 143, 3–26.Google Scholar
Bannink, A, Smits, MCJ, Kebreab, E, Mills, JAN, Ellis, JL, Klop, A, France, J, Dijkstra, J 2010. Simulating the effects of grassland management and grass ensiling on methane emission from lactating cows. Journal of Agricultural Science 148, 55–72.Google Scholar
Bauman, DE, Harvatine, KJ, Lock, AL 2011. Nutrigenomics, rumen-derived bioactive fatty acids, and the regulation of milk fat synthesis. Annual Review of Nutrition 31, 299–319.Google Scholar
Beukes, PC, Gregorini, P, Romera, AJ 2011. Estimating greenhouse gas emissions from New Zealand dairy systems using a mechanistic whole farm model and inventory methodology. Animal Feed Science and Technology 166–167, 708–720.Google Scholar
Blok, MC, André, G, Brandsma, GG, Van Straalen, WM, Van Duinkerken, G 2007. Voeropnamemodel Melkvee, versie 2007. CVB-documentatierapport 51, CVB, Productschap Diervoeder, NL.Google Scholar
Cant, JP 2005. Integration of data in feed evaluation systems. In Quantitative aspects of ruminant digestion and metabolism, 2nd edition (ed. J Dijkstra, JM Forbes and J France), pp. 707–725. CAB International, Wallingford, UK.Google Scholar
Dijkstra, J, Oenema, O, Bannink, A 2011. Dietary strategies to reducing N excretion from cattle: implications for methane emissions. Current Opinion in Environmental Sustainability 3, 414–422.Google Scholar
Dijkstra, J, Neal, HD, Beever, DE, France, J 1992. Simulation of nutrient digestion, absorption and outflow in the rumen: model description. Journal of Nutrition 122, 2239–2256.Google Scholar
Ellis, JL, Bannink, A, France, J, Kebreab, E, Dijkstra, J 2010. Evaluation of enteric methane prediction equations for dairy cows used in whole farm models. Global Change Biology 16, 3246–3256.Google Scholar
Ellis, JL, Dijkstra, J, Kebreab, E, Bannink, A, Odongo, NE, McBride, BW, France, J 2008. Aspects of rumen microbiology central to mechanistic modelling of methane production in cattle. Journal of Agricultural Science 146, 213–233.Google Scholar
Ellis, JL, Dijkstra, J, France, J, Parsons, AJ, Edwards, GR, Rasmussen, S, Kebreab, E 2012. Effect of high-sugar grasses on methane emissions simulated using a dynamic model. Journal of Dairy Science 95, 272–285.Google Scholar
Garnsworthy, PC, Craigon, J, Hernandez-Medrano, JH, Saunders, N 2012. Variation among individual dairy cows in methane measurements made on farm during milking. Journal of Dairy Science 95, 3181–3189.Google Scholar
IPCC 2006. IPCC guidelines for national greenhouse gas inventories. Intergovernmental. Panel on Climate Change, IGES, Japan.Google Scholar
Janssen, PH 2010. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Animal Feed Science and Technology 160, 1–22.Google Scholar
Johnson, KA, Johnson, DE 1995. Methane emission in cattle. Journal of Animal Science 89, 1302–1310.Google Scholar
Kelsey, JA, Corl, BA, Collier, RJ, Bauman, DE 2003. The effect of breed, parity, and stage of lactation on conjugated linoleic acid (CLA) in milk fat from dairy cows. Journal of Dairy Science 86, 2588–2597.Google Scholar
Mach, N, Blum, Y, Bannink, A, Causeur, D, Houee-Bigot, M, Lagarrigue, S, Smits, MA 2012. Pleiotropic effects of polymorphism of the gene diacylglycerol-O-transferase 1 (DGAT1) in the mammary gland tissue of dairy cows. Journal of Dairy Science 95, 4989–5000.CrossRefGoogle ScholarPubMed
Mills, JN, Dijkstra, J, Bannink, A, Cammell, B, Kebreab, E, France, J 2001. A mechanistic model of whole-tract digestion and metahnogenesis in the lactating dairy cow: model development, evaluation, and application. Journal of Animal Science 79, 1584–1597.Google Scholar
Murray, PJ, Gill, E, Balsdon, SL, Jarvis, SC 2001. A comparison of methane emissions from sheep grazing pastures with differing management intensities. Nutrient Cycling in Agroecosystems 60, 93–97.Google Scholar
Oba, M, Allen, MS 2003. Effects of corn grain conservation method on ruminal digestion kinetics for lactating dairy cows at two dietary starch concentrations. Journal of Dairy Science 86, 184–194.CrossRefGoogle ScholarPubMed
Reijs, JW 2007. Improving slurry by diet adjustments: a novelty to reduce N losses from grassland based dairy farms. PhD Thesis, Wageningen University, The Netherlands, 203pp.Google Scholar
Reynolds, CK, Kristensen, NB 2008. Nitrogen recycling through the gut and the nitrogen economy of ruminants: an asynchronous symbiosis. Journal of Animal Science 86, 293–305.Google Scholar
Robinson, PH, Tamminga, S, Van Vuuren, AM 1987. Influence of declining level of feed intake and varying the proportion of starch in the concentrate on rumen ingesta quantity, composition and kinetics of ingesta turnover in dairy cows. Livestock Production Science 17, 37–62.Google Scholar
Schennink, A, Stoop, WM, Visker, MH, Heck, JM, Bovenhuis, H, Van der Poel, JJ, Van Valenberg, HJ, Van Arendonk, JA 2007. DGAT1 underlies large genetic variation in milk-fat composition of dairy cows. Animal Genetics 38, 467–473.Google Scholar
Schils, RLM, Olesen, JE, Del Prado, A, Soussana, JF 2007. A review of farm level modelling approaches for mitigating greenhouse gas emissions from ruminant livestock systems. Livestock Science 112, 240–251.CrossRefGoogle Scholar
Schröder, JJ, Bannink, A, Kohn, RA 2005. Improving the efficiency of nutrient use on cattle operations. In Nitrogen and phosphorus nutrition in cattle (ed. E Pfeffer and A Hristov), pp. 255–279. CAB International, Wallingford, UK.Google Scholar
Šebek, LBJ, Van Riel, J, De Jong, G 2007. The breeding value for milk urea as predictor for the efficiency of protein utilization in dairy cows. Animal Sciences Group, Report 81. Lelystad, The Netherlands.Google Scholar
Spek, JW, Dijkstra, J, Van Duinkerken, G, Bannink, A 2013. A review of factors influencing milk urea concentration and its relationship with urinary urea excretion in lactating dairy cattle. Journal of Agricultural Science 151, 407–423.Google Scholar
Spek, JW, Bannink, A, Gort, G, Hendriks, WH, Dijkstra, J 2012. Effect of sodium chloride intake on urine volume, urinary urea excretion, and milk urea concentration in lactating dairy cattle. Journal of Dairy Science 95, 7288–7298.Google Scholar
Steinfeld, H, Gerber, P, Wassenaar, T, Castel, V, Rosales, M, de Haan, C 2006. Livestock's long shadow: environmental issues and options. FAO, Rome.Google Scholar
Van Knegsel, AT, Van den Brand, H, Dijkstra, J, Van Straalen, WM, Heetkamp, MJ, Tamminga, S, Kemp, B 2007. Dietary energy source in dairy cows in early lactation: energy partitioning and milk composition. Journal of Dairy Science 90, 1467–1476.Google Scholar
Van Vuuren, AM, Hindle, VA, Klop, A, Cone, JW 2010. Effect of maize starch concentration in the diet on starch and cell wall digestion in the dairy cow. Journal of Animal Physiology and Animal Nutrition 94, 319–329.Google Scholar
Van Zijderveld, SM, Gerrits, WJJ, Dijkstra, J, Newbold, JR, Hulshof, RBA, Perdok, HB 2011. Persistency of methane mitigation by dietary nitrate supplementation in dairy cows. Journal of Dairy Science 94, 4028–4038.Google Scholar
Vlaeminck, B, Fievez, V, Tamminga, S, Dewhurst, RJ, Van Vuuren, AM, DeBrabander, D, Demeyer, D 2006. Milk odd- and branched-chain fatty acids in relation to the rumen fermentation pattern. Journal of Dairy Science 89, 3954–3964.CrossRefGoogle Scholar
Zom, RLG, André, G, Van Vuuren, AM 2012. Development of a model for the prediction of feed intake by dairy cows: 1. Prediction of feed intake. Livestock Science 143, 43–57.Google Scholar