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Breeding strategies to reduce environmental footprint in dairy cattle

Published online by Cambridge University Press:  27 September 2013

Donagh P. Berry
Donagh P. Berry*
Affiliation:
Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland
*
†E-mail: [email protected]
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Abstract

Animal breeding should be considered as a permanent and cumulative approach to reducing the environmental footprint of dairy cattle production systems within an overall national and global mitigation strategy. Current international dairy cattle breeding goals do not explicitly include environmental traits, but observed improvements in milk production and both fertility and longevity contribute substantially to improving the environmental footprint relative to output. Ideally, however, environmental related traits, most notably greenhouse gas emissions and nitrogen excretion, should be explicitly included in national breeding goals with their own economic weight. Access to routine phenotypic observations for the environmental traits or other information including genomic information or information on heritable correlated traits is required for inclusion in the selection index. There is, however, a considerable paucity of information on the genetic parameters for, in particular, greenhouse gas emissions in dairy cattle; these parameters include genetic variance estimates, as well as genetic and phenotypic (co)variances with other performance traits. Large studies with well phenotyped animals across a range of environments are needed to estimate such parameters and also investigate the extent, if any, of genotype-by-environment interactions across contrasting environments. Considerable genetic variation in milk urea nitrogen, as a proxy for nitrogen excretion in the urine, exist and suggest that breeding programmes to improve nitrogen use efficiency will be fruitful. However, because of the antagonistic genetic correlations between milk urea nitrogen and milk production, genetic gain in milk yield is expected to be compromised within a breeding goal that includes milk urea nitrogen.

Keywords

environmentgeneticgreenhouse gasnitrogen
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. 28 - 36
Copyright
Copyright © The Animal Consortium 2013 

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References

Arunvipas, P, VanLeeuwen, JA, Dohoo, IR, Keefe, GP 2003. Evaluation of the reliability and repeatability of automated milk urea nitrogen testing. Canadian Journal of Veterinary Research 67, 60–63.Google Scholar
Bannink, A, France, J, Lopez, S, Gerrits, WJJ, Kenreab, E, Tamminga, S, Dijkstra, J 2008. Modelling the implications of feed strategy on rumen fermentation and functioning of the rumen wall. Animal Feed Science and Technology 143, 3–26.Google Scholar
Bastin, C, Berry, DP, Soyeurt, H, Gengler, N 2012. Genetic correlations of days open with production traits and contents in milk of major fatty acids predicted by mid-infrared spectrometry. Journal of Dairy Science 95, 6113–6121.Google Scholar
Berry, DP, Cromie, AR 2009. Associations between age at first calving and subsequent performance in Irish spring calving Holstein-Friesian dairy cows. Livestock Science 123, 44–54.Google Scholar
Berry, DP, Crowley, JJ 2013. Genetics of feed efficiency in dairy and beef cattle. Journal of Animal Science 91, 1594–1613.CrossRefGoogle ScholarPubMed
Berry, DP, Bermingham, M, Good, M, More, SJ 2011a. Genetics of animal health and disease in cattle. Irish Veterinary Journal 64, 5.Google Scholar
Berry, DP, Kearney, JF, Twomey, K, Evans, RD 2013. Genetics of reproductive performance in seasonal calving dairy cattle production systems. The Irish Journal of Agricultural and Food Research 52, 1–16.Google Scholar
Berry, DP, Buckley, F, Dillon, PG, Evans, RD, Rath, M, Veerkamp, RF 2003a. Estimation of genotype x environment interactions, in a grass-based system, for milk yield, body condition score, and body weight using random regression models. Livestock Production Science 83, 191–203.Google Scholar
Berry, DP, Buckley, F, Dillon, PG, Evans, RD, Rath, M, Veerkamp, RF 2003b. Genetic relationships among body condition score, body weight, milk yield and fertility in dairy cows. Journal of Dairy Science 86, 2193–2204.Google Scholar
Berry, DP, Meade, G, Butler, S, Diskin, MG, Morris, DG, Creevey, CJ 2011b. The integration of omic disciplines and systems biology in cattle breeding. Animal 5, 493–505.Google Scholar
Berry, DP, Horan, B, O'Donovan, M, Buckley, F, Kennedy, E, McEvoy, M, Dillon, PG 2007a. Genetics of grass dry matter intake, energy balance, and digestibility in grazing Irish dairy cows. Journal of Dairy Science 90, 4835–4845.CrossRefGoogle ScholarPubMed
Berry, DP, Shalloo, L, Cromie, AR, Olori, V, Veerkamp, RF, Dillon, P, Amer, PR, Evans, RD, Kearney, JF, Wickham, B 2007b. The economic breeding index: a generation on. Technical Report to the Irish Cattle Breeding Federation. Retrieved September 16, 2012, from http://www.icbf.com/publications/files/The_Economic_breeding_a_generation_on_Dec_2007.pdf Google Scholar
Cameron, ND 1997. Selection indices and prediction of genetic merit in animal breeding. CAB International, Wallingford, UK.Google Scholar
Chilliard, Y, Martin, C, Rouel, J, Doreau, M 2009. Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with CH4 output. Journal of Dairy Science 92, 5199–5211.CrossRefGoogle ScholarPubMed
Ciszuk, AU, Gebregziabher, T 1994. Milk urea as an estimate of urine nitrogen of dairy cows and goats. Acta Agriculturae Scandinavica 44, 87–95.Google Scholar
Coleman, J, Pierce, KM, Berry, DP, Brennan, A, Horan, B 2009. The influence of genetic selection and feed system on the reproductive performance of spring-calving dairy cows within future pasture-based production systems. Journal of Dairy Science 92, 5258–5269.Google Scholar
Crowley, JJ, McGee, M, Kenny, DA, Crews, DH Jr, Evans, RD, Berry, DP 2010. Phenotypic and genetic parameters for different measures of feed efficiency in different breeds of Irish performance tested beef bulls. Jouranl of Animal Science 88, 885–894.Google Scholar
Daetwyler, HD, Villanueva, B, Woolliams, JA 2008. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 3, e3395.CrossRefGoogle ScholarPubMed
De Haas, Y, Windig, JJ, Calus, MPL, Dijkstra, J, De Haan, M, Bannink, A, veerkamp, RF 2011. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. Journal of Dairy Science 94, 6122–6134.Google Scholar
De Marchi, M, Bonfatti, V, Cecchinato, A, Di Martino, G, Carnier, P 2009b. Prediction of protein composition of individual cow milk using mid-infrared spectroscopy. Italian Journal of Animal Science 8, 399–401.Google Scholar
De Marchi, M, Fagan, CC, O'Donnell, CP, Cecchinato, A, Dal Zotto, R, Cassandro, M, Penasa, M, Bittante, G 2009a. Prediction of coagulation properties, titratable acidity, and pH of bovine milk using mid-infrared spectroscopy. Journal of Dairy Science 92, 423–432.CrossRefGoogle ScholarPubMed
De Stoop, WM, Bovenhuis, H, van Arendonk, JAM 2007. Genetic parameters for milk urea nitrogen in relation to milk production traits. Journal of Dairy Science 90, 1981–1986.CrossRefGoogle Scholar
Dehareng, F, Delfosse, C, Froidmont, E, Soyeurt, H, Martin, C, Gengler, N, Vanlierde, A, Dardenne, P 2012. Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows. Animal 6, 1694–1701.Google Scholar
Demeyer, D, Fievez, V 2000. Ruminants et environment: la méthanogenése. Annales de Zootechie 49, 95–112.Google Scholar
Durunna, ON, Colazo, MG, Ambrose, DJ, McCartney, D, Baron, VS, Basarb, JA 2012. Evidence of residual feed intake reranking in crossbred replacement heifers. Journal of Animal Science (in press).Google Scholar
Durunna, ON, Mujibi, FDN, Goonewardene, L, Okine, EK, Basarab, JA, Wang, Z, Moore, SS 2011. Feed efficiency differences and reranking in beef steers fed grower and finisher diets. Journal of Animal Science 89, 158–167.CrossRefGoogle ScholarPubMed
Falconer, DS, Mackay, TFC 1996. Introduction to quantitative genetics, 4th edition. Longman, Essex, UK.Google Scholar
FAO 2009. Declaration of the World Food Summit on Food Security, Rome, 16–18 November 2009. Rome: FAO. Retrieved September 25, 2010, from ftp://ftp.fao.org/docrep/fao/Meeting/018/k6050e.pdf Google Scholar
Garnsworthy, PC 2004. The environmental impact of fertility in dairy cows: a modelling approach to predict methane and ammonia emissions. Animal Feed Science and Technology 112, 211–223.Google Scholar
Goopy, JP, Hegarty, RS 2004. Repeatability of methane production in cattle fed concentrate and forage diets. Journal of Animal Feed and Science 13, 75–78.Google Scholar
Gunsett, FC 1984. Linear index selection to improve traits defined as ratios. Journal of Animal Science 59, 1185–1193.Google Scholar
Gustafsson, AH, Palmquist, DL 1993. Diurinal variation of rumen ammonia, serum urea, and milk urea in dairy cows and high and low yields. Journal of Dairy Science 76, 475–484.Google Scholar
Hayes, BJ, Bowman, PJ, Chamberlain, AJ, Goddard, ME 2009. Invited review: genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science 92, 433–443.Google Scholar
Hegarty, RS, Goopy, JP, herd, RM, McCorkell, B 2007. Cattle elected for lower residual feed intake have reduced daily methane production. Journal of Animal Science 85, 1479–1486.CrossRefGoogle Scholar
Huntington, GB, Leonard, ES, Burns, JC 2011. Technical Note: Use of near-infrared reflectance spectroscopy to predict intake and digestibility in bulls and steers. Journal of Animal Science 89, 1163–1166.Google Scholar
Johnson, KA, Johnson, DE 1995. Methane emissions from cattle. Journal of Animal Science 73, 2483–2492.Google Scholar
Jonker, JS, Kohn, RA, Erdman, RA 1998. Using milk urea nitrogen to predict nitrogen excretion and utilisation efficiency in lactating dairy cows. Journal of Dairy Science 81, 2681–2692.Google Scholar
Koch, RM, Swiger, LA, Chambers, D, Gregory, KE 1963. Efficiency of feed use in beef cattle. Journal of Animal Science 22, 486–494.Google Scholar
Koenen, EPC, Veerkamp, RF 1998. Genetic covariance functions for live weight, condition score, and dry-matter intake measured at different lactation stages of Holstein-Friesian heifers. Livestock Production Science 57, 67–77.Google Scholar
König, S, Chang, YM, Borstel, UUv, Gianola, D, Simianer, H 2008. Genetic and phenotypic relationships among milk urea nitrogen, fertility, and milk yield in Holstein cows. Journal of Dairy Science 91, 4372–4382.Google Scholar
Lassen, J, Løvendahl, P, Madsen, J 2012. Accuracy of noninvasive breath measurements using Fourier transform infrared methods on individual cows. Journal of Dairy Science 95, 890–898.Google Scholar
McGinn, SM, Beauchemin, KA, Iwaasa, AD, McAllister, TA 2006. Assessment of the sulfur hexafluoride (SF6) tracer technique for measuring enteric methane emissions from cattle. Journal of Environmental Quality 35, 1686–1691.CrossRefGoogle ScholarPubMed
McParland, S, Banos, G, Wall, E, Coffey, MP, Soyeurt, H, Veerkamp, RF, Berry, DP 2011. The use of mid-infrared spectrometry to predict body energy status of Holstein cows. Journal of Dairy Science 94, 3651–3661.Google Scholar
McParland, S, Banos, G, McCarthy, B, Lewis, E, Coffey, MP, O'Neill, B, O'Donovan, M, Wall, E, Berry, DP 2012. Validation of mid-infrared spectrometry in milk for predicting body energy status in Holstein-Friesian cows. Journal of Dairy Science (in press).Google Scholar
Meuwissen, T, Hayes, B, Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.Google Scholar
Miglior, F, Muir, BL, Van Doormaal, BJ 2005. Selection indices in Holstein cattle of various countries. Journal of Dairy Science 88, 1255–1263.Google Scholar
Miglior, F, Sewalem, A, Jamrozik, J, Bohmanova, J, Lefebvre, DM, Moore, RK 2007. Genetic analysis of milk urea nitrogen and lactose and their relationship with other production traits in Canadian Holstein cattle. Journal of Dairy Science 90, 2468–2479.Google Scholar
Mitchell, RG, Rogers, GW, Dechow, CD, Vallimont, JE, Cooper, JB 2005. Milk urea nitrogen concentration: heritability and genetic correlations with reproductive performance and disease. Journal of Dairy Science 88, 4434–4440.Google Scholar
Mucha, S, Strandberg, E 2011. Genetic analysis of milk urea nitrogen and relationships with yield and fertility across lactation. Journal of Dairy Science 94, 5665–5672.Google Scholar
Nielsen, HM, Amer, PR 2007. An approach to derive economic weights in breeding objectives using partial profile choice experiments. Animal 1, 1254–1262.Google Scholar
O'Brien, D, Shalloo, L, Grainger, C, Buckley, F, Horan, B, Wallace, M 2010. the influence of strain of Holstein-Friesian cow and feeding system on greenhouse gas emissions from pastoral dairy farms. Journal of Dairy Science 93, 3390–3402.Google Scholar
O'Mara, FP 2011. The significance of livestock as a contributor to global greenhouse gas emissions today and the near future. Animal Feed Science and Technology 166–167, 7–15.Google Scholar
Rendel, J, Robertson, A 1950. Estimation of genetic gain in milk yield by selection in a closed herd of dairy cattle. Journal of Genetics 50, 1–8.Google Scholar
Roche, JR, Friggens, NC, Kay, JK, Fisher, MW, Stafford, KJ, Berry, DP 2009. Body condition score and its association with dairy cow productivity, health and welfare. Journal of Dairy Science 92, 5769–5801.Google Scholar
Sellner, EM, Kim, JW, McClure, MC, Taylor, KH, Schnabel, RD, Taylor, JF 2007. Applications of genomic information in livestock. Journal of Animal Science 85, 3148–3158.Google Scholar
Shalloo, L, Dillon, P, Rath, M, Wallace, M 2004. Description and validation of the Moorepark dairy systems model (MDSM). Journal of Dairy Science 87, 1945–1959.Google Scholar
Soyeurt, H, Dehareng, F, Gengler, N, McParland, S, Wall, E, Berry, DP, Coffey, M 2011. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. Journal of Dairy Science 94, 1657–1667.CrossRefGoogle ScholarPubMed
Thackaberry, MH. Deighton, BM. O'Loughlin, TM, Boland, KM Pierce, Buckley, F. 2011. A comparison of methane emissions by Holstein-Friesian, Jersey and Jersey × Holstein-Friesian dairy cows under varying stocking rates. In proceedings of Agricultural Research Forum 2011, Tullamore Court Hotel, Tullamore, C. Offaly, 14th and 15th March, ISBN-13 978-1-84170-573-6.Google Scholar
Van Es, AJH 1978. feed evaluation for ruminants. 1. Systems in use from May 1977 onwards in Netherlands. Livestock Production Science 5, 331–345.Google Scholar
Van Raden, PM, Van Tassell, CP, Wiggans, GR, Schnabel RD Talyor, JF, Schenkel, FS 2009. Invited Review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16–24.Google Scholar
Veerkamp, RF, Beerda, B 2007. Genetics and genomics to improve fertility in high producing dairy cows. Theriology 68S, S266–S273.Google Scholar
Vlaming, JB, Lopez-Villalobos, N, Brookes, IM, Hoskin, SO, 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
Weiske, A, Vabitsch, A, Olsen, JE, Schelde, K, Michel, J, Friedrich, R, Kaltschmitt, M 2006. Mitigation of greenhouse gas emissions in European conventional and organic dairy farming. Agriculture Ecosystems & Environment 112, 221–232.Google Scholar
Wood, GM, Boettcher, PJ, Jamrozik, J, Jansen, GB, Kelton, DF 2003. Estimation of genetic parameters for concentrations of milk urea nitrogen. Journal of Dairy Science 86, 2462–2469.Google Scholar