Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2025-01-03T18:31:29.192Z Has data issue: false hasContentIssue false
  • English
  • Français

Effects of changing cow production and fitness traits on profit and greenhouse gas emissions of UK dairy systems

Published online by Cambridge University Press:  09 September 2014

M. J. BELL ,
P. C. GARNSWORTHY ,
A. W. STOTT  and
J. E. PRYCE
M. J. BELL
Affiliation:
The University of Nottingham, School of Biosciences, Sutton Bonington Campus, Loughborough LE12 5RD, UK
P. C. GARNSWORTHY*
Affiliation:
The University of Nottingham, School of Biosciences, Sutton Bonington Campus, Loughborough LE12 5RD, UK
A. W. STOTT
Affiliation:
SRUC, West Mains Road, Edinburgh, EH9 3JG, UK
J. E. PRYCE
Affiliation:
Biosciences Research Division, Department of Primary Industries, Agribio, 5 Ringroad, Bundoora, Vic. 3083, Australia Futures Cooperative Research Centre, Bundoora, Victoria, 3083, Australia
*
*To whom all correspondence should be addressed. Email:[email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

The aim of the present study was to compare the effect of changing a range of biological traits on farm profit and greenhouse gas (GHG) emissions (expressed as carbon dioxide equivalent, CO2-eq.) in the UK dairy cow population. A Markov chain approach was used to describe the steady-state herd structure of the average milk-recorded UK dairy herd, as well as to estimate the CO2-eq. emissions per cow, and per kilogram of milk solids (MS). Effects of changing each herd production and fitness trait by one unit (e.g. 1 kg milk; 1% mastitis incidence) were assessed, with derived values for change in profit (economic values) being used in a multi-trait selection index. Of the traits studied, an increase in survival and reductions in milk volume, live weight, residual feed intake, somatic cell count, mastitis incidence, lameness incidence and calving interval were traits that would be both profitable and reduce CO2-eq. emissions per cow and per kg MS of a dairy herd. A multi-trait selection index was used to estimate the annual response in production and fitness traits and the economic response, with an estimate of annual profit per cow from selection on multiple traits. Milk volume, milk fat and protein yield, live weight, survival and dry matter intake were estimated to increase each year and body condition score, residual feed intake, somatic cell count, mastitis incidence, lameness incidence and calving interval were estimated to decrease, with selection on these traits estimated to result in an annual increase of 1% per year in GHG emissions per cow, but a reduction of 0·9% per unit product. Improved efficiencies of production associated with a reduction in milk volume (and increasing fat and protein content), live weight and feed intake (gross and metabolic efficiency, respectively), and increase in health, fertility and overall survival will increase farm annual profit of UK dairy systems and reduce their environmental impact.

Type
Modelling Animal Systems Research Papers
Information
The Journal of Agricultural Science , Volume 153 , Issue 1 , January 2015 , pp. 138 - 151
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Banos, G., Coffey, M. P., Wall, E. & Brotherstone, S. (2006). Genetic relationship between first-lactation body energy and later-life udder health in dairy cattle. Journal of Dairy Science 89, 2222–2232.CrossRefGoogle ScholarPubMed
Barker, Z.E (2007). Epidemiology of Lameness in Dairy Cows. PhD thesis, University of Warwick.Google Scholar
Bell, M., Wall, E., Russell, G., Simm, G. & Stott, A. (2011). The effect of improving cow productivity, fertility, and longevity on the global warming potential of dairy systems. Journal of Dairy Science 94, 3662–3678.CrossRefGoogle ScholarPubMed
Bell, M. J., Eckard, R. J. & Pryce, J. E. (2012). Breeding dairy cows to reduce greenhouse gas emissions. In Livestock Production (Ed. Javed, K.), pp. 47–58. Rijeka, Croatia: InTech Publishing.Google Scholar
Bell, M. J., Eckard, R. J., Haile-Mariam, M. & Pryce, J. E. (2013 a). The effect of changing cow production and fitness traits on net income and greenhouse gas emissions from Australian dairy systems. Journal of Dairy Science 96, 7918–7931.CrossRefGoogle ScholarPubMed
Bell, M. J., Eckard, R. J., Moate, P. J., Craigon, J. & Garnsworthy, P. C. (2013 b). Modelling the effect of diet composition on enteric methane emissions. Poster presentation. Advances in Animal Biosciences 4, 441.Google Scholar
Brown, K., Cardenas, L., MacCarthy, J., Murrells, T., Pang, Y., Passant, N., Thistlethwaite, G., Thomson, A. & Webb, N. (2010). UK Greenhouse Gas Inventory 1990 to 2010: Annual Report for Submission under the Framework Convention on Climate Change. London: Defra.Google Scholar
Capper, J. L., Cady, R. A. & Bauman, D. E. (2009). The environmental impact of dairy production: 1944 compared with 2007. Journal of Animal Science 87, 2160–2167.Google Scholar
Chagunda, M. G. G., Römer, D. A. M. & Roberts, D. J. (2009). Effect of genotype and feeding regime on enteric methane, non-milk nitrogen and performance of dairy cows during the winter feeding period. Livestock Science 122, 323–332.CrossRefGoogle Scholar
Cottle, D. J. & Coffey, M. P. (2013). The sensitivity of predicted financial and genetic gains in Holsteins to changes in the economic value of traits. Journal of Animal Breeding and Genetics 130, 41–54.Google Scholar
de Haas, Y., Veerkamp, R. F., Barkema, H. W., Gröhn, Y. T. & Schukken, Y. H. (2004). Associations between pathogen-specific cases of clinical mastitis and somatic cell count patterns. Journal of Dairy Science 87, 95–105.Google Scholar
de Haas, Y., Windig, J. J., Calus, M. P. L., Dijkstra, J., de Haan, M., Bannink, A. & Veerkamp, R. F. (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 Haas, Y., Calus, M. P. L., Veerkamp, R. F., Wall, E., Coffey, M. P., Daetwyler, H. D., Hayes, B. J. & Pryce, J. E. (2012). Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets. Journal of Dairy Science 95, 6103–6112.CrossRefGoogle ScholarPubMed
Emmans, G. C. (1994). Effective energy: a concept of energy utilisation applied across species. British Journal of Nutrition 71, 801–821.Google Scholar
FAO (2010). Greenhouse Gas Emissions from the Dairy Sector – A Life Cycle Assessment. Rome: FAO.Google Scholar
Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D. W., Haywood, J., Lean, J., Lowe, D. C., Myhre, G., Nganga, J., Prinn, R., Raga, G., Schulz, M. & Van Dorland, R. (2007). Changes in atmospheric constituents and in radiative forcing. In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (Eds Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M. & Miller, H. L.). Cambridge, UK and New York: Cambridge University Press.Google Scholar
Garnsworthy, P. C. (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.CrossRefGoogle Scholar
Garnsworthy, P. C., Craigon, J., Hernandez-Medrano, J. H. & 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
Guinée, J. B., Gorree, M., Heijungs, R., Huppes, G., Kleijn, R., de Koning, A., van Oers, L., Wegener Sleeswijk, A., Suh, S., Udo de Haes, H. A., de Bruijn, H., van Duin, R., Huijbregts, M. A. J., Lindeijer, E., Roorda, A. A. H., van der Ven, B. L. & Weidema, B. P. (2002). Handbook on Life Cycle Assessment: Operational Guide to the ISO Standards. Dordrecht: Kluwer Academic Publishers.Google Scholar
Hayes, B. J., van der Werf, J. H. J. & Pryce, J. E. (2011). Economic benefit of genomic selection for residual feed intake (as a measure of feed conversion efficiency) in Australian dairy cattle. Recent Advances in Animal Nutrition – Australia 18, 31–35.Google Scholar
Heikkilä, A. M., Nousiainen, J. I. & Pyörälä, S. (2012). Costs of clinical mastitis with special reference to premature culling. Journal of Dairy Science 95, 139–150.Google Scholar
Huhtanen, P., Rinne, M. & Nousiainen, J. (2009). A meta-analysis of feed digestion in dairy cows. 2. The effects of feeding level and diet composition on digestibility. Journal of Dairy Science 92, 5031–5042.Google Scholar
Intergovernmental Panel on Climate Change (IPCC) (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Agriculture, Forestry and Other Land Use. Vol. 4: Agriculture, Forestry and Other Land Use (Eds Eggleston, S., Buendia, L., Miwa, K., Ngara, T. & Tanabe, K.). Hayama, Japan: Institute for Global Environmental Strategies (IGES).Google Scholar
Jones, H. E., Warkup, C. C., Williams, A. & Audsley, E. (2008). The effect of genetic improvement on emission from livestock systems. In Book of Abstracts of the 59th Annual Meeting of the European Association for Animal Production, 24–27 August, Vilnius, Lithuania, p. 28. Wageningen, The Netherlands: Wageningen Academic Publishers.Google Scholar
Kossaibati, M. A. & Esslemont, R. J. (1997). The cost of production diseases in dairy herds in England. Veterinary Journal 154, 41–51.Google Scholar
Moran, D., Barnes, A. & McVittie, A. (2007). The Rationale for Defra Investment in R&D Underpinning the Genetic Improvement of Crops and Animals (IF0101). Final Report to Defra. London: Defra.Google Scholar
Minson, D. J. & McDonald, C. K. (1987). Estimating forage intake from the growth of beef cattle. Tropical Grasslands 21, 116–122.Google Scholar
NRC (2001). Nutrient Requirements of Dairy Cattle, 7th edn. Washington, DC: National Academies Press.Google Scholar
Pritchard, T., Coffey, M., Mrode, R. & Wall, E. (2013). Genetic parameters for production, health, fertility and longevity traits in dairy cows. Animal 7, 34–46.Google Scholar
Pryce, J. E., van der Werf, J. H. J., Haile-Mariam, M., Malcolm, B. & Goddard, M. E. (2009). Updated index weights for the Australian Profit Ranking in dairy. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 18, 143–146.Google Scholar
Redman, G. (2012). The John Nix Farm Management Pocketbook 2012. Melton Mowbray: Agro Business Consultants Ltd.Google Scholar
Robertson, A. & Rendel, J. M. (1950). The use of progeny testing with artificial insemination with dairy cattle. Journal of Genetics 50, 21–31.Google Scholar
Rutherford, K. M. D., Langford, F. M., Jack, M. C., Sherwood, L., Lawrence, A. B. & Haskell, M. J. (2009). Lameness prevalence and risk factors in organic and non-organic dairy herds in the United Kingdom. Veterinary Journal 180, 95–105.Google Scholar
Stott, A. W., Veerkamp, R. F. & Wassell, T. R. (1999). The economics of fertility in the dairy herd. Animal Science 68, 49–57.Google Scholar
Stott, A. W., Coffey, M. P. & Brotherstone, S. (2005). Including lameness and mastitis in a profit index for dairy cattle. Animal Science 80, 41–52.Google Scholar
Takeda, H. & Kiriyama, S. (1979). Correlation between the physical properties of dietary fibers and their protective activity against amaranth toxicity in rats. Journal of Nutrition 109, 388–396.Google Scholar
Vallimont, J. E., Dechow, C. D., Daubert, J. M., Dekleva, M. W., Blum, J. W., Barlieb, C. M., Liu, W., Varga, G. A., Heinrichs, A. J. & Baumrucker, C. R. (2010). Genetic parameters of feed intake, production, body weight, body condition score, and selected type traits of Holstein cows in commercial tie-stall barns. Journal of Dairy Science 93, 4892–4901.Google Scholar
Vallimont, J. E., Dechow, C. D., Daubert, J. M., Dekleva, M. W., Blum, J. W., Liu, W., Varga, G. A., Heinrichs, A. J. & Baumrucker, C. R. (2013). Feed utilization and its associations with fertility and productive life in 11 commercial Pennsylvania tie-stall herds. Journal of Dairy Science 96, 1251–1254.Google Scholar
van de Haar, M. J. & St Pierre, N. (2006). Major advances in nutrition: relevance to the sustainability of the dairy industry. Journal of Dairy Science 89, 1280–1291.Google Scholar
Veerkamp, R. F. (1998). Selection for economic efficiency of dairy cattle using information on live weight and feed intake: A review. Journal of Dairy Science 81, 1109–1119.Google Scholar
Veerkamp, R. F. & Brotherstone, S. (1997). Genetic correlations between linear type traits, food intake, live weight and condition score in Holstein Friesian dairy cattle. Animal Science 64, 385–392.Google Scholar
Veerkamp, R. F., Emmans, G. C., Cromie, A. R. & Simm, G. (1995). Variance components for residual feed intake in dairy cows. Livestock Production Science 41, 111–120.Google Scholar
Visscher, P. M., Bowman, P. J. & Goddard, M. E. (1994). Breeding objectives for pasture based dairy production systems. Livestock Production Science 40, 123–137.Google Scholar
Wainman, F. W., Dewy, P. J. S. & Boyne, A. W. (1981). Feedingstuffs Evaluation Unit Report 3: Compound Feedingstuffs for Ruminants. Aberdeen, UK: Rowett Research Institute.Google Scholar
Wall, E., Ludemann, C., Jones, H., Audsley, E., Moran, D., Roughsedge, T. & Amer, P. (2010 a). The Potential for Reducing Greenhouse Gas Emissions for Sheep and Cattle in the UK using Genetic Selection (FGG0808). Final Report to Defra. London: Defra.Google Scholar
Wall, E., Simm, G. & Moran, D. (2010 b). Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal 4, 366–376.Google Scholar
Wilkinson, J. M. & Audsley, E. (2013) Options from life-cycle analysis for reducing greenhouse gas emissions from crop and livestock production systems. International Journal of Agricultural Management 2, 70–80.Google Scholar
Willshire, J. A. & Bell, N. J. (2009). An economic review of cattle lameness. Cattle Practice 17, 136–141.Google Scholar
Williams, Y. J., Pryce, J. E., Grainger, C., Wales, W. J., Linden, N., Porker, M. & Hayes, B. J. (2011). Variation in residual feed intake in Holstein-Friesian dairy heifers in southern Australia. Journal of Dairy Science 94, 4715–4725.Google Scholar
Wright, I. A. (1982). Studies on the body composition of beef cows. Ph.D. Thesis, University of Edinburgh.Google Scholar