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Evidence for increasing digestive and metabolic efficiency of energy utilization with age of dairy cattle as determined in two feeding regimes

Published online by Cambridge University Press:  24 July 2017

F. Grandl
ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zurich, Switzerland
J. O. Zeitz
Institute of Animal Nutrition and Nutritional Physiology, Justus-Liebig-University Giessen, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
M. Clauss
Vetsuisse Faculty, Clinic for Zoo Animals, Exotic Pets and Wildlife, University of Zurich, Winterthurerstrasse 260, 8057 Zurich, Switzerland
M. Furger
Agricultural Education and Advisory Centre Plantahof, Kantonsstrasse 17, 7302 Landquart, Switzerland
M. Kreuzer*
ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zurich, Switzerland
A. Schwarm
ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zurich, Switzerland
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The changes taking place with age in energy turnover of dairy cattle are largely unknown. It is unclear whether the efficiency of energy utilization in digestion (characterized by faecal and methane energy losses) and in metabolism (characterized by urine and heat energy losses) is altered with age. In the present study, energy balance data were obtained from 30 lactating Brown Swiss dairy cows aged between 2 and 10 years, and 12 heifers from 0.5 to 2 years of age. In order to evaluate a possible dependence of age effects on diet type, half of the cattle each originated from two herds kept at the same farm, which were fed either on a forage-only diet or on the same forage diet but complemented with 5 kg/day of concentrate since their first calving. During 2 days, the gaseous exchange of the animals was quantified in open-circuit respiration chambers, followed by an 8-day period of feed, faeces, urine and milk collection. Daily amounts and energy contents were used to calculate complete energy balances. Age and feeding regime effects were analysed by parametric regression analysis where BW, milk yield and hay proportion in forage as consumed were considered as covariates. Relative to intake of gross energy, the availability of metabolizable energy (ME) increased with age. This was not the result of an increasing energy digestibility, but of proportionately lower energy losses with methane (following a curvilinear relationship with the greatest losses in middle-aged cows) and urine (continuously declining). The efficiency of utilization of ME for milk production (kl) increased with age. Potential reasons include an increase in the propionate-to-acetate ratio in the rumen because of a shift away from fibre degradation and methane formation as well as lower urine energy losses. The greater kl allowed older cows to accrete more energy reserves in the body. As expected, offering concentrate enhanced digestibility, metabolizability and metabolic utilization of energy. Age and feeding regime did not interact significantly. In conclusion, older cows seem to have digestive and metabolic strategies to use dietary energy to a certain degree more efficiently than younger cows.

Research Article
© The Animal Consortium 2017 

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Agnew, RE and Yan, T 2000. Impact of recent research on energy feeding systems for dairy cattle. Livestock Production Science 66, 197215.CrossRefGoogle Scholar
Agroscope 2017. Feeding recommendations and nutrient tables for ruminants (in German). Online version. Retrieved on 15 February 2017 from Scholar
Akaike, H 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716723.CrossRefGoogle Scholar
Allen, MS 2000. Effects of diet on short-term regulation of feed intake by lactating dairy cattle. Journal of Dairy Science 83, 15981624.CrossRefGoogle ScholarPubMed
Association of Official Analytical Chemists (AOAC) 1995. Official methods of analysis. AOAC International, Arlington, VA, USA.Google Scholar
Bell, MJ, Eckard, RE, Haile-Mariam, M and Pryce, JE 2013. 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, 79187931.CrossRefGoogle ScholarPubMed
Blaxter, KL and Boyne, AW 1978. The estimation of the nutritive value of feeds as energy sources for ruminants and the derivation of feeding systems. Journal of Agricultural Science 90, 4768.CrossRefGoogle Scholar
Brouwer, E 1965. Report of subcommittee on constants and factors. In Energy metabolism of farm animals. Third symposium on energy metabolism (ed. KL Blaxter), pp. 441443. EAAP Publication no. 11. Academic Press, London, UK.Google Scholar
Buehler, K and Wanner, M 2014. Chapter 6: Metabolic centre of the University of Zurich and ETH Zurich (under construction). In Technical manual on respiration chamber designs (eds. C Pinares and G Waghorn), pp. 89106. Ministry of Agriculture and Forestry, Wellington, New Zealand. Retrieved on 21 February 2017 from Scholar
Calcagno, V 2013. glmulti: Model selection and multimodel inference made easy. Retrieved on 21 February 2017 from Scholar
Cassidy, ES, West, PC, Gerber, JS and Foley, JA 2013. Redefining agricultural yields: from tonnes to people nourished per hectare. Environmental Research Letters 8, 034015.CrossRefGoogle Scholar
Chwalibog, A, Jensen, K and Thorbek, G 1996. Oxidation of nutrients in bull calves treated with β-adrenergic agonists. Archives of Animal Nutrition 49, 255261.Google ScholarPubMed
Coppock, CE, Flatt, WP, Moore, LA and Stewart, WE 1964. Effect of hay to grain ratio on utilization of metabolizable energy for milk production by dairy cows. Journal of Dairy Science 47, 13301338.CrossRefGoogle Scholar
de Vries, A 2015. Culling/longevity versus genetic progress from heifers. WCDS Advances in Dairy Technology 27, 345355.Google Scholar
Dijkstra, J, Oenema, O, van Groenigen, JW, Spek, JW, van Vuuren, AM and Bannink, A 2013. Diet effects on urine composition of cattle and N2O emissions. Animal 7 (suppl. 2), 292302.CrossRefGoogle ScholarPubMed
Dong, LF, Ferris, CP, McDowell, DA and Yan, T 2015. Effects of diet forage proportion on maintenance energy requirement and the efficiency of metabolizable energy use for lactation by lactating dairy cows. Journal of Dairy Science 98, 88468855.CrossRefGoogle ScholarPubMed
Ferris, CP, Gordon, FJ, Patterson, DC, Porter, MG and Yan, T 1999. The effect of genetic merit and concentrate proportion in the diet on nutrient utilization by lactating dairy cows. The Journal of Agricultural Science 132, 483490.CrossRefGoogle Scholar
Fox, J 2003. Effect displays in R for generalised linear models. Journal of Statistical Software 8, 127.CrossRefGoogle Scholar
Freetly, HC, Nienaber, JA and Brown-Brandl, T 2006. Changes in heat production by mature cows after changes in feeding level. Journal of Animal Science 84, 14291438.CrossRefGoogle ScholarPubMed
Gesellschaft für Ernährungsphysiologie, Ausschuss für Bedarfsnormen (GfE) 2001. Empfehlungen zur Energie und Nährstoffversorgung der Milchkühe und Aufzuchtrinder, 8th revised edition. DLG-Verlag, Frankfurt, Germany.Google Scholar
Grandl, F, Luzi, SP, Furger, M, Zeitz, JO, Leiber, F, Ortmann, S, Clauss, M, Kreuzer, M and Schwarm, A 2016a. Biological implications of longevity in dairy cows: 1. Changes in feed intake, feeding behavior and digestion with age. Journal of Dairy Science 99, 34573471.CrossRefGoogle ScholarPubMed
Grandl, F, Amelchanka, SL, Furger, M, Clauss, M, Zeitz, JO, Kreuzer, M and Schwarm, A 2016b. Biological implications of longevity in dairy cows: 2. Changes in methane emissions and efficiency with age. Journal of Dairy Science 99, 34723485.CrossRefGoogle ScholarPubMed
Hammond, KJ, Humphries, DJ, Crompton, LA, Kirton, P and Reynolds, CK 2015. Effects of forage source and extruded linseed supplementation on methane emissions from growing dairy cattle of differing body weights. Journal of Dairy Science 98, 80668077.CrossRefGoogle ScholarPubMed
Hammond, KJ, Humphries, DJ, Westbury, DB, Thompson, A, Crompton, LA, Kirton, P, Green, C and Reynolds, CK 2014. The inclusion of forage mixtures in the diet of growing dairy heifers: impacts on digestion, energy utilisation, and methane emissions. Agriculture, Ecosystems and Environment 197, 8895.CrossRefGoogle Scholar
Hoffmann, L and Klein, M 1980. Die Abhängigkeit der Harnenergie vom Kohlenstoff und Stickstoffgehalt im Harn bei Rindern, Schafen, Schweinen und Ratten. Archiv für Tierernaehrung 30, 743750.CrossRefGoogle Scholar
Horn, M, Knaus, W, Kirner, L and Steinwidder, A 2012. Economic evaluation of longevity in organic dairy cows. Organic Agriculture 2, 127143.CrossRefGoogle Scholar
Huhtanen, P, Miettinen, H and Ylinen, M 1993. Effect of increasing ruminal butyrate on milk yield and blood constituents in dairy cows fed a grass silage-based diet. Journal of Dairy Science 76, 11141124.CrossRefGoogle ScholarPubMed
Jiao, HP, Yan, T, Wills, DA and McDowell, DA 2015. Maintenance energy requirements of young Holstein cattle from calorimetric measurements at 6, 12, 18, and 22 months of age. Livestock Science 178, 150157.CrossRefGoogle Scholar
Kebreab, E, France, J, Agnew, RE, Yan, T, Dhanoa, MS, Dijkstra, J, Beever, DE and Reynolds, CK 2003. Alternatives to linear analysis of energy balance data from lactating dairy cows. Journal of Dairy Science 86, 29042913.CrossRefGoogle ScholarPubMed
Knaus, W 2009. Dairy cows trapped between performance demands and adaptability. Journal of the Science of Food and Agriculture 89, 11071114.CrossRefGoogle Scholar
Knaus, W 2013. Re-thinking dairy cow feeding in light of food security. AgroLife Scientific Journal 2, 3640.Google Scholar
Moss, A, Jouany, JP and Newbold, J 2000. Methane production by ruminants: its contribution to global warming. Annales de Zootechnie 49, 231253.CrossRefGoogle Scholar
Mowrey, A and Spain, JN 1999. Results of a nationwide survey to determine feedstuffs fed to lactating dairy cows. Journal of Dairy Science 82, 445451.CrossRefGoogle ScholarPubMed
R Core Team 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Scholar
Reynolds, CK, Crompton, LA and Mills, JAN 2011. Improving the efficiency of energy utilisation in cattle. Animal Production Science 51, 612.CrossRefGoogle Scholar
Reynolds, CK, Tyrrell, HF and Reynolds, PJ 1991. Effects of diet forage-to-concentrate ratio and intake on energy metabolism in growing beef heifers: whole body energy and nitrogen balance and visceral heat production. The Journal of Nutrition 121, 9941003.CrossRefGoogle ScholarPubMed
Strathe, AB, Dijkstra, J, France, J, Lopez, S, Yan, T and Kebreab, E 2011. A Bayesian approach to analyze energy balance data from lactating dairy cows. Journal of Dairy Science 94, 25202531.CrossRefGoogle ScholarPubMed
Symonds, MRE and Moussalli, A 2011. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behavioral Ecology and Sociobiology 63, 1321.CrossRefGoogle Scholar
Van Es, AJH 1975. Feed evaluation for dairy cows. Livestock Production Science 2, 95107.CrossRefGoogle Scholar
Yan, T, Gordon, FJ, Agnew, RE, Porter, MG and Patterson, DC 1997. The metabolisable energy requirement for maintenance and the efficiency of utilisation of metabolisable energy for lactation by dairy cows offered grass silage-based diets. Livestock Production Science 51, 141150.CrossRefGoogle Scholar
Zehetmeier, M, Hoffmann, H, Sauer, J, Hofmann, G, Dorfner, G and O’Brien, D 2014. A dominance analysis of greenhouse gas emissions, beef output and land use of German dairy farms. Agricultural Systems 129, 5567.CrossRefGoogle Scholar