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Isolating the cow-specific part of residual energy intake in lactating dairy cows using random regressions

Published online by Cambridge University Press:  11 December 2017

A. Fischer ,
N. C. Friggens ,
D. P. Berry  and
P. Faverdin
A. Fischer
Affiliation:
Department of Animal Husbandry Techniques and Environment, Institut de l’élevage, Monvoisin, 35650 Le Rheu, France UMR 1348 PEGASE, INRA, Agrocampus-Ouest, 16 Le Clos, 35590 Saint-Gilles, France
N. C. Friggens
Affiliation:
INRA UMR 0791 Modélisation Systémique Appliquée aux Ruminants, INRA, AgroParisTech, Université Paris-Saclay, 75005 Paris, France
D. P. Berry
Affiliation:
Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy Co. Cork, Ireland
P. Faverdin*
Affiliation:
UMR 1348 PEGASE, INRA, Agrocampus-Ouest, 16 Le Clos, 35590 Saint-Gilles, France
*
†E-mail: [email protected]
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Abstract

The ability to properly assess and accurately phenotype true differences in feed efficiency among dairy cows is key to the development of breeding programs for improving feed efficiency. The variability among individuals in feed efficiency is commonly characterised by the residual intake approach. Residual feed intake is represented by the residuals of a linear regression of intake on the corresponding quantities of the biological functions that consume (or release) energy. However, the residuals include both, model fitting and measurement errors as well as any variability in cow efficiency. The objective of this study was to isolate the individual animal variability in feed efficiency from the residual component. Two separate models were fitted, in one the standard residual energy intake (REI) was calculated as the residual of a multiple linear regression of lactation average net energy intake (NEI) on lactation average milk energy output, average metabolic BW, as well as lactation loss and gain of body condition score. In the other, a linear mixed model was used to simultaneously fit fixed linear regressions and random cow levels on the biological traits and intercept using fortnight repeated measures for the variables. This method split the predicted NEI in two parts: one quantifying the population mean intercept and coefficients, and one quantifying cow-specific deviations in the intercept and coefficients. The cow-specific part of predicted NEI was assumed to isolate true differences in feed efficiency among cows. NEI and associated energy expenditure phenotypes were available for the first 17 fortnights of lactation from 119 Holstein cows; all fed a constant energy-rich diet. Mixed models fitting cow-specific intercept and coefficients to different combinations of the aforementioned energy expenditure traits, calculated on a fortnightly basis, were compared. The variance of REI estimated with the lactation average model represented only 8% of the variance of measured NEI. Among all compared mixed models, the variance of the cow-specific part of predicted NEI represented between 53% and 59% of the variance of REI estimated from the lactation average model or between 4% and 5% of the variance of measured NEI. The remaining 41% to 47% of the variance of REI estimated with the lactation average model may therefore reflect model fitting errors or measurement errors. In conclusion, the use of a mixed model framework with cow-specific random regressions seems to be a promising method to isolate the cow-specific component of REI in dairy cows.

Keywords

feed efficiencymixed modelcattlelactationindividual variation
Type
Research Article
Information
animal , Volume 12 , Issue 7 , July 2018 , pp. 1396 - 1404
Copyright
© The Animal Consortium 2017 

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References

Aggrey, SlE and Rekaya, R 2013. Dissection of Koch’s residual feed intake: implications for selection. Poultry Science 92, 2600–2605.CrossRefGoogle ScholarPubMed
Bazin, S 1984. Grille de notation de l'état d’engraissement des vaches Pie Noires. L’Institut Technique de l’Elevage Bovin, Paris, France.Google Scholar
Berry, DP and Crowley, JJ 2013. Cell Biology Symposium: genetics of feed efficiency in dairy and beef cattle. Journal of Animal Science 91, 1594–1613.CrossRefGoogle ScholarPubMed
Berry, DP, Horan, B, O’Donovan, M, Buckley, F, Kennedy, E, McEvoy, M and Dillon, P 2007. Genetics of grass dry matter intake, energy balance, and digestibility in grazing Irish dairy cows. Journal of Dairy Science 90, 4835–4845.Google Scholar
Cannas, A, Atzori, AS, Teixeira, IAMA, Sainz, RD and Oltjen, JW 2010. The energetic cost of maintenance in ruminants: from classical to new concepts and prediction systems. In Energy and protein metabolism and nutrition (ed. Crovetto GM), pp 531–542. Wageningen Academic Publishers, Wageningen, the Netherlands.Google Scholar
Chilliard, Y, Rémond, B, Agabriel, J, Robelin, J and Verite, R 1987. Variations du contenu digestif et des réserves corporelles au cours du cycle gestation-lactation. Bulletin Technique Centre de Recherches Zootechniques et Vétérinaires de Theix 70, 117–131.Google Scholar
Connor, EE, Hutchison, JL, Norman, HD, Olson, KM, Van Tassell, CP, Leith, JM and Baldwin, RL 2013. Use of residual feed intake in Holsteins during early lactation shows potential to improve feed efficiency through genetic selection. Journal of Animal Science 91, 3978–3988.CrossRefGoogle ScholarPubMed
Dekkers, JC and Gilbert, H 2010. Genetic and biological aspect of residual feed intake in pigs. In Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, 1–6 August 2010, Leipzig, Germany, pp. 1–8.Google Scholar
Fischer, A, Luginbühl, T, Delattre, L, Delouard, JM and Faverdin, P 2015. Rear shape in 3 dimensions summarized by principal component analysis is a good predictor of body condition score in Holstein dairy cows. Journal of Dairy Science 98, 4465–4476.Google Scholar
Fischer, A 2017. Study of the between-cows variability of feed efficiency in dairy cows, PhD thesis, Agrocampus-Ouest, Rennes, France.Google Scholar
Hurley, AM, López-Villalobos, N, McParland, S, Kennedy, E, Lewis, E, O’Donovan, M, Burke, JL and Berry, DP 2016. Inter-relationships among alternative definitions of feed efficiency in grazing lactating dairy cows. Journal of Dairy Science 99, 468–479.Google Scholar
Institut National de la Recherche Agronomique (INRA) 2010. Alimentation des bovins, ovins et caprins. Besoins des animaux - valeurs des aliments : tables Inra 2007 mise à jour 2010. Editions Quae, Versailles, France.Google Scholar
Manafiazar, G, McFadden, T, Goonewardene, L, Okine, E, Basarab, J, Li, P and Wang, Z 2013. Prediction of residual feed intake for first-lactation dairy cows using orthogonal polynomial random regression. Journal of Dairy Science 96, 7991–8001.Google Scholar
Mantysaari, P, Liinamo, AE and Mantysaari, EA 2012. Energy efficiency and its relationship with milk, body, and intake traits and energy status among primiparous Nordic Red dairy cattle. Journal of Dairy Science 95, 3200–3211.CrossRefGoogle ScholarPubMed
Mehtiö, T, Rinne, M, Nyholm, L, Mäntysaari, P, Sairanen, A, Mäntysaari, EA, Pitkänen, T and Lidauer, MH 2016a. Cow-specific diet digestibility predictions based on near-infrared reflectance spectroscopy scans of faecal samples. Journal of Animal Breeding and Genetics 133, 115–125.Google Scholar
Mehtiö, T, Negussie, E, Mäntysaari, P, Mäntysaari, EA and Lidauer, MH 2016b. Partitioning genetic variance of metabolizable energy efficiency in dairy cows. In 67th Meeting of the European Federation of Animal Science (EAAP), 29 August–2 September 2016, Belfast, UK, pp. 453.Google Scholar
Potts, SB, Boerman, JP, Lock, AL, Allen, MS and VandeHaar, MJ 2017. Relationship between residual feed intake and digestibility for lactating Holstein cows fed high and low starch diets. Journal of Dairy Science 100, 265–278.Google Scholar
R Core Team 2016. R: A language and environment for statistical computing. In R foundation for Statistical Computing, Vienna, Austria.Google Scholar
Robinson, DL 2005. Assessing the accuracy of modelling weight gain of cattle using feed efficiency data. Livestock Production Science 95, 187–200.Google Scholar
Savietto, D, Berry, DP and Friggens, NC 2014. Towards an improved estimation of the biological components of residual feed intake in growing cattle. Journal of Animal Science 92, 467–476.Google Scholar
Thorup, VM, Edwards, D and Friggens, NC 2012. On-farm estimation of energy balance in dairy cows using only frequent body weight measurements and body condition score. Journal of Dairy Science 95, 1784–1793.Google Scholar
Xi, YM, Wu, F, Zhao, DQ, Yang, Z, Li, L, Han, ZY and Wang, GL 2016. Biological mechanisms related to differences in residual feed intake in dairy cows. Animal 10, 1311–1318.Google Scholar
Yao, C, Spurlock, DM, Armentano, LE, Page, CD, VandeHaar, MJ, Bickhart, DM and Weigel, KA 2013. Random forests approach for identifying additive and epistatic single nucleotide polymorphisms associated with residual feed intake in dairy cattle. Journal of Dairy Science 96, 6716–6729.Google Scholar