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Phenotyping of robustness and milk quality

Published online by Cambridge University Press:  30 July 2013

D. P. Berry*
Affiliation:
Animal & Bioscience Research Department, Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland
S. McParland
Affiliation:
Animal & Bioscience Research Department, Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland
C. Bastin
Affiliation:
Agricultural Sciences Department, Gembloux Agro-Bio Tech, University of Liège, Passage des Déportés 2, B-5030 Gembloux, Belgium
E. Wall
Affiliation:
SRUC, Easter Bush, Penicuik, Midlothian, EH25 9RG, Scotland
N. Gengler
Affiliation:
Agricultural Sciences Department, Gembloux Agro-Bio Tech, University of Liège, Passage des Déportés 2, B-5030 Gembloux, Belgium National Fund for Scientific Research (F.R.S.-FNRS), B-1000 Brussels, Belgium
H. Soyeurt
Affiliation:
Agricultural Sciences Department, Gembloux Agro-Bio Tech, University of Liège, Passage des Déportés 2, B-5030 Gembloux, Belgium National Fund for Scientific Research (F.R.S.-FNRS), B-1000 Brussels, Belgium
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Abstract

A phenotype describes the outcome of the interacting development between the genotype of an individual and its specific environment throughout life. Animal breeding currently exploits large data sets of phenotypic and pedigree information to estimate the genetic merit of animals. Here we describe rapid, low-cost phenomic tools for dairy cattle. We give particular emphasis to infrared spectroscopy of milk because the necessary spectral data are already routinely available on milk samples from individual cows and herds, and therefore the operational cost of implementing such a phenotyping strategy is minimal. The accuracy of predicting milk quality traits from mid-infrared spectroscopy (MIR) analysis of milk, although dependent on the trait under investigation, is particularly promising for differentiating between good and poor-quality dairy products. Many fatty acid concentrations in milk, and in particular saturated fatty acid content, can be very accurately predicted from milk MIR. These results have been confirmed in many international populations. Albeit from only two studied populations investigated in the RobustMilk project, milk MIR analysis also appears to be a reasonable predictor of cow energy balance, a measure of animal robustness; high accuracy of prediction was not expected as the gold standard method of measuring energy balance in those populations was likely to contain error. Because phenotypes predicted from milk MIR are available routinely from milk testing, longitudinal data analyses could be useful to identify animals of superior genetic merit for milk quality and robustness, as well as for monitoring changes in milk quality and robustness because of management, while simultaneously accounting for the genetic merit of the animals. These sources of information can be very valuable input parameters in decision-support tools for both milk producers and processors.

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Full Paper
Copyright
Copyright © The Animal Consortium 2013 

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References

Arnould, VM-R, Soyeurt, H 2009. Genetic variability of milk fatty acids. Journal of Applied Genetics 50, 2939.Google Scholar
Baker, EN 2005. Lactoferrin: a multi-tasking protein par excellence. Cellular and Molecular Life Sciences 62, 25292530.Google Scholar
Banos, G, Coffey, MP 2010. Genetic association between body energy measured throughout lactation and fertility in dairy cattle. Animal 4, 189199.Google Scholar
Barber, MC, Clegg, RA, Travers, MT, Vernon, RG 1997. Lipid metabolism in the lactating mammary gland. Biochimica et Biophysica Acta 1347, 101126.Google Scholar
Bastin, C, Laloux, L, Gillon, A, Miglior, F, Soyeurt, H, Hammami, H, Bertozzi, C, Gengler, N 2009. Modeling milk urea of Walloon dairy cows in management perspectives. Journal of Dairy Science 92, 35293540.Google Scholar
Beam, SW, Butler, WR 1999. Effects of energy balance on follicular development and first ovulation in postpartum dairy cows. Journal of Reproduction and Fertility. Suppl. 54, 411424.Google Scholar
Berry, DP, Veerkamp, RF, Dillon, PG 2006. Phenotypic profiles for body weight, body condition score, energy intake, and energy balance across different parities and concentrate feeding levels. Livestock Science 104, 112.Google Scholar
Berry, DP, O'Donovan, M, Dillon, P 2009. Potential to genetically alter intake and energybalance in grass fed dairy cows. Breeding for Robustness in Cattle, Wageningen Academic Publishers, Wageningen,The Netherlands pp. 219–224, EAAP Publ. no. 126.Google Scholar
Berry, DP, Bermingham, M, Good, M, More, SJ 2011. Genetics of animal health and disease in cattle. Irish Veterinary Journal 64, 5.Google Scholar
Berry, DP, Horan, B, O'Donovan, M, Buckley, F, Kennedy, E, McEvoy, M, Dillon, P 2007. Genetics of grass dry matter intake, energy balance and digestibility in Irish dairy cows. Journal of Dairy Science 90, 48354845.Google Scholar
Bonfatti, V, Di Martino, G, Carnier, P 2011. Effectiveness of mid-infrared spectroscopy for the prediction of detailed protein composition and contents of protein genetic variants of individual milk of Simmental cows. Journal of Dairy Science 94, 57765785.Google Scholar
Bowman, JC 1974. An introduction to animal breeding. Edward Arnold Ltd, London, UK.Google Scholar
Cassandro, M, Comin, A, Ojala, M, Dal Zotto, R, De Marchi, M, Gallo, L, Carnier, P, Bittante, G 2008. Genetic parameters of milk coagulation properties and their relationships with milk yield and quality traits in Italian Holstein cows. Journal of Dairy Science 91, 371376.Google Scholar
Chillard, Y, Ferlay, A, Doreau, M 2001. Effect of different types of forages, animal fat or marine oils in cow's diet on milk fat secretion and composition, especially conjugated linoleic acid (CLA) and polyunsaturated fatty acids. Livestock Production Science 70, 3148.Google Scholar
Coffey, MP, Mottram, TB, McFarlane, N 2003a. A feasibility study on the automatic recording of condition score in dairy cows. Proceedings of the 2003 British Society of Animal Science Winter Meeting. March, York, 131pp.Google Scholar
Coffey, MP, Simm, G, Hill, WG, Brotherstone, S 2003b. Genetic evaluations of dairy bulls for daughter energy balance profiles using linear type scores and body condition score analyzed using random regression. Journal of Dairy Science 86, 22052212.Google Scholar
Collard, BL, Dekkers, JCM, Petitclerc, D, Schaeffer, LR 2000. Relationships between energy balance and health traits of dairy cattle in early lactation. Journal of Dairy Science 83, 26832690.Google Scholar
Dal Zotto, R, De Marchi, M, Cecchinato, A, Penasa, A, Cassandro, M, Carnier, P, Gallo, L, Bittante, G 2008. Reproducibility and repeatability of measures of milk coagulation properties and predictive ability of mid-infrared reflectance spectroscopy. Journal of Dairy Science 91, 41034112.Google Scholar
De Marchi, M, Fagan, CC, O'Donnell, CP, Cecchinato, A, Dal Zotto, R, Cassandro, M, Penasa, M, Bittante, G 2009. Prediction of coagulation properties, titratable acidity, and pH of bovine milk using mid-infrared spectroscopy. Journal of Dairy Science 92, 423432.Google Scholar
De Roos, APW, van den Bijgaart, HJCM, Hørlyk, J, De Jong, G 2007. Screening for subclinical ketosis in dairy cattle by fourier transform infrared spectroscopy. Journal of Dairy Science 90, 17611766.Google Scholar
Ferguson, JD, Azzaro, G, Licitra, G 2006. Body condition assessment using digital images. Journal of Dairy Science 89, 38333841.Google Scholar
Friggens, NC, Ridder, C, Lovendahl, P 2007b. On the use of milk composition measures to predict energy balance of dairy cows. Journal of Dairy Science 90, 54535467.Google Scholar
Friggens, NC, Berg, P, Theilgaard, P, Korsgaard, IR, Ingvartsen, KL, Løvendahl, PL, Jensen, J 2007a. Breed and parity effects on energy balance profiles through lactation: evidence for genetically driven body reserve change. Journal of Dairy Science 90, 52915305.Google Scholar
Grieve, DG, Korver, S, Rijpkema, YS, Hof, G 1986. Relationship between milk composition and some nutritional parameters in early lactation. Livestock Production Science 14, 239254.CrossRefGoogle Scholar
Grummer, RR 1991. Effect on feed on the composition of milk fat. Journal of Dairy Science 74, 32283243.Google Scholar
Hansen, PW 1999. Screening of dairy cows for ketosis by use of infrared spectroscopy and multivariate calibration. Journal of Dairy Science 82, 20052010.Google Scholar
Haug, A, Høstmark, AT, Harstad, OM 2007. Bovine milk in human nutrition – a review. Lipids in Health and Disease 6, 25.Google Scholar
Heuer, C, Van Straalen, WM, Schukken, YH, Dirkzwager, A, Noordhuizen, JPTM 2000. Prediction of energy balance in a high yielding dairy herd in early lactation: model devilment and precision. Livestock Production Science 65, 91105.Google Scholar
Heuer, C, Luinge, HJ, Lutz, ETG, Schukken, H, van der Maas, JH, Wilmink, H, Noordhuizen, JPTM 2001. Determination of acetone in cow milk by fourier transform infrared spectroscopy for the detection of subclinical ketosis. Journal of Dairy Science 84, 575582.Google Scholar
Ipema, AH, Goense, D, Hogewerf, PH, Houwers, HWJ, van Roest, H 2008. Pilot study to monitor body temperature of dairy cows with a rumen bolus. Computers and Electronics in Agriculture 64, 4952.Google Scholar
Maurice-Van Eijndhoven, MHT, Soyeurt, H, Dehareng, F, Calus, MPL 2013. Validation of fatty acid predictions in milk using mid-infrared spectrometry across cattle breeds. Animal 7, 348354.Google Scholar
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, 36513661.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 95, 72257235.Google Scholar
Melfsen, A, Hartung, E, Haeussermann, A 2012. Accuracy of in-line milk composition analysis with diffuse reflectance near-infrared spectroscopy. Journal of Dairy Science 95, 112.Google Scholar
Nguyen, HN, Dehareng, F, Hammida, M, Baeten, V, Froidmont, E, Soyeurt, H, Niemöller, A 2011. Potential of near infrared spectroscopy for on-line analysis at the milking parlour using a fiber-optic probe presentation. NIRnews 22, 1113.Google Scholar
Palmquist, DL, Baulieu, AD, and Barbano, DM 1993. Feed and animal factors influencing milk fat composition. Journal of Dairy Science 76, 17531771.Google Scholar
Polat, B, Colak, A, Cengiz, M, Yanmaz, LE, Oral, H, Bastan, A, Kaya, S, Hayirli, A 2010. Sensitivity and specificity of infrared thermography in detection of subclinical mastitis in dairy cows. Journal of Dairy Science 93, 35253532.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, 57695801. http://www.journalofdairyscience.org/search/resultsGoogle Scholar
Rutten, MJM, Bovenhuis, H, Heck, JML, van Arendonk, JAM 2011. Predicting bovine milk protein composition based on Fourier transform infrared spectra. Journal of Dairy Science 94, 56835690.Google Scholar
Rutten, MJM, Bovenhuis, H, Hettinga, KA, Van Vanlenberg, HJF, Van Arendonk, JAM 2009. Predicting bovine milk fat composition using infrared spectroscopy based on milk samples collected in winter and summer. Journal of Dairy Science 92, 62026209.Google Scholar
Soyeurt, H, Misztal, I, Gengler, N 2010. Genetic variability of milk components based on mid-infrared spectral data. Journal of Dairy Science 93, 17221728.Google Scholar
Soyeurt, H, Bruwier, D, Romnee, JM, Gengler, N, Bertozzi, C, Veselko, D, Dardenne, P 2009. Potential estimation of mineral contents in cow milk using mid-infrared spectrometry. Journal of Dairy Science 92, 24442454.Google Scholar
Soyeurt, H, Dehareng, F, Gengler, N, McParland, S, Wall, E, Berry, DP, Coffey, M, Dardenne, P 2011. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. Journal of Dairy Science 84, 16571667.Google Scholar
Soyeurt, H, Bastin, C, Colinet, FG, Arnould, VMR, Berry, DP, Wall, W, Dehareng, F, Nguyen, HN, Dardenne, P, Schefers, J, Vandenplas, J, Weigel, K, Coffey, M, Théron, L, Detilleux, J, Reding, E, Gengler, N, McParland, S 2012. Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis. Animal 6, 18301838.Google Scholar
Veerkamp, RF, Beerda, B 2007. Genetics and genomics to improve fertility in high producing dairy cows. Theriogenology 68S, S266S273.Google Scholar
Veerkamp, RF, Oldenbroek, JJ, Van Der Gaast, HJ, Van Der Werf, JHJ 2000. Genetic correlation between days until start of luteal activity and milk yield, energy balance and live weights. Journal of Dairy Science 83, 577583.Google Scholar
Williams, P, Norris, K 2001. Near-infrared technology in the agricultural and food industries, 2nd edition. American Association of Cereal Chemists, St. Paul, Minnesota.Google Scholar