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Phenotyping of robustness and milk quality

  • D. P. Berry (a1), S. McParland (a1), C. Bastin (a2), E. Wall (a3), N. Gengler (a2) (a4) and H. Soyeurt (a2) (a4)...


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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Arnould, VM-R, Soyeurt, H 2009. Genetic variability of milk fatty acids. Journal of Applied Genetics 50, 2939.
Baker, EN 2005. Lactoferrin: a multi-tasking protein par excellence. Cellular and Molecular Life Sciences 62, 25292530.
Banos, G, Coffey, MP 2010. Genetic association between body energy measured throughout lactation and fertility in dairy cattle. Animal 4, 189199.
Barber, MC, Clegg, RA, Travers, MT, Vernon, RG 1997. Lipid metabolism in the lactating mammary gland. Biochimica et Biophysica Acta 1347, 101126.
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.
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.
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.
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.
Berry, DP, Bermingham, M, Good, M, More, SJ 2011. Genetics of animal health and disease in cattle. Irish Veterinary Journal 64, 5.
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.
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.
Bowman, JC 1974. An introduction to animal breeding. Edward Arnold Ltd, London, UK.
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.
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.
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.
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.
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.
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.
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.
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.
Ferguson, JD, Azzaro, G, Licitra, G 2006. Body condition assessment using digital images. Journal of Dairy Science 89, 38333841.
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.
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.
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.
Grummer, RR 1991. Effect on feed on the composition of milk fat. Journal of Dairy Science 74, 32283243.
Hansen, PW 1999. Screening of dairy cows for ketosis by use of infrared spectroscopy and multivariate calibration. Journal of Dairy Science 82, 20052010.
Haug, A, Høstmark, AT, Harstad, OM 2007. Bovine milk in human nutrition – a review. Lipids in Health and Disease 6, 25.
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.
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.
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.
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.
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.
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.
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.
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.
Palmquist, DL, Baulieu, AD, and Barbano, DM 1993. Feed and animal factors influencing milk fat composition. Journal of Dairy Science 76, 17531771.
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.
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.
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.
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.
Soyeurt, H, Misztal, I, Gengler, N 2010. Genetic variability of milk components based on mid-infrared spectral data. Journal of Dairy Science 93, 17221728.
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.
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.
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.
Veerkamp, RF, Beerda, B 2007. Genetics and genomics to improve fertility in high producing dairy cows. Theriogenology 68S, S266S273.
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.
Williams, P, Norris, K 2001. Near-infrared technology in the agricultural and food industries, 2nd edition. American Association of Cereal Chemists, St. Paul, Minnesota.


Phenotyping of robustness and milk quality

  • D. P. Berry (a1), S. McParland (a1), C. Bastin (a2), E. Wall (a3), N. Gengler (a2) (a4) and H. Soyeurt (a2) (a4)...


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