[en] The use of test-day models to model milk mid-infrared (MIR) spectra for genetic purposes has already been explored; however, little attention has been given to their use to predict milk MIR spectra for management purposes. The aim of this paper was to study the ability of a test-day mixed model to predict milk MIR spectra for management purposes. A data set containing 467,496 test-day observations from 53,781 Holstein dairy cows in first lactation was used for model building. Principal component analysis was implemented on the selected 311 MIR spectral wavenumbers to reduce the number of traits for modeling; 12 principal components (PC) were retained, explaining approximately 96% of the total spectral variation. Each of the retained PC was modeled using a single trait test-day mixed model. The model solutions were used to compute the predicted scores of each PC, followed by a back-transformation to obtain the 311 predicted MIR spectral wavenumbers. Four new data sets, containing altogether 122,032 records, were used to test the ability of the model to predict milk MIR spectra in 4 distinct scenarios with different levels of information about the cows. The average correlation between observed and predicted values of each spectral wavenumber was 0.85 for the modeling data set and ranged from 0.36 to 0.62 for the scenarios. Correlations between milk fat, protein, and lactose contents predicted from the observed spectra and from the modeled spectra ranged from 0.83 to 0.89 for the modeling set and from 0.32 to 0.73 for the scenarios. Our results demonstrated a moderate but promising ability to predict milk MIR spectra using a test-day mixed model. Current and future MIR traits prediction equations could be applied on the modeled spectra to predict all MIR traits in different situations instead of developing one test-day model separately for each trait. Modeling MIR spectra would benefit farmers for cow and herd management, for instance through prediction of future records or comparison between observed and expected wavenumbers or MIR traits for the detection of health and management problems. Potential resulting tools could be incorporated into milk recording systems.
Disciplines :
Agriculture & agronomy
Author, co-author :
Delhez, Pauline ; Université de Liège - ULiège > Département GxABT > Modélisation et développement
Colinet, Frédéric ; Université de Liège - ULiège > Département GxABT > Ingénierie des productions animales et nutrition
Vanderick, Sylvie ; Université de Liège - ULiège > Département GxABT > Ingénierie des productions animales et nutrition
Bertozzi, Carlo
Gengler, Nicolas ; Université de Liège - ULiège > Département GxABT > Ingénierie des productions animales et nutrition
Soyeurt, Hélène ; Université de Liège - ULiège > Département GxABT > Modélisation et développement
Language :
English
Title :
Predicting milk mid-infrared spectra from first parity Holstein cows using a test-day mixed model with the perspective of herd management
Publication date :
2020
Journal title :
Journal of Dairy Science
ISSN :
0022-0302
eISSN :
1525-3198
Publisher :
American Dairy Science Association, United States - Illinois
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Bibliography
Bastin, C., Laloux, L., Gillon, A., Miglior, F., Soyeurt, H., Hammami, H., Bertozzi, C., Gengler, N., Modeling milk urea of Walloon dairy cows in management perspectives. J. Dairy Sci. 92 (2009), 3529–3540 https://doi.org/10.3168/jds.2008-1904 19528631.
Belay, T.K., Dagnachew, B.S., Kowalski, Z.M., Ådnøy, T., An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle. J. Dairy Sci. 100 (2017), 6312–6326 https://doi.org/10.3168/jds.2016-12252 28571989.
Bittante, G., Cecchinato, A., Genetic analysis of the Fourier-transform infrared spectra of bovine milk with emphasis on individual wavelengths related to specific chemical bonds. J. Dairy Sci. 96 (2013), 5991–6006 https://doi.org/10.3168/jds.2013-6583 23810593.
Bonfatti, V., Di Martino, G., Carnier, P., Effectiveness of mid-infrared spectroscopy for the prediction of detailed protein composition and contents of protein genetic variants of individual milk of Simmental cows. J. Dairy Sci. 94 (2011), 5776–5785 https://doi.org/10.3168/jds.2011-4401 22118068.
Bonfatti, V., Vicario, D., Degano, L., Lugo, A., Carnier, P., Comparison between direct and indirect methods for exploiting Fourier transform spectral information in estimation of breeding values for fine composition and technological properties of milk. J. Dairy Sci. 100 (2017), 2057–2067 https://doi.org/10.3168/jds.2016-11951 28109603.
Caccamo, M., Veerkamp, R.F., De Jong, G., Pool, M.H., Petriglieri, R., Licitra, G., Variance components for test-day milk, fat, and protein yield, and somatic cell score for analyzing management information. J. Dairy Sci. 91 (2008), 3268–3276 https://doi.org/10.3168/jds.2007-0805 18650304.
Capuano, E., Rademaker, J., van den Bijgaart, H., van Ruth, S.M., Verification of fresh grass feeding, pasture grazing and organic farming by FTIR spectroscopy analysis of bovine milk. Food Res. Int. 60 (2014), 59–65 https://doi.org/10.1016/j.foodres.2013.12.024.
Caredda, M., Addis, M., Ibba, I., Leardi, R., Scintu, M.F., Piredda, G., Sanna, G., Building of prediction models by using Mid-Infrared spectroscopy and fatty acid profile to discriminate the geographical origin of sheep milk. Lebensm. Wiss. Technol. 75 (2017), 131–136 https://doi.org/10.1016/j.lwt.2016.08.053.
Dagnachew, B.S., Kohler, A., Ådnøy, T., Genetic and environmental information in goat milk Fourier transform infrared spectra. J. Dairy Sci. 96 (2013), 3973–3985 https://doi.org/10.3168/jds.2012-5972 23548299.
Dagnachew, B.S., Meuwissen, T.H.E., Ådnøy, T., Genetic components of milk Fourier-transform infrared spectra used to predict breeding values for milk composition and quality traits in dairy goats. J. Dairy Sci. 96 (2013), 5933–5942 https://doi.org/10.3168/jds.2012-6068 23831101.
De Marchi, M., Penasa, M., Cecchinato, A., Mele, M., Secchiari, P., Bittante, G., Effectiveness of mid-infrared spectroscopy to predict fatty acid composition of Brown Swiss bovine milk. Animal 5 (2011), 1653–1658 https://doi.org/10.1017/S1751731111000747 22440358.
de Roos, A.P.W., Harbers, A.G.F., De Jong, G., Random herd curves in a test-day model for milk, fat, and protein production of dairy cattle in the Netherlands. J. Dairy Sci. 87 (2004), 2693–2701 https://doi.org/10.3168/jds.S0022-0302(04)73396-2 15328295.
Duffield, T.F., Kelton, D.F., Leslie, K.E., Lissemore, K.D., Lumsden, J.H., Use of test day milk fat and milk protein to detect subclinical ketosis in dairy cattle in Ontario. Can. Vet. J. 38 (1997), 713–718 9360791.
Ferragina, A., Cipolat-Gotet, C., Cecchinato, A., Bittante, G., The use of Fourier-transform infrared spectroscopy to predict cheese yield and nutrient recovery or whey loss traits from unprocessed bovine milk samples. J. Dairy Sci. 96 (2013), 7980–7990 https://doi.org/10.3168/jds.2013-7036 24094534.
Gengler, N., Soyeurt, H., Dehareng, F., Bastin, C., Colinet, F., Hammami, H., Vanrobays, M.-L., Lainé, A., Vanderick, S., Grelet, C., Vanlierde, A., Froidmont, E., Dardenne, P., Capitalizing on fine milk composition for breeding and management of dairy cows. J. Dairy Sci. 99 (2016), 4071–4079 https://doi.org/10.3168/jds.2015-10140 26778306.
Gillon, A., Abras, S., Mayeres, P., Bertozzi, C., Gengler, N., Adding value to test-day data by using modified best prediction method. Proc. ICAR 37th Annu. Meet., Riga, Latvia, 2010, ICAR, Rome, Italy, 171–178.
Grelet, C., Bastin, C., Gelé, M., Davière, J.-B., Johan, M., Werner, A., Reding, R., Fernandez Pierna, J.A., Colinet, F.G., Dardenne, P., Gengler, N., Soyeurt, H., Dehareng, F., Development of Fourier transform mid-infrared calibrations to predict acetone, β-hydroxybutyrate, and citrate contents in bovine milk through a European dairy network. J. Dairy Sci. 99 (2016), 4816–4825 https://doi.org/10.3168/jds.2015-10477 27016835.
Hamann, J., Krömker, V., Potential of specific milk composition variables for cow health management. Livest. Prod. Sci. 48 (1997), 201–208 https://doi.org/10.1016/S0301-6226(97)00027-4.
Hammami, H., Rekik, B., Soyeurt, H., Bastin, C., Bay, E., Stoll, J., Gengler, N., Accessing genotype by environment interaction using within- and across-country test-day random regression sire models. J. Anim. Breed. Genet. 126 (2009), 366–377 https://doi.org/10.1111/j.1439-0388.2008.00794.x 19765163.
Iñón, F.A., Garrigues, S., De La Guardia, M., Nutritional parameters of commercially available milk samples by FTIR and chemometric techniques. Anal. Chim. Acta 513 (2004), 401–412 https://doi.org/10.1016/j.aca.2004.03.014.
International Committee for Animal Recording (ICAR). Section 2 – Guidelines for Dairy Cattle Milk Recording. 2017, ICAR, Rome, Italy.
Kim, H.-Y., Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restor. Dent. Endod 38 (2013), 52–54 https://doi.org/10.5395/rde.2013.38.1.52 23495371.
Koivula, M., Nousiainen, J.I., Nousiainen, J., Mäntysaari, E.A., Use of herd solutions from a random regression test-day model for diagnostic dairy herd management. J. Dairy Sci. 90 (2007), 2563–2568 https://doi.org/10.3168/jds.2006-517 17430961.
Lainé, A., Bastin, C., Grelet, C., Hammami, H., Colinet, F.G., Dale, L.M., Gillon, A., Vandenplas, J., Dehareng, F., Gengler, N., Assessing the effect of pregnancy stage on milk composition of dairy cows using mid-infrared spectra. J. Dairy Sci. 100 (2017), 2863–2876 https://doi.org/10.3168/jds.2016-11736 28131584.
Leclercq, G., Gengler, N., Soyeurt, H., Bastin, C., Genetic variability of the mid-infrared prediction of lactoferrin content in milk for Walloon Holstein first-parity cows. Livest. Sci. 151 (2013), 158–162 https://doi.org/10.1016/j.livsci.2012.11.002.
Mayeres, P., Stoll, J., Bormann, J., Reents, R., Gengler, N., Prediction of daily milk, fat, and protein production by a random regression test-day model. J. Dairy Sci. 87 (2004), 1925–1933 https://doi.org/10.3168/jds.S0022-0302(04)73351-2 15453510.
Mayeres, P., Stoll, J., Reents, R., Gengler, N., Alternative modeling of fixed effects in test-day models to increase their usefulness for management decisions. Interbull Bull. 29 (2002), 128–132.
McParland, S., Banos, G., Wall, E., Coffey, M.P., Soyeurt, H., Veerkamp, R.F., Berry, D.P., The use of mid-infrared spectrometry to predict body energy status of Holstein cows. J. Dairy Sci. 94 (2011), 3651–3661 https://doi.org/10.3168/jds.2010-3965 21700055.
Mineur, A., Köck, A., Grelet, C., Gengler, N., Egger-Danner, C., Sölkner, J., First results in the use of milk mid-infrared spectra in the detection of lameness in Austrian dairy cows. ACS Agric. Conspec. Sci. 82 (2017), 163–166.
Misztal, I., BLUPF90 Family of Programs. http://nce.ads.uga.edu/wiki/doku.php/, 2018.
Owen, A.J., Uses of derivative spectroscopy, UV-visible spectroscopy, application note. 1995, Agilent Technologies, Santa Clara, CA.
Palm, R., L'analyse en composantes principales: Principes et applications. 1998, Faculté universitaire des Sciences agronomiques de Gembloux.
Picque, D., Lefier, D., Grappin, R., Corrieu, G., Monitoring of fermentation by infrared spectrometry. Alcoholic and lactic fermentations. Anal. Chim. Acta 279 (1993), 67–72 https://doi.org/10.1016/0003-2670(93)85067-T.
R Core Team, R: A language and environment for statistical computing. 2017, R foundation for statistical computing, Vienna, Austria https://www.R-project.org/.
Rinnan, Å., Nørgaard, L., Van Den Berg, F., Data Pre-processing. Infrared spectroscopy for food quality analysis and control, 2009, Academic Press, New York, NY, 29–50.
Rovere, G., de los Campos, G., Tempelman, R.J., Vazquez, A.I., Miglior, F., Schenkel, F., Cecchinato, A., Bittante, G., Toledo-Alvarado, H., Fleming, A., A landscape of the heritability of Fourier-transform infrared spectral wavelengths of milk samples by parity and lactation stage in Holstein cows. J. Dairy Sci. 102 (2019), 1354–1363 https://doi.org/10.3168/jds.2018-15109 30580946.
Scampicchio, M., Eisenstecken, D., De Benedictis, L., Capici, C., Ballabio, D., Mimmo, T., Robatscher, P., Kerschbaumer, L., Oberhuber, M., Kaser, A., Huck, C.W., Cesco, S., Multi-method approach to trace the geographical origin of alpine milk: A case study of Tyrol region. Food Anal. Methods 9 (2016), 1262–1273 https://doi.org/10.1007/s12161-015-0308-2.
Shallue, C.J., Lee, J., Antognini, J., Sohl-Dickstein, J., Frostig, R., Dahl, G.E., Measuring the effects of data parallelism on neural network training. J. Mach. Learn. Res. 20 (2019), 1–49.
Sivakesava, S., Irudayaraj, J., Rapid determination of tetracycline in milk by FT-MIR and FT-NIR spectroscopy. J. Dairy Sci. 85 (2002), 487–493 https://doi.org/10.3168/jds.S0022-0302(02)74099-X 11949850.
Socrates, G., Infrared and Raman characteristic group frequencies contents: Tables and charts. 3rd ed., 2001, Wiley, Chichester, NY.
Soyeurt, H., Bruwier, D., Romnee, J.-M., Gengler, N., Bertozzi, C., Veselko, D., Dardenne, P., Potential estimation of major mineral contents in cow milk using mid-infrared spectrometry. J. Dairy Sci. 92 (2009), 2444–2454 https://doi.org/10.3168/jds.2008-1734 19447976.
Soyeurt, H., Dehareng, F., Gengler, N., McParland, S., Wall, E., Berry, D.P., Coffey, M., Dardenne, P., Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. J. Dairy Sci. 94 (2011), 1657–1667 https://doi.org/10.3168/jds.2010-3408 21426953.
Soyeurt, H., Froidmont, E., Dufrasne, I., Hailemariam, D., Wang, Z., Bertozzi, C., Colinet, F.G., Dehareng, F., Gengler, N., Contribution of milk mid-infrared spectrum to improve the accuracy of test-day body weight predicted from stage, lactation number, month of test and milk yield. Livest. Sci. 227 (2019), 82–89 https://doi.org/10.1016/j.livsci.2019.07.007.
Soyeurt, H., Misztal, I., Gengler, N., Genetic variability of milk components based on mid-infrared spectral data. J. Dairy Sci. 93 (2010), 1722–1728 https://doi.org/10.3168/jds.2009-2614 20338450.
Tsuruta, S., Misztal, I., Strandén, I., Use of the preconditioned conjugate gradient algorithm as a generic solver for mixed-model equations in animal breeding applications. J. Anim. Sci. 79 (2001), 1166–1172 https://doi.org/10.2527/2001.7951166x 11374535.
Van de Voort, F.R., How does material resource adequacy affect innovation project performance? A meta-analysis. J. Prod. Innov. Manage. 34 (1992), 842–863 https://doi.org/10.1111/jpim.12368.
Vanlierde, A., Soyeurt, H., Gengler, N., Colinet, F.G., Froidmont, E., Kreuzer, M., Grandl, F., Bell, M., Lund, P., Olijhoek, D.W., Eugène, M., Martin, C., Kuhla, B., Dehareng, F., Short communication: Development of an equation for estimating methane emissions of dairy cows from milk Fourier transform mid-infrared spectra by using reference data obtained exclusively from respiration chambers. J. Dairy Sci. 101 (2018), 7618–7624 https://doi.org/10.3168/jds.2018-14472 29753478.
Visentin, G., McDermott, A., McParland, S., Berry, D.P., Kenny, O.A., Brodkorb, A., Fenelon, M.A., De Marchi, M., Prediction of bovine milk technological traits from mid-infrared spectroscopy analysis in dairy cows. J. Dairy Sci. 98 (2015), 6620–6629 https://doi.org/10.3168/jds.2015-9323 26188572.
Wang, Q., Hulzebosch, A., Bovenhuis, H., Genetic and environmental variation in bovine milk infrared spectra. J. Dairy Sci. 99 (2016), 6793–6803 https://doi.org/10.3168/jds.2015-10488 27179856.
Wiggans, G.R., Goddard, M.E., A computationally feasible test day model for genetic evaluation of yield traits in the United States. J. Dairy Sci. 80 (1997), 1795–1800 https://doi.org/10.3168/jds.S0022-0302(97)76113-7 9276821.
Zaalberg, R.M., Shetty, N., Janss, L., Buitenhuis, A.J., Genetic analysis of Fourier transform infrared milk spectra in Danish Holstein and Danish Jersey. J. Dairy Sci. 102 (2019), 503–510 https://doi.org/10.3168/jds.2018-14464 30343907.
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