[en] We predicted dry matter intake of dairy cows using parity, week of lactation, milk yield, milk mid-infrared (MIR) spectrum, and MIR-based predictions of bodyweight, fat, protein, lactose, and fatty acids content in milk. The dataset comprised 10,711 samples of 534 dairy cows with a geographical diversity (Australia, Canada, Denmark, and Ireland). We set up partial least square (PLS) regressions with different constructs and a one-hidden-layer artificial neural network (ANN) using the highest contribution variables. In the ANN, we replaced the spectra with their projections to the 25 first PLS factors explaining 99% of the spectral variability to reduce the model complexity. Cow-independent 10 × 10-fold cross-validation (CV) achieved the best performance with root mean square errors (RMSECV) of 3.27 ± 0.08 kg for the PLS regression and 3.25 ± 0.13 kg for ANN. Although the available data were significantly different, we also performed a country-independent validation (CIV) to measure the models' performance fairly. We found RMSECIV varying from 3.73 to 6.03 kg for PLS and 3.69 to 5.08 kg for ANN. Ultimately, based on the country-independent validation, we discussed the developed models' performance with those achieved by the National Research Council's equation.
Disciplines :
Agriculture & agronomy
Author, co-author :
Tedde, Anthony ; Université de Liège - ULiège > TERRA Research Centre ; National Funds for Scientific Research, 1000 Brussels, Belgium
Grelet, Clément ; Walloon Agricultural Research Center (CRA-W), 5030 Gembloux, Belgium
Ho, Phuong N; Agriculture Victoria Research, Centre for AgriBioscience, AgriBio, Bundoora, VIC 3083, Australia
Pryce, Jennie E; Agriculture Victoria Research, Centre for AgriBioscience, AgriBio, Bundoora, VIC 3083, Australia ; School of Applied Systems Biology, La Trobe University, 5 Ring Road, Bundoora, VIC 3083, Australia
Hailemariam, Dagnachew; Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada
Wang, Zhiquan; Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada
Plastow, Graham; Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada
Gengler, Nicolas ; Université de Liège - ULiège > Département GxABT > Ingénierie des productions animales et nutrition
Froidmont, Eric ; Université de Liège - ULiège > Département de chimie (sciences) > Chimie analytique et électrochimie
Data Availability Statement: Restrictions apply to the availability of these data. Data were obtained from the Walloon Breeding Association (Ciney, Belgium), the Walloon Agricultural Research Center (Gembloux, Belgium), the Ellinbank Research Farm belonging to Agriculture Victoria Research (Australia), the Faculty of Agricultural, Life and Environmental Sciences, from the University of Alberta (Canada), and from the Genotype Plus Environment (GplusE) Project, namely, data from the Agri-Food and Biosciences Institute (Ireland), the Aarhus University (Denmark), and the University College Dublin (Ireland). They are available from the authors with the permission of the related aforementioned third parties. Some results outside the scope of the modeling described in the Materials and Methods section were mentioned to provide some insights to the reader without being described in detail. They are followed by "(results not shown)." Acknowledgments: The Genotype Plus Environment (GplusE) Project has received funding from the European Union’s Seventh Framework Programme for research, technological development, and demonstration under grant agreement no. 613689. List of authors in the GplusE consortium: Mark Crowe, Alan Fahey, Fiona Carter, Elizabeth Matthews, An-dreia Santoro, Colin Byrne, Pauline Rudd, Roisin O’Flaherty, Sinead Hallinan, Claire Wathes, Mazdak Salavati, Zhangrui Cheng, Ali Fouladi, Geoff Pollott, Dirk Werling, Beatriz Sanz Bernardo, Conrad Ferris, Alistair Wylie, Matt Bell, Mieke Vaneetvelde, Kristof Hermans, Miel Hostens, Geert Opsomer, Sander Moer-man, Jenne De Koster, Hannes Bogaert, Jan Vandepitte, Leila Vandevelde, Bonny Vanranst, Klaus Ingvartsen, Martin Tang Sorensen, Johanna Hoglund, Susanne Dahl, Soren Ostergaard, Janne Rothmann, Mogens Krogh, Else Meyer, Leslie Foldager, Charlotte Gaillard, Jehan Ettema, Tine Rousing, Torben Larsen, Victor H. Silva de Oliveira, Cinzia Marchitelli, Federica Signorelli, Francesco Napolitano, Bianca Moioli, Alessandra Crisà, Luca Buttazzoni, Jennifer McClure, Daragh Matthews, Francis Kearney, Andrew Cromie, Matt McClure, Shujun Zhang, Xing Chen, Huanchun Chen, Junlong Zhao, Liguo Yang, Guohua Hua, Chen Tan, Guiqiang Wang, Michel Bonneau, Marlène Sciarretta, Armin Pearn, Arnold Evertson, Linda Kosten, Anders Fogh, Thomas Andersen, Matthew Lucy, Chris Elsik, Gavin Conant, Jerry Taylor, Deborah Triant, Nicolas Gengler, Michel Georges, Frederic Colinet, Marilou Ramos Pamplona, Hedi Hammami, Catherine Bastin, Haruko Takeda, Aurelie Laine, Anne-Sophie Van Laere, Rodrigo Mota, Saied Naderi Darbagshahi, Frederic Dehareng, Clement Grelet, Amelie Vanlierde, Eric Froidmont, Frank Becker, Martin Schulze, and Sergio Palma Vera. The views expressed in this publication are the authors’ sole responsibility and do not necessarily reflect the views of the European Commission. The authors acknowledge the laboratory “Comité du Lait” (Battice, Belgium) for the Walloon milk mid-infrared spectra. The University of Alberta researchers acknowledge support from Alberta Ministry of Agriculture and Forestry, Genome Alberta, and Genome Canada. The authors acknowledge the financial support of the National Fund for the Scientific Research (F.R.S-FNRS) for the SimBa research project (T02211). This project is also co-financed by the Luxembourg National Research Fund (FNR). Computational resources have been provided by the Consortium des Équipements de Calcul Intensif (CÉCI), funded by the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under Grant No. 2.5020.11 and by the Walloon Region.Funding: This work is supported by the Luxembourg National Research Fund (INTER/FNRS/18/12987586/SimBa) and by the Fonds de la Recherche Scientifique-FNRS under Grant n° T.0221.19.
National Research Council. Nutrient Requirements of Dairy Cattle, 7th ed.; Natl. Acad. Press: Washington, DC, USA, 2001; p. 9825.
Berry, D.; Lee, J.; Macdonald, K.; Stafford, K.; Matthews, L.; Roche, J. Associations Among Body Condition Score, Body Weight, Somatic Cell Count, and Clinical Mastitis in Seasonally Calving Dairy Cattle. J. Dairy Sci. 2007, 90, 637–648, doi:10.3168/jds.s0022-0302(07)71546-1.
Westwood, C.; Lean, I.; Garvin, J. Factors Influencing Fertility of Holstein Dairy Cows: A Multivariate Description. J. Dairy Sci. 2002, 85, 3225–3237, doi:10.3168/jds.s0022-0302(02)74411-1.
Vallimont, J.; Dechow, C.; Daubert, J.; Dekleva, M.; Blum, J.; Barlieb, C.; Liu, W.; Varga, G.; Heinrichs, A.; Baumrucker, C. Short communication: Heritability of gross feed efficiency and associations with yield, intake, residual intake, body weight, and body condition score in 11 commercial Pennsylvania tie stalls. J. Dairy Sci. 2011, 94, 2108–2113, doi:10.3168/jds.2010-3888.
Walsh, S.; Williams, E.; Evans, A. A review of the causes of poor fertility in high milk producing dairy cows. Anim. Reprod. Sci. 2011, 123, 127–138, doi:10.1016/j.anireprosci.2010.12.001.
Grummer, R. Strategies to improve fertility of high yielding dairy farms: Management of the dry period. Theriogenology 2007, 68, S281–S288, doi:10.1016/j.theriogenology.2007.04.031.
Van Knegsel, A.T.; Brand, H.V.D.; Dijkstra, J.; Tamminga, S.; Kemp, B. Effect of dietary energy source on energy balance, pro-duction, metabolic disorders and reproduction in lactating dairy cattle. Reprod. Nutr. Dev. 2005, 45, 665–688, doi:10.1051/rnd:2005059.
Lake, S.L.; Weston, T.R.; Scholljegerdes, E.J.; Murrieta, C.M.; Alexander, B.M.; Rule, D.C.; Moss, G.E.; Hess, B.W. Effects of postpartum dietary fat and body condition score at parturition on plasma, adipose tissue, and milk fatty acid composition of lactating beef cows1. J. Anim. Sci. 2007, 85, 717–730, doi:10.2527/jas.2006-353.
Roche, J.; Friggens, N.; Kay, J.K.; Fisher, M.W.; Stafford, K.J.; Berry, D.P. Invited review: Body condition score and its association with dairy cow productivity, health, and welfare. J. Dairy Sci. 2009, 92, 5769–5801, doi:10.3168/jds.2009-2431.
Connor, E.E.; Hutchison, J.L.; Olson, K.M.; Norman, H.D. TRIENNIAL LACTATION SYMPOSIUM: Opportunities for improving milk production efficiency in dairy cattle1,2. J. Anim. Sci. 2012, 90, 1687–1694, doi:10.2527/jas.2011-4528.
Shetty, N.; Løvendahl, P.; Lund, M.; Buitenhuis, A. Prediction and validation of residual feed intake and dry matter intake in Danish lactating dairy cows using mid-infrared spectroscopy of milk. J. Dairy Sci. 2017, 100, 253–264, doi:10.3168/jds.2016-11609.
Basarab, J.A.; Beauchemin, K.A.; Baron, V.S.; Ominski, K.H.; Guan, L.L.; Miller, S.P.; Crowley, J.J. Reducing GHG emissions through genetic improvement for feed efficiency: Effects on economically important traits and enteric methane production. Animals 2013, 7, 303–315, doi:10.1017/s1751731113000888.
Nutrient requirements of dairy cattle by the Subcommittee on Dairy Cattle Nutrition, Board on Agriculture, National Research Council, 6th ed.; Natl. Acad. Press: Washington, DC, USA, 1988.
Lahart, B.; McParland, S.; Kennedy, E.; Boland, T.; Condon, T.; Williams, M.; Galvin, N.; McCarthy, B.; Buckley, F. Predicting the dry matter intake of grazing dairy cows using infrared reflectance spectroscopy analysis. J. Dairy Sci. 2019, 102, 8907–8918, doi:10.3168/jds.2019-16363.
Wallén, S.; Prestløkken, E.; Meuwissen, T.; McParland, S.; Berry, D. Milk mid-infrared spectral data as a tool to predict feed intake in lactating Norwegian Red dairy cows. J. Dairy Sci. 2018, 101, 6232–6243, doi:10.3168/jds.2017-13874.
Grelet, C.; Froidmont, E.; Foldager, L.; Salavati, M.; Hostens, M.; Ferris, C.; Ingvartsen, K.; Crowe, M.; Sorensen, M.; Pierna, J.F.; et al. Potential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation. J. Dairy Sci. 2020, 103, 4435–4445, doi:10.3168/jds.2019-17910.
Grelet, C.; Dardenne, P.; Soyeurt, H.; Fernandez, J.; Vanlierde, A.; Steevens, F.; Gengler, N.; Dehareng, F. Large-scale phenotyp-ing in dairy sector using milk MIR spectra: Key factors affecting the quality of predictions. Methods 2021, 186, 97–111, doi:10.1016/j.ymeth.2020.07.012.
Vanlierde, A.; Dehareng, F.; Gengler, N.; Froidmont, E.; McParland, S.; Kreuzer, M.; Bell, M.; Lund, P.; Martin, C.; Kuhla, B.; et al. Improving robustness and accuracy of predicted daily methane emissions of dairy cows using milk mid‐infrared spectra. J. Sci. Food Agric. 2020, doi:10.1002/jsfa.10969.
Soyeurt, H.; Dehareng, F.; Gengler, N.; McParland, S.; Wall, E.; Berry, D.; Coffey, M.; Dardenne, P. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. J. Dairy Sci. 2011, 94, 1657–1667, doi:10.3168/jds.2010-3408.
Soyeurt, H.; Grelet, C.; McParland, S.; Calmels, M.; Coffey, M.; Tedde, A.; Delhez, P.; Dehareng, F.; Gengler, N. A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra. J. Dairy Sci. 2020, 103, 11585–11596, doi:10.3168/jds.2020-18870.
Soyeurt, H.; Froidmont, E.; Dufrasne, I.; Hailemariam, D.; Wang, Z.; Bertozzi, C.; Colinet, F.; 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. 2019, 227, 82–89, doi:10.1016/j.livsci.2019.07.007.
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. 2009, 92, 2444–2454, doi:10.3168/jds.2008-1734.
Gross, J.; A Van Dorland, H.; Bruckmaier, R.M.; Schwarz, F.J. Milk fatty acid profile related to energy balance in dairy cows. J. Dairy Res. 2011, 78, 479–488, doi:10.1017/s0022029911000550.
McCulloch, W.S.; Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biol. 1943, 5, 115–133, doi:10.1007/bf02478259.
Ho, P.N.; Marett, L.C.; Wales, W.J.; Axford, M.; Oakes, E.M.; Pryce, J.E. Predicting milk fatty acids and energy balance of dairy cows in Australia using milk mid-infrared spectroscopy. Anim. Prod. Sci. 2020, 60, 164, doi:10.1071/an18532.
Grelet, C.; Pierna, J.F.; Dardenne, P.; Baeten, V.; Dehareng, F. Standardization of milk mid-infrared spectra from a European dairy network. J. Dairy Sci. 2015, 98, 2150–2160, doi:10.3168/jds.2014-8764.
Mahalanobis, P. C. On the generalised distance in statistics. Proc. Natl. Inst. Sci. India 1936, 12, 49–55.
De Maesschalck, R.; Jouan-Rimbaud, D.; Massart, D.L. The Mahalanobis distance. Chemom. Intell. Lab. Syst. 2000, 50, 1–18, doi:10.1016/s0169-7439(99)00047-7.
Garrido-Varo, A.; Garcia-Olmo, J.; Fearn, T. A note on Mahalanobis and related distance measures in WinISI and The Unscram-bler. J. Near Infrared Spectrosc. 2019, 27, 253–258, doi:10.1177/0967033519848296.
Delhez, P.; Ho, P.; Gengler, N.; Soyeurt, H.; Pryce, J. Diagnosing the pregnancy status of dairy cows: How useful is milk mid-infrared spectroscopy? J. Dairy Sci. 2020, 103, 3264–3274, doi:10.3168/jds.2019-17473.
Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence—Volume 2, Montreal, QC, Canada, 20–25 August 1995; pp. 1137–1143.
R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020.
Bjørn-Helge, M. Wehrens, R. Hovde Liland, K. pls: Partial Least Squares and Principal Component Regression; R package version 2.7-2, 2019, https://CRAN.R-project.org/package=pls (accessed on 29 April 2021).
Kuhn, M. Caret: Classification and Regression Training; R package version 6.0-86, 2020, https://CRAN.R-project.org/package=caret (accessed on 29 April 2021).
Breiman, L.Classification And Regression Trees; Wadsworth and Brook: Belmont, CA, USA, 1984.
Venables, W. N.; Ripley, B. D. Modern Applied Statistics with S, Fourth; Springer: New York, NY, USA, 2002.
Wold, S.; Johansson, E.; Cocchi, M. PLS: Partial Least Squares Projections to Latent Structures; KLUWER ESCOM Science Publisher: Germany, 1993; pp. 523–550.
Wold, S. PLS for multivariate linear modeling. In Chemometric Methods in Molecular Design; Weily-VCH: Germany, 1995; pp. 195–218.
Kolver, E.S.; Muller, L.D. Performance and Nutrient Intake of High Producing Holstein Cows Consuming Pasture or a Total Mixed Ration. J. Dairy Sci. 1998, 81, 1403–1411, doi:10.3168/jds.s0022-0302(98)75704-2.
Chilliard, Y.; Glasser, F.; Ferlay, A.; Bernard, L.; Rouel, J.; Doreau, M. Diet, rumen biohydrogenation and nutritional quality of cow and goat milk fat. Eur. J. Lipid Sci. Technol. 2007, 109, 828–855, doi:10.1002/ejlt.200700080.
Dardenne, P. Some Considerations about NIR Spectroscopy: Closing Speech at NIR-2009. NIR News 2010, 21, 8–14, doi:10.1255/nirn.1165.
Mayes, R.W.; Lamb, C.S.; Colgrove, P.M. The use of dosed and herbage n-alkanes as markers for the determination of herbage intake. J. Agric. Sci. 1986, 107, 161–170, doi:10.1017/s0021859600066910.
Enevoldsen, C.; Kristensen, T. Estimation of Body Weight from Body Size Measurements and Body Condition Scores in Dairy Cows. J. Dairy Sci. 1997, 80, 1988–1995, doi:10.3168/jds.s0022-0302(97)76142-3.
Kuhn, M.; Johnson, K. Linear Regression and Its Cousins. In Applied Predictive Modeling; Kuhn, M., Johnson, K., Eds.; Springer: New York, NY, USA, 2013; pp. 101–139.
Gulati, A.; Galvin, N.; Lewis, E.; Hennessy, D.; O’Donovan, M.; McManus, J.J.; Fenelon, M.A.; Guinee, T.P. Outdoor grazing of dairy cows on pasture versus indoor feeding on total mixed ration: Effects on gross composition and mineral content of milk during lactation. J. Dairy Sci. 2018, 101, 2710–2723, doi:10.3168/jds.2017-13338.
