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Potential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation

(2020) JOURNAL OF DAIRY SCIENCE. 103(5). p.4435-4445
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Abstract
Improving nitrogen use efficiency (NUE) at both the individual cow and the herd level has become a key target in dairy production systems, for both environmental and economic reasons. Cost-effective and large-scale phenotyping methods are required to improve NUE through genetic selection and by feeding and management strategies. The aim of this study was to evaluate the possibility of using mid-infrared (MIR) spectra of milk to predict individual dairy cow NUE during early lactation. Data were collected from 129 Holstein cows, from calving until 50 d in milk, in 3 research herds (Denmark, Ireland, and the UK). In 2 of the herds, diets were designed to challenge cows metabolically, whereas a diet reflecting local management practices was offered in the third herd. Nitrogen intake (kg/d) and nitrogen excreted in milk (kg/d) were calculated daily. Nitrogen use efficiency was calculated as the ratio between nitrogen in milk and nitrogen intake, and expressed as a percentage. Individual daily values for NUE ranged from 9.7 to 81.7%, with an average of 36.9% and standard deviation of 10.4%. Milk MIR spectra were recorded twice weekly and were standardized into a common format to avoid bias between apparatus or sampling periods. Regression models predicting NUE using milk MIR spectra were developed on 1,034 observations using partial least squares or support vector machines regression methods. The models were then evaluated through (1) a cross-validation using 10 subsets, (2) a cow validation excluding 25% of the cows to be used as a validation set, and (3) a diet validation excluding each of the diets one by one to be used as validation sets. The best statistical performances were obtained when using the support vector machines method. Inclusion of milk yield and lactation number as predictors, in combination with the spectra, also improved the calibration. In cross-validation, the best model predicted NUE with a coefficient of determination of cross-validation of 0.74 and a relative error of 14%, which is suitable to discriminate between low- and high-NUE cows. When performing the cow validation, the relative error remained at 14%, and during the diet validation the relative error ranged from 12 to 34%. In the diet validation, the models showed a lack of robustness, demonstrating difficulties in predicting NUE for diets and for samples that were not represented in the calibration data set. Hence, a need exists to integrate more data in the models to cover a maximum of variability regarding breeds, diets, lactation stages, management practices, seasons, MIR instruments, and geographic regions. Although the model needs to be validated and improved for use in routine conditions, these preliminary results showed that it was possible to obtain information on NUE through milk MIR spectra. This could potentially allow large-scale predictions to aid both further genetic and genomic studies, and the development of farm management tools.
Keywords
DIETARY-PROTEIN, FARMS, STANDARDIZATION, PERFORMANCE, POLLUTION, Fourier-transform mid-infrared spectrometry, nutrition, environment, modeling

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MLA
Grelet, C., et al. “Potential of Milk Mid-Infrared Spectra to Predict Nitrogen Use Efficiency of Individual Dairy Cows in Early Lactation.” JOURNAL OF DAIRY SCIENCE, vol. 103, no. 5, 2020, pp. 4435–45, doi:10.3168/jds.2019-17910.
APA
Grelet, C., Froidmont, E., Foldager, L., Salavati, M., Hostens, M., Ferris, C. P., … Vera, S. P. (2020). Potential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation. JOURNAL OF DAIRY SCIENCE, 103(5), 4435–4445. https://doi.org/10.3168/jds.2019-17910
Chicago author-date
Grelet, C., E. Froidmont, L. Foldager, M. Salavati, Miel Hostens, C. P. Ferris, K. L. Ingvartsen, et al. 2020. “Potential of Milk Mid-Infrared Spectra to Predict Nitrogen Use Efficiency of Individual Dairy Cows in Early Lactation.” JOURNAL OF DAIRY SCIENCE 103 (5): 4435–45. https://doi.org/10.3168/jds.2019-17910.
Chicago author-date (all authors)
Grelet, C., E. Froidmont, L. Foldager, M. Salavati, Miel Hostens, C. P. Ferris, K. L. Ingvartsen, M. A. Crowe, M. T. Sorensen, J. A. Fernandez Pierna, A. Vanlierde, N. Gengler, F. Dehareng, Mark Crowe, Alan Fahey, Fiona Carter, Elizabeth Matthews, Andreia 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 Van Eetvelde, Kristof Hermans, Miel Hostens, Geert Opsomer, Sander Moerman, Jenne De Koster, Hannes Bogaert, Jan Vandepitte, Leila Vande Velde, Bonifacius Van Ranst, 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 Crisa, 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, Guigiang Wang, Michel Bonneau, Marlene 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. 2020. “Potential of Milk Mid-Infrared Spectra to Predict Nitrogen Use Efficiency of Individual Dairy Cows in Early Lactation.” JOURNAL OF DAIRY SCIENCE 103 (5): 4435–4445. doi:10.3168/jds.2019-17910.
Vancouver
1.
Grelet C, Froidmont E, Foldager L, Salavati M, Hostens M, Ferris CP, et al. Potential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation. JOURNAL OF DAIRY SCIENCE. 2020;103(5):4435–45.
IEEE
[1]
C. Grelet et al., “Potential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation,” JOURNAL OF DAIRY SCIENCE, vol. 103, no. 5, pp. 4435–4445, 2020.
@article{8664263,
  abstract     = {Improving nitrogen use efficiency (NUE) at both the individual cow and the herd level has become a key target in dairy production systems, for both environmental and economic reasons. Cost-effective and large-scale phenotyping methods are required to improve NUE through genetic selection and by feeding and management strategies. The aim of this study was to evaluate the possibility of using mid-infrared (MIR) spectra of milk to predict individual dairy cow NUE during early lactation. Data were collected from 129 Holstein cows, from calving until 50 d in milk, in 3 research herds (Denmark, Ireland, and the UK). In 2 of the herds, diets were designed to challenge cows metabolically, whereas a diet reflecting local management practices was offered in the third herd. Nitrogen intake (kg/d) and nitrogen excreted in milk (kg/d) were calculated daily. Nitrogen use efficiency was calculated as the ratio between nitrogen in milk and nitrogen intake, and expressed as a percentage. Individual daily values for NUE ranged from 9.7 to 81.7%, with an average of 36.9% and standard deviation of 10.4%. Milk MIR spectra were recorded twice weekly and were standardized into a common format to avoid bias between apparatus or sampling periods. Regression models predicting NUE using milk MIR spectra were developed on 1,034 observations using partial least squares or support vector machines regression methods. The models were then evaluated through (1) a cross-validation using 10 subsets, (2) a cow validation excluding 25% of the cows to be used as a validation set, and (3) a diet validation excluding each of the diets one by one to be used as validation sets. The best statistical performances were obtained when using the support vector machines method. Inclusion of milk yield and lactation number as predictors, in combination with the spectra, also improved the calibration. In cross-validation, the best model predicted NUE with a coefficient of determination of cross-validation of 0.74 and a relative error of 14%, which is suitable to discriminate between low- and high-NUE cows. When performing the cow validation, the relative error remained at 14%, and during the diet validation the relative error ranged from 12 to 34%. In the diet validation, the models showed a lack of robustness, demonstrating difficulties in predicting NUE for diets and for samples that were not represented in the calibration data set. Hence, a need exists to integrate more data in the models to cover a maximum of variability regarding breeds, diets, lactation stages, management practices, seasons, MIR instruments, and geographic regions. Although the model needs to be validated and improved for use in routine conditions, these preliminary results showed that it was possible to obtain information on NUE through milk MIR spectra. This could potentially allow large-scale predictions to aid both further genetic and genomic studies, and the development of farm management tools.},
  author       = {Grelet, C. and Froidmont, E. and Foldager, L. and Salavati, M. and Hostens, Miel and Ferris, C. P. and Ingvartsen, K. L. and Crowe, M. A. and Sorensen, M. T. and Pierna, J. A. Fernandez and Vanlierde, A. and Gengler, N. and Dehareng, F. and Crowe, Mark and Fahey, Alan and Carter, Fiona and Matthews, Elizabeth and Santoro, Andreia and Byrne, Colin and Rudd, Pauline and O'Flaherty, Roisin and Hallinan, Sinead and Wathes, Claire and Salavati, Mazdak and Cheng, Zhangrui and Fouladi, Ali and Pollott, Geoff and Werling, Dirk and Bernardo, Beatriz Sanz and Ferris, Conrad and Wylie, Alistair and Bell, Matt and Van Eetvelde, Mieke and Hermans, Kristof and Hostens, Miel and Opsomer, Geert and Moerman, Sander and De Koster, Jenne and Bogaert, Hannes and Vandepitte, Jan and Vande Velde, Leila and Van Ranst, Bonifacius and Ingvartsen, Klaus and Sorensen, Martin Tang and Hoglund, Johanna and Dahl, Susanne and Ostergaard, Soren and Rothmann, Janne and Krogh, Mogens and Meyer, Else and Foldager, Leslie and Gaillard, Charlotte and Ettema, Jehan and Rousing, Tine and Larsen, Torben and de Oliveira, Victor H. Silva and Marchitelli, Cinzia and Signorelli, Federica and Napolitano, Francesco and Moioli, Bianca and Crisa, Alessandra and Buttazzoni, Luca and McClure, Jennifer and Matthews, Daragh and Kearney, Francis and Cromie, Andrew and McClure, Matt and Zhang, Shujun and Chen, Xing and Chen, Huanchun and Zhao, Junlong and Yang, Liguo and Hua, Guohua and Tan, Chen and Wang, Guigiang and Bonneau, Michel and Sciarretta, Marlene and Pearn, Armin and Evertson, Arnold and Kosten, Linda and Fogh, Anders and Andersen, Thomas and Lucy, Matthew and Elsik, Chris and Conant, Gavin and Taylor, Jerry and Triant, Deborah and Gengler, Nicolas and Georges, Michel and Colinet, Frederic and Pamplona, Marilou Ramos and Hammami, Hedi and Bastin, Catherine and Takeda, Haruko and Laine, Aurelie and Van Laere, Anne-Sophie and Mota, Rodrigo and Darbagshahi, Saied Naderi and Dehareng, Frederic and Grelet, Clement and Vanlierde, Amelie and Froidmont, Eric and Becker, Frank and Schulze, Martin and Vera, Sergio Palma},
  issn         = {0022-0302},
  journal      = {JOURNAL OF DAIRY SCIENCE},
  keywords     = {DIETARY-PROTEIN,FARMS,STANDARDIZATION,PERFORMANCE,POLLUTION,Fourier-transform mid-infrared spectrometry,nutrition,environment,modeling},
  language     = {eng},
  number       = {5},
  pages        = {4435--4445},
  title        = {Potential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation},
  url          = {http://dx.doi.org/10.3168/jds.2019-17910},
  volume       = {103},
  year         = {2020},
}

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