Predicting milk fatty acids and energy balance of dairy cows in Australia using milk mid-infrared spectroscopy
P. N. Ho
A E , L. C. Marett B , W. J. Wales B , M. Axford C , E. M. Oakes C and J. E. Pryce A D
+ Author Affiliations
- Author Affiliations
A Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Vic. 3083, Australia.
B Agriculture Victoria, Ellinbank Centre, 1301 Hazeldean Road, Ellinbank, Vic. 3821, Australia.
C DataGene Ltd, Bundoora, 5 Ring Road, Vic. 3083, Australia.
D School of Applied Systems Biology, La Trobe University, Bundoora, Vic. 3083, Australia.
E Corresponding author. Email: phuong.ho@ecodev.vic.gov.au
Animal Production Science 60(1) 164-168 https://doi.org/10.1071/AN18532
Submitted: 31 August 2018 Accepted: 19 March 2019 Published: 24 April 2019
Abstract
Mid-infrared spectroscopy (MIRS) is traditionally used for analysing milk fat, protein and lactose concentrations in dairy production, but there is growing interest in using it to predict difficult, or expensive-to-measure, phenotypes on a large scale. The resulting prediction equations can be applied to MIRS data from commercial herd-testing, to facilitate management and feeding decisions, or for genomic selection purposes. We investigated the ability of MIRS of milk samples to predict milk fatty acids (FAs) and energy balance (EB) of dairy cows in Australia. Data from 240 Holstein lactating cows that were part of two 32-day experiments, were used. Milk FAs were measured twice during the experimental period. Prediction models were developed using partial least-square regression with a 10-fold cross-validation. Measures of prediction accuracy included the coefficient of determination (R2cv) and root mean-square error. Milk FAs with a chain length of ≤16 were accurately predicted (0.89 ≤ R2cv ≤ 0.95), while prediction accuracy for FAs with a chain length of ≥17 was slightly lower (0.72 ≤ R2cv ≤ 0.82). The accuracy of the model prediction was moderate for EB, with the value of R2cv of 0.48. In conclusion, the ability of MIRS to predict milk FAs was high, while EB was moderately predicted. A larger dataset is needed to improve the accuracy and the robustness of the prediction models.
Additional keywords: metabolic status, performance, prediction accuracy.
References
Banos G, Coffey MP (2010) Genetic association between body energy measured throughout lactation and fertility in dairy cattle. Animal 4, 189–199.Barbano D, Mellili C (2017) Milk fatty acid composition to support management of feeding, health and reproduction in dairy cows. In ‘71st annual convention: 2017 Virginia State Feed Association and Nutritional Management ‘Cow’ College’, 15–16 February 2017, Roanoke, Virginia, USA.
Bell MJ, Tzimiropoulos G (2018) Novel monitoring systems to obtain dairy cattle phenotypes associated with sustainable production. Frontiers in Sustainable Food Systems 2,
Coffey MP, Emmans GC, Brotherstone S (2001) Genetic evaluation of dairy bulls for energy balance traits using random regression. Animal Science 73, 29–40.
| Genetic evaluation of dairy bulls for energy balance traits using random regression.Crossref | GoogleScholarGoogle Scholar |
Coffey MP, Simm G, Brotherstone S (2002) Energy balance profiles for the first three lactations of dairy cows estimated using random regression. Journal of Dairy Science 85, 2669–2678.
| Energy balance profiles for the first three lactations of dairy cows estimated using random regression.Crossref | GoogleScholarGoogle Scholar | 12416821PubMed |
Conte G, Dimauro C, Serra A, Macciotta NPP, Mele M (2018) A canonical discriminant analysis to study the association between milk fatty acids of ruminal origin and milk fat depression in dairy cows. Journal of Dairy Science 101, 6497–6510.
| A canonical discriminant analysis to study the association between milk fatty acids of ruminal origin and milk fat depression in dairy cows.Crossref | GoogleScholarGoogle Scholar | 29627248PubMed |
De Marchi M, Penasa M, Cecchinato A, Mele M, Secchiari P, Bittante G (2011) Effectiveness of mid-infrared spectroscopy to predict fatty acid composition of Brown Swiss bovine milk. Animal 5, 1653–1658.
De Marchi M, Toffanin V, Cassandro M, Penasa M (2014) Invited review: mid-infrared spectroscopy as phenotyping tool for milk traits. Journal of Dairy Science 97, 1171–1186.
| Invited review: mid-infrared spectroscopy as phenotyping tool for milk traits.Crossref | GoogleScholarGoogle Scholar | 24440251PubMed |
Emmans GC (1994) Effective energy: a concept of energy utilization applied across species. British Journal of Nutrition 71, 801–821.
| Effective energy: a concept of energy utilization applied across species.Crossref | GoogleScholarGoogle Scholar | 8031731PubMed |
Gengler N, Soyeurt H, Dehareng F, Bastin C, Colinet F, Hammami H, Vanrobays ML, Lainé A, Vanderick S, Grelet C, Vanlierde A, Froidmont E, Dardenne P (2016) Capitalizing on fine milk composition for breeding and management of dairy cows. Journal of Dairy Science 99, 4071–4079.
| Capitalizing on fine milk composition for breeding and management of dairy cows.Crossref | GoogleScholarGoogle Scholar | 26778306PubMed |
Gengler N, Mineur A, Vanderick S, Hammami H, Consortium G (2018) Milk mid-infrared spectra based biomarkers contributing to genetic improvement for udder health, fertility and longevity. ICAR technical series no. 23. pp. 221–227.
Gottardo P, Penasa M, Righi F, Lopez-Villalobos N, Cassandro M, De Marchi M (2017) Fatty acid composition of milk from Holstein–Friesian, Brown Swiss, Simmental and Alpine Grey cows predicted by mid-infrared spectroscopy. Italian Journal of Animal Science 16, 380–389.
| Fatty acid composition of milk from Holstein–Friesian, Brown Swiss, Simmental and Alpine Grey cows predicted by mid-infrared spectroscopy.Crossref | GoogleScholarGoogle Scholar |
Gross J, van Dorland HA, Bruckmaier RM, Schwarz FJ (2011) Milk fatty acid profile related to energy balance in dairy cows. The Journal of Dairy Research 78, 479–488.
| Milk fatty acid profile related to energy balance in dairy cows.Crossref | GoogleScholarGoogle Scholar | 21843394PubMed |
Hein L, Sørensen LP, Kargo M, Buitenhuis AJ (2018) Genetic analysis of predicted fatty acid profiles of milk from Danish Holstein and Danish Jersey cattle populations. Journal of Dairy Science 101, 2148–2157.
| Genetic analysis of predicted fatty acid profiles of milk from Danish Holstein and Danish Jersey cattle populations.Crossref | GoogleScholarGoogle Scholar | 29248226PubMed |
Hewavitharana AK, van Brakel B (1997) Fourier transform infrared spectrometric method for the rapid determination of casein in raw milk. Analyst (London) 122, 701–704.
| Fourier transform infrared spectrometric method for the rapid determination of casein in raw milk.Crossref | GoogleScholarGoogle Scholar |
James G, Witten D, Hastie T, Tibshirani R (2013) ‘An introduction to statistical learning. Vol. 112.’ (Springer: New York)
Jorjong S, van Knegsel ATM, Verwaeren J, Lahoz MV, Bruckmaier RM, De Baets B, Kemp B, Fievez V (2014) Milk fatty acids as possible biomarkers to early diagnose elevated concentrations of blood plasma nonesterified fatty acids in dairy cows. Journal of Dairy Science 97, 7054–7064.
| Milk fatty acids as possible biomarkers to early diagnose elevated concentrations of blood plasma nonesterified fatty acids in dairy cows.Crossref | GoogleScholarGoogle Scholar | 25200787PubMed |
Knutsen TM, Olsen HG, Tafintseva V, Svendsen M, Kohler A, Kent MP, Lien S (2018) Unravelling genetic variation underlying de novo-synthesis of bovine milk fatty acids. Scientific Reports 8, 2179
| Unravelling genetic variation underlying de novo-synthesis of bovine milk fatty acids.Crossref | GoogleScholarGoogle Scholar | 29391528PubMed |
Lainé A, Bastin C, Grelet C, Hammami H, Colinet FG, Dale LM, Gillon A, Vandenplas J, Dehareng F, Gengler N (2017) Assessing the effect of pregnancy stage on milk composition of dairy cows using mid-infrared spectra. Journal of Dairy Science 100, 2863–2876.
| Assessing the effect of pregnancy stage on milk composition of dairy cows using mid-infrared spectra.Crossref | GoogleScholarGoogle Scholar | 28131584PubMed |
Lassen J, Poulsen NA, Larsen MK, Buitenhuis AJ (2016) Genetic and genomic relationship between methane production measured in breath and fatty acid content in milk samples from Danish Holsteins. Animal Production Science 56, 298–303.
| Genetic and genomic relationship between methane production measured in breath and fatty acid content in milk samples from Danish Holsteins.Crossref | GoogleScholarGoogle Scholar |
Luinge HJ, Hop E, Lutz ETG, van Hemert JA, de Jong EAM (1993) Determination of the fat, protein and lactose content of milk using Fourier transform infrared spectrometry. Analytica Chimica Acta 284, 419–433.
| Determination of the fat, protein and lactose content of milk using Fourier transform infrared spectrometry.Crossref | GoogleScholarGoogle Scholar |
Luke TDW, Rochfort S, Wales WJ, Pryce JE (2018) Prediction of serum metabolic profile biomarkers in early lactation dairy cows using mid-infrared spectroscopy of milk. In ‘International Council of Animal Recording (ICAR) annual meeting’, 8–11 February 2018, Auckland, New Zealand.
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, 3651–3661.
| The use of mid-infrared spectrometry to predict body energy status of Holstein cows.Crossref | GoogleScholarGoogle Scholar | 21700055PubMed |
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, 7225–7235.
| Validation of mid-infrared spectrometry in milk for predicting body energy status in Holstein–Friesian cows.Crossref | GoogleScholarGoogle Scholar | 23040020PubMed |
McParland S, Lewis E, Kennedy E, Moore SG, McCarthy B, O’Donovan M, Butler ST, Pryce JE, Berry DP (2014) Mid-infrared spectrometry of milk as a predictor of energy intake and efficiency in lactating dairy cows. Journal of Dairy Science 97, 5863–5871.
| Mid-infrared spectrometry of milk as a predictor of energy intake and efficiency in lactating dairy cows.Crossref | GoogleScholarGoogle Scholar | 24997658PubMed |
Mevik B-H, Wehrens R (2007) The pls package: principal component and partial least squares regression in R. Journal of Statistical Software 18, 1–23.
Moate PJ, Jacobs JL, Hannah MC, Morris GL, Beauchemin KA, Alvarez Hess PS, Eckard RJ, Liu Z, Rochfort S, Wales WJ, Williams SRO (2018) Adaptation responses in milk fat yield and methane emissions of dairy cows when wheat was included in their diet for 16 weeks. Journal of Dairy Science 101, 7117–7132.
| Adaptation responses in milk fat yield and methane emissions of dairy cows when wheat was included in their diet for 16 weeks.Crossref | GoogleScholarGoogle Scholar | 29729908PubMed |
Rutten MJM, Bovenhuis H, Hettinga KA, van Valenberg 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, 6202–6209.
| Predicting bovine milk fat composition using infrared spectroscopy based on milk samples collected in winter and summer.Crossref | GoogleScholarGoogle Scholar |
Savitzky A, Golay MJ (1964) Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36, 1627–1639.
| Smoothing and differentiation of data by simplified least squares procedures.Crossref | GoogleScholarGoogle Scholar |
Shenk JS, Westerhaus MO (1995) Forage analysis by near infrared spectroscopy. In ‘Forages. Vol. II. The Science of Grassland Agriculture’. 5th edn. (Eds RF Barnes, DA Miller, CJ Nelson) pp. 111–120. (Iowa State University Press: Ames, IA)
Smith S, Hicks V, Coffey M, Winters EM, Wall E (2017) A Star Tech, the Final FrontMIR: estimated breeding values for mid-infrared derived predictions of energy traits in dairy cows. In ‘The 41st ICAR conference’, 14–16 June 2017, Edinburgh, UK.
Soyeurt H, Dardenne P, Dehareng F, Lognay G, Veselko D, Marlier M, Bertozzi C, Mayeres P, Gengler N (2006) Estimating fatty acid content in cow milk using mid-infrared spectrometry. Journal of Dairy Science 89, 3690–3695.
| Estimating fatty acid content in cow milk using mid-infrared spectrometry.Crossref | GoogleScholarGoogle Scholar | 16899705PubMed |
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 94, 1657–1667.
| Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries.Crossref | GoogleScholarGoogle Scholar | 21426953PubMed |
Thorup VM, Edwards D, Friggens NC (2012) On-farm estimation of energy balance in dairy cows using only frequent body weight measurements and body condition score. Journal of Dairy Science 95, 1784–1793.
| On-farm estimation of energy balance in dairy cows using only frequent body weight measurements and body condition score.Crossref | GoogleScholarGoogle Scholar | 22459827PubMed |
Toledo-Alvarado H, Vazquez AI, de los Campos G, Tempelman RJ, Gabai G, Cecchinato A, Bittante G (2018) Changes in milk characteristics and fatty acid profile during the estrous cycle in dairy cows. Journal of Dairy Science 101, 9135–9153.
| Changes in milk characteristics and fatty acid profile during the estrous cycle in dairy cows.Crossref | GoogleScholarGoogle Scholar | 30055916PubMed |
van Gastelen S, Mollenhorst H, Antunes-Fernandes EC, Hettinga KA, van Burgsteden GG, Dijkstra J, Rademaker JLW (2018) Predicting enteric methane emission of dairy cows with milk Fourier-transform infrared spectra and gas chromatography-based milk fatty acid profiles. Journal of Dairy Science 101, 5582–5598.
| Predicting enteric methane emission of dairy cows with milk Fourier-transform infrared spectra and gas chromatography-based milk fatty acid profiles.Crossref | GoogleScholarGoogle Scholar | 29550122PubMed |
Wallén SE, Prestløkken E, Meuwissen THE, McParland S, Berry DP (2018) Milk mid-infrared spectral data as a tool to predict feed intake in lactating Norwegian Red dairy cows. Journal of Dairy Science 101, 6232–6243.
| Milk mid-infrared spectral data as a tool to predict feed intake in lactating Norwegian Red dairy cows.Crossref | GoogleScholarGoogle Scholar | 29605317PubMed |
Williams P (2004) ‘Near-infrared technology: getting the best out of light: a short course in the practical implementation of near-infrared spectroscopy for the user.’ (PDK Projects, Incorporated: Nanaimo, Canada)
