Standardisation of milk MIR spectra: a first step to create new tools of dairy farm management

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Merging of spectral datasets from different MIR instruments used in the routine analysis of milk

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Genetic analysis of milk citrate predicted by milk mid-infrared spectra of Holstein cows in early lactation.

peer reviewedMilk citrate is regarded as an early biomarker of negative energy balance (NEB) in dairy cows during early lactation and serves as a suitable candidate phenotype for genomic selection due to its wide availability across a large number of cows through milk mid-infrared spectra prediction. However, its genetic background is not well known. Therefore, the objectives of this study were to (1) analyze the genetic parameters of milk citrate; (2) identify genomic regions associated with milk citrate; (3) analyze the functional annotation of candidate genes and quantitative trait loci (QTL) related to milk citrate in Walloon Holstein cows. In total, 134,517 test-day milk citrate phenotypes (mmol/L) collected within the first 50 d in milk (DIM) on 52,198 Holstein cows were used. These milk citrate phenotypes, predicted by milk mid-infrared spectra, were divided into 3 traits according to the first (citrate1), second (citrate2), and third to fifth parity (citrate3+). Genomic information for 566,170 SNPs was available for 4,479 animals. A multiple-trait repeatability model was used to estimate genetic parameters. A single-step genome-wide association study (GWAS) was used to identify candidate genes for citrate and post-GWAS analysis was done to investigate relationship and function of the identified candidate genes. The heritabilities estimated for citrate1, citrate2 and citrate3+ were 0.40, 0.37 and 0.35, respectively. The genetic correlations between the 3 traits ranged from 0.98 to 0.99. The genomic correlations between the 3 traits were also nearly 1.00 across the genomic regions (1 Mb) in the whole genome, which means that citrate can be considered as a single trait in the first 5 parities. In total, 603 significant SNPs located on 3 genomic regions (chromosome7 68.569 - 68.575 Mb, 14 1.31 - 3.05 Mb, and 20 54.00 - 64.28 Mb), were identified to be associated with milk citrate. We identified 89 candidate genes including GPT, ANKH, PPP1R16A and 32 QTL reported in the literature related to the identified significant SNPs. These identified QTL were mainly reported associated with milk fatty acids and metabolic diseases in dairy cows. This study suggests that milk citrate in Holstein cows is highly heritable and has the potential to be used as an early proxy for the NEB of Holstein cows in a breeding objective

Prediction of pregnancy state from milk mid-infrared (MIR) spectroscopy in dairy cows

Submitted 2020-07-14 | Accepted 2020-08-18 | Available 2020-12-01https://doi.org/10.15414/afz.2020.23.mi-fpap.224-232Pregnancy assessment is a very important tool for the reproductive management in efficient and profitable dairy farms. Nowadays, mid-infrared (MIR) spectroscopy is the method of choice in the routine milk recording system for quality control and to determine standard milk components. Since it is well known that there are changes in milk yield and composition during pregnancy, the aim of this study was to develop a discriminant model to predict the pregnancy state from routinely recorded MIR spectral data. The data for this study was from the Austrian milk recording system. Test day records of Fleckvieh, Brown Swiss and Holstein Friesian cows between 3 and 305 days of lactation were included in the study. As predictor variables, the first derivative of 212 selected MIR spectral wavenumbers were used. The data set contained roughly 400,000 records from around 40,000 cows and was randomly split into calibration and validation set by farm. Prediction was done with Partial Least Square Discriminant Analysis. Indicators of model fit were sensitivity, specificity, balanced accuracy and Area Under Receiver Operating Characteristic Curve (AUC). In a first approach, one discriminant model for all cows across the whole lactation and gestation lengths was applied. The sensitivity and specificity of this model in validation were 0.856 and 0.836, respectively. Splitting up the results for different lactation stages showed that the model was not able to predict pregnant cases before the third month of lactation and vice versa not able to predict non-pregnancy after the third month of lactation. Consequently, in the second approach a prediction model for each different (expected) pregnancy stage and lactation stage was developed. Balanced accuracies ranged from 0.523 to 0.918. Whether prediction accuracies from this study are sufficient to provide farmers with an additional tool for fertility management, it needs to be explored in discussions with farmers and breeding organizations.Keywords: MIR spectroscopy, pregnancy prediction, dairy cow, PLSReferencesBalhara, A. K., Gupta, M., Singh, S., Mohanty, A. K., & Singh, I. (2013). Early pregnancy diagnosis in bovines: Current status and future directions. The Scientific World Journal, 2013. hhttps://doi.org/10.1155/2013/958540Bekele, N., Addis, M., Abdela, N., & Ahmed, W. M. (2016). Pregnancy Diagnosis in Cattle for Fertility Management: A Review. Global Veterinaria, 16(4), 355–364. https://doi.org/10.5829/idosi.gv.2016.16.04.103136Benedet, A., Franzoi, M., Penasa, M., Pellattiero, E., & De Marchi, M. (2019). Prediction of blood metabolites from milk mid-infrared spectra in early-lactation cows. Journal of Dairy Science, 102(12), 11298–11307. https://doi.org/10.3168/jds.2019-16937Delhez, P., Ho, P. N., Gengler, N., Soyeurt, H., & Pryce, J. E. (2020). Diagnosing the pregnancy status of dairy cows: How useful is milk mid-infrared spectroscopy? Journal of Dairy Science, 103(4), 3264–3274. https://doi.org/10.3168/jds.2019-17473Egger-Danner, C., Fürst, C., Mayerhofer, M., Rain, C., & Rehling, C. (2018). ZuchtData Jahresbericht 2018. Vienna. [Online]. Available at: https://zar.at/Downloads/Jahresberichte/ZuchtData-Jahresberichte.html. [Accessed: 2020, May 15].Gengler, N., Tijani, A., Wiggans, G. R., & Misztal, I. (1999). Estimation of (Co)variance function coefficients for test day yield with a expectation-maximization restricted maximum likelihood algorithm. Journal of Dairy Science, 82(8), 1849.e1-1849.e23. https://doi.org/10.3168/jds.S0022-0302(99)75417-2Grelet, C., Fernández Pierna, J. A., Dardenne, P., Baeten, V., & Dehareng, F. (2015). Standardization of milk mid-infrared spectra from a European dairy network. Journal of Dairy Science, 98(4), 2150–2160. https://doi.org/10.3168/jds.2014-8764Grelet, C., Bastin, C., Gelé, M., Davière, J. B., Johan, M., Werner, A., Reding, R., Fernandes Pierna, J. A., Colinet, F. G., Dardenne, P., Gendler, N., Soyeurt, H. & Dehareng, F. (2016). Development of Fourier transform mid-infrared calibrations to predict acetone, β-hydroxybutyrate, and citrate contents in bovine milk through a European dairy network. Journal of Dairy Science, 99(6), 4816–4825. https://doi.org/10.3168/jds.2015-10477Hirpa, A., Yehualaw, B., Wube, A., Asnake, A., Jemberu, A., Medicine, V., & Box, P. O. (2018). Review on Pregnancy Diagnosis in Dairy Cows, 9(2), 45–55. https://doi.org/10.5829/idosi.jri.2018.45.55Ho, P. N., Bonfatti, V., Luke, T. D. W., & Pryce, J. E. (2019). Classifying the fertility of dairy cows using milk mid-infrared spectroscopy. Journal of Dairy Science. https://doi.org/10.3168/jds.2019-16412Humblot, P. (2001). Monitor Pregnancy and Determine the Timing , Frequencies and Sources of Embryonic Mortality in Ruminants. Theriogenology, 56(01), 1417–1433.Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 28(5), 1–26.Lainé, A., Bel Mabrouk, H., Dale, L. M., Bastin, C., & Gengler, N. (2014). How to use mid-infrared spectral information from milk recording system to detect the pregnancy status of dairy cows. Communications in Agricultural and Applied Biological Sciences, 79(1), 33–38.Lainé, A., Bastin, C., Grelet, C., Hammami, H., Colinet, F. G., Dale, L. M., Gillon, A., Vandenplas, J., Deharend, 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(4), 2863–2876.https://doi.org/10.3168/jds.2016-11736Lantz, B. (2015). Machine Learning with R. Machine Learning (Second Edi). Packt Publishing Ltd. https://doi.org/10.1002/9781119642183.ch14Mineur, A., Köck, A., Grelet, C., Gengler, N., Egger-Danner, C., & Sölkner, J. (2017). First Results in the Use of Milk Mid-infrared Spectra in the Detection of Lameness in Austrian Dairy Cows Genomic evaluation View project MACSUR View project. Agriculturae Conspectus Scientifi Cus, Vol. 82(No. 2 (163-166)), (163-166). Retrieved from https://www.researchgate.net/publication/325450513Olori, V. E., Brotherstone, S., Hill, W. G., & McGuirk, B. J. (1997). Effect of gestation stage on milk yield and composition in Holstein Friesian dairy cattle. Livestock Production Science, 52(2), 167–176. https://doi.org/10.1016/S0301-6226(97)00126-7Pohler, K. G., Franco, G. A., Reese, S. T., Dantas, F. G., Ellis, M. D., & Payton, R. R. (2016). Past, present and future of pregnancy detection methods. Applied Reproductive Strategies in Beef Cattle 7-8 September 2016, 251–259.Rienesl, L., Khayatzadeh, N., Köck, A., Dale, L., Werner, A., Grelet, C., Gengler, N., Auer, F-J., Egger-Danner, C., Massart, X. & Sölkner, J. (2019). Mastitis detection from milk mid-infrared (MIR) spectroscopy in dairy cows. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 67(5), 1221–1226. https://doi.org/10.11118/actaun201967051221Santos, J. E. P., Thatcher, W. W., Chebel, R. C., Cerri, R. L. A., & Galvão, K. N. (2004). The effect of embryonic death rates in cattle on the efficacy of estrus synchronization programs. Animal Reproduction Science, 82–83, 513–535. https://doi.org/10.1016/j.anireprosci.2004.04.015SAS Institute Inc. (2017). SAS software 9.4. SAS Institute Inc., Cary, NC, USA.Soyeurt, H., Dehareng, F., Gengler, N., McParland, S., Wall, E., Berry, D. P., Coffey, P. & Dardenne, P. (2011). Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. Journal of Dairy Science, 94(4), 1657–1667. https://doi.org/10.3168/jds.2010-3408Soyeurt, H., Bastin, C., Colinet, F. G., Arnould, V. M.-R., Berry, D. P., Wall, E., Dehareng, F., Nguyen, H. N., Pardenne, 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(11), 1830–1838. https://doi.org/10.1017/s1751731112000791Toffanin, V., De Marchi, M., Lopez-Villalobos, N., & Cassandro, M. (2015). Effectiveness of mid-infrared spectroscopy for prediction of the contents of calcium and phosphorus, and titratable acidity of milk and their relationship with milk quality and coagulation properties. International Dairy Journal, 41, 68–73. https://doi.org/10.1016/j.idairyj.2014.10.002Vanlierde, A., Vanrobays, M.-L., Dehareng, F., Froidmont, E., Soyeurt, H., McParland, S., S., Lewis, E., Deighton, M. H., Grandl, F., Kreuzer, M., Gredler, B., Dardenne, P. & Gengler, N. (2015). Hot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra. Journal of Dairy Science, 98(8), 5740–5747. https://doi.org/10.3168/jds.2014-8436Vanlierde, A., Soyeurt, H., Gengler, N., Colinet, F. G., Froidmont, E., Kreuzer, M., Grandl, F., Bell, M., Lund, P., Olijhoek, D. W., Eugéne M., Martin, C., Kuhla, B. & Dehareng, F. (2018). 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. Journal of Dairy Science, 101(8). https://doi.org/10.3168/jds.2018-14472

Contrôle approprié des données pour améliorer la pertinence des données prédictes par la spectrométrie moyen infrarouge du lait

peer reviewedThe use of abnormal milk mid-infrared (MIR) spectrum strongly affects prediction quality, even if the prediction equations used are accurate. So, this record must be detected after or before the prediction process to avoid erroneous spectral extrapolation or the use of poor-quality spectral data by dairy herd improvement (DHI) organizations. For financial or practical reasons, adapting the quality protocol currently used to improve the accuracy of fat and protein contents is unfeasible. This study proposed three different statistical methods that would be easy to implement by DHI organizations to solve this issue: the deletion of 1% of the extreme high and low predictive values (M1), the deletion of records based on the Global-H (GH) distance (M2), and the deletion of records based on the absolute fat residual value (M3). Additionally, the combinations of these three methods were investigated. A total of 346,818 milk samples were analyzed by MIR spectrometry to predict the contents of fat, protein, and fatty acids. Then, the same traits were also predicted externally using their corresponded standardized MIR spectra. The interest in cleaning procedures was assessed by estimating the root mean square differences (RMSDs) between those internal and external predicted phenotypes. All methods allowed for a decrease in the RMSD, with a gain ranging from 0.32% to 41.39%. Based on the obtained results, the "M1 and M2" combination should be preferred to be more parsimonious in the data loss, as it had the higher ratio of RMSD gain to data loss. This method deleted the records based on the 2% extreme predictions and a GH threshold set at 5. However, to ensure the lowest RMSD, the "M2 or M3" combination, considering a GH threshold of 5 and an absolute fat residual difference set at 0.30 g/dL of milk, was the most relevant. Both combinations involved M2 confirming the high interest of calculating the GH distance for all samples to predict. However, if it is impossible to estimate the GH distance due to a lack of relevant information to compute this statistical parameter, the obtained results recommended the use of M1 combined with M3. The limitation used in M3 must be adapted by the DHI, as this will depend on the spectral data and the equation used. The methodology proposed in this study can be generalized for other MIR-based phenotypes

Associations between Circulating IGF-1 Concentrations, Disease Status and the Leukocyte Transcriptome in Early Lactation Dairy Cows

Publication history: Accepted - 19 November 2021; Published - 25 November 2021.Peripartum dairy cows commonly experience negative energy balance (EB) and immunosuppression together with high incidences of infectious and metabolic disease. This study investigated mechanisms linking EB status with immune defense in early lactation. Data were collected from multiparous Holstein cows from six herds and leukocyte transcriptomes were analyzed using RNA sequencing. Global gene expression was related to circulating IGF-1 (as a biomarker for EB) by subdividing animals into three groups, defined as IGF-1 LOW (100 ng/mL, n = 43) at 14 ± 4 days in milk (DIM). Differentially expressed genes between groups were identified using CLC Genomics Workbench V21, followed by cluster and KEGG pathway analysis, focusing on the comparison between LOW and HIGH IGF-1 cows. LOW cows were older and had significantly lower dry matter intakes and EB values, whereas HIGH cows produced more milk. During the first 35 DIM, 63% of LOW cows had more than one health problem vs. 26% HIGH cows, including more with clinical mastitis and uterine infections. Gene expression analysis indicated that leukocytes in LOW cows switched energy metabolism from oxidative phosphorylation to aerobic glycolysis (PGM, LDH, and PDK4). Many antimicrobial peptides were up-regulated in LOW cows (e.g., PTX3, DMBT1, S100A8, and S100A9) together with genes associated with inflammation, platelet activation and the complement cascade. HIGH cows had greater expression of genes regulating T and B cell function and the cytoskeleton. Overall, results suggested an ongoing cycle of poor EB and higher infection rates in LOW IGF-1 cows which was reflected in altered leukocyte functionality and reduced milk production.This project received funding from the European Union’s Seventh Framework Programme (Brussels, Belgium) for research, technological development, and demonstration under grant agreement no. 61368

Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms.

peer reviewedKnowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points

Promoting international prediction models through standardization of milk mid-infrared spectra

Analysis of milk by Fourier transform mid-infrared (FT-MIR) spectrometry provides a large amount of information on the physico-chemical composition of individual milk samples. Hence, it has been used for decades to predict fat, protein and lactose contents, and more recently fine milk composition, milk processing qualities and status of cows. This fast and cost-effective technology is a perfect candidate to provide new information for the management of individual cows. However, its concrete use by field organizations is still suboptimal given the difficulty of sharing data and models among spectrometers. The aim of this research was to optimize the use of FT-MIR analysis of milk with the final purpose of enabling the development of new management tools for dairy farmers. In order to harmonize spectral responses among instruments and allow sharing of data and models, the first objective was to test a standardization method, well known from the NIR sector, in the frame of FT-MIR spectrometers dedicated to milk analysis. The possibility of standardizing such instruments was assessed by using the Piece-wise Direct Standardization (PDS) method and common raw milk samples constituted from the IDF norm (ISO 9622:2013 | IDF 141:2013). The performances of spectral harmonization were assessed by the transfer of a robust fat model from a master instrument into 21 slave instruments. Regressions were performed between master and each slave fat predictions, before and after PDS. The biases and the root mean square errors between the predictions decreased after PDS from 0.378 to 0.000 and from 0.461 to 0.016 (g of fat/100 mL of milk), respectively. These preliminary results showed that the PDS method permits a reduction of the inherent spectral variability between instruments and the use of common robust models by all the spectrometers included in the constituted network. The second objective was to ensure that models of interest with low precision could also be transferred from instrument to instrument. The effect of standardization on network spectral reproducibility was assessed on 66 instruments from 3 different brands by comparing the spectral variability of the slave instruments with and without standardization. With standardization, the standardized Mahalanobis distance (GH) between the slaves and master spectra was reduced on average from 2,656 to 14. The transfer of models from instrument to instrument was then tested using 3 FT-MIR models predicting the quantity of daily methane emitted by dairy cows, the concentration of polyunsaturated fatty acids in milk, and the fresh cheese yield. The differences, in terms of root mean squared error, between master and slaves predictions were reduced after standardization on average from 103 to 17 g/d for methane, from 0.032 to 0.005 g/100 mL of milk for polyunsaturated fatty acids, and from 2.55 to 0.49 g of curd/100 g of milk for fresh cheese yield. For all models, an improvement of prediction reproducibility within the network has also been observed. Concretely, the spectral standardization allows the transfer and use of multiple models on all instruments as well as the improvement of spectral and prediction reproducibility within the network. The method offers opportunities for data exchange as well as the creation and use of common database and models, at an international level, to provide more information for the management of dairy herds. After ensuring the possibility of using spectral data under optimal conditions, the third objective was to concretize the development of models providing information on cow status to be used as management tools by dairy farmers. This work aimed to develop models to predict milk citrate, reflecting early energy imbalance, and milk acetone and β-hydroxybutyrate (BHB) as indicators of (sub)clinical ketosis. Milk samples were collected in commercial and experimental farms in Luxembourg, France, and Germany. Milk mid-infrared spectra were recorded locally and standardized. Prediction equations were developed using partial least square regression. The coefficient of determination (R²) of cross-validation was 0.73 for acetone, 0.71 for BHB, and 0.90 for citrate with root mean square error of 0.248, 0.109, and 0.70 mmol/L, respectively. Finally, an external validation was performed and R² obtained were 0.67 for acetone, 0.63 for BHB, and 0.86 for citrate, with a root mean square error of validation of 0.196, 0.083, and 0.76 mmol/L, respectively. The results demonstrated the potential of FT-MIR spectrometry to predict citrate content with good accuracy and to supply indicative contents of BHB and acetone in milk, thereby providing rapid and cost-effective tools to manage ketosis and negative energy balance in dairy farms. This research highlights new knowledge and possibilities regarding the harmonization of spectral format from different instruments in order to create, share and use FT-MIR models providing information for the management of dairy cows. More concretely, it contributes outputs as procedures to standardize instruments in routine and models to predict indicators of negative energy balance and ketosis to help farmers in the management of early lactation period