References of "Soyeurt, Hélène"
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See detailLarge-scale phenotyping in dairy sector using milk MIR spectra: Key factors affecting the quality of predictions
Grelet, Clément; Dardenne, Pierre; Soyeurt, Hélène ULiege et al

in Methods (in press)

Methods and technologies enabling the estimation at large scale of important traits for the dairy sector are of great interest. Those phenotypes are necessary to improve herd management, animal genetic ... [more ▼]

Methods and technologies enabling the estimation at large scale of important traits for the dairy sector are of great interest. Those phenotypes are necessary to improve herd management, animal genetic evaluation, and milk quality control. In the recent years, the research was very active to predict new phenotypes from the mid-infrared (MIR) analysis of milk. Models were developed to predict phenotypes such as fine milk composition, milk technological properties or traits related to cow health, fertility and environmental impact. Most of models were developed within research contexts and often not designed for routine use. The implementation of models at a large scale to predict new traits of interest brings new challenges as the factors influencing the robustness of models are poorly documented. The first objective of this work is to highlight the impact on prediction accuracy of factors such as the variability of the spectral and reference data, the spectral regions used and the complexity of models. The second objective is to emphasize methods and indicators to evaluate the quality of models and the quality of predictions generated under routine conditions. The last objective is to outline the issues and the solutions linked with the use and transfer of models on large number of instruments. Based on partial least square regression and 10 datasets including milk MIR spectra and reference quantitative values for 57 traits of interest, the impact of the different factors is illustrated by evaluating the influence on the validation root mean square error of prediction (RMSEP). In the displayed examples, all factors, when well set up, increase the quality of predictions, with an improvement of the RMSEP ranging from 12% to 43%. This work also aims to underline the need for and the complementarity between different validation procedures, statistical parameters and quality assurance methods. Finally, when using and transferring models, the impact of the spectral standardization on the prediction reproducibility is highlighted with an improvement up to 86% with the tested models, and the monitoring of individual spectrometer stability over time appears essential. This list inspired from our experience is of course not exhaustive. The displayed results are only examples and not general rules and other aspects play a role in the quality of final predictions. However, this work highlights good practices, methods and indicators to increase and evaluate quality of phenotypes predicted at a large scale. The results obtained argue for the development of guidelines at international levels, as well as international collaborations in order to constitute large and robust datasets and enable the use of models in routine conditions. [less ▲]

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See detailAssessing animal welfare: Deriving individual welfare phenotypes from existing milk recording data
Franceschini, Sébastien ULiege; Leblois, Julie ULiege; Lepot, F. et al

Poster (2020, June)

Animal welfare is an increasing concern in dairy production. Consumers want an ethical production while farmers want to ensure the health of the animals. Animal welfare measurements at the herd level such ... [more ▼]

Animal welfare is an increasing concern in dairy production. Consumers want an ethical production while farmers want to ensure the health of the animals. Animal welfare measurements at the herd level such as the Welfare Quality® (WQ®) Protocol already exist but are time-consuming and costly. Moreover, assessing the overall well-being at the animal level becomes a challenge as herd measures for welfare can not be directly translated to the animal level. Two projects, active in the Walloon Region of Belgium, HappyMoo (Interreg NWE) and ScorWelCow, are trying to define individual welfare scores (IWS) and their prediction from routinely measured milk recording data, including mid-infrared spectral data representing fine milk composition. Data from WQ® Protocol and routine milk recording was collected during the same timeframe in 18 dairy farms with 1386 cows, the majority being genotyped. Two approaches to assess and to predict individual animal welfare were developed. The first approach consisted of two steps: translating the WQ® principles into IWS and predicting these from milk recording data. The variation observed in the first step while regressing WQ® animal measures on WQ® principles was considered representative of the biological variation between cows. IWS prediction Partial Least Square regression for the 4 principles of the welfare quality scores have R2 between 0.65 and 0.77. Moreover, results from this first approach showed a significant welfare assessor effect suggesting that welfare measurements were strongly human interpretation-dependent. This suggested the need for an alternative approach. The second approach directly used milk recording data such as spectral data to cluster cows in different groups, bypassing a priori definition of welfare by WQ®. Those groups were compared to results from the first approach and showed possible discrimination for herds with enhanced WQ® score ( Specificity = 1.00 but Sensitivity = 0.10) thus suggesting further unsupervised analysis. Based on this research, novel individual welfare traits could be developed allowing future genomic selection for improved welfare. [less ▲]

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See detailDiagnosing the pregnancy status of dairy cows: How useful is milk mid-infrared spectroscopy?
Delhez, Pauline ULiege; Ho, Phuong; Gengler, Nicolas ULiege et al

in Journal of Dairy Science (2020), 103(4), 3264-3274

Pregnancy diagnosis is an essential part of successful breeding programs on dairy farms. Milk composition alters with pregnancy, and this is well documented. Fourier-transform mid-infrared (MIR ... [more ▼]

Pregnancy diagnosis is an essential part of successful breeding programs on dairy farms. Milk composition alters with pregnancy, and this is well documented. Fourier-transform mid-infrared (MIR) spectroscopy is a rapid and cost-effective method for providing milk spectra that reflect the detailed composition of milk samples. Therefore, the aim of this study was to assess the ability of MIR spectroscopy to predict the pregnancy status of dairy cows. The MIR spectra and insemination records were available from 8,064 Holstein cows of 19 commercial dairy farms in Australia. Three strategies were studied to classify cows as open or pregnant using partial least squares discriminant analysis models with random cow-independent 10-fold cross-validation and external validation on a cow-independent test set. The first strategy considered 6,754 MIR spectra after insemination used as independent variables in the model. The results showed little ability to detect the pregnancy status as the area under the receiver operating characteristic curve was 0.63 and 0.65 for cross-validation and testing, respectively. The second strategy, involving 1,664 records, aimed to reduce noise in the MIR spectra used as predictors by subtracting a spectrum before insemination (i.e., open spectrum) from the spectrum after insemination. The accuracy was comparable with the first approach, showing no the superiority of the method. Given the limited results for these models when using combined data from all stages after insemination, the third strategy explored separate models at 7 stages after insemination comprising 348 to 1,566 records each (i.e., progressively greater gestation) with single MIR spectra after insemination as predictors. The models developed using data recorded after 150 d of pregnancy showed promising prediction accuracy with the average value of area under the receiver operating characteristic curve of 0.78 and 0.76 obtained through cross-validation and testing, respectively. If this can be confirmed on a larger data set and extended to somewhat earlier stages after insemination, the model could be used as a complementary tool to detect fetal abortion. [less ▲]

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See detailStrategies of the Walloon dairy producersfaced to the uncertain dairy future
Dalcq, Anne-Catherine ULiege; Dogot, Thomas ULiege; Soyeurt, Hélène ULiege et al

Conference (2020, January 31)

This study observes the strategies, and their determinants, of the Walloon dairy producersfaced to the post quota perspective through the realisation of 245 surveys, conducted from November 2014 to ... [more ▼]

This study observes the strategies, and their determinants, of the Walloon dairy producersfaced to the post quota perspective through the realisation of 245 surveys, conducted from November 2014 to February 2015. It highlights how dairy production companies plan to evolve to cope with this great change in the sector and so how will move the production of our dairy products. Three kinds of strategical variables were defined and related to the evolution of milk production (MP) [the producerswho increase MP (HighMP) vs. keep constant MP (ConstantMP) vs. stop MP]; the valorisation of MP [alternative (ValMP)vs. classical] and the diversification of activities [with (DivMP) vs. without such activities]. The relationships between the chosen strategies and the quantitative technical variables were studied using generalised linear models. The independence between qualitative technical variables and the strategical variables was tested using Chi Square test. HighMP and ConstantMP producersrepresent 38.4% and 53.9% of respondents, respectively. HighMP producerswere significantly more declared as legal entity (p-value = 0.03), had more family members on the farm (p-value<0.01), larger agricultural area in property (p-value = 0.03) and higher MP quota(p-value = 0.01)compared to ConstantMP producers. Only 9.8% of respondents decide to valorise differently MP. ValMP producerstend to have more employees (p-value = 0.08) and an agricultural area less fragmented (p-value = 0.07)than classical producers. A total of 7.8% of respondents decide to develop other activities. DivMP producerstend to have more employees (p-value = 0.10), more agricultural area in property (p-value = 0.03) and a more recent year of installation (p-value < 0.01). Finally, 44.9% of ConstantMP producersdo not want to start an alternative valorisation of MP and diversify their activities. In conclusion, a relationship exists between, amongst others, the legal status, workforce available, characteristics of the agricultural area, the dairy production and the strategy chosen by the Walloon dairy producers. [less ▲]

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See detailManaging the high variability of compressed sward heights to model grass growth on pastures using satellite images
Nickmilder, Charles ULiege; Soyeurt, Hélène ULiege; Dufrasne, Isabelle ULiege et al

Poster (2020, January 31)

ROADSTEP is a Walloon research program aiming to develop decision tools to help farmers in their daily herd monitoring on pastures. One of the aims is to develop a modelling tool to predict the ... [more ▼]

ROADSTEP is a Walloon research program aiming to develop decision tools to help farmers in their daily herd monitoring on pastures. One of the aims is to develop a modelling tool to predict the availability of pasture feeding based on satellite images, meteorological variables and soil characteristics. So, 72,975 compressed sward heights (CSH) have been measured on 30 parcels located in 3 farms using Jenquip EC20G platemeter in 2018 and 2019. CSH records (175 ± 53 mm) seemed to be normally distributed based on the low values of skewness (-1.96) and kurtosis (3.28). However, CSH gathered per parcel and per date showed a trend to unfit a normal distribution and seemed to be dependent on the location of the measurement spot on the parcel. Indeed, the observed kurtosis per parcel and test date were comprised between 0.64 and 27.40. Skewness values ranged from -4.39 to -1.38. These high kurtosis values highlight that CSH records were not normally distributed per parcel. Therefore, the current way to use an average CSH to represent a parcel is not the best choice as this value is not representative. This implies the need to adopt an unbiased approach that enables the comparison of CSH and other variables between dates. The chosen method consists in splitting the parcels in square sub-blocks. Each cell of this grid gathers all the climatic-soil related-satellite-median CSH data and is used as the unitary entity to train the predictive model of the biomass available in the pasture. [less ▲]

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See detailAssessment of Walloon dairy farms eco-efficiency using Data Envelopment Analysis and easily-accessible environmental and economic indicators: a preliminary study
Delhez, Pauline ULiege; Reding, Edouard; Gengler, Nicolas ULiege et al

Poster (2020, January 31)

Achieving economically viable and environmentally friendly food production is a key challenge today. In this context, the aim of this study was to (i) analyse the economic and environmental efficiency (i ... [more ▼]

Achieving economically viable and environmentally friendly food production is a key challenge today. In this context, the aim of this study was to (i) analyse the economic and environmental efficiency (i.e., eco-efficiency) of a sample of specialised dairy farms in the Walloon region of Belgium; and (ii) to identify key management factors that differ between efficient and inefficient farms. Eco-efficiency was estimated with the productive efficiency benchmarking method Data Envelopment Analysis (DEA). DEA is a well-known technique for measuring the relative efficiency of comparable decision-making units using several inputs to produce one or more outputs. In our study, input and output variables were selected based on their economic and environmental relevance, as well as on their availability in the accounting database of the Walloon Breeding Association (awé). The chosen DEA inputs and output included economic-oriented variables such as fat and protein corrected milk yield and simple environmental indicators like land use, livestock units, fertiliser and pesticide application, purchased feed and on-farm energy use. Preliminary results on 174 dairy farms in 2017 suggested contrasting levels of eco-efficiency in our sample. Hypotheses concerning the determinants of eco-efficiency will be tested. The findings of this study will help inform policy-making towards dairy farm management that can increase dairy production at the least environmental costs. [less ▲]

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See detailCan fungal volatile organic compounds be used to develop aflatoxin-specific sensors?
Josselin, Laurie ULiege; De Boevre, Marthe; De Clerck, Caroline ULiege et al

Poster (2020, January 31)

Foodstuff (corn, wheat, rice, etc.) can be contaminated by several filamentous fungal species in pre or post-harvest conditions. Some of these, such as Aspergillus, Fusarium and Penicillium produce ... [more ▼]

Foodstuff (corn, wheat, rice, etc.) can be contaminated by several filamentous fungal species in pre or post-harvest conditions. Some of these, such as Aspergillus, Fusarium and Penicillium produce secondary metabolites, highly toxic at low concentrations to all vertebrates including humans: they can cause severe illnesses upon chronic exposure and can even lead to death after acute exposure. These non-volatile molecules are named mycotoxins and current methods to detect them, involving the use of ELISA tests or HPLC, are quite time consuming and expensive. At present there is no rapid test that does not require extensive sample preparation to detect the presence of mycotoxin directly in a production line (e.g. grain storage companies). Therefore, the aim of this work is to identify volatile organic compounds (VOCs) markers, specific of mycotoxins’ production in foodstuff. Using the SPME technique, we have characterized and compared the VOCs produced in vitro by non-aflatoxigenic (not producing aflatoxins) and aflatoxigenic strains of Aspergillus flavus (producing aflatoxins B1, B2 and G2, three types of mycotoxins). Preliminary analyses have shown similarities and differences between the two strains. Both of them emit VOCs as 1-octen-3-ol, 3-methylbutan-1-ol, octan-3-one, 2-methylbutanal, 3-methylbutanal, known in the literature to be specific of fungi. In particular, we have identified several strain-specific terpenes that are of interest for the development of the future molecular foot-print sensor. The next step is to study the VOCs produced in in vivo conditions, when the fungi are growing on stored cereals; and the correlation between specific VOCs and mycotoxin production. [less ▲]

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See detailShort communication: Potential prediction of vitamin B12 concentration based on mid-infrared spectral data using Holstein Dairy Herd Improvement milk samples
Duplessis, M.; Pellerin, D.; Girard, C. L. et al

in Journal of Dairy Science (2020), 103(8), 7540-7546

The purpose of this study was (1) to predict the quantitative concentration of vitamin B12 in milk using mid-infrared (MIR) spectrometry, and (2) to evaluate the potential of MIR spectra to discriminate ... [more ▼]

The purpose of this study was (1) to predict the quantitative concentration of vitamin B12 in milk using mid-infrared (MIR) spectrometry, and (2) to evaluate the potential of MIR spectra to discriminate different clusters of records based on their B12 concentration. Milk samples were collected from 4,340 Holstein cows between 3 and 592 d in milk and located in 100 herds. Samples were taken using in-line milk meters and divided into 2 aliquots: one for MIR spectrometry and the other for B12 concentration reference analyses by radioassay. Analyses were performed on 311 selected spectral wavelengths. A partial least squares regression model was built to quantify B12 concentration. Discriminant analysis was used to isolate B12 concentration clusters. A B12 concentration threshold was set at 442 ng/dL, because this represents the cutoff value for a 250-mL glass of milk to fulfill 46% of the daily vitamin B12 recommended dietary allowance for individuals 14 yr or older. For each analysis, records coming from two-thirds of herds were used to calibrate prediction equations, and the remaining records (one-third of herds for validation) were used to assess the prediction performance. In the case of discriminant analysis, validation sets were divided into evaluation sets (one-third of herds) to obtain alternate probability cutoffs and in test sets (two-thirds of herds) to validate equations. Spectral and B12 concentration outliers were identified by calculating standardized Mahalanobis distance and with a residual analysis, respectively (n = 3,154). Regarding quantitative B12 concentration, cross-validation and validation coefficients of determination averaged 0.51 and 0.46, respectively, which are relatively low, which would limit the potential use of the developed quantitative equations. In addition, root mean square errors of prediction of cross validation and validation sets averaged 88.9 and 94.7 ng/dL, respectively. Area under the receiver operating characteristic curve of test sets averaged 0.81 based on the 442 ng/dL threshold, which could be considered to represent good accuracy of classification. However, the false discovery rate averaged 36%. In summary, models predicting quantitative B12 concentration had low cross-validation and validation coefficients of determination, limiting their use, but the proposed discriminant models could be used to identify milk samples with naturally high B12. © 2020 American Dairy Science Association [less ▲]

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See detailPredicting milk mid-infrared spectra from first parity Holstein cows using a test-day mixed model with the perspective of herd management
Delhez, Pauline ULiege; Colinet, Frédéric ULiege; Vanderick, Sylvie ULiege et al

in Journal of Dairy Science (2020)

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 ... [more ▼]

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. [less ▲]

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See detailAdvanced monitoring of milk quality to address the demand of added-value dairy products
Bastin, Catherine; Dehareng, Frédéric; Gengler, Nicolas ULiege et al

in Book of Abstracts of the 71st Annual Meeting of the European Federation of Animal Science (2020)

Consumers are seeking for local, healthy and direct-from-producers dairy products. Hence, assessing the suitability of milk to be processed in dairy products either directly in the farms or through local ... [more ▼]

Consumers are seeking for local, healthy and direct-from-producers dairy products. Hence, assessing the suitability of milk to be processed in dairy products either directly in the farms or through local dairy plants is of great interest. Moreover, several studies demonstrated the usefulness of mid-infrared (MIR) spectrometry for the prediction of various traits related to the nutritional and technological properties of milk. This study presents 5 groups of MIR predicted traits related to various aspects of dairy products: (1) milk coagulation traits, (2) cheese and butter yields, (3) nutritional quality, (4) texture and, (5) sensory quality. The MIR prediction equations of these 5 groups of traits were applied on standardized spectra from individual milk samples collected in the frame of the Walloon milk recording scheme. After edits, more than 780,000 records collected between 2017 and 2019 were used. The MIR predictions for coagulation time and curd firmness were combined to define a new trait with 5 levels assessing the overall milk coagulation property, from poorly coagulating milk to optimal milk for coagulation. Casein and calcium contents and titrable acidity were also studied in relation to milk coagulation properties. The nutritional quality of milk was assessed through the content in fat of PUFA and the health promoting index (i.e., UFA / (C12 + C14 x 4 + C16). The spreadability of butter was defined as the ratio of C18:1 cis-9 to C16:0. The sensory quality of milk was assessed through SCC and the content of free fatty acids. Results showed that about 10% of the records were classified as poorly coagulating milk; these records had lower calcium and casein contents with lower MIR predicted cheese yield. Also, 60% of the cows were never classified in the poorly coagulating milk class while 5% of the cow produced poorly coagulating milk at least half of the time. Moreover, cheese yield was highly correlated with the protein and fat contents. The results also showed the influence of days in milk, parity, herd and breed on all traits. Local breeds (i.e., Dual Purpose Belgian Blue and Eastern Red) showed favourable milk fat profile for nutritional quality of milk and texture of dairy products even if these breeds tend to have higher proportion of suboptimal milk for coagulation. These results indicates that dairy farmers have the opportunities to monitor and improve milk quality for the production of added-value dairy products. [less ▲]

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See detailThe use of mid-infrared spectral data to predict traits for genetic selection in dairy cattle
Gengler, Nicolas ULiege; Soyeurt, Hélène ULiege

in van der Werf, Julius; Pryce, Jennie (Eds.) Advances in breeding of dairy cattle (2019)

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See detailContribution 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
Soyeurt, Hélène ULiege; Froidmont, Éric; Dufrasne, Isabelle ULiege et al

in Livestock Science (2019), 227(2019), 82-89

A regular and repeated recording of body weight (BW) is useful information for herd management. BW can bepredicted regularly from animal characteristics such as age, lactation number, or lactation stage ... [more ▼]

A regular and repeated recording of body weight (BW) is useful information for herd management. BW can bepredicted regularly from animal characteristics such as age, lactation number, or lactation stage. Those traits areunfortunately animal unspecific. Adding animal specific information, which can be easily obtained on a largescale, to the BW prediction would be of utmost importance. There are good scientific reasons to suspect linksbetween BW and animal specific characteristics, available in a repeated fashion, such as milk yield and milkcomposition. This study aimed to demonstrate the feasibility of predicting test-day BW from stage, lactationnumber, month of test, milk yield and mid-infrared spectra, representing milk composition. Five models weretested initially from 721 BW records collected in 6 herds: day in milk + number of lactation (equation 1a);equation1a + milk yield (equation 1b); only spectral data (equation 1c); equation 1c + equation 1a (equation2); equation 2 + milk yield (equation 3). Then 3 other equations included the same explicative variables, exceptthat the spectral data were regressed using second order Legendre Polynomials (PL) to take into account changesof spectral data within lactation. Equation 1a and 1b were built using linear regressions and equation 1c until 3were built using partial least square regressions. These 3 last equations had a higher number of factors. Adding ofMIR data in the equation increased of 7% the values of cross-validation R² (R²cv). Potential BW outliers werediscarded using a residual analysis based on equation 3. From 662 records, the following statistical parameterswere obtained: the calibration coefficient of determination (R²c) = 0.65, R²cv = 0.61, calibration root meansquared error of prediction (RMSEP)=38 kg, and RMSEPcv=40 kg. Low variation of R²c and RMSEPc valuesobtained from the herd validation confirmed the herd independence of predictions. However, large variabilitywas observed for RMSEPv (37 to 64 kg) suggesting the need to increase the dataset in order to improve therobustness of the equation. By applying the equations on a large spectral database, it was confirmed that theaddition of MIR data allows to better model the BW evolution within lactation. Based on these preliminaryresults, and if a larger validation confirms thesefindings, this approach could be used to develop equations thatare better able to assess BW throughout lactation(s), BW being an important element for management andselection tools. [less ▲]

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See detailSuccessful economic management differs between intensive and extensive dairy farms
Dalcq, Anne-Catherine ULiege; Beckers, Yves ULiege; Wyzen, Benoit et al

Conference (2019, August 26)

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See detailGplusE: Mid-infrared milk analysis based technologies adding value to gene banks
Gengler, Nicolas ULiege; Grelet, Clément ULiege; Soyeurt, Hélène ULiege et al

in Book of Abstracts of the 70th Annual Meeting of the European Federation of Animal Science (2019, August)

Fitter farms through the use of animal gene banks need an excellent level of characterization of the genetic material represented in these banks. If genomic characterization is straight forward the ... [more ▼]

Fitter farms through the use of animal gene banks need an excellent level of characterization of the genetic material represented in these banks. If genomic characterization is straight forward the phenotypic characterization of specific traits of interest is less. Even worse, some traits may even not be known as of interest when the material was conserved. Here mid-infrared (MIR) spectra based milk analysis strategies as studied during the recently finished EU FP7 project GplusE would allow to add value to gene bank collection. Required for this would be the recording of spectra for individuals of preserved breeds, if possible from spectrometers which were at the same time participating to a standardization procedure, and the preservation for at least a part of these MIR records the corresponding preserved (frozen) milk. There are at least four ways MIR based technologies can add value. First, MIR prediction equations developed well after the conservation of the MIR data can be used to determine a posteriori novel phenotypes for these old animals. Prediction would be more reliable when made on standardized spectra and can be done with the sole knowledge of the spectra so even for larger groups of animals then those preserved (e.g. daughters of preserved sires). Also, second, preserved frozen samples associated to some MIR records, can be used to validate, or even improve these equations adding variability that has maybe disappeared since preservation. These frozen samples can also be useful because novel reference methods (e.g. proteomics) may appear and these samples can contribute to better novel equations because they increase variability of reference calibration dataset. Finally, MIR spectra data, but also MIR based predictions can be used to establish breed differences. Using directly MIR has the advantage of a phenotypic characterization (i.e. milk phenome) close the genome. As for the use of the genome for selection of candidates for gene banks, the use of this milk phenome could be a strategy to assess and to cover the existing variability in a given breed and among breeds to preserve. [less ▲]

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See detailNew phenotypes from milk MIR spectra: challenges to obtain reliable predictions
Grelet, Clément ULiege; Dardenne, Pierre; Soyeurt, Hélène ULiege et al

in Book of Abstracts of the 70th Annual Meeting of the European Federation of Animal Science (2019, August)

In the recent years, the research aiming to predict new phenotypes from the FT-MIR analysis of milk was very active. Models were developed to predict phenotypes such as fine milk composition, cow health ... [more ▼]

In the recent years, the research aiming to predict new phenotypes from the FT-MIR analysis of milk was very active. Models were developed to predict phenotypes such as fine milk composition, cow health and environmental impact or technological properties of milk. Those models could be of great interest in order to perform genetic studies as they could allow generating large amount of data at large scale and with reasonable cost. To achieve this, it is nonetheless necessary to insure that the models provide reliable predictions when applied on the large diversity of spectral data met on real field conditions. The robustness of models -its capacity to be ‘all terrain’ and provide good results in various conditions- is therefore essential to ensure reliability of predictions. Robustness could be estimated by evaluating the error in external validation (RMSEP), the reproducibility of predictions between instruments and the ability of the calibration dataset to cover the variability of routine field data. However, in current literature, the model robustness is often omitted. Models are frequently developed on reduced dataset, with limited number of herds, breeds and diets. Additionally, models are evaluated by looking to the statistical performances, through the R2 and the standard error (RMSE or SEC), while the robustness is rarely assessed. Finally, only a limited number of models is used in routine and faces the large variability of real field conditions to provide phenotypes for management of cows or genetic studies. The objective of this work is consequently to evaluate the impact of different factors influencing robustness on prediction quality. The impact of sampling scheme (oriented vs random), and model development are investigated. Effect of inclusion of variability in the model by adding countries, breeds, MIR instruments and days in milk are also investigated. The obtained results encourage for international collaborations in order to constitute large and robust datasets and enable the use of models in routine conditions. [less ▲]

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See detailZootechnical parameters added to the milk MIR spectra as predictive value to estimate CH4 emissions
Vanlierde, Amélie ULiege; Dehareng, Frédéric ULiege; Gengler, Nicolas ULiege et al

in Book of Abstracts of the 70th Annual Meeting of the European Federation of Animal Science (2019, August)

Breeding needs tools to quantify at large scale greenhouse gases emissions in order to develop levers to reduce them. Milk mid-infrared (MIR) spectrum is a promising proxy to estimate daily methane (CH4 ... [more ▼]

Breeding needs tools to quantify at large scale greenhouse gases emissions in order to develop levers to reduce them. Milk mid-infrared (MIR) spectrum is a promising proxy to estimate daily methane (CH4) emissions of individual dairy cows. A model has been developed based on 1,089 reference measurements combining milk MIR spectra and CH4 records, collected using the SF6 tracer technique (n=513) and respiration chambers (n=576). These data came from 7 countries (BE, IRL, CH, UK, FR, DK and D) and from cows of 5 major breeds: Holstein (74%), Brown Swiss (13%), Jersey (3%), Red Holstein (3%) and Swedish Red Crossed (2.5%). Using a 5 groups cross-validation (CV), statistics reached an R2 of 0.64 with a SE of 61 g of CH4/day. A cow and country dependent external validation (CCDEV) has been performed: all the data from 20% of the cows per country are removed, calibration was then done on the 80% remaining cows from all countries and validated on the 20% removed. After 500 repetitions, the R2 and the RMSEP of CCDEV were 0.55±0.07 and 70±4.5 g of CH4/day respectively. To asses improving these results, parity, milk yield and breed information have been added individually and by combination to the MIR spectra. This led to 7 new calibration models. Statistics were improved with the parameters included, with an optimum found for the version combining milk MIR spectra, milk yield, parity and breed as predictors. The statistics reached a R2cv of 0.68, a SECV of 57 g of CH4/day, and R2 and RMSEP of CCDEV of 0.6±0.06 and 65±4.1 respectively. Including these routinely available cow parameters improves the model prediction performance. Practical applications are still required to observe and confirm the relevance of these new predictions. [less ▲]

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See detailValidation of the prediction of body weight from dairy cow characteristics and milk MIR spectra
Soyeurt, Hélène ULiege; Froidmont, E.; Dufrasne, Isabelle ULiege et al

in Book of Abstracts of the 70th Annual Meeting of the European Association for Animal Production (2019, August)

Body weight (BW) recording is of interest to optimize herd management and environmental fingerprint. Due to its cost, a weighing system is not installed in many farms. Linear type traits are mostly ... [more ▼]

Body weight (BW) recording is of interest to optimize herd management and environmental fingerprint. Due to its cost, a weighing system is not installed in many farms. Linear type traits are mostly available only once in the lifetime of the cow. So, an interest exists to develop a method to predict routinely BW with traits easily recorded and cheap. First investigation was conducted one year ago to build a prediction equation of BW from parity, test month, milk yield, days in milk and milk MIR spectrum. This study based on 717 records obtained a herd validation root mean square error (RMSEv) ranged from 37 to 64 kg. Cross-validation (cv) R2 was equal to 0.51 with RMSE of 50 kg. This equation was applied on 1,161 milk MIR spectra collected in the GplusE project from Holstein cows. Validation R2 was 0.51 with RMSEv of 65 kg. A total of 109 spectra had a global H distance higher than 3 suggesting spectral outliers. After their removal, R2v increased slightly (0.54). Difference of RMSEv can be explained by a lack of spectral variability in the calibration set. So, the 2 datasets were merged to build a PLS regression including the same predictive traits as the first study. After a spectral cleaning based on GH and residual analysis, the best equation used 1,837 records and gave a 10 fold R2cv of 0.64±0.02 with a RMSEv of 46±2 kg. The ability to predict BW was improved by adding this new data. Lowest errors were observed for BW ranged from 500 to 750 kg which represents 94% of the set. So, low and high BW lack in the calibration set. This study confirms the preliminary results and the potentiality to predict an indicator of body weight. This alternative BW prediction could explain a part of the BW variability not already covered by other BW predictors as those based on linear scores. Moreover, this method allows to consider the past information if the spectral data is available. This approach should help research on BW changes and selection for this trait in the future. [less ▲]

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See detailUsing of spectral global H distance improves the accuracy of milk MIR based predictions
Zhang, Lei ULiege; Li, Chong ULiege; Dehareng, Frédéric ULiege et al

in Book of Abstracts of the 70th Annual Meeting of the European Federation of Animal Science (2019, August)

Milk MIR spectrometry predicts traits related to quality, health and environment. Standardizing MIR data enables to share prediction equations. The prediction accuracy depends partly on the calibration ... [more ▼]

Milk MIR spectrometry predicts traits related to quality, health and environment. Standardizing MIR data enables to share prediction equations. The prediction accuracy depends partly on the calibration spectral variability. So, the calculation of spectral global H (GH) distance is of interest. The effect of GH on the accuracy of MIR predictions were studied using 198,394 milk samples collected from Chinese cows and analysed on 3 Bentley FTS machines. The content of fat, protein, monounsaturated fatty acid (MFA), unsaturated FA (UFA), saturated FA (SFA), polyunsaturated FA (PFA) predicted by manufacturer’s models were the reference values. Fat, protein, MFA, PFA, SFA and UFA averages were 3.97, 3.43, 0.86, 0.07, 2.62 and 0.93 g/dl of milk. Bentley MIR spectra were standardized to master MIR spectra according to the method developed by the European Milk Recording network. Then the studied traits were predicted on those spectra using published MIR equations. The averaged predicted content of fat, protein, MFA, PFA, SFA, and UFA were 3.99, 3.53, 1.15, 0.15, 2.64, 1.29 g/dl of milk. GH ranged from 0 to 475. Correlation values between predicted and reference contents ranged from 0.92 to 0.98, except for PFA (0.59). Root mean square errors (RMSE) were 0.19, 0.18, 0.32, 0.09, 0.21, and 0.39 g/dl for fat, protein, MFA, PFA, SFA, and UFA. Correlation values between the squared residuals and GH were positive (0.17-0.42). This suggests a positive effect of GH on the prediction accuracy. When a threshold of GH≤5 was applied, the data loss ranged from 3.83 to 9.27%. Correlation values between predicted and reference contents increased (0.94 to 0.98, 0.64 for PFA). RMSE decreased from 1.26 to 7.37%. Considering GH limits spectral extrapolation. To improve the accuracy, samples with GH>5 must be included in the calibration set to cover the spectral variability. Moreover, the spectral standardization must be performed on a regular basis in order to check the potential spectral deviation of an instrument. In this study, the standardization was performed once. [less ▲]

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See detailPotential use of MIR spectra in the prediction of hoof disorders in Holstein Friesians
Mineur, Axelle ULiege; Verduijn, E. C.; Knijn, H. M. et al

in Book of Abstracts of the 70th Annual Meeting of the European Association for Animal Production (2019, August)

The prediction of hoof disorders using MIR (mid-infrared) could be a promising approach for genomic selection of locomotion in dairy cattle and management of hoof problems in herds. Previously, we studied ... [more ▼]

The prediction of hoof disorders using MIR (mid-infrared) could be a promising approach for genomic selection of locomotion in dairy cattle and management of hoof problems in herds. Previously, we studied the temporal relationship between locomotion scores and MIR based biomarkers. This time we focussed on hoof disorder scores and MIR spectra directly instead of the biomarker concentrations derived from the spectra. The data provided by CRV through the ClawMIR project, consisted of 638,904 hoof disorder records and 5,708,128 MIR records, coming from 261,647 Holstein Friesian cows in 1,983 herds between 2013 and 2018. Each lactation was subdivided into 30-day month classes. Pre-processing of the spectral data consisted of the first derivative, applied with a window size of 5 wavenumbers. The MIR data and hoof disorder scores were corrected for animal-lactation, herd-testday and lactation group (1, 2 or 3+) with a fixed effect model. The spectral data and hoof disorder severity scores were then averaged over animals and month classes. Only the first five months of lactation were investigated as that is the period when the cow is most at risk of developing a metabolism related locomotion disorder. The first step of this research was to establish correlations between the hoof disorders at a specific month and each of the 212 wavenumbers during the 1,2, 3 or 4 months before the hoof disorders. Looking at the results, certain patterns appeared in these correlations. White Line disorders have the highest correlations with absorbance values taken the last month before their occurrence (r between 0.06 and 0.08). Sole Haemorrhage and Sole Ulcer scores did not make a distinction between the months preceding their occurrence, but inside each month, they have the highest correlations with the same groups of wavenumbers (r between 0.03 and 0.05 for certain wavenumbers for Sole Haemorrhage and r between 0.06 and 0.09 for similar wavenumbers for Sole Ulcer). These patterns showed time-dependent but also intra-waveband patterns that are potentially very useful in the establishment of early warning equations for diseases. [less ▲]

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