152 results on '"Gengler, Nicolas"'
Search Results
2. Estimation of genetic parameters and single-step genome-wide association studies for eating time and rumination time in Holstein dairy cows
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Atashi, Hadi, Lemal, Pauline, Tran, Marie-Nguyet, and Gengler, Nicolas
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- 2024
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3. Genetic analysis of milk citrate predicted by milk mid-infrared spectra of Holstein cows in early lactation
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Chen, Yansen, Hu, Hongqing, Atashi, Hadi, Grelet, Clément, Wijnrocx, Katrien, Lemal, Pauline, and Gengler, Nicolas
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- 2024
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4. Defining valid breeding goals for animal breeds
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Wellmann, Robin, Gengler, Nicolas, Bennewitz, Jörn, and Tetens, Jens
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- 2023
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5. Mining data from milk mid-infrared spectroscopy and animal characteristics to improve the prediction of dairy cow's liveweight using feature selection algorithms based on partial least squares and Elastic Net regressions
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Zhang, Lei, Tedde, Anthony, Ho, Phuong, Grelet, Clément, Dehareng, Frédéric, Froidmont, Eric, Gengler, Nicolas, Brostaux, Yves, Hailemariam, Dagnachew, Pryce, Jennie, and Soyeurt, Hélène
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- 2021
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6. Recent genetic advances on boar taint reduction as an alternative to castration: a review
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Duarte, Darlene Ana Souza, Schroyen, Martine, Mota, Rodrigo Reis, Vanderick, Sylvie, and Gengler, Nicolas
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- 2021
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7. Investigation on heroin and cocaine quality in Luxembourg
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Bourmaud, Adèle, Dahm, Georges, Meys, François, Gengler, Nicolas, Origer, Alain, and Schneider, Serge
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- 2021
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8. Towards a Unified Architecture Powering Scalable Learning Models with IoT Data Streams, Blockchain, and Open Data.
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Debauche, Olivier, Nkamla Penka, Jean Bertin, Hani, Moad, Guttadauria, Adriano, Ait Abdelouahid, Rachida, Gasmi, Kaouther, Ben Hardouz, Ouafae, Lebeau, Frédéric, Bindelle, Jérôme, Soyeurt, Hélène, Gengler, Nicolas, Manneback, Pierre, Benjelloun, Mohammed, and Bertozzi, Carlo
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MACHINE learning ,INTERNET of things ,BLOCKCHAINS ,DATA modeling ,ALGORITHMS ,EDGE computing - Abstract
The huge amount of data produced by the Internet of Things need to be validated and curated to be prepared for the selection of relevant data in order to prototype models, train them, and serve the model. On the other side, blockchains and open data are also important data sources that need to be integrated into the proposed integrative models. It is difficult to find a sufficiently versatile and agnostic architecture based on the main machine learning frameworks that facilitate model development and allow continuous training to continuously improve them from the data streams. The paper describes the conceptualization, implementation, and testing of a new architecture that proposes a use case agnostic processing chain. The proposed architecture is mainly built around the Apache Submarine, an unified Machine Learning platform that facilitates the training and deployment of algorithms. Here, Internet of Things data are collected and formatted at the edge level. They are then processed and validated at the fog level. On the other hand, open data and blockchain data via Blockchain Access Layer are directly processed at the cloud level. Finally, the data are preprocessed to feed scalable machine learning algorithms. [ABSTRACT FROM AUTHOR]
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- 2023
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9. The use of a genomic relationship matrix for breed assignment of cattle breeds: comparison and combination with a machine learning method.
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Wilmot, Hélène, Niehoff, Tobias, Soyeurt, Hélène, Gengler, Nicolas, and Calus, Mario P. L.
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To develop a breed assignment model, three main steps are generally followed: 1) The selection of breed informative single nucleotide polymorphism (SNP); 2) The training of a model, based on a reference population, that allows to classify animals to their breed of origin; and 3) The validation of the developed model on external animals i.e., that were not used in previous steps. However, there is no consensus in the literature about which methodology to follow for the first step, nor about the number of SNP to be selected. This can raise many questions when developing the model and lead to the use of sophisticated methodologies for selecting SNP (e.g., with iterative algorithms, partitions of SNP, or combination of several methods). Therefore, it may be of interest to avoid the first step by the use of all the available SNP. For this purpose, we propose the use of a genomic relationship matrix (GRM), combined or not with a machine learning method, for breed assignment. We compared it with a previously developed model based on selected informative SNP. Four methodologies were investigated: 1) The PLS_NSC methodology: selection of SNP based on a partial least square-discriminant analysis (PLS-DA) and breed assignment by classification based on the nearest shrunken centroids (NSC) method; 2) Breed assignment based on the highest mean relatedness of an animal to the reference populations of each breed (referred to mean_GRM); 3) Breed assignment based on the highest SD of the relatedness of an animal to the reference populations of each breed (referred to SD_GRM) and 4) The GRM_SVM methodology: the use of means and SD of the relatedness defined in mean_GRM and SD_GRM methodologies combined with the linear support vector machine (SVM), a machine learning method used for classification. Regarding mean global accuracies, results showed that the use of mean_GRM or GRM_SVM was not significantly different (Bonferroni corrected P > 0.0083) than the model based on a reduced SNP panel (PLS_NSC). Moreover, the mean_GRM and GRM_SVM methodology were more efficient than PLS_NSC as it was faster to compute. Therefore, it is possible to bypass the selection of SNP and, by the use of a GRM, to develop an efficient breed assignment model. In routine, we recommend the use of GRM_SVM over mean_GRM as it gave a slightly increased global accuracy, which can help endangered breeds to be maintained. The script to execute the different methodologies can be accessed on [ABSTRACT FROM AUTHOR]
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- 2023
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10. Farmer and field survey in cassava growing districts of Rwanda reveals key factors associated with cassava brown streak disease incidence and cassava productivity
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NYIRAKANANI, Chantal, Bizimana, Jean Pierre, Kwibuka, Yves, Nduwumuremyi, Athanase, Bigirimana, Vincent de Paul, Bucagu, Charles, Lassois, Ludivine, Malice, Eléonore, Gengler, Nicolas, Massart, Sébastien, Bragard, Claude, Habtu, Michael, Brostaux, Yves, Thonar, Cécile, and Vanderschuren, Hervé
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Sciences bio-médicales et agricoles ,Sciences exactes et naturelles - Abstract
info:eu-repo/semantics/inPress
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- 2021
11. Lipid Serum Profiling of Boar-Tainted and Untainted Pigs Using GC×GC–TOFMS: An Exploratory Study.
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Bhatt, Kinjal, Dejong, Thibaut, Dubois, Lena M., Markey, Alice, Gengler, Nicolas, Wavreille, José, Stefanuto, Pierre-Hugues, and Focant, Jean-François
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BLOOD lipids ,SATURATED fatty acids ,UNSATURATED fatty acids ,SWINE ,ION mobility ,ION mobility spectroscopy ,LIPIDS - Abstract
Mass spectrometry (MS)-based techniques, including liquid chromatography coupling, shotgun lipidomics, MS imaging, and ion mobility, are widely used to analyze lipids. However, with enhanced separation capacity and an optimized chemical derivatization approach, comprehensive two-dimensional gas chromatography (GC×GC) can be a powerful tool to investigate some groups of small lipids in the framework of lipidomics. This study describes the optimization of a dedicated two-stage derivatization and extraction process to analyze different saturated and unsaturated fatty acids in plasma by two-dimensional gas chromatography–time-of-flight mass spectrometry (GC×GC–TOFMS) using a full factorial design. The optimized condition has a composite desirability of 0.9159. This optimized sample preparation and chromatographic condition were implemented to differentiate between positive (BT) and negative (UT) boar-tainted pigs based on fatty acid profiling in pig serum using GC×GC–TOFMS. A chemometric screening, including unsupervised (PCA, HCA) and supervised analysis (PLS–DA), as well as univariate analysis (volcano plot), was performed. The results suggested that the concentration of PUFA ω-6 and cholesterol derivatives were significantly increased in BT pigs, whereas SFA and PUFA ω-3 concentrations were increased in UT pigs. The metabolic pathway and quantitative enrichment analysis suggest the significant involvement of linolenic acid metabolism. [ABSTRACT FROM AUTHOR]
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- 2022
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12. Genome‐wide association for milk urea concentration in Dual‐Purpose Belgian Blue cows.
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Atashi, Hadi, Chen, Yansen, Wilmot, Hélène, Vanderick, Sylvie, Hubin, Xavier, and Gengler, Nicolas
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GENOME-wide association studies ,UREA ,LACTATION ,HERITABILITY ,COWS ,GENETIC correlations ,CATTLE ,SINGLE nucleotide polymorphisms - Abstract
The objectives of this study were to estimate genetic parameters and identify genomic regions associated with milk urea concentration (MU) in Dual‐Purpose Belgian Blue (DPBB) cows. The data were 29,693 test‐day records of milk yield (MY), fat yield (FY), protein yield (PY), fat percentage (FP), protein percentage (PP) and MU collected between 2014 and 2020 on 2498 first parity cows (16,935 test‐day records) and 1939 second‐parity cows (12,758 test‐day records) from 49 herds in the Walloon Region of Belgium. Data of 28,266 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes (BTA), on 1699 animals (639 males and 1060 females) were used. Random regression test‐day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method using a single chain of 100,000 iterations after a burn‐in period of 20,000. SNP solutions were estimated using a single‐step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by windows of 25 consecutive SNPs (with an average size of ~2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. The mean (SD) of MU was 22.89 (10.07) and 22.35 (10.07) mg/dl for first and second parity, respectively. The mean (SD) heritability estimates for daily MU were 0.18 (0.01) and 0.22 (0.02), for first and second parity, respectively. The mean (SD) genetic correlations between daily MU and MY, FY, PY, FP and PP were −0.05 (0.09), −0.07 (0.11), −0.03 (0.13), −0.05 (0.08) and −0.03 (0.11) for first parity, respectively. The corresponding values estimated for second parity were 0.02 (0.10), −0.02 (0.09), 0.02 (0.08), −0.08 (0.06) and −0.05 (0.05). The genome‐wide association analyses identified three genomic regions (BTA2, BTA3 and BTA13) associated with MU. The identified regions showed contrasting results between parities and among different stages within each parity. This suggests that different groups of candidate genes underlie the phenotypic expression of MU between parities and among different lactation stages within a parity. The results of this study can be used for future implementation and use of genomic evaluation to reduce MU in DPBB cows. [ABSTRACT FROM AUTHOR]
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- 2022
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13. Prediction of Indirect Indicators of a Grass-Based Diet by Milk Fourier Transform Mid-Infrared Spectroscopy to Assess the Feeding Typologies of Dairy Farms.
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Soyeurt, Hélène, Gerards, Cyprien, Nickmilder, Charles, Bindelle, Jérôme, Franceschini, Sébastien, Dehareng, Frédéric, Veselko, Didier, Bertozzi, Carlo, Gengler, Nicolas, Marvuglia, Antonino, Bayram, Alper, and Tedde, Anthony
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FOURIER transform spectroscopy ,DAIRY farms ,CATTLE feeding & feeds ,CATTLE nutrition ,MILKFAT ,MILK ,MID-infrared spectroscopy - Abstract
Simple Summary: The dairy industry is interested in developing a detection tool for milk produced from pasture to certify the protected designation of origin of certain dairy products. As the cattle's grazing influences the milk composition, the milk mid-infrared (MIR) spectra will be modified. Therefore, this study aims to develop a predictive model allowing one to use milk MIR spectrometry to classify milk as produced from a cattle diet either based on grass or not. To achieve this objective, the collection of grazing calendars from farms is needed. Unfortunately, this is hardly possible, as this information is rarely recorded. Therefore, the innovation of this research consists of using the usual farming practices developed in the southern part of Belgium combined with a large-scale milk database containing 48 milk composition-related traits. Indeed, in this geographical area, the cows are mainly on pasture between April and September. Therefore, the month of testing can be considered indirect information about their feeding. The developed models were able to distinguish with a precision of around 90% the supposed grass-based diet. Therefore, the probability of belonging to the GRASS class could be used in a tool counting the number of grazing days to confirm the labeling of dairy products. This research aims to develop a predictive model to discriminate milk produced from a cattle diet either based on grass or not using milk mid-infrared spectrometry and the month of testing (an indirect indicator of the feeding ration). The dataset contained 3,377,715 spectra collected between 2011 and 2021 from 2449 farms and 3 grazing traits defined following the month of testing. Records from 30% of the randomly selected farms were kept in the calibration set, and the remaining records were used to validate the models. Around 90% of the records were correctly discriminated. This accuracy is very good, as some records could be erroneously assigned. The probability of belonging to the GRASS modality allowed confirmation of the model's ability to detect the transition period even if the model was not trained on this data. Indeed, the probability increased from the spring to the summer and then decreased. The discrimination was mainly explained by the changes in the milk fat, mineral, and protein compositions. A hierarchical clustering from the averaged probability per farm and year highlighted 12 groups illustrating different management practices. The probability of belonging to the GRASS class could be used in a tool counting the number of grazing days. [ABSTRACT FROM AUTHOR]
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- 2022
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14. Multiple Country Approach to Improve the Test-Day Prediction of Dairy Cows’ Dry Matter Intake
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Tedde, Anthony, Grelet, Clément, Ho, Phuong, Pryce, Jennie, Hailemariam, Dagnachew, Wang, Zhiquan, Plastow, Graham, Gengler, Nicolas, Froidmont, Eric, Dehareng, Frédéric, Bertozzi, Carlo, Crowe, Mark, Soyeurt, Hélène, and Consortium, on
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Veterinary medicine ,Feed conversion ratio ,Article ,03 medical and health sciences ,chemistry.chemical_compound ,Animal science ,Milk yield ,Lactation ,SF600-1100 ,feed efficiency ,medicine ,Dry matter ,dairy cows ,Lactose ,030304 developmental biology ,Mathematics ,Uncategorized ,dimensionality reduction ,dry matter intake ,0303 health sciences ,General Veterinary ,0402 animal and dairy science ,Geographical diversity ,04 agricultural and veterinary sciences ,040201 dairy & animal science ,Regression ,medicine.anatomical_structure ,machine learning ,QL1-991 ,chemistry ,Test day ,mid infrared spectra ,partial least square ,feed intake ,Animal Science and Zoology ,Zoology ,artificial neural network - Abstract
Simple Summary Dry matter intake, related to the number of nutrients available to an animal to meet its production and health needs, is crucial for the economic, environmental, and welfare management of dairy herds. Because the equipment required to weigh the ingested food at an individual level is not broadly available, we propose some new ways to approach the actual dry matter consumed by a dairy cow for a given day. To do so, we used regression models using parity (number of lactations), week of lactation, milk yield, milk mid-infrared spectrum, and prediction of bodyweight, fat, protein, lactose, and fatty acids content in milk. We chose these elements to predict individual dry matter intake because they are either easily accessible or routinely provided by regional dairy organizations (often called “dairy herd improvement” associations). We succeeded in producing a model whose dry matter intake predictions were moderately related to the actual values. Abstract We predicted dry matter intake of dairy cows using parity, week of lactation, milk yield, milk mid-infrared (MIR) spectrum, and MIR-based predictions of bodyweight, fat, protein, lactose, and fatty acids content in milk. The dataset comprised 10,711 samples of 534 dairy cows with a geographical diversity (Australia, Canada, Denmark, and Ireland). We set up partial least square (PLS) regressions with different constructs and a one-hidden-layer artificial neural network (ANN) using the highest contribution variables. In the ANN, we replaced the spectra with their projections to the 25 first PLS factors explaining 99% of the spectral variability to reduce the model complexity. Cow-independent 10 × 10-fold cross-validation (CV) achieved the best performance with root mean square errors (RMSECV) of 3.27 ± 0.08 kg for the PLS regression and 3.25 ± 0.13 kg for ANN. Although the available data were significantly different, we also performed a country-independent validation (CIV) to measure the models’ performance fairly. We found RMSECIV varying from 3.73 to 6.03 kg for PLS and 3.69 to 5.08 kg for ANN. Ultimately, based on the country-independent validation, we discussed the developed models’ performance with those achieved by the National Research Council’s equation.
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- 2021
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15. Estimation of genetic parameters for predicted nitrogen use efficiency and losses in early lactation of Holstein cows
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Chen, Y., Vanderick, S., Mota, R.R., Grelet, Clement, and Gengler, Nicolas
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mid-infrared spectrum ,indirect selection ,repeatability model ,heritability ,genetic correlation - Abstract
The objective of this study was to estimate genetic parameters of predicted N use efficiency (PNUE) and N losses (PNL) as proxies of N use and loss for Holstein cows. Furthermore, we have assessed approximate genetic correlations between PNUE, PNL, and dairy production, health, longevity, and conformation traits. These traits are considered important in many countries and are currently evaluated by the International Bull Evaluation Service (Interbull). The values of PNUE and PNL were obtained by using the combined milk mid-infrared (MIR) spectrum, parity, and milk yield–based prediction equations on test-day MIR records with days in milk (DIM) between 5 and 50 d. After editing, the final data set comprised 46,163 records of 21,462 cows from 154 farms in 5 countries. Each trait was divided into primiparous and multiparous (including second to fifth parity) groups. Genetic parameters and breeding values were estimated by using a multitrait (2-trait, 2-parity classes) repeatability model. Herd-year-season of calving, DIM, age of calving, and parity were used as fixed effects. Random effects were defined as parity (within-parity permanent environment), nongenetic cow (across-parity permanent environment), additive genetic animal, and residual effects. The estimated heritability of PNUE and PNL in the first and later parity were 0.13, 0.12, 0.14, and 0.13, and the repeatability values were 0.49, 0.40, 0.55, and 0.43, respectively. The estimated approximate genetic correlations between PNUE and PNL were negative and high (from −0.89 to −0.53), whereas the phenotypic correlations were also negative but relatively low (from −0.45 to −0.11). At a level of reliability of more than 0.30 for all novel traits, a total of 504 bulls born after 1995 had also publishable Interbull multiple-trait across-country estimated breeding values (EBV). The approximate genetic correlations between PNUE and the other 30 traits of interest, estimated as corrected correlations between EBV of bulls, ranged from −0.46 (udder depth) to 0.47 (milk yield). Obtained results showed the complex genetic relationship between efficiency, production, and other traits: for instance, more efficient cows seem to give more milk, which is linked to deeper udders, but seem to have lower health, fertility, and longevity. Additionally, the approximate genetic correlations between PNL, lower values representing less loss of N, and the 30 other traits, were from −0.32 (angularity) to 0.57 (direct calving ease). Even if further research is needed, our results provided preliminary evidence that the PNUE and PNL traits used as proxies could be included in genetic improvement programs in Holstein cows and could help their management.
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- 2021
16. Genomic prediction and genetic correlations estimated for milk production and fatty acid traits in Walloon Holstein cattle using random regression models.
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Paiva, José Teodoro, Mota, Rodrigo Reis, Lopes, Paulo Sávio, Hammami, Hedi, Vanderick, Sylvie, Oliveira, Hinayah Rojas, Veroneze, Renata, Silva, Fabyano Fonseca e, and Gengler, Nicolas
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HOLSTEIN-Friesian cattle ,DAIRY cattle ,MILK yield ,GENETIC correlations ,REGRESSION analysis ,FAT content of milk ,STATISTICAL weighting ,STATISTICAL reliability - Abstract
The aims of this study were to: (1) estimate genetic correlation for milk production traits (milk, fat and protein yields and fat and protein contents) and fatty acids (FA: C16:0, C18:1 cis-9, LCFA, SFA, and UFA) over days in milk, (2) investigate the performance of genomic predictions using single-step GBLUP (ssGBLUP) based on random regression models (RRM), and (3) identify the optimal scaling and weighting factors to be used in the construction of the H matrix. A total of 302 684 test-day records of 63.875 first lactation Walloon Holstein cows were used. Positive genetic correlations were found between milk yield and fat and protein yield (r
g from 0.46 to 0.85) and between fat yield and milk FA (rg from 0.17 to 0.47). On the other hand, negative correlations were estimated between fat and protein contents (rg from −0.22 to −0.59), between milk yield and milk FA (rg from −0.22 to −0.62), and between protein yield and milk FA (rg from −0.11 to −0.19). The selection for high fat content increases milk FA throughout lactation (rg from 0.61 to 0.98). The test-day ssGBLUP approach showed considerably higher prediction reliability than the parent average for all milk production and FA traits, even when no scaling and weighting factors were used in the H matrix. The highest validation reliabilities (r2 from 0.09 to 0.38) and less biased predictions (b1 from 0.76 to 0.92) were obtained using the optimal parameters (i.e., ω = 0.7 and α = 0.6) for the genomic evaluation of milk production traits. For milk FA, the optimal parameters were ω = 0.6 and α = 0.6. However, biased predictions were still observed (b1 from 0.32 to 0.81). The findings suggest that using ssGBLUP based on RRM is feasible for the genomic prediction of daily milk production and FA traits in Walloon Holstein dairy cattle. [ABSTRACT FROM AUTHOR]- Published
- 2022
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17. Prediction of Acute and Chronic Mastitis in Dairy Cows Based on Somatic Cell Score and Mid-Infrared Spectroscopy of Milk.
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Rienesl, Lisa, Khayatzdadeh, Negar, Köck, Astrid, Egger-Danner, Christa, Gengler, Nicolas, Grelet, Clément, Dale, Laura Monica, Werner, Andreas, Auer, Franz-Josef, Leblois, Julie, and Sölkner, Johann
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MASTITIS ,SOMATIC cells ,DAIRY cattle ,DAIRY farm management ,RECEIVER operating characteristic curves ,HEALTH of cattle ,MID-infrared spectroscopy - Abstract
Simple Summary: Mid-infrared (MIR) spectroscopy is the method of choice to determine milk components like fat, protein and urea. We examined the potential of MIR spectra analyses for the prediction of clinical mastitis events of dairy cows additionally, or alternatively, to somatic cell count, which is routinely used as an indicator for mastitis monitoring. Prediction models based on MIR spectra and a somatic cell count-derived score (SCS) were developed and compared. A model based on MIR spectra and SCS proved more accurate at predicting mastitis than models based on either indicator alone. Consequently, MIR spectra analyses add extra value in the prediction of clinical mastitis, making them potentially useful for dairy farm management and as an auxiliary trait for the genetic evaluation of udder health. Monitoring for mastitis on dairy farms is of particular importance, as it is one of the most prevalent bovine diseases. A commonly used indicator for mastitis monitoring is somatic cell count. A supplementary tool to predict mastitis risk may be mid-infrared (MIR) spectroscopy of milk. Because bovine health status can affect milk composition, this technique is already routinely used to determine standard milk components. The aim of the present study was to compare the performance of models to predict clinical mastitis based on MIR spectral data and/or somatic cell count score (SCS), and to explore differences of prediction accuracies for acute and chronic clinical mastitis diagnoses. Test-day data of the routine Austrian milk recording system and diagnosis data of its health monitoring, from 59,002 cows of the breeds Fleckvieh (dual purpose Simmental), Holstein Friesian and Brown Swiss, were used. Test-day records within 21 days before and 21 days after a mastitis diagnosis were defined as mastitis cases. Three different models (MIR, SCS, MIR + SCS) were compared, applying Partial Least Squares Discriminant Analysis. Results of external validation in the overall time window (−/+21 days) showed area under receiver operating characteristic curves (AUC) of 0.70 when based only on MIR, 0.72 when based only on SCS, and 0.76 when based on both. Considering as mastitis cases only the test-day records within 7 days after mastitis diagnosis, the corresponding areas under the curve were 0.77, 0.83 and 0.85. Hence, the model combining MIR spectral data and SCS was performing best. Mastitis probabilities derived from the prediction models are potentially valuable for routine mastitis monitoring for farmers, as well as for the genetic evaluation of the trait udder health. [ABSTRACT FROM AUTHOR]
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- 2022
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18. Random regression test‐day models to describe milk production and fatty acid traits in first lactation Walloon Holstein cows.
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Paiva, José Teodoro, Mota, Rodrigo Reis, Lopes, Paulo Sávio, Hammami, Hedi, Vanderick, Sylvie, Oliveira, Hinayah Rojas, Veroneze, Renata, Fonseca e Silva, Fabyano, and Gengler, Nicolas
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LACTATION ,MILK yield ,FATTY acids ,REGRESSION analysis ,COWS ,MILK proteins ,PALMITIC acid ,OLEIC acid - Abstract
We investigated the use of different Legendre polynomial orders to estimate genetic parameters for milk production and fatty acid (FA) traits in the first lactation Walloon Holstein cows. The data set comprised 302,684 test‐day records of milk yield, fat and protein contents, and FAs generated by mid‐infrared (MIR) spectroscopy, C16:0 (palmitic acid), C18:1 cis‐9 (oleic acid), LCFAs (long‐chain FAs), SFAs (saturated FAs) and UFAs (unsaturated FAs) were studied. The models included random regression coefficients for herd‐year of calving (h), additive genetic (a) and permanent environment (p) effects. The selection of the best random regression model (RRM) was based on the deviance information criterion (DIC), and genetic parameters were estimated via a Bayesian approach. For all analysed random effects, DIC values decreased as the order of the Legendre polynomials increased. Best‐fit models had fifth‐order (degree 4) for the p effect and ranged from second‐ to fifth‐order (degree 1–4) for the a and h effects (LEGhap: LEG555 for milk yield and protein content; LEG335 for fat content and SFA; LEG545 for C16:0 and UFA; and LEG535 for C18:1 cis‐9 and LCFA). Based on the best‐fit models, an effect of overcorrection was observed in early lactation (5–35 days in milk [DIM]). On the contrary, third‐order (LEG333; degree 2) models showed flat residual trajectories throughout lactation. In general, the estimates of genetic variance tended to increase over DIM, for all traits. Heritabilities for milk production traits ranged from 0.11 to 0.58. Milk FA heritabilities ranged from low‐to‐high magnitude (0.03–0.56). High Spearman correlations (>0.90 for all bulls and >0.97 for top 100) were found among breeding values for 155 and 305 DIM between the best RRM and LEG333 model. Therefore, third‐order Legendre polynomials seem to be most parsimonious and sufficient to describe milk production and FA traits in Walloon Holstein cows. [ABSTRACT FROM AUTHOR]
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- 2022
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19. Direct In Vivo Analysis of CBD- and THC-Acid Cannabinoids and Classification of Cannabis Cultivars Using SpiderMass.
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Ogrinc, Nina, Schneider, Serge, Bourmaud, Adèle, Gengler, Nicolas, Salzet, Michel, and Fournier, Isabelle
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CANNABINOIDS ,CULTIVARS ,CANNABIS (Genus) ,CULTIVATED plants ,MASS spectrometers ,NUTRITIONAL value - Abstract
In recent years, cannabis and hemp-based products have become increasingly popular for recreational use, edibles, beverages, health care products, and medicines. The rapid detection and differentiation of phytocannabinoids is, therefore, essential to assess the potency and the therapeutic and nutritional values of cannabis cultivars. Here, we implemented SpiderMass technology for in vivo detection of cannabidiolic acid (CBDA) and ∆
9 -tetrahydrocannabinolicacid (∆9 -THCA), and other endogenous organic plant compounds, to access distribution gradients within the plants and differentiate between cultivars. The SpiderMass system is composed of an IR-laser handheld microsampling probe connected to a mass spectrometer through a transfer tube. The analysis was performed on different plant organs from freshly cultivated cannabis plants in only a few seconds. SpiderMass analysis easily discriminated the two acid phytocannabinoid isomers via MS/MS, and the built statistical models differentiated between four cannabis cultivars. Different abundancies of the two acid phytocannabinoids were found along the plant as well as between different cultivars. Overall, these results introduce direct analysis by SpiderMass as a compelling analytical alternative for rapid hemp analysis. [ABSTRACT FROM AUTHOR]- Published
- 2022
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20. The pattern of linkage disequilibrium in Dual‐Purpose Belgian Blue cattle.
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Atashi, Hadi, Wilmot, Hélène, and Gengler, Nicolas
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LINKAGE disequilibrium ,SINGLE nucleotide polymorphisms ,CATTLE ,HOLSTEIN-Friesian cattle ,ALLELES ,LOCUS (Genetics) ,CATTLE breeds ,PERCENTILES - Abstract
Quantifying the level of linkage disequilibrium (LD), non‐random association of alleles at two or more loci, is important to determine the number of markers needed for genomic selection. The aims of this study were to evaluate the extent of LD in Dual‐Purpose Belgian Blue (DPBB) and to compare the level of LD in DPBB with that of Walloon Holstein. Data of 28,427 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes (BTA), of 639 DPBB and 398 Holstein bulls were used. The level of LD between pairwise SNPs separated by up to 10 Mb was evaluated, separately for each breed, using the squared correlation of the alleles at two loci. The analysis of molecular variance showed that the percentage of variation within populations (85.48%) was higher than between populations (14.52%). However, permutation tests showed a significant genetic differentiation between the two studied populations (p <.01). The average LD found between adjacent SNP pairs in DPBB (0.16 (SD = 0.22)) was generally lower than in Holstein (0.23 (SD = 0.27)). The proportion of SNPs in useful LD (r2 > 0.30) within a genomic distance of ≤0.10 Mb between SNPs was 18.58% and 28.23% in DPBB and Holstein bulls, respectively. In both breeds, the effective population size decreased over generations; however, the decline was greater in DPBB than that in Holstein. Based on results, it can be concluded that at least 68,000 SNPs are needed for implementing genomic selection in DPBB cattle with enough accuracy. [ABSTRACT FROM AUTHOR]
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- 2022
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21. Genome‐wide association for milk production and lactation curve parameters in Holstein dairy cows
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Atashi, Hadi, Salavati, Mazdak, De Koster, Jenne, Ehrlich, Jim, Crowe, Mark, Opsomer, Geert, McLoughlin, Niamh, Fahey, Alan, Matthews, Elizabeth, Santoro, Andreia, Byrne, Colin, Rudd, Pauline, O'Flaherty, Roisin, Hallinan, Sinead, Wathes, Claire, Cheng, Zhangrui, Fouladi, Ali, Pollott, Geoff, Werling, Dirk, Bernardo, Beatriz Sanz, Wylie, Alistair, Bell, Matt, Van Eetvelde, Mieke, Hermans, Kristof, Moerman, Sander, Bogaert, Hannes, Vandepitte, Jan, Vande Velde, Leila, Van Ranst, Bonifacius, Hoglund, Johanna, Dahl, Susanne, Ostergaard, Soren, Rothmann, Janne, Krogh, Mogens, Meyer, Else, Gaillard, Charlotte, Ettema, Jehan, Rousing, Tine, Signorelli, Federica, Napolitano, Francesco, Moioli, Bianca, Crisa, Alessandra, Buttazzoni, Luca, McClure, Jennifer, Matthews, Daragh, Kearney, Francis, Cromie, Andrew, McClure, Matt, Zhang, Shujun, Chen, Xing, Chen, Huanchun, Zhao, Junlong, Yang, Liguo, Hua, Guohua, Tan, Chen, Wang, Guiqiang, Bonneau, Michel, Pompozzi, Andrea, Pearn, Armin, Evertson, Arnold, Kosten, Linda, Fogh, Anders, Andersen, Thomas, Lucey, Matthew, Elsik, Chris, Conant, Gavin, Taylor, Jerry, Gengler, Nicolas, Georges, Michel, Colinet, Frederic, Pamplona, Marilou Ramos, Hammami, Hedi, Bastin, Catherine, Takeda, Haruko, Laine, Aurelie, Van Laere, Anne-Sophie, Schulze, Martin, Vera, Sergio Palma, Ferris, Conrad, Marchitelli, Cinzia, and Hostens, Miel
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Candidate gene ,Genotype ,QTL ,CATTLE ,PREDICTIONS ,Genome-wide association study ,Biology ,Best linear unbiased prediction ,Quantitative trait locus ,Protein lipidation ,Polymorphism, Single Nucleotide ,milk yield ,Animal science ,Food Animals ,Pregnancy ,Lactation ,Genetic variation ,medicine ,Animals ,Veterinary Sciences ,PERSISTENCY ,genome‐wide association study ,QUANTITATIVE TRAIT LOCI ,Genome ,GENETIC-PARAMETERS ,genome-wide association study ,Holstein ,General Medicine ,Original Articles ,SOMATIC-CELL SCORE ,Dairying ,Parity ,medicine.anatomical_structure ,Milk ,Phenotype ,YIELD ,Yield (chemistry) ,Cattle ,Female ,Original Article ,SHAPE ,Animal Science and Zoology ,ENRICHMENT ,lactation curve - Abstract
The aim of this study was to identify genomic regions associated with 305‐day milk yield and lactation curve parameters on primiparous (n = 9,910) and multiparous (n = 11,158) Holstein cows. The SNP solutions were estimated using a weighted single‐step genomic BLUP approach and imputed high‐density panel (777k) genotypes. The proportion of genetic variance explained by windows of 50 consecutive SNP (with an average of 165 Kb) was calculated, and regions that accounted for more than 0.50% of the variance were used to search for candidate genes. Estimated heritabilities were 0.37, 0.34, 0.17, 0.12, 0.30 and 0.19, respectively, for 305‐day milk yield, peak yield, peak time, ramp, scale and decay for primiparous cows. Genetic correlations of 305‐day milk yield with peak yield, peak time, ramp, scale and decay in primiparous cows were 0.99, 0.63, 0.20, 0.97 and −0.52, respectively. The results identified three windows on BTA14 associated with 305‐day milk yield and the parameters of lactation curve in primi‐ and multiparous cows. Previously proposed candidate genes for milk yield supported by this work include GRINA, CYHR1, FOXH1, TONSL, PPP1R16A, ARHGAP39, MAF1, OPLAH and MROH1, whereas newly identified candidate genes are MIR2308, ZNF7, ZNF34, SLURP1, MAFA and KIFC2 (BTA14). The protein lipidation biological process term, which plays a key role in controlling protein localization and function, was identified as the most important term enriched by the identified genes.
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- 2020
22. Potential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation
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Grelet, C., Froidmont, E., Foldager, L., Salavati, M., Hostens, Miel, Ferris, C. P., Ingvartsen, K. L., Crowe, M. A., Sorensen, M. T., Pierna, J. A. Fernandez, Vanlierde, A., Gengler, N., Dehareng, F., Crowe, Mark, Fahey, Alan, Carter, Fiona, Matthews, Elizabeth, Santoro, Andreia, Byrne, Colin, Rudd, Pauline, O'Flaherty, Roisin, Hallinan, Sinead, Wathes, Claire, Salavati, Mazdak, Cheng, Zhangrui, Fouladi, Ali, Pollott, Geoff, Werling, Dirk, Bernardo, Beatriz Sanz, Ferris, Conrad, Wylie, Alistair, Bell, Matt, Van Eetvelde, Mieke, Hermans, Kristof, Opsomer, Geert, Moerman, Sander, De Koster, Jenne, Bogaert, Hannes, Vandepitte, Jan, Vande Velde, Leila, Van Ranst, Bonifacius, Ingvartsen, Klaus, Sorensen, Martin Tang, Hoglund, Johanna, Dahl, Susanne, Ostergaard, Soren, Rothmann, Janne, Krogh, Mogens, Meyer, Else, Foldager, Leslie, Gaillard, Charlotte, Ettema, Jehan, Rousing, Tine, Larsen, Torben, de Oliveira, Victor H. Silva, Marchitelli, Cinzia, Signorelli, Federica, Napolitano, Francesco, Moioli, Bianca, Crisa, Alessandra, Buttazzoni, Luca, McClure, Jennifer, Matthews, Daragh, Kearney, Francis, Cromie, Andrew, McClure, Matt, Zhang, Shujun, Chen, Xing, Chen, Huanchun, Zhao, Junlong, Yang, Liguo, Hua, Guohua, Tan, Chen, Wang, Guigiang, Bonneau, Michel, Sciarretta, Marlene, Pearn, Armin, Evertson, Arnold, Kosten, Linda, Fogh, Anders, Andersen, Thomas, Lucy, Matthew, Elsik, Chris, Conant, Gavin, Taylor, Jerry, Triant, Deborah, Gengler, Nicolas, Georges, Michel, Colinet, Frederic, Pamplona, Marilou Ramos, Hammami, Hedi, Bastin, Catherine, Takeda, Haruko, Laine, Aurelie, Van Laere, Anne-Sophie, Mota, Rodrigo, Darbagshahi, Saied Naderi, Dehareng, Frederic, Grelet, Clement, Vanlierde, Amelie, Froidmont, Eric, Becker, Frank, Schulze, Martin, and Vera, Sergio Palma
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nutrition ,FARMS ,POLLUTION ,Fourier-transform mid-infrared spectrometry ,DIETARY-PROTEIN ,modeling ,Veterinary Sciences ,STANDARDIZATION ,PERFORMANCE ,environment - 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.
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- 2020
23. Development of a genomic tool for breed assignment by comparison of different classification models: Application to three local cattle breeds.
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Wilmot, Hélène, Bormann, Jeanne, Soyeurt, Hélène, Hubin, Xavier, Glorieux, Géry, Mayeres, Patrick, Bertozzi, Carlo, and Gengler, Nicolas
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CATTLE breeding ,CATTLE breeds ,CLASSIFICATION ,GENOTYPES ,CENTROID - Abstract
Assignment of individual cattle to a specific breed can often not rely on pedigree information. This is especially the case for local breeds for which the development of genomic assignment tools is required to allow individuals of unknown origin to be included to their herd books. A breed assignment model can be based on two specific stages: (a) the selection of breed‐informative markers and (b) the assignment of individuals to a breed with a classification method. However, the performance of combination of methods used in these two stages has been rarely studied until now. In this study, the combination of 16 different SNP panels with four classification methods was developed on 562 reference genotypes from 12 cattle breeds. Based on their performances, best models were validated on three local breeds of interest. In cross‐validation, 14 models had a global cross‐validation accuracy higher than 90%, with a maximum of 98.22%. In validation, best models used 7,153 or 2,005 SNPs, based on a partial least squares‐discriminant analysis (PLS‐DA) and assigned individuals to breeds based on nearest shrunken centroids. The average validation sensitivity of the first two best models for the three local breeds of interest were 98.33% and 97.5%. Moreover, results reported in this study suggest that further studies should consider the PLS‐DA method when selecting breed‐informative SNPs. [ABSTRACT FROM AUTHOR]
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- 2022
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24. Linking first lactation survival to milk yield and components and lactation persistency in Tunisian Holstein cows
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Grayaa, Marwa, Vanderick, Sylvie, Rekik, Boulbaba, Ben Gara, Abderrahman, Hanzen, Christian, Grayaa, Siwar, Reis Mota, Rodrigo, Hammami, Hedi, and Gengler, Nicolas
- Abstract
Genetic parameters were estimated for first lactation survival defined as a binary trait (alive or dead to second calving) and the curve shape traits of milk yield, fat and protein percentages using information from 25 981 primiparous Tunisian Holsteins. For each trait, shape curves (i.e. peak lactation, persistency), level of production adjusted to 305 days in milk (DIMs) for total milk yield (TMY), and average fat (TF %) and protein (TP %) percentages were defined. Variance components were estimated with a linear random regression model under three bivariate animal models. Production traits were modelled by fixed herd × test-day (TD) interaction effects, fixed classes of 25 DIMs × age of calving × season of calving interaction effects, fixed classes of pregnancy, random environment effects and random additive genetic effects. Survival was modelled by fixed herd × year of calving interaction effects and age of calving × season of calving interaction effects, random permanent environment effects, and random additive genetic effects. Heritability (h2) estimates were 0.03 (±0.01) for survival and 0.23 (±0.01), 0.31 (±0.01) and 0.31 (±0.01) for TMY, TF % and TP %, respectively. Genetic correlations between survival and TMY, TF % and TP % were 0.26 (±0.08), −0.24 (±0.06) and −0.13 (±0.06), respectively. Genetic correlations between survival and persistency for fat and protein percentages were −0.35 (±0.09) and −0.19 (±0.09), respectively. Cows that had higher persistencies for fat and protein percentages were more likely not to survive.
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- 2019
25. Chromosome regions affecting MIR spectra indirect predictors of cheese--making properties in cattle
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Mota, R.R., Hammami, Hedi, Vanderick, S., Naderi, S., and Gengler, Nicolas
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- 2019
26. Creation of a functional spectral database -Can the calculation of a spectral Global h distance ensure the quality of international based MIR predictions?
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Zhang, Lei, Yabin Ma, Dehareng, Frédéric, Grelet, Clément, Colinet, Frédéric, Gengler, Nicolas, and Soyeurt, Hélène
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- 2019
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27. Using milk MIR spectra to identify candidate genes associated with climate--smart traits in cattle
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Hammami, Hedi, Mota, R.R., Vanderick, S., Naderi, S., Bastin, Catherine, and Gengler, Nicolas
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- 2019
28. GplusE: Mid-infrared milk analysis based technologies adding value to gene banks
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Gengler, Nicolas, Grelet, Clément, Soyeurt, H., and Dehareng, Frédéric
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- 2019
29. Validation of the prediction of body weight from dairy cow characteristics and milk MIR spectra
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Soyeurt, H., Froidmont, Eric, Dufrasne, I., Wang, Z., Gengler, Nicolas, Dehareng, Frédéric, and Grelet, Clément
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- 2019
30. Improving robustness and accuracy of predicted daily methane emissions of dairy cows using milk mid‐infrared spectra.
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Vanlierde, Amélie, Dehareng, Frédéric, Gengler, Nicolas, Froidmont, Eric, McParland, Sinead, Kreuzer, Michael, Bell, Matthew, Lund, Peter, Martin, Cécile, Kuhla, Björn, and Soyeurt, Hélène
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DAIRY cattle ,MILK ,LACTATION ,METHANE ,REGRESSION analysis ,MILK yield - Abstract
BACKGROUND: A robust proxy for estimating methane (CH4) emissions of individual dairy cows would be valuable especially for selective breeding. This study aimed to improve the robustness and accuracy of prediction models that estimate daily CH4 emissions from milk Fourier transform mid‐infrared (FT‐MIR) spectra by (i) increasing the reference dataset and (ii) adjusting for routinely recorded phenotypic information. Prediction equations for CH4 were developed using a combined dataset including daily CH4 measurements (n = 1089; g d−1) collected using the SF6 tracer technique (n = 513) and measurements using respiration chambers (RC, n = 576). Furthermore, in addition to the milk FT‐MIR spectra, the variables of milk yield (MY) on the test day, parity (P) and breed (B) of cows were included in the regression analysis as explanatory variables. RESULTS: Models developed based on a combined RC and SF6 dataset predicted the expected pattern in CH4 values (in g d−1) during a lactation cycle, namely an increase during the first weeks after calving followed by a gradual decrease until the end of lactation. The model including MY, P and B information provided the best prediction results (cross‐validation statistics: R2 = 0.68 and standard error = 57 g CH4 d−1). CONCLUSIONS: The models developed accounted for more of the observed variability in CH4 emissions than previously developed models and thus were considered more robust. This approach is suitable for large‐scale studies (e.g. animal genetic evaluation) where robustness is paramount for accurate predictions across a range of animal conditions. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]
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- 2021
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31. CH4 emitted by dairy cows estimated from milk MIR spectra: model based on data collected in 7 countries
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Vanlierde, Amélie, Dehareng, Frédéric, Gengler, Nicolas, Mathot, Michaël, Kreuzer, Michael, Grandl, Florian, Kuhla, Bjoern, Lund, Peter, Olijhoek, Dana, Eugène, Maguy, Martin, Cécile, Bell, Matthew J., McParland, Sinead, Soyeurt, Hélène, Centre Wallon de Recherches Agronomiques (CRA-W), Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, and Vanlierde, Amélie
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methane ,[SDV]Life Sciences [q-bio] ,food and beverages ,mir ,prediction ,mid-infrared spectroscopie ,lait ,produit laitier ,[SHS]Humanities and Social Sciences ,spectre ,gaz à effet de serre ,[INFO]Computer Science [cs] ,méthane - Abstract
International audience; Greenhouse gases (GHG) emissions from livestock and more especially methane (CH4) emissions from cattle related to ruminal fermentations remain the most important source of GHG within the agricultural sector. Main levers to reduce those emissions are the diet and the genetic selection. To study the impact of those levers and how reduce CH4 emissions, a large amount of reference measurements are needed. However, existing techniques to measure CH4 emissions from dairy cows are expensive, time consuming and difficult to apply on a large amount of animals. This is why the availability of a robust proxy to estimate individual daily CH4 emissions from dairy cows would be valuable. Estimate CH4 emissions from milk mid infrared (MIR) spectrum present potential to meet this aim as it can be obtained routinely at reasonable cost through milk recording process. Develop this equation is particularly challenging as the CH4 prediction equation from milk MIR spectra as CH4 is not a direct milk component but an indirect phenotype linked to milk composition through ruminal fermentations which theoretically influence both. To increase the variability of the calibration set, two datasets (CH4 measurements and milk MIR spectra) have been merged: A) 532 data from 156 cows of Ireland and Belgium using the SF6 tracer technique; B) 584 data from 147 cows of Switzerland, United Kingdom, France, Denmark and Germany collected in respiration chambers. In addition of the calibration using the raw reference values, a second calibration was performed with a reduction of 8 % to CH4 values from chambers evaluate the need to correct the potential method bias in accordance with literature. A 5-groups cross-validation was performed to test the robustness of the models. Those equations showed a R² and a standard error of cross-validation of 0.63 and 62 g/day respectively for the calibration on raw values and 0.65 and 59 g/day when respiration chamber values are adjusted. The slight improvement due to adjustment of chamber measurement is not significant. With errors around 60 g/day, the current equations does not permit to distinguish slight variation of CH4 emissions as it is often required in nutritional context. However, more variability is included (cows, breeds, diets, and country specific information), marked trends can be differentiated and statistics confirming its potential as proxy especially for genetic evaluations or life cycle analyses.
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- 2018
32. Defining and using novel milk composition based heat stress resilience traits in the context of genomic selection for more robust dairy cows in Wallonia
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Mineur, A., Mota, R., Gengler, Nicolas, Vanderick, S., Hammami, H., Bryant, J., Burke, M., Cook, R., Harris, B., Mosconi, C., and Wickham, B.
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genetic improvement ,milk MIR spectra ,resilience ,genomic selection - Abstract
Recent research showed the usefulness of using estimated breeding values (EBV)for mid-infrared (MIR) based biomarkers in genetic improvement. A novel class ofbiomarkers was defined based on modelling responses of milk composition(e.g., mid-infrared (MIR) based) to stress expressed on continuous scales usingreaction norm models. Heat stress is an important aspect of dairy production even in temperate climates as shown in recent studies. Implementation of genomic selection for tolerance to heat stress is therefore not only an issue for Australian dairy cattle, a country that recently introduced such an evaluation. The question remains open if using milk composition based heat stress resilience genomically enhanced EBV (GEBV) is not a viable option. Genetic parameters were estimated for production, udder health, and milk composition traits. Data included 155,977 test-day records for milk, fat, and protein yields, fat and protein percentages, 9 individual milk fatty acids (FA), 7 FA groups, 5 minerals, and 3 ketone bodies predicted by mid-infrared spectrometry, and 7 FA groups. Data were from 21,375 first-lactation Holstein cows in 473 herds in the Walloon region of Belgium and were collected between 2008 and 2014. Test-day records were merged with daily temperature-humidity index (THI) values based on meteorological records from public weather stations. The maximumdistance between each farm and its corresponding weather station was 13 km. Linear reaction norm models were used to estimate the intercept and slope responses of 23 traits to increasing THI values. Most yield and FA traits had phenotypic and genetic declines as THI increased, whereas C18:0, C18:1 cis-9, and 4 FA groups (unsaturated FA, monounsaturated FA, polyunsaturated FA, and long-chain FA) increased with THI. Moreover, the latter traits had the largest slope-to-intercept genetic variance ratios, which indicate that they are more affected by heat stress at high THI levels, and therefore good candidate traits. Among all traits, C18:1 cis-9 was the most sensitive to heat stress. As this trait is known to reflect body reserve mobilization, using its response to THI could be a very affordable milk biomarker of heat stress for dairy cattle, expressing the equilibrium between intake and mobilization, and therefore adaptation, under warm conditions. By including novel milk based composition traits to traditional production traits, correlations between EBVs and GEBVs of those milk based traits and udder health, fertility and longevity increased considerably. Thisstudy demonstrated that milk composition resilience heat stress traits could be used as early indicators of robustness traits. Our results also suggest that marker information tend to lead to higher accuracies of prediction. Therefore, new options to improve robustness through genomic selection in Walloon Holsteins are now available.
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- 2018
33. Milk mid-infrared spectra based biomarkers contributing to genetic improvement for udder health, fertility and longevity
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Gengler, Nicolas, Mineur, A., Vanderick, S., Hammami, H., Bryant, J., Burke, M., Cook, R., Harris, B., Mosconi, C., and Wickham, B.
- Subjects
fertility ,genetic improvement ,longevity ,udder health ,blood biomarkers ,milk MIR spectra - Abstract
Recent research showed the usefulness of using estimated breeding values (EBV)for mid-infrared (MIR) based biomarkers in genetic improvement. Similarly, research has also shown that genetic variation is contained in the absorbance traits along the MIR band of wavelengths. Targeted extraction of the useful genetic variance can be achieved by the combination of EBV. Direct estimation of EBV for absorbance traits was demonstrated. Our first objective was to show that the reduction of the rank of the (co)variance structure among spectral traits is possible by imposing linear functions, even if these functions represent lower accuracy MIR biomarkers. MIR based biomarkers traits were derived from ongoing research in the FP7 GplusE project. In this study, the pathway from MIR spectra to the use in genetic improvement will be described. First, blood reference phenotypic data was collected on Holstein cows, at early lactation for IGF-1, glucose, urea, cholesterol, fructosamine, beta-hydroxybutyric (BHB) acid and non-esterified fatty acids (NEFA). These traits were calibrated against corresponding MIR spectral data. Calibration Rcv 2 ranged from 0.21 to 0.51, very low from a chemometrical point of view, but potentially sufficient to extract useful spectral variation. This was validated, using EBV that were based on these MIR predictions for 144,623 records (closest to days in milk 25), from 73,378cows, in the Walloon region of Belgium. Single-trait, but multi-lactation (1, 2, 3+)models yielded h2 estimates ranging from 0.07 to 0.27. At least 20 daughters with novel traits and official EBV for udder health, fertility and longevity, with minimum reliabilities of 70% were required; a total of 124 bulls met this criteria. Standard selection index theory would usually rely on prediction error variance minimisation and estimated population (co)variances. Alternatively in this study, Partial Least Squares were applied to EBV for the milk MIR based biomarkers to develop novel genetic predictors for udder health, fertility and longevity, by extracting genetic variation along the wave band after rank reduction. Using all bulls, correlations between best predictors and EBV for udder health, fertility and longevity were at least 0.63, 0.67 and 0.62. Using selection index theory and based on significant increases of prediction abilities of longevity (0.76 compared to 0.68 from udder health or fertility alone) using milk MIR based blood biomarkers, their potential contribution to genetic improvement of udder health, fertility and longevity will be demonstrated.
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- 2018
34. Combining heterogeneous across-country data for prediction of enteric methane from proxies in dairy cattle
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Negussi, Enyew, González Recio, Oscar, de Haas, Yvette, Gengler, Nicolas, Soyeurt, Hélène, Vanlierde, Amélie, Peiren, Nico, Pszczola, Marcin, Garnsworthy, Phil, Battagin, Mara, Bayat, Alireza, Lassen, Jan, Yan, Tianhai, Boland, Tommy, Kuhla, Björn, Strabel, Tomasz, Schwarm, Angela, and Biscarini, Filippo
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methane proxies ,prediction accuracy ,dairy cattle ,WIAS ,Life Science ,heterogeneous data ,Fokkerij & Genomica ,Enteric methane ,Heterogeneous data ,Prediction accuracy ,Methane proxies ,Random forest ,Dairy cattle ,random forest ,enteric methane ,Animal Breeding & Genomics - Abstract
Large-scale measurement of enteric methane (CH4) from individual animals is a requisite for estimation of genetic parameters and prediction of breeding values. Direct measurement of individual CH4 emissions is logistically demanding and expensive, and correlated traits (proxies) or models can be used instead as a means to predict emissions. However, most predictive models tend to be specific and are valid mainly within the circumstances under which they were developed. Robust prediction models that work across countries and production environments may be built by combining heterogeneous data from several sources. However, combining heterogeneous individual animal observations on CH4 proxies from several sources is challenging and reports are scant in literature. The main objective of this study was to combine heterogeneous individual animal observations on CH4 proxies to develop robust enteric CH4 prediction models. Data on dairy cattle CH4 emissions and related proxies from 16 herds were made available by 13 research centers across 9 European countries within the Methagene EU COST Action FA1302 consortium on “Large-scale methane measurements on individual ruminants for genetic evaluations”. After a through edition and harmonization, the final dataset comprised 48,804 observations from 2,391 cows. Random Forest (RF) models were used to predict CH4 emissions and to estimate the relative importance of proxies for across-country predictions. Principal component analysis (PCA) was used to detect potential data stratifications. Milk yield, milk fat, DIM, BW, herd and country of origin appeared to be the most relevant proxies in the prediction model. An overall prediction accuracy of 0.81 was estimated from the combined heterogeneous data. This study is a first attempt to develop methods and approaches to combine heterogeneous individual animal data on proxies for CH4 to build robust models for prediction of CH4 emissions across diverse production systems and environments. The methodology outlined here can be extended to combining heterogeneous data, pedigree information and genome-wide dense marker information for estimation of genetic parameters and prediction of breeding values for traits related to dairy system CH4 emissions. Keywords: enteric methane, heterogeneous data, prediction accuracy, methane proxies, random forest, dairy cattle., Proceedings of the World Congress on Genetics Applied to Livestock Production. Volume: Methods and Tools - Prediction 2
- Published
- 2018
35. Is it possible to predict milk processing into butter using infrared spectroscopy?
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Lefébure, Émilie, Troch, Thibault, Noutfia, Younés, Colinet, Frédéric, Gérard, Amaury, Dehareng, Frédéric, Baeten, Vincent, Gengler, Nicolas, and Sindic, Marianne
- Subjects
BUTTER ,DAIRY processing ,INFRARED spectroscopy ,INFRARED spectra ,COLOR of water ,DAIRY products ,NEAR infrared spectroscopy - Abstract
Copyright of Biotechnologie, Agronomie, Societe et Environnement is the property of Les Presses Agronomiques de Gembloux and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2021
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36. Estimation of CH4 emissions from milk MIR spectra using respiration chamber as reference technique
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Vanlierde, Amelie, Gengler, Nicolas, Soyeurt, Hélène, Grandl, Florian, Kreuzer, Michael, Lund, Peter, Olijhoek, Dana, Ferris, Conrad P., and Dehareng, Frédéric
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- 2017
37. Targeted combination of estimated breeding values for lower accuracy mid-infrared biomarkers increases their usefulness in genetic evaluation of dairy cattle
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Gengler, Nicolas
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- 2017
38. Genetic analysis of milk MIR predicted blood and milk biomarkers linked to the physiological status
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Hammami, Hedi, Colinet, Frederic G., Bastin, Catherine, Grelet, Clément, Vanlierde, Amelie, Dehareng, Frédéric, and Gengler, Nicolas
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- 2017
39. National single-step genomic method that integrates multi-national genomic information
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Vandenplas, Jeremie, Špehar, Marija, Potočnik, Klemen, Gengler, Nicolas, and Gorjanc, Gregor
- Subjects
combination ,single-step genomic BLUP ,WIAS ,genomically enhanced estimated breeding values ,multi-national ,Fokkerij & Genomica ,Animal Breeding & Genomics - Abstract
The aim of this paper was to develop a national single-step genomic BLUP that integrates multi-national genomic estimated breeding values (EBV) and associated reliabilities without double counting dependent data contributions from the different evaluations. Simultaneous use of all data, including phenotypes, pedigree, and genotypes, is a condition to obtain unbiased EBV. However, this condition is not always fully met, mainly due to unavailability of foreign raw data for imported animals. In dairy cattle genetic evaluations, this issue is traditionally tackled through the multiple across-country evaluation (MACE) of sires, performed by Interbull Centre (Uppsala, Sweden). Multiple across-country evaluation regresses all the available national information onto a joint pedigree to obtain country-specific rankings of all sires without sharing the raw data. In the context of genomic selection, the issue is handled by exchanging sire genotypes and by using MACE information (i.e., MACE EBV and reliabilities), as a valuable source of "phenotypic" data. Although all the available data are considered, these "multi-national" genomic evaluations use multi-step methods assuming independence of various sources of information, which is not met in all situations. We developed a method that handles this by single-step genomic evaluation that jointly (1) uses national phenotypic, genomic, and pedigree data; (2) uses multi-national genomic information; and (3) avoids double counting dependent data contributions from an animal's own records and relatives' records. The method was demonstrated by integrating multi-national genomic EBV and reliabilities of Brown Swiss sires, included in the InterGenomics consortium at Interbull Centre, into the national evaluation in Slovenia. The results showed that the method could (1) increase reliability of a national (genomic) evaluation; (2) provide consistent ranking of all animals: bulls, cows, and young animals; and (3) increase the size of a genomic training population. These features provide more efficient and transparent selection throughout a breeding program.
- Published
- 2017
40. Selection against metabolic diseases
- Author
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Heringstad, Bjørg, Pryce, J.E., Egger-Danner, Christa, Stock, K.F., Cole, J. B., Gengler, Nicolas, Miglior, F., Bradley, A. J., Parker Graddis, K.L., Koeck, Astrid, Bastin, C., and Abdelsayed, M.
- Abstract
Metabolic diseases, such as ketosis and milk fever, are among the most common diseases affecting dairy cattle. Genetic improvement of ability to resist metabolic diseases can be achieved by direct selection with genetic evaluation based on clinically observed traits, or by indirect selection based on indicators or predictors of metabolic diseases. The most prevalent metabolic diseases in dairy cattle, for which genetic parameters have been published, are ketosis, displaced abomasum, milk fever, and tetany. In this review we present genetic parameters for these metabolic diseases, give a status of genetic and genomic evaluations, and discuss possible indicator traits.
- Published
- 2017
41. Additional considerations to the use of single‐step genomic predictions in a dominance setting.
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Mota, Rodrigo R., Vanderick, Sylvie, Colinet, Frédéric G., Hammami, Hedi, Wiggans, George R., and Gengler, Nicolas
- Subjects
MATRIX inversion ,PREDICTION theory ,SOCIAL dominance - Abstract
Recent publications indicate that single‐step models are suitable to estimate breeding values, dominance deviations and total genetic values with acceptable quality. Additive single‐step methods implicitly extend known number of allele information from genotyped to non‐genotyped animals. This theory is well derived in an additive setting. It was recently shown, at least empirically, that this basic strategy can be extended to dominance with reasonable prediction quality. Our study addressed two additional issues. It illustrated the theoretical basis for extension and validated genomic predictions to dominance based on single‐step genomic best linear unbiased prediction theory. This development was then extended to include inbreeding into dominance relationships, which is a currently not yet solved issue. Different parametrizations of dominance relationship matrices were proposed. Five dominance single‐step inverse matrices were tested and described as C1, C2, C3, C4 and C5. Genotypes were simulated for a real pig population (n = 11,943 animals). In order to avoid any confounding issues with additive effects, pseudo‐records including only dominance deviations and residuals were simulated. SNP effects of heterozygous genotypes were summed up to generate true dominance deviations. We added random noise to those values and used them as phenotypes. Accuracy was defined as correlation between true and predicted dominance deviations. We conducted five replicates and estimated accuracies in three sets: between all (S1), non‐genotyped (S2) and inbred non‐genotyped (S3) animals. Potential bias was assessed by regressing true dominance deviations on predicted values. Matrices accounting for inbreeding (C3, C4 and C5) best fit. Accuracies were on average 0.77, 0.40 and 0.46 in S1, S2 and S3, respectively. In addition, C3, C4 and C5 scenarios have shown better accuracies than C1 and C2, and dominance deviations were less biased. Better matrix compatibility (accuracy and bias) was observed by re‐scaling diagonal elements to 1 minus the inbreeding coefficient (C5). [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
42. Optimizing Limited Deep Phenotyping Capacity for Genetic Studies and Routine Genetic Evaluations: Application to Boar Taint.
- Author
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Markey, Alice, Wavreille, José, Mayeres, Patrick, Huet, Anne-Catherine, and Gengler, Nicolas
- Subjects
BOARS ,CATTLE growth ,ODORS ,ARTIFICIAL insemination ,BIRTH weight ,ANIMAL weaning ,SWINE breeding - Abstract
In the context of genetic research, but even more for routine genetic evaluations, a recurrent problem is the difficulty to obtain measurements for certain required phenotypes. This can be caused by limited facilities, difficulties in sampling, expensive analyses, limited staff relative to number of animals, and many other reasons. It may therefore be necessary to select which animals to phenotype while maintaining the phenotyping quality and avoiding bias or omitting some animals of interest. This implies developing strategies to do the phenotyping in a non-biased way using a priori available minimum knowledge. An example could be predicting post weaning growth of cattle based on earlier information as birth weight and weaning weight to optimize the use of feed intake or methane measurement facilities. In pigs, similar situations might occur for growth or emissions. However, another important example is boar taint (BT) which shows that not only sequential weighing, but also partial measurements of BT may require attention. BT is defined as urinary and fecal and associated with 2 major compounds, androstenone (AND) and skatole (SKA). The classical method of odor discrimination, "Human Nose Score" (HNS), is notoriously unreliable. Even if odor is known to be complex, adding AND and SKA to HNS would help reveal better the boar taint condition (i.e., phenotype). However, the measurements of AND and SKA or other compounds in fat are expensive. Therefore, it would be important to optimize these measurements by selecting relevant fat samples taken at slaughterhouses. We tested the following optimization strategy. The objective was to obtain a good distribution of a posteriori measured AND and SKA in the selected samples given initial asymmetric populations with positive HNS (3 to 10% declared positives). In a first step, the strategy pooled 2 available HNS for 997 across 22 contemporary groups (CG) and added limited chemical measurements (218/997) available for older samples. The data were obtained from a boar progeny testing scheme; therefore, CG were linked through connecting sires from artificial insemination having sons in different CG. A multi-trait BLUP model was used adjusting for metabolic weight using a 4-generation pedigree. A total of 15% of the animals in the last 6 contemporary groups were selected using first positive HNS (17/300), then keeping the highest and lowest predictions and maintaining diversity by not repeating boars from the same sire-family (i.e., when a family was already present, the next animal was taken). A posteriori chemical analyses were performed on selected samples to compare predicted and observed classifications in tainted and untainted. Research showed an accuracy of 0.73 (Table 1), leading to 58% tainted and 42% untainted compared with the expected ratio. Improvements of this strategy are currently in development, including using genomics. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
43. Validation Strategies of Sensor Data in the Context of Heat Stress Detection.
- Author
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Lemal, Pauline, Tran, Marie-Nguyet, Schroyen, Martine, and Gengler, Nicolas
- Subjects
ANIMAL herds ,BEEF cattle ,GENETIC correlations ,COMPOSITION of milk ,DETECTORS ,DAIRY cattle ,VARIANCES ,CONFIRMATORY factor analysis - Abstract
Evaluating genetically heat stress (HS) of cattle is still a major challenge, also because of the need to have access to HS sensitive phenotypes relating their reaction to temperature and humidity as described by the temperature-humidity index (THI). A solution could be to add the information of data from sensors. However, activity (ACT) and rumination (RUM) sensors reflect no "production" [i.e., milk yields (MY), milk composition, but also intake or growth] related aspects which is suboptimal. Therefore, the objectives of this study were thus to assess the possible usefulness for adding sensors data for HS genetic evaluation based on a dairy situation. But the context described can be easily enlarged and include beef cattle and pigs. Strategy was illustrated using daily SenseHub collar records that were obtained from October 2019 to July 2022 in six dairy herds in Wallonia in Southern Belgium. A total of 453 Walloon Holstein cows were followed during this period for activity time. Meteorological data and a HS sensitive "production" trait (here MY) were also obtained from 2015 to 2022 for 1,740 cows from the same herds. In total 32,154 MY, 130,867 ACT and 130,848 RUM records were available. The thresholds at which the different traits start to be affected by HS were estimated at a THI of 63, 64 and 66, respectively, for MY, ACT and RUM. A three-trait random regression reaction norm model was fitted with these different thresholds. Estimated heritability values were 0.19 ± 0.03, 0.14 ± 0.06 and 0.21 ± 0.05 respectively for MY, ACT and RUM using AIREML. Ratios of variance associated to THI and constant genetic effect were 0.002, 0.02 and 0.04 indicating a stronger reaction by RUM but also ACT than MY to THI. However, it is uncertain if the reactions of these sensor traits are reflected in the production traits. Estimated genetic correlations of regressions on THI for ACT and RUM showed values of 0.51 ± 0.70 and 0.08 ± 0.54 with MY and 0.77 ± 0.69 between ACT and RUM. The first eigenvector of the correlation matrix explained 65% of the total variability and standardized coefficients of this eigenvector were 0.42, 0.69 and 0.59 for MY, ACT and RUM. A total of 50% of the variance associated with the regression on THI for MY could be explained by the regressions on THI for ACT and RUM. Adding the constant genetic effects for ACT and RUM only marginally increased to 51% the ability to explain the effect of THI on MY. Based on these results, reactions of different indicator traits, here sensor data, may reflect a different reaction than production and therefore economically important traits. Additional research, also beyond dairy cattle, should continue to address this topic that is very important for the validation of sensor data detecting HS. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
44. Potential use of milk MIR spectra to obtain new health phenotypes for dairy cows in the framework of the GplusE project
- Author
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Vanlierde, Amelie, Grelet, Clement, Gengler, Nicolas, Ferris, Conrad Peter, Sørensen, Martin Tang, Höglund, Johanna, Carter, Fiona, Ingvartsen, Klaus Lønne, Santoro, Andreia, Dardenne, Pierre, and Dehareng, Frédéric
- Published
- 2016
45. MASTITIS DETECTION FROM MILK MID-INFRARED (MIR) SPECTROSCOPY IN DAIRY COWS.
- Author
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Rienesl, Lisa, Khayatzadeh, Negar, Köck, Astrid, Dale, Laura, Werner, Andreas, Grelet, Clément, Gengler, Nicolas, Auer, Franz-Josef, Egger-Danner, Christa, Massart, Xavier, and Sölkner, Johann
- Subjects
MASTITIS ,COMPOSITION of milk ,COWS ,DISCRIMINANT analysis ,SPECTROMETRY ,GENETIC correlations ,MILK ,LACTOSE - Abstract
Mid-infrared (MIR) spectroscopy is the method of choice for the standard milk recording system, to determine milk components including fat, protein, lactose and urea. Since milk composition is related to health and metabolic status of a cow, MIR spectra could be potentially used for disease detection. In dairy production, mastitis is one of the most prevalent diseases. The aim of this study was to develop a calibration equation to predict mastitis events from routinely recorded MIR spectra data. A further aim was to evaluate the use of test day somatic cell score (SCS) as covariate on the accuracy of the prediction model. The data for this study is from the Austrian milk recording system and its health monitoring system (GMON). Test day data including MIR spectra data was merged with diagnosis data of Fleckvieh, Brown Swiss and Holstein Friesian cows. As prediction variables, MIR absorbance data after first derivatives and selection of wavenumbers, corrected for days in milk, were used. The data set contained roughly 600,000 records and was split into calibration and validation sets by farm. Calibration sets were made to be balanced (as many healthy as mastitis cases), while the validation set was kept large and realistic. Prediction was done with Partial Least Squares Discriminant Analysis, key indicators of model fit were sensitivity and specificity. Results were extracted for association between spectra and diagnosis with different time windows (days between diagnosis and test days) in validation. The comparison of different sets of predictor variables (MIR, SCS, MIR + SCS) showed an advantage in prediction for MIR + SCS. For this prediction model, specificity was 0.79 and sensitivity was 0.68 in time window -7 to +7 days (calibration and validation). Corresponding values for MIR were 0.71 and 0.61, for SCS they were 0.81 and 0.62. In general, prediction of mastitis performed better with a shorter distance between test day and mastitis event, yet even for time windows of -21 to +21 days, prediction accuracies were still reasonable, with sensitivities ranging from 0.50 to 0.57 and specificities remaining unchanged (0.71 to 0.85). Additional research to further improve prediction equation, and studies on genetic correlations among clinical mastitis, SCS and MIR predicted mastitis are planned. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
46. Bioassays to Quantify Hygienic Behavior in Honey Bee (Apis Mellifera L.) Colonies: A Review.
- Author
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Leclercq, Gil, Francis, Frédéric, Gengler, Nicolas, and Blacquière, Tjeerd
- Published
- 2018
- Full Text
- View/download PDF
47. Development of infrared reflectance spectroscopy databases for efficient livestock managements
- Author
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Decruyenaere, V., Boval, Maryline, Dardenne, Pierre, Gengler, Nicolas, and Philippe LECOMTE
- Subjects
L02 - Alimentation animale ,P01 - Conservation de la nature et ressources foncières ,U30 - Méthodes de recherche - Abstract
There is a strong demand towards livestock for high quality products and services while limiting the impact on the environment. That goes for different management systems as outdoor for which consumer expectation is high. Decision Support Tools (DST) are therefore needed to achieve various food production and other agricultural resources, as manure. This synthesis aim is to highlight the potential of Near and Mid-Infrared Reflectance Spectroscopy (NIRS and MIRS) as tool for enhancing the value of livestock systems. NIRS and MIRS are non-destructive technologies that estimate simultaneously several parameters as chemical composition, nutritive value of various products, feeding characteristics (digestibility, intake), animal physiological status (pregnancy), detection of metabolic disorders (acidosis, mastitis) or enteric greenhouse gas emission (GES). A lot of products can be analysed by IRS: forages and feeds, effluents, faeces, milk, meat. All spectral information can be used in qualitative and quantitative ways to develop DST able to improve livestock system sustainability, herd management in regard to diet and animal welfare, to select efficient animals and this, for various animal species. Many developments are underway to merge databases of several research centres. So, NIRS is used to predict forage chemical composition; to estimate, from faeces analysis, parameters reflecting diet feeding value, in tropical and temperate areas. MIRS analysis of milk is another example of pooling databases for dairy system management. Prediction of new parameters as GES production from milk analysis and performances of livestock grazing on natural areas are studied. These examples illustrate the potential of NIRS and MIRS for the development of effective DST.
- Published
- 2013
48. WP 1. Building and exploring datasets and climate models on climate change in relation to livestock and grassland - Task L1.1: Identification of datasets on climate change in relation to livestock productivity (production and fitness traits) and livestock infectious disease
- Author
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Bartley, Dave, Köchy, Martin, Harstad, Odd Magne, Meyer-Aurich, Andreas, Wall, Eileen, Gengler, Nicolas, Wilson, Anthony, and Scollan, Nigel
- Subjects
Climate Change ,Agriculture ,Food Security - Abstract
Datasets from Germany and the United Kingdom containing information on geographic (European Union 27 countries), climatic, meteorological, host and infectious agents’ parameters (figure 2) have been completed and are now available for preliminary analysis relating to data quality and consistency. Data set information will continue to be added over the next 12 months.
- Published
- 2013
- Full Text
- View/download PDF
49. WPL 1. Building and exploring datasets and climate models on climate change in relation to livestock and grassland - Task L1.2. National and transnational dairy cows biometeorological datasets linked to productive, reproductive and health performances data
- Author
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Lacetera, Nicola, Vitali, Andrea, Gengler, Nicolas, Hammami, Hedi, Dufrasne, Isabelle, Baudouin, Nicks, Tychon, Bernard, Velarde, Antonio, Blanco, Isabel, Faverdin, Philippe, and Boudon, Anne
- Subjects
food and beverages ,Climate Change ,Agriculture ,Food Security - Abstract
Different datasets have been completed and are now available for the analysis of interannual and seasonal variations of productive, reproductive or health data relative to intensively dairy cows and also to establish the relationships between temperature humidity index (THI) and dairy cow performances. Datasets are referred to different European countries (Italy, Belgium, Luxembourg and Slovenia) with different climatic features. All these datasets have data relative to Animal Pedigree (Cow ID, Birth date, Breed, Sire ID and Dam ID), Test-day records (Cow ID, Herd ID, Parity, Calving date, Test date, Milk yield, Milk fat and protein (%), Milk somatic cell score), Reproductive events (Cow ID, Herd ID, Parity, Calving date, AI date, Sire ID, Days Open, NRR-56 day), and Daily meteorological records (Meteo station ID, Zip code of the meteo station, Observation date, Max temperature, Min temperature, Mean temperature, Max relative humidity, Min relative humidity, Mean relative humidity, Solar radiation, Wind speed). The dataset relative to Italy includes also Mortality data (Animal ID, Herd ID, Death date) and Bulk milk quality data (Herd ID, Test date, Fat & protein (%), Somatic cell score, Bacterial count, Herd latitude, Herd longitude, Herd elevation). An additional database is still under construction and will be based on Spanish data from organic dairy farms.
- Published
- 2013
- Full Text
- View/download PDF
50. Hygienic removal of freeze-killed brood does not predict Varroa-resistance traits in unselected stocks.
- Author
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Leclercq, Gil, Blacquière, Tjeerd, Gengler, Nicolas, and Francis, Frédéric
- Published
- 2018
- Full Text
- View/download PDF
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