130 results on '"Gengler, Nicolas"'
Search Results
2. 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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3. 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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4. Genetic analysis of milk citrate predicted by milk mid-infrared spectra of Holstein cows in early lactation
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Chen, Yansen, primary, Hu, Hongqing, additional, Atashi, Hadi, additional, Grelet, Clément, additional, Wijnrocx, Katrien, additional, Lemal, Pauline, additional, and Gengler, Nicolas, additional
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- 2023
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5. 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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6. Towards a Unified Architecture Powering Scalable Learning Models with IoT Data Streams, Blockchain, and Open Data
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Debauche, Olivier, primary, Nkamla Penka, Jean Bertin, additional, Hani, Moad, additional, Guttadauria, Adriano, additional, Ait Abdelouahid, Rachida, additional, Gasmi, Kaouther, additional, Ben Hardouz, Ouafae, additional, Lebeau, Frédéric, additional, Bindelle, Jérôme, additional, Soyeurt, Hélène, additional, Gengler, Nicolas, additional, Manneback, Pierre, additional, Benjelloun, Mohammed, additional, and Bertozzi, Carlo, additional
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- 2023
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7. 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, primary, Niehoff, Tobias, additional, Soyeurt, Hélène, additional, Gengler, Nicolas, additional, and Calus, Mario P L, additional
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- 2023
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8. Integrating heterogeneous across-country data for proxy-based random forest prediction of enteric methane in dairy cattle
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Negussie, Enyew, González-Recio, Oscar, Battagin, Mara, Bayat, Ali-Reza, Boland, Tommy M., de Haas, Yvette, García-Rodríguez, Aser, Garnsworthy, P. C., Gengler, Nicolas, Kreuzer, Michael, Kuhla, Björn, Lassen, Jan, Peiren, Nico, Pszczola, Marcin, Schwarm, Angela, Soyeurt, Hélène, Vanlierde, Amélie, Yan, Tianhai, Biscarini, Filippo, European Commission, Negussie, Enyew [0000-0003-4892-9938], González-Recio, Oscar [0000-0002-9106-4063], Battagin, Mara [0000-0001-7309-6793], Bayat, Ali-Reza [0000-0002-4894-0662], Boland, Tommy [0000-0002-7433-130X], de Haas, Yvette [0000-0002-4331-4101], Garcia-Rodriguez, Aser [0000-0001-5519-6766], Garnsworthy, Philip C [0000-0001-5131-3398], Gengler, Nicolas [0000-0002-5981-5509], Kreuzer, Michael [0000-0002-9978-1171], Kuhla, Björn [0000-0002-2032-5502, Lassen, Jan [0000-0002-1338-8644], Peiren, Nico [0000-0001-5500-1607], Pszczola, Marcin [0000-0003-2833-5083], Schwarm, Angela [0000-0002-5750-2111], Soyeurt, Hélène [0000-0001-9883-9047], Vanlierde, Amélie [0000-0002-4619-1936], Yan, Tianhai [0000-0002-1994-5202], Biscarini, Filippo [0000-0002-3901-2354], Negussie, Enyew, González-Recio, Oscar, Battagin, Mara, Bayat, Ali-Reza, Boland, Tommy, de Haas, Yvette, Garcia-Rodriguez, Aser, Garnsworthy, Philip C, Gengler, Nicolas, Kreuzer, Michael, Kuhla, Björn, Lassen, Jan, Peiren, Nico, Pszczola, Marcin, Schwarm, Angela, Soyeurt, Hélène, Vanlierde, Amélie, Yan, Tianhai, and Biscarini, Filippo
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methane emissions ,Enteric methane ,proxies ,Prediction models ,predictive model ,Intestine, Small ,Machine learning ,Genetics ,Animals ,Lactation ,dairy cows ,Fokkerij & Genomica ,prediction models ,Proxies for methane ,Diet ,Milk ,machine learning ,WIAS ,enteric methane ,proxies for methane ,Cattle ,Female ,Animal Science and Zoology ,Methane ,random forest ,Animal Breeding & Genomics ,Food Science - Abstract
Direct measurements of methane (CH4) from individual animals are difficult and expensive. Predictions based on proxies for CH4 are a viable alternative. Most prediction models are based on multiple linear regressions (MLR) and predictor variables that are not routinely available in commercial farms, such as dry matter intake (DMI) and diet composition. The use of machine learning (ML) algorithms to predict CH4 emissions from across-country heterogeneous data sets has not been reported. The objectives were to compare performances of ML ensemble algorithm random forest (RF) and MLR models in predicting CH4 emissions from proxies in dairy cows, and assess effects of imputing missing data points on prediction accuracy. Data on CH4 emissions and proxies for CH4 from 20 herds were provided by 10 countries. The integrated data set contained 43,519 records from 3,483 cows, with 18.7% missing data points imputed using k-nearest neighbor imputation. Three data sets were created, 3k (no missing records), 21k (missing DMI imputed from milk, fat, protein, body weight), and 41k (missing DMI, milk fat, and protein records imputed). These data sets were used to test scenarios (with or without DMI, imputed vs. nonimputed DMI, milk fat, and protein), and prediction models (RF vs. MLR). Model predictive ability was evaluated within and between herds through 10-fold cross-validation. Prediction accuracy was measured as correlation between observed and predicted CH4, root mean squared error (RMSE) and mean normalized discounted cumulative gain (NDCG). Inclusion of DMI in the model improved within and between-herd prediction accuracy to 0.77 (RMSE = 23.3%) and 0.58 (RMSE = 31.9%) in RF and to 0.50 (RMSE = 0.327) and 0.13 (RMSE = 42.71) in MLR, respectively than when DMI was not included in the predictive model. When missing DMI records were imputed, within and between-herd accuracy increased to 0.84 (RMSE = 18.5%) and 0.63 (RMSE = 29.9%), respectively. In all scenarios, RF models out-performed MLR models. Results suggest routinely measured variables from dairy farms can be used in developing globally robust prediction models for CH4 if coupled with state-of-the-art techniques for imputation and advanced ML algorithms for predictive modeling., Journal of Dairy Science, 105 (6), ISSN:0022-0302, ISSN:1525-3198
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- 2022
9. Lipid Serum Profiling of Boar-Tainted and Untainted Pigs Using GC×GC–TOFMS: An Exploratory Study
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Bhatt, Kinjal, primary, Dejong, Thibaut, additional, Dubois, Lena M., additional, Markey, Alice, additional, Gengler, Nicolas, additional, Wavreille, José, additional, Stefanuto, Pierre-Hugues, additional, and Focant, Jean-François, additional
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- 2022
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10. 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, primary, Gerards, Cyprien, additional, Nickmilder, Charles, additional, Bindelle, Jérôme, additional, Franceschini, Sébastien, additional, Dehareng, Frédéric, additional, Veselko, Didier, additional, Bertozzi, Carlo, additional, Gengler, Nicolas, additional, Marvuglia, Antonino, additional, Bayram, Alper, additional, and Tedde, Anthony, additional
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- 2022
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11. 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, primary, Khayatzdadeh, Negar, additional, Köck, Astrid, additional, Egger-Danner, Christa, additional, Gengler, Nicolas, additional, Grelet, Clément, additional, Dale, Laura Monica, additional, Werner, Andreas, additional, Auer, Franz-Josef, additional, Leblois, Julie, additional, and Sölkner, Johann, additional
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- 2022
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12. 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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Male ,endocrine system ,Castration alternatives ,Indoles ,urogenital system ,Swine ,Quantitative Trait Loci ,Genetic selection ,Food Contamination ,Androsterone ,Animal Genetics • Review ,Skatole ,Androstenone ,Phenotype ,Odorants ,Pork Meat ,Animals ,Pigs ,Castration - Abstract
Boar taint is an unpleasant odor in male pig meat, mainly caused by androstenone, skatole, and indole, which are deposited in the fat tissue. Piglet castration is the most common practice to prevent boar taint. However, castration is likely to be banished in a few years due to animal welfare concerns. Alternatives to castration, such as genetic selection, have been assessed. Androstenone and skatole have moderate to high heritability, which makes it feasible to select against these compounds. This review presents the latest results obtained on genetic selection against boar taint, on correlation with other traits, on differences in breeds, and on candidate genes related to boar taint. QTLs for androstenone and skatole have been reported mainly on chromosomes 6, 7, and 14. These chromosomes were reported to contain genes responsible for synthesis and degradation of androstenone and skatole. A myriad of work has been done to find markers or genes that can be used to select animals with lower boar taint. The selection against boar taint could decrease performance of some reproduction traits. However, a favorable response on production traits has been observed by selecting against boar taint. Selection results have shown that it is possible to reduce boar taint in few generations. In addition, modifications in diet and environment conditions could be associated with genetic selection to reduce boar taint. Nevertheless, costs to measure and select against boar taint should be rewarded with incentives from the market; otherwise, it would be difficult to implement genetic selection.
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- 2021
13. Direct In Vivo Analysis of CBD- and THC-Acid Cannabinoids and Classification of Cannabis Cultivars Using SpiderMass
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Ogrinc, Nina, primary, Schneider, Serge, additional, Bourmaud, Adèle, additional, Gengler, Nicolas, additional, Salzet, Michel, additional, and Fournier, Isabelle, additional
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- 2022
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14. 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
15. 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, primary, Bizimana, Jean Pierre, additional, Kwibuka, Yves, additional, Nduwumuremyi, Athanase, additional, Bigirimana, Vincent de Paul, additional, Bucagu, Charles, additional, Lassois, Ludivine, additional, Malice, Eléonore, additional, Gengler, Nicolas, additional, Massart, Sébastien, additional, Bragard, Claude, additional, Habtu, Michael, additional, Brostaux, Yves, additional, Thonar, Cécile, additional, and Vanderschuren, Hervé, additional
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- 2021
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16. 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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17. 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
18. Multiple Breeds and Countries’ Predictions of Mineral Contents in Milk from Milk Mid-Infrared Spectrometry
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Christophe, Octave S., primary, Grelet, Clément, additional, Bertozzi, Carlo, additional, Veselko, Didier, additional, Lecomte, Christophe, additional, Höeckels, Peter, additional, Werner, Andreas, additional, Auer, Franz-Josef, additional, Gengler, Nicolas, additional, Dehareng, Frédéric, additional, and Soyeurt, Hélène, additional
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- 2021
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19. Appropriate Data Quality Checks Improve the Reliability of Values Predicted from Milk Mid-Infrared Spectra
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Zhang, Lei, primary, Li, Chunfang, additional, Dehareng, Frédéric, additional, Grelet, Clément, additional, Colinet, Frédéric, additional, Gengler, Nicolas, additional, Brostaux, Yves, additional, and Soyeurt, Hélène, additional
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- 2021
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20. Is it possible to predict milk processing into butter using infrared spectroscopy?
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Lefébure, Émilie, primary, Troch, Thibault, primary, Noutfia, Younés, primary, Colinet, Frédéric, primary, Gérard, Amaury, primary, Dehareng, Frédéric, primary, Baeten, Vincent, primary, Gengler, Nicolas, primary, and Sindic, Marianne, primary
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- 2021
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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. Can We Observe Expected Behaviors at Large and Individual Scales for Feed Efficiency-Related Traits Predicted Partly from Milk Mid-Infrared Spectra?
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Zhang, Lei, primary, Gengler, Nicolas, additional, Dehareng, Frédéric, additional, Colinet, Frédéric, additional, Froidmont, Eric, additional, and Soyeurt, Hélène, additional
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- 2020
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24. Short communication: Investigation of the temporal relationships between milk mid-infrared predicted biomarkers and lameness events in later lactation
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Mineur, Axelle, primary, Hammami, Hedi, additional, Grelet, Clément, additional, Egger-Danner, Christa, additional, Sölkner, Johann, additional, and Gengler, Nicolas, additional
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- 2020
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25. Mastitis Detection from Milk Mid-Infrared (MIR) Spectroscopy in Dairy Cows
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Rienesl, Lisa, primary, Khayatzadeh, Negar, additional, Köck, Astrid, additional, Dale, Laura, additional, Werner, Andreas, additional, Grelet, Clément, additional, Gengler, Nicolas, additional, Auer, Franz-Josef, additional, Egger-Danner, Christa, additional, Massart, Xavier, additional, and Sölkner, Johann, additional
- Published
- 2019
- Full Text
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26. Linking first lactation survival to milk yield and components and lactation persistency in Tunisian Holstein cows
- Author
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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.
- Published
- 2019
27. CH4 emitted by dairy cows estimated from milk MIR spectra: model based on data collected in 7 countries
- Author
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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
- Subjects
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.
- Published
- 2018
28. Combining heterogeneous across-country data for prediction of enteric methane from proxies in dairy cattle
- Author
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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
- Subjects
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
29. Linking first lactation survival to milk yield and components and lactation persistency in Tunisian Holstein cows
- Author
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Grayaa, Marwa, primary, Vanderick, Sylvie, additional, Rekik, Boulbaba, additional, Ben Gara, Abderrahman, additional, Hanzen, Christian, additional, Grayaa, Siwar, additional, Reis Mota, Rodrigo, additional, Hammami, Hedi, additional, and Gengler, Nicolas, additional
- Published
- 2019
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30. 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
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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
31. 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
32. Impact of UGT1A1 polymorphisms on Raltegravir and its glucuronide plasma concentrations in a cohort of HIV-1 infected patients
- Author
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Belkhir, Leïla, primary, Seguin-Devaux, Carole, additional, Elens, Laure, additional, Pauly, Caroline, additional, Gengler, Nicolas, additional, Schneider, Serge, additional, Ruelle, Jean, additional, Haufroid, Vincent, additional, and Vandercam, Bernard, additional
- Published
- 2018
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33. Chemical Evaluation of Electronic Cigarettes: Multicomponent Analysis of Liquid Refills and their Corresponding Aerosols
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Beauval, Nicolas, primary, Antherieu, Sébastien, additional, Soyez, Mélissa, additional, Gengler, Nicolas, additional, Grova, Nathalie, additional, Howsam, Michael, additional, Hardy, Emilie M, additional, Fischer, Marc, additional, Appenzeller, Brice M.R., additional, Goossens, Jean-François, additional, Allorge, Delphine, additional, Garçon, Guillaume, additional, Lo-Guidice, Jean-Marc, additional, and Garat, Anne, additional
- Published
- 2017
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34. Genomics of a revived breed: Case study of the Belgian campine cattle
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François, Liesbeth, primary, Wijnrocx, Katrien, additional, Colinet, Frédéric G., additional, Gengler, Nicolas, additional, Hulsegge, Bettine, additional, Windig, Jack J., additional, Buys, Nadine, additional, and Janssens, Steven, additional
- Published
- 2017
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35. Cow milk coagulation: process description, variation factors and evaluation methodologies. A review
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Troch, Thibault, primary, Lefébure, Émilie, primary, Baeten, Vincent, primary, Colinet, Frédéric, primary, Gengler, Nicolas, primary, and Sindic, Marianne, primary
- Published
- 2017
- Full Text
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36. Challenges and priorities for modelling livestock health and pathogens in the context of climate change
- Author
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Özkan, Şeyda, primary, Vitali, Andrea, additional, Lacetera, Nicola, additional, Amon, Barbara, additional, Bannink, André, additional, Bartley, Dave J., additional, Blanco-Penedo, Isabel, additional, de Haas, Yvette, additional, Dufrasne, Isabelle, additional, Elliott, John, additional, Eory, Vera, additional, Fox, Naomi J., additional, Garnsworthy, Phil C., additional, Gengler, Nicolas, additional, Hammami, Hedi, additional, Kyriazakis, Ilias, additional, Leclère, David, additional, Lessire, Françoise, additional, Macleod, Michael, additional, Robinson, Timothy P., additional, Ruete, Alejandro, additional, Sandars, Daniel L., additional, Shrestha, Shailesh, additional, Stott, Alistair W., additional, Twardy, Stanislaw, additional, Vanrobays, Marie-Laure, additional, Ahmadi, Bouda Vosough, additional, Weindl, Isabelle, additional, Wheelhouse, Nick, additional, Williams, Adrian G., additional, Williams, Hefin W., additional, Wilson, Anthony J., additional, Østergaard, Søren, additional, and Kipling, Richard P., additional
- Published
- 2016
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37. 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.
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- 2013
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38. 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
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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
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- View/download PDF
39. Chemical Evaluation of Electronic Cigarettes: Multicomponent Analysis of Liquid Refills and their Corresponding Aerosols.
- Author
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Antherieu, Sébastien, Soyez, Mélissa, Allorge, Delphine, Garçon, Guillaume, Lo-Guidice, Jean-Marc, Garat, Anne, Beauval, Nicolas, Gengler, Nicolas, Fischer, Marc, Grova, Nathalie, Hardy, Emilie M., Appenzeller, Brice M. R., Howsam, Michael, and Goossens, Jean-François
- Subjects
ELECTRONIC cigarettes ,HEALTH ,SMOKING ,PHYSIOLOGICAL effects of tobacco ,PHYSIOLOGY - Abstract
Electronic cigarette use has raised concern worldwide regarding potential health risks and its position in tobacco cessation strategies. As part of any toxicity assessment, the chemical characterization of e-liquids and their related vapors are among fundamental data to be determined. Considering the lack of available reference methods, we developed and validated several analytical procedures in order to conduct a multicomponent analysis of six e-liquid refills and their resultant vapor emissions (generated by a smoking machine), and compared them with tobacco smoke. We combined several techniques including gas-chromatography, high and ultra-performance liquid chromatography and inductively coupled plasma with mass spectrometry or ultraviolet and flame ionization detection in order to identify the main e-liquid constituents (propylene glycol, glycerol and nicotine), as well as multiple potentially harmful components (trace elements, polycyclic aromatic hydrocarbons (PAHs), pesticides and carbonyl compounds). Regarding propylene glycol, glycerol and nicotine concentrations, the six tested e-liquids comply with the advertised composition and contain only traces of pollutants. Noticeable lower concentrations of trace elements (≤3.4 pg/ mL puff), pesticides (
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- 2017
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40. Predictions of Daily Milk and Fat Yields, Major Groups of Fatty Acids, and C18:1 cis-9 from Single Milking Data without a Milking Interval
- Author
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Arnould, Valérie, primary, Reding, Romain, additional, Bormann, Jeanne, additional, Gengler, Nicolas, additional, and Soyeurt, Hélène, additional
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- 2015
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41. Unified method to integrate and blend several, potentially related, sources of information for genetic evaluation
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Vandenplas, Jérémie, primary, Colinet, Frederic G, additional, and Gengler, Nicolas, additional
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- 2014
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42. Inversion of a part of the numerator relationship matrix using pedigree information
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Faux, Pierre, primary and Gengler, Nicolas, additional
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- 2013
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43. Genetic variability in the Skyros pony and its relationship with other Greek and foreign horse breeds
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Bömcke, Elisabeth, primary, Gengler, Nicolas, additional, and Cothran, E. Gus, additional
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- 2010
- Full Text
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44. Assessing variability of literature based methane indicator traits in a large dairy cow population.
- Author
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Kandel, Purna Bhadra, Gengler, Nicolas, and Soyeurt, Hélène
- Subjects
DAIRY products industry ,DAIRY cattle ,METHANE ,BIOINDICATORS ,POPULATION biology ,CATTLE breeding ,FATTY acid content of milk - 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.)
- Published
- 2015
45. Genetics of body condition score as an indicator of dairy cattle fertility. A review.
- Author
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Bastin, Catherine and Gengler, Nicolas
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DAIRY cattle ,CATTLE fertility ,BIOENERGETICS ,HERITABILITY ,LACTATION - 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.)
- Published
- 2013
46. Genetic variability of fatty acids in bovine milk.
- Author
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Soyeurt, Hélène and Gengler, Nicolas
- Subjects
FATTY acids ,MILKFAT ,LINOLEIC acid ,GENETICS ,PUBLIC health ,DIGLYCERIDES ,PHOSPHOLIPIDS ,STEROLS ,HYDROCARBONS - 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.)
- Published
- 2008
47. Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms.
- Author
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Tedde, Anthony, Grelet, Clément, Ho, Phuong N., Pryce, Jennie E., Hailemariam, Dagnachew, Wang, Zhiquan, Plastow, Graham, Gengler, Nicolas, Brostaux, Yves, Froidmont, Eric, Dehareng, Frédéric, Bertozzi, Carlo, Crowe, Mark A., Dufrasne, Isabelle, Soyeurt, Hélène, Duplessis, Mélissa, and Fadul-Pacheco, Liliana
- Subjects
FEATURE selection ,DAIRY cattle ,STANDARD deviations ,DAIRY processing ,DAIRY farm management ,ANIMAL welfare ,FORECASTING - Abstract
Simple Summary: First, the current work consisted of validating the feasibility of large-scale dairy cow bodyweight prediction from models involving the day in milk, milk yield, parity, and milk mid-infrared spectrum. Second, it aimed to improve the accuracy of predictive models by using feature selection algorithms to decrease the number of predictors to limit overfitting. The models, using accessible and low-cost measurements, provided highly reproducible predictions. These could be easily obtained on an individual basis throughout a cow's productive life by dairy herd improvement organizations, thus providing potentially relevant information for the dairy farmer at three levels: economics (reproductive performance), animal welfare (disease detection), and environment (methane production). Knowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
48. A Preliminary Investigation of Social Network Analysis Applied to Dairy Cow Behavior in Automatic Milking System Environments.
- Author
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Fadul-Pacheco, Liliana, Liou, Michael, Reinemann, Douglas J., Cabrera, Victor E., and Gengler, Nicolas
- Subjects
SOCIAL network analysis ,DAIRY cattle ,SOCIAL dynamics ,DAIRY farms ,MILK yield ,MILK ,FACTORS of production - Abstract
Simple Summary: Cows are social animals, therefore understanding the ways that they interact can help improve their management and welfare. We used social network analysis (SNA) to data on voluntary cow movement through a sort gate in an automatic milking system to identify pairs of cows that repeatedly passed through a sort gate in close succession (affinity pairs). Results from this exploratory study showed that when cows were separated from their affinity-pair cow the day-day variability in milk production increased by a factor of 3, a possible indicator of stress. The results of this exploratory study suggest that SNA could be used as a tool to better understand the social dynamics of dairy cows and inform group and regrouping process to produce positive outcomes. We have applied social network analysis (SNA) to data on voluntary cow movement through a sort gate in an automatic milking system to identify pairs of cows that repeatedly passed through a sort gate in close succession (affinity pairs). The SNA was applied to social groups defined by four pens on a dairy farm, each served by an automatic milking system (AMS). Each pen was equipped with an automatic sorting gate that identified when cows voluntarily moved from the resting area to either milking or feeding areas. The aim of this study was two-fold: to determine if SNA could identify affinity pairs and to determine if milk production was affected when affinity pairs where broken. Cow traffic and milking performance data from a commercial guided-flow AMS dairy farm were used. Average number of milked cows was 214 ± 34, distributed in four AMS over 1 year. The SNA was able to identify clear affinity pairs and showed when these pairings were formed and broken as cows entered and left the social group (pen). The trend in all four pens was toward higher-than-expected milk production during periods of affinity. Moreover, we found that when affinities were broken (separation of cow pairs) the day-to-day variability in milk production was three times higher than for cows in an affinity pair. The results of this exploratory study suggest that SNA could be potentially used as a tool to reduce milk yield variation and better understand the social dynamics of dairy cows supporting management and welfare decisions. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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49. Influence of Calving Ease on In-Line Milk Lactose and Other Milk Components.
- Author
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Antanaitis, Ramūnas, Juozaitienė, Vida, Malašauskienė, Dovilė, Televičius, Mindaugas, Urbutis, Mingaudas, Baumgartner, Walter, Gengler, Nicolas, Turini, Luca, and Bertelloni, Fabrizio
- Subjects
MILKFAT ,MILK proteins ,MILK ,ELECTRIC conductivity ,DAIRY farms ,LACTOSE - Abstract
Simple Summary: Calving is a difficult moment in a cow's life that causes stress, and the ease of calving determines the course of further lactation. The hypothesis of our study was to investigate how the difficulty of calving may influence changes in lactose concentration and other milk components and how well these two factors correlate between each other. We found a statistically significant (p < 0.001) negative correlation of calving ease score with milk lactose % (r = −0.376) and positive correlation coefficients with milk lactose yield (kg) (r = 0.277) as well as milk fat/lactose % ratio (r = 0.191). The analysis showed a regular increase (p < 0.001) with decreasing calving ease scores for milk electric conductivity and milk somatic cell count. The aim of our study was to determine how the ease of calving of cows may influence changes in lactose concentration and other milk components and whether these two factors correlate with each other. To achieve this, we compared data of calving ease scores and average percentage of in-line registered milk lactose and other milk components. A total of 4723 dairy cows from nine dairy farms were studied. The cows were from the second to the fourth lactation. All cows were classified according to the calving ease: group 1 (score 1)—no problems; group 2 (score 2)—slight problems; group 3 (score 3)—needed assistance; group 4 (score 4)—considerable force or extreme difficulty. Based on the data from the milking robots, during complete lactation we recorded milk indicators: milk yield MY (kg/day), milk fat (MF), milk protein (MP), lactose (ML), milk fat/lactose ratio (MF/ML), milk protein/lactose ratio (MP/ML), milk urea (MU), and milk electrical conductivity (EC) of all quarters of the udder. According to the results, we found that cows that had no calving difficulties, also had higher milk lactose concentration. ML > 4.7% was found in 58.8% of cows without calving problems. Cows with more severe calving problems had higher risk of mastitis (SCC and EC). Our data indicates that more productive cows have more calving problems compared to less productive ones. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
50. Production of Bovine Equol-Enriched Milk: A Review.
- Author
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Křížová, Ludmila, Křešťáková, Veronika, Dadáková, Kateřina, Kašparovský, Tomáš, and Gengler, Nicolas
- Subjects
FOOD of animal origin ,NUTRITION ,FUNCTIONAL foods ,BACTERIAL metabolism ,DAIRY products ,MILK - Abstract
Simple Summary: Milk and dairy products contain many substances beneficial to human health; moreover, the contents of some of these substances can be enhanced. This is also the case of isoflavones which are compounds of plant origin that can be ingested and metabolized by cattle and, subsequently, secreted into bovine milk. An especially healthful substance called equol is ranked among isoflavone metabolites, commonly produced in the digestive tract of cattle. Equol content in milk can be modified by using feedstuffs with different contents of isoflavones or by milk processing and storage. Milk and dairy products are important sources of nutrients in the human diet because they contain a number of essential substances and other biologically active components. Many of these substances can be modified, and thus offer opportunities to use milk and dairy products as functional food. Isoflavones are particularly important in human nutrition due to their diverse pharmacological and antioxidant properties. The clinical effectiveness of isoflavone-rich products is believed to be dependent on their ability to metabolize daidzein to equol, which may directly exert cancer preventive effects. However, only approximately 30–40% of humans are able to produce equol, while animals, in general, produce equol. Equol is the predominant product of bacterial metabolism of isoflavones and can be found in various amounts in some food of animal origin, especially in milk. Therefore, milk and dairy products can be considered to be sources of equol for humans who are not able to produce this metabolite. When the content of isoflavones in milk is to be modified, two groups of factors should be considered, i.e., dietary factors that include the source of isoflavones and the processing effects on feedstuffs and animal factors that include the intake of isoflavones, ruminal and postruminal changes, and the health and physiological status of animals. The approximate content of isoflavones in milk can be predicted using carry-over rates for different dietary sources or using a formula that describes the relationship between equol concentration in milk and formononetin intake. Processing and storage can affect the content and profile of isoflavones in milk and dairy products. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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