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Author

Y. de Haas

Observed names: De Haas, Y., De Haas, Yvette, Y De Haas, Y de Haas, Y. De Haas, Yvette De Haas, Yvette de Haas, de Haas, Y, de Haas, Y., de Haas, Yvette
Observed institutions: University of Wisconsin–Madison, ETH Zurich, Hendrix Genetics (Netherlands), CRV (Netherlands), Topigs Norsvin (Netherlands), Wageningen University & Research
ORCID: 0000-0002-4331-4101
Observed ORCID: 0000-0002-4331-4101
OpenAlex ID: A5004464084
Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection
2011 · Y. de Haas, J.J. Windig, et al. · Journal of Dairy Science · 271
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Animal board invited review: genetic possibilities to reduce enteric methane emissions from ruminants
2015 · N.K. Pickering, V.H. Oddy, et al. · animal · 195
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Invited review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and their potential for use in management and breeding decisions
2017 · E. Negussie, Y. de Haas, et al. · Journal of Dairy Science · 191
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The Effect of Pathogen-Specific Clinical Mastitis on the Lactation Curve for Somatic Cell Count
2002 · Y. de Haas, H.W. Barkema, et al. · Journal of Dairy Science · 166
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Selective breeding as a mitigation tool for methane emissions from dairy cattle
2021 · Y. de Haas, R.F. Veerkamp, et al. · animal · 163
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Genomic selection for feed efficiency in dairy cattle
2013 · J.E. Pryce, W.J. Wales, et al. · animal · 161
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Associations Between Pathogen-Specific Cases of Clinical Mastitis and Somatic Cell Count Patterns
2004 · Y. de Haas, R.F. Veerkamp, et al. · Journal of Dairy Science · 149
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Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying
2016 · Y. de Haas, M. Pszczola, et al. · Journal of Dairy Science · 142
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Heterogeneity in genetic and nongenetic variation and energy sink relationships for residual feed intake across research stations and countries
2015 · R.J. Tempelman, D.M. Spurlock, et al. · Journal of Dairy Science · 142
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International genetic evaluations for feed intake in dairy cattle through the collation of data from multiple sources
2014 · D.P. Berry, M.P. Coffey, et al. · Journal of Dairy Science · 142
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Animal Board Invited Review: Comparing conventional and organic livestock production systems on different aspects of sustainability
2017 · C.P.A. van Wagenberg, Y. de Haas, et al. · animal · 126
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Somatic Cell Count Distributions During Lactation Predict Clinical Mastitis
2004 · M.J. Green, L.E. Green, et al. · Journal of Dairy Science · 109
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Genetic and phenotypic parameters for conformation and yield traits in three Swiss dairy cattle breeds
2007 · Y. De Haas, L.L.G. Janss, et al. · Journal of Animal Breeding and Genetics · 107
The genetic and biological basis of feed efficiency in mid-lactation Holstein dairy cows
2017 · L.C. Hardie, M.J. VandeHaar, et al. · Journal of Dairy Science · 106
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Alternative Somatic Cell Count Traits as Mastitis Indicators for Genetic Selection
2008 · Y. de Haas, W. Ouweltjes, et al. · Journal of Dairy Science · 95
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Applicability of day-to-day variation in behavior for the automated detection of lameness in dairy cows
2013 · R.M. de Mol, G. André, et al. · Journal of Dairy Science · 90
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Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets
2012 · Y. de Haas, M.P.L. Calus, et al. · Journal of Dairy Science · 84
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Genetic parameters across lactation for feed intake, fat- and protein-corrected milk, and liveweight in first-parity Holstein cattle
2014 · C.I.V. Manzanilla Pech, R.F. Veerkamp, et al. · Journal of Dairy Science · 76
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Invited review: Determination of large-scale individual dry matter intake phenotypes in dairy cattle
2019 · D.J. Seymour, A. Cánovas, et al. · Journal of Dairy Science · 72
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Predicted accuracy of and response to genomic selection for new traits in dairy cattle
2012 · M.P.L. Calus, Y. de Haas, et al. · animal · 72
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Genomic prediction of dry matter intake in dairy cattle from an international data set consisting of research herds in Europe, North America, and Australasia
2015 · Y. de Haas, J.E. Pryce, et al. · Journal of Dairy Science · 69
Resilience: reference measures based on longer-term consequences are needed to unlock the potential of precision livestock farming technologies for quantifying this trait
2022 · N.C. Friggens, I. Adriaens, et al. · Peer Community Journal · 67
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Genomewide association study of methane emissions in Angus beef cattle with validation in dairy cattle1
2016 · C. I. V. Manzanilla-Pech, Y. De Haas, et al. · Journal of Animal Science · 67
Genetic parameters between feed-intake-related traits and conformation in 2 separate dairy populations—the Netherlands and United States
2015 · C.I.V. Manzanilla-Pech, R.F. Veerkamp, et al. · Journal of Dairy Science · 64
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Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population
2013 · M. Pszczola, R.F. Veerkamp, et al. · animal · 63
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Works count: 291
Citation count: 4,451
H-index: 36
I10-index: 68
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