World's Best Scientists 2026 revealed!
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Animal Science and Veterinary
USA
2023

D-Index & Metrics

Animal Science and Veterinary

D-Index
69
Citations
14357
World Ranking
188
National Ranking
58

Kent A. Weigel publication distribution in Animal Science and Veterinary in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Animal Science and Veterinary in 2026. The highlighted bar marks where Kent A. Weigel sits on this spectrum.

29–33 publications: 1 scientists 34–38 publications: 6 scientists 39–43 publications: 17 scientists 44–48 publications: 23 scientists 49–53 publications: 28 scientists 54–58 publications: 44 scientists 59–63 publications: 66 scientists 64–68 publications: 75 scientists 69–73 publications: 71 scientists 74–78 publications: 85 scientists 79–83 publications: 108 scientists 84–88 publications: 105 scientists 89–93 publications: 84 scientists 94–98 publications: 103 scientists 99–103 publications: 97 scientists 104–108 publications: 95 scientists 109–113 publications: 89 scientists 114–118 publications: 88 scientists 119–123 publications: 109 scientists 124–128 publications: 93 scientists 129–133 publications: 89 scientists 134–138 publications: 95 scientists 139–143 publications: 92 scientists 144–148 publications: 80 scientists 149–153 publications: 75 scientists 154–158 publications: 74 scientists 159–163 publications: 67 scientists 164–168 publications: 53 scientists 169–173 publications: 62 scientists 174–178 publications: 63 scientists 179–183 publications: 50 scientists 184–188 publications: 55 scientists 189–193 publications: 50 scientists 194–198 publications: 27 scientists 199–203 publications: 44 scientists 204–208 publications: 31 scientists 209–213 publications: 28 scientists 214–218 publications: 38 scientists 219–223 publications: 33 scientists 224–228 publications: 36 scientists 229–233 publications: 29 scientists 234–238 publications: 35 scientists 239–243 publications: 23 scientists 244–248 publications: 24 scientists 249–253 publications: 29 scientists 254–258 publications: 28 scientists 259–263 publications: 21 scientists 264–268 publications: 25 scientists 269–273 publications: 11 scientists 274–278 publications: 18 scientists 279–283 publications: 13 scientists 284–288 publications: 18 scientists 289–293 publications: 10 scientists 294–298 publications: 12 scientists 299–303 publications: 11 scientists 304–308 publications: 15 scientists 309–313 publications: 14 scientists 314–318 publications: 14 scientists 319–323 publications: 8 scientists 324–328 publications: 8 scientists 329–333 publications: 13 scientists 334–338 publications: 14 scientists 339–343 publications: 13 scientists 344–348 publications: 10 scientists 349–353 publications: 11 scientists 354–358 publications: 5 scientists 359–363 publications: 8 scientists 364–368 publications: 8 scientists 369–373 publications: 6 scientists 374–378 publications: 7 scientists 379–383 publications: 8 scientists 384–388 publications: 3 scientists 389–393 publications: 4 scientists 394–398 publications: 5 scientists 399–403 publications: 8 scientists 404–408 publications: 5 scientists 409–413 publications: 4 scientists 414–418 publications: 3 scientists 419–423 publications: 8 scientists 424–428 publications: 5 scientists 429–433 publications: 3 scientists 434–438 publications: 5 scientists 439–441 publications: 5 scientists 442+ publications: 99 scientists
29 publications 442+

This scientist: 246 publications — 83rd percentile

83% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 442 publications or more.

Kent A. Weigel D-index placement in Animal Science and Veterinary in 2026

The chart shows the D-index (discipline H-index) distribution of Animal Science and Veterinary scientists ranked by Research.com in 2026. The highlighted bar marks where Kent A. Weigel sits on this spectrum.

20 D-Index: 5 scientists 21 D-Index: 22 scientists 22 D-Index: 31 scientists 23 D-Index: 44 scientists 24 D-Index: 53 scientists 25 D-Index: 66 scientists 26 D-Index: 80 scientists 27 D-Index: 101 scientists 28 D-Index: 106 scientists 29 D-Index: 116 scientists 30 D-Index: 154 scientists 31 D-Index: 158 scientists 32 D-Index: 143 scientists 33 D-Index: 137 scientists 34 D-Index: 126 scientists 35 D-Index: 134 scientists 36 D-Index: 112 scientists 37 D-Index: 115 scientists 38 D-Index: 105 scientists 39 D-Index: 111 scientists 40 D-Index: 107 scientists 41 D-Index: 87 scientists 42 D-Index: 74 scientists 43 D-Index: 62 scientists 44 D-Index: 61 scientists 45 D-Index: 41 scientists 46 D-Index: 51 scientists 47 D-Index: 43 scientists 48 D-Index: 28 scientists 49 D-Index: 47 scientists 50 D-Index: 40 scientists 51 D-Index: 34 scientists 52 D-Index: 33 scientists 53 D-Index: 43 scientists 54 D-Index: 16 scientists 55 D-Index: 32 scientists 56 D-Index: 21 scientists 57 D-Index: 18 scientists 58 D-Index: 28 scientists 59 D-Index: 33 scientists 60 D-Index: 17 scientists 61 D-Index: 21 scientists 62 D-Index: 18 scientists 63 D-Index: 21 scientists 64 D-Index: 9 scientists 65 D-Index: 20 scientists 66 D-Index: 11 scientists 67 D-Index: 16 scientists 68 D-Index: 12 scientists 69 D-Index: 17 scientists 70 D-Index: 13 scientists 71 D-Index: 15 scientists 72 D-Index: 11 scientists 73 D-Index: 11 scientists 74 D-Index: 14 scientists 75 D-Index: 5 scientists 76 D-Index: 6 scientists 77+ D-Index: 100 scientists
20 D-Index 77+

This scientist: 69 D-Index — 94th percentile

94% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 77 D-Index or more.

Research.com Recognitions

  • 2023 - Research.com Animal Science and Veterinary in United States Leader Award
  • 2010 - J. L. Lush Award in Animal Breeding, American Dairy Science Association
  • 2003 - Cargill Animal Nutrition Young Scientist Award, American Dairy Science Association

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Genetics
  • Gene

Kent A. Weigel mainly investigates Dairy cattle, Genetics, Herd, Animal science and Selection. His biological study spans a wide range of topics, including Sperm, Milking, Culling and Biotechnology. His studies deal with areas such as Regression analysis and Statistics as well as Genetics.

His studies in Herd integrate themes in fields like Heritability, Ice calving and Sire. His Sire research includes elements of Veterinary medicine and Progeny testing. His study in Animal science is interdisciplinary in nature, drawing from both Fertility, Artificial insemination, Standard error, Pregnancy rate and Insemination.

His most cited work include:

  • Predicting Quantitative Traits With Regression Models for Dense Molecular Markers and Pedigree (412 citations)
  • Genomic Prediction of Breeding Values when Modeling Genotype × Environment Interaction using Pedigree and Dense Molecular Markers (306 citations)
  • Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods (204 citations)

What are the main themes of his work throughout his whole career to date?

Kent A. Weigel mainly focuses on Statistics, Dairy cattle, Animal science, Sire and Genetics. Kent A. Weigel interconnects Selection and Econometrics in the investigation of issues within Statistics. Kent A. Weigel has researched Dairy cattle in several fields, including Culling, Herd, Biotechnology, Breed and Algorithm.

The various areas that Kent A. Weigel examines in his Herd study include Milking, Milk yield and Gene–environment interaction. His studies deal with areas such as Feed conversion ratio, Residual feed intake and Ice calving, Lactation as well as Animal science. His Sire study combines topics from a wide range of disciplines, such as Fertility, Progeny testing and Heritability.

He most often published in these fields:

  • Statistics (35.45%)
  • Dairy cattle (35.00%)
  • Animal science (28.64%)

What were the highlights of his more recent work (between 2014-2021)?

  • Dairy cattle (35.00%)
  • Animal science (28.64%)
  • Residual feed intake (10.45%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Dairy cattle, Animal science, Residual feed intake, Feed conversion ratio and Biotechnology. He interconnects Statistics, Genome, Selection and Genotype in the investigation of issues within Dairy cattle. His Selection study incorporates themes from Quantitative genetics and Linear model.

His Animal science research is multidisciplinary, relying on both Genomic selection, Ice calving, Lactation and Heritability. His Heritability research includes themes of Single-nucleotide polymorphism and Genetic correlation. His work is dedicated to discovering how Residual feed intake, Genetics are connected with Covariate and other disciplines.

Between 2014 and 2021, his most popular works were:

  • Harnessing the genetics of the modern dairy cow to continue improvements in feed efficiency (65 citations)
  • Heterogeneity in genetic and nongenetic variation and energy sink relationships for residual feed intake across research stations and countries. (56 citations)
  • Genomic prediction of dry matter intake in dairy cattle from an international data set consisting of research herds in Europe, North America, and Australasia (42 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Genetics
  • Gene

His primary areas of study are Dairy cattle, Residual feed intake, Feed conversion ratio, Heritability and Animal science. His studies in Dairy cattle integrate themes in fields like Population genetics, Quantitative genetics, Biotechnology, Linear model and Selection. Residual feed intake and Genetics are commonly linked in his work.

His research integrates issues of Regression analysis, Machine learning and Artificial intelligence in his study of Genetics. His Heritability research incorporates elements of Single-nucleotide polymorphism, Genotype and Econometrics. The Animal science study combines topics in areas such as Ice calving and Lactation.

Best Publications

  • Predicting Quantitative Traits With Regression Models for Dense Molecular Markers and Pedigree

    Gustavo de los Campos;Hugo Naya;Daniel Gianola;José Crossa

  • Genomic Prediction of Breeding Values when Modeling Genotype × Environment Interaction using Pedigree and Dense Molecular Markers

    Juan Burgueño;Gustavo de los Campos;Kent Weigel;José Crossa

  • Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods

    Gustavo De Los Campos;Daniel Gianola;Guilherme J. M. Rosa;Kent A. Weigel

  • Survey of management practices on reproductive performance of dairy cattle on large US commercial farms.

    D.Z. Caraviello;K.A. Weigel;P.M. Fricke;M.C. Wiltbank

  • Genomic evaluations with many more genotypes

    Paul M VanRaden;Jeffrey R O'Connell;George R Wiggans;Kent A Weigel

  • Genetic Selection for Health Traits Using Producer-Recorded Data. I. Incidence Rates, Heritability Estimates, and Sire Breeding Values

    N.R. Zwald;K.A. Weigel;Y.M. Chang;R.D. Welper

  • Evaluation of inbreeding depression in Holstein cattle using whole-genome SNP markers and alternative measures of genomic inbreeding

    D.W. Bjelland;K.A. Weigel;N. Vukasinovic;J.D. Nkrumah

  • Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat

    Daniel Gianola;Hayrettin Okut;Kent A Weigel;Guilherme Jm Rosa

  • Controlling Inbreeding in Modern Breeding Programs

    K.A. Weigel

  • Fertility of Dairy Cows after Resynchronization of Ovulation at Three Intervals Following First Timed Insemination

    P.M. Fricke;D.Z. Caraviello;K.A. Weigel;M.L. Welle

  • Harnessing the genetics of the modern dairy cow to continue improvements in feed efficiency

    M.J. VandeHaar;L.E. Armentano;K. Weigel;D.M. Spurlock

  • Exploring the Role of Sexed Semen in Dairy Production Systems

    K.A. Weigel

  • Machine Learning Classification Procedure for Selecting SNPs in Genomic Selection : Application to Early Mortality in Broilers

    N. Long;D. Gianola;G.J.M. Rosa;K.A. Weigel

  • Applied animal genomics: results from the field.

    Alison L. Van Eenennaam;Kent A. Weigel;Amy E. Young;Matthew A. Cleveland

  • Investigation of factors affecting voluntary and involuntary culling in expanding dairy herds in Wisconsin using survival analysis.

    K.A. Weigel;R.W. Palmer;D.Z. Caraviello

  • Genetic Selection for Health Traits Using Producer-Recorded Data. II. Genetic Correlations, Disease Probabilities, and Relationships with Existing Traits

    N.R. Zwald;K.A. Weigel;Y.M. Chang;R.D. Welper

  • Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers.

    K.A. Weigel;G. de los Campos;O. González-Recio;H. Naya

  • International genetic evaluations for feed intake in dairy cattle through the collation of data from multiple sources

    Donagh P. Berry;M.P. Coffey;J.E. Pryce;Y. De Haas

  • Genomic selection in dairy cattle: Integration of DNA testing into breeding programs

    Jonathan M. Schefers;Kent A. Weigel

  • Identification of Factors That Cause Genotype by Environment Interaction Between Herds of Holstein Cattle in Seventeen Countries

    N.R. Zwald;K.A. Weigel;W.F. Fikse;R. Rekaya

  • Technical note: an R package for fitting generalized linear mixed models in animal breeding.

    A. I. Vazquez;D. M. Bates;G. J. M. Rosa;D. Gianola

  • Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms.

    Saleh Shahinfar;David Page;Jerry Guenther;Victor Cabrera

  • Genetic parameters for reproductive traits of Holstein cattle in California and Minnesota.

    K.A. Weigel;R. Rekaya

  • Prospects for improving reproductive performance through genetic selection.

    Kent A. Weigel

  • Analysis of reproductive performance of lactating cows on large dairy farms using machine learning algorithms.

    D.Z. Caraviello;K.A. Weigel;M. Craven;D. Gianola

  • Indirect Prediction of Herd Life in Guernsey Dairy Cattle

    J. Cruickshank;K.A. Weigel;M.R. Dentine;B.W. Kirkpatrick

  • A multiple-trait herd cluster model for international dairy sire evaluation.

    K.A. Weigel;R. Rekaya

  • Assessment of the Impact of Somatic Cell Count on Functional Longevity in Holstein and Jersey Cattle Using Survival Analysis Methodology

    D.Z. Caraviello;K.A. Weigel;G.E. Shook;P.L. Ruegg

Frequent Co-Authors

Daniel Gianola
Daniel Gianola University of Wisconsin–Madison
Guilherme J. M. Rosa
Guilherme J. M. Rosa University of Wisconsin–Madison
Louis E. Armentano
Louis E. Armentano University of Wisconsin–Madison
M.J. VandeHaar
M.J. VandeHaar Michigan State University
Y. de Haas
Y. de Haas Wageningen University & Research
Robert J. Tempelman
Robert J. Tempelman Michigan State University
Roel F. Veerkamp
Roel F. Veerkamp Wageningen University & Research
Mike Coffey
Mike Coffey Scotland's Rural College
C.R. Staples
C.R. Staples University of Florida
Erin E. Connor
Erin E. Connor University of Delaware

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Related Online Degrees & Career Pathways

For students interested in Animal Science and Veterinary studies, exploring related online degrees can open diverse career opportunities. For instance, if you're drawn to the counseling aspect of animal care or animal behavior, consider advanced options such as online doctoral programs in counseling. These can complement your animal science background and lead to specialized roles in therapy and rehabilitation.

Career-wise, the field offers many well-paying options. To learn more about jobs with animals that pay well, it’s worth investigating roles beyond traditional veterinary work, such as wildlife management, animal nutrition, or zoological sciences.

Additionally, understanding leadership and health-focused roles can enhance your career prospects. For example, if you're considering managing sports programs related to animals or animal therapy, exploring what an athletic director does can provide valuable insights into organizational and managerial skills.

Lastly, coupling your animal science credentials with human health expertise is increasingly popular. You might want to study exercise science online to broaden your understanding of physical health and rehabilitation, which can be relevant for animal-assisted therapy or performance improvement roles.

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