Heather M. White

2.8k total citations
102 papers, 2.0k citations indexed

About

Heather M. White is a scholar working on Agronomy and Crop Science, Genetics and Animal Science and Zoology. According to data from OpenAlex, Heather M. White has authored 102 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 53 papers in Agronomy and Crop Science, 49 papers in Genetics and 30 papers in Animal Science and Zoology. Recurrent topics in Heather M. White's work include Genetic and phenotypic traits in livestock (47 papers), Reproductive Physiology in Livestock (46 papers) and Ruminant Nutrition and Digestive Physiology (35 papers). Heather M. White is often cited by papers focused on Genetic and phenotypic traits in livestock (47 papers), Reproductive Physiology in Livestock (46 papers) and Ruminant Nutrition and Digestive Physiology (35 papers). Heather M. White collaborates with scholars based in United States, New Zealand and Canada. Heather M. White's co-authors include Shawn S. Donkin, Ryan S. Pralle, T.L. Chandler, Stephanie L. Koser, Mark R. Wick, Andrew L. Mason, Robert Perrillo, P.H. Doane, K.A. Weigel and Bradley W. Bolling and has published in prestigious journals such as The Lancet, SHILAP Revista de lepidopterología and Gastroenterology.

In The Last Decade

Heather M. White

96 papers receiving 1.9k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Heather M. White United States 25 845 646 420 394 239 102 2.0k
Mitsuyoshi Suzuki Japan 19 228 0.3× 444 0.7× 261 0.6× 229 0.6× 132 0.6× 182 1.4k
M. A. Lomax United Kingdom 24 792 0.9× 474 0.7× 357 0.8× 186 0.5× 184 0.8× 64 1.8k
EN Bergman 21 827 1.0× 391 0.6× 352 0.8× 267 0.7× 215 0.9× 25 1.9k
Brigitte Siliart France 21 167 0.2× 300 0.5× 98 0.2× 359 0.9× 555 2.3× 44 1.8k
Guangjun Chang China 21 536 0.6× 127 0.2× 149 0.4× 105 0.3× 483 2.0× 68 1.3k
S.J. Bertics United States 36 2.8k 3.3× 1.5k 2.4× 637 1.5× 156 0.4× 309 1.3× 59 3.7k
A. D. Beaulieu Canada 29 1.1k 1.3× 614 1.0× 1.3k 3.0× 80 0.2× 463 1.9× 99 3.5k
Cheng Xia China 17 427 0.5× 261 0.4× 145 0.3× 140 0.4× 213 0.9× 82 961
M. J. Fettman United States 21 346 0.4× 219 0.3× 95 0.2× 49 0.1× 161 0.7× 61 1.3k
H.W. Symonds United States 21 589 0.7× 278 0.4× 263 0.6× 96 0.2× 97 0.4× 58 1.3k

Countries citing papers authored by Heather M. White

Since Specialization
Citations

This map shows the geographic impact of Heather M. White's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Heather M. White with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Heather M. White more than expected).

Fields of papers citing papers by Heather M. White

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Heather M. White. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Heather M. White. The network helps show where Heather M. White may publish in the future.

Co-authorship network of co-authors of Heather M. White

This figure shows the co-authorship network connecting the top 25 collaborators of Heather M. White. A scholar is included among the top collaborators of Heather M. White based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Heather M. White. Heather M. White is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Daddam, Jayasimha Rayalu, et al.. (2025). Differences in amino acid and fatty acid metabolism contribute to variability in dairy cattle feed efficiency. Journal of Dairy Science. 108(8). 8367–8379.
2.
Gaddis, Kristen L. Parker, James E. Koltes, Robert J. Tempelman, et al.. (2025). Impact of heat stress on dry matter intake and residual feed intake in mid-lactation dairy cows. Journal of Dairy Science. 108(7). 7345–7353.
3.
Toghiani, Sajjad, P.M. VanRaden, M.J. VandeHaar, et al.. (2024). Dry matter intake in US Holstein cows: Exploring the genomic and phenotypic impact of milk components and body weight composite. Journal of Dairy Science. 107(9). 7009–7021. 2 indexed citations
4.
Balaguer, María Angels de Luis, Tiago Bresolin, Ranveer Chandra, et al.. (2024). Multi-modal machine learning for the early detection of metabolic disorder in dairy cows using a cloud computing framework. Computers and Electronics in Agriculture. 227. 109563–109563. 9 indexed citations
5.
White, Heather M., et al.. (2024). Behavioral consistency of competitive behaviors and feeding patterns in lactating dairy cows across stocking densities at the feed bunk. Frontiers in Veterinary Science. 11. 1302573–1302573. 1 indexed citations
6.
Gaddis, Kristen L. Parker, R.L. Baldwin, J.E.P. Santos, et al.. (2023). Consistency of dry matter intake in Holstein cows: Heritability estimates and associations with feed efficiency. Journal of Dairy Science. 107(2). 1054–1067. 6 indexed citations
7.
Schenkel, Flávio S., F. Miglior, J. Jamrozik, et al.. (2023). Estimation of genetic parameters for feed efficiency traits using random regression models in dairy cattle. Journal of Dairy Science. 107(3). 1523–1534. 3 indexed citations
8.
9.
10.
Gaddis, Kristen L. Parker, R.L. Baldwin, J.E.P. Santos, et al.. (2023). Impact of parity differences on residual feed intake estimation in Holstein cows. SHILAP Revista de lepidopterología. 4(3). 201–204. 2 indexed citations
11.
White, Heather M., et al.. (2023). Social interactions, feeding patterns, and feed efficiency of same- and mixed-parity groups of lactating cows. Journal of Dairy Science. 106(12). 9410–9425. 4 indexed citations
12.
Bresolin, Tiago, et al.. (2023). Increasing the prepartum dose of rumen-protected choline: Effects on milk production and metabolism in high-producing Holstein dairy cows. Journal of Dairy Science. 106(9). 5988–6004. 10 indexed citations
13.
Koltes, James E., et al.. (2022). Predicting dry matter intake in mid-lactation Holstein cows using point-in-time data streams available on dairy farms. Journal of Dairy Science. 105(12). 9666–9681. 5 indexed citations
14.
Pralle, Ryan S., et al.. (2021). Circulating Metabolites Indicate Differences in High and Low Residual Feed Intake Holstein Dairy Cows. Metabolites. 11(12). 868–868. 14 indexed citations
15.
Gaddis, Kristen L. Parker, P.M. VanRaden, K.A. Weigel, et al.. (2021). Implementation of Feed Saved evaluations in the U.S.. Bulletin - International Bull Evaluation Service/Interbull bulletin. 147–152. 11 indexed citations
16.
Dórea, J.R.R., M.R. Borchers, R.L. Wallace, et al.. (2021). Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables. Journal of Dairy Science. 104(8). 8765–8782. 29 indexed citations
17.
18.
Caprarulo, V., T.L. Chandler, M.G. Zenobi, et al.. (2020). The effects of prepartum energy intake and peripartum rumen-protected choline supplementation on hepatic genes involved in glucose and lipid metabolism. Journal of Dairy Science. 103(12). 11439–11448. 7 indexed citations
19.
White, Heather M., et al.. (2020). Improving nutritional accuracy and economics through a multiple ration-grouping strategy. Journal of Dairy Science. 103(4). 3774–3785. 14 indexed citations
20.
Mason, Andrew L., Mark R. Wick, Heather M. White, et al.. (1993). Increased hepatocyte expression of hepatitis B virus transcription in patients with features of fibrosing cholestatic hepatitis. Gastroenterology. 105(1). 237–244. 50 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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