Evidence map›Paper›PMID 39526828›Full record

ArticleJournal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie2025

Genetic Characterisation of Feeding Patterns in Lactating Holstein Cows and Their Association With Feed Efficiency Traits.

Ligia Cavani, Kristen L Parker Gaddis, Ransom L Baldwin, José E P Santos, James E Koltes, Robert J Tempelman, Michael J VandeHaar, Heather M White, Francisco Peñagaricano, Kent A Weigel

Abstract read
In one paragraph

Article in Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Ligia CavaniDepartment of Animal and Dairy Sciences, University of Wisconsin, Madison, Wisconsin, USA.
Kristen L Parker GaddisCouncil on Dairy Cattle Breeding, Bowie, Maryland, USA.
Ransom L BaldwinAnimal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, Maryland, USA.
José E P SantosDepartment of Animal Sciences, University of Florida, Gainesville, Florida, USA.
James E KoltesDepartment of Animal Science, Iowa State University, Ames, Iowa, USA.
Robert J TempelmanDepartment of Animal Science, Michigan State University, East Lansing, Michigan, USA.
Michael J VandeHaarDepartment of Animal Science, Michigan State University, East Lansing, Michigan, USA.
Heather M WhiteDepartment of Animal and Dairy Sciences, University of Wisconsin, Madison, Wisconsin, USA.
Francisco PeñagaricanoDepartment of Animal and Dairy Sciences, University of Wisconsin, Madison, Wisconsin, USA.
Kent A WeigelDepartment of Animal and Dairy Sciences, University of Wisconsin, Madison, Wisconsin, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Feeding behaviour traits, such as number, duration or intake per feeder visit, have been associated with feed efficiency in dairy cattle. Those traits, however, do not fully capture cows' feeding patterns throughout the day. The goal of this study was to propose a new phenotype for characterising within-day feeding patterns and estimate its heritability and genetic correlations with dry matter intake (DMI), secreted milk energy, metabolic body weight and residual feed intake. Feeding patterns were evaluated using 4.8 million bunk visits from 1684 midlactation Holstein cows collected from 2009 to 2023 with an Insentec system. Feed efficiency traits were available from 6099 lactating Holstein cows at six research stations across the United States. Daily bunk visits were ordered, with Time 0 designated as the time of first feed delivery. Intake proportions were calculated by visit for each cow by dividing feed intake per visit by the total intake of the cow for that day. Feeding patterns were characterised by the area under the curve of cumulative feed intake proportions for each cow throughout the day. The feeding pattern phenotype per cow was defined as the average of areas under the curve across days, whereas consistency of feeding pattern was calculated as the natural logarithm of variance of daily area under the curve values. Estimates of heritability and genetic correlations were performed using Bayesian inference with an animal model, considering lactation, days in milk and cohort (trial-treatment) as fixed effects and animal as a random effect. Heritability estimates for average area under the curve and variance of daily area under the curve were 0.35 ± 0.05 and 0.16 ± 0.05, respectively. The genetic correlation between average area under the curve and secreted milk energy was -0.30 ± 0.14. Genetic correlations between average area under the curve and DMI, metabolic body weight and residual feed intake were not statistically significant. Variance of daily area under the curve was genetically correlated with DMI (0.47 ± 0.15), secreted milk energy (0.40 ± 0.17) and metabolic body weight (0.28 ± 0.13). The genetic correlation between variance of daily area under the curve and residual feed intake was not significant. Overall, we provided a reliable method to truly characterise feeding patterns in midlactation dairy cows. Feeding pattern and its consistency were heritable, indicating that a significant proportion of phenotypic variation is explained by additive genetic effects. Genetic correlation estimates indicate that cows with more consistent daily feeding patterns have lower DMI, lower secreted milk energy and lower metabolic body weight.

Indexed as

Feeding BehaviorLactationAnimal FeedAnimalsBody WeightCattleEatingFemaleMilkPhenotypearea under the curveheritabilityintakemilk energy

Identifiers

PMID39526828
PMCPMC12149497

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.