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Record W4410799761 · doi:10.3168/jds.2024-26191

Early-life management practices and their association with dairy herd longevity, productivity, and profitability

2025· article· en· W4410799761 on OpenAlexafffundabout
Gabriel Machado Dallago, D. Warner, E. Vasseur

Bibliographic record

VenueJournal of Dairy Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsSte. Anne's HospitalMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNovalaitDairy Farmers of CanadaNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsLongevityProfitability indexProductivityHerdAgricultural scienceAssociation (psychology)BiologyAnimal scienceBusinessEconomicsPsychologyFinance

Abstract

fetched live from OpenAlex

Although improving the management of lactating cows to reduce health and reproductive issues can enhance cow longevity, the long-term effects of early-life management practices are less understood. The objectives of this study were to characterize dairy farms based on their early-life management practices and analyze their associations with herd longevity, productivity, and profitability. In this cross-sectional observational study, early-life management practices regarding colostrum feeding, milk feeding, solid feed and weaning, and housing were collected from 1,658 dairy farms in Québec, Canada, using a questionnaire between February 2020 and February 2021. Length of productive life and the percentage of cows in their third or greater lactation, estimated from DHI testing data, were used as herd longevity indicators, whereas lifetime cumulative ECM production and lifetime cumulative milk value, also derived from DHI records, served as indicators of productivity and profitability, respectively. Cluster analysis was performed to characterize farms based on their early-life management practices. Cluster stability assessment was used to determine the best clustering algorithm and number of clusters. Associations between herd longevity, productivity, profitability, and early-life management practices were assessed using multivariate linear regression models. Due to missing data (ranging from 0.1% to 15.4% across variables), multiple imputation was employed, and significant practices were identified by iteratively applying likelihood ratio tests (α < 0.05) across the imputed datasets. Two clusters were identified and denominated as traditionally or modernly managed farms. The traditionally managed farms cluster (n = 600; 36.2%) was characterized by feeding nonpasteurized or nonacidified milk (whole or waste) with individual buckets, not measuring the concentration of IgG in the colostrum, and housing calves individually. Modernly managed farms (n = 1,058; 63.8%) were characterized by feeding calves powdered milk replacer through automated systems and group housing calves both before and after weaning. Practices adopted by traditionally managed farms were associated with increased longevity but lower productivity and profitability, whereas practices adopted by modernly managed farms were associated with lower longevity but increased productivity and profitability. Our results highlight that early-life management practices are linked with herd longevity, productivity, and profitability, but further research is needed to understand the underlying factors contributing to these associations and to guide dairy farmers in making informed management decisions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.263

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.244
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes3
Has abstractyes

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