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Record W4417007168 · doi:10.1139/cjas-2025-0043

Genetic evaluation of profitability measures in Holstein dairy cows

2025· article· en· W4417007168 on OpenAlexvenueno aff
Davoud Rostami, Abbas Pakdel

Bibliographic record

VenueCanadian Journal of Animal Science · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
Fundersnot available
KeywordsProfitability indexHeritabilityHerdProfit (economics)Genetic correlationDairy cattleSelection (genetic algorithm)Genetic gain

Abstract

fetched live from OpenAlex

This study aimed to assess and estimate the genetic parameters for profit indices and identify the most reliable index for selection purposes. A total of 4 637 629 test-day records were collected from 255 804 cows across 124 herds over 15 years (2009–2024). However, the economic data were collected only from five dairy herds. Pedigree data included 575 707 animals (185 608 with records), traced using DMU Trace and structured with CFC software. Genetic parameters were estimated using single- and multiple-trait animal models with fixed effects of herd-year-season, lactation number, age at first calving, and random additive genetic effects. A DMU program was used for genetic evaluations and the best linear unbiased prediction of breeding values for the studied traits. The profitability indices include profit, income over feed cost (IOFC), income equal to feed cost (IEFC), money-corrected milk, and milk-to-feed price ratio. The results of this study revealed that profit has a moderate heritability (0.25), indicating a potential for genetic improvement. The IOFC per 100 kg of milk (IOFC milk ) had the highest genetic ( r = 0.97) and phenotypic ( r = 0.92) correlation with profit, making it the most reliable profit alternative. Furthermore, IEFC per 100 kg of milk (IEFC milk ) was genetically negatively correlated with profit (−0.80), indicating that cows with high IEFC were less economically efficient. While milk yield had improved, profitability had shown a negative genetic trend, which means that an exclusive focus on higher milk production is detrimental to long-term economic efficiency. Generally, indices such as IOFC milk and IEFC milk should be considered in breeding programs to increase profitability, efficiency, and sustainability within industrial dairy farms. This study emphasizes the importance of integrating profitability indicators into genetic evaluations. Providing novel selection criteria contributes to more efficient and sustainable breeding strategies for Holstein dairy cows.

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)0.002
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.287
Teacher spread0.259 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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