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Record W4399865649 · doi:10.3168/jds.2024-24766

Association between body condition profiles, milk production, and reproduction performance in Holstein and Normande cows

2024· article· en· W4399865649 on OpenAlexfundno aff
Charlotte Dezetter, Fabrice Bidan, Luc Delaby, Fabienne Blanc, Sandrine Fréret, Nicolas Bédère

Bibliographic record

VenueJournal of Dairy Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsnot available
FundersAPIS-GENEMinistère de l'Agriculture et de l'AlimentationAgence Nationale de la RechercheMinistry of Agriculture and Food
KeywordsReproductionMilk productionAnimal scienceDairy cattleBiologyHolstein CattleInternal medicineEndocrinologyMedicineGenetics

Abstract

fetched live from OpenAlex

Body condition dynamics are known to affect the different steps of reproduction in cattle (cyclicity, estrus expression, fertilization, embryo development). This has led to a widespread idea that there is an ideal-target optimal body condition, but no clear profile has yet been identified. Here we investigated the relationships between BCS profiles and reproductive performance in dairy cows. Data were from Holstein or Normande herds in 6 French experimental farms. In the Holstein breed, we discriminated 4 BCS profiles based on combining BCS at calving (Low indicates BCS ∼2.6 points at calving; High indicates BCS ∼3.3 points at calving) with BCS loss after calving (Moderate [M] indicates BCS loss of ≤1.0 points at calving; Severe [S] indicates BCS loss of >1.0 points at calving). The Low-M profile mostly included multiparous cows with higher milk yield and lower reproductive performance than cows in the 3 other profiles. Low-M cows that experienced abnormal ovarian activity had lower reproductive performance than their profile-mates. Moreover, 67% of Low-M cows kept the same profile at the following lactation. The High-S profile mostly included primiparous cows with lower milk yield and higher reproductive performance than cows in other profiles. In High-S cows, higher milk yields correlated with higher risk of failure to calf on first insemination. Moreover, 38% of High-S cows kept the same profile at the following lactation, and none changed to High-M. The other 2 BCS profiles (Low-S and High-M) were intermediate in terms of milk yield and reproductive performance. In Normande, we discriminated 3 BCS profiles based on combining BCS at calving (Low: ∼2.6 points; High: ∼3.5 points) with BCS loss after calving (Flat [F]: flat with no loss; M: ∼0.5 points; or S: ∼1.0 point). The Low-M and High-S profiles included cows with similar performance, even though High-S-profile cows showed better but not significantly different milk yield and reproduction performance. The High-F profile included cows that were more likely to experience abnormal ovarian activity and fail at first insemination than cows in other profiles. More than 50% of Normande cows with 2 successive lactations kept in the same BCS profile at the next lactation. Even though a low BCS at calving combined with severe BCS loss (more than 1 point) after calving was found to increase reproductive failure, there was no evidence of an optimal BCS profile for reproduction in dairy cows, and reproductive success or failure is multifactorial.

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)0.001
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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.017
GPT teacher head0.252
Teacher spread0.235 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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