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Enregistrement W4417013648 · doi:10.3168/jdsc.2025-0878

Milking dynamics following individual quarter dry-off in Holstein cows in an automatic milking system

2025· article· en· W4417013648 sur OpenAlexaboutno aff
Clara Ibarguren, Jason E. Lombard, Juan Vélez, Constanza Hernández-Gotelli, Pablo Pinedo

Notice bibliographique

RevueJDS Communications · 2025
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueMilk Quality and Mastitis in Dairy Cows
Établissements canadiensnon disponible
Organismes subventionnairesU.S. Department of AgricultureDeLaval
Mots-clésMilkingQuarter (Canadian coin)Automatic milkingDairy cattleWork (physics)

Résumé

récupéré en direct d'OpenAlex

Individual quarter dry-off (QDO) is a targeted management strategy used to address persistent intramammary infections that lead to chronic subclinical mastitis, as well as cases of clinical mastitis that are recurrent or unresponsive to treatment.Although the interest in the use of individual QDO as a non-antimicrobial strategy for mastitis control is growing, the impact of this management on subsequent milk production has not been widely explored.Moreover, detailed information on the individual performance of the remaining functional quarters, following QDO is scarce.The objective of this observational study was to investigate the effect of lactational QDO following clinical mastitis on short-term milk yield in the remaining individual quarters.This retrospective study was conducted in a commercial dairy farm located in northeast Colorado, USA, equipped with an automatic milking system.The analysis included 114 multiparous cows with one quarter dried off through abrupt cessation of milking following unresponsive clinical mastitis therapy.For comparison, one healthy control cow was matched to each affected cow based on DIM and parity number.Individual quarter milk yield of the remaining functional quarters and from control cows was collected for each milking visit from the on-farm management software and summed as a daily value per quarter for the 30 d following QDO.The herd average DIM at the peak of lactation (68 DIM) was considered to categorize the study cows based on their DIM at QDO into pre-peak and post-peak groups.All the analyses were conducted separately for these 2 stage of lactation groups and cows were also categorized based on their dry quarter location (DQL).Least squares means (SE) for daily average milk yield per functional quarter and per cow up to 30 d post QDO were calculated and compared among DQL groups (including matched control cows) using ANOVA for repeated measures analysis, with cow ID as the repeated statement, with compound symmetry selected as the covariance structure.Multivariable models included DQL as explanatory variable of interest and DIM at QDO and calving season as potential covariates.In addition, milk yield curves up to 30 d post QDO were built for milk yield per DQL using daily LSM calculated by repeated measures analysis.Cow-level milk yield was also compared between DQL groups, including unaffected control cows using t-test for repeated measures analysis.Differences in quarter milk yield were only identified for the pre-peak group (68 DIM).Milk yield from the left rear and right rear quarters was smaller in control cows than in cows with the right front quarter dried off.When total milk yield per cow was compared within the pre-peak group, control cows had greater yield than cows subjected to QDO of the right rear quarter.In the post-peak group, control cows had the highest milk yield compared with the 4 groups of cows with a dry quarter.In conclusion, following QDO, the levels of milk yield compensation in the remaining functional quarters were variable and smaller when QDO occurred post peak (>68 DIM).Nonetheless, in most cases the cow-level milk yield remained lower in 3-quarter cows compared with unaffected controls.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,468
Score d'incertitude au seuil0,921

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,036
Tête enseignante GPT0,295
Écart entre enseignants0,258 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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