A validation study of the pre-recorded data-based herd status index for dairy herd welfare identification
Notice bibliographique
Résumé
Groups of researchers have proposed pre-screening assessment tools using pre-recorded data with the objective of remote detection of herds with the highest welfare issues, and, therefore, reducing the number of farm visits to those herds in need of intervention. Nevertheless, applying a new assessment method requires the evaluation of its validity relative to an existing assessment method. Such an evaluation is needed to ensure that the proposed method is reliable and corresponds to its objective for which it was developed. Hence, this thesis aimed to determine the validity of the herd status index (HSI) to identify an overall state of dairy cattle welfare at the herd level by identifying its performance level and the correspondence of its indicators relative to the proAction® on-farm outcome welfare assessment method.Farm-level data for five outcome measures of welfare – lameness, body condition, hock, neck, and knee injuries scores, collected as a part of the proAction® Quality Assurance Program were integrated with pre-recorded test-day dairy herd improvement (DHI) data for the three years before the on-farm assessment, extracted from the Lactanet Inc. (Sainte-Anne-de-Bellevue, QC, Canada) database. Two-stage cluster analysis was performed to partition study herds into subgroups based on five-dimensions – outcome measures of welfare, which resulted in four distinct groups of herds classified as groups with the least (C1), the highest (C4), and average (C1, C3) welfare issues. The clusters significantly differed (P < 0.05) from each other regarding all five-dimensions, except for the prevalence of neck injuries of herds in C1 and C2. Followed by the cluster analysis, the HSI was calculated for each of the study herds based on the method developed by Warner et al. (2020). The findings showed that the HSI based on twelve pre-recorded DHI indicators could identify herds with the highest welfare issues relative to herds’ classification based on proAction® on-farm welfare assessment data.With regards to individual pre-recorded indicators of the HSI, five out of twelve indicators – involuntary replacement and mortality rates, herd management and transition cow indexes, and prevalence of cows with high SCC > 400,000 cells/ml in milk significantly differed between herds in C2 and C4, that was in complete correspondence with the classification of herds based on outcome measures of welfare. Thus, these five indicators’ contribution to the HSI’s overall performance level is substantial and corresponds to the objective, making the HSI a comparatively valid method to identify herds with the highest welfare issues. However, in terms of the remaining indicators of the HSI, this study revealed no significant results between study clusters; therefore, it may indicate that the contribution of these indicators into the overall performance level of the HSI is not essential and they should be reconsidered based on scientific evidence and can be replaced by those which were shown to hold high potential
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,032 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».