Making stall beds more comfortable: the effect of longitudinal space and deep-bedding on the comfort and welfare of tie-stall-housed dairy cows
Notice bibliographique
Résumé
Understanding cow comfort in tie-stall barns is an important issue for the Canadian dairy industry, and one area of the stall that contributes greatly to cow comfort is the stall bed. The stall bed is defined both by its size (stall width and longitudinal space) but also by its material components (bedding depth, bedding type, and stall base type). This thesis aimed to determine if making the stall bed more comfortable increased cow welfare in two parts: a literature review and an experimental study. The literature review portion of this thesis made the following conclusions based on the available literature for each sub-topic. Some areas, such as bedding, had been more thoroughly investigated compared to others, such as stall longitudinal space. Of all the material components of the stall bed, bedding depth appears to have the most positive impact on cow welfare as a deeper level of bedding in the stall can negate hard stall bases and more abrasive bedding types. Longitudinal space in tie-stalls is defined both by stall bed length and by the front limit of the stall: the manger wall. Longer stall bed lengths have been shown to increase lying time and decrease injury and lameness prevalence, but are often not utilized by producers due to concerns about cleanliness. The impact of manger wall height on cow welfare has not been researched extensively, but may work in conjunction with other stall components to define the space available to the cow. The experimental portion of this thesis aimed to maximize the comfort of the stall bed by investigating the combined effect of three aspects of the stall bed: stall bed length, manger wall height, and bedding depth. Two rows of 12 tie-stalls were modified. Each row was modified to be a different length: short (178 cm, length commonly found in Quebec) and long (188 cm). Two manger wall height treatments were applied randomly to the stalls in each row: high (20 cm, upper limit of recommendation) and low (5 cm). A 7.6 cm-deep straw bedding layer was added to all stalls through the use of a bedding keeper installed at the end of all stalls. The twenty-four cows were divided into 6 blocks based on parity (2.7 ± 0.32) and DIM (115 ± 13.2 d). Four groups were then formed with 1 cow from each block in each group (n = 6/group). Two groups were assigned to each row and subjected to both manger wall treatments in a crossover design (1-week habituation, 6-week data collection/treatment). Injuries were scored once per week at 17 different body locations and analyzed as a difference from baseline for each period. Lying behaviours were recorded continuously via leg-mounted accelerometers. Data were analyzed using a mixed model with length, sequence, block, treatment, and period as fixed effects, week as a repeated measure, and cow as a random effect.All initially injured areas on the cow healed over the 14-week study. Improvement in hock injury was observed from weeks 1 to 6 for all treatments (P ≤ 0.001, lateral tarsal; P ≤ 0.01, lateral calcanei). Cows in longer (188-cm) stalls were found to spend more time lying (14.1 vs. 13.3 h/d; P < 0.05) and had longer lying bouts than cows in short stalls (74.1 vs. 52.9 min/bout; P < 0.05). Manger wall height did not affect injury or lying time. The higher lying times observed were comparable to those reported in deep-bedded compost packs, indicating that cows with more bedding, especially those in long stalls, were more comfortable. It was confirmed that increased bedding depth has a protective effect on injuries, allowing them to heal within a short time period. Deep-bedded straw stalls with bedding keepers appear to be applicable on tie-stall farms and would be a useful modification for producers that should result in cows that are more comfortable, lie-down for longer, and have fewer injuries
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 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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 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) | 0,003 | 0,000 |
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.
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 ».