A survey of dairy goat kid-rearing practices on Canadian farms and their associations with self-reported farm performance.


Journal

Journal of dairy science
ISSN: 1525-3198
Titre abrégé: J Dairy Sci
Pays: United States
ID NLM: 2985126R

Informations de publication

Date de publication:
Sep 2021
Historique:
received: 06 04 2020
accepted: 21 02 2021
pubmed: 9 6 2021
medline: 25 8 2021
entrez: 8 6 2021
Statut: ppublish

Résumé

Although future production of dairy goats is influenced by kid-rearing practices, little is known regarding which practices maximize kid growth, welfare, and future production success. The objectives of this survey study were to (1) identify common rearing practices of Canadian commercial dairy goat farms and evaluate their associations with 6 farm performance indicators and (2) determine if farms could be grouped by management style on the basis of the 6 performance indicators and compare rearing practices common across the different groups. A survey was sent by post or electronic media to reach dairy goat producers across Canada. The questionnaire contained 70 questions on the following areas of kid rearing: kidding management, care of newborn, colostrum management, milk and solid feeding in the preweaning period, health management, disbudding, housing conditions, weaning strategies, record keeping and growth monitoring, and farm performance data. Performance indicators, calculated on self-reported data, were 305-d milk production, preweaning mortality rate, diarrhea and respiratory disease prevalence, average daily gain from birth to weaning, herd milk production, and replacement rate. A total of 175 questionnaires were returned. After applying inclusion criteria, including herd size (≥40 goats) and completeness of surveys, 104 respondents from Ontario (n = 72, 69%), Québec (n = 23, 22%), and the Western provinces (n = 9, 9%) were retained for analysis, representing 29% of all Canadian producers. Farm sizes ranged from 42 to 2,500 (median = 190) goats. A large amount of variation in rearing practices and farm performance was found between farms. Colostrum and milk feeding management were found to be associated with all performance indicators except for kid respiratory disease prevalence, with timing of colostrum delivery and feeding method accounting for most the associations within each of the 2 areas. Replacement rate was mostly affected by whether or not kids were reared with their dam. Herds surveyed in the study could successfully be divided into 3 distinct groups (production-focused, longevity-focused, and low performance), representing different management styles on the basis of farm self-reported performance levels. Rearing practices found to be associated with higher farm performance could be targeted by advisory services to help improve management practices on Canadian dairy goat farms.

Identifiants

pubmed: 34099298
pii: S0022-0302(21)00635-4
doi: 10.3168/jds.2020-18663
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

9999-10009

Informations de copyright

Copyright © 2021 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Auteurs

S Bélanger-Naud (S)

Department of Animal Science, McGill University, Sainte-Anne-de-Bellevue, Québec H9X 3V9, Canada.

D Cinq-Mars (D)

Département des Sciences Animales, Université Laval, Québec, Québec G1V 0A6, Canada.

C Julien (C)

Centre de Recherche en Sciences Animales de Deschambault, Deschambault, Québec G0A 1S0, Canada.

J Arsenault (J)

Département de Pathologie et Microbiologie, Faculté de Médecine Vétérinaire de l'Université de Montréal, Saint-Hyacinthe, Québec J2S 2M2, Canada.

S Buczinski (S)

Département des Sciences Cliniques, Faculté de Médecine Vétérinaire de l'Université de Montréal, Saint-Hyacinthe, Québec J2S 2M2, Canada.

J Lévesque (J)

Centre de Recherche en Sciences Animales de Deschambault, Deschambault, Québec G0A 1S0, Canada.

E Vasseur (E)

Department of Animal Science, McGill University, Sainte-Anne-de-Bellevue, Québec H9X 3V9, Canada. Electronic address: elsa.vasseur@mcgill.ca.

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Classifications MeSH