| Literature DB >> 20587037 |
Victoriya V Volkova1, J Allen Byrd, Sue Ann Hubbard, Danny Magee, Richard H Bailey, Robert W Wills.
Abstract
BACKGROUND: Lighting is used during conventional broiler grow-out to modify bird behaviour to reach the goals of production and improve bird welfare. The protocols for lighting intensity vary. In a field study, we evaluated if the lighting practices impact the burden of Salmonella in broiler flocks.Entities:
Mesh:
Year: 2010 PMID: 20587037 PMCID: PMC2914049 DOI: 10.1186/1751-0147-52-46
Source DB: PubMed Journal: Acta Vet Scand ISSN: 0044-605X Impact factor: 1.695
Odds-ratios of detecting Salmonella in broiler flock and house litter depending on parameters of grow-out lighting.
| Parameter/its association with the outcome | |||||
|---|---|---|---|---|---|
| Outcome sample type | Parameter | Mean | OR (Wald-type 95% CI) | ||
| Feathered carcass rinsates 1 week before the end of rearing | 58 | 10% increase of the hours of full lights during grow-out | 25.5% | 1.38 | 0.061 |
| 58 | 10% increase of the hours of black-out during grow-out | 12.4% | 0.32 | 0.060 | |
| Post-harvest drag swabs of litter from grow-out house | 50 | Day of grow-out when dim lights for ≥18 hours per day started | 15 | 0.89 | 0.065 |
| Feathered carcass rinsates at arrival for processing | 50 | Day of grow-out when dim lights for ≥18 hours per day started | 15 | 0.93 | 0.098 |
| Post-chilling carcass rinsates | 54 | 10% increase of the hours of full lights during grow-out | 25.5% | 1.31 | 0.062 |
| 54 | 10% increase of the hours of dim lights during grow-out | 62.1% | 0.77 | 0.091 | |
Association between a parameter of grow-out lighting and an outcome was tested in a multi-level mixed logistic regression model that accounted for variation in the Salmonella burden among grow-out farms within a broiler complex, complexes within a company, and between companies. The lighting parameter was tested in this model as a single fixed-effects factor, and was considered to be associated with the outcome if P ≤ 0.100; only such parameters are presented. In all these models, the variation among grow-out farms within a complex significantly (P ≤ 0.100) contributed to variability in the outcome, but not the variation among complexes within a company or between companies (all P > 0.500). n - number of flocks.