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Review
. 2008 Jan;14(1):5-20.
doi: 10.13031/2013.24120.

Identification of risk factors for sub-optimal housing conditions in Australian piggeries: Part 1. Study justification and design

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Free article
Review

Identification of risk factors for sub-optimal housing conditions in Australian piggeries: Part 1. Study justification and design

T M Banhazi et al. J Agric Saf Health. 2008 Jan.
Free article

Abstract

We undertook a literature search related to pig production facilities with two major aims: first, to review all the likely benefits that might be gained from air quality improvements; and second, to review previous research that had identified statistically significant factors affecting airborne pollutants and environmental parameters, so that these factors could be considered in a multifactorial analysis aimed at explaining variations in air pollutant concentrations. Ammonia, carbon dioxide, viable bacteria, endotoxins, and inhalable and respirable particles were identified as major airborne pollutants in the review. We found that high concentrations of airborne pollutants in livestock buildings could increase occupational health and safety risks, compromise the health, welfare, and production efficiency of animals, and affect the environment. Therefore, improving air quality could reduce environmental damage and improve animal and worker health. To achieve a reduction in pollutant concentrations, a better understanding of the factors influencing airborne pollutant concentrations in piggery buildings is required. Most of the work done previously has used simple correlation matrices to identify relationships between key factors and pollutant concentrations, without taking into consideration multifactorial effects simultaneously in a model. However, our review of this prior knowledge was the first important step toward developing a more inclusive statistical model. This review identified a number of candidate risk factors, which we then took into consideration during the development of multifactorial statistical models. We used a general linear model (GLM) to model measured internal concentrations, emissions, and environmental parameters in order to predict and potentially control the building environment.

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