Thomas Banhazi

Identification of risk factors for sub-optimal housing conditions in Australian piggeries: Part 2. Airborne pollutants.

The concentrations of total airborne bacteria, respirable endotoxins, ammonia, and respirable and inhalable particles were monitored in 160 piggery buildings in Australia between autumn 1997 and autumn 1999. The overall mean airborne bacteria, respirable endotoxins, ammonia (NH3), and inhalable and respirable particle concentrations measured were 1.17 x 10(5) cfu m(-3), 33.1 EU m(-3), 3.7 ppm, 1.74 mg m(-3), and 0.26 mg m(-3), respectively. The characteristics of the buildings and management systems used were documented at the time of sampling. A multifactorial general linear model (GLM) statistical procedure was used to analyze the effects of housing and management factors on the concentrations of the airborne pollutants. Both airborne bacteria and respirable endotoxin concentrations were affected by building classification (type), and respirable endotoxin concentrations were positively correlated with increasing humidity. The concentrations of airborne bacteria increased as the level of pen hygiene (cleanliness) decreased. The NH3 concentrations were primarily affected by level of pen hygiene, building volume, pig flow management, and season. Building classification, pig flow management, season, building volume, ventilation rates, and temperature affected inhalable particle concentrations. Respirable particle concentrations were primarily affected by building classification, pen hygiene, pig flow management, season, ventilation rates, temperature, and humidity. These findings suggest that environmental improvement strategies (such as improved cleaning, ventilation, and temperature control) are likely to reduce airborne pollutant concentrations in pig buildings and in the environment, thus improving the health and welfare of both pigs and farm staff.

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

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.

Identification of risk factors for sub-optimal housing conditions in Australian piggeries: part 3. Environmental parameters

Between autumn 1997 and autumn 1999, we measured ventilation rates (using a CO2 balance method), air temperatures, and relative humidity (using self-contained dataloggers with built-in sensors) in 160 pig housing facilities in Queensland, South Australia, Victoria, and Western Australia, in each case over a 60 h period. In some buildings, the internal air velocities above the animals were also recorded. While the monitoring instruments were being set up, a detailed questionnaire was used to collect data on major housing features and management factors. This information was statistically analyzed to quantify the effects of housing and management factors on the resulting environment conditions using a multifactorial analysis. The overall mean air temperature, relative humidity, internal air velocity, and ventilation rate were 20.3 degrees C, 58.9%, 0.12 m s(-1), and 663.9 m3 h(-1) 500 kg(-1) live weight, respectively, across all buildings. Internal building temperature and humidity were affected statistically by the type of insulation material used, the classification of buildings, and external climatic conditions. Ventilation rates were primarily affected by the type of ventilation system used, height (size) of ventilation openings, stocking density (kg m(-3)), and length, width, and height of buildings. These findings should aid the development of strategies for the industry to improve environmental control in piggery buildings.

Identification of Risk Factors for Sub-Optimal Housing Conditions in Australian Piggeries: Part 4. Emission Factors and Study Recommendations

The internal concentrations and emission rates of ammonia (NH3), total bacteria, respirable endotoxins, and inhalable and respirable particles were monitored in 160 piggery buildings in four states of Australia (Queensland, Victoria, Western Australia, and South Australia) between autumn 1997 and autumn 1999. Emissions were calculated for individual buildings as a product of internal concentration and ventilation rate, which were estimated by a carbon dioxide balance method. Relative humidity and temperature were also measured. The overall mean emission rates of NH3, total bacteria, respirable endotoxins, inhalable particles, and respirable particles per 500 kg live weight from Australian piggery buildings were 1442.5 mg h(-1), 82.2 x 10(6) cfu h(-1), 20.1 x 10(3) EU h(-1), 1306.7 mg h(-1), and 254.7 mg h(-1), respectively. Internal concentrations of key airborne pollutants have been reported in companion articles. Building characteristics and management systems used in the piggeries were documented at the time of sampling and used in the subsequent statistical modeling of variations in pollutant emission rates. The emissions model used all statistically significant factors identified during prior modeling conducted for individual pollutant concentrations and ventilation airflow. The identification of highly significant factors affecting emission rates and internal concentrations should aid the development of strategies for the industry to reduce emission rates from individual buildings, thus improving the environmental performance of piggery operations. In the second part of the article, specific recommendations are made based on the overall study results.

Precision Livestock Farming: A Suite of Electronic Systems to Ensure the Application of Best Practice Management on Livestock Farms

Abstract The sophisticated global market place for livestock products demands safe, uniform, cheap, and environmentally–and welfare-friendly products. However, best-practice management procedures are not always implemented on livestock farms to ensure that these market requirements are consistently satisfied. Therefore, improvements are needed in the way livestock farms are managed. Information-based and electronically-controlled livestock production systems are needed to ensure that the best of available knowledge can be readily implemented on farms. New technologies introduced on farms as part of Precision Livestock Farming (PLF) systems will have the capacity to activate livestock management methods that are more responsive to market signals. PLF technologies encompass methods for measuring electronically the critical components of the system that indicate efficiency of resource use, software technologies aimed at interpreting the information captured, and controlling processes to ensure optimum efficiency of resource use and animal productivity. These envisaged real-time monitoring and control systems should dramatically improve production efficiency of livestock enterprises. However, as some of the components of PLF systems are not yet sufficiently developed to be readily implemented, further research and development is required. In addition, an overall strategy for the adoption and commercial exploitation of PLF systems needs to be developed in collaboration with private companies. This article outlines the potential role PLF can play in ensuring that existing and new knowledge is implemented effectively on farms to improve returns to livestock producers, quality of products, welfare of animals and sustainability of the farm environment.

Weight estimation using image analysis and statistical modelling: a preliminary study

The weighing of pigs on a farm is traditionally performed manually, making the process time-consuming and laborious. An automated weighing system could thus greatly improve the efficiency of the weighing process. Former studies have demonstrated that an animals weight may be estimated via analysis of an image of that animal. A recent study conducted at the University of Adelaide aimed to implement an automatic weight estimation system for pigs, and use this system to confirm the results of previous studies while investigating new features, such as additional statistical modelling. A system was designed and implemented using off-the-shelf hardware. It was found that the system was able to estimate a pigs weight with an acceptable error.

Improved image analysis based system to reliably predict the live weight of pigs on farm: Preliminary results

Abstract A computer vision system was developed to automatically measure the live weight of pigs without human intervention. The system was trialled on both research and commercial farms to demonstrate the ability of the system to cope with different conditions and non-uniform lighting conditions. Early results demonstrate that the system can achieve sufficient practical accuracy. The results of the initial trials demonstrated that weight of the pigs can be predicted with an average error of 1.18 kg. Precision, reliability and repeatability are likely to be increased in future through improved weight prediction models, increased image resolution and algorithm enhancement.

An Assessment of Direct on-Farm Energy Use for High Value Grain Crops Grown under Different Farming Practices in Australia

I use energy cost share to characterize the role of energy in the economy. Specifically, I use an estimate of monetary expenditures for primary energy on an annualized basis for forty-four countries from 1978 to 2010 for natural gas, coal, petroleum, and electricity. I show that global energy cost share is significantly correlated to a one-year lag in the change in gross domestic product as well as measures of total factor productivity. Given the historical reduction in the relative cost of energy (including food and fodder for animate power) since the start of the Industrial Revolution, combined with a global energy cost share estimate, I conclude that the turn of the 21st Century represents the time period with the cheapest energy in the history of human civilization (to date). This potential historical nadir for energy expenditures around 2000 has important ramifications for strategies to solve future social, economic, and environmental problems such as reducing annual emissions of greenhouse gases (GHGs). Rapidly decreasing annual GHG emissions while internalizing their costs into the economy might feedback to increase energy expenditures to such a degree as to prevent economic growth during that transition.

Review of the Consequences and Control of High Air Temperatures in Intensive Livestock Buildings

Abstract The behaviour and physiology of intensively housed animals will be negatively affected when the environmental temperature is above their thermo-neutral zone (TNZ). It is likely that production efficiency, welfare, health and reproductive capacity of the animals will be compromised. Traditional technologies, such as the use of different cooling systems and better building design, can be used to alleviate the negative effects of high temperatures on animals. However, there are real opportunities to further develop climate control technologies and create intelligent environmental control systems that will be able to predict and therefore control both the responses of animals and the buildings in relation to selected control interventions.

Growth recorded automatically and continuously by a machine vision system for finisher pigs

Abstract Conventional livestock weighing methods require direct contact with the animals. This contact creates a physically demanding and hazardous situation for those undertaking the weighing activities. Alternatively, the weight of livestock can be estimated from their body measurements using non-invasive methods. This article presents recent improvements in the ongoing development of a completely automatic, two-dimensional machine vision system labelled the piGUI system, designed to obtain body measurements of pigs to estimate their live weight. Results comparing pig weights obtained by a weigh-scale and the vision-based method are reported for pigs in their finisher stage of growth. During offline testing of a video dataset, the piGUI system demonstrated that it was capable of estimating the average group weight within a 2.5% error relative to the actual group average weight. In addition, the weight deviation of the groups was estimated within a ±1 kg error of the actual group weight deviation. During on-farm testing the average group weight was accurate to 2.5% relative error and the estimated weight deviation was within a ±2 kg error of the actual weight deviation. Continuous recording of livestock growth is important as growth data can be used to measure animals’ responses to various factors such as the surrounding climate, housing environment and nutrition. Assessing the animals’ responses to these conditions is essential in improving the efficiency and welfare of livestock in both research and commercial settings.