Artificial olfaction system for on-site odour measurement

Odour impacts and concerns are an impediment to the growth of the Australian chicken meat industry. To manage these, the industry has to be able to demonstrate the efficacy of its odour reduction strategies scientifically and defensibly; however, it currently lacks reliable, cost effective and objective tools to do so. This report describes the development of an artificial olfaction system (AOS) to measure meat chicken farm odour. This report describes the market research undertaken to determine the demand for such a tool, the development and evaluation of three AOS prototypes, data analysis and odour prediction modelling, and the development of two complementary odour measurement tools, namely, a volatile organic compound (VOC) pre-concentrator and a field olfactometer. This report is aimed at investors in poultry odour research and those charged with, or interested in, assessment of odour on chicken farms, including farm managers, integrators, their consultants, regulators and researchers. The findings will influence the focus of future environmental odour measurement research.

Measurement of genetic and environmental variation in barley (Hordeum vulgare) grain hardness

In this study, we assessed a broad range of barley breeding lines and commercial varieties by three hardness methods (two particle size methods and one crush resistance method (SKCS—Single-Kernel Characterization System), grown at multiple sites to see if there was variation in barley hardness and if that variation was genetic or environmentally controlled. We also developed near-infrared reflectance (NIR) calibrations for these three hardness methods to ascertain if NIR technology was suitable for rapid screening of breeding lines or specific populations. In addition, we used this data to identify genetic regions that may be associated with hardness. There were significant (p<0.05) genetic effects for the three hardness methods. There were also environmental effects, possibly linked to the effect of protein on hardness, i.e. increasing protein resulted in harder grain. Heritability values were calculated at >85% for all methods. The NIR calibrations, with R2 values of >90%, had Standard Error of Prediction values of 0.90, 72 and 4.0, respectively, for the three hardness methods. These equations were used to predict hardness values of a mapping population which resulted in genetic markers being identified on all chromosomes but chromosomes 2H, 3H, 5H, 6H and 7H had markers with significant LOD scores. The two regions on 5H were on the distal end of both the long and short arms. The region that showed significant LOD score was on the long arm. However, the region on the short arm associated with the hardness (hordoindoline) genes did not have significant LOD scores. The results indicate that barley hardness is influenced by both genotype and environment and that the trait is heritable, which would allow breeders to develop very hard or soft varieties if required. In addition, NIR was shown to be a reliable tool for screening for hardness. While the data set used in this study has a relatively low variation in hardness, the tools developed could be applied to breeding populations that have large variation in barley grain hardness.

Process studies of odour emissions from effluent ponds using machine-based odour measurement

Replicable experimental studies using a novel experimental facility and a machine-based odour quantification technique were conducted to demonstrate the relationship between odour emission rates and pond loading rates. The odour quantification technique consisted of an electronic nose, AromaScan A32S, and an artificial neural network. Odour concentrations determined by olfactometry were used along with the AromaScan responses to train the artificial neural network. The trained network was able to predict the odour emission rates for the test data with a correlation coefficient of 0.98. Time averaged odour emission rates predicted by the machine-based odour quantification technique, were strongly correlated with volatile solids loading rate, demonstrating the increased magnitude of emissions from a heavily loaded effluent pond. However, it was not possible to obtain the same relationship between volatile solids loading rates and odour emission rates from the individual data. It is concluded that taking a limited number of odour samples over a short period is unlikely to provide a representative rate of odour emissions from an effluent pond. A continuous odour monitoring instrument will be required for that more demanding task.

Measurement and Interpretation of Salinity Tolerance in Four Perennial Grasses

Whilst the topic of soil salinity has received a substantive research effort over the years, the accurate measurement and interpretation of salinity tolerance data remain problematic. The tolerance of four perennial grass species (non-halophytes) to sodium chloride (NaCl) dominated salinity was determined in a free-flowing sand culture system. Although the salinity tolerance of non-halophytes is often represented by the threshold salinity model (bent-stick model), none of the species in the current study displayed any observable salinity threshold. Further, the observed yield decrease was not linear as suggested by the model. On re-examination of earlier datasets, we conclude that the threshold salinity model does not adequately describe the physiological processes limiting growth of non-halophytes in saline soils. Therefore, the use of the threshold salinity model is not recommended for non-halophytes, but rather, a model which more accurately reflects the physiological response observed in these saline soils, such as an exponential regression curve.

Potential of VIS-NIR Spectroscopy to predict perceived ‘muddy’ taint in australian farmed barramundi

Sensory analysis of food involves the measurement, interpretation and understanding of human responses to the properties of food perceived by the senses such as sight, smell, and taste (Cozzolino et al. 2005). It is important to have a quantitative means for assessing sensory properties in a reasonable way, to enable the food industry to rapidly respond to the changing demands of both consumers and the market. Aroma and flavour are among the most important properties for the consumer, and numerous studies have been performed in attempts to find correlations between sensory qualities and objective instrumental measurements. Rapid instrumental methods such as near infrared spectroscopy (NIR) might be advantageous to predict quality of different foods and agricultural products due to the speed of analysis, minimum sample preparation and low cost. The advantages of such technologies is not only to assess chemical structures but also to build an spectrum, characteristic of the sample, which behaves as a “finger print” of the sample.

Exploring the potential of VIS-NIR spectroscopy to predict sensory properties of foods

Sensory analysis of food involves the measurement, interpretation and understanding of human responses to the properties of food perceived by the senses such as sight, smell, and taste (Cozzolino et al. 2005). It is important to have a quantitative means for assessing sensory properties in a reasonable way, to enable the food industry to rapidly respond to the changing demands of both consumers and the market. Aroma and flavour are among the most important properties for the consumer and numerous studies have been performed in attempts to find correlations between sensory qualities and objective instrumental measurements. Rapid, non-destructive instrumental methods such as near infrared spectroscopy (NIR) might be advantageous to predict quality of food and agricultural products due to the speed of analysis, minimum sample preparation and low cost. The advantages of such technologies are not only to assess chemical structures but also to build a spectrum, characteristic of the sample, which behaves as a “finger print”.

Objective `Safe' Grazing Capacities for South-West Queensland Australia: Model Application and Evaluation

This paper describes the employment of two experienced graziers as consultants to apply and evaluate a model for calculating 'safe' long-term grazing capacities of individual properties. The model was based on ecological principles and entailed estimates of average annual forage grown (kglha) on the different land systems on each property and the calculation of the number of livestock (dry sheep equivalents, DSE) required to 'safely' utilise this forage. The grazier consultants applied and evaluated the 'safe' grazing capacity model on 20 properties of their choosing. For evaluation, model results were compared with; (a) the Department of Lands rated carrying capacities for those properties and (b) the grazing capacity assessed independently by the owners of those properties. For the 20 properties, the average 'safe' grazing capacity calculated by the model (21.0 DSE/kmZ) was 8% lighter than the average of the owner assessed capacities (22.7 DSE/kmZ), which in tum was 37% lighter than the average of the pre-1989 Department of Lands rated carrying capacity (31.0 DSE/kmZ). The grazing land management and administrative implications of these results and the role graziers played as consultants are discussed.

Objective `Safe' Grazing Capacities for South-West Queensland Australia: Development of a Model for Individual Properties

Few tools are available to assist graziers, land administrators and financiers in making objective grazing capacity decisions on Australian rangelands, despite existing knowledge regarding stocking rate theory and the impact of stocking rates on land condition. To address this issue a model for objectively estimating 'safe' grazing capacities on individual grazing properties in south-west Queensland was developed. The method is based on 'safe' levels of utilisation (15%-20%) by domestic livestock of average annual forage grown for each land system on a property. Average annual forage grown (kglha) was calculated as the product of the rainfall use efficiency (kglhdmm) and average annual rainfall (mm) for a land system. This estimate included the impact of tree and shrub cover on forage production. The 'safe' levels of forage utilisation for south- west Queensland pastures were derived from the combined experience of (1) re-analysis of the results of grazing trials, (2) reaching a consensus on local knowledge and (3) examination of existing grazing practice on 'benchmark' grazing properties. We recognise the problems in defining, determining and using grazing capacity values, but consider that the model offers decision makers a tool that can be used to assess the grazing capacity of individual properties.

Field measurement of beef pen manure methane and nitrous oxide reveals a surprise for inventory calculations

Few data exist on direct greenhouse gas emissions from pen manure at beef feedlots. However, emission inventories attempt to account for these emissions. This study used a large chamber to isolate N2O and CH4 emissions from pen manure at two Australian commercial beef feedlots (stocking densities, 13-27 m(2) head) and related these emissions to a range of potential emission control factors, including masses and concentrations of volatile solids, NO3-, total N, NH4+, and organic C (OC), and additional factors such as total manure mass, cattle numbers, manure pack depth and density, temperature, and moisture content. Mean measured pen N2O emissions were 0.428 kg ha(-1) d(-1) (95% confidence interval [CI], 0.252-0.691) and 0.00405 kg ha(-1) d(-1) (95% CI, 0.00114-0.0110) for the northern and southern feedlots, respectively. Mean measured CH4 emission was 0.236 kg ha(-1) d(-1) (95% CI, 0.163-0.332) for the northern feedlot and 3.93 kg ha(-1) d(-1) (95% CI, 2.58-5.81) for the southern feedlot. Nitrous oxide emission increased with density, pH, temperature, and manure mass, whereas negative relationships were evident with moisture and OC. Strong relationships were not evident between N2O emission and masses or concentrations of NO3- or total N in the manure. This is significant because many standard inventory calculation protocols predict N2O emissions using the mass of N excreted by the animal.