Comparison of odour emission rates measured from various sources using two sampling devices

Two commonly used sampling devices (a wind tunnel and the US EPA dynamic emission chamber), were used to collect paired samples of odorous air from a number of agricultural odour sources. The odour samples were assessed using triangular, forced-choice dynamic olfactometry. The odour concentration data was combined with the flushing rate data to calculate odour emission rates for both devices on all sources. Odour concentrations were consistently higher in samples collected with a flux chamber (ratio ranging from 10:7 to 5:1, relative to wind tunnel samples), whereas odour emission rates were consistently larger when derived from wind tunnels (ratio ranging from 60:1 to 240:1, relative to flux chamber values). A complex relationship existed between emission rate estimates derived from each device, apparently influenced by the nature of the emitting surface. These results have great significance for users of odour dispersion models, for which an odour emission rate is a key input parameter.

Odour emissions from anaerobic piggery ponds. 2: Improving estimates of emission rate through recognition of spatial variability

Odour emission rates were measured for seven different anaerobic ponds treating piggery wastes at six to nine discrete locations across the surface of each pond on each sampling occasion over a thirteen month period. Significant variability in emission rates were observed for each pond. Measurement of a number of water quality variables in pond liquor samples collected at the same time and from the same locations as the odour samples indicated that the composition of the pond liquor was also variable. The results indicated that spatial variability was a real phenomenon and could have a significant impact on odour assessment practices. Considerably more odour samples would be required to characterise pond emissions than currently recommended by most practitioners, or regulatory agencies.

Estimating crop area using seasonal time series of enhanced vegetation index from MODIS satellite imagery

Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state, and national scales. There is now an increasing drive from industry for more accurate and cost-effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. Multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April-November) were investigated to estimate crop area for wheat, barley, chickpea, and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period; (ii) harmonic analysis of the time series (HANTS) of the EVI values; and (iii) principal component analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea, and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at pixel scale (>98% correct classification) for identifying overall winter cropping. However, discrimination among crops was less accurate. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire scale, the result contradicted the poor pixel-scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped-area estimates before harvest.

Enhanced diagnostic platforms for Post Entry Quarantine (PEQ)

Enhanced diagnostic platforms for Post Entry Quarantine (PEQ) and market access (Phase 1).

PA0187 Enhanced On-farm Monitoring and Mitigation of Pesticide and Nutrient Transport

Enhanced On-farm Monitoring and Mitigation of Pesticide and Nutrient Transport.

New strawberry breeding lines – Enhanced phytochemical composition and bioaccessibility

Screening of phytochemicals has been of interest in strawberry genotypes as there is emerging evidence from epidemiological and clinical studies that consumption of phytochemical-rich strawberry cultivars may provide health benefits. The aim of the present study was (1) to quantify selected phytochemicals in new strawberry breeding lines (BL) and (2) to assess the in vitro bioaccessibility of phytochemicals as an initial measure to predict their bioavailability.

Large-scale expansion of no-take closures within the Great Barrier Reef has not enhanced fishery production

A rare opportunity to test hypotheses about potential fishery benefits of large-scale closures was initiated in July 2004 when an additional 28.4% of the 348 000 km2 Great Barrier Reef (GBR) region of Queensland, Australia was closed to all fishing. Advice to the Australian and Queensland governments that supported this initiative predicted these additional closures would generate minimal (10%) initial reductions in both catch and landed value within the GBR area, with recovery of catches becoming apparent after three years. To test these predictions, commercial fisheries data from the GBR area and from the two adjacent (non-GBR) areas of Queensland were compared for the periods immediately before and after the closures were implemented. The observed means for total annual catch and value within the GBR declined from pre-closure (2000–2003) levels of 12 780 Mg and Australian $160 million, to initial post-closure (2005–2008) levels of 8143 Mg and $102 million; decreases of 35% and 36% respectively. Because the reference areas in the non-GBR had minimal changes in catch and value, the beyond-BACI (before, after, control, impact) analyses estimated initial net reductions within the GBR of 35% for both total catch and value. There was no evidence of recovery in total catch levels or any comparative improvement in catch rates within the GBR nine years after implementation. These results are not consistent with the advice to governments that the closures would have minimal initial impacts and rapidly generate benefits to fisheries in the GBR through increased juvenile recruitment and adult spillovers. Instead, the absence of evidence of recovery in catches to date currently supports an alternative hypothesis that where there is already effective fisheries management, the closing of areas to all fishing will generate reductions in overall catches similar to the percentage of the fished area that is closed.

Estimating daily methane production in individual cattle with irregular feed intake patterns from short-term methane emission measurements

Spot measurements of methane emission rate (n = 18 700) by 24 Angus steers fed mixed rations from GrowSafe feeders were made over 3- to 6-min periods by a GreenFeed emission monitoring (GEM) unit. The data were analysed to estimate daily methane production (DMP; g/day) and derived methane yield (MY; g/kg dry matter intake (DMI)). A one-compartment dose model of spot emission rate v. time since the preceding meal was compared with the models of Wood (1967) and Dijkstra et al. (1997) and the average of spot measures. Fitted values for DMP were calculated from the area under the curves. Two methods of relating methane and feed intakes were then studied: the classical calculation of MY as DMP/DMI (kg/day); and a novel method of estimating DMP from time and size of preceding meals using either the data for only the two meals preceding a spot measurement, or all meals for 3 days prior. Two approaches were also used to estimate DMP from spot measurements: fitting of splines on a 'per-animal per-day' basis and an alternate approach of modelling DMP after each feed event by least squares (using Solver), summing (for each animal) the contributions from each feed event by best-fitting a one-compartment model. Time since the preceding meal was of limited value in estimating DMP. Even when the meal sizes and time intervals between a spot measurement and all feeding events in the previous 72 h were assessed, only 16.9% of the variance in spot emission rate measured by GEM was explained by this feeding information. While using the preceding meal alone gave a biased (underestimate) of DMP, allowing for a longer feed history removed this bias. A power analysis taking into account the sources of variation in DMP indicated that to obtain an estimate of DMP with a 95% confidence interval within 5% of the observed 64 days mean of spot measures would require 40 animals measured over 45 days (two spot measurements per day) or 30 animals measured over 55 days. These numbers suggest that spot measurements could be made in association with feed efficiency tests made over 70 days. Spot measurements of enteric emissions can be used to define DMP but the number of animals and samples are larger than are needed when day-long measures are made.