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.

An identification tool for the Australian weedy Sporobolus species based on random amplified polymorphic DNA (RAPD) profiles

Based on morphological features alone, there is considerable difficulty in identifying the 5 most economically damaging weed species of Sporobolus [viz. S. pyramidalis P. Beauv., S. natalensis (Steud.) Dur and Schinz, S. fertilis (Steud.) Clayton, S. africanus (Poir.) Robyns and Tourney, and S. jacquemontii Kunth.] found in Australia. A polymerase chain reaction (PCR)-based random amplified polymorphic DNA (RAPD) technique was used to create a series of genetic markers that could positively identify the 5 major weeds from the other less damaging weedy and native Sporobolus species. In the initial RAPD profiling experiment, using arbitrarily selected primers and involving 12 species of Sporobolus, 12 genetic markers were found that, when used in combination, could consistently identify the 5 weedy species from all others. Of these 12 markers, the most diagnostic were UBC51490 for S. pyramidalis and S. natalensis; UBC43310.2000.2100 for S. fertilis and S. africanus; and ORA20850 and UBC43470 for S. jacquemontii. Species-specific markers could be found only for S. jacquemontii. In an effort to understand why there was difficulty in obtaining species-specific markers for some of the weedy species, a RAPD data matrix was created using 40 RAPD products. These 40 products amplified by 6 random primers from 45 individuals belonging to 12 species, were then subjected to numerical taxonomy and multivariate system (NTSYS pc version 1.70) analysis. The RAPD similarity matrix generated from the analysis indicated that S. pyramidalis was genetically more similar to S. natalensis than to other species of the 'S. indicus complex'. Similarly, S. jacquemontii was more similar to S. pyramidalis, and S. fertilis was more similar to S. africanus than to other species of the complex. Sporobolus pyramidalis, S. jacquemontii, S. africanus, and S. creber exhibited a low within-species genetic diversity, whereas high genetic diversity was observed within S. natalensis, S. fertilis, S. sessilis, S. elongates, and S. laxus. Cluster analysis placed all of the introduced species (major and minor weedy species) into one major cluster, with S. pyramidalis and S. natalensis in one distinct subcluster and S. fertilis and S. africanus in another. The native species formed separate clusters in the phenograms. The close genetic similarity of S. pyramidalis to S. natalensis, and S. fertilis to S. africanus may explain the difficulty in obtaining RAPD species-specific markers. The importance of these results will be within the Australian dairy and beef industries and will aid in the development of integrated management strategy for these weeds.

Spontaneous fermentation of traditional sago starch in Papua New Guinea

Sago starch is an important dietary carbohydrate in lowland Papua New Guinea (PNG). An investigation was conducted to determine whether microbes play a role in its preservation using traditional methods. In 12 stored sago samples collected from PNG villages, lactic acid bacteria (LAB) were present (>= 3.6 x 10(4) cfu/g) and pH ranged from 6.8 to 4.2. Acetic and propionic acids were detected in all samples, while butyric, lactic and valeric acids were present in six or more. In freshly prepared sago, held in sealed containers in the laboratory at 30 degrees C, spontaneous fermentation by endogenous microflora of sago starch was observed. This was evident by increasing concentrations of acetic, butyric and lactic acids over 4 weeks, and pH reducing from 4.9 to 3.1: both LAB and yeasts were involved. Survival of potential bacterial pathogens was monitored by seeding sago starch with similar to 10(4)/g of selected organisms. Numbers of Bacillus cereus, Listeria monocytogenes and Staphylococcus aureus fell to <30/g within 7 days. Salmonella sp. was present only in low numbers after 7 days (<36/g), but Escherichia coli was still detectable after three weeks (>10(2)/g). Fermentation appeared to increase the storability and safety of the product.

High-resolution melt-curve analysis of random-amplified-polymorphic-DNA markers, for the characterisation of pathogenic Leptospira.

A new test for pathogenic Leptospira isolates, based on RAPD-PCR and high-resolution melt (HRM) analysis (which measures the melting temperature of amplicons in real time, using a fluorescent DNA-binding dye), has recently been developed. A characteristic profile of the amplicons can be used to define serovars or detect genotypes. Ten serovars, of leptospires from the species Leptospira interrogans (serovars Australis, Robinsoni, Hardjo, Pomona, Zanoni, Copenhageni and Szwajizak), L. borgpetersenii (serovar Arborea), L. kirschneri (serovar Cynopteri) and L. weilii (serovar Celledoni), were typed against 13 previously published RAPD primers, using a real-time cycler (the Corbett Life Science RotorGene 6000) and the optimised reagents from a commercial kit (Quantace SensiMix). RAPD-HRM at specific temperatures generated defining amplicon melt profiles for each of the tested serovars. These profiles were evaluated as difference-curve graphs generated using the RotorGene software package, with a cut-off of at least 8 'U' (plus or minus). The results demonstrated that RAPD-HRM can be used to measure serovar diversity and establish identity, with a high degree of stability. The characterisation of Leptospira serotypes using a DNA-based methodology is now possible. As an objective and relatively inexpensive and rapid method of serovar identification, at least for cultured isolates, RAPD-HRM assays show convincing potentia.

High-resolution melt-curve analysis of random amplified polymorphic DNA (RAPD-HRM) for the characterisation of pathogenic leptospires: intra-serovar divergence, interserovar convergence, and evidence of attenuation in Leptospira reference collections.

High-resolution melt-curve analysis of random amplified polymorphic DNA (RAPD-HRM) is a novel technology that has emerged as a possible method to characterise leptospires to serovar level. RAPD-HRM has recently been used to measure intra-serovar convergence between strains of the same serovar as well as inter-serovar divergence between strains of different serovars. The results indicate that intra-serovar heterogeneity and inter-serovar homogeneity may limit the application of RAPD-HRM in routine diagnostics. They also indicate that genetic attenuation of aged, high-passage-number isolates could undermine the use of RAPD-HRM or any other molecular technology. Such genetic attenuation may account for a general decrease seen in titres of rabbit hyperimmune antibodies over time. Before RAPD-HRM can be further advanced as a routine diagnostic tool, strains more representative of the wild-type serovars of a given region need to be identified. Further, RAPD-HRM analysis of reference strains indicates that the routine renewal of reference collections, with new isolates, may be needed to maintain the genetic integrity of the collections.

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.