9 resultados para Migration measurement

em eResearch Archive - Queensland Department of Agriculture


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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.

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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.

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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.

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The Wet Tropics bioregion of north Queensland has been identified as an area of global significance. The world-heritage-listed rainforests have been invaded by feral pigs (Sus scrofa) that are perceived to cause substantial environmental damage. A community perception exists of an annual altitudinal migration of the feral-pig population. The present study describes the movements of 29 feral pigs in relation to altitudinal migration (highland, transitional and lowland areas). Feral pigs were sedentary and stayed within their home range throughout a 4-year study period. No altitudinal migration was detected; pigs moved no more than a mean distance of 1.0 km from the centre of their calculated home ranges. There was no significant difference between the mean (+/- 95% confidence interval) aggregate home ranges for males (8.7 +/- 4.3 km², n = 15) and females (7.2 +/- 1.8 km², n = 14). No difference in home range was detected among the three altitudinal areas: 7.2 +/- 2.4 km² for highland, 6.2 +/- 3.9 km² for transitional and 9.9 +/- 5.3 km² for lowland areas. The aggregate mean home range for all pigs in the present study was 8.0 +/- 2.4 km². The study also assessed the influence seasons had on the home range of eight feral pigs on the rainforest boundary; home ranges did not significantly vary in size between the tropical wet and dry seasons, although the mean home range in the dry season (7.7 +/- 6.9 km²) was more than twice the home range in the wet season (2.9 +/- 0.8 km²). Heavier pigs tended to have larger home ranges. The results of the present study suggest that feral pigs are sedentary throughout the year so broad-scale control techniques need to be applied over sufficient areas to encompass individual home ranges. Control strategies need a coordinated approach if a long-term reduction in the pig population is to be achieved.

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A highly polymorphic genetic locus of Stout Whiting was examined for evidence of geographical subdivision amongst samples collected from three locales in southern Queensland waters. Statistical indicators of subdivision were not significantly different from zero, suggesting that it is unlikely that the Stout Whiting resource in southern Queensland is genetically subdivided into separate stocks. It is recommended that the full-scale genetic program not proceed and that the resource be managed as a single stock.

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Coastal seagrass habitats in tropical and subtropical regions support aggregations of resident green turtles (Chelonia mydas) from several genetically distinct breeding populations. Migration of individuals to their respective dispersed breeding sites provides a complex pattern of migratory connectivity among nesting and feeding habitats of this species. An understanding of this pattern is important in regions where the persistence of populations is under threat from anthropogenic impacts. The present study uses mitochondrial DNA and mixed-stock analyses to assess the connectivity among seven feeding grounds across the north Australian coast and adjacent areas and 17 genetically distinct breeding populations from the Indo-Pacific region. It was hypothesised that large and geographically proximate breeding populations would dominate at nearby feeding grounds. As expected, each sampled feeding area appears to support multiple breeding populations, with two aggregations dominated by a local breeding population. Geographic distance between breeding and feeding habitat strongly influenced whether a breeding population contributed to a feeding ground (wi = 0.654); however, neither distance nor size of a breeding population was a good predictor of the extent of their contribution. The differential proportional contributions suggest the impact of anthropogenic mortality at feeding grounds should be assessed on a case-by-case basis.

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Turnip mosaic virus (TuMV) is a potyvirus that is transmitted by aphids and infects a wide range of plant species. We investigated the evolution of this pathogen by collecting 32 isolates of TuMV, mostly from Brassicaceae plants, in Australia and New Zealand. We performed a variety of sequence-based phylogenetic and population genetic analyses of the complete genomic sequences and of three non-recombinogenic regions of those sequences. The substitution rates, divergence times and phylogeographical patterns of the virus populations were estimated. Six inter- and seven intralineage recombination-type patterns were found in the genomes of the Australian and New Zealand isolates, and all were novel. Only one recombination-type pattern has been found in both countries. The Australian and New Zealand populations were genetically different, and were different from the European and Asian populations. Our Bayesian coalescent analyses, based on a combination of novel and published sequence data from three nonrecombinogenic protein-encoding regions, showed that TuMV probably started to migrate from Europe to Australia and New Zealand more than 80 years ago, and that distinct populations arose as a result of evolutionary drivers such as recombination. The basal-B2 subpopulation in Australia and New Zealand seems to be older than those of the world-B2 and -B3 populations. To our knowledge, our study presents the first population genetic analysis of TuMV in Australia and New Zealand. We have shown that the time of migration of TuMV correlates well with the establishment of agriculture and migration of Europeans to these countries.

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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.