9 resultados para Stratigrafia, GIS, Tunisia

em eResearch Archive - Queensland Department of Agriculture


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Two examples of GIS-based multiple-criteria evaluations of plantation forests are presented. These desktop assessments use available topographical, geological and pedological information to establish the risk of occurrence of certain environmentally detrimental processes. The first case study is concerned with the risk that chemical additives (i.e. simazine) applied within the forestry landscape may reach the drainage system. The second case study assesses the vulnerability of forested areas to landslides. The subject of the first multiple-criteria evaluation (MCE) was a 4 km2 logging area, which had been recently site-prepared for a Pinus plantation. The criteria considered relevant to the assessment were proximity to creeks, slope, soil depth to the restrictive layer (i.e. potential depth to a perched water table) and soil erodability (based on clay content). The output of the MCE was in accordance with field observations, showing that this approach has the potential to provide management support by highlighting areas vulnerable to waterlogging, which in turn can trigger overland flow and export of pollutants to the local stream network. The subject of the second evaluation was an Araucaria plantation which is prone to landslips during heavy rain. The parameters included in the assessment were drainage system, the slope of the terrain and geological features such as rocks and structures. A good correlation between the MCE results and field observations was found, suggesting that this GIS approach is useful for the assessment of natural hazards. Multiple-criteria evaluations are highly flexible as they can be designed in either vector or raster format, depending on the type of available data. Although tested on specific areas, the MCEs presented here can be easily used elsewhere and assist both management intervention and the protection of the adjacent environment by assessing the vulnerability of the forest landscape to either introduced chemicals or natural hazards.

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Australian researchers have been developing robust yield estimation models, based mainly on the crop growth response to water availability during the crop season. However, knowledge of spatial distribution of yields within and across the production regions can be improved by the use of remote sensing techniques. Images of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, available since 1999, have the potential to contribute to crop yield estimation. The objective of this study was to analyse the relationship between winter crop yields and the spectral information available in MODIS vegetation index images at the shire level. The study was carried out in the Jondaryan and Pittsworth shires, Queensland , Australia . Five years (2000 to 2004) of 250m resolution, 16-day composite of MODIS Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images were used during the winter crop season (April to November). Seasonal variability of the profiles of the vegetation index images for each crop season using different regions of interest (cropping mask) were displayed and analysed. Correlation analysis between wheat and barley yield data and MODIS image values were also conducted. The results showed high seasonal variability in the NDVI and EVI profiles, and the EVI values were consistently lower than those of the NDVI. The highest image values were observed in 2003 (in contrast to 2004), and were associated with rainfall amount and distribution. The seasonal variability of the profiles was similar in both shires, with minimum values in June and maximum values at the end of August. NDVI and EVI images showed sensitivity to seasonal variability of the vegetation and exhibited good association (e.g. r = 0.84, r = 0.77) with winter crop yields.

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To remain competitive, many agricultural systems are now being run along business lines. Systems methodologies are being incorporated, and here evolutionary computation is a valuable tool for identifying more profitable or sustainable solutions. However, agricultural models typically pose some of the more challenging problems for optimisation. This chapter outlines these problems, and then presents a series of three case studies demonstrating how they can be overcome in practice. Firstly, increasingly complex models of Australian livestock enterprises show that evolutionary computation is the only viable optimisation method for these large and difficult problems. On-going research is taking a notably efficient and robust variant, differential evolution, out into real-world systems. Next, models of cropping systems in Australia demonstrate the challenge of dealing with competing objectives, namely maximising farm profit whilst minimising resource degradation. Pareto methods are used to illustrate this trade-off, and these results have proved to be most useful for farm managers in this industry. Finally, land-use planning in the Netherlands demonstrates the size and spatial complexity of real-world problems. Here, GIS-based optimisation techniques are integrated with Pareto methods, producing better solutions which were acceptable to the competing organizations. These three studies all show that evolutionary computation remains the only feasible method for the optimisation of large, complex agricultural problems. An extra benefit is that the resultant population of candidate solutions illustrates trade-offs, and this leads to more informed discussions and better education of the industry decision-makers.

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Parthenium hysterophorus is a weed of global significance causing severe economic, environmental, human and animal health problems in Asia, Africa, Australia and the Pacific. In South Asia, P. hysterophorus occurs in India, Pakistan, Sri Lanka, Bangladesh and Nepal. A host-specific leaf-feeding beetle Zygogramma bicolorata from Mexico was introduced into India in 1984, as a biological control agent for P. hysterophorus. In this study, a GIS-based distribution map of P. hysterophorus and its biological control agent Z. bicolorata in South Asia based on meta-analysis is presented. The map highlights the limited published information on P. hysterophorus incidence in many of the states and territories in India, as well as in neighbouring Bangladesh, Bhutan, Nepal and Pakistan. Incidence of Z. bicolorata was recorded as three distinct clusters, covering many states in India. In Pakistan, Z. bicolorata was recorded in the Punjab region bordering India. A CLIMEX model based on the current distribution of Z. bicolorata in India suggests that the geographic range of this agent in India and Pakistan can extend to other P. hysterophorus-infested areas in the region. The CLIMEX model also suggests that all of Bangladesh and Sri Lanka, and parts of Nepal are climatically suitable for Z. bicolorata.

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Resistance to the root-lesion nematode Pratylenchus thornei was sought in wheat from the West Asia and North Africa (WANA) region in the Watkins Collection (148 bread and 139 durum wheat accessions) and the McIntosh Collection (59 bread and 43 durum wheat accessions). It was considered that landraces from this region, encompassing the centres of origin of wheat and where P. thornei also occurs, could be valuable sources of resistance for use in wheat breeding. Resistance was determined by number of P. thornei/kg soil after the growth of the plants in replicated glasshouse experiments. On average, durum accessions produced significantly lower numbers of P. thornei than bread wheat accessions in both the Watkins and McIntosh Collections. Selected accessions with low P. thornei numbers were re-tested and 13 bread wheat and 10 durum accessions were identified with nematode numbers not significantly different from GS50a, a partially resistant bread wheat line used as a reference standard. These resistant accessions, which originated in Iran, Iraq, Syria, Egypt, Sudan, Morocco, and Tunisia, represent a resource of resistance genes in the primary wheat gene pool, which could be used in Australian wheat breeding programs to reduce the economic loss from P. thornei.

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Commercialisation and adoption of remote sensing and GIS technologies for improved production forecasting, productivity, quality and paddock- to- plate tracking within the Australian Peanut Industry.

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Background: The Queensland East Coast Otter Trawl Fishery (ECOTF) for penaeid shrimp fishes within Australia's Great Barrier Reef World Heritage Area (GBRWHA). The past decade has seen the implementation of conservation and fisheries management strategies to reduce the impact of the ECOTF on the seabed and improve biodiversity conservation. New information from electronic vessel location monitoring systems (VMS) provides an opportunity to review the interactions between the ECOTF and spatial closures for biodiversity conservation. Methodology and Results: We used fishing metrics and spatial information on the distribution of closures and modelled VMS data in a geographical information system (GIS) to assess change in effort of the trawl fishery from 2001-2009 and to quantify the exposure of 70 reef, non-reef and deep water bioregions to trawl fishing. The number of trawlers and the number of days fished almost halved between 2001 and 2009 and new spatial closures introduced in 2004 reduced the area zoned available for trawl fishing by 33%. However, we found that there was only a relatively minor change in the spatial footprint of the fishery as a result of new spatial closures. Non-reef bioregions benefited the most from new spatial closures followed by deep and reef bioregions. Conclusions/Significance: Although the catch of non target species remains an issue of concern for fisheries management, the small spatial footprint of the ECOTF relative to the size of the GBRWHA means that the impact on benthic habitats is likely to be negligible. The decline in effort as a result of fishing industry structural adjustment, increasing variable costs and business decisions of fishers is likely to continue a trend to fish only in the most productive areas. This will provide protection for most benthic habitats without any further legislative or management intervention.

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Remote detection of management-related trend in the presence of inter-annual climatic variability in the rangelands is difficult. Minimally disturbed reference areas provide a useful guide, but suitable benchmarks are usually difficult to identify. We describe a method that uses a unique conceptual framework to identify reference areas from multitemporal sequences of ground cover derived from Landsat TM and ETM+ imagery. The method does not require ground-based reference sites nor GIS layers about management. We calculate a minimum ground cover image across all years to identify locations of most persistent ground cover in years of lowest rainfall. We then use a moving window approach to calculate the difference between the window's central pixel and its surrounding reference pixels. This difference estimates ground-cover change between successive below-average rainfall years, which provides a seasonally interpreted measure of management effects. We examine the approach's sensitivity to window size and to cover-index percentiles used to define persistence. The method successfully detected management-related change in ground cover in Queensland tropical savanna woodlands in two case studies: (1) a grazing trial where heavy stocking resulted in substantial decline in ground cover in small paddocks, and (2) commercial paddocks where wet-season spelling (destocking) resulted in increased ground cover. At a larger scale, there was broad agreement between our analysis of ground-cover change and ground-based land condition change for commercial beef properties with different a priori ratings of initial condition, but there was also some disagreement where changing condition reflected pasture composition rather than ground cover. We conclude that the method is suitably robust to analyse grazing effects on ground cover across the 1.3 x 10(6) km(2) of Queensland's rangelands. Crown Copyright (c) 2012 Published by Elsevier Inc. All rights reserved.

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During the past 15 years, surveys to identify virus diseases affecting cool-season food legume crops in Australia and 11 CWANA countries (Algeria, China, Egypt, Ethiopia, Lebanon, Morocco, Sudan, Syria, Tunisia, Uzbekistan and Yemen) were conducted. More than 20,000 samples were collected and tested for the presence of 14 legume viruses by the tissue-blot immunoassay (TBIA) using a battery of antibodies, including the following Luteovirus monoclonal antibodies (McAbs): a broad-spectrum legume Luteovirus (5G4), BLRV, BWYV, SbDV and CpCSV. A total of 195 Luteovirus samples were selected for further testing by RT-PCR using 7 primers (one is degenerate, and can detect a wide range of Luteoviridae virus species and the other six are species-specific primers) at the Virology Laboratory, QDAF, Australia, during 2014. A total of 145 DNA fragments (represented 105 isolates) were sequenced. The following viruses were characterized based on molecular analysis: BLRV from Lebanon, Morocco, Tunisia and Uzbekistan; SbDV from Australia, Syria and Uzbekistan; BWYV from Algeria, China, Ethiopia, Lebanon, Morocco, Sudan, Tunisia and Uzbekistan; CABYV from Algeria, Lebanon, Syria, Sudan and Uzbekistan; CpCSV from Algeria, Ethiopia, Lebanon, Morocco, Syria and Tunisia, and unknown Luteoviridae species from Algeria, Ethiopia, Morocco, Sudan, Uzbekistan and Yemen. This study has clearly shown that there are a number of Polerovirus species, in addition to BWYV, all can produce yellowing/stunting symptoms in pulses (e.g. CABYV, CpCSV, and other unknown Polerovirus species). Based on our knowledge this is the first report of CABYV affecting food legumes. Moreover, there was about 95% agreement between results obtained from serological analysis (TBIA) and molecular analysis for the detection of BLRV and SbDV. Whereas, TBIA results were not accurate when using CpCSV and BWYV McAbs . It seems that the McAbs for CpCSV and BWYV used in this study and those available worldwide, are not virus species specific. Both antibodies, reacted with other Polerovirus species (e.g. CABYV, and unknown Polerovirus). This highlights the need for more accurate characterization of existing antibodies and where necessary the development of better, virus-specific antibodies to enable their use for accurate diagnosis of Poleroviruses.