29 resultados para Geo-transformare

em CentAUR: Central Archive University of Reading - UK


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Paul Crutzen (2006) has suggested a research initiative to consider whether it would be feasible to artificially enhance the albedo of the planet Earth to counteract greenhouse warming. The enhancement of albedo would be achieved by intentionally injecting sulfur into the stratosphere. The rational for proposing the experiment is the observed cooling of the atmosphere following the recent major volcanic eruptions by El Chichon in 1984 and Mount Pinatubo in 1991 (Hansen et al., 1992). Although I am principally not against a research initiative to study such a potential experiment, I do have important reservations concerning its general feasibility. And its potential feasibility, I believe, must be the key motivation for embarking on such a study. Here I will bring up three major issues, which must be more thoroughly understood before any geo-engineering of climate could be considered, if at all. The three issues are (i) the lack of accuracy in climate prediction, (ii) the huge difference in timescale between the effect of greenhouse gases and the effect of aerosols and (iii) serious environmental problems which may be caused by high carbon dioxide concentration irrespective of the warming of the climate.

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Soil data and reliable soil maps are imperative for environmental management. conservation and policy. Data from historical point surveys, e.g. experiment site data and farmers fields can serve this purpose. However, legacy soil information is not necessarily collected for spatial analysis and mapping such that the data may not have immediately useful geo-references. Methods are required to utilise these historical soil databases so that we can produce quantitative maps of soil propel-ties to assess spatial and temporal trends but also to assess where future sampling is required. This paper discusses two such databases: the Representative Soil Sampling Scheme which has monitored the agricultural soil in England and Wales from 1969 to 2003 (between 400 and 900 bulked soil samples were taken annually from different agricultural fields); and the former State Chemistry Laboratory, Victoria, Australia where between 1973 and 1994 approximately 80,000 soil samples were submitted for analysis by farmers. Previous statistical analyses have been performed using administrative regions (with sharp boundaries) for both databases, which are largely unrelated to natural features. For a more detailed spatial analysis that call be linked to climate and terrain attributes, gradual variation of these soil properties should be described. Geostatistical techniques such as ordinary kriging are suited to this. This paper describes the format of the databases and initial approaches as to how they can be used for digital soil mapping. For this paper we have selected soil pH to illustrate the analyses for both databases.

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This paper analyses historic records of agricultural land use and management for England and Wales from 1931 and 1991 and uses export coefficient modelling to hindcast the impact of these practices on the rates of diffuse nitrogen (N) and phosphorus (P) export to water bodies for each of the major geo-climatic regions of England and Wales. Key trends indicate the importance of animal agriculture as a contributor to the total diffuse agricultural nutrient loading on waters, and the need to bring these sources under control if conditions suitable for sustaining 'Good Ecological Status' under the Water Framework Directive are to be generated. The analysis highlights the importance of measuring changes in nutrient loading in relation to the catchment-specific baseline state for different water bodies. The approach is also used to forecast the likely impact of broad regional scale scenarios on nutrient export to waters and highlights the need to take sensitive land out of production, introduce ceilings on fertilizer use and stocking densities, and controls on agricultural practice in higher risk areas where intensive agriculture is combined with a low intrinsic nutrient retention capacity, although the uncertainties associated with the modelling applied at this scale should be taken into account in the interpretation of model output. The paper advocates the need for a two-tiered approach to nutrient management, combining broad regional policies with targeted management in high risk areas at the catchment and farm scale.

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We report the results of a multi-instrument, multi-technique, coordinated study of the solar eruptive event of 13 May 2005. We discuss the resultant Earth-directed (halo) coronal mass ejection (CME), and the effects on the terrestrial space environment and upper Earth atmosphere. The interplanetary CME (ICME) impacted the Earth’s magnetosphere and caused the most-intense geomagnetic storm of 2005 with a Disturbed Storm Time (Dst) index reaching −263 nT at its peak. The terrestrial environment responded to the storm on a global scale. We have combined observations and measurements from coronal and interplanetary remote-sensing instruments, interplanetary and near-Earth in-situ measurements, remote-sensing observations and in-situ measurements of the terrestrial magnetosphere and ionosphere, along with coronal and heliospheric modelling. These analyses are used to trace the origin, development, propagation, terrestrial impact, and subsequent consequences of this event to obtain the most comprehensive view of a geo-effective solar eruption to date. This particular event is also part of a NASA-sponsored Living With a Star (LWS) study and an on-going US NSF-sponsored Solar, Heliospheric, and INterplanetary Environment (SHINE) community investigation.

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Many producers of geographic information are now disseminating their data using open web service protocols, notably those published by the Open Geospatial Consortium. There are many challenges inherent in running robust and reliable services at reasonable cost. Cloud computing provides a new kind of scalable infrastructure that could address many of these challenges. In this study we implement a Web Map Service for raster imagery within the Google App Engine environment. We discuss the challenges of developing GIS applications within this framework and the performance characteristics of the implementation. Results show that the application scales well to multiple simultaneous users and performance will be adequate for many applications, although concerns remain over issues such as latency spikes. We discuss the feasibility of implementing services within the free usage quotas of Google App Engine and the possibility of extending the approaches in this paper to other GIS applications.

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The aim of this study was to evaluate in vitro the influence of fermentable carbohydrates on the activity of porcine microbiota and survival of Salmonella Typhimurium in a batch culture system simulating the porcine hindgut. The carbohydrates tested were xylooligosaccharides, a mixture of fructooligosaccharides/inulin (FIN), fructooligosaccharides (FOS), gentiooligosaccharides (GEO) and lactulose (LAC). These ingredients stimulated the growth of selected Bifidobacterium and Lactobacillus species in pure cultures. In batch cultures, the carbohydrates influenced some fermentation parameters. For example, GEO and FIN significantly increased lactic acids compared with the control (no added carbohydrate). With the exception of LAC, the test carbohydrates increased the production of short-chain fatty acid (SCFA) and modified SCFA profiles. Quantitative analysis of bacterial populations by FISH revealed increased counts of the Bifidobacterium group compared with control and, with exception of FOS, increased Lactobacillus, Leuconostoc and Weissella spp. counts. Salmonella numbers were the lowest during the fermentation of LAC. This work has looked at carbohydrate metabolism by porcine microbiota in a pH-controlled batch fermentation system. It provides an initial model to analyse interactions with pathogens.

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The project investigated whether it would be possible to remove the main technical hindrance to precision application of herbicides to arable crops in the UK, namely creating geo-referenced weed maps for each field. The ultimate goal is an information system so that agronomists and farmers can plan precision weed control and create spraying maps. The project focussed on black-grass in wheat, but research was also carried out on barley and beans and on wild-oats, barren brome, rye-grass, cleavers and thistles which form stable patches in arable fields. Farmers may also make special efforts to control them. Using cameras mounted on farm machinery, the project explored the feasibility of automating the process of mapping black-grass in fields. Geo-referenced images were captured from June to December 2009, using sprayers, a tractor, combine harvesters and on foot. Cameras were mounted on the sprayer boom, on windows or on top of tractor and combine cabs and images were captured with a range of vibration levels and at speeds up to 20 km h-1. For acceptability to farmers, it was important that every image containing black-grass was classified as containing black-grass; false negatives are highly undesirable. The software algorithms recorded no false negatives in sample images analysed to date, although some black-grass heads were unclassified and there were also false positives. The density of black-grass heads per unit area estimated by machine vision increased as a linear function of the actual density with a mean detection rate of 47% of black-grass heads in sample images at T3 within a density range of 13 to 1230 heads m-2. A final part of the project was to create geo-referenced weed maps using software written in previous HGCA-funded projects and two examples show that geo-location by machine vision compares well with manually-mapped weed patches. The consortium therefore demonstrated for the first time the feasibility of using a GPS-linked computer-controlled camera system mounted on farm machinery (tractor, sprayer or combine) to geo-reference black-grass in winter wheat between black-grass head emergence and seed shedding.

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Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).

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In the earth sciences, data are commonly cast on complex grids in order to model irregular domains such as coastlines, or to evenly distribute grid points over the globe. It is common for a scientist to wish to re-cast such data onto a grid that is more amenable to manipulation, visualization, or comparison with other data sources. The complexity of the grids presents a significant technical difficulty to the regridding process. In particular, the regridding of complex grids may suffer from severe performance issues, in the worst case scaling with the product of the sizes of the source and destination grids. We present a mechanism for the fast regridding of such datasets, based upon the construction of a spatial index that allows fast searching of the source grid. We discover that the most efficient spatial index under test (in terms of memory usage and query time) is a simple look-up table. A kd-tree implementation was found to be faster to build and to give similar query performance at the expense of a larger memory footprint. Using our approach, we demonstrate that regridding of complex data may proceed at speeds sufficient to permit regridding on-the-fly in an interactive visualization application, or in a Web Map Service implementation. For large datasets with complex grids the new mechanism is shown to significantly outperform algorithms used in many scientific visualization packages.

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The rate and magnitude of predicted climate change require that we urgently mitigate emissions or sequester carbon on a substantial scale in order to avoid runaway climate change. Geo- and bioengineering solutions are increasingly proposed as viable and practical strategies for tackling global warming. Biotechnology companies are already developing transgenic “super carbon-absorbing” trees, which are sold as a cost-effective and relatively low-risk means of sequestering carbon. The question posed in this article is, Do super carbon trees provide real benefits or are they merely a fanciful illusion? It remains unclear whether growing these trees makes sense in terms of the carbon cost of production and the actual storage of carbon. In particular, it is widely acknowledged that “carbon-eating” trees fail to sequester as much carbon as they oxidize and return to the atmosphere; moreover, there are concerns about the biodiversity impacts of large-scale monoculture plantations. The potential social and ecological risks and opportunities presented by such controversial solutions warrant a societal dialogue.

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Expanding national services sectors and global competition aggravate current and perceived future market pressures on traditional manufacturing industries. These perceptions of change have provoked a growing intensification of geo-political discourses on technological innovation and ‘learning’, and calls for competency in design among other professional skills. However, these political discourses on innovation and learning have paralleled public concerns with the apparent ‘growth pains’ from factory closures and subsequent increases in unemployment, and its debilitating social and economic implications for local and regional development. In this respect the following investigation sets out to conceptualize change through the complementary and differing perceptions of industry and regional actors’ experiences or narratives, linking these perceptions to their structure-determined spheres of agent-environment interactivity. It aims to determine whether agents’ differing perceptions of industry transformation can have a role in the legitimization of their interests in, and in sustaining their organizational influence over the process of industry-regional transformation. It argues that industry and regional agent perceptions are among the cognitive aspects of agent-environment interactivity that permeate agency. It stresses agents’ ability to reason and manipulate their work environments to preserve their self-regulating interests in, and task representative influence over the multi-jurisdictional space of industry-regional transformation. The contributions of this investigation suggest that agents’ varied perceptions of industry and regional change inform or compete for influence over the redirection of regional, industry and business strategies. This claim offers a greater appreciation for the reflexive and complex institutional dimensions of industry planning and development, and the political responsibility to socially just forms of regional development. It positions the outcomes of this investigation at the nexus of intensifying geo-political discourses on the efficiency and equity of territorial development in Europe.

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Countries throughout the sub-Saharan (SSA) region have a complex linguistic heritage having their origins in opportunistic boundary changes effected by Western colonial powers at the Berlin Conference 1884-85. Postcolonial language-in-education policies valorizing ex-colonial languages have contributed at least in part to underachievement in education and thus the underdevelopment of human resources in SSA countries. This situation is not likely to improve whilst unresolved questions concerning the choice of language(s) that would best support social and economic development remain. Whilst policy attempts to develop local languages have been discussed within the framework of the African Union, and some countries have experimented with models of multilingual education during the past decade, the goalposts have already changed as a result of migration and trade. This paper argues that language policy makers need to be cognizant of changing language ecologies and their relationship with emerging linguistic and economic markets. The concept of language, within such a framework, has to be viewed in relation to the multiplicity of language markets within the shifting landscapes of people, culture, economics and the geo-politics of the 21st Century. Whilst, on the one hand, this refers to the hegemony of dominant powerful languages and the social relations of disempowerment, on the other hand, it also refers to existing and evolving social spaces and local language capabilities and choices. Within this framework the article argues that socially constructed dominant macro language markets need to be viewed also in relation to other, self-defined, community meso- and individual micro- language markets and their possibilities for social, economic and political development. It is through pursuing this argument that this article assesses the validity of Omoniyi’s argument in this volume, for the need to focus on the concept of language capital within multilingual contexts in the SSA region as compared to Bourdieu’s concept of linguistic capital.  

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During propagation, Magnetic Clouds (MC) interact with their environment and, in particular, may reconnect with the solar wind around it, eroding away part of its initial magnetic flux. Here we quantitatively analyze such an interaction using combined, multipoint observations of the same MC flux rope by STEREO A, B, ACE, WIND and THEMIS on November 19–20, 2007. Observation of azimuthal magnetic flux imbalance inside a MC flux rope has been argued to stem from erosion due to magnetic reconnection at its front boundary. The present study adds to such analysis a large set of signatures expected from this erosion process. (1) Comparison of azimuthal flux imbalance for the same MC at widely separated points precludes the crossing of the MC leg as a source of bias in flux imbalance estimates. (2) The use of different methods, associated errors and parametric analyses show that only an unexpectedly large error in MC axis orientation could explain the azimuthal flux imbalance. (3) Reconnection signatures are observed at the MC front at all spacecraft, consistent with an ongoing erosion process. (4) Signatures in suprathermal electrons suggest that the trailing part of the MC has a different large-scale magnetic topology, as expected. The azimuthal magnetic flux erosion estimated at ACE and STEREO A corresponds respectively to 44% and 49% of the inferred initial azimuthal magnetic flux before MC erosion upon propagation. The corresponding average reconnection rate during transit is estimated to be in the range 0.12–0.22 mV/m, suggesting most of the erosion occurs in the inner parts of the heliosphere. Future studies ought to quantify the influence of such an erosion process on geo-effectiveness.