Identifying management zones in agricultural fields using spatially constrained classification of soil and ancillary data

Site-specific management requires accurate knowledge of the spatial variation in a range of soil properties within fields. This involves considerable sampling effort, which is costly. Ancillary data, such as crop yield, elevation and apparent electrical conductivity (ECa) of the soil, can provide insight into the spatial variation of some soil properties. A multivariate classification with spatial constraint imposed by the variogram was used to classify data from two arable crop fields. The yield data comprised 5 years of crop yield, and the ancillary data 3 years of yield data, elevation and ECa. Information on soil chemical and physical properties was provided by intensive surveys of the soil. Multivariate variograms computed from these data were used to constrain sites spatially within classes to increase their contiguity. The constrained classifications resulted in coherent classes, and those based on the ancillary data were similar to those from the soil properties. The ancillary data seemed to identify areas in the field where the soil is reasonably homogeneous. The results of targeted sampling showed that these classes could be used as a basis for management and to guide future sampling of the soil.

A geostatistical analysis of soil, vegetation, and image data characterizing land surface variation

The elucidation of spatial variation in the landscape can indicate potential wildlife habitats or breeding sites for vectors, such as ticks or mosquitoes, which cause a range of diseases. Information from remotely sensed data could aid the delineation of vegetation distribution on the ground in areas where local knowledge is limited. The data from digital images are often difficult to interpret because of pixel-to-pixel variation, that is, noise, and complex variation at more than one spatial scale. Landsat Thematic Mapper Plus (ETM+) and Satellite Pour l'Observation de La Terre (SPOT) image data were analyzed for an area close to Douna in Mali, West Africa. The variograms of the normalized difference vegetation index (NDVI) from both types of image data were nested. The parameters of the nested variogram function from the Landsat ETM+ data were used to design the sampling for a ground survey of soil and vegetation data. Variograms of the soil and vegetation data showed that their variation was anisotropic and their scales of variation were similar to those of NDVI from the SPOT data. The short- and long-range components of variation in the SPOT data were filtered out separately by factorial kriging. The map of the short-range component appears to represent the patterns of vegetation and associated shallow slopes and drainage channels of the tiger bush system. The map of the long-range component also appeared to relate to broader patterns in the tiger bush and to gentle undulations in the topography. The results suggest that the types of image data analyzed in this study could be used to identify areas with more moisture in semiarid regions that could support wildlife and also be potential vector breeding sites.

Information servers, present and future

Mainframes, corporate and central servers are becoming information servers. The requirement for more powerful information servers is the best opportunity to exploit the potential of parallelism. ICL recognized the opportunity of the 'knowledge spectrum' namely to convert raw data into information and then into high grade knowledge. Parallel Processing and Data Management Its response to this and to the underlying search problems was to introduce the CAFS retrieval engine. The CAFS product demonstrates that it is possible to move functionality within an established architecture, introduce a different technology mix and exploit parallelism to achieve radically new levels of performance. CAFS also demonstrates the benefit of achieving this transparently behind existing interfaces. ICL is now working with Bull and Siemens to develop the information servers of the future by exploiting new technologies as available. The objective of the joint Esprit II European Declarative System project is to develop a smoothly scalable, highly parallel computer system, EDS. EDS will in the main be an SQL server and an information server. It will support the many data-intensive applications which the companies foresee; it will also support application-intensive and logic-intensive systems.

Modeling the user knowledge by belief networks

This paper describes the user modeling component of EPIAIM, a consultation system for data analysis in epidemiology. The component is aimed at representing knowledge of concepts in the domain, so that their explanations can be adapted to user needs. The first part of the paper describes two studies aimed at analysing user requirements. The first one is a questionnaire study which examines the respondents' familiarity with concepts. The second one is an analysis of concept descriptions in textbooks and from expert epidemiologists, which examines how discourse strategies are tailored to the level of experience of the expected audience. The second part of the paper describes how the results of these studies have been used to design the user modeling component of EPIAIM. This module works in a two-step approach. In the first step, a few trigger questions allow the activation of a stereotype that includes a "body" and an "inference component". The body is the representation of the body of knowledge that a class of users is expected to know, along with the probability that the knowledge is known. In the inference component, the learning process of concepts is represented as a belief network. Hence, in the second step the belief network is used to refine the initial default information in the stereotype's body. This is done by asking a few questions on those concepts where it is uncertain whether or not they are known to the user, and propagating this new evidence to revise the whole situation. The system has been implemented on a workstation under UNIX. An example of functioning is presented, and advantages and limitations of the approach are discussed.

The climate sensitivity and its components diagnosed from Earth Radiation Budget data

One of the major uncertainties in the ability to predict future climate change, and hence its impacts, is the lack of knowledge of the earth's climate sensitivity. Here, data are combined from the 1985-96 Earth Radiation Budget Experiment (ERBE) with surface temperature change information and estimates of radiative forcing to diagnose the climate sensitivity. Importantly, the estimate is completely independent of climate model results. A climate feedback parameter of 2.3 +/- 1.4 W m(-2) K-1 is found. This corresponds to a 1.0-4.1-K range for the equilibrium warming due to a doubling of carbon dioxide (assuming Gaussian errors in observable parameters, which is approximately equivalent to a uniform "prior" in feedback parameter). The uncertainty range is due to a combination of the short time period for the analysis as well as uncertainties in the surface temperature time series and radiative forcing time series, mostly the former. Radiative forcings may not all be fully accounted for; however, all argument is presented that the estimate of climate sensitivity is still likely to be representative of longer-term climate change. The methodology can be used to 1) retrieve shortwave and longwave components of climate feedback and 2) suggest clear-sky and cloud feedback terms. There is preliminary evidence of a neutral or even negative longwave feedback in the observations, suggesting that current climate models may not be representing some processes correctly if they give a net positive longwave feedback.

Too much information? : Drama teachers’ perceptions of the value of subject knowledge

This paper reports on research into what drama teachers consider they really need to know as drama specialists. In the first instance the very concept of knowledge is discussed as it pertains to education in the arts as is the current situation in England regarding the extent to which new drama teachers’ subject specialist knowledge has been formally accredited and what the implications of this may be to an evolving curriculum. The research itself initially involved using a questionnaire to investigate the way in which drama teachers prioritised different aspects of professional knowledge. Results of this survey were deemed surprising enough to warrant further investigation through the use of interviews and a multiple-sorting exercise which revealed why the participants prioritised in the way they did. Informed by the work of Bourdieu, Foucault and Kelly, a model is proposed which may help explain the tensions experienced by drama teachers as they try to balance and prioritise different aspects of professional knowledge.

Century-of-Information Research (CIR): a strategy for research and innovation in the century of information

More data will be produced in the next five years than in the entire history of human kind, a digital deluge that marks the beginning of the Century of Information. Through a year-long consultation with UK researchers, a coherent strategy has been developed, which will nurture Century-of-Information Research (CIR); it crystallises the ideas developed by the e-Science Directors' Forum Strategy Working Group. This paper is an abridged version of their latest report which can be found at: http://wikis.nesc.ac.uk/escienvoy/Century_of_Information_Research_Strategy which also records the consultation process and the affiliations of the authors. This document is derived from a paper presented at the Oxford e-Research Conference 2008 and takes into account suggestions made in the ensuing panel discussion. The goals of the CIR Strategy are to facilitate the growth of UK research and innovation that is data and computationally intensive and to develop a new culture of 'digital-systems judgement' that will equip research communities, businesses, government and society as a whole, with the skills essential to compete and prosper in the Century of Information. The CIR Strategy identifies a national requirement for a balanced programme of coordination, research, infrastructure, translational investment and education to empower UK researchers, industry, government and society. The Strategy is designed to deliver an environment which meets the needs of UK researchers so that they can respond agilely to challenges, can create knowledge and skills, and can lead new kinds of research. It is a call to action for those engaged in research, those providing data and computational facilities, those governing research and those shaping education policies. The ultimate aim is to help researchers strengthen the international competitiveness of the UK research base and increase its contribution to the economy. The objectives of the Strategy are to better enable UK researchers across all disciplines to contribute world-leading fundamental research; to accelerate the translation of research into practice; and to develop improved capabilities, facilities and context for research and innovation. It envisages a culture that is better able to grasp the opportunities provided by the growing wealth of digital information. Computing has, of course, already become a fundamental tool in all research disciplines. The UK e-Science programme (2001-06)—since emulated internationally—pioneered the invention and use of new research methods, and a new wave of innovations in digital-information technologies which have enabled them. The Strategy argues that the UK must now harness and leverage its own, plus the now global, investment in digital-information technology in order to spread the benefits as widely as possible in research, education, industry and government. Implementing the Strategy would deliver the computational infrastructure and its benefits as envisaged in the Science & Innovation Investment Framework 2004-2014 (July 2004), and in the reports developing those proposals. To achieve this, the Strategy proposes the following actions: support the continuous innovation of digital-information research methods; provide easily used, pervasive and sustained e-Infrastructure for all research; enlarge the productive research community which exploits the new methods efficiently; generate capacity, propagate knowledge and develop skills via new curricula; and develop coordination mechanisms to improve the opportunities for interdisciplinary research and to make digital-infrastructure provision more cost effective. To gain the best value for money strategic coordination is required across a broad spectrum of stakeholders. A coherent strategy is essential in order to establish and sustain the UK as an international leader of well-curated national data assets and computational infrastructure, which is expertly used to shape policy, support decisions, empower researchers and to roll out the results to the wider benefit of society. The value of data as a foundation for wellbeing and a sustainable society must be appreciated; national resources must be more wisely directed to the collection, curation, discovery, widening access, analysis and exploitation of these data. Every researcher must be able to draw on skills, tools and computational resources to develop insights, test hypotheses and translate inventions into productive use, or to extract knowledge in support of governmental decision making. This foundation plus the skills developed will launch significant advances in research, in business, in professional practice and in government with many consequent benefits for UK citizens. The Strategy presented here addresses these complex and interlocking requirements.

Predicting the profile of nutrients available for absorption: from nutrient requirement to animal response and environmental impact

Current feed evaluation systems for dairy cattle aim to match nutrient requirements with nutrient intake at pre-defined production levels. These systems were not developed to address, and are not suitable to predict, the responses to dietary changes in terms of production level and product composition, excretion of nutrients to the environment, and nutrition related disorders. The change from a requirement to a response system to meet the needs of various stakeholders requires prediction of the profile of absorbed nutrients and its subsequent utilisation for various purposes. This contribution examines the challenges to predicting the profile of nutrients available for absorption in dairy cattle and provides guidelines for further improved prediction with regard to animal production responses and environmental pollution. The profile of nutrients available for absorption comprises volatile fatty acids, long-chain fatty acids, amino acids and glucose. Thus the importance of processes in the reticulo-rumen is obvious. Much research into rumen fermentation is aimed at determination of substrate degradation rates. Quantitative knowledge on rates of passage of nutrients out of the rumen is rather limited compared with that on degradation rates, and thus should be an important theme in future research. Current systems largely ignore microbial metabolic variation, and extant mechanistic models of rumen fermentation give only limited attention to explicit representation of microbial metabolic activity. Recent molecular techniques indicate that knowledge on the presence and activity of various microbial species is far from complete. Such techniques may give a wealth of information, but to include such findings in systems predicting the nutrient profile requires close collaboration between molecular scientists and mathematical modellers on interpreting and evaluating quantitative data. Protozoal metabolism is of particular interest here given the paucity of quantitative data. Empirical models lack the biological basis necessary to evaluate mitigation strategies to reduce excretion of waste, including nitrogen, phosphorus and methane. Such models may have little predictive value when comparing various feeding strategies. Examples include the Intergovernmental Panel on Climate Change (IPCC) Tier II models to quantify methane emissions and current protein evaluation systems to evaluate low protein diets to reduce nitrogen losses to the environment. Nutrient based mechanistic models can address such issues. Since environmental issues generally attract more funding from governmental offices, further development of nutrient based models may well take place within an environmental framework.