User-friendly Decision Support System Research Articles

Barley yellow dwarf disease risk assessment based on Bayesian modelling of aphid population dynamics

A stochastic population dynamics model is proposed to improve integrated pest management strategies against the aphid Rhopalosiphum padi, the main Barley yellow dwarf virus (BYDV) vector in winter cereals during autumn in Europe. The model is based on a temperature-dependent simulation of R. padi population dynamics. The model requires a single early assessment of the proportion of plants infested by aphids. To account for sampling errors and for uncertainty caused by the numerous factors acting on aphid population dynamics under field conditions, Bayesian statistical inference was used. The model allows assessment of the probability distribution of the area under the curve of the percentage of plants infested by R. padi during autumn, a predictor of the need for insecticide sprays against BYDV vectors. The accuracy of model predictions was tested on an independent data set collected from 1995 to 1998 in the main French small grain production areas. The use of this model as a basis for a user-friendly decision support system improving BYDV management is discussed.

MODELKEY. Models for assessing and forecasting the impact of environmental key pollutants on freshwater and marine ecosystems and biodiversity (5 pp)

Triggered by the requirement of Water Framework Directive for a good ecological status for European river systems till 2015 and by still existing lacks in tools for cause identification of insufficient ecological status MODELKEY (http:// www.modelkey.org), an Integrated Project with 26 partners from 14 European countries, was started in 2005. MODELKEY is the acronym for 'Models for assessing and forecasting the impact of environmental key pollutants on freshwater and marine ecosystems and biodiversity'. The project is funded by the European Commission within the Sixth Framework Programme. MODELKEY comprises a multidisciplinary approach aiming at developing interlinked tools for an enhanced understanding of cause-effect-relationships between insufficient ecological status and environmental pollution as causative factor and for the assessment and forecasting of the risks of key pollutants on fresh water and marine ecosystems at a river basin and adjacent marine environment scale. New modelling tools for risk assessment including generic exposure assessment models, mechanistic models of toxic effects in simplified food chains, integrated diagnostic effect models based on community patterns, predictive component effect models applying artificial neural networks and GIS-based analysis of integrated risk indexes will be developed and linked to a user-friendly decision support system for the prioritisation of risks, contamination sources and contaminated sites. Modelling will be closely interlinked with extensive laboratory and field investigations. Early warning strategies on the basis of sub-lethal effects in vitro and in vivo are provided and combined with fractionation and analytical tools for effect-directed analysis of key toxicants. Integrated assessment of exposure and effects on biofilms, invertebrate and fish communities linking chemical analysis in water, sediment and biota with in vitro, in vivo and community level effect analysis is designed to provide data and conceptual understanding for risk arising from key toxicants in aquatic ecosystems and will be used for verification of various modelling approaches. The developed tools will be verified in case studies representing European key areas including Mediterranean, Western and Central European river basins. An end-user-directed decision support system will be provided for cost-effective tool selection and appropriate risk and site prioritisation.

NGAUGE: A decision support system to optimise N fertilisation of British grassland for economic and environmental goals

The poor efficiency with which nitrogen (N) is often used on grassland farms is well documented, as are the potential consequences of undesirable emissions of nitrogen. As fertiliser represents a major input of nitrogen to such systems, its improved management has good potential for increasing the efficiency of nitrogen use and enhancing environmental and economic performance. This paper describes the development, structure and potential application of a new decision support system for fertiliser management for British grassland. The underlying empirically-based model simulates monthly nitrogen flows within and between the main components of the livestock production system according to user inputs describing site conditions and farm management characteristics. The user-friendly decision support system (‘NGAUGE’) has a user interface that was produced in collaboration with livestock farmers to ensure availability of all required inputs. NGAUGE is an improvement on existing nitrogen fertiliser recommendation systems in that it relates production to environmental impact and is therefore potentially valuable to policy makers and researchers for identifying pollution mitigation strategies and blueprints for novel, more sustainable systems of livestock production. One possible application is the simulation of the phenomenon of pollution swapping, whereby, for example, the adoption of strategies for the reduction of nitrate leaching may exacerbate emissions of ammonia and nitrous oxide. Outputs of the decision support system include a field- and target-specific N fertiliser recommendation together with farm- and field-based N budgets, comprising amounts of N in both production and loss components of the system. Recommendations may be updated on a monthly basis to take account of deviations of weather conditions from the 30-year mean. The optimisation procedure within NGAUGE enables user-specified targets of herbage production, N loss or fertiliser use to be achieved while maximising efficiency of N use. Examples of model output for a typical grassland management scenario demonstrate the effect on model predictions of site and management properties such as soil texture, weather zone, grazing and manure applications. Depending on existing management and site characteristics, simulations with NGAUGE suggest that it is possible to reduce nitrate leaching by up to 46% (compared with a fertiliser distribution from existing fertiliser recommendations), and fertiliser by 33%, without sacrificing herbage yield. The greatest improvements in efficiency are possible on sandy-textured soils, with moderate N inputs.

Supporting the evidence-based treatment of individual drug users: The case for ‘patient profile decision analysis’

Practitioners treating problematic drug users face a significant challenge to (1) evaluate the evolving evidence base regarding treatment options, and (2) apply guidelines to individual patients with their unique combinations of characteristics, risks and histories. In this commentary we highlight patient profile decision analysis (PPDAS) as a rigorous and updateable means of assisting doctors deliver evidence-based care at a more patient focused level than current guidelines. PPDAS uses decision analytical modelling to calculate the evidence-based risks and benefits of different treatment options for multiple patient profiles. The output is presented in a user-friendly decision support system, enabling doctors and patients to quickly access the data to support their decision making. We outline the main features of PPDAS and review its potential for developing profile specific guidelines for the treatment of drug users.

Potato Late Blight Management in the Toluca Valley: Field Validation of SimCast Modified for Cultivars with High Field Resistance.

Management of potato late blight in the highland tropics is very costly and remains difficult. Reducing the impact of late blight through the use of resistant cultivars in combination with a fungicide forecasting system could lower the number of costly fungicide applications. Previously, we evaluated the fungicide advisory SimCast for use with Mexican cultivars of high levels of field resistance for use in the central highlands of Mexico and found that it predicted too many fungicide applications for cultivars of moderate to high levels of resistance. We adapted SimCast for cultivars with higher levels of resistance, and field validations conducted in 1999 and 2000 show that SimCast resulted in good disease control on cultivars ranging from susceptible to highly resistant. The number of fungicide applications forecast for cultivars with moderate to high levels of resistance was reduced. Precipitation was the environmental variable responsible for most of the forecasts made. Our work demonstrates that a predictive system can be portable from the temperate climate to a tropical highland climate. A user-friendly decision support system consisting of just a rain-gauge and the exclusive use of SimCast's fungicide units could be a valuable and affordable tool in managing potato late blight in the highland tropics.

Principles and Practices for Managing Rangeland Invasive Plants

Invasive plants reduce the capacity of ecosystems to provide goods and services required by society, alter ecological processes, and can displace desirable species. They can reduce wildlife habitat quality, riparian area integrity, rangeland economic value, and enterprise net returns. The invasion process is regulated by characteristics of the invading plant and the community being invaded. The presence and spread of invasive plants is often symptomatic of underlying management problems that must be corrected before acceptable, long-term rangeland improvement can be achieved. Disturbance appears to be important early in the invasion process because it creates vacant niches that alien plants can occupy. Control of invasive plants may only open niches for establishment of other undesirable plants unless desirable plants are present to fill the vacated niches. In many instances, rangelands have deteriorated to the point that desirable species are either not present, or in such low abundance that plant community recovery is slow or will not occur without revegetation after invasive plants are controlled. Integrated weed management employs the planned, sequential use of multiple tactics (e.g. chemical, biological, cultural, and mechanical control measures) to improve ecosystem function (energy flow and nutrient cycling) and maintain invasive plant damage below economic levels, and emphasizes managing rangeland ecosystem functions to meet objectives rather than emphasizing a particular weed or control method. Sustainable, integrated invasive plant management strategies require assessing plant impacts, understanding and managing the processes influencing invasion, knowledge of invasive plant biology and ecology, and are based on ecological principles. Invasive plant management programs must be compatible with and integrated into overall rangeland resource management objectives and plans. Because of the complexity of managing invasive plants, it is imperative that relevant ecological and economic information be synthesized into user-friendly decision support systems. DOI:10.2458/azu_jrm_v54i5_masters

Principles and practices for managing rangeland invasive plants

Invasive plants reduce the capacity of ecosystems to provide goods and services required by society, alter ecological processes, and can displace desirable species. They can reduce wildlife habitat quality, riparian area integrity, rangeland economic value, and enterprise net returns. The invasion process is regulated by characteristics of the invading plant and the community being invaded. The presence and spread of invasive plants is often symptomatic of underlying management problems that must be corrected before acceptable, long-term rangeland improvement can be achieved. Disturbance appears to be important early in the invasion process because it creates vacant niches that alien plants can occupy. Control of invasive plants may only open niches for establishment of other undesirable plants unless desirable plants are present to fill the vacated niches. In many instances, rangelands have deteriorated to the point that desirable species are either not present, or in such low abundance that plant community recovery is slow or will not occur without revegetation after invasive plants are controlled. Integrated weed management employs the planned, sequential use of multiple tactics (e.g. chemical, biological, cultural, and mechanical control measures) to improve ecosystem function (energy flow and nutrient cycling) and maintain invasive plant damage below economic levels, and emphasizes managing rangeland ecosystem functions to meet objectives rather than emphasizing a particular weed or control method. Sustainable, integrated invasive plant management strategies require assessing plant impacts, understanding and managing the processes influencing invasion, knowledge of invasive plant biology and ecology, and are based on ecological principles. Invasive plant management programs must be compatible with and integrated into overall rangeland resource management objectives and plans. Because of the complexity of managing invasive plants, it is imperative that relevant ecological and economic information be synthesized into user-friendly decision support systems.

A GIS-based environmental decision support system to assess the transfer of long-lived radiocaesium through food chains in areas contaminated by the Chernobyl accident

As part of the EC-financed RESTORE project ('Restoration of radioactively contaminated ecosystems'), an operational, user-friendly GIS-based Environmental Decision Support System (EDSS) has been developed. The RESTORE EDSS comprises a GIS-embedded modelling tool to assess the transfer of radiocaesium via food chains and external radiation exposures to humans based on an understanding of the nature of contamination, geo-chemical, hydrological, and biological processes, the different pathways for radiocaesium, and human behaviour. The EDSS accounts for spatial and temporal variation of these factors and is applicable to a variety of ecosystems. The overall aim of the EDSS is to identify vulnerable areas in terms of enhanced radionuclide transfer into food chains and/or the presence of 'critical population groups' that suffer enhanced internal and/or external exposure to radionuclides. Therefore, it provides information to support decisions about where to implement countermeasures and where to restore contaminated land most effectively. Predictions made by the EDSS use maps of soil contamination, soil type, and land use, production and production rates, and consumption habits of the affected population. The EDSS is illustrated by an analysis of the entire contaminated area of Ukraine, Belarus, and Russia.

Computer-supported poisonous plant information system for veterinary medicine

Animals poisoned by plants are the subject of an increasing number of inquiries made to poison control centres. The most frequent questions are concerned with the identification of potentially toxic species and the choice of adequate therapeutic strategies. To meet this growing demand for information, we generated a database on poisonous plants to be used by veterinary practitioners. Relevant data were selected from the scientific literature and organised according to the requirements of a structured database. As a result, we now introduce a user-friendly decision support system that is equipped with several search functions for fast and efficient retrieval of data. The information provided for each plant includes the degree of toxicity, major toxic constituents, their mechanism of action, pathological findings, clinical symptoms with brief case reports, therapeutic guidelines and references. In addition, each species is accompanied by a botanical description with photographic illustrations. This information tool on poisonous plants is available via the internet (http:www.vetpharm.unizh.ch) or compact disc, and can be accessed on Macintosh, Windows or UNIX using a browser that supports HTML 3.

Model management system for IRT-based test construction decision support system

Decision support system (DSS) has become widespread for some specific domains in recent years. However, DSS for IRT-based (item response theory) test construction has not yet been developed. This domain basically imposes a semi-structured or unstructured decision and, therefore, involves a very complex modeling process. This study develops a model management system (MMS) architecture to assist a non-expert user in manipulating test construction process efficiently and effectively. This architecture consists of four components: problem analysis, model type selection, model formulation and solver. The model type selection subsystem is further organized into three levels of hierarchy, i.e., environment, structure and parameter. A prototype is presented to demonstrate the feasibility of this architecture. The results indicate that this approach can be applied for providing an integrated, flexible and user-friendly DSS environment for producing better quality of results in less solution time.

Multiproduct cost-volume-profit model with product limits

A multiproduct cost-volume-profit model is extended to incorporate sales or product capacity limits for each product, required rate of return on sales, and the tax rate depending on profit levels as determined by the government. The model also deals with overhead costs that are traceable to a group of products but which cannot be allocated to individual products within the group to any reasonable accuracy. To solve the model, an algorithm is constructed to determine the required volume for each product that will achieve the best possible rate of return on sales revenue. Based upon the dBase Database Management System and the CVP model, a user-friendly menu-driven interactive decision support system is developed. The model algorithm and decision support system is illustrated with an example consisting of five products. Various reports generated by the interactive decision support system are also presented.

The state of multiple criteria decision support software

Maturation of the field of operations research has resulted in giving more attention to providing direct support for the process of managerial decision making. The desire for models which more accurately reflect the real problems that need to be solved has led to the sub-field of multiple criteria decision making (MCDM). However, many multiple criteria decision making procedures proposed in the literature are not being applied in real decision problems to the extent one might expect. Developments in the field of information systems, previously known as data processing, have led to a shift in focus from automating business transactions to more directly supporting managerial decision making. Advances in informa-tion technology have made interactive and user-friendly decision support systems (DSS) feasible. In this paper we provide a survey of currently available DSSs that use MCDM methodology. The availability and increased awareness of these software packages may lead to improved utilization of MCDM methodology and further development of the software.

Utilization of Neural Network for Constructing a User Friendly Decision Support System to Deal Stocks

Utilization of Neural Network for Constructing a User Friendly Decision Support System to Deal Stocks

Agricultural lenders' diagnostic system (ALDS)

ALDS is a user-friendly decision support system designed to help agricultural lenders score and rank loan applications. The system includes a number of predefined categories for evaluating the client. In contrast to most loan scoring models, ALDS evaluates both quantitative financial ratios and qualitative characteristics. Moreover, the ranking procedures can be adapted to fit the views of an individual bank or lender. Through the use of ‘pop up’ windows and an editing feature, the user can modify the weights on the various categories, financial ratios and qualitative characteristics. This flexible system also allows multiple lenders to identify their own scoring weights and save them in an individual file. The program generates a 0 to 10 score for each client. Final scores can be used in considering the risk of an individual loan application or in comparison with a number of applications. The lender can also examine strengths and weaknesses of particular loan applications by observing the score associated with the various categories used in the decision support system. Printed reports of individual financial records and multiple client scores are available as output from the model.