14,730 research outputs found
Determining the probability of cyanobacterial blooms: the application of Bayesian networks in multiple lake systems
A Bayesian network model was developed to assess the combined influence of nutrient conditions and climate on the occurrence of cyanobacterial blooms within lakes of diverse hydrology and nutrient supply. Physicochemical, biological, and meteorological observations were collated from 20 lakes located at different latitudes and characterized by a range of sizes and trophic states. Using these data, we built a Bayesian network to (1) analyze the sensitivity of cyanobacterial bloom development to different environmental factors and (2) determine the probability that cyanobacterial blooms would occur. Blooms were classified in three categories of hazard (low, moderate, and high) based on cell abundances. The most important factors determining cyanobacterial bloom occurrence were water temperature, nutrient availability, and the ratio of mixing depth to euphotic depth. The probability of cyanobacterial blooms was evaluated under different combinations of total phosphorus and water temperature. The Bayesian network was then applied to quantify the probability of blooms under a future climate warming scenario. The probability of the "high hazardous" category of cyanobacterial blooms increased 5% in response to either an increase in water temperature of 0.8°C (initial water temperature above 24°C) or an increase in total phosphorus from 0.01 mg/L to 0.02 mg/L. Mesotrophic lakes were particularly vulnerable to warming. Reducing nutrient concentrations counteracts the increased cyanobacterial risk associated with higher temperatures
Recommendation domains for pond aquaculture
This publication introduces the methods and results of a research project that has developed a set of decision-support tools to identify places and sets of conditions for which a particular target aquaculture technology is considered feasible and therefore good to promote. The tools also identify the nature of constraints to aquaculture development and thereby shed light on appropriate interventions to realize the potential of the target areas. The project results will be useful for policy planners and decision makers in national, regional and local governments and development funding agencies, aquaculture extension workers in regional and local governments, and researchers in aquaculture systems and rural livelihoods. (Document contains 40 pages
Expert Elicitation for Reliable System Design
This paper reviews the role of expert judgement to support reliability
assessments within the systems engineering design process. Generic design
processes are described to give the context and a discussion is given about the
nature of the reliability assessments required in the different systems
engineering phases. It is argued that, as far as meeting reliability
requirements is concerned, the whole design process is more akin to a
statistical control process than to a straightforward statistical problem of
assessing an unknown distribution. This leads to features of the expert
judgement problem in the design context which are substantially different from
those seen, for example, in risk assessment. In particular, the role of experts
in problem structuring and in developing failure mitigation options is much
more prominent, and there is a need to take into account the reliability
potential for future mitigation measures downstream in the system life cycle.
An overview is given of the stakeholders typically involved in large scale
systems engineering design projects, and this is used to argue the need for
methods that expose potential judgemental biases in order to generate analyses
that can be said to provide rational consensus about uncertainties. Finally, a
number of key points are developed with the aim of moving toward a framework
that provides a holistic method for tracking reliability assessment through the
design process.Comment: This paper commented in: [arXiv:0708.0285], [arXiv:0708.0287],
[arXiv:0708.0288]. Rejoinder in [arXiv:0708.0293]. Published at
http://dx.doi.org/10.1214/088342306000000510 in the Statistical Science
(http://www.imstat.org/sts/) by the Institute of Mathematical Statistics
(http://www.imstat.org
The safety case and the lessons learned for the reliability and maintainability case
This paper examine the safety case and the lessons learned for the reliability and maintainability case
A Bayesian Belief Network to Infer Incentive Mechanisms to Reduce Antibiotic Use in Livestock Production
Efficient policy intervention to reduce antibiotic use in livestock production requires knowledge about the rationale underlying antibiotic usage. Animal health status and management quality are considered the two most important factors that influence farmers’ decision-making concerning antibiotic use. Information on these two factors is therefore crucial in designing incentive mechanisms. In this paper, a Bayesian belief network (BBN) is built to represent the knowledge on how these factors can directly and indirectly determine antibiotic use and the possible impact on economic incentives. Since both factors are not directly observable (i.e. latent), they are inferred from measurable variables (i.e. manifest variables) which are influenced by these factors. Using farm accounting data and registration data on antibiotic use and veterinary services in specialized finisher pig production farms, a confirmatory factor analysis was carried out to construct these factors. The BBN is then parameterized through regression analysis on the constructed factors and manifest variables. Using the BBN, possible incentive mechanisms through prices and management training are discussed.Livestock Production/Industries,
Recommendation domains for pond aquaculture
This publication introduces the methods and results of a research project that has developed a set of decision-support tools to identify places and sets of conditions for which a particular target aquaculture technology is considered feasible and therefore good to promote. The tools also identify the nature of constraints to aquaculture development and thereby shed light on appropriate interventions to realize the potential of the target areas. The project results will be useful for policy planners and decision makers in national, regional and local governments and development funding agencies, aquaculture extension workers in regional and local governments, and researchers in aquaculture systems and rural livelihoods.Pond culture, Freshwater aquaculture, GIS
Use of a Bayesian belief network to predict the impacts of commercializing non-timber forest products on livelihoods
Commercialization of non-timber forest products (NTFPs) has been widely promoted as a means of sustainably developing tropical forest resources, in a way that promotes forest conservation while supporting rural livelihoods. However, in practice, NTFP commercialization has often failed to deliver the expected benefits. Progress in analyzing the causes of such failure has been hindered by the lack of a
suitable framework for the analysis of NTFP case studies, and by the lack of predictive theory. We address
these needs by developing a probabilistic model based on a livelihood framework, enabling the impact of
NTFP commercialization on livelihoods to be predicted. The framework considers five types of capital
asset needed to support livelihoods: natural, human, social, physical, and financial. Commercialization of
NTFPs is represented in the model as the conversion of one form of capital asset into another, which is
influenced by a variety of socio-economic, environmental, and political factors. Impacts on livelihoods are
determined by the availability of the five types of assets following commercialization. The model,
implemented as a Bayesian Belief Network, was tested using data from participatory research into 19 NTFP
case studies undertaken in Mexico and Bolivia. The model provides a novel tool for diagnosing the causes
of success and failure in NTFP commercialization, and can be used to explore the potential impacts of
policy options and other interventions on livelihoods. The potential value of this approach for the
development of NTFP theory is discussed
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