Influence diagrams as decision-making tools for pesticide risk management

被引:23
作者
Carriger, John F.
Newman, Michael C.
机构
[1] College of William and Mary-VIMS, Gloucester Point, VA 23062, PO Box 1346
[2] US Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Gulf Ecology Division, Gulf Breeze
关键词
BAYESIAN BELIEF NETWORKS; MODEL; JUDGMENT; PROBABILITY; SCIENCE; SCALE;
D O I
10.1002/ieam.268
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The pesticide policy arena is filled with discussion of probabilistic approaches to assess ecological risk, however, similar discussions about implementing formal probabilistic methods in pesticide risk decision making are less common. An influence diagram approach is proposed for ecological risk-based decisions about pesticide usage. Aside from technical data, pesticide risk management relies on diverse sources, such as stakeholder opinions, to make decisions about what, how, where, and when to spray. Bayesian influence diagrams allow multiple lines of evidence, including process related information from existing data and expert judgment, in 1 inclusive decision model. In ecological risk assessments, data informally incorporated for pesticide usage decisions, such as field and laboratory effect studies along with chemical monitoring and modeling data, can be formally incorporated and expressed in linked causal diagrams. A case study is presented from the perspective of an environmental manager wishing to efficiently control pests while minimizing risk to local aquatic receptors. Exposure modeling results and toxicity studies were incorporated, and an ecological risk assessment was carried out but combined with hypothetical information on spraying efficacy and valuation of outcomes that would be necessary for making risk management decisions. The variables and their links in the influence diagram are ones that are important to a manager and can be manipulated to optimally control pests while protecting nontarget resources. Integr Environ Assess Manag 2012; 8: 339-350. (C) 2011 SETAC
引用
收藏
页码:339 / 350
页数:12
相关论文
共 68 条
[1]   Using Bayesian networks to model watershed management decisions: an East Canyon Creek case study [J].
Ames, DP ;
Neilson, BT ;
Stevens, DK ;
Lall, U .
JOURNAL OF HYDROINFORMATICS, 2005, 7 (04) :267-282
[2]  
[Anonymous], 2006, SIGNIFICANCE
[3]  
[Anonymous], 2001, GUIDELINES USING BAY
[4]  
[Anonymous], 2001, Making hard decisions with decision tools
[5]   Modeling and Measuring Individuals' Mental Representations of Complex Spatio-Temporal Decision Problems [J].
Arentze, Theo A. ;
Dellaert, Benedict G. C. ;
Timmermans, Harry J. P. .
ENVIRONMENT AND BEHAVIOR, 2008, 40 (06) :843-869
[6]   Belief network models of land manager decisions and land use change [J].
Bacon, PJ ;
Cain, JD ;
Howard, DC .
JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2002, 65 (01) :1-23
[7]   Structural, elicitation and computational issues faced when solving complex decision making problems with influence diagrams [J].
Bielza, C ;
Gómez, M ;
Ríos-Insua, S ;
del Pozo, JAF .
COMPUTERS & OPERATIONS RESEARCH, 2000, 27 (7-8) :725-740
[8]   Modeling challenges with influence diagrams: Constructing probability and utility models [J].
Bielza, C. ;
Gomez, M. ;
Shenoy, P. P. .
DECISION SUPPORT SYSTEMS, 2010, 49 (04) :354-364
[9]   The role of science in federal policy development on a regional to global scale: Personal commentary [J].
Bierbaum, R .
ESTUARIES, 2002, 25 (4B) :878-885
[10]   The use of Hugin® to develop Bayesian networks as an aid to integrated water resource planning [J].
Bromley, J ;
Jackson, NA ;
Clymer, OJ ;
Giacomello, AM ;
Jensen, FV .
ENVIRONMENTAL MODELLING & SOFTWARE, 2005, 20 (02) :231-242