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A Bayesian network approach to knowledge integration and representation of farm irrigation: 3. Spatial application
Author(s) -
Robertson D. E.,
Wang Q. J.,
McAllister A. T.,
Abuzar M.,
Malano H. M.,
Etchells T.
Publication year - 2009
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2006wr005421
Subject(s) - bayesian network , intervention (counseling) , outcome (game theory) , bayesian probability , computer science , representation (politics) , environmental resource management , environmental science , mathematics , artificial intelligence , politics , political science , law , psychology , mathematical economics , psychiatry
Catchment managers are interested in understanding impacts of the management options they promote at both farm and regional scales. In this third paper of this series, we use Inteca‐Farm, a Bayesian network model of farm irrigation in the Shepparton Irrigation Region of northern Victoria, Australia, to assess the current condition of management outcome measures and the impact of historical and future management intervention. To help overcome difficulties in comprehending modeling results that are expressed as probability distributions, to capture uncertainties, we introduce methods to spatially display and compare the output from Bayesian network models and to use these methods to compare model predictions for three management scenarios. Model predictions suggest that management intervention has made a substantial improvement to the condition of management outcome measures and that further improvements are possible. The results highlight that the management impacts are spatially variable, which demonstrates that farm modeling can provide valuable evidence in substantiating the impact of catchment management intervention.