Bayesian networks (Bn) are efficient tools for integrating different domain models, knowledge bases and data (e.g. economic, social, biological and hydrological domains) in a transparant, coherent and equitable way. The paper reports on the construction of a Bn to assess impacts of pesticide management instruments on groundwater and drinking water quality. Within the Bn agricultural management data, groundwater monitoring data and expert knowledge were combined in a focused dialogue among domain experts with outputs from socio-economic models. The instruments analysed were taxes on pesticides, pesticide-free buffer zones around field edges and water boreholes, and an increase in the proportion of organic farming (subsidies). The analysis showed that pesticide-free buffer zones around field margins and water boreholes are recommended. Bns indicates that benefits with respect to biodiversity and ground water quality exceed the social consts of the reduced agricultural productivity. However, a tax on herbicides (i.e. on weed killers) is the most effective instrument with respect to protection of the groundwater resource, including groundwater reserves not currently utilised for drinking water.
|Conference||XXIV Nordic Hydrological Conference. NORDIC WATER 2006. |
|Abbreviated title||NHC 2006|
|Period||6/08/06 → 9/08/06|
- Programme Area 2: Water Resources