Using Monte-Carlo simulations and Bayesian Networks to quantify and demonstrate the impact of fertiliser best management practices

Authors:
David Nash;Murray Hannah
Affiliations:
Future Farming Systems Research, Department of Primary Industries - Ellinbank Center, 1301 Hazeldean Rd. Ellinbank, Victoria 3821, Australia;Future Farming Systems Research, Department of Primary Industries - Ellinbank Center, 1301 Hazeldean Rd. Ellinbank, Victoria 3821, Australia
Venue:
Environmental Modelling & Software
Year:
2011

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Abstract

Nutrient exports from agriculture contribute to eutrophication of rivers and lakes. In many jurisdictions ''Best Management Practices'' (BMP's) are the cornerstone of mitigation efforts. In this paper we examine the use of Monte-Carlo simulations to combine fertiliser distribution, grazing and runoff data, and regression equations developed from field-scale monitoring, to estimate the maximal effect of fertiliser BMP's on phosphorus (P) exports. The simulation data are then compared with a Bayesian Network that can be used to quickly evaluate the effects of different management scenarios on P exports and communicate those results to landholders. Both techniques demonstrate that for systems similar to those for which the regression equations were derived, improved fertiliser management is unlikely to have a major impact on Total P (TP) exports (i.e.