Nitrate pollution of groundwater is still an issue of concern at many drinking water wells located in the Swiss lowlands, where agricultural areas are the main pollution source. Extensification measures (e.g. conversion of arable land to extensive grassland, reduction of vegetable/potato areas in favor of cereals) are generally considered to be effective to reduce nitrate leaching to groundwater. However, these measures are also associated with large losses in agricultural productivity and can thus only be implemented on small focused areas within contribution zones of drinking water wells. It is hypothesized here that the trade-offs between agricultural production and groundwater protection can better be managed if more nuanced mitigation strategies are implemented at a broader scale. Such strategies would target at an improved synchrony between plant nitrogen demands and soil nutrient availabilities (e.g. by inclusion of cover crops and optimizing crop rotations, through reduced soil management and demand-driven fertilization practices). Since evaluating the effects of such strategies is anything but trivial given the high complexity of the process interactions and the strong influence of climatic variability, it is the aim of this work to train a mechanistic field scale model that simulates soil water and nutrient dynamics at a field scale in response to soil, climate and management drivers (DAISY model). The calibration builds on an extensive dataset from the lysimeter station Zurich Reckenholz including detailed data since 2009 on nitrate leaching, seepage water generation, soil moisture, water tension, soil temperature, and crop yields for a series of different experiments including non-inversion tillage, cover cropping as well as different fertilization types and amounts. In this presentation, results from calibration and validation will be presented as well as preliminary results of scenario simulations.
