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A three-day workshop on modelling weed population dynamics partially sponsored by the European Weed Research Society (EWRS) was held in October 2006 at Research Centre Flakkebjerg. 24 participants attended the workshop from 8 countries, mainly Europe and USA. The aim of the workshop was to gather some of those active and interested in the field, i.e. most of the participants had either published papers on weed population dynamic models or had a model under development. By gathering these people, it was our hope to learn from each other and possibly benefit by increasing the knowledge and use of each other's work. The EWRS saw the workshop as an opportunity to interconnect some of the work going on in (at least) three working groups: Weed Seed Germination and Early Growth, Crop-Weed Interactions and Physical and Cultural Weed Control. The preparations of the workshop were initiated by the keynote paper: "Field weed population dynamics: a review of model approaches and applications" presented by N. Holst at the EWRS Symposium in Bari, Italy, 2005, which has recently been published in Weed Research. In connection with this, a database with a bibliography of publications on weed population dynamic models was created, and is public online at http://www.blackwell-synergy.com/doi/abs/10.1111/j.1365-3180.2007.00534.x. At the workshop, ten weed population dynamics models were presented, and all of these were available on computers for the participants to try. Many of the models are freely available on the Internet, although some must be paid for, and some are still under development. The themes discussed at the workshop were presented under five headings: 1. Using models for understanding weed biology, 2. Modelling tools for understanding the dynamics of weed populations, 3. Using models for prediction purposes and decision support, 4. Understanding basic spatial-temporal dynamics through use of models and 5. Model analysis. A common understanding at the workshop, in respect

Weeds are a major problem in organic farming. Preventive as well as curative measures must be utilised to manage the weeds and avoid proliferation. Besides direct weed control measures, many different aspects of planning and management in the cropping system affect the proliferation of weeds. However, it has rarely been investigated how the whole system affects weed populations.

We evaluated models of weed population dynamics based on an analysis of their assumptions, biological rationale, flexibility, documentation, accessibility, demand for parameter estimation and documented validity. We arrived at general recommendations regarding which modelling approach should be applied in order to address different application domains.

Experiments with the germination of two weed species from different origins under different climatic conditions has led to modelling their germination and emergence.

For implementation of simple yield loss models into threshold-based weed management systems, a thorough validation is needed over a great diversity of sites. Yield losses by competition wsth Sinapis alba L. (white mustard) as a model weed, were studied in 12 experiments in sugar beet (Beta vulgaris L.) and in 11 experiments in spring wheat (Triticum aestivum L.). Most data sets were heller described by a model based on the relative leaf area of the weed than by a hyperbolic model based on weed density. This leaf area model accounted for (part of) the effect of different emerging times of the S. alba whereas the density model did not. A parameter that allows the maximum yield loss to be smaller than 100% was mostly not needed to describe the effects of weed competition. The parameter that denotes the competitiveness of the weed species with respect to the crop decreased the later the relative leaf area of the mustard was determined. This decrease could be estimated from the differences in relative growth rate of the leaf area of crop and S. alba. However, the accuracy of this estimation was poor. The parameter value of the leaf area model varied considerably between sites and years. The results strongly suggest that the predictive ability of the leaf area model needs to be improved before it can be applied in weed management systems. Such improvement would require additional information about effects of abiotic factors on plant development and morphology and the definition of a time window for predictions with an acceptable level of error.