The use of seeds dressed with pesticides is widespread and has important advantages. The most important disadvantage of this technique is that there is a possible emission of pesticide laden dust particles during sowing. The main factors affecting this emission are seed treatment quality, the environmental conditions and the seed drilling technology used. Due to the bee killing incidents observed in several countries, the industry has made important improvements with regard to seed treatment quality. In several countries, the use of air deflectors on sowing machines is made mandatory for certain products but they are not always used in practice. Additionally, there are still questions about their drift reducing effect and the optimal machine settings, the effect of environmental and soil conditions, the relevance of dust drift and its impact, etc. Therefore, a 4 year research project 'MASTER' was started which aims at quantifying the risk of dust drift and developing dust drift reducing measures and innovations in sowing techniques using an integrated experimental and modelling approach. In a first step, the dust drift potential of several treated seed batches was assessed using the Heubach method and information about the chemical and physical characteristics of abraded pesticide-coated seed particles was gathered. Then the airflow pattern and dust emission of different types of sowing machines at different settings were measured under controlled conditions. Using this information, a Computational Fluid Dynamics (CFD) sowing machine model was developed to simulate the dust behaviour in and around the machine in order to evaluate the effect of machine design and settings. A CFD dust drift emission model will simulate the behaviour of dust in the environment. Dust drift field experiments will be done to evaluate the effect of machine, seed treatment quality and environmental conditions on dust drift and to validate the CFD dust drift emission model.

Pesticide-laden dust emission from treated seeds during seed drilling using a combined experimental and modelling approach

248th National Meeting (2014)

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