Modelling plant disease and pest effects on crop performances.

Modelling the effects of plant diseases and pests on crop performance, starting with crop yield, is an important new challenge MACSUR wants to address. We have established a small "Pest and Disease" group within MACSUR, where we address this question, with particular emphasis on wheat and grapevine. In the case of wheat, a reference data set from Denmark is being used as a key reference set for wheat - septoria tritici blotch - leaf rust interaction. In a first step an ensemble of seven wheat growth models of different complexity implement defined mechanisms for damages through pest and diseases using field data of a "pest-free" treatment for crop model calibration and idealised (temporal) patterns of injuries represented by simplified disease progress curves. In a second step field data of non-protected field plots are provided together with disease severity data to test simulations of real disease effects on crop yield loss against observed data. In parallel, we collected information on available data for pest and disease impacts by a questionnaire to evaluate their suitability for crop growth as well as for pest and disease modelling. We shall report our results in this exercise, and outline the approach we envision to (i) continue this work on wheat, and (ii) expand it to other crops such as grapevine

Comparing the site sensitivity of crop models using spatially variable field data from precision agriculture.

Impacts of climate change on crop production depend strongly on the site conditions and properties. Vulnerability of crop production to changing climate conditions is highly determined by the ability of the site to buffer periods of adverse climatic situations like water scarcity or excessive rainfall. Therefore, the capability of models to reflect crop responses and water and nutrient dynamics under different site conditions is essential to assess climate impact on a regional scale. To test and improve sensitivity of models to various site properties such as soil variability and hydrological boundary conditions, spatial variable data sets from precision farming of two fields in Germany and Italy were provided to modellers. For the German 20 ha field soil and management data for 60 grid points for 3 years (2 years wheat, 1 year triticale) were provided. For the Italian field (12 ha) information for 100 grid points were available for three growing seasons of durum wheat. Modellers were asked to run their models using a) the model specific procedure to estimate soil hydraulic properties from texture using their standard procedure and use in step b) fixed values for field capacity and wilting point derived from soil taxonomy. Only the phenology and crop yield of one grid point provided for a basic calibration. In step c) information for all grid points of the first year (yield, soil water and mineral N content for Germany, yield, biomass and LAI for Italy were provided. Results of twelve models are compared against measured state variables analyzing their site response and consistency across crop and soil variables