Development of generic management rules for crop growth simulation models

International audience; Crop growth simulation models are powerful tools for assessing the effects of management practices or policy changes on the environment and agricultural production (Van Ittersum & Donatelli, 2003). Assessments can be made at a very fine scale, e.g., for one farm, or at a much coarser scale, e.g. assessing the impacts of the implementation of the Water Framework Directive on European agricultural systems. These models simulate the behaviour of crops or cropping systems taking into account soil, climate, agro-management and crop characteristics. An example of such a model is the Agricultural Production and Externalities Simulator (APES; Donatelli et al., 2009), which allows entire crop rotations to be simulated taking into account the specific characteristics of such systems. Whatever the scale of the assessment, the usually large data demands of these cropping systems models have to be met. Obtaining the required input data becomes a challenge when cropping systems models are applied to assessments for large and heterogeneous areas such as Europe. Soil and daily climate data are available from the European databases assembled by the Joint Research Centre of the European Union. However, little detailed data is available on the management of crops, i.e. the timing, amount and type of input used and the application method. This agro-management information refers to all operations relevant for crop production, for example tillage, sowing, fertilization and irrigation (crop protection is not taken into account). Surveys aimed at collecting detailed management information are cumbersome as they require the involvement of many experts and they only provide average information on agro-management that is in fact flexible and variable in practice as it depends on factors associated with soil, climate and crop conditions. In this paper, we describe an approach that has been applied to generate agro-management information for 21 crops and 19 regions in the EU on the basis of ‘easily-obtainable’ survey data and generic expert rules. We compare this agro-management information with detailed agromanagement information from a survey carried out in four EU regions. This research has been conducted within the SEAMLESS project (Van Ittersum et al., 2008).