Farm modelling has been widely used over the past ten years for the assessment of agricultural systems in face of policy changes, technological innovation, economic and climate changes. Beyond the various modelling methods (optimisation, rule-based, agent-based...), these models are framed by their objectives -defining input data and users- which can be: policy assessment, technological innovation assessment, farming systems resilience studies, optimisation of water management at regional level, agricultural landscape design, a " boundary object " for strategic thinking in a stakeholder arena. Indeed these farm modelling tools are still poorly used for the design of innovative cropping systems which remains mostly based on field level experiments and modelling. Nevertheless driving forces and assessment criteria for the design of sustainable cropping systems are increasingly expressed at larger scales than field or even farm. For exemple most environmental services are provisionned at landscape (e.g. biodiversity) or whatershed levels (e.g. water quality), at regional level (e.g. climate mitigation) and in more global socio-systems (e.g. food security, rural development). On the other hand, a large part of the functions supporting these services are operating at field level where the interactions between farmer's decisions (crop selection, combination and management) and biophysical processes (soil, plants, pests, weeds and diseases) are occuring. A large part of these biophysical processes also occur at landscape level (biodiversity, water flows…) but they require information at field level to be upscaled in a landscape mosaïc. In-between these two levels (field and region) the farm level plays a key role in agricultural systems simulation: (i) it is the decision level of farm activities (crops, livestock, trees…) further applied to field level ; (ii) it is the first level of expression of socio-economical services (labour, income, food production…) and (iii) farm