Models are increasingly being used to investigate agro-ecosystems dynamics, although processes interacting at different scales remain difficult to consider. When upscaled or downscaled based on aggregation or disaggregation methods, information is generally distorted. This study explores agro-ecosystem modelling using an interaction graph-based modelling approach that explicitly link elements at different scales without up or downscaling. The study area/time frame is the cotton region of West Burkina Faso over the last fifteen years. Field, plot, farm and climate entities are linked in graphs that evolve according to functions computed along different time steps. Three main processes and their interrelations are simulated, occurring at different spatial and temporal scales: crop area expansion, crop rotation and crop production. Three simulation examples are presented to illustrate the analytical possibilities allowed by the approach. These examples test i) the geographical distribution of plots as a means to face climatic risks, ii) the effect of fallowing practice in a spatially constrained cotton dominated landscape and iii) the consequences of reduced access to credit for farmers to buy fertilizers. Model outputs enable quantifying and mapping the respective effects of processes at different scales. Results show that modelling across scales is achievable without resorting to methods of aggregation or disaggregation, which opens new perspectives in multi-scalar analyses of agro-ecosystems that link land production and land use and land cover.