Our diverse field and in silico research is integrated through a conceptual and quantitative framework: the simulation model. Models are simplifications of a system designed to provide insights into the system functioning.

In our case the models are Cycles, CropSyst, and PIHM. Models are powerful research tools in two different ways. First, building a systems' model requires assembling model components in a coherent way. As a result we gain not only a deeper understanding of how the system of interest work: we also uncover knowledge gaps. These knowledge gaps are the seed of many research projects in our lab. Second, models help us test interactions and scenarios that are experimentally impractical (for example unusual or expensive agricultural management practices or multiple climate scenarios), or that are simply impossible to accomplish experimentally in a meaningful way (e.g. changes in diffuse radiation, changes in air humidity, re-arrangement of land use in watersheds).

Currently, Cycles, Cycles-L, CropSyst and FLUX-PIHM are part the backbone of a variety of projects: