This chapter examines improving data flow and integration in models assessing the impact of climate change on agriculture. It starts by first describing model-data integration, focusing on multi-criteria calebration of mechanistic agro-ecosystem models. The chapter moves on to review informing spatio-temporal simulations through methods such as remote sensing, proximal sensing and distributed data. A section on the assimilation of data in spatio-temporal simulations is also provided, followed by an analysis of workflows for massive parallel computing. The chapter reviews model-model integration as well as granularity and modular design for model improvement, reuse, exchange and interoperability. A section on the concepts for distrubted modelling is also provided.