Improving data flow and integration in models assessing the impact of climate change on agriculture

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.