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

Publication date: 24/07/2023
Extent: 36 pages

Contributions by: Claas Nendel, Roland Baatz, Michael Berg-Mohnicke and Gohar Ghazaryan, Leibniz Centre for Agricultural Landscape Research (ZALF), Germany; Sander Janssen, Wageningen Environmental Research, The Netherlands; Pierre Martre, INRAE, France; and Cheryl Porter, University of Florida, USA

Chapter synopsis:

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.

DOI: 10.19103/AS.2022.0115.03