A semi-automated data integration workflow to enable high-throughput crop modelling

Read full text
Authors: Benjamin M. L. Leroy, Sebastian Burkhart, Lara Kempf, Joseph Gitahi, Marija Knezevic, Andreas Donaubauer, Muhammad Arslan, David Gackstetter, Giada Matheisen, Senthold Asseng, Thomas H. Kolbe, Patrick Noack
Cited by: 0

Abstract

Crop simulation models are important tools for yield projection and analysis but are rarely used for real-time decision support due to persistent bottlenecks in data management and processing workflows to generate input datasets. We present a blueprint for a semi-automated data management workflow that enables high-throughput simulation, integrating diverse source data into a rapid, scalable pipeline. The modular workflow leverages established standards in geospatial science, agronomy, and crop modeling to assemble high-quality inputs for crop models. The blueprint outlines the technical requirements to operationalize crop models, enabling broader applications as decision-support tools.

Fields of Study & Subjects

Smart Agriculture and AI Remote Sensing in Agriculture Scientific Computing and Data Management

Publication details

Journal / source
Gesellschaft für Informatik (GI)
Publication date
1 January 2026
Language
EN