Lernen aus unvollständigen Daten
Dieser Use Case befasst sich mit der Frage, wie mit unvollständigen Daten aus Langzeitexperimenten umgegangen werden kann.
Partners:
Leibniz Centre for Agricultural Landscape Research (ZALF)
Information Centre for Life Sciences (ZB MED)
University of Bonn
World Agricultural Systems Center of Technical University Munich
Background
Data from long-term field agricultural field experiments (LTE) are of high interest in agrosystem sciences, especially for use in model validation and simulations. Although numerous LTEs with different factors are conducted both nationally and internationally (e.g., lte.bonares.de), their data have rarely been published and thus are not available for application in crop models. LTE studies often aim to identify the respective major constraints of crop yield in a particular setting, e.g., the effects of pests and diseases, water or nutrient deficiency, or detrimental climate conditions and try to understand long-term effects on soil carbon to support measures for enhancing soil carbon sequestration. However, usually only one or two factors are considered, providing only limited information about the respective setting, which can be a major obstacle in terms of using LTE data for more generic meta studies or the application of biophysical or machine learning yield models. Furthermore, the use of LTE data for crop model calibration and validation requires knowledge about the data’s quality and plausibility, which is often missing in published datasets.
Objectives
This UC aims to improve availability and to complement LTE data, which are of special interest for crop modellers or meta-studies. One activity in this use case will be the support of LTE experimentalists in publishing their datasets in existing FAIRagro infrastructures. Furthermore, to make LTE data applicable for generic studies, additional contextual information will be collected complementing LTE data. An iterative approach will be developed, automated as far as possible and published as a guideline on how to complement existing LTE datasets for further use. Additionally, a service will be developed to analyse and assess data quality and plausibility, which is needed to reuse LTE data in model applications.
Actions
- Support and standardize new LTE data publication
- Data curation supplementing already published incomplete LTE data
- Implement a service to assess data plausibility
More use cases
Use case 1: Nutzung der Wechselwirkungen zwischen Genotyp, Standort, Jahr und Bewirtschaftung für eine nachhaltige Pflanzenproduktion
Use case 2: Bewertung von Trade-offs für ein optimales Stickstoffmanagement bei Pflanzen
Use case 3: Optimierung von Schädlings- und Krankheitsdaten zur Förderung des integrierten Pflanzenschutz
Use case 4: Lernen aus unvollständigen Daten
Use case 5: Nichtinvasive Phänotypisierung mit autonomen Robotern
Use case 6: Automatisierte Datenflüsse für Pflanzenmodelle