Learning from incomplete data
This use cases addresses challenges how to deal with incomplete data on the case of data from long-term experiments.
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
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.
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.
- 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: Exploiting genotype × location × year × management interactions for sustainable crop production
Use case 2: Assessing tradeoffs for optimal crop nitrogen management
Use case 3: Streamlining pest and disease data to advance integrated pest management
Use case 4: Learning from incomplete data
Use case 5: Noninvasive phenotyping with autonomous robots
Use case 6: Automated data flows for crop simulation models