Janssen, S., Hansen, J. G., Jorgensen, J., & Jørgensen, M. S. (2015). Operational database for storing and extracting data (Vol. 6).
Abstract: This deliverable lays out the work as done as part of MACSUR CropM on data, with the focus on improving data management and have shared data curation for future use. The issue was tackled with help from the MACSUR central hub coordination in the form of Jason Jargenson from University of Reading. The data management as proposed and implemented in this deliverable is very much a bottom up process, in which partners in a meeting in Spring 2013 in Aarhus investigated the best way forward for data management across activities in CropM.As a follow up to this, the work was mainly divided in three parts: 1. The Open Data Journal for Agricultural Research, mainly focused on long term data archival and citation of data sets, as input and outputs to the modelling work, as part of MACSUR, lead by Wageningen UR 2. The Geonetwork data catalog hosted at Aarhus Universitet, that allows for operational access and storage of data sets as part of the ongoing work, also for restricted access of the consortium, and as a first step to visualization, lead by Aarhus Universitet. 3. The work on rating data sets, that provides a tool for improving data set access in an early phase for connecting them to models, lead by Reading University. At the end of the deliverable some next steps are giving for data activities in the context of AgMIP and beyond. No Label
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Ewert, F., Rötter, R. P., Bindi, M., Webber, H., Trnka, M., Kersebaum, K., et al. (2015). Crop modelling for integrated assessment of risk to food production from climate change (Vol. 6).
Abstract: The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches. No Label
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