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Janssen, S. (2015). Inventory of data and data sharing mechanism for model linking and scaling exercises (Vol. 6).
Abstract: This deliverable lays out the work as done as part of MACSUR CropM on ‘Inventory of data and data sharing mechanism for model linking and scaling exercises’. In summary not much work was done, as it was found that there was not real demand for the activity in this task. The task in itself was servicing the other work as part of MACSUR, and as the service was not in demand, it was decided to take a low profile and wait for specific requests by partners for data in relation to model linking and upscaling. No Label
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Hoffmann, H., & Ewert, F. (2015). Review on scaling methods for crop models (Vol. 6).
Abstract: Agricultural systems cover a range of organisational levels and spatial and temporal scales. To capture multi-scale problems of sustainable management in agricultural systems, Integrated assessment modelling (IAM) including crop models is often applied which require methods of scale changes (scaling methods). Scaling methods, however, are often not well understood and are therefore sources of uncertainty in models. The present report summarizes scaling methods as developed and applied in recent years (e.g. in SEAMLESS-IF and MACSUR) in a classification scheme based on Ewert et al. (2011, 2006). Scale changes refer to different spatial, temporal and functional scales with changes in extent, resolution, and coverage rate. Accordingly, there are a number of different scaling methods that can include data extrapolation, aggregation and disaggregation, sampling and nested simulation. Comparative quantitative analysis of alternative scaling methods are currently under way and covered by other reports in MACSUR and several publications (e.g. Ewert et al., 2014; Hoffmann et al., 2015; Zhao et al., 2015). The following classification of scaling methods assists to structure such analysis. Improved integration of scaling methods in IAM may help to overcome modelling limitations that are related to high data demand, complexity of models and scaling methods considered. No Label
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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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Sharif, B., Mankowski, D., Kersebaum, K. C., Trnka, M., Schelde, K., & Olsesen, J. E. (2015). Empirical analysis on crop-weather relationships (Vol. 6).
Abstract: There have been several studies, where process-based crop models are developed, used and compared in order to project crop production and corresponding model uncertainties under climate change. Despite many advances in this field, there are some correlations between climate variables and crop growth, such as pest and diseases, that is often absent in process-based models. Such relationships can be simulated using empirical models. In this study, several statistical techniques were applied on winter oilseed rape data collected in some European countries. The empirical models were then used to predict yield of winter oilseed rape in the field experiments during more than 20 years, up to 2013. Results suggest that newly developed regression techniques such as shrinkage methods work well both in yield projections and finding the influential climatic variables. Many of regression techniques agree in terms of yield prediction; however, choice of significant climate variables is rather sensitive to the choice of regression technique. No Label
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Janssen, S., Houtkamp, J., De Groot, H., & Schils, R. (2015). Online web tool for data visualization (Vol. 6).
Abstract: This deliverable lays out the work as done as part of MACSUR CropM on data, with the focus on providing a web tool for visualization of model output. It was decided early on that not a specific MACSUR web tool would be developed as part of MACSUR for phase 1, and mostly results would be visualized in other available tools, such as the Global Yield Gap Atlas, which are recognised resources for visualizations. Only in relationship to the MACSUR Geonetwork data catalog hosted at Aarhus University some developments where started. Operationally speaking, most data was still being generated during phase 1, so there was not enough to visualize on specific websites and partners did not commit financial resources to their development, and only in kind was available. No Label
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