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Bellocchi, G., Rivington, M., & Acutis, M. (2014). Protocol for model evaluation (Vol. 3).
Abstract: This deliverable focuses on the development of methods for model evaluation in order to have unambiguous indications derived from the use of several evaluation metrics. The information about model quality is aggregated into a single indicator using a fuzzy expert system that can be applied to a wide range of model estimates where suitable test data are available. This is a cross-cutting activity between CropM (C1.4) and LiveM (L2.2). No Label
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Bellocchi, G., Ma, S., Köchy, M., & Braunmiller, K. (2013). Datasets classification and criteria for data requirements (Vol. 2).
Abstract: This deliverable focuses on the collation, screening, and consolidation of data for selected grassland sites in Europe and peri-Mediterranean regions. No Label
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Bellocchi, G., Ma, S., Köchy, M., & Braunmiller, K. (2013). Identified grassland-livestock production systems and related models (Vol. 2).
Abstract: This report describes grassland-livestock production systems, as selected for model-basedstudies. A list of grassland models was identified for evaluation against such datasets(WP2) and application at reference farm (WP3) and regions (WP4) across Europe and peri-European countries. No Label
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Rolinski, S., & Sætnan, E. (2013). Uncertainties in climate change prediction and modelling (Vol. 1).
Abstract: As models become increasingly complex and integrated, uncertainty among model parameters, variables and processes become critical for evaluating model outcomes and predictions. A framework for understanding uncertainty in climate modelling has been developed by the IPCC and EEA which provides a framework for discussion of uncertainty in models in general. Here we report on a review of this framework along with the results of a survey of sources of uncertainty in livestock and grassland models. Along with the identification of key sources of uncertainty in livestock and grassland modelling, the survey highlighted the need for a development of a common typology for uncertainty. When collaborating across traditionally separate research fields, or when communicating with stakeholders, differences in understanding, interpretation or emphasis can cause confusion. Further work in MACSUR should focus on improving model intercomparison methods to better understand model uncertainties, and improve availability of high quality datasets which can reduce model uncertainties. No Label
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Kipling, R., Topp, K., & Don, A. (2015). The availability of carbon sequestration data in Europe (Vol. 4).
Abstract: With growing interest in the carbon sequestration potential of soils, experimental research and mapping projects have produced a wealth of datasets in this subject area. However, the coverage, quality and scope of available data vary widely across Europe, and the extent to which these data are accessible to experimental researchers and modellers is also highly variable. This report describes the availability of soil carbon data at the global and European levels, and reviews the on-line resources for accessing these data and meta-data. The extent to which researchers in the field share findings, based on institutional links in projects and on-line resources, is investigated. Future priorities for research and data accessibility relating to carbon sequestration are discussed. Many soil data resources are available online. Global and European soil data portals draw together much information from across Europe, and include the outcomes of major soil carbon mapping exercises. However, much project and national research is not accessible through these portals, and information on datasets derived from many research initiatives is difficult or impossible to locate online. Data on carbon sequestration (carbon fluxes in soils) specifically is more limited, although some such datasets are available through the general soil data resources described. Improved clarity in the presentation of research, and work to link more national and sub-national data to European and global online resources is required, with initiatives such as GSIF (Global Soil Information Facility) active in encouraging direct reporting of soil-related data at the global level. Priorities for research on SOC stocks include measuring carbon storage below the topsoil (>30cm), improving records of SOC in peatlands, improving the number and distribution of samples available for Europe-wide soil carbon mapping, and developing recognised methodological standards to allow easier comparisons of datasets. In the field of carbon sequestration research specifically, priorities include linking long-term SOC data to historical land use, developing understanding of the movement of SOC between top-soil and sub-soil and increasing dialogue between modellers and empirical researchers to improve dynamic modelling of SOC. No Label
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