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Ma, S.; Acutis, M.; Barcza, Z.; Ben, T., H.; Doro, L.; Hidy, D.; Köchy, M.; Minet, J.; Lellei-Kovács, E.; Perego, A.; Rolinski, S.; Ruget, F.; Seddaiu, G.; Wu, L.; Bellocchi, G. |
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The grassland model intercomparison of the MACSUR (Modelling European Agriculture with Climate Change for Food Security) European knowledge hub |
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2014 |
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LiveM |
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7th International Congress on Environmental Modelling and Software, 2014-06-15 to 2014-06-19 |
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MA @ admin @ |
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2618 |
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Author |
Acutis, M.; Bellocchi, G. |
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Title |
Briefing on CropM-LiveM model intercomparison protocol |
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2013 |
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CropM |
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JPI FACCE MACSUR CropM and LiveM cross-cutting activity Helsinki, Finland, 2013-05-06 to 2013-05-06 |
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MA @ admin @ |
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2272 |
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Bellocchi, G.; Rivington, M.; Acutis, M. |
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Title |
Protocol for model evaluation |
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Report |
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2014 |
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FACCE MACSUR Reports |
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3 |
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D-L2.2/D |
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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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no |
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MA @ admin @ |
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2229 |
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Sanna, M.; Acutis, M.; Bellocchi, G. |
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Title |
Interrelationship between evaluation metrics to assess agro-ecological models |
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2014 |
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FACCE MACSUR Reports |
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3 |
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Sp3-5 |
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When evaluating the performances of simulation models, the perception of the quality of the outputs may depend on the statistics used to compare simulated and observed data. In order to have a comprehensive understanding of model performance, the use of a variety of metrics is generally advocated. However, since they may be correlated, the use of two or more metrics may convey the same information, leading to redundancy. This study intends to investigate the interrelationship between evaluation metrics, with the aim of identifying the most useful set of indicators, for assessing simulation performance. Our focus is on agro-ecological modelling. Twenty-three performance indicators were selected to compare simulated and observed data of four agronomic and meteorological variables: above-ground biomass, leaf area index, hourly air relative humidity and daily solar radiation. Indicators were calculated on large data sets, collected to effectively apply correlation analysis techniques. For each variable, the interrelationship between each pair of indicators was evaluated, by computing the Spearman’s rank correlation coefficient. A definition of “stable correlation” was proposed, based on the test of heterogeneity, allowing to assess whether two or more correlation coefficients are equal. An optimal subset of indicators was identified, striking a balance between number of indicators, amount of provided information and information redundancy. They are: Index of Agreement, Squared Bias, Root Mean Squared Relative Error, Pattern Index, Persistence Model Efficiency and Spearman’s Correlation Coefficient. The present study was carried out in the context of CropM-LiveM cross-cutting activities of MACSUR knowledge hub. No Label |
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MA @ admin @ |
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2222 |
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Sándor, R.; Ma, S.; Acutis, M.; Barcza, Z.; Ben Touhami, H.; Doro, L.; Hidy, D.; Köchy, M.; Lellei-Kovács, E.; Minet, J.; Perego, A.; Rolinski, S.; Ruget, F.; Seddaiu, G.; Wu, L.; Bellocchi, G. |
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Title |
Uncertainty in simulating biomass yield and carbon–water fluxes from grasslands under climate change |
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Journal Article |
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2015 |
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Advances in Animal Biosciences |
Abbreviated Journal |
Advances in Animal Biosciences |
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6 |
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01 |
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49-51 |
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Keywords |
grassland productivity; carbon balance; model simulation; uncertainty; sensitivity |
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English |
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2040-4700 |
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CropM, LiveM, ft_macsur |
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MA @ admin @ |
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4651 |
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