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Rocca, A.; Bellocchi, G.; Giussani, A.; Sanna, M.; Perego, A.; Fumagalli, M.; Carozzi, M.; Chiodini, M.; Bregaglio, S.; Confalonieri, R.; Acutis, M. |
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Correlation between evaluation model indicators |
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2013 |
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CropM |
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XVI National congress of Agrometeorology. Firenze, Italy, 2013-06-04 to 2013-06-06 |
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MA @ admin @ |
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2748 |
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Perego, A.; Sanna, M.; Bellocchi, G.; Acutis |
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Simulazione di flussi di carbonio da ecosistemi pratensi: applicazione del modello colturale ARMOSA al sito di Laqueuille (Francia) |
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2014 |
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LiveM |
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XLIII Congress of the Italian Society for Agronomy, 2014-09-17 to 2014-09-19 |
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MA @ admin @ |
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2713 |
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Ma, S.; Ben, T., H.; Lellei-Kovács, E.; Barcza, Z.; Hidy, D.; Bellocchi, G. |
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Title |
Grassland model intercomparison of the knowledge hub MACSUR: illustrative results from the models PaSim and Biome-BGC MuSo |
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2014 |
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LiveM |
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XIII ESA congress, Debrecen, Hungary, 2014-08-25 to 2014-08-29 |
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MA @ admin @ |
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2617 |
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Kipling, P.; Saetnan, R.; van den Pol-van Dasselaar, A.; Scollan, D.; Bartley, D.; Bellocchi, G.; Hutchings, J.; Dalgaard, T. |
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Modelling interactions between climate and livestock pathogen transmission, Pirbright Institute, UK |
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2014 |
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Workshop: Modelling interactions between climate and livestock pathogen transmission, 2014-01-22 to 2014-01-22 |
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no |
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MA @ admin @ |
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2534 |
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Author |
Bellocchi, G. |
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Title |
Fuzzy-logic based multi-site crop model evaluation |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-5 |
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The most common way to evaluate simulation models is to quantify the agreement between observations and simulations via statistical metrics such as the root mean squared error and the linear regression coefficient of determination. It is agreed that the aggregation of metrics of different nature intro integrated indicators offers a valuable way to assess models. Expanded notions of model evaluation that have recently emerged, based on the trade-off between properties of the model and agreement between predictions and actual data under contrasting conditions, integrate sensitivity analysis measures and information criteria for model selection, as well as concepts of model robustness, and point to expert judgments to explore the importance of different metrics. As a FACCE MACSUR CropM-LiveM action, a composite indicator (MQIm: Model Quality Indicator for multi-site assessment) was elaborated, by a group of specialists, on metrics commonly used to evaluate crop models (with extension to grassland models) while also integrating aspects of model complexity and stability of performances. The indicator, based on fuzzy bounds applied to a set of weighed metrics, was first revised by a broader group of modellers and then assessed via questionnaire survey of scientists and end-users. We document a crop model evaluation in Europe and assess to what extent the MQIm reflects the main components of model quality and supports inferences about model performances. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2120 |
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