| Records |
| Author |
Rocca, A.; Bellocchi, G.; Giussani, A.; Sanna, M.; Perego, A.; Fumagalli, M.; Carozzi, M.; Chiodini, M.; Bregaglio, S.; Confalonieri, R.; Acutis, M. |
| Title |
Correlation between evaluation model indicators |
Type |
Conference Article |
| Year |
2013 |
Publication |
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Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
CropM |
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Conference |
XVI National congress of Agrometeorology. Firenze, Italy, 2013-06-04 to 2013-06-06 |
| Notes |
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Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
2748 |
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| Author |
Sanna, M.; Bellocchi, G.; Fumagalli, M.; Acutis, M. |
| Title |
Interrelationship and optimal choice of indicators to evaluate performance of agrometeorological models |
Type |
Manuscript |
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Abbreviated Journal |
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Issue |
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Pages |
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| Keywords |
CropM; LiveM |
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Conference |
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| Notes |
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Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
2790 |
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| Author |
Perego, A.; Sanna, M.; Bellocchi, G.; Acutis |
| Title |
Simulazione di flussi di carbonio da ecosistemi pratensi: applicazione del modello colturale ARMOSA al sito di Laqueuille (Francia) |
Type |
Conference Article |
| Year |
2014 |
Publication |
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Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
LiveM |
| Abstract |
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Conference |
XLIII Congress of the Italian Society for Agronomy, 2014-09-17 to 2014-09-19 |
| Notes |
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Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
2713 |
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| Author |
Sanna, M.; Acutis, M.; Bellocchi, G. |
| Title |
Interrelationship between evaluation metrics to assess agro-ecological models |
Type |
Report |
| Year |
2014 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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| Volume |
3 |
Issue |
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Pages |
Sp3-5 |
| Keywords |
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| Abstract |
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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| Notes |
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Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
2222 |
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| Author |
Sanna, M.; Acutis, M.; Bellocchi, G. |
| Title |
Interrelationship between evaluation metrics to assess agro-ecological models |
Type |
Conference Article |
| Year |
2014 |
Publication |
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Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
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. |
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Abbreviated Series Title |
FACCE MACSUR Mid-term Scientific Conference |
| Series Volume |
3(S) Sassari, Italy |
Series Issue |
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Edition |
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ISBN |
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Expedition |
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Conference |
FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
| Notes |
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Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5092 |
| Permanent link to this record |
