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Sanna, M., Bellocchi, G., Fumagalli, M., & Acutis, M. (2015). A new method for analysing the interrelationship between performance indicators with an application to agrometeorological models. Env. Model. Softw., 73, 286–304.
Abstract: The use of a variety of metrics is advocated to assess model performance but correlated metrics may convey the same information, thus leading to redundancy. Starting from this assumption, a method was developed for selecting, from among a collection of performance indicators, one or more subsets providing the same information as the entire set. The method, based on the definition of “stable correlation”, was applied to 23 performance indicators of agrometeorological models, calculated on large sets of simulated and observed data of four agronomic and meteorological variables: above-ground biomass, leaf area index, hourly air relative humidity and daily solar radiation. Two subsets were determined: {Squared Bias, Root Mean Squared Relative Error, Coefficient of Determination, Pattern Index, Modified Modelling Efficiency}, {Persistence Model Efficiency, Root Mean Squared Relative Error, Coefficient of Determination, Pattern Index}. The method needs corroboration but is statistically founded and can support the implementation of standardized evaluation tools. (C) 2015 Elsevier Ltd. All rights reserved.
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Pulina, A., Bellocchi, G., Seddaiu, G., & Roggero, P. P. (2016). Scenario analysis of alternative management options on the forage production and greenhouse gas emissions in Mediterranean grasslands. (Vol. 116, pp. 263–266).
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Sanna, M., Bellocchi, G., Fumagalli, M., & Acutis, M. Interrelationship and optimal choice of indicators to evaluate performance of agrometeorological models.
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Sándor, R., Ma, S., Acutis, M., Barcza, Z., Ben, T., H., Doro, L., et al. (2014). Uncertainty in simulating biomass yield and carbon-water fluxes from Euro-Mediterranean grasslands under climate changes..
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Saetnan, E., Kipling, P., Scollan, D., Bartley, D., Bellocchi, G., Hutchings, J., et al. (2014). MACSUR LiveM – a knowledge-hub for integrated modelling of climate change impacts on livestock production systems: lessons learned and future developments..
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