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Bellocchi, G., Rivington, M., & Acutis, M. (2014). Deliberative processes for comprehensive evaluation of agro-ecological models. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Biophysical models are acknowledged for examining interactions of agro-ecological systems and fostering communication between scientists, managers and the public. As the role of models grows in importance, there is an increase in the need to assess their quality and performance (Bellocchi et al., 2010). However, the heterogeneity of factors influencing model outputs makes it difficult a full assessment of model features. Where models are used with or for stakeholders then model credibility depends not only on the outcomes of well-structured statistical evaluation but also less tangible factors may need to be addressed using complementary deliberative processes. To expand our horizons in the evaluation of crop and grassland models, approaches have been reviewed with emphasis on using combined metrics. Comprehensive evaluation of simulation models was developed to integrate expectations of stakeholders via a weighting system where lower and upper fuzzy bounds are applied to a set of evaluation metrics. A questionnaire-based survey helped understanding the multi-faceted knowledge and experience required and the substantial challenges posed by the deliberative process. MACSUR knowledge hub holds potential to advance in good modelling practice in relation with model evaluation (including access to appropriate software tools), an activity which is frequently neglected in the context of time-limited projects.
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Sanna, M., Acutis, M., & Bellocchi, G. (2014). Interrelationship between evaluation metrics to assess agro-ecological models. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
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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Rocca, A., Bellocchi, G., Giussani, A., Sanna, M., Perego, A., Fumagalli, M., et al. (2013). Correlation between evaluation model indicators..
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Acutis, M., & Bellocchi, G. (2013). Briefing on CropM-LiveM model intercomparison protocol..
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Perego, A., Giussani, A., Fumagalli, M., Sanna, M., Chiodini, M., Carozzi, M., et al. (2013). Crop rotation, fertilizer types and application timing affecting nitrogen leaching in nitrate vulnerable zones in Po Valley. Italian Journal of Agrometeorology, 3(2), 39–50.
Abstract: A critical analysis was performed to evaluate the potential risk of nitrate leaching towards groundwater in three Nitrate Vulnerable Zones (NVZs) of the Lombardia plain by applying the ARMOSA crop simulation model over a 20 years period (1988-2007). Each studied area was characterized by (i) two representative soil types, (ii) a meteorological data set, (iii) four crop rotations according to the regional land use, (iv) organic N load, calculated on the basis of livestock density. We simulated 3 scenarios defined by different fertilization time and amount of mineral and organic fertilizers. The A scenario involved no limitation in organic N application, while under the B and C scenarios the N organic amount was 170 and 250 kg N ha(-1)y(-1), respectively. The C scenario was compliant with the requirement of the 2012 Italian derogation, allowing only the use of organic manure with an efficiency greater than 65%. The model results highlighted that nitrate leaching was significantly reduced passing from the A scenario to the B and C ones (p<0.01); on average nitrogen losses decreased by up to 53% from A to B and up to 75% from A to C.
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