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Kipling, P., Saetnan, E., Scollan, D., Bartley, D., Bellocchi, G., Hutchings, J., et al. (2014). Modelling livestock and grassland systems under climate change..
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Ben Touhami, H., & Bellocchi, G. (2014). Bayesian calibration of the Pasture Simulation model (PaSim) to simulate emissions from long-term grassland sites: a European perspective..
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Bellocchi, G., & Ehrhardt, F. (2014). Collaborations with initiatives and projects outside MACSUR and AgMIP – Grassland & Livestock..
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Bellocchi, G., & Ma, S. (2014). Results of uncalibrated grassland model runs (Vol. 3).
Abstract: This deliverable focuses on the some illustrative results obtained with the grassland models selected (D-L2.1.1) to simulate biomass and flux data from grassland sites in Europe and peri-Mediterranean regions (D-L2.1.1 and D-L2.1.2). This is a blind exercise, carried out without model calibration. The complete set of results will include simulations from calibrated models. The results shown are illustrative of the methodology adopted for grassland model intercomparison in MACSUR. The insights gained from this ongoing study are relevant for some crop and vegetation models, which in some cases proved comparable to grassland-specific models to simulate biomass data from managed grasslands. The results reported here cannot be considered conclusive. Additional results will be published as they become available together with calibration results, as well as the comprehensive evaluation of models with fuzzy logic-based indicators. No Label
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Bellocchi, G., Rivington, M., & Acutis, M. (2014). Protocol for model evaluation (Vol. 3).
Abstract: 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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