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Wallach, D.; Rivington, M. |
Title |
A framework for assessing the uncertainty in crop model predictions |
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2014 |
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FACCE MACSUR Reports |
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3 |
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D-C4.1.2 |
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It is of major importance in modeling to understand and quantify the uncertainty in model predictions, both in order to know how much confidence to have in those predictions, and as a first step toward model improvement. Here we show that there are basically three different approaches to evaluating uncertainty, and we explain the advantages and drawbacks of each. This is a necessary first step toward developing protocols for evaluation of uncertainty and so obtaining a clearer picture of the reliability of crop models. No Label |
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MA @ admin @ |
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2231 |
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Author |
Kersebaum, K.; C, |
Title |
Results of uncalibrated model runs available (ROTATIONEFFECTS) |
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2014 |
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FACCE MACSUR Reports |
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3 |
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D-C1.5 |
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The study ROTATIONEFFECT aims to compare the output of different models simulating field data sets with multi-year crop rotations including different treatments. Data sets for 5 locations in Europe were distributed to 19 interested modeller groups comprising a total of 201 crop growth seasons. In a first step only minimal information for calibration were provided to the modellers. In total 14 modelling teams sent their “uncalibrated” results as single-year calculations and/or calculations of rotation depending on the capability of the model. 7-10 models were capable to run the rotations as continuous runs. Up to 12 models provided single year simulations of at least one crop. Comparing results of models which provided both single year and continuous runs, show a little lower root mean square error for the continuous rotations runs. Cereal crop yields were generally better simulated than tuber/beet yields. Additionally, the models’ response to various treatments (irrigation/rainfed, nitrogen level, CO2 level, residue management/ tillage, catch crops) were compared to observed differences. First indicators of model performance have been developed and presented at international conferences. No Label |
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MA @ admin @ |
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2230 |
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Bellocchi, G.; Rivington, M.; Acutis, M. |
Title |
Protocol for model evaluation |
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Report |
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2014 |
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FACCE MACSUR Reports |
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3 |
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D-L2.2/D |
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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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MA @ admin @ |
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2229 |
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Bellocchi, G.; Martin, R.; Shtiliyanova, A.; Ben Touhami, H.; Carrère, P. |
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Vul’Clim – Climate change vulnerability studies in the region Auvergne (France) |
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2014 |
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FACCE MACSUR Reports |
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3 |
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Sp3-6 |
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The region Auvergne (France) is a major livestock territory in Europe (beef and dairy cattle with permanent grasslands), with a place in climate change regional studies assisting policy makers and actors in identifying adaptation and mitigation measures. Vul’Clim is a research grant (Bourse Recherche Filière) of the region Auvergne (February 2014-September 2015) to develop model-based vulnerability analysis approaches for a detailed assessment of climate change impacts at regional scale. Its main goal is the creation of a computer-aided platform for vulnerability assessment of grasslands, in interaction with stakeholders from a cluster of eco-enterprises. A modelling engine provided by the mechanistic, biogeochemical model PaSim (Pasture Simulation model) is the core of the platform. An action studies the changes of scales by varying the granularity of the data available at a given scale (e.g. climate data supplied by global scenarios) to let them being exploited at another scale (e.g. high-resolution pixels). Another action is to develop an assessment framework linking modelling tools to entry data and outputs, including a variety of components: data-entry manager at different spatial resolutions; automatic computation of indicators; gap-filling and data quality check; simulation kernel with the model(s) used; device to represent results as maps and integrated indicators. No Label |
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MA @ admin @ |
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2223 |
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Author |
Sanna, M.; Acutis, M.; Bellocchi, G. |
Title |
Interrelationship between evaluation metrics to assess agro-ecological models |
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Report |
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2014 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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3 |
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Sp3-5 |
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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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MA @ admin @ |
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2222 |
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