The goal of this framework is to rapidly bridge from prototypes to operational applications, enabling running and comparing different modelling solutions. A key aspect of the framework is the transparency which allows for quality evaluation of outputs in the various steps of the modelling workflow. The framework is based on framework-independent components, both for the modelling solutions and the graphical user's interfaces. The goal is not only to provide a framework for model development and operational use but also, and of no lesser importance, to provide a loose collection of objects re-usable either standalone or in different frameworks. The software is developed using Microsoft C# language in the .NET framework.
The simulation system is discretized in layers, each with its own features and requirements. Such layers are the Model Layer (ModL), where fine granularity models are implemented as discrete units, the Composition Layer (CompL), where basic models are linked into more complex, aggregated models, and the Configuration Layer (ConfL), which allows providing context specific parameterization (in the software sense) for operational use. Applications can span from simple console applications to user-interacting applications based on the model-view-controller pattern, in the simplest cases linking either directly to either the ModL or the CompL, or accessing model ConfL. In all cases, the component oriented architecture allows implementing a set of functionalities which impact on the richness of functionality of the system and on its transparency. Layers implement no top-down dependency among them, hence facilitating the independent reuse of tools, utilities, and model components in different applications and frameworks.
Applications can be built based on the libraries as in the following figure. The libraries can be extended implementing new models, as shown in the software development kits, and new libraries can be added.
Model components and tools can be autonomously downloaded with the SDK at the components' portal. Same for modelling solutions (the portal is being renovated).
Acces to modelling solutions as SaaS need to be requested.
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Semenov M.A. Donatelli M., Stratonovitch P., Chatzidaki E., Baruth B., 2010. ELPIS: a dataset of local-scale daily climate scenarios for Europe. Climate Research, 44: 3-15. https://www.int-res.com/articles/cr_oa/c044p003.pdf
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Bregaglio S., Hossard l., Cappelli G., Resmond R., Bocchi S., Barbier J-M., Ruget F., Delmotte S., 2017. Identifying trends and associated uncertainties in potential rice production under climate change in Mediterranean area. Agricultural and Forest Meteorology, 237-238: 219-232. https://www.sciencedirect.com/science/article/pii/S0168192317300485
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Donatelli M., Srivastava A.K., Duveiller G., Niemeyer S., Fumagalli D., 2015. Climate change impact and potential adaptation strategies under alternate realizations of climate scenarios for three major crops in Europe, Environ. Res. Lett. 10 http://iopscience.iop.org/1748-9326/10/7/075005/pdf/1748-9326_10_7_075005.pdf
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Bregaglio S., Orlando F., Forni E., De Gregorio T., Falzoi S., Boni C., Pisetta M., Confalonieri R., 2016. Development and evaluation of new modelling solutions to simulate hazelnut (Corylus avellana L.) growth and development. Ecol. Modell., 329: 86–99 https://www.sciencedirect.com/science/article/pii/S0304380016300655
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Maiorano A., Fanchini D., Donatelli M., 2014. MIMYCS. Moisture, a process-based model of moisture content in developing maize kernels. European Journal of Agronomy, 59: 86-95. http://www.sciencedirect.com/science/article/pii/S1161030114000781?via%3Dihub
Confalonieri R., Francone C., Cappelli G., Stella T., Frasso N., Carpani M., Bregaglio S., Acutis M., Tubiello, F.N., Fernandes E., 2012. A multi-approach software library for estimating crop suitability to environment.Computers and Electronics in Agriculture 90: 170-175. http://www.sciencedirect.com/science/article/pii/S0168169912002384
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Bregaglio S., Titone P., Cappelli G., Tamborini L., Mongiano G., Confalonieri R., 2016. Coupling a generic disease model to the warm rice simulator to assess leaf and panicle blast impacts in a temperate climate. European Journal of Agronomy, 76: 107-117. https://www.sciencedirect.com/science/article/pii/S1161030116300417
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Donatelli M., Bellocchi G., Habyarimana E., Bregaglio S., Baruth B., 2010. AirTemperature: Extensible Software Library to Generate Air Temperature Data, SRX Computer Science, vol. 2010 https://dx.doi.org/10.3814/2010/812789
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Donatelli M., Bellocchi G., Habyarimana E., Confalonieri R., Micale F., 2009. An extensible model library for generating wind speed data. Computers and Electronics in Agriculture, 69:165-170 http://www.sciencedirect.com/science/article/pii/S0168169909001549
Carlini L., Bellocchi G., Donatelli M., 2006. Rain, a software component to generate synthetic precipitation data. Agronomy Journal, 98: 1312-1317 https://www.agronomy.org/publications/aj/abstracts/98/5/1312
Donatelli M., Carlini L., Bellocchi G., 2006. A software component for estimating solar radiation. Environmental Modelling and Software 21, 3:411-416 http://www.sciencedirect.com/science/article/pii/S1364815205000794
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Bellocchi G., Acutis M., Fila G., Donatelli M., 2002. An indicator of solar radiation model performance based on a fuzzy expert system. Agron. J., 94: 1222–1233 Archived 2018-01-16 at the Wayback Machine. http://www.cracin.it/sipeaa/tools/RadEst/Irad_AgJ_Rev.pdf
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Donatelli M., Stöckle C.O., Nelson R.L., Bellocchi G., 2003. ET_CSDLL: a dynamic link library for the computation of reference and crop evapotranspiration. Agron J., 95: 1334-1336. https://dl.sciencesocieties.org/publications/aj/abstracts/95/5/1334?access=0&view=pdf
Acutis M., Donatelli M., Lanza Filippi G. 2008. PTF: an Extensible Component for Sharing and Using Knowledge on Pedo-Transfer Functions, International Congress on Environmental Modelling and Software. Proceedings of the iEMSs Fourth Biennial Meeting, Barcelona, Catalonia 7–10 July 2008: 759-765
PDF https://www.researchgate.net/publication/236120172_PTF_an_Extensible_Component_for_Sharing_and_Using_Knowledge_on_Pedo-Transfer_Functions/file/9c9605162979a4aae4.pdf
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Cappelli G., Confalonieri R., Romani M., Feccia S., Pagani M.A., Cappa C., Bocchi S., Bregaglio S., 2017. Boundaries and perspectives from a multi-model study on rice grain quality in Northern Italy. Field Crops Research, 215: 140-148. https://www.sciencedirect.com/science/article/pii/S0378429017305993
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Donatelli M., Confalonieri R., Cerrani I., Fanchini D., Acutis M., Tarantola S., Baruth B., 2009. LUISA (Library User Interface for Sensitivity Analysis ): a generic software component for sensitivity analysis of bio-physical models, in: 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, pp. 2377–2383. https://www.mssanz.org.au/modsim09/C3/donatelli_C3b.pdf
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Gilardelli C., Stella T., Frasso N., Cappelli G.A., Bregaglio S., Chiodini M.E., Scaglia B., Confalonieri R., 2016. WOFOST-GTC: A new model for the simulation of winter rapeseed production and oil quality. Field Crop Research, 197: 125-132. https://www.sciencedirect.com/science/article/pii/S0378429016302234
Stella T, Francone C, Yamaç SS, Ceotto E, Pagani V, Pilu R, Confalonieri R., 2015. Reimplementation and reuse of the Canegro model: from sugarcane to giant reed. Comput Electron Agr, 113: 193-202. https://www.sciencedirect.com/science/article/pii/S0168169915000514
Donatelli M., Rizzoli A., 2008. A design for framework-independent model components of biophysical systems. International Congress on Environmental Modelling and Software, Proceedings of the iEMSs Fourth Biennial Meeting, Barcelona, Catalonia 7–10 July 2008: 727-734 PDF https://www.researchgate.net/publication/236120180_A_Design_for_Framework-Independent_Model_Components_of_Biophysical_Systems/file/3deec5162982e7cbe6.pdf?
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