BioMA

BioMA (Biophysical Model Applications) is a public domain software framework designed and implemented for developing, parameterizing and running modelling solutions based on biophysical models in the domains of agriculture and environment.Donatelli M., Cerrani I., Fanchini D., Fumagalli. D., Rizzoli A. 2012. Enhancing Model Reuse via Component-Centered Modeling Frameworks: the Vision and Example Realizations. In: International Environmental Modelling and Software Society (iEMSs), 2012 International Congress on Environmental Modelling and Software, Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig, Germany, R. Seppelt, A.A. Voinov, S. Lange, D. Bankamp (Eds.) [http://www.iemss.org/iemss2012/proceedings/D3_1_0847_Donatelli_et_al.pdf PDF] It is based on discrete conceptual units codified in freely extensible software components .Donatelli M., Rizzoli A. 2008 A design for framework-independent model components of biophysical systems International Congress onEnvironmental Modelling and Software iEMSs 2008 Proceedings of theiEMSs Fourth Biennial Meeting, Barcelona, Catalonia 7–10 July 2008: 727-734 [http://www.iemss.org/iemss2008/uploads/Main/Vol2-iEMSs2008-Proceedings.pdf PDF]

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 framework is a development of the work carried out under the APESDonatellli M., G. Russell, A.E Rizzoli, et al. 2010 A component-based framework for simulating agricultural production and externalities. [https://www.springer.com/life+sciences/agriculture/book/978-90-481-3618-6 In: Environmental and agricultural modelling: Integrated approaches for policy impact assessment, F.Brouwer and M. van Ittersum editors, Springer, 63-108] [http://www.apesimulator.org/ task] of the 6th EU Framework Program {{usurped|1=[https://web.archive.org/web/20050211042918/http://www.seamless-ip.org/ SEAMLESS project]}}.

Deployments of the platform and its tools and components have been used:

• to create weather datasets for biophysical simulation,:Donatelli M., Fumagalli D., Zucchini A., Duveiller G., Nelson R.L., Baruth B. 2012. A EU27 Database of Daily Weather Data Derived from Climate Change Scenarios for Use with Crop Simulation Models. In: International Environmental Modelling and Software Society (iEMSs), 2012 International Congress on Environmental Modelling and Software, Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig, Germany, R. Seppelt, A.A. Voinov, S. Lange, D. Bankamp (Eds.) [http://www.iemss.org/iemss2012/proceedings/C3_0815_Donatelli_et_al.pdf PDF]Duveiller G., Donatelli M., Fumagalli D., Zucchini A., Baruth B., 2015. A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios. [https://link.springer.com/article/10.1007/s00704-015-1650-4 Theoretical and Applied Climatology, 127: 573-585.]Semenov M.A. Donatelli M., Stratonovitch P., Chatzidaki E., Baruth B., 2010. ELPIS: a dataset of local-scale daily climate scenarios for Europe. [https://www.int-res.com/articles/cr_oa/c044p003.pdf Climate Research, 44: 3-15.]
• to assess the impact on crop production in Europe,Donatelli M., Duveiller G., Fumagalli D., Srivastava A., Zucchini A., Angileri V., Fasbender D., Loudjani P., Kay S., Juskevicius V., Toth T., Haastrup P., Míbarek R., Espinosa M., Ciaian P., Niemeyer S. 2011 Assessing Agriculture Vulnerabilities for the design of Effective Measures for Adaption to Climate Change AVEMAC project. [http://mars.jrc.ec.europa.eu/mars/content/download/2677/13635/file/AVEMAC_FinalReport.pdf PDF]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. [https://www.sciencedirect.com/science/article/pii/S0168192317300485 Agricultural and Forest Meteorology, 237-238: 219-232.] and adaptation,Donatelli M., Srivastava A., Duveiller G., Niemeyer S. 2012. Estimating Impact Assessment and Adaptation Strategies under Climate Change Scenarios for Crops at EU27 Scale. In: International Environmental Modelling and Software Society (iEMSs), 2012 International Congress on Environmental Modelling and Software, Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig, Germany, R. Seppelt, A.A. Voinov, S. Lange, D. Bankamp (Eds.) [http://www.iemss.org/iemss2012/proceedings/A6_0757_Srivastava_et_al.pdf PDF]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, [http://iopscience.iop.org/1748-9326/10/7/075005/pdf/1748-9326_10_7_075005.pdf Environ. Res. Lett. 10]
• to simulate the development of soil pathogens under climate change,Manici L., Donatelli M., Fumagalli D., Lazzari A., Bregaglio S. 2012 Potential Response of Soil-Borne Fungal Pathogens Affecting Crops to a Scenario of Climate Change in Europe. In: International Environmental Modelling and Software Society (iEMSs), 2012 International Congress on Environmental Modelling and Software, Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig, Germany, R. Seppelt, A.A. Voinov, S. Lange, D. Bankamp (Eds.) [http://www.iemss.org/iemss2012/proceedings/C1_0703_Manici_et_al.pdf PDF]Manici L. M., Bregaglio S., Fumagalli D., Donatelli M. 2014. Modelling soil borne fungal pathogens of arable crops under climate change, [https://link.springer.com/article/10.1007/s00484-014-0808-6 International Journal of Biometereology]
• to reproduce the growth and development of tree species,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. [https://www.sciencedirect.com/science/article/pii/S0304380016300655 Ecol. Modell., 329: 86–99]
• to estimate the survival of insects damaging maize under climate changeMaiorano A, Cerrani I, Fumagalli D, Donatelli M, 2013. New biological model to manage the impact of climate warming on maize corn borers. [https://link.springer.com/article/10.1007%2Fs13593-013-0185-2 Agronomy for Sustainable Development],Maiorano A., Bregaglio S., Donatelli M., Fumagalli D., Zucchini A., 2012. Comparison of modelling approaches to simulate the phenology of the European corn borer under future climate scenarios. [https://www.sciencedirect.com/science/article/pii/S0304380012001615 Ecological Modelling, 245: 65-74.]Maiorano A., Fanchini D., Donatelli M., 2014. MIMYCS. Moisture, a process-based model of moisture content in developing maize kernels. [http://www.sciencedirect.com/science/article/pii/S1161030114000781?via%3Dihub European Journal of Agronomy, 59: 86-95.]
• to estimate crop suitability to environment,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.[http://www.sciencedirect.com/science/article/pii/S0168169912002384 Computers and Electronics in Agriculture 90: 170-175.]
• to perform modelling solutions comparison at sub-model level,Donatelli M., Bregaglio S., Confalonieri R., De Mascellis R., Acutis M., 2014. A generic framework for evaluating hybrid models by reuse and composition – A case study on soil temperature simulation, ISSN 1364-8152, [https://dx.doi.org/10.1016/j.envsoft.2014.04.011 Environmental Modelling & Software]
• to develop a library of reusable models for crop development and growth,Stella T., Frasso N., Negrini G., Bregaglio S., Cappelli G., Acutis M., Confalonieri R., 2014. Model simplification and development via reuse, sensitivity analysis and composition: A case study in crop modelling. Environmental Modelling & Software, 59:44–58 [https://dx.doi.org/10.1016/j.envsoft.2014.05.007]Bregaglio S., Frasso N., Pagani V., Stella T., Francone C., Cappelli G., Acutis M., Balaghi R., Ouabbou H., Paleari L., Confalonieri R., 2015. New multi-model approach gives good estimations of wheat yield under semi-arid climate in Morocco. [https://link.springer.com/article/10.1007/s13593-014-0225-6 Agronomy for Sustainable Development, 35: 157-167]
• to estimate the impact of climate change on crop production in Latin America,Confalonieri R., Donatelli M., Bregaglio S., Tubiello F.N., Fernandes E. 2012. Agroecological Zones Simulator (AZS): A component based, open-access, transparent platform for climate change Crop productivity impact assessment in Latin America. In: International Environmental Modelling and Software Society (iEMSs), 2012 International Congress on Environmental Modelling and Software, Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig, Germany, R. Seppelt, A.A. Voinov, S. Lange, D. Bankamp (Eds.) [http://www.iemss.org/iemss2012/proceedings/C3_0915_Confalonieri_et_al.pdf PDF]
• to simulate fungal infectionsBregaglio, S.; Donatelli, M.; Confalonieri, R. 2013. Fungal infections of rice, wheat, and grape in Europe in 2030-2050.[https://link.springer.com/article/10.1007%2Fs13593-013-0149-6 Agronomy for Sustainable Development 33: 4,767-776]Bregaglio, 2012. Definition and implementation of plant disease simulation models in interaction with crop models, Ph.D. Thesis, University of Milan [http://www.biomamodelling.org/public/documents/bregaglio_thesis.pdf PDF]Bregaglio, S., Cappelli, G., Donatelli, M., 2012. Evaluating the suitability of a generic fungal infection model for pest risk assessment studies. [http://www.sciencedirect.com/science/article/pii/S0304380012004012 Ecological Modelling 247, 58-63] and the dynamics of plant epidemics,Bregaglio S., Donatelli M., 2015. A set of software components for the simulation of plant airborne diseases. [https://www.sciencedirect.com/science/article/pii/S1364815215001589 Environ. Modell. Softw., 72: 426–444.]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. [https://www.sciencedirect.com/science/article/pii/S1161030116300417 European Journal of Agronomy, 76: 107-117.]Donatelli M., Magarey R.D., Bregaglio S., Willocquet L., Whish J.P.M., Savary S., 2017. Modelling the impacts of pests and diseases on agricultural systems. [https://www.sciencedirect.com/science/article/pii/S0308521X1730104X Agricultural Systems, 155: 213-224.]
• to estimate agro-meteorological variables,Bregaglio S., Donatelli M., Confalonieri R., Acutis M., Orlandini S., 2011. Multi metric evaluation of leaf wetness models for large-area application of plant disease models. [http://www.sciencedirect.com/science/article/pii/S016819231100116X Agricultural and Forest Meteorology, 151: 1163-1172.]Bregaglio S., Donatelli M., Confalonieri R., Acutis M., Orlandini S., 2010. An integrated evaluation of thirteen modelling solutions for the generation of hourly values of air relative humidity. [https://link.springer.com/article/10.1007%2Fs00704-010-0274-y Theoretical and Applied Climatology 102:429-438]Donatelli M., Bellocchi G., Habyarimana E., Bregaglio S., Baruth B., 2010. AirTemperature: Extensible Software Library to Generate Air Temperature Data, [https://dx.doi.org/10.3814/2010/812789 SRX Computer Science, vol. 2010]Confalonieri R., Bellocchi G., Donatelli M., 2010. A software component to compute agro-meteorological indicators. [http://www.sciencedirect.com/science/article/pii/S1364815208002144 Environmental Modelling & Software, 25:1485-1486]Donatelli M., Bellocchi G., Habyarimana E., Confalonieri R., Micale F., 2009. An extensible model library for generating wind speed data. [http://www.sciencedirect.com/science/article/pii/S0168169909001549 Computers and Electronics in Agriculture, 69:165-170]Carlini L., Bellocchi G., Donatelli M., 2006. Rain, a software component to generate synthetic precipitation data. [https://www.agronomy.org/publications/aj/abstracts/98/5/1312 Agronomy Journal, 98: 1312-1317]Donatelli M., Carlini L., Bellocchi G., 2006. A software component for estimating solar radiation. [http://www.sciencedirect.com/science/article/pii/S1364815205000794 Environmental Modelling and Software 21, 3:411-416]Donatelli M., Bellocchi G., Carlini L., 2006. Sharing knowledge via software components: models on reference evapotranspiration. [http://www.sciencedirect.com/science/article/pii/S1161030105000754 European Journal of Agronomy 24, 2:186-192]Bellocchi G., Acutis M., Fila G., Donatelli M., 2002. An indicator of solar radiation model performance based on a fuzzy expert system. [http://www.cracin.it/sipeaa/tools/RadEst/Irad_AgJ_Rev.pdf Agron. J., 94: 1222–1233] {{Webarchive|url=https://web.archive.org/web/20180116081223/http://www.cracin.it/sipeaa/tools/RadEst/Irad_AgJ_Rev.pdf |date=2018-01-16 }}.Donatelli M., Carlini L., Bellocchi G., Colauzzi M.. 2005. [https://www.mssanz.org.au/modsim05/papers/donatelli.pdf CLIMA: A component-based weather generator.] p. 627–633. In A. Zerger and R.M. Argent (ed.) MODSIM 2005. International Congress on Modelling and Simulation. Melbourne, Australia. 12–15 Dec. 2005. Modelling and Simulation Society of Australia and New Zealand. Society, Burlington, Vermont.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. [https://dl.sciencesocieties.org/publications/aj/abstracts/95/5/1334?access=0&view=pdf Agron J., 95: 1334-1336.]
• to develop a library of functions to estimate soil hydraulic properties,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

[https://www.researchgate.net/publication/236120172_PTF_an_Extensible_Component_for_Sharing_and_Using_Knowledge_on_Pedo-Transfer_Functions/file/9c9605162979a4aae4.pdf PDF]Fila G., Bellocchi G.. Donatelli M., Acutis M., 2006. PTFIndicator: An IRENE_DLL-based application to evaluate estimates of pedotransfer functions by integrated indices. [http://www.sciencedirect.com/science/article/pii/S1364815205000034 Env. Modell. Softw., 21: 107-100.]

• to estimate quality of agricultural products.Cappelli, G., Bregaglio, S., Romani, M., Feccia, S., Confalonieri, R., 2014. A software component implementing a library of models for the simulation of pre-harvest rice grain quality. Computers and Electronics in Agriculture, 104, 18-24 [https://dx.doi.org/10.1016/j.compag.2014.03.002]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. [https://www.sciencedirect.com/science/article/pii/S0378429017305993 Field Crops Research, 215: 140-148.]
• to simulate the timing and the application of agricultural management practicesDonatelli M., Bregaglio S., Stella T., Fila G., 2016. Modelling agricultural management in multi-model simulation systems. In: Crop modelling for agriculture and food security under global change, [https://www.agroscope.admin.ch/agroscope/de/home/.../SWQ9MzYyMDY=.pdf Proceedings of the International Crop Modelling Symposium, 2016] (eds: Ewert F, Boote K.J., Rotter R.P., Thorburn P., Nendel C.), 15–17 March 2016, Berlin.Donatelli M., Van Evert F.K., Di Guardo A., Adam M., Kansou K., 2006. A component to simulate agricultural management. In: Voinov A., Jakeman A.J., Rizzoli A.E. (Eds.), [https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3130&context=iemssconference iEMSs Third Biannual Meeting: “Summit on Environmental Modelling and Software]”. International Environmental Modelling and Software
• to develop a library to perform sensitivity analysis on agricultural modelsDonatelli 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: [https://www.mssanz.org.au/modsim09/C3/donatelli_C3b.pdf 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.
• to define a library to evaluate crop model performances in reproducing field experimentsFila G., Bellocchi G., Acutis M., Donatelli M., 2003a. IRENE: a software to evaluate model performance. [https://www.sciencedirect.com/science/article/pii/S1161030102001296 Eur. J. Agron., 18: 369–372.]
• to develop a new model of quantitative and qualitative aspects of winter rapeseed productionsGilardelli 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. [https://www.sciencedirect.com/science/article/pii/S0378429016302234 Field Crop Research, 197: 125-132.]
• to adapt the Canegro sugar cane model for giant reedStella 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. [https://www.sciencedirect.com/science/article/pii/S0168169915000514 Comput Electron Agr, 113: 193-202.]

BioMA applications and modelling solutions are the simulation tools used by the [http://mars.jrc.ec.europa.eu/ MARS unit] of the European Commission to simulate agricultural production under scenarios of climate change. BioMA is also used in the EU FP7 project [http://www.modextreme.org/ MODEXTREME].

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,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 [https://www.researchgate.net/publication/236120180_A_Design_for_Framework-Independent_Model_Components_of_Biophysical_Systems/file/3deec5162982e7cbe6.pdf? PDF] 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.

In the context of the [https://www.crea.gov.it/en/-/agridigit-en AgriDigit] project, carried out at [https://www.crea.gov.it/en/home CREA], the BioMA framework has been adapted to execution in the Cloud via a SaaS architecture. Model calls will be treated as an HTTP invocation, so the Model View Controller architecture is no longer needed. Hence, the Configuration Layer has been eliminated (it is not used) for cloud services. Also the Composition Layer has been simplified.

File:BioMA libraries nologo.png

Advanced applications can be grouped under two categories:

• BioMA-Spatial, were models are run iteratively against spatially explicit units, as either grid cells or polygons. These application can include a layer to model interaction among the spatial units;
• BioMA-Site, were models are run against specific sites. These applications can be specialized for specific crops, and in general allow a more detailed access to model constituent blocks and outputs.

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.

Code of core components is available under the MIT license, however, the reuse of binaries falls under the Creative Commons license as below, implying the no-commercial, share-alike clauses.

Application and tools are available under the Creative Commons license as binaries, however code can be shared under specific agreements between parties. Model component developers may make code available, however, they must make binaries available for reuse.[https://creativecommons.org/licenses/by-nc-sa/4.0/ Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) ]

{{Reflist|30em}}

Category:Simulation software

Category:Agronomy