BioMA explained

BioMA
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Header1:	Biophysical Model Applications
Data2:	BioMA is a public domain software framework for developing, parameterizing and running modelling solutions in the domains of agriculture and environment.
Data3:	Model components and modelling solutions are reusable under different frameworks.
Data4:	The software is developed using Microsoft C# of the .NET framework

Modelling frameworks are used in modelling and simulation and can consist of a software infrastructure to develop and run mathematical models. They have provided a substantial step forward in the area of biophysical modelling with respect to monolithic implementations.^{[1] [2] [3] [4]} The separation of algorithms from data, the reusability of I/O procedures and integration services, and the isolation of modelling solutions in discrete units has brought a solid advantage in the development of simulation systems. Modelling frameworks for agriculture have evolved over time, with different approaches and targets^[5]

BioMA is a software framework developed focusing on platform-independent, re-usable components, including multi-model implementations at fine granularity.

BioMA - Biophysical Model Applications

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.^[6] It is based on discrete conceptual units codified in freely extensible software components .^[7]

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 APES^[8] task of the 6th EU Framework Program SEAMLESS project.

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

• to create weather datasets for biophysical simulation,:^{[9] [10] [11]}
• to assess the impact on crop production in Europe,^{[12] [13]} and adaptation,^{[14] [15]}
• to simulate the development of soil pathogens under climate change,^{[16] [17]}
• to reproduce the growth and development of tree species,^[18]
• to estimate the survival of insects damaging maize under climate change^{[19] [20] [21]}
• to estimate crop suitability to environment,^[22]
• to perform modelling solutions comparison at sub-model level,^[23]
• to develop a library of reusable models for crop development and growth,^{[24] [25]}
• to estimate the impact of climate change on crop production in Latin America,^[26]
• to simulate fungal infections^{[27] [28] [29]} and the dynamics of plant epidemics,^{[30] [31] [32]}
• to estimate agro-meteorological variables,^{[33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43]}
• to develop a library of functions to estimate soil hydraulic properties,^{[44] [45]}
• to estimate quality of agricultural products.^{[46] [47]}
• to simulate the timing and the application of agricultural management practices^{[48] [49]}
• to develop a library to perform sensitivity analysis on agricultural models^[50]
• to define a library to evaluate crop model performances in reproducing field experiments^[51]
• to develop a new model of quantitative and qualitative aspects of winter rapeseed productions^[52]
• to adapt the Canegro sugar cane model for giant reed^[53]

BioMA applications and modelling solutions are the simulation tools used by the MARS unit of the European Commission to simulate agricultural production under scenarios of climate change. BioMA is also used in the EU FP7 project MODEXTREME.

The architecture

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,^[54] 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.

Model layer: fine grained/composite models implemented in components Composition layer: modeling solutions from model components Configuration layer: adapters for advanced functionalities in controllers Applications: from console to advanced MVC implementations Development Tools: tools mostly using code generation

Re-usable components implementing model libraries are composed into modelling solutions. Modeling solutions are not specific to one modelling framework. An adapter creates a version of the modelling solution specific to a framework application, such as BioMA. The semantically explicit interfaces allow creating rich applications

Cloud Architecture

In the context of the AgriDigit project, carried out at 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.

Applications

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.

Availability

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.

The BioMA Intellectual Property Rights model

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.^[55]

References

Notes and References

1. Donatelli, M., J. Bolte, F. van Evert and W. Wang, 2003 Which software designs for evolution. In: van Ittersum M.K., Donatelli M. (Eds.), Modelling cropping systems: science, software and applications.European Journal of Agronomy 18, 193-195.
2. Rizzoli A.E., G. Leavesley, J.C. Ascough II, R.M. Argent, I.N. Athanasiadis, V. Brilhante, F.H.A. Claeys, O. David, M. Donatelli i, P. Gijsbers, D. Havlik, A. Kassahun, P. Krause 2008 Environmental modelling, software and decision support - state of the art and new perspectives Elsevier 101-119
3. Argent, R.M., 2004. An overview of model integration for environmental applicationsócomponents, frameworks and semantics, Environmental Modelling & Software, Volume 19, 3:219-234
4. Athanasiadis I.N., Rizzoli A.E., Donatelli M., Carlini L., 2011. Enriching environmental software model interfaces through ontology-based tools. Int. J. Advanced Systemic Studies, 4: 94-105.
5. Holzworth D.P., Snow V., Janssen S., Athanasiadis I.N., Donatelli M., Hoogenboom G., White J.W., Thorburn P., 2015. Agricultural production systems modelling and software: Current status and future prospects, Enrironmental Modelling and Software http://www.sciencedirect.com/science/article/pii/S1364815214003703
6. 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.) PDF
7. 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 PDF
8. Donatellli M., G. Russell, A.E Rizzoli, et al. 2010 A component-based framework for simulating agricultural production and externalities. In: Environmental and agricultural modelling: Integrated approaches for policy impact assessment, F.Brouwer and M. van Ittersum editors, Springer, 63-108
9. 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.) PDF
10. 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. Theoretical and Applied Climatology, 127: 573-585.
11. 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.
12. 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. PDF
13. 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.
14. 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.) PDF
15. 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
16. 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.) PDF
17. Manici L. M., Bregaglio S., Fumagalli D., Donatelli M. 2014. Modelling soil borne fungal pathogens of arable crops under climate change, International Journal of Biometereology
18. 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
19. Maiorano A, Cerrani I, Fumagalli D, Donatelli M, 2013. New biological model to manage the impact of climate warming on maize corn borers. Agronomy for Sustainable Development,
20. 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. Ecological Modelling, 245: 65-74.
21. 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.
22. 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.
23. 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, Environmental Modelling & Software
24. 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
25. 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. Agronomy for Sustainable Development, 35: 157-167
26. 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.) PDF
27. Bregaglio, S.; Donatelli, M.; Confalonieri, R. 2013. Fungal infections of rice, wheat, and grape in Europe in 2030-2050.Agronomy for Sustainable Development 33: 4,767-776
28. Bregaglio, 2012. Definition and implementation of plant disease simulation models in interaction with crop models, Ph.D. Thesis, University of Milan PDF
29. Bregaglio, S., Cappelli, G., Donatelli, M., 2012. Evaluating the suitability of a generic fungal infection model for pest risk assessment studies. Ecological Modelling 247, 58-63
30. Bregaglio S., Donatelli M., 2015. A set of software components for the simulation of plant airborne diseases. Environ. Modell. Softw., 72: 426–444.
31. 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.
32. 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. Agricultural Systems, 155: 213-224.
33. 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. Agricultural and Forest Meteorology, 151: 1163-1172.
34. 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. Theoretical and Applied Climatology 102:429-438
35. 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
36. Confalonieri R., Bellocchi G., Donatelli M., 2010. A software component to compute agro-meteorological indicators. Environmental Modelling & Software, 25:1485-1486
37. 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
38. Carlini L., Bellocchi G., Donatelli M., 2006. Rain, a software component to generate synthetic precipitation data. Agronomy Journal, 98: 1312-1317
39. Donatelli M., Carlini L., Bellocchi G., 2006. A software component for estimating solar radiation. Environmental Modelling and Software 21, 3:411-416
40. Donatelli M., Bellocchi G., Carlini L., 2006. Sharing knowledge via software components: models on reference evapotranspiration. European Journal of Agronomy 24, 2:186-192
41. 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 .
42. Donatelli M., Carlini L., Bellocchi G., Colauzzi M.. 2005. 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.
43. 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.
44. 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: 759-765PDF
45. Fila G., Bellocchi G.. Donatelli M., Acutis M., 2006. PTFIndicator: An IRENE_DLL-based application to evaluate estimates of pedotransfer functions by integrated indices. Env. Modell. Softw., 21: 107-100.
46. 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
47. 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.
48. Donatelli 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, 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.
49. 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.), iEMSs Third Biannual Meeting: “Summit on Environmental Modelling and Software”. International Environmental Modelling and Software
50. 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.
51. Fila G., Bellocchi G., Acutis M., Donatelli M., 2003a. IRENE: a software to evaluate model performance. Eur. J. Agron., 18: 369–372.
52. 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.
53. 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.
54. 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
55. https://creativecommons.org/licenses/by-nc-sa/4.0/ Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)