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.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.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 101-119Argent, R.M., 2004. An overview of model integration for environmental applicationsócomponents, frameworks and semantics, Environmental Modelling & Software, Volume 19, 3:219-234Athanasiadis 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. 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 targetsHolzworth 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
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.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 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 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. In: Environmental and agricultural modelling: Integrated approaches for policy impact assessment, F.Brouwer and M. van Ittersum editors, Springer, 63-108 task of the 6th EU Framework Program .
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.) PDFDuveiller 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.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. 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. PDFBregaglio 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. 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.) PDFDonatelli 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 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.) PDFManici L. M., Bregaglio S., Fumagalli D., Donatelli M. 2014. Modelling soil borne fungal pathogens of arable crops under climate change, 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. 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. 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. 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. 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.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, 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 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 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.) PDF to simulate fungal infectionsBregaglio, S.; Donatelli, M.; Confalonieri, R. 2013. Fungal infections of rice, wheat, and grape in Europe in 2030-2050.Agronomy for Sustainable Development 33: 4,767-776Bregaglio, 2012. Definition and implementation of plant disease simulation models in interaction with crop models, Ph.D. Thesis, University of Milan PDFBregaglio, 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 and the dynamics of plant epidemics,Bregaglio S., Donatelli M., 2015. A set of software components for the simulation of plant airborne diseases. 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. 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. Agricultural Systems, 155: 213-224.