BioMA

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BioMA logo 2013.PNG
Biophysical Model Applications
BioMA is a public domain software framework for developing, parameterizing and running modelling solutions in the domains of agriculture and environment.
Model components and modelling solutions are reusable under different frameworks.
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]

Contents

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:

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.

Architectural layers of the BioMA simulation system Framework 3.png
Architectural layers of the BioMA simulation system
  • 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
From model components to modelling solutions, and to adapters From Model to ConfigurationLayer.PNG
From model components to modelling solutions, and to adapters

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

Model libraries used in BioMA to build modelling solutions BioMA libraries nologo.png
Model libraries used in BioMA to build modelling solutions

Advanced applications can be grouped under two categories:

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]

Related Research Articles

Agricultural science Academic field within biology

Agricultural science is a broad multidisciplinary field of biology that encompasses the parts of exact, natural, economic and social sciences that are used in the practice and understanding of agriculture. Professionals of the agricultural science are called agricultural scientists or agriculturists.

Agroecology is an applied science that studies ecological processes applied to agricultural production systems. Bringing ecological principles to bear can suggest new management approaches in agroecosystems. The term is often used imprecisely, as the term can be used as a science, a movement, or an agricultural practice. Agroecologists study a variety of agroecosystems. The field of agroecology is not associated with any one particular method of farming, whether it be organic, regenerative, integrated, or industrial, intensive or extensive, although some use the name specifically for alternative agriculture.

Decision support system Information system that supports business or organizational decision-making activities

A decision support system (DSS) is an information system that supports business or organizational decision-making activities. DSSs serve the management, operations and planning levels of an organization and help people make decisions about problems that may be rapidly changing and not easily specified in advance—i.e. unstructured and semi-structured decision problems. Decision support systems can be either fully computerized or human-powered, or a combination of both.

Nutrient management

Nutrient management is the science and practice directed to link soil, crop, weather, and hydrologic factors with cultural, irrigation, and soil and water conservation practices to achieve optimal nutrient use efficiency, crop yields, crop quality, and economic returns, while reducing off-site transport of nutrients (fertilizer) that may impact the environment. It involves matching a specific field soil, climate, and crop management conditions to rate, source, timing, and place of nutrient application.

The Earth System Modeling Framework (ESMF) is open-source software for building climate, numerical weather prediction, data assimilation, and other Earth science software applications. These applications are computationally demanding and usually run on supercomputers. The ESMF is considered a technical layer, integrated into a sophisticated common modeling infrastructure for interoperability. Other aspects of interoperability and shared infrastructure include: common experimental protocols, common analytic methods, common documentation standards for data and data provenance, shared workflow, and shared model components.

Component-based software engineering Branch of software engineering

Component-based software engineering (CBSE), also called component-based development (CBD), is a branch of software engineering that emphasizes the separation of concerns with respect to the wide-ranging functionality available throughout a given software system. It is a reuse-based approach to defining, implementing and composing loosely coupled independent components into systems. This practice aims to bring about an equally wide-ranging degree of benefits in both the short-term and the long-term for the software itself and for organizations that sponsor such software.

Gianni Bellocchi

Gianni Bellocchi is a researcher in agricultural and related sciences. He is credited with the development of approaches and tools in validation of estimates and measurements. Introduction of fuzzy logic in the context of validation is often considered to be the most significant contribution to the field of model and method validation.

Ecosystem model A typically mathematical representation of an ecological system

An ecosystem model is an abstract, usually mathematical, representation of an ecological system, which is studied to better understand the real system.

Spot blotch is a leaf disease of wheat caused by Cochliobolus sativus. Cochliobolus sativus also infects other plant parts and in conjunction with other pathogens causes common root rot and black point.

The Community Surface Dynamics Modeling System (CSDMS) deals with the Earth's surface and the observable and projected changes constantly taking place – the ever-changing dynamic interface between lithosphere, hydrosphere, cryosphere and atmosphere.

Common modeling infrastructure refers to software libraries that can be shared across multiple institutions in order to increase software reuse and interoperability in complex modeling systems. Early initiatives were in the climate and weather domain, where software components representing distinct physical domains tended to be developed by domain specialists, often at different organizations. In order to create complete applications, these needed to be combined, using for instance a general circulation model, that transfers data between different components. An additional challenge is that these models generally require supercomputers to run, to account for the collected data and for data analyses. Thus, it was important to provide an efficient massively parallel computer system, and the processing hardware and software, to account for all the different workloads and communication channels.

Deficit irrigation (DI) is a watering strategy that can be applied by different types of irrigation application methods. The correct application of DI requires thorough understanding of the yield response to water and of the economic impact of reductions in harvest. In regions where water resources are restrictive it can be more profitable for a farmer to maximize crop water productivity instead of maximizing the harvest per unit land. The saved water can be used for other purposes or to irrigate extra units of land. DI is sometimes referred to as incomplete supplemental irrigation or regulated DI.

Environmental informatics is the science of information applied to environmental science. As such, it provides the information processing and communication infrastructure to the interdisciplinary field of environmental sciences aiming at data, information and knowledge integration, the application of computational intelligence to environmental data as well as the identification of environmental impacts of information technology. The UK Natural Environment Research Council defines environmental informatics as the "research and system development focusing on the environmental sciences relating to the creation, collection, storage, processing, modelling, interpretation, display and dissemination of data and information." Kostas Karatzas defined environmental informatics as the "creation of a new 'knowledge-paradigm' towards serving environmental management needs." Karatzas argued further that environmental informatics "is an integrator of science, methods and techniques and not just the result of using information and software technology methods and tools for serving environmental engineering needs."

Agent-based models have many applications in biology, primarily due to the characteristics of the modeling method. Agent-based modeling is a rule-based, computational modeling methodology that focuses on rules and interactions among the individual components or the agents of the system. The goal of this modeling method is to generate populations of the system components of interest and simulate their interactions in a virtual world. Agent-based models start with rules for behavior and seek to reconstruct, through computational instantiation of those behavioral rules, the observed patterns of behavior.

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Knowledge Engineering and Machine Learning Group

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"Envirome" is a concept that relates the core of environmental conditions with the successful biological performance of living beings. This concept was created in genetic epidemiology, in which an envirome is defined as the total set of environmental factors, both present, and past, that affect the state, and in particular the disease state, of an organism. The study of the envirome and its effects is termed enviromics. The term was first coined in the field of psychiatric epidemiology by J.C. Anthony in 1995. More recently, use of the term has been extended to the cellular domain, where cell functional enviromics studies both the genome and envirome from a systems biology perspective. In plants, enviromics is directly related to complex ecophysiology, in which the wide environment of the plants, into an omics scale, can be dissected and understood as a mosaic of possible growing factors and the balance of diverse resources available. In ecology, this concept can be related to the Shelford's law of tolerance. The enviromics is conceived as a pillar of the Modern Plant Breeding, capable to connect the design and development of breeding goals concealing it with the agronomic targets for a climate-smart agriculture. It also has the ability to bridge the knowledge gaps between the different levels of systems biology and phenomics in the context of Gene–environment interaction.

The Decision Support System for Agrotechnology Transfer (DSSAT) is a set of computer programs for simulating agricultural crop growth. It has been used in over 100 countries by agronomists for evaluating farming methods. One application has been assessing the possible impacts on agriculture of climate change and testing adaptation methods.

eWater is a non-profit organisation established by Australian Federal and State Governments. The role of eWater is to support integrated water resources management in Australia through development and implementation of the national hydrological modelling strategy (NHMS).

BAITSSS is biophysical Evapotranspiration (ET) computer model that determines water use, primarily in agriculture landscape, using remote sensing-based information. It was developed and refined by Ramesh Dhungel and the water resources group at University of Idaho's Kimberly Research and Extension Center since 2010. It has been used in different areas in the United States including Southern Idaho, Northern California, northwest Kansas, and Texas.

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
  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
  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 Archived 2018-01-16 at the Wayback Machine .
  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: 759-765 PDF
  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
  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. Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)