1. XV AIAM Congress, Palermo, June 5 - 7, 2012 CROP MONITORING IN EUROPE MARS-AGRI4CAST activities on crop monitoring, yield forecasting and climate change Giovanna Fontana On behalf of the MARS –AGRI4CAST team IES - Institute for Environment and Sustainability Ispra - Italy http://mars.jrc.ec.europa.eu/ http://ies.jrc.ec.europa.eu/ 1

2. XV AIAM Congress, Palermo, June 5 - 7, 2012 Outline The JRC MARS Unit Scenario analysis in agriculture The modelling system The BioMA platform 2 17 February 201417 February 201417 February 2014

3. XV AIAM Congress, Palermo, June 5 - 7, 2012 The IES MARS Unit mission… Focusing on crop production and agricultural activities, the MARS Unit provides timely forecasts, early assessments and the scientific underpinning for efficient monitoring and control systems. The work serves the Agriculture and Food policies of the European Union, their impact on rural economies and on the environment, encompassing the global issues of food security and climate change. 3 17 February 201417 February 201417 February 2014

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5. XV AIAM Congress, Palermo, June 5 - 7, 2012 MARS (Monitoring Agricultural ResourceS) Unit: AGRI4CAST: crop production forecasts initially in EU member states, then in neighboring countries; scenario analysis for current and future climate AGRI-ENV: Integration of Environment concerns into Agriculture FoodSec (Food Security Assessment): crop monitoring & early warning for DG DEVCO mainly in sub-Saharan Africa GeoCAP: Geomatics for the Common Agricultural Policy control

6. The IES MARS-AGRI4CAST Action runs: The Crop Growth Monitoring System (CGMS), providing in season production estimates to DG AGRI; Scenario analysis of climate change impact on agriculture, providing software tools, inclusive of data and models; MARS-AGRI4CAST activities have led to the development of: Several weather database covering Europe, and areas in Latin America, Asia, and Africa; A modelling platform, BioMA (Biophysical Models Applications), which allows running an extensible set of modelling solutions against a spatially explicit database.

7. XV AIAM Congress, Palermo, June 5 - 7, 2012 Outline The JRC MARS Unit Scenario analysis in agriculture The modelling system The BioMA platform 7 17 February 201417 February 201417 February 2014

8. XV AIAM Congress, Palermo, June 5 - 7, 2012 Scenario analysis in agriculture MARS uses data on weather, soils, crops, and agricultural management to make impact assessment and to estimate the outcome of adaptation strategies. Times series of weather data are used as driving force with process based biophysical models to simulate the dynamics of the system. The analysis can integrate several aspects of a production system to simulate its performance (e.g. yields and water use of a cropping system), or may target specific sub-systems (e.g. potential pressure of a diseases on a crop). In the following slides examples of both procedures and results taken from analysis carried out are shown, as well as basic elements of the modelling and software systems. 8 17 February 201417 February 201417 February 2014

9. Weather data From climate scenario data to model inputs 917 February 2014

10. XV AIAM Congress, Palermo, June 5 - 7, 2012 Weather data workflow Grid of 25 x 25 km

11. XV AIAM Congress, Palermo, June 5 - 7, 2012 Scenarios of future climate are extremely variable even within the same hypothesis of green house gases emission.

12. XV AIAM Congress, Palermo, June 5 - 7, 2012 Climate change impacts in LAC Datasets – Climate (GCM: HadleyUK MetOffice; NCAR) ECMWF ERA-Interim reanalysis (1989 – present, 25×25 km spatial resolution); IPCC AR4 emission scenarios A1B and B1; Hadley3 and NCAR CGMs; 2020 – 2050 time frames. CLIMAK weather generator coupled to CLIMA libraries

13. Model simulation workflows Impact assessment and definition of adaptation strategies 1317 February 2014

14. XV AIAM Congress, Palermo, June 5 - 7, 2012 Simulating impact and adaptation

15. XV AIAM Congress, Palermo, June 5 - 7, 2012 Wheat – yield gap (water availability)

16. XV AIAM Congress, Palermo, June 5 - 7, 2012 Wheat – water limited, no adaptation

17. XV AIAM Congress, Palermo, June 5 - 7, 2012 Wheat – best adaptation (%)

18. XV AIAM Congress, Palermo, June 5 - 7, 2012 Wheat – best adaptation (t ha -1 )

19. XV AIAM Congress, Palermo, June 5 - 7, 2012 Wheat – adaptation technique

20. XV AIAM Congress, Palermo, June 5 - 7, 2012 Quality: Rice Amylose/Amilopectin ratio

21. XV AIAM Congress, Palermo, June 5 - 7, 2012 Quality: Rice grains chalkiness

22. XV AIAM Congress, Palermo, June 5 - 7, 2012 Soil-Borne plant diseases 22 17 February 201417 February 201417 February 2014

23. XV AIAM Congress, Palermo, June 5 - 7, 2012 Outline The JRC MARS Unit Scenario analysis in agriculture The modelling system The BioMA platform 23 17 February 201417 February 201417 February 2014

24. XV AIAM Congress, Palermo, June 5 - 7, 2012 The modelling system 24 17 February 201417 February 201417 February 2014

25. XV AIAM Congress, Palermo, June 5 - 7, 2012 Plant libraries 25 17 February 201417 February 201417 February 2014 Many crop models are available in the literature, sharing part of the approaches and differing for others. There is not such a thing as the universal best model; instead, under specific conditions of applications and according to the specific objective of the modelling study, different tools can be adequate. If modelling knowledge is shared in software libraries including different approaches, it becomes possible to compare models and to further develop them by adding either new or missing approaches.

26. XV AIAM Congress, Palermo, June 5 - 7, 2012 The CropML and CropML-WL libraries CropML and CropML_WL are two libraries of models for crop growth and development under potential and water limiting conditions. They currently implement modelling solutions with different approaches for biomass accumulation, photosynthates allocation to plant organs and leaf area evolution CropSyst (generic crop / grasses simulator) Wofost (generic crop simulator) WARM (rice) STICS (being implemented - generic crop / grasses simulator) CaneGro (being implemented - specific for sugarcane) 26 17 February 201417 February 201417 February 2014

27. XV AIAM Congress, Palermo, June 5 - 7, 2012 Air-borne plant diseases simulation Despite of their key role in determining actual production levels, the impact of plant diseases on the year-to-year yield fluctuations is completely ignored in crop yield simulation systems. Forecasting frameworks have been developed for a number of plant diseases, but the coupling of disease forecasting models to crop growth models is not yet operational. it is crucial to deal with the implementation of models for the simulation of the dynamics of plant diseases and of the plant- pathogen interactions aiming at quantifying biotic yield losses.

28. XV AIAM Congress, Palermo, June 5 - 7, 2012 The Diseases components are software libraries consisting of four components which provide a generic frame to simulate disease development: DiseaseProgress, InoculumPressure, ImpactsOnPlants, AgromanagementImpact. Each component is developed as a generic model unit to simulate various aspects of a polycyclic epidemic caused by fungal pathogens.

29. XV AIAM Congress, Palermo, June 5 - 7, 2012 The framework is targeted at simulating a generic fungal polycyclic epidemic considering the impact of weather variables, agricultural management and host characteristics (resistance, phenology, growth); The development of the Diseases components followed the guidelines drawn by Donatelli and Rizzoli (2008): Independent software unit Extensible by third parties Fine granularity of the modelling approaches implemented Bridge, Strategy, Composite, and Context Design patterns Inputs, outputs and the parameters in Domain classes Unit tests, model and code documentation available.

30. XV AIAM Congress, Palermo, June 5 - 7, 2012 Outline The JRC MARS Unit Scenario analysis in agriculture The modelling system The BioMA platform 30 17 February 201417 February 201417 February 2014

31. XV AIAM Congress, Palermo, June 5 - 7, 2012 The BioMA framework Analyzing agriculture and climate change implies targeting multiple objectives, in a variety of environmental and agro-management contexts; New relevant data layers are becoming available and are planned for the near future; Impact of GHG emissions on climate must be studied with global models, but impact on production systems must be analyzed at local level; There is also a clear demand for integration with other domains, to add economic, social and environmental dimension to the analysis; These points directly define requirements for the tools needed, which have been leading to the design of a flexible modeling platform open for collaborative development.

32. XV AIAM Congress, Palermo, June 5 - 7, 2012 What is BioMA? BioMA (Biophysical Models Applications) is a software framework designed and developed for analyzing, parameterizing and running modeling solutions based on biophysical models against database which include spatially explicit units; The framework is based on framework-independent components, both for the modeling solutions and the graphical user's interface; The component-based structure allows BioMA to implement diverse modeling solutions targeted to specific modeling goals, allowing also for adding new modeling solutions independently by third parties; The goal of this framework is to rapidly bridge from prototypes to operational applications, enabling also running and comparing different modeling solutions.

33. XV AIAM Congress, Palermo, June 5 - 7, 2012

34. BioMA features and peculiarities The guidelines followed during its development aimed at maximizing: Extensibility with new modeling solutions Transparency of the modeling domain Ease of customization in new environments Ease of deployment Possibility for extension independently by third parties The reuse of existing models, besides their re-implementation to ensure the functionalities of the platform, was also chosen as a base for making available modeling options.

35. XV AIAM Congress, Palermo, June 5 - 7, 2012 BioMA current modelling solutions The current version of BioMA includes heterogeneous modelling solutions: WARM (Rice simulation) CropSyst-Water Limited (Generic crop/cropping systems simulator) WOFOST-Water Limited (Generic crop simulator) APES (Cropping systems simulator) DSSAT- Canegro (Sugarcane – being developed) PotentialDiseaseInfection (Airborne plant diseases) PotentialSoilDiseaseIfection (Soilborne plant diseases) Diseases (Plant diseases linked to crops) GrainQuality (Currently rice) ClimIndices (Climatic indices) All model components can be extended (e.g. the new WOFOST).

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37. BioMA structure The software architectural layers 3717 February 2014

38. XV AIAM Congress, Palermo, June 5 - 7, 2012 BioMA: from models to viewers 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

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40. XV AIAM Congress, Palermo, June 5 - 7, 2012 Production levels Modelling solutions in BioMA allow estimating different production levels, increasingly integrating yield limiting factors: Potential production (P: crop growth solar radiation and temperature driven) Water limited (WL: all factors of P and water limitation) Abiotic stress limited (AL: P and effects due to temperature stresses of extreme events for crops) Disease limited (DL: P and impact from one crop-specific disease) Multiple-factor limited (MFL: P, WL, AL, and DL limited) P set limits which can be potentially overcome by genetic improvement/choice of species, WL/DL set limits which can be overcome via agricultural management

41. XV AIAM Congress, Palermo, June 5 - 7, 2012 BioMA features and peculiarities BioMA is provided with supporting tools for developers and users: LUISA: Monte Carlo based sensitivity analysis, implementing 7 sensitivity analysis methods; Optimizer: Automatic calibration extensible for objective functions and solvers; IMMA: Model evaluation, based on simple and composite metrics for quantifying models performances and complexity. These tools are extensible and re-usable also outside BioMA.

42. BioMA graphical user interfaces Data visualization 4217 February 2014

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45. XV AIAM Congress, Palermo, June 5 - 7, 2012

46. click Variables of time series are selected by the user

47. XV AIAM Congress, Palermo, June 5 - 7, 2012 BioMA documentation

48. XV AIAM Congress, Palermo, June 5 - 7, 2012 BioMA and components availability

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50. XV AIAM Congress, Palermo, June 5 - 7, 2012 Conclusions – The BioMA platform Applications developed on the base of the BioMA framework are currently capable to address many aspects related to the biophysics of agricultural production; BioMA neither is a model nor proposes a model; instead, it is an open platform to make available in operational software the results of research on biophysical modeling in agriculture; Adopting a component oriented development, extended both to models and tools, fosters reusability without forcing third parties toward investing exclusively on a specific framework they do not own; We make available BioMA as a platform, but also, and of no lesser importance, as a loose collection of model objects and software tools reusable in other platforms.

51. XV AIAM Congress, Palermo, June 5 - 7, 2012 Muchas gracias por su atención… BioMA Scientific Leader Marcello Donatelli Biophysical Modellers Marcello Donatelli, Roberto Confalonieri, Simone Bregaglio, Giovanni Cappelli, Caterina Francone, Amit Srivastava, Marco Acutis, Francesco Tubiello Software Engineers Iacopo Cerrani, Davide Fanchini, Davide Fumagalli, Andrea Rizzoli, Antonio Zucchini Credits … and all the scientist that have developed in the last decades of research many of the models made available as base modeling reference in the platform!