Presentation of the tasks and activity planning for the work group BioMA WP 3: BioMA (Multi-model crop yield estimates) Roberto Confalonieri & Marcello Donatelli - E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

WP 3: Objectives Collection of data to properly adapt the BioMA platform to monitoring and forecasting in China (rice) and Morocco (wheat) Calibration of the parameters of the BioMA models (WARM, CropSyst, WOFOST) for rice in China and wheat in Morocco Evaluation of the BioMA suitability for multi-model monitoring and yield forecasting of rice in China and wheat in Morocco Deployment of adapted software platform to local users E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

Outline WP 3 tasks description Scientific issues related with WP 3 Technology used to meet the project requirements Activity planning E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

Outline WP 3 tasks description Scientific issues related with WP 3 Technology used to meet the project requirements Activity planning E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

WP 3 tasks description Task 3.1: Ground data collection for BioMA Task 3.2: Adaptation of BioMA for multi-model rice monitoring in China Task 3.3: BioMA piloting for multi-model rice monitoring and yield forecasting in JIANGHUAI Plain, China Task 3.4: Adaptation of BioMA for multi-model wheat monitoring in Morocco Task 3.5: BioMA piloting for multi-model wheat monitoring and yield forecasting in Morocco E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium) Task 3.1 Task 3.2 Task 3.3 Task 3.4 Task 3.5 Rice, China Wheat, Morocco BioMA adaptation BioMA piloting

Task 3.1 description Task leader: JAAS; partners: JAAS, INRA Activity 3.1.1: Identification of the group of cultivars to be calibrated for the BioMA crop models (WARM, CropSyst, WOFOST) Activity 3.1.2: Identification of measurable key variables and parameters needed for a robust calibration of the BioMA models Activity 3.1.3: Collection of data (i) for each group of cultivar [3.1.1], (ii) for suitable variables [3.1.2], (iii) for different combinations site year Activity 3.1.4: Development of a database for the parameterization and calibration activities according to specifications provided by Task 3.2 E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium) Task 3.1 Task 3.2 Task 3.3 Task 3.4 Task 3.5 Rice, China Wheat, Morocco BioMA adaptation BioMA piloting

Tasks 3.2 & 3.4 description Task leaders: UNIMI, JRC; partners: UNIMI, JRC Activity 3.2(4).1: Spatially distributed sensitivity analysis of the BioMA models to identify the most relevant parameters Activity 3.2(4).2: Parameters calibration for each model and group of cultivars Activity 3.2(4).3: Evaluation of the BioMA models for field-scale simulations for each group of cultivars Activity 3.2(4).4: Evaluation of the BioMA models for large-area simulations using official yield statistics E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium) Task 3.1 Task 3.2 Task 3.3 Task 3.4 Task 3.5 Rice, China Wheat, Morocco BioMA adaptation BioMA piloting

Tasks 3.3 & 3.5 description Task leaders: UNIMI, JRC; partners: UNIMI, JRC, JAAS, INRA Activity 3.3(5).1: Evaluation of the suitability of the BioMA platform for rice/wheat monitoring and yield forecasts in China/Morocco Activity 3.3(5).2: Evaluation of the usefulness of the multi-model approach for monitoring and forecasting activities Activity 3.3(5).3: Evaluation of possible improvements in monitoring and forecasting capabilities due to the injection in the models of exogenous data (i.e., forcing state variables using NDVI or LAI data) E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium) Task 3.1 Task 3.2 Task 3.3 Task 3.4 Task 3.5 Rice, China Wheat, Morocco BioMA adaptation BioMA piloting

Outline WP 3 tasks description Scientific issues related with WP 3 Technology used to meet the project requirements Activity planning E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

Scientific issues related with WP 3 The scientific background we will start from is represented by the prototype of rice yield forecasting system developed by the JRC in 2008 for the whole China The steps forward will be represented by: the simulation of diseases and abiotic damages impact on productions (!!!) the adoption of a multi-model approach to crop simulation the evaluation of the dynamic use of remote sensed information to force crop models state variables E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

Outline WP 3 tasks description Scientific issues related with WP 3 Technology used to meet the project requirements Activity planning E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

BioMA BioMA is an extensible platform for running biophysical models on generic spatial units. Simulations are carried out on modelling solutions, that are discrete simulation engines where different models are selected and integrated to run simulations for a specific goal. Each modelling solution makes use of extensible components. The guidelines followed during its development aim at maximizing: Extensibility with new modelling solutions Ease of customization in new environment Ease of deployment E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

BioMA Since the lack of a real modules reuse has been one of the main reasons that delayed model development in the last decades, the component oriented programming paradigm was followed. Modules are therefore implemented in generic (framework-independent) software units (i.e., components). A component can be defined as: A unit of composition with contractually specified interfaces and explicit context dependencies only. A software component can be deployed independently and is subject by composition by third parties. The component-based paradigm strongly push scientists to think of models in modular terms E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

BioMA A modular conceptualization of models allows: An easier transfer of research results into operational tools; The comparison of different approaches; A greater transparency; More rapid application development; Re-use of models of known quality; Independent extensibility by third parties; Avoiding duplications. E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

BioMA E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

Modelling solutions Six modelling solutions will be developed and evaluated within E-AGRI Rice in China: multi-model simulations with and without forcing the models with RS data WARM (Confalonieri et al., 2010) WOFOST (Van Keulen and Wolf, 1986) CropSyst (Stöckle et al., 2003) Wheat in Morocco: multi-model simulations WOFOST CropSyst E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

Modelling solutions Each modelling solutions will include models for: Crop growth and development (CropML and CropML_WL) Soil water dynamics (SoilRE and SoilW) Diseases (DiseasesProgress, ImpactsOnPlant, BlastDiseases) Abiotic damages (AbioticDamage) Forcing models state variables with exogenous data (i.e., NDVI or NDVI-derived leaf area index) (used only for rice in China) (Forcing) Micrometeorology (TRIS) Each modelling solution performs simulation for different production levels, keeping separated the outputs of the levels themselves E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

Modelling solutions Example: Rice South America potential water limited diseases limited abiotic damage E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

Components Most of these components are already developed and operationally used within different projects related with crop monitoring and yield forecasting, food security, and evaluation of the impact of climate change E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

Supporting tools BioMA is provided with supporting tools (extensible and re-usable outside BioMA) for developers and users: LUISA: Monte Carlo based sensitivity analysis, implementing 7 sensitivity analysis methods; Optimizer: Automatic calibration extensible for objective functions and solvers. It currently implements different solvers based on the downhill simplex (Nelder and Mead, 1965); IMMA: model evaluation based on simple and composite metrics for quantifying model performances and complexity E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

Example of spatially distributed sensitivity analysis E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

Example of spatially distributed sensitivity analysis E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

Data display Maps and time series E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium) click

Outline WP 3 tasks description Scientific issues related with WP 3 Technology used to meet the project requirements Activity planning E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

Activity planning GANTT diagram for the whole WP 3 E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

Activity planning GANTT diagram for Task 3.1 E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

Activity planning GANTT diagram for Tasks 3.2 and 3.4 E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

Activity planning GANTT diagram for Tasks 3.3 and 3.5 E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

…Thank you for your kind attention