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BioMA workgroup (Multi-model crop yield estimates) Roberto Confalonieri & Marcello Donatelli -

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Presentation on theme: "BioMA workgroup (Multi-model crop yield estimates) Roberto Confalonieri & Marcello Donatelli -"— Presentation transcript:

1 BioMA workgroup (Multi-model crop yield estimates) Roberto Confalonieri & Marcello Donatelli - E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

2 WP 3 Multi-model approach to crop yield estimates Simulation of the impact of diseases (!!!) and abiotic damages on crop productivity Dynamic forcing crop models state variables using exogenous information, i.e., NDVI or NDVI derived leaf area index (only for rice) Provide the statistical tool (generating the forecast using simulated and remote sensed data, and historical yield series) with different typologies of simulated information: same state variables (simulated indicator) simulated using different models simulated state variables without forcing (to be statistically post- processed together with remote sensing indicators) and the same variables resulting by dynamic forcing of crop models (to be post- processed alone) E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

3 WP 3 tasks 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

4 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

5 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

6 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

7 Models 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)

8 Models calibration (1) Model parameters will be calibrated for different groups of rice and wheat varieties with similar morphological and physiological features Different models would need different measured data (parameters, state variables, driving variables, etc.) for a rigorous calibration, e.g., CropSyst needs specific leaf area (SLA) at emergence, WARM needs SLA at emergence and at mid-tillering, WOFOST would need SLA periodically (about times during the crop cycle length) Different production levels (e.g., potential, water limited) would need different data for the calibration activities E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

9 Models calibration (2) The first steps in BioMA adaptation for rice in China and wheat in Morocco will be: the identification of groups of similar varieties, of their spatial distribution, and of information on their management [JAAS and INRA will be in charge for this] definition of protocols for data collection to allow the creation of databases with measured data suitable for the calibration of the parameters of all the models [UNIMI will provide the protocols] E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

10 Models calibration (3) The second step is to understand which are the most relevant parameters for each model (those on which to concentrate the effort during the calibration) under the conditions explored This will be carried out by: running spatially distributed sensitivity analysis comparing the spatial patterns of the groups of cultivars and the sensitivity analysis results E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

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

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

13 Models evaluation Once key parameters will be set to values derived from measurements, the others will be calibrated using automatic optimization algorithms Calibrated parameters will be validated using independent sets of field observations Models performances for yield estimates will be evaluated at administrative level using historical yield series by directly comparing simulated and official yields by comparing post-processed (statistical tool) simulated results and official yields (cross-validation) Performances of the different models (with and without dynamic forcing for rice) will be evaluated using different evaluation metrics E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

14 Piloting BioMA BioMA can be easily deployed: no installation is required a data-layer component allows for interfacing BioMA with whatever typology of database (Oracle, Access, …) and with whatever typology of database structure Integrated tools for zonation and automatic calibration are available in BioMA, thus allowing people in China and Morocco to autonomously refine calibrations in case further observations become available Tools for data display (maps, different typology of charts, etc.) are integrated, showing also agroclimatic indices, thus favouring the work of the analyst E-AGRI kick-off meeting, March 2011, VITO (Mol, Belgium)

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

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

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

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

19 …Thank you for your kind attention


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