Ecophysiological models - revisited

1. Ecophysiological models - revisited 16000 14000 12000 10000 Grain yield (kg/ha) 8000 6000 4000 2000 0 10 15 20 25 30 35 Temperature ( C) Jeff White USDA-ARS, ALARC, Maricopa

2. Objectives • Remind/inform people of basic capabilities of ecophysiological models and associated tools • Compare an existing model + software shell (DSSAT) to the iPlant G-to-P Modeling Workflow proposed by Steve • Show parallels between the two • Comment on lessons from a DSSAT-type approach • Identify opportunities for iPlant

3. Fourth Assessment Report of IPCC. Response of wheat yields (%) to global warming and elevated CO2 based on simulations with ecophysiological models. Elevated CO2 Ambient CO2

4. Simplified* flow diagram Initial inputs: start date, cultivar, soil, fertilizers … Management Water & N balance Daily inputs: weather, management, pests ... Phenology Photosynthesis Respiration Output Partitioning No Maturity? Final output Yes Senescence *CSM has > 270 routines

5. Relative effect of temperature on leaf photosynthesis for wheat. Source: P. Bindraban, 1997

7. Simulated vs observed growth of winter wheat at Manhattan, Kansas

8. iPG2Pproposed workflow

9. DSSAT4.5 • Over 25 crop species • Large user base • 15+ years • Over 100 countries • Public and private sector • Numerous training events • Developed through collaboration among US and other universities, international centers, etc. • Partially supported through software license ($200 per copy) Other models & shells exist! 

10. DSSAT4.5 is a shell • Dataset preparation • Runs cumpliant models such as Cropping Systems Model • Tools for model applications: • Parameter estimation • Cross-validation • Sensitivity analysis • Time series analysis • Spatial analysis

12. iPG2Pproposed workflow

13. Tools for parameter estimation: - GenCalc - GLUE

14. Two tools for sensitivity analysis:- Embedded in CSM model ( a legacy tool)- DSSAT Sensitivity Analysis V 4.5

15. Tools for visualization: - GBuild - EasyGrapher - Others incorporate graphics: weather, seasonal analysis, etc.

16. Simulated response of common bean to elevated temperature for 96 combinations of alleles at six loci

17. iPG2P proposed workflow compared to DSSAT Workflow boxes & DSSAT tools: Model entry Parameter estimation Sensitivity analysis Visualization of model inputs & outputs Verification Missing in workflow boxes: Weather data preparation Soil data preparation Management data preparation Cross-validation data preparation “Generic” applications: Time series Spatial Missing in DSSAT True modular model development Ability to import sub-models Applications for QTL & association mapping Links to genetic/genomic data

18. DSSAT4.5 • Positives • Widely used – “it works” • Promoted standardization of data • via the ICASA standards • Promoted use of systems approaches in research • Limitations • Models are only partially modular • Source code is not truly open • - Scares off contributors • - Painfully inefficient for software maintenance • Diverse GUIs for tools – confusing to users • One person maintains one tool – high risk for users • Tools have overlapping functionality – confusing to users • Incomplete documentation – confusing & frustrating • Main GUI is inefficient for many applications – more frustration

19. Key opportunities for iPG2P C.I. • Open, modular framework for modeling from pathway/organ scales to whole plant scale • Generic tools for: • Model development at different scales • Model evaluation • Dataset preparation – relates to data integration • Model applications • Parameter estimation • Time series analyses (e.g., multiple years or seasons) • Visualization is required throughout (and in numerous layouts) • G-to-P tools • Association and QTL mapping • Genetic data as inputs to models (parameter estimation) • Keys to success: • Open source – requires training for crop modeling community • Guidelines on “look and feel” or GUI • Learn from or adapt features of existing tools (not just DSSAT) • Tests cases that challenge multiple facets of the IPG2P C.I.