Software and data as scaffolds for integrative science
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This document discusses using software and data as scaffolds for integrative science. It describes challenges in agriculture and scientific problems that require knowledge across many scales and formats. Computational solutions are proposed to combine expert knowledge, data, and mechanistic models through platforms like PEcAn and TERRA REF. PEcAn provides common formats and tools to link models and data. TERRA REF aims to create open reference datasets from automated phenotyping to enable interdisciplinary collaboration. Future directions include expanding software, data sharing, and training to support more scientists in data-driven research.
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Software and data as scaffolds for integrative science
- 2. Outline • Overview: Problems and Approach • Combining Information • Models: integration across domains • PEcAn: integration of models and data • TERRA REF: automated data collection and analysis • Future Directions 2
- 3. Challenges we face • Agricultural Production: • Feeding 9bn by 2050 • Climate is changing • Resources are becoming scarce • Scientific Problems: • How do genes control traits? • How can leverage data and computing? 3Tilman et al, Nature 2002 Yield Fertilizer Pesticides
- 4. Technical Solutions for Science and Agriculture • Knowledge is Spread Across Many Scales and Formats: • Expert Knowledge • Data • Mechanistic Models • Integrating these will enable: • Stronger Inference and Prediction • More Science and Engineering 4 Marshall-Colon et al 2017 Frontiers in Plant Science
- 6. Outline • Conceptual Overview • Computational Solutions • Crop Models • PEcAn • TERRA REF • Future Directions Zhu, Lynch, LeBauer, Millar, Stitt, Long, 2015 Plant Cell & Environment 6
- 10. Scaling Up & Predicting the Future IPCC AR5 Warszawski et al. PNAS Temperature Precipitation Climate Forecasts (2040-2050)
 CMIP5: 5 Climate models x 4 CO2 emissions Scenarios 10
- 11. Effects of Climate on Sugarcane Yield in Brazil 2040-2050 Climate Impact (metric Tons / ha) Jaiswal, DeSouza, Larsen, LeBauer, Miguez, Sparovek, Bollero, Buckeridge, Long, 2017 Scaling leaf-level CO2 x T x H2O response 11
- 12. Outline • Conceptual Overview • Computational Solutions • Crop Models • PEcAn: Linking Models and Data • TERRA REF • Future Directions 12 PE Ecol
- 14. Traits System States Prediction Soil Meteorology Parameters Boundary Conditions Drivers Publications Primary Data Repositories Wild Data Relevant Information Configuration Sensitivity Calibration Validation AnalysesRun Model Outputs Just Running a Model is Hard Most of this work is model independent, so solutions can be shared 14
- 15. Data Sources Analyses Ecosystem Models BioCro ED2 CLM SIPNET ... n=12 The Standard Approach: Redundant, Labor Intensive, Error Prone Converter For Met, need one converter per driver (m) x model (n) combination Prediction NARR NOAA Fluxnet CMIP5 … m = 10 Met Station Calibration Sensitivity Validation Visualization 15
- 16. PEcAn common formats: Many users use, reuse, test, and improve components Common Format Common Format Ecosystem Models BioCro ED2 CLM SIPNET ... n=12 Converter Only need n+m (not n×m) converters Less work, more robust and valid results Diverse Met Data NARR NOAA Fluxnet CMIP5 … n = 10 Met Station Analyses Prediction Calibration Sensitivity Validation Visualization 16
- 22. PEcAn is a community project 42 Contributors > 50 citations Textbook 100s of students trained 22
- 24. PEcAn now provides a place for shared models, data access, and tools Tools: Web front end PostGIS database* Met Scaling and Gap filling Data Ingest Meta-Analysis* Sensitivity & Uncertainty Analysis* Ensemble Prediction Parameter Data Assimilation State Data Assimilation Benchmarking Visualization* Data Modeling: Radiative Transfer Photosynthesis Tree Rings Models: BioCro* CABLE CLM DALEC ED* FATES G’Day JULES Linkages LPJguess MAAT MAESPA PRELES SIPNET Data: Literature* Field Measurements Expert Priors* Meteorology Soils PalEON Fluxnet ORNL NEON TERRA REF* LTER … github.com/pecanproject/pecan pecanproject.org 24
- 25. Outline • Conceptual Overview • Computational Solutions • Crop Models • PEcAn: Linking Models and Data • TERRA REF • Future Directions 25
- 26. High Throughput Phenotyping • High Throughput Phenotyping: • Replace manual with sensor-based measurements • Measure more traits with higher frequency • But … sensors are expensive and data are difficult to interpret • Terra program major investment to push this forward http://bulletin.ipm.illinois.edu/print.php?id=513 26
- 27. TERRA REF • Motivation: • Automated Measurements —> Stronger Inference • Software & Data —> Framework for Interdisciplinary Collaboration • Solutions: • Reference Datasets • Modular and Interoperable • Open Data, Software, Computing 27
- 28. A Phenomics Pipeline for Crop Improvement Sensors Traits Genotypes Selection Genomics Higher Yield Yield Stability Nutrition Stress Tolerance and more … Automated Measurements Component & Aggregate Genomic Prediction Pan Genome 28
- 29. Diverse Scientific Disciplines Sensors Traits Genotypes Selection Genomics Engineering Robotics Computer Vision (Eco)Physiology Agronomy Biology Breeding Statistics & Machine Learning 29
- 32. Field Scanner Sensors terraref.org/articles/lemnatec-scanalyzer-field-sensors/ VNIR Imaging Spectrometer 380-1000nm SWIR Imaging Spectrometer 900-2500 nm IR Temperature Sensor NDVI (1 down, 1 up) 650, 800 nm PRI Sensor 531, 570 nm PAR Sensor 410-655 nm Color Sensor 410-655 nm 3D Scanners: 2 Side View, 1 Down RGB: 2 Side View, 1 Down (1) Active Reflectance 670, 730, 780 nm PS II Fluorescence Environmental: wind, temperature, humidity, light, rain, CO2 32
- 33. Approach: Integrate Software and Databases • What do people currently use? • What domain specific software and databases exist? • How can we connect these? • What standards & conventions to adopt? 33
- 35. Data Formats, Standards & Conventions Sensors Traits Genotypes Selection CF Conventions OGC geoTIFF NetCDF-CF LAS PEcAn Crop Ontology AgMIP/ICASA BRAPI BAM, FASTQ, VCF, BED, FASTA, GFF Genomics 35
- 39. Data Analysis Environments Any Linux Configuration + Large Filesystem + Databases + Compute Workflows: Analyze ! Share ! Publish Develop ! Deploy workbench.terraref.org 39
- 44. Thermal 44
- 68. Get involved • Sign up for beta release of software and data • terraref.org/data • Use and provide feedback on software and data formats • github.com/terraref • Collaborate • Field measurements • Software • Algorithms • Colocated Sensors 68
- 69. Outline • Conceptual Overview • Computational Solutions • Crop Models • PEcAn: Linking Models and Data • TERRA REF • Future Directions 69
- 75. Purdue Phenomics & IoT Platforms • Develop Cyberinfrastructure • Make data useable • Facilitate interdisciplinary research • Assess existing capabilities, current roadblocks, future needs • Work with Library, RCAC, faculty to facilitate data publishing • QA/QC • Community Standards and Common Interfaces 75 Funding: NSF Advances in Biological Infrastructure USDA NIFA Food and Agriculture Cyberinformatics and Tools
- 76. Agricultural Technology Once we understand how these systems work, we can engineer for ecosystem services rather that solely for yield: • Climate control • Soil improvement, carbon storage • Roots, mycorrhizae, microbiome • Pharmaceuticals • Petrochemical Substitutes • … anything plants can do NASA Ames Research 76
- 77. Todd Mockler Project Lead Nadia Shakoor Project Director Noah Fahlgren Phenotyping & Bioinformatics Erica Fishel Technology Transfer Solmaz Hajmohammadi Sensor Fusion Stephen Kresovich Breeding Jeremy Schmutz Sequencing Geoff Morris Gene-trait Associations William Rooney Breeding Pedro Andrade-Sanchez Agronomy & Phenomics Michael Ottman Physiology Maria Newcomb Field Measurements Jeff White Agronomy David LeBauer Informatics & Computing Robert Pless Image Analysis Roman Garnett Prediction Algorithms Wasit Walamu Sensing & Physiology Max Burnette Craig Willis Rob Kooper Jeff Terstreip Zongyang Li Zhenbin Hu Nick Heyek Charlie Zender Henry Butowsky Team 77
- 78. • Mike Dietze, Boston University • David LeBauer, University of Illinois • Shawn Serbin, Brookhaven National Lab • Ankur Desai, University of Wisconsin • Kenton McHenry, National Center for Supercomputing Applications • and many other user/contributors 78