Using Maps, Models and Open Science in Plant Pathology 

–G. E. P. Box “Essentially, all models are wrong, but some are useful.” 

Guiding policy How does climate change affect rice diseases in Tanzania? Mitigating The Impact Of Climate Change On Rice Disease Resistance In East Africa [MICCORDEA Project] 

= Losses due to pest injuries Actual Yield - Production-Situation Driven Crop growth model (Attainable Yield) RICEPEST 

• Simulates yield losses due to pests • Data requirements o Radiation o Temperature • Output: Attainable and Actual yield o Sheath blight o Sheath rot o Weeds o Bacterial leaf blight o Brown spot o Leaf blast o Neck blast o Brown plant hopper o Defoliators o White heads o Dead heart o Stem borer o Disease severity o Production Situation RICEPEST 

Bacterial Leaf Blight Average AUDPC 1983-1998 EPIRICE Data: NASA/POWER 

• Simulates unmanaged epidemics • Data requirements o Precipitation o Temperature o Relative humidity o Planting date • Output: Unmanaged disease severity (AUDPC) o Sheath blight o Tungro o Bacterial leaf blight o Brown spot o Leaf blast EPIRICE 

Duku, C., Sparks, A. H. and Zwart, S. 2015. Spatial modelling of rice yield losses in Tanzania due to bacterial leaf blight and leaf blast in a changing climate. Climatic Change 135(3). EPIRICE RICEPEST Data: General Circulation Model (GCM), Commonwealth Scientific and Industrial Research Organization mark 3 (CSIRO-MK3) 

5 20 3 0 18 10 20 25 27 24 25 23 RICEPEST Model { = 3.5 3.2 3.6 3.6 Satellite derived, georeferenced data x y x y x y Georeferenced Yield Loss Estimates x y Precip Temp Rad Tons/HA Fertilizer Irrigation, etc. Production Situation EPIRICE Model (% Disease Severity) 25 20 30 19 27 24 25 23 + x y x y Blast BLB + 

Duku, C., Sparks, A. H. and Zwart, S. 2015. Spatial modelling of rice yield losses in Tanzania due to bacterial leaf blight and leaf blast in a changing climate. Climatic Change 135(3). Change in yield loss due to BLB 

Guiding breeding efforts Disease and submergence or drought Stress-Tolerant Rice for Africa and South Asia (STRASA) 

International Rice Research Institute 

FAO’s Global Administrative Unit Layers (GAUL) NCEI’s Global Surface Summary of the Day – GSOD 2001-2008*, interpolated NASA/JAXA Tropical Rainfall Measuring Mission (TRMM) 2001- 2008* Data sources 

EPIRICE BB Output with GAUL overlaid AUDPC 

The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by IRRI. 

The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by IRRI. 

May 2015, New Delhi Met with partners in person 

The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by IRRI. 

The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by IRRI. 

Guiding breeding efforts Pathogen population monitoring GRDC Project GRI2007-001RTX 

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Measuring impact Rice variety releases in Philippines and Indonesia 

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Studying effects of climate change New methods to address gaps in data availability 

• Forms of data available • Policy needs • Climate change evaluations Yuan-Min Shen, Taichung District Agricultural Research and Extension Station, Bugwood.org Modelling and global mapping of potato late blight 

Metamodels Metamodel (GAM*) Daily or Monthly Data SimCast Hourly Data Blight Units *Generalised additive model 

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BU = s(T, RH, k = 150) 

1:1 Fitted R-squared: 0.7963 

BU = s(T, RH, k = 150) Metamodel (GAM*) Daily or Monthly Data SimCast Hourly Data Blight Units Data: CRU CL2.0* and World Climate Research Programme’s (WCRP) Coupled Model Intercomparison Project phase 3 (CMIP3) 

Tactical Decision Making In the paddock, in the moment 

blackleg.sporacle R package output Example! 

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Can we spatially enable? Field pea blackspot spore showers Source: Sentinel 2 and CSIRO Melloy and Sparks (unpublished) 

Where to from here? Whole farm models for liming Acid Management Strategy Comparison All net return forecasts are relative to an “acidification not managed” strategy whereby no addition of lime occurs. Net present value of future production ($/ha) Minimum (1:10) 20 Mean 93 Maximum (1:10) 173 Lime applied at 1.5 times acidification rate on paddocks below target pH and at 1.0 times acidification rate on paddocks at target pH on a target 5 year cycle with additions proportional to previous season’s cash flow Strategy 1 Remediation responsive to cash flow Minimum cash flow ($/ha) Minimum (1:10) -78 Mean -53 Maximum (1:10) -26 Likely Gain Risk 

–G. E. P. Box “Essentially, all models are wrong, but some are useful.” 

Open and Reproducible Research 

Science Collect Analyse Publish Write Review Summarise Reproduce Re-analyse (meta-analysis) Share data Open repository Share code open/free tools Collaborative tools Citation Pre-prints Open Access Ideal simplified research life-cycle 

• Sponsors/journals require data (standard in molecular) • Allows reproducibility (data and/or methods) • Technology (less cumbersome) is becoming available • Enhanced visibility/transparency • Multiple citable outcomes: data, code, manuscript, etc. Why embrace Open Practices? 

• Lack of interest/knowledge (supplemental rarely posted) • Low incentive/pressure - that may change! • Takes (huge) time and effort • Document data and code • Versioning code and maintaining • FOBS - Fear of being scooped? Barriers for open practices? 

www.openplantpathology.org How can we change that? 

Open Plant Pathology (OPP) fosters a diverse community culture that values open, transparent and reproducible research using shared data and reusable software Vision and mission 

By creating a social network with a welcoming and sharing scientific community openplantpathology.slack.com Expanding network Sharing knowledge Brainstorming Building capacity (Hopefully!) more transparent, reproducible, efficient and reliable Plant Pathology research 1. Social Workspace 

to promote the initiative and publish community outcomes Community chat Website & Blog Data repository Code & Data Files 2. Infrastructure 

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Twitter 

GitHub 

Website 

R package: hagis 

ICPP2018 - Workshops ● Network Analysis in Plant Pathology ● Applications of Information Theory in Plant Disease Management: Theory and Practice ● Introduction to Multivariate Statistics Using R ● Population Genomics in R APPS 2019 - Workshop ● R Markdown for Scientists Previous activities 

Repositories • AfricaRice/IRRI Tanzania work • https://github.com/adamhsparks/MICCORDEA • STRASA mapping disease in India • https://github.com/adamhsparks/STRASA-Biotic-Stress-Maps • Metamodelling potato late blight • https://github.com/adamhsparks/Global-Late-Blight-MetaModelling 

R Package Resources epicrop, the EPIRICE (and hopefully EPIWHEAT) model https://github.com/adamhsparks/epicrop GSODR/nasapower, global daily weather data https://github.com/ropensci/GSODR (station) https://github.com/ropensci/nasapower (satellite/model) chirps, rainfall from TRMM https://github.com/ropensci/chirps (satellite/model) getCRUCL2.0, CRU Climate data https://github.com/ropensci/getCRUCLdata hagis, Plant pathogen pathotype complexities, distributions and analysis https://github.com/openplantpathology/hagis 

Thank you Visit dpird.wa.gov.au Important disclaimer The Chief Executive Officer of the Department of Primary Industries and Regional Development and the State of Western Australia accept no liability whatsoever by reason of negligence or otherwise arising from the use or release of this information or any part of it. © State of Western Australia 2018