Why Models Need Standards Update on AgGateway Initiative

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Egbert Sullivan
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1 Why Models Need Standards Update on AgGateway Initiative Joe Russo, Jean Batzer, Mark Gleason and Roger Magarey Midwest Weather Working Group 6 th Annual Meeting Austin, Texas August 9, 2013

Models/Data/Risk in Management Decision Making Data Viewing In Situ Sensors Alerts Guidance Record Keeping Equip Sensors As-Applied Remote Sensing April May June July August September Yield Monitor

3 Models/Data/Risk in Management Decision Making Data Viewing In Situ Sensors Alerts Guidance Record Keeping Equip Sensors As-Applied Remote Sensing April May June July August September Yield Monitor Planting Irrigation Application Scouting Tillage Harvest Data Management Field Communication Field Communication Irrigation Scheduling Fertilizer/Chemical Applications Soil/Field Sampling Degree Day Tracking Scouting Web Access Models Models Models Models Risk Analysis April May June July August September Data Copyright 2013 ZedX Inc. Offsite Data Sources

4 Why Models Need Standards Convert input weather and other data to standard spatial and temporal scales. Quantify spatial and temporal uncertainties associated with standard input (weather) data scales. The combined uncertainties can define input accuracy. Create a standard glossary of variables and units. Create a standard variable nomenclature. Establish standard truth sets to evaluate model output precision and accuracy as a function of input accuracy. Create standard terms for model output. Specify experimental protocols for model verification. Define a standard application programming interface (API) for exchanging model configuration and output files in a standard format.

5 Scale of Weather Data Sources Relative to Farm, Field, Canopy CFSv2 GFS, NOGAPS NAM, RUC CFSv2 + NWS station network WRF, RTMA, GFSMOS + NWS graphical forecast (grid) m SkyBit grid m PRISM grid Agricultural decision-making scales in blue box Precision Agriculture Copyright 2013 ZedX Inc.

Grid Versus Station Weather Data........ Weather Stations Source: http://www.

10 Why Models Need Standards Convert input weather and other data to standard spatial and temporal scales.

12 Value Value Uncertainty Associated With Temporal Scales and Forecasts Observation 1- hour 6-hour 1-day 1-week 1-month 6-month 1-year Forecast 6-day 5-day 4-day 3-day 2-day 1-day Observation Copyright 2013 ZedX Inc.

13 Why Models Need Standards Convert input weather and other data to standard spatial and temporal scales. Quantify spatial and temporal uncertainties associated with standard input (weather) data scales. The combined uncertainties can define input accuracy.

15 Why Models Need Standards Convert input weather and other data to standard spatial and temporal scales. Quantify spatial and temporal uncertainties associated with standard input (weather) data scales. The combined uncertainties can define input accuracy. Create a standard glossary of variables and units. Copyright 2013 ZedX Inc.

16 Standard Variable Nomenclature (1) Give variable name. Examples, temperature maximum temperature (2) Give original sampling interval. Examples, hourly temperature daily maximum temperature (3) State any statistics used to create a new variable from observations. Examples, daily average temperature (1/2maximum + 1/2minimum) (4) State any statistics used to derive a mean of the original observations for longer periods. You must state period of mean as days, months, years, etc. Examples, monthly mean of daily average temperature for 1961 monthly means of daily average temperature for 1961 thru 1995 annual mean of hourly temperature for 1961 annual means of hourly temperature for 1961 thru 1995 (5) State any statistics used to derive a normal for a long-term climatology. You must state period of normal as days, months, years, etc. Examples, 30-year normal of monthly means of daily average temperature for year normal of annual means of hourly temperature for 1961 thru 1970 Note: It is implicitly understood that the monthly mean is for the same period as the normal. Copyright 2013 ZedX Inc.

17 Why Models Need Standards Convert input weather and other data to standard spatial and temporal scales. Quantify spatial and temporal uncertainties associated with standard input (weather) data scales. The combined uncertainties can define input accuracy. Create a standard glossary of variables and units. Create a standard variable nomenclature.

18 Model Output Precision and Accuracy Input Output Model (Black Box) ~ Model output compared (~) to truth set = Observation/ Forecast Input Accuracy = Model Output Precision = Model Output Accuracy Copyright 2013 ZedX Inc.

19 Model Output Precision and Accuracy How do we measure dew? Truth vs. Electric resistance threshold establishment droplet size sensor surface sensor angle compass direction canopy location

20 Why Models Need Standards Convert input weather and other data to standard spatial and temporal scales. Quantify spatial and temporal uncertainties associated with standard input (weather) data scales. The combined uncertainties can define input accuracy. Create a standard glossary of variables and units. Create a standard variable nomenclature. Establish standard truth sets to evaluate model output precision and accuracy as a function of input accuracy. Copyright 2013 ZedX Inc.

21 Terms for Plant Pathology Presence or absence of a disease in a canopy. Disease incidence is the proportion of a plant community that is infected. Disease severity is the proportion of a plant that is infected. Pathogen (urediniospore) intensity is the number of urediniospores per unit host area (cm 2 ). Disease counts are the number of uredinia or pustules per unit area. Primary disease counts are the number of uredinia resulting from primary infection due to the first deposition of urediniospores.

area or fruit volume destroyed by a pathogen Disease

22 Terms for crop loss assessment depends on the disease Disease severity: percentage or proportion of plant area or fruit volume destroyed by a pathogen Disease incidence: proportion of a plant community that is diseased

23 Why Models Need Standards Convert input weather and other data to standard spatial and temporal scales. Quantify spatial and temporal uncertainties associated with standard input (weather) data scales. The combined uncertainties can define input accuracy. Create a standard glossary of variables and units. Create a standard variable nomenclature. Establish standard truth sets to evaluate model output precision and accuracy as a function of input accuracy. Create standard terms for model output.

Observational Protocol for Model Verification Can you use a 5 km area model to predict disease in

Observational Protocol for Model Verification Biglerville,

26 Observational Protocol for Model Verification Biglerville, PA Experimental Site Credit: Jim Travis, Noemi Halbrendt, Penn State Fruit Research & Extension Station, Biglerville, PA

28 % Disease % Disease % Disease % Disease Observational Protocol for Model Verification 2009 Scab Incidence on Shoot Leaves, Red Delicious University Drive Orchard, PSU-FREC, Biglerville, PA North Top of Block (R1) Center Canopy Date Top Canopy Slope Side of Block (R2 Center Canopy Top Canopy Date Center Block (R3) South Block (R4 Center Canopy Top Canopy Center Canopy Top Canopy Date Date Credit: Jim Travis, Noemi Halbrendt, Penn State Fruit Research & Extension Station, Biglerville, PA

% Disease % Disease % Disease % Disease 100 80 60 40 20 0 Observational Protocol for Model Verification 2009 Scab Incidence on Shoot Leaves, Red Delicious University Drive Orchard, PSU-FREC,

29 % Disease % Disease % Disease % Disease Observational Protocol for Model Verification 2009 Scab Incidence on Shoot Leaves, Red Delicious University Drive Orchard, PSU-FREC, Biglerville, PA North Top of Block (R1) Center Canopy PA-PIPE 5 km Model Date Top Canopy Slope Side of Block (R2 Center Canopy Top Canopy Date Center Block (R3) South Block (R4 Center Canopy Top Canopy Center Canopy Top Canopy Date Date Credit: Jim Travis, Noemi Halbrendt, Penn State Fruit Research & Extension Station, Biglerville, PA

30 Why Models Need Standards Convert input weather and other data to standard spatial and temporal scales. Quantify spatial and temporal uncertainties associated with standard input (weather) data scales. The combined uncertainties can define input accuracy. Create a standard glossary of variables and units. Create a standard variable nomenclature. Establish standard truth sets to evaluate model output precision and accuracy as a function of input accuracy. Create standard terms for model output. Specify experimental protocols for model verification.

31 Standard Model Configuration and Output Files Model Configuration File File Name: corn_country_state_xxx (county fips)_xxx (user number).conf Group= Corn Models Country= US State= PA (042) County= 027 (Centre) Site Name= ZedX Site ID= US_PA_027_001 Latitude, LAT= Longitude, LON= Elevation, ELV= 329 (meters) Standard Time Offset= -5 Daylight Start Date= Daylight End Date= Data Type= Obs Data Type= Fct Product= Standard Corn Phenology Sim Start Date= Sim End Date= User Para= crop= crn User Para= var= pioneer 3394 User Para= cmr= 110 Std Para= oset Std Para= phen User Para= seed= 2 Std Para= crdx User Para= pldate= User Para= stex= sicl User Para= sdep= 20 Std Data= gcrnut Std Data= gcrnuc User Data= scrn Std Data= crdcrn

Output XML FMIS Standard XML FMIS XML ISO XML ISO Standard XML Telematics Controller Core Data Consultant FMIS 1 AgFleet

32 Standard Model Application Programming Interface (API) Manufacturer Seed Company 1 API Models (3 rd Party) API Management Template (USDA) Farmer FMIS 2 Wireless Media Seed Company 2 Equipment Company 1 AgGateway API API Product XML Equip XML Output XML FMIS Standard XML FMIS XML ISO XML ISO Standard XML Telematics Controller Core Data Consultant FMIS 1 AgFleet Service Provider FMIS 3 Local Data Implement Tractor XML AgGateway Standard XML FMIS XML Standard XML ISO Standard

33 Why Models Need Standards Convert input weather and other data to standard spatial and temporal scales. Quantify spatial and temporal uncertainties associated with standard input (weather) data scales. The combined uncertainties can define input accuracy. Create a standard glossary of variables and units. Create a standard variable nomenclature. Establish standard truth sets to evaluate model output precision and accuracy as a function of input accuracy. Create standard terms for model output. Specify experimental protocols for model verification. Define a standard application programming interface (API) for exchanging model configuration and output files in a standard format.

34 Offsite (Model) Data Specifications 1. Name of Data Set 2. Source (as in organization or individual) 3. Contact (to receive data) 4. Content of Data Set 5. Units (metric will be the standard) 6. Record Length (first to last date of availability) 7. Quality Control/Quality Assurance Procedure 8. Data Collection Protocol (material and method) 9. Domain (extent of area where data are available) 10. Spatial Resolution 11. Temporal Resolution 12. Update Frequency 13. Data Format (for transfer between source and user) 14. Storage Requirements (minimum size on user end) 15. Usage Policy (any restriction on type of user) 16. Citation Policy (when distributing data) 17. Privacy Policy 18. Cost 19. Access Instructions (for transfer between source and user) 20. Comment

35 Why Models Need Standards Convert input weather and other data to standard spatial and temporal scales. Quantify spatial and temporal uncertainties associated with standard input (weather) data scales. The combined uncertainties can define input accuracy. Create a standard glossary of variables and units. Create a standard variable nomenclature. Establish standard truth sets to evaluate model output precision and accuracy as a function of input accuracy. Create standard terms for model output. Specify experimental protocols for model verification. Define a standard application programming interface (API) for exchanging model configuration and output files in a standard format. Identify offsite (model) data specifications.

36 Thank You! Questions?

Application of the Integrated Aerobiology Modeling System to Soybean Rust Forecasting in 2006

Application of the Integrated Aerobiology Modeling System to Soybean Rust Forecasting in 2006 Scott A. Isard Penn State University & Joseph M. Russo ZedX Inc. Integrated Aerobiology Modeling System (IAMS)