Forecast and Virtual Weather Driven Plant Disease Risk Modeling System L. Coop 1, A. Fox 2, W....
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Forecast and Virtual Weather Driven Plant Disease Risk Modeling System L. Coop 1 , A. Fox 2 , W. Mahaffee 3 , D. Gent 3 , W. Pfender 3 , C. Daly 4 , C. Thomas 5 , P. Jepson 1 INTRODUCTION & OBJECTIVES An important approach to mitigating risks and costs of damage from crop diseases and pests is to use management decision aids based on warning models. Fully-validated, weather- dependent models now exist for many of the high-value crops grown in the Western US. Lack of nearby weather stations and integrated forecasts have remained a major constraint to adoption of pest models. The Western weather system workgroup has been focusing for several years on development of solutions to these problems, and here we show progress to date (largely funded by USDA-NRI Plant Biosecurity Program and USDA NPDN (National Plant Diagnostics Network) in the practical development of virtual weather stations, which we define as estimated site- specific weather data that can serve as a direct substitute for an actual weather observation station. Objectives include the research and development of errors and uncertainties in estimation of near-realtime, real-time,and forecasted weather data, integration with hourly weather driven plant disease risk models, web delivery, and full field and statistical testing of virtual weather and disease risk models in multiple cropping systems and growing regions. Status Summary: Virtual data are showing excellent potential for current implementation while ongoing and proposed improvements are expected to greatly benefit wider application, both for plant biosecurity and IPM needs. Strategy for Virtual Weather Development and Implementation: - All existing stations have virtual analogs, available for statistical performance evaluations (examples below), for fill-in of missing data (QA system, in development), and gridded and mapped model outputs (proposed) - Offer end user-selected virtual stations for “My Pest Models” page (in development, USDA WRIPM grant), including a) user selects location lat- longs, b) Nearby actual weather selected to allow performance statistics, c) user selects models from pull-down list, d) choice of forecast engines including NWS Digital and Fox Weather LLC http://uspest.org/wea - Home Page 13,000+ weather stations – used with QA/QC and distance elevation weighted regressio n (DEWR) for type V2 virtual weather creation. 1 Integrated Plant Protection Center, Oregon State University, Corvallis, OR, 2 Fox Weather LLC, Fortuna, CA, 3 USDA ARS, Corvallis, OR, 4 College of Oceanic and Atmospheric Sciences, OSU, Corvallis, OR, 5 UC Davis, Plant Pathology, Davis, CA Plant Disease risk model interface with multiple models (11 currently available), using virtual (left) and actual (right) weather data. Online statistical analysis of virtual weather data. Several standard measures are included: MAE, ME (bias), R 2 , index of precision (d), for numerous evolving virtual data types. Histograms shown; scatterplots are in development. A field trial in small plots of 5 vines and reps/treatment. Sulfur application for powdery mildew began at 6in (6inches of shoot growth) or 10/2.5 (daily average temp 10 C with 2.5 mm rain fall) or calendar (every 10-14 days). Water control - same volume of water but no fungicide. 10/2.5 GT uses site specific data to run Gubler Thomas model while the 6in interpolated GT uses virtual data from the site. Comparison of multiple virtual data types. Previous day weather used as persistence skill score comparitor. To date, V2 (DEWR) out-performs all forecast-based data for all weather parameters, while further improvements have been proposed including full PRISM based estimation. Virtual data can be gridded and used for high-resolution terrain-sensitive plant disease risk maps, such as this prototype PRISM-based risk forecast map for grass seed stem rust under a cool weather pattern (left) and warm, dry weather pattern later in the season (right). Field Research Results. Left side - Measurement error associated with temperature sensor placement in 3 crops (A: grapes, B: hop, C: grass seed). Right side - In-canopy (open triangles) and standard-height (closed triangles) temperature measurements: model outputs and management actions (sprays). Powdery mildew index for grapes (A) and hop (B). Simulation model for grass seed (C), where observed disease (bottom two lines) are included with simulated total (latent plus active) disease (upper two lines) and action threshold (top, dashed line) that is triggered by total disease calculation.
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Transcript of Forecast and Virtual Weather Driven Plant Disease Risk Modeling System L. Coop 1, A. Fox 2, W....
Forecast and Virtual Weather Driven Plant Disease Risk Modeling SystemL. Coop1, A. Fox2, W. Mahaffee3, D. Gent3, W. Pfender3, C. Daly4, C. Thomas5, P. Jepson1
INTRODUCTION & OBJECTIVESAn important approach to mitigating risks and costs of damage from crop diseases and pests is to use management decision aids based on warning models. Fully-validated, weather-dependent models now exist for many of the high-value crops grown in the Western US. Lack of nearby weather stations and integrated forecasts have remained a major constraint to adoption of pest models.The Western weather system workgroup has been focusing for several years on development of solutions to these problems, and here we show progress to date (largely funded by USDA-NRI Plant Biosecurity Program and USDA NPDN (National Plant Diagnostics Network) in the practical development of virtual weather stations, which we define as estimated site-specific weather data that can serve as a direct substitute for an actual weather observation station. Objectives include the research and development of errors and uncertainties in estimation of near-realtime, real-time,and forecasted weather data, integration with hourly weather driven plant disease risk models, web delivery, and full field and statistical testing of virtual weather and disease risk models in multiple cropping systems and growing regions. Status Summary: Virtual data are showing excellent potential for current implementation while ongoing and proposed improvements are expected to greatly benefit wider application, both for plant biosecurity and IPM needs.
Strategy for Virtual Weather Development and Implementation: - All existing stations have virtual analogs, available for statistical performance evaluations (examples below), for fill-in of missing data (QA system, in development), and gridded and mapped model outputs (proposed)
- Offer end user-selected virtual stations for “My Pest Models” page (in development, USDA WRIPM grant), including a) user selects location lat-longs, b) Nearby actual weather selected to allow performance statistics, c) user selects models from pull-down list, d) choice of forecast engines including NWS Digital and Fox Weather LLC
http://uspest.org/wea - Home Page
13,000+ weather stations – used with QA/QC and distance elevation weighted regression (DEWR) for type V2 virtual weather creation.
1Integrated Plant Protection Center, Oregon State University, Corvallis, OR, 2Fox Weather LLC, Fortuna, CA, 3USDA ARS, Corvallis, OR, 4College of Oceanic and Atmospheric Sciences, OSU, Corvallis, OR, 5UC Davis, Plant Pathology, Davis, CA
Plant Disease risk model interface with multiple models (11 currently available), using virtual (left) and actual (right) weather data.
Online statistical analysis of virtual weather data. Several standard measures are included: MAE, ME (bias), R2, index of precision (d), for numerous evolving virtual data types. Histograms shown; scatterplots are in development.
A field trial in small plots of 5 vines and reps/treatment. Sulfur application for powdery mildew began at 6in (6inches of shoot growth) or 10/2.5 (daily average temp 10 C with 2.5 mm rain fall) or calendar (every 10-14 days). Water control - same volume of water but no fungicide. 10/2.5 GT uses site specific data to run Gubler Thomas model while the 6in interpolated GT uses virtual data from the site.
Comparison of multiple virtual data types. Previous day weather used as persistence skill score comparitor. To date, V2 (DEWR) out-performs all forecast-based data for all weather parameters, while further improvements have been proposed including full PRISM based estimation.
Virtual data can be gridded and used for high-resolution terrain-sensitive plant disease risk maps, such as this prototype PRISM-based risk forecast map for grass seed stem rust under a cool weather pattern (left) and warm, dry weather pattern later in the season (right).
Field Research Results. Left side - Measurement error associated with temperature sensor placement in 3 crops (A: grapes, B: hop, C: grass seed). Right side - In-canopy (open triangles) and standard-height (closed triangles) temperature measurements: model outputs and management actions (sprays). Powdery mildew index for grapes (A) and hop (B). Simulation model for grass seed (C), where observed disease (bottom two lines) are included with simulated total (latent plus active) disease (upper two lines) and action threshold (top, dashed line) that is triggered by total disease calculation.
