national agricultural decision support system (nadss) pi: steve goddard an application of...
TRANSCRIPT
National Agricultural National Agricultural Decision Support Decision Support System (NADSS)System (NADSS)
PI: Steve Goddard
An Application of Geo-Spatial Decision Support to Agriculture Risk Management
What is NADSS?What is NADSS? The National Agricultural Decision Support The National Agricultural Decision Support
System (NADSS) is a distributed web-based System (NADSS) is a distributed web-based application to help decision makers assess application to help decision makers assess various risk factorsvarious risk factors our research has focused primarily on droughtour research has focused primarily on drought
we are investigating ways to use the system to we are investigating ways to use the system to create tools to aide in the identification of risk create tools to aide in the identification of risk areasareas
Using various data and computational indices Using various data and computational indices we are able to create tabular data for analysis we are able to create tabular data for analysis as well as maps for further spatial analysisas well as maps for further spatial analysis
The PartnershipThe Partnership
National Science Foundation’s Digital Government Program
National Drought Mitigation Center, University of Nebraska--Lincoln
High Plains Regional Climate Center, UNL
USDA Risk Management Agency, Natural Resources Conservation Service, National Agricultural Statistics Service, and the Farm
Service Agency
USGS EROS Data Center
Nebraska Research Initiative on Geospatial Decision Support Systems
GIS Workshop
FundingFunding
Source: Source: NSF: $1 Million, 7/01—1/05NSF: $1 Million, 7/01—1/05
Title: Title: DIGITAL GOVERNMENT: A Geospatial Decision DIGITAL GOVERNMENT: A Geospatial Decision Support System for Drought Risk ManagementSupport System for Drought Risk Management
Principal InvestigatorsPrincipal Investigators: Steve Goddard, Jitender Deogun, Michael J. : Steve Goddard, Jitender Deogun, Michael J. Hayes, Kenneth G. Hubbard, Stephen Reichenbach, Peter Revesz, W.J. Hayes, Kenneth G. Hubbard, Stephen Reichenbach, Peter Revesz, W.J. Waltman, Donald A. Wilhite, and Mark D. Svoboda, University of Waltman, Donald A. Wilhite, and Mark D. Svoboda, University of Nebraska-Lincoln (UNL), Lincoln, Nebraska 68588-0115. Nebraska-Lincoln (UNL), Lincoln, Nebraska 68588-0115. ([email protected])([email protected])Co-InvestigatorsCo-Investigators: Sheri K. Harms, University of Nebraska-Kearney; : Sheri K. Harms, University of Nebraska-Kearney; J.S. Peake, University of Nebraska-Omaha; Ray Sinclair and Sharon J.S. Peake, University of Nebraska-Omaha; Ray Sinclair and Sharon Waltman, USDA Natural Resources Conservation Service, National Waltman, USDA Natural Resources Conservation Service, National Soil Survey Center, Lincoln, NE; and Marcus Tooze, GIS Workshop, Soil Survey Center, Lincoln, NE; and Marcus Tooze, GIS Workshop, Lincoln, NE.Lincoln, NE.
FundingFunding
Source: Source: USDA RMA/FCIC: $1.3 Million, 10/02—3/05USDA RMA/FCIC: $1.3 Million, 10/02—3/05
Title: Title: RISK ASSESSMENT AND EXPOSURE ANALYSIS RISK ASSESSMENT AND EXPOSURE ANALYSIS ON THE AGRICULTURAL LANDSCAPE: A Holistic ON THE AGRICULTURAL LANDSCAPE: A Holistic Approach to Spatio-Temporal Models and Tools for Agricultural Approach to Spatio-Temporal Models and Tools for Agricultural Risk Assessment and Exposure AnalysisRisk Assessment and Exposure Analysis
Principal InvestigatorsPrincipal Investigators: Steve Goddard, Jitender Deogun, Michael J. : Steve Goddard, Jitender Deogun, Michael J. Hayes, Kenneth G. Hubbard, H. Douglas Jose, Stephen Reichenbach, Hayes, Kenneth G. Hubbard, H. Douglas Jose, Stephen Reichenbach, W.J. Waltman, Donald A. Wilhite, and Mark D. Svoboda, University of W.J. Waltman, Donald A. Wilhite, and Mark D. Svoboda, University of Nebraska-Lincoln (UNL), Lincoln, Nebraska 68588-0115. Nebraska-Lincoln (UNL), Lincoln, Nebraska 68588-0115. ([email protected])([email protected])Co-InvestigatorsCo-Investigators: Norman Bliss, EROS Data Center; Sioux Falls, SD: : Norman Bliss, EROS Data Center; Sioux Falls, SD: Sheri K. Harms, University of Nebraska-Kearney; and J.S. Peake, Sheri K. Harms, University of Nebraska-Kearney; and J.S. Peake, University of Nebraska-Omaha; Ray Sinclair and Sharon Waltman, University of Nebraska-Omaha; Ray Sinclair and Sharon Waltman, USDA Natural Resources Conservation Service, National Soil Survey USDA Natural Resources Conservation Service, National Soil Survey Center, Lincoln, NE; and Marcus Tooze, GIS Workshop, Lincoln, NE.Center, Lincoln, NE; and Marcus Tooze, GIS Workshop, Lincoln, NE.
NADSS Web SiteNADSS Web Site
http://nadss.unl.edu/http://nadss.unl.edu/
Current ToolsCurrent Tools Our current tools apply Our current tools apply
risk analysis risk analysis methodologies to the methodologies to the study of droughtstudy of drought Integration of basic Integration of basic
models with data models with data generates “information” generates “information” for analysis by decision for analysis by decision makersmakers
Information can be Information can be gathered at any gathered at any resolution for which we resolution for which we have datahave data
http://nadss.unl.eduhttp://nadss.unl.edu
Current NADSS ToolsCurrent NADSS Tools
Current Current NADSS NADSS ToolsTools
Planting date guide tool with date sliders, numerical information, and navigation buttons.
Sample risk analysis maps of growing non-irrigated corn in NE and Custer county.
Proposed NADSS Proposed NADSS ToolsTools
An Irrigation Scheduling tool that will help producers better manage their limited water resources, decrease the use of energy for pumping, and decrease the risk of drought stress.
Irrigation Schedule
02468
10121416
120 130 140 150 160 170 180 190 200
Calendar Day
So
il M
ois
ture
(in
ches
)Saturation
Field Cap
Wilting Pt
Actual
Another Another Proposed NADSS Proposed NADSS
ToolToolA Crop-Specific Yield Prediction tool that will provide the producer with an estimate of yield based on the weather up to the current date and projections of what it might be from the current date to the end of the growing season. CSDI Ensemble Projection from day 195 to the end of the season
(CSDI=Y/Yp)
0.4
0.5
0.6
0.7
0.8
0.9
1
100 120 140 160 180 200 220 240 260
Calendar Day
CS
DI
Ensemble Prediction from day 195 to end of season
0
20
40
60
80
100
0.45 0.55 0.65 0.75 0.85 0.95
CSDI
Pro
b.
of
CS
DI
>sh
ow
n
Another Another Proposed NADSS Proposed NADSS
ToolToolA Field Analyst tool that can, for example, analyze the soil quality for a particular field based on the NRCS Soil Rating for Plant Growth (SRPG) index.
It can also be used by a producer to evaluate “value added” when new fields are put into service or removed from service.
Another Proposed Another Proposed NADSS Tool: Field NADSS Tool: Field Analyst continuedAnalyst continued
Following the example using an SRPG analysis, when both an original field and field addition have been digitized, the Field Analyst provides the user with the SRPG of the combined fields, and whether the field addition had a positive or negative affect on the overall soil quality.
Building a Spatial ViewBuilding a Spatial View Data from information and knowledge layers are Data from information and knowledge layers are
translated spatially and interpolated to provide a translated spatially and interpolated to provide a “risk view” for a defined area“risk view” for a defined area
Drought Indices
Soil Data
Climate Data
Reported Yields
Raster interpolation of data points within various windows
Inverse Distance Weighting
Spline
Kriging
Re-summarization of raster data
Generation of displayable images
Risk Indicators Surfacing Display
Other Data Type
Combining Risk FactorsCombining Risk Factors
By combining several domain specific factors By combining several domain specific factors from our “information layer” we are able to from our “information layer” we are able to create maps displaying the risk for states, create maps displaying the risk for states, regions or countriesregions or countries
The user adjusts weight factors for
each variable
The result is a “spatial” view of risk
Variables are spatially rendered
ConclusionConclusion
We have developed the framework for We have developed the framework for a a Distributed Geospatial Decision Distributed Geospatial Decision Support SystemSupport System architecture that can architecture that can be applied to other problems and be applied to other problems and domainsdomains
For example, we can integrate water For example, we can integrate water models, economic models and even models, economic models and even threat models into the system.threat models into the system.