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. (goddard@cse.unl.edu)(goddard@cse.unl.edu)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. (goddard@cse.unl.edu)(goddard@cse.unl.edu)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

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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

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CS

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ow

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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.