1. National Agricultural Decision Support System (NADSS) A Geospatial Decision Support System for Agricultural Risk Management Principal Investigator: S. Goddard, Co-Principals: J. Deogun, M.J. Hayes, K.G. Hubbard, S.E. Reichenbach, P.Z. Revesz, W.J. Waltman, and D.A. Wilhite Co-Investigators: M.E. Tooze, S.K. Harms, J.S. Peake, and T. Tadesse

2. The Partnership lNational Science Foundation’s Digital Government Program lNational Drought Mitigation Center, University of Nebraska--Lincoln lHigh Plains Regional Climate Center, UNL lUSDA Risk Management Agency, Natural Resources Conservation Service, National Agricultural Statistics Service, and the Farm Service Agency lUSGS EROS Data Center lNebraska Research Initiative on Geospatial Decision Support Systems lGIS Workshop

3. Funding Source: NSF: $1 Million, 7/01—6/04 Title: DIGITAL GOVERNMENT: A Geospatial Decision Support System for Drought Risk Management Principal Investigators: Steve Goddard, Jitender Deogun, Michael J. Hayes, Kenneth G. Hubbard, Stephen Reichenbach, Peter Revesz, W.J. Waltman, Donald A. Wilhite, and Mark D. Svoboda, University of Nebraska-Lincoln (UNL), Lincoln, Nebraska 68588-0115. (goddard@cse.unl.edu) Co-Investigators: Sheri K. Harms, University of Nebraska-Kearney; J.S. Peake, University of Nebraska-Omaha; Ray Sinclair and Sharon Waltman, USDA Natural Resources Conservation Service, National Soil Survey Center, Lincoln, NE; and Marcus Tooze, GIS Workshop, Lincoln, NE.

4. Funding Source: USDA RMA/FCIC: $1.3 Million, 10/02—9/04 Title: RISK ASSESSMENT AND EXPOSURE ANALYSIS ON THE AGRICULTURAL LANDSCAPE: A Holistic Approach to Spatio-Temporal Models and Tools for Agricultural Risk Assessment and Exposure Analysis Principal Investigators: Steve Goddard, Jitender Deogun, Michael J. Hayes, Kenneth G. Hubbard, H. Douglas Jose, Stephen Reichenbach, W.J. Waltman, Donald A. Wilhite, and Mark D. Svoboda, University of Nebraska-Lincoln (UNL), Lincoln, Nebraska 68588-0115. (goddard@cse.unl.edu) Co-Investigators: Norman Bliss, EROS Data Center; Sioux Falls, SD: Sheri K. Harms, University of Nebraska-Kearney; and J.S. Peake, University of Nebraska-Omaha; Ray Sinclair and Sharon Waltman, USDA Natural Resources Conservation Service, National Soil Survey Center, Lincoln, NE; and Marcus Tooze, GIS Workshop, Lincoln, NE.

5. Project Goals Develop a support system of geospatial analyses that will enhance agricultural risk assessment and exposure analysis. Initial emphasis is on drought. Develop a support system of geospatial analyses that will enhance agricultural risk assessment and exposure analysis. Initial emphasis is on drought. Compute and map drought indices at increased spatial and temporal resolutions. Compute and map drought indices at increased spatial and temporal resolutions. Provide transparent access to distributed geospatial, relational, and constraint databases. Provide transparent access to distributed geospatial, relational, and constraint databases. Develop new algorithms (using data mining and knowledge discovery techniques) that seek out patterns between weather stations, crop yields, and ENSO events. Develop new algorithms (using data mining and knowledge discovery techniques) that seek out patterns between weather stations, crop yields, and ENSO events. Develop new geospatial analyses to better visualize the emergence, evolution, and movement of drought. Develop new geospatial analyses to better visualize the emergence, evolution, and movement of drought.

6. National Agricultural Decision Support System (NADSS) http://nadss.unl.edu/ http://nadss.unl.edu/

7. Current Tools Our current tools apply risk analysis methodologies to the study of drought Our current tools apply risk analysis methodologies to the study of drought Integration of basic models with data generates “information” for analysis by decision makers Integration of basic models with data generates “information” for analysis by decision makers Information can be gathered at any resolution for which we have data Information can be gathered at any resolution for which we have data http://nadss.unl.edu http://nadss.unl.edu

8. Current NADSS Tools

9. Prototype planting date guide tool with climograph, date sliders, numerical information, and navigation buttons. Sample Climograph and Soil Moisture Regime probability analysis map

10. Building a Spatial View Data from information and knowledge layers are translated spatially and interpolated to provide a “risk view” for a defined area Data from information and knowledge layers are translated spatially and interpolated to provide a “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 SurfacingDisplay Other Data Type

11. Risk Assessment in Practice By combining several domain specific factors from our “information layer” we are able to create maps displaying the risk for states, regions or countries By combining several domain specific factors from our “information layer” we are able to create maps displaying the risk for states, regions or countries The user adjusts weight factors for each variable The result is a “spatial” view of risk Variables are spatially rendered

12. Risk Assessment Total Market Value Dairy Farms Beef Farms Projecting Potential Impacts for Decision- Makers as County Profiles Congressional Delegation State Legislature USDA and State Agencies Commodity Groups and Agribusiness

13. Data Mining and Knowledge Discovery Corn Grain Yields (Bu/acre) Clinton County 1966 1973-1974 1991? 1988 Annual Milk Production Year ENSO events and other El Nino/La Nina processes can serve as a trigger mechanism for drought. Mining the patterns between crop yields and ENSO signals may provide new insights to risk management and fore- casting potential impacts on cropping systems. Genetic Improvement and Management Year NIR Corn Yields In Nebraska Through Time

14. Distributed Geospatial Decision Support System Architecture HTTP IIOP RMI TCP Presentation (User Interface) e.g., Web Interface, Java applet Knowledge Layer e.g., Exposure Analysis, Risk Assessment Data cache Distributed Spatial and Relational Data e.g., Climatic Variables, Agricultural Statistics Data cache e.g., Drought Indices, Regional Crop Losses Information Layer HTTP IIOP RMI TCP Presentation (User Interface) e.g., Web Interface, Java applet Presentation (User Interface) e.g., Web Interface, Java applet Knowledge Layer e.g., Exposure Analysis, Risk Assessment Data cache Knowledge Layer e.g., Data Mining, Exposure Analysis, Risk Assessment Data cache Distributed Spatial and Relational Data e.g., Climatic Variables, Agricultural Statistics Data cache e.g., Drought Indices, Regional Crop Losses Information Layer Data cache e.g., Drought Indices, Regional Crop Losses Information Layer

15. 4-Layer Architecture for NADSS

16. NADSS Benefits and Impacts Improving spatial and temporal analysis for drought risk management Improving spatial and temporal analysis for drought risk management State level to County level to Field level State level to County level to Field level Monthly Index to Weekly Index (SPI, PDSI, Newhall Simulation Model) Monthly Index to Weekly Index (SPI, PDSI, Newhall Simulation Model) Responding to drought events more effectively Responding to drought events more effectively

17. Conclusion We are addressing Data Interpretation and Data Integration problems by creating a Distributed Geospatial Decision Support System architecture We are addressing Data Interpretation and Data Integration problems by creating a Distributed Geospatial Decision Support System architecture The Distributed Geospatial Decision Support System architecture is applicable to many other distributed geospatial decision support systems The Distributed Geospatial Decision Support System architecture is applicable to many other distributed geospatial decision support systems

18. Application Layer (user interface) e.g. Web interface, EJB, servlets Knowledge Layer e.g. Data Mining, Exposure Analysis, Risk Assessment Information Layer e.g. Drought Indices, Regional Crop Losses Data Layer e.g. Climate Variables, Agriculture Statistics Spatial Layer e.g. spatial analysis and rendering tools Any component can communication with components in other layers above or below it Any component can communication with components in other layers above or below it Each layer is tied to the spatial layer, allowing the data from any layer to be rendered spatially Each layer is tied to the spatial layer, allowing the data from any layer to be rendered spatially