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National Agricultural Decision Support System (NADSS) 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 A Geospatial Decision Support System for Agricultural Risk Management The 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 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. ([email protected]) 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. 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. ([email protected]) 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. Project Goals 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. 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 geospatial analyses to better visualize the emergence, evolution, and movement of drought. National Agricultural Decision Support System (NADSS) http://nadss.unl.edu/ Current Tools 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 Information can be gathered at any resolution for which we have data http://nadss.unl.edu Current NADSS Tools Current NADSS Tools Prototype planting date guide tool with climograph, date sliders, numerical information, and navigation buttons. Sample Climograph and Soil Moisture Regime probability analysis map Building a Spatial View Data from information and knowledge layers are translated spatially and interpolated to provide a “risk view” for a defined area Risk Indicators Drought Indices Soil Data Climate Data Reported Yields Other Data Type Surfacing Raster interpolation of data points within various windows Inverse Distance Weighting Spline Kriging Display Re-summarization of raster data Generation of displayable images 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 The user adjusts weight factors for each variable Variables are spatially rendered The result is a “spatial” view of risk Risk Assessment Total Market Value Projecting Potential Impacts for DecisionMakers as County Dairy Farms Profiles Beef Farms Congressional Delegation State Legislature USDA and State Agencies Commodity Groups and Agribusiness Data Mining and Knowledge Discovery NIR Corn Yields In Nebraska Through Time Annual Milk Production Non-Irrigated Corn Yields of Nebraska Through Time Corn Yield (Bushels/Acre) 140 Clinton County 120 y = 1.6509x - 3195 100 R2 = 0.7646 80 60 1991? 1973-1974 40 1988 1966 20 0 1940 1950 1960 1970 Year Year 1980 1990 2000 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 forecasting potential impacts on cropping systems. Distributed Geospatial Decision Support System Architecture Presentation (User Interface) e.g., Web Interface, Java applet Data cache Knowledge Layer e.g., Data e.g., Mining, Exposure Exposure Analysis, Analysis, Risk Assessment Risk Assessment Data cache Information Layer e.g., Drought Indices, Regional Crop Losses Data cache Distributed Spatial and Relational Data e.g., Climatic Variables, Agricultural Statistics HTTP IIOP RMI TCP 4-Layer Architecture for NADSS User Interface Web Client Java Client Local Database Cache Knowledge Layer Data Mining Tools Drought Exposure Analysis Tools Drought Vu lnerability and Soil Climate Reg ime Analysis Tools Local Database Cache Information Layer Index Producing Service (SPI, PDSI, NSM) Wrapped legacy Mapping Service (ArcPlot, GRASS) Interpolation Service (Spline, Kriging, IDW ) Local Database Cache Data Layer (Local) Attribute Data Cache CGI data access (client) Data Layer (Distributed) Relat ional DB UCA N client Geodata Cache DiscoveryLink (Wrapper) UCA N server Structure flat files OGDI (driver stub) OGDI driver MLPQ (Constrained DB) Geospatial DB HTTP IIOP RMI NADSS Benefits and Impacts Improving spatial and temporal analysis for drought risk management State level to County level to Field level Monthly Index to Weekly Index (SPI, PDSI, Newhall Simulation Model) Responding to drought events more effectively Conclusion 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