1. NADSS Overview
An Application of Geo-Spatial Decision Support to Agriculture Risk Management

2. What is NADSS
The National Agriculture Decision Support System (NADSS) is a distributed web based application to help decision makers assess various risk factors
our research has focused primarily on drought
we are investigating ways to use the system to create tools to aide in the identification of risk areas
Using various data and computational indices we are able to create tabular data for analysis as well as maps for further spatial analysis
Current research is focused on distributing the system allowing the system to execute on Prairie Fire

3. Drought Tools: SPI Standard Precipitation Index
Built to quantify deficit or excess moisture conditions at a location for a specified time interval
Values computed using precipitation records for a location
represents the number of standard deviations from the normalized mean
Can quantify both deficit and excess precipitation over multiple time scales

4. Drought Tools: PDSI Palmer Drought Severity Index
Built to quantify the severity of drought conditions
is one of the most widely used drought tools
Unlike the SPI, the PDSI uses temperature as well as precipitation data
Computations are based on a supply demand model for the amount of moisture in soil
NADSS uses a unique implementation of the PDSI that dynamically calculates certain coefficients used in the computation so that extreme periods a reported with a predictable frequency of occurrence for rare events.

5. Drought Tools: NSM Newhall Simulation Model
Used by USDA services to estimate soil moisture regimes as defined by Soil Taxonomies
Runs on monthly normals for both precipitation and temperature
generally for 30 year normals
NADSS implemented a revision of the model to tun on monthly records for individual years
We currently include “centennial stations” or stations with 100 years or more of data
Allows us to determine where new or alternative crops can be adapted to the landscape

6. Planting Date Guide
A tool to assist farmers and crop consultants with decision of matching corn hybrids with growing season characteristics
Locates a given farm and field and then describes the growing season window
users can select dates and generate probabilities of accumulated heat units
Can be used to identify production fields on the ecological edge, where crops are being grown at the boundary of adaptation

7. Planting Date Guide

8. NADSS Architecture
The NADSS currently under development utilizes a layered architecture with individual components residing together in layers
this approach allows us to more easily develop, distribute, and deploy new components; allowing for greater flexibility and performance
The bulk of computing is done on a component server by CORBA objects designed to deal solely with data requests
component logic can be combined (connected) to create unique requests
The application front-end is further partitioned into individual EJB modules to create the connection of the CORBA calls

9. 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
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
Spatial Layer
e.g. spatial analysis and rendering tools

10. Application of Layering
By combining several domain specific factors from different layers we are able to create maps (in this case: displaying the risk for crop failure) that show data for states, counties, farm or even field level
The result is a “spatial” view of risk
Variables are spatially rendered
The user adjusts weight factors for each variable

11. Next Steps
We are currently working towards unification of our tools under a common interface, architecture and data set
our tools as they exist today provide proof of concept only
Maintain a quality controlled data set, minimizing windows of missing climate data to achieve more accurate results
Focused on human centric design to increase the usability of our tools thereby providing broader access to producers
Create a fully distributable architecture allowing us to more easily integrate other projects for other research facilities
provides better support for the needs of producers