NADSS Overview

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

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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
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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