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

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

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

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.

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

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

Planting Date Guide

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

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

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

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