Submitted to: National Cotton Council Beltwide Cotton Conference
Publication Type: Proceedings
Publication Acceptance Date: 1/7/1999
Publication Date: N/A
Interpretive Summary: Crop models have been used for a number of years to assist farmers in making management decisions. These models, however, have been difficult to use in large scale farming operations. Recent advances in Geographical Information System (GIS) technologies have provided an avenue for reducing much of the record keeping demand associated with site-specific application of crop models. The first stage of a fully integrated cotton model in a desktop GIS has reduced the complexity of record keeping and management. It has also enabled users to view predictions on a field map. The integrated model was used to monitor and visualize results from a variable rate nitrogen study conducted in the summer of 1998. The integrated model will allow researchers to study what resolution is actually necessary or sufficient for precision management. Using the new model will help determine at what scale the cost of acquiring additional input data becomes greater than the expected return. Additional work also needs to be done on including decision support systems such as expert systems. Eventually, decision support software needs to be written to fully utilize capabilities of GIS and precision application equipment.
Technical Abstract: Process level crop models have been shown to be effective management tools when adequate resources are available to utilize them. They have, however, been difficult to use in large scale farming operations. Recent advances in Geographical Information System (GIS) technology have provided an avenue for reducing much of the overhead associated with site-specific application of such tools. Using GIS, it is possible to develop the tools necessary to reduce the record keeping demand and implement crop simulation models in the precision farming paradigm. The first stage of a fully integrated crop model in a desktop GIS has been accomplished and shown to reduce the complexity of site-specific model implementation at farm level.