1a.Objectives (from AD-416):
This research seeks to provide improved management tools and information technologies to aid commercial cotton production in the Mid South through the application of precision agriculture practices. It seeks to reduce costs of cotton production through the acquisition, storage, processing, and application of geo-referenced information. Combines remotely sensed data with insect sampling methods to improve decisions regarding control for thrips and stink bugs in cotton. Develops appropriate statistical anslysis concepts and models for precision agriculture. Develops local area networking software and decision aids for digital data collection and use in precision agriculture.
1b.Approach (from AD-416):
Statistical models will be developed for geo-referenced measurement of precision agriculture cotton production practices. Improvements to mixed model analysis methodology will be developed using archived data as well as new data from remotely sensed images and field measurements. Statistical models to analyze spatial application decisions from data that involves a disproportionate number of zero counts and has small sample sizes will be developed and improved. Light Detection and Ranging (LiDAR) and Normalized Difference Vegetation Index (NDVI) data will be combined to facilitate site-specific management of thrips and stink bugs in cotton. Concepts and software will be built upon a wireless network to manage large quantities of geo-referenced data.
Discussions continue with several commercial companies, producers, and consultants regarding principles developed in this project for site-specific management decisions. For the first time, a variable-rate corn seeding prescription was created using concepts from research and based upon the fusion of a digital elevation model from the 2012 planting season and the spring 2013 Veris cart data collected by a consultant in West Texas. The effort involved adaptation of the categorical fusion algorithm developed in the project research. A recent visit to this commercial field suggests that the adaptation of the algorithm to variable-rate seeding was robust. The yield data for 2013 harvest will be analyzed using an experimental procedure already developed in this project to evaluate the effect of variable-rate seeding. In its third year, another experiment involving a fumigant for nematode control and a spatially variable planting pattern was also continued. This study represents extension of the project research concepts into a factorial treatment structure within a commercial farm-field.