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United States Department of Agriculture

Agricultural Research Service

Research Project: DEVELOPMENT OF PRECISION AGRICULTURE SYSTEMS IN COTTON PRODUCTION
2009 Annual Report


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.


3.Progress Report
A remote sensing processing model was built in commercially available software to accomplish the integration of elevation (at 1 m spatial resolution) with a vegetative index of cotton plant vigor to produce field scouting maps at mid-June and mid-July for two commercial fields. These maps, with geo-referenced sampling information for tarnished plant bugs, were used to time pesticide applications. The integrated map may also have applicability to the stink bug complex in cotton. Efforts continue to document the utility of these types of maps. A first year effort in pest management in soybean has begun, where the remotely sensed variables are LiDAR elevations and a kriged surface of shallow apparent electrical conductivity (ECa) readings from a Veris machine. Another effort examines the correlation structure between two remotely sensed attributes by an intensive sampling with replacement model. This study involves the shallow and deep ECa readings as the response and independent variables, respectively. Preliminary results suggest a classification map based upon the deep attribute can be produced where the means of sparsely selected samples of the shallow attribute do not overlap, and the correlation between the two attributes is small within each derived class. This represents the first empirical confirmation of the hypothesis that site-specific sampling does not need large numbers of samples for insect counts because the information provided by the densely sampled attribute measureable by remote sensing system is a surrogate for that requirement. This evidence that the correlation structure is small, within each class derived from remote sensing information, has utility for the simplification for the design and analysis of site-specific experiments reducing the model and spatial autocorrelation structure. This will allow the development of simpler statistical models for analyses. A one day workshop was conducted, July 2009, with scientists at the Laboratory for Manufacturing and Productivity, Massachusetts Institute of Technology, Cambridge, MA, to determine how to develop a prototype system to automate the analyses of site-specific management practices in commercial fields. However, advances in hardware and software are making it possible to quickly collect the necessary information at geographical coordinates and at small ground resolutions. This makes it reasonable that an automated system for the analysis of geo-referenced information can be designed.


4.Accomplishments
1. New Hypothesis for Economic Thresholds in Cotton Pest Management. Reduced cost for control of insect pest management is an important goal in improving the profitability of cotton production. We developed the hypothesis that site-specific management of cotton insect pests is facilitated by using pest management practices based upon the extreme count values of insect samples within a zone, as opposed to the means of insect sample counts within zones. Thus, with site-specific applications of pesticides to zones having high extremes, applications are triggered sooner, with the expected result that, fewer sprays on a field-wide basis are required. If the hypothesis is supported by research data its impact in reducing costs, conserving beneficial insects, and maintaining or improving yield could be substantial for cotton producers.


Review Publications
McKinion, J.M. 2009. Role of telecommunications in precision agriculture. Lee, I., editor. Handbook of Research on Telecommunications Planning and Management for Business. Macomb, IL: IGI Global. p. 832-846.

Willers, J.L., Jenkins, J.N., McKinion, J.M., Gerard, P., Hood, K.B., Bassie, J.R., Cauthen, M.D. 2009. Methods of analysis for georeferenced sample counts of tarnished plant bugs in cotton. Precision Agriculture. 10:189-212.

McKinion, J.M., Jenkins, J.N., Willers, J.L., Zumanis, A. 2009. Spatially variable insecticide applications for early season control of cotton insect pests. Computers and Electronics in Agriculture. 67:71-79.

Willers, J.L., Jin, M., Eksioglu, B., Zusmanis, A., O'Hara, C., Jenkins, J.N. 2008. A post-processing step error correction algorithm for overlapping LiDAR strips from agricultural landscapes. Computers and Electronics in Agriculture. 64:183-193.

Willers, J.L., Jallas, E., McKinion, J.M., Seal, M.R., Turner, S. 2009. Precision farming, myth or reality: Selected case studies from Mississippi cotton fields. In: Papajorgiji, P.J., Pardalos, P.M., editors. Advances in Modeling Agricultural Systems. New York, NY: Springer Science. p. 243-272.

Last Modified: 11/22/2014
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