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ARS Home » Southeast Area » Tifton, Georgia » Crop Protection and Management Research » Research » Publications at this Location » Publication #120189
Title: VALIDATION OF COTTON HERBICIDE APPLICATION DECISION SUPPORT SYSTEM (HADDS) IN GEORGIA.

Author
item Webster, Theodore
item CULPEPPER, A - UNIVERSITY OF GEORGIA
item HARDISON, G - UNIVERSITY OF GEORGIA
item WILKERSON, G - NORTH CAROLINA STATE UNIV
item BENNETT, A - NORTH CAROLINA STATE UNIV

Submitted to: Proceedings of Southern Weed Science Society
Publication Type: Abstract Only
Publication Acceptance Date: 5/1/2000
Publication Date: 2/1/2001
Citation: Webster, T.M., Culpepper, A.S., Hardison, G.B., Wilkerson, G.G., Bennett, A.C. 2001. Validation of cotton herbicide application decision support system (HADSS) in Georgia [abstract]. Proceedings of Southern Weed Science Society. 54:184.

Interpretive Summary:

Technical Abstract: Many factors that affect weed-crop interactions cannot, as of yet, be predicted early in the growing season. Currently, the best method to estimate potential crop yield loss involves quantifying weed densities. Using grower supplied weed densities, the Herbicide Application Decision Support System (HADSS) provides growers with an interface to information on the competitiveness of and herbicide efficacy against a weed complex. HADSS is a valuable tool in teaching, designing, and implementing weed management systems. Prior to implementing this in Georgia, we must determine: (1) How accurate is the system in selecting an appropriate treatment? and (2) Do we have to count every weed? Field studies were conducted in 1999 (2 locations) and 2000 (4 locations) in south Georgia. Net returns from our expert recommendations were compared to those from HADSS. Equivalent net returns were found 49% of the time; differences of <15% in net return were found 83% of the time. Counting weeds is a laborious chore, an alternative system of classifying weed densities as low, medium, high, or very high was successful. There was a linear relation between total competitive load based on class data and count data (r2 = 0.85). Evaluation of net return from class and count data indicated a very strong linear relation with a slope of 1.0 (r2 = 0.99). We conclude that estimating weed density by categories would have been accurate enough in our fields to recommend the correct weed control program.