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Title: USDA-ARS RESEARCH IN APPLICATION TECHNOLOGY FOR PEST MANAGEMENT

Author
item Smith, Lowrey
item Thomson, Steven

Submitted to: Pest Management Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/11/2002
Publication Date: 6/1/2003
Citation: SMITH, L.A., THOMSON, S.J. 2003. USDA-ARS RESEARCH IN APPLICATION TECHNOLOGY FOR PEST MANAGEMENT. PEST MANAGEMENT SCI 59:699-707.

Interpretive Summary: The Agricultural Research Service (ARS) is conducting research in pesticide application to reduce drift and increase application effectiveness. A research summary is presented that emphasizes ARS achievements over the past two to three years in application technology. Many facets are covered including innovative application systems, drift management techniques, methods to improve application effectiveness for pest control, and remote sensing of pests so spray can be targeted only to the pest. The latter prevents the entire field from having to be sprayed, saving chemical and reducing drift to off-target areas. Research is also being conducted to determine crop and soil properties that are favorable for weed growth and insect proliferation. This information can be used to predict where pests are most likely to be, providing additional information for early-season control and targeted spraying.

Technical Abstract: The Agricultural Research Service (ARS) is conducting research in pesticide application to reduce drift and increase application effectiveness. A research summary is presented that emphasizes ARS achievements over the past two to three years in application technology. Many facets are covered including innovative application systems, drift management techniques, methods to improve application effectiveness for pest control, and remote sensing of pests so spray can be targeted only to the pest. The latter prevents the entire field from having to be sprayed, saving chemical and reducing drift to off-target areas. Research is also being conducted to determine crop and soil properties that are favorable for weed growth and insect proliferation. This information can be used to predict where pests are most likely to be, providing additional information for early-season control and targeted spraying.