Skip to main content
ARS Home » Southeast Area » Mississippi State, Mississippi » Crop Science Research Laboratory » Genetics and Sustainable Agriculture Research » Research » Publications at this Location » Publication #246722

Title: Geographical approaches for integrated pest management of arthropods in forestry and row crops

item Willers, Jeffrey
item RIGGINS, JOHN - Mississippi State University

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 12/2/2009
Publication Date: 7/1/2010
Citation: Willers, J.L., Riggins, J.J. 2010. Geographical approaches for integrated pest management of arthropods in forestry and row crops. In: Oerke, E.C., Gerhards, R., Menz, G., Sikora, R.A., editors. Precision Crop Protection - the Challenge and Use of Heterogeneity. New York, NY: Springer. p. 183-202.

Interpretive Summary: Remote sensing technology offers the forest and row crop entomologist new opportunities to approach Integrated Pest Management issues. The technology leads to innovative analyses of forest and row crop ecosystems because of spatial and spectral resolutions of these sensors near 4m x 4m pixels or smaller. The availability of these resolutions at this scale leads to innovative changes in sampling for insects. We describe the basic concepts of how to alter sampling methods for forest and row crop insect pests if remote sensing information is available.

Technical Abstract: With the proper technology and access to geographical information, it is more important to spend time on developing an excellent classification scheme of a remotely sensed attribute of crop and forest vigor rather than spending large amounts of time collecting many samples of insect counts. The ability to define zones from remote sensing images of crop or forest systems is essential for controlling the sample variability of insect counts by sampling them from more homogenous plant populations. Perspectives on defining zones from remote sensing information including an examination of their relationships to other sample attributes not measurable by remote sensing techniques are discussed.