Abstract
Cotton insect control has been weakened by historical blanket applications of pesticides over large groups of fields at similar times, which has contributed to landscape-level patterns of resistance, dramatic increases in production costs, and environmental concerns. Classified remotely-sensed imagery facilitates a way to change these trends. To improve use of remote-sensing-based sampling methodologies for insect control, an extant simulation model was reconfigured to investigate how assessment of pest dispersion would vary with changes in sample unit size and pest density, based on an assumption that the pest is randomly dispersed within a particular simulated habitat class. Lloyd’s index of patchiness was the statistic used to compare outcomes from the simulation trials. Index of patchiness estimates closer to zero suggest regular dispersion patterns, while those nearer to one indicate random dispersion, and values increasingly larger than one suggest aggregation. Using this framework of knowledge, cotton scouts can use appropriately classified remote sensing imagery, subset by sprayer characteristics, to set boundaries for habitat classes and geo-spatially define sample units to create an information network for implementing site-specific insect pest management decisions.
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