Insect control: Combine remote sensing with insect scouting data, and generate pest-density maps uploaded to a DGPS ground sprayer for variable-rate applications of pesticide and/or plant growth regulator. Impact: Application of technologies that decrease the amount of pesticide applied while maintaining yield potential, reduce environmental impact, and enhance grower profits.

Plant stress: Field and laboratory studies to relate measurements of narrow-waveband spectral reflectance in leaves and canopies to plant stress physiology Impact: Spectral signatures sensitive to specific nutrient and water stresses will enhance our ability to intervene before cotton yield is impaired.

Spatial variability: On-farm research to measure soil type/nutrition, topography, drainage patterns, crop growth and yield in a geo-referenced format to delineate soil/crop management zones within a field. Impact: Management of small land units with the aid of ARS Cotton Model will optimize grower inputs.

Insect control: Management of large amounts of data promptly and efficiently; inability of current variable-rate controllers to apply a spray prescription at fine spatial scales; expert system to write prescriptions; steep learning curve to become proficient with appropriate GIS and statistical analysis (SAS) software tools.

Insect control: Management of large amounts of data promptly and efficiently; inability of current variable-rate controllers to apply a spray prescription at fine spatial scales; expert system to write prescriptions; steep learning curve to become proficient with appropriate GIS and statistical analysis (SAS) software tools.

Spatial variability: Knowledge of statistical designs that can utilize remotely-measured variables (uncontrolled factors) as covariates with traditional in-field measurements.

Insect control: Work with growers, farm consultants, and industry to build spatially- variable insecticide maps completely on site and at minimal cost. Further explore the value of multi-temporal information in remote images. Examine relationship between spider mites and canopy hyperspectral reflectance.

Plant stress: Develop and test spectral algorithms indicative of single and multiple stress factors. Use algorithms to estimate critical levels of physiological constituents (e.g., N, P, K, water ) in plants under stress.

Spatial variability: Use information from multipsectral and hyperspectral images, yield monitors, plant maps, soil analysis, and crop simulation to accurately predict yield, including spatial variability.