Early season yield prediction in irrigated corn using remotely sensed imagery. (4668)
Authors:
D. Inman* - Colorado State University
R. Khosla - Colorado State University, Fort Collins, CO M.A. Lefsky - Colorado State University, Fort Collins, CO R.M. Reich - Colorado State University, Fort Collins, CO D.G. Westfall - Colorado State University, Fort Collins, CO
Abstract:
Accurate grain yield prediction during the early growing season to optimize N inputs to meet the crops specific N needs could potentially improve nitrogen use efficiency (NUE). Normalized vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) derived from remotely sensed imagery are well correlated to leaf area index (LAI) and thus photosynthetic capacity, crop productivity, and potential yield. The objective of this study was to develop and to assess the utility of an NDVI and GNDVI-based predictive model for early season corn grain yield. The study was on a commercially operated continuous corn irrigated production field in northeastern Colorado. The study site was classified into three site-specific productivity management zones (high, medium, and low).
Imagery was aerially acquired at the eight-leaf crop growth stage. Statistical modeling was performed using least squares regression analysis with indicator variables to account for site-specific management zones. NDVI had a higher correlation with relative yield than GNDVI (r = 0.74 p
Speaker Information: Daniel Inman, Colorado State University, 318 W. Laurel Apt. B, Fort Collins, CO 80521; Phone: 970-217-0931; E-mail: [email protected]
Session Information: Tuesday, November 2, 2004, 8:30 AM-10:45 AM Presentation Start: 9:20 AM
Keywords: Remote Sensing; Precision Agriculture; Nutrient Management; Soil Fertility
