Tuesday, 8 November 2005 - 10:15 AM
179-7

Field Validation of a Remote Sensing-Based Late-Season Nitrogen Application Decision System in Corn.

Ravi P. Sripada1, Ronnie W. Heiniger2, Jeffrey G. White1, and Alan D. Meijer2. (1) Dept of Soil Science, North Carolina State University, Campus Box 7619, 4123A Williams Hall, North Carolina State University, Raleigh, NC 27695, (2) Dept of Crop Science, North Carolina State University, Vernon James Res. and Ext. Cent., 207 Research Rd.,, Plymouth, NC 27962

    Previous research indicated that a linear-plateau function using relative green difference vegetation index (RGDVI) from aerial color-infrared (CIR) photography could be used to predict optimum N rates in corn (Zea mays L.) at tasseling (VT). The objective of this research was to validate this RGDVI-based remote sensing technique for determining in-season N requirements for corn at the VT growth stage, and to test the robustness of the model across years. A two-way factorial experimental design was implemented as a split-plot in randomized complete blocks with N at planting (NPL) as main plot factor and sidedress N at VT (NVT) as sub-plot factor at 10 irrigated and non-irrigated sites in North Carolina during 2003. Results indicate that the linear-plateau model describing the relationship between economic optimum NVT rates and RGDVI was the best predictor and as observed in previous research appears robust over a variety of moisture regimes and years. The difference between predicted and observed optimum NVT rates ranged from -30 to 90 kg N ha-1. A greater difference between predicted and observed N rates was observed when N requirement was high and was attributed to lower yield potential observed in this study compared to model development years. Overall, the remote sensing technique was successful in predicting optimum NVT rates (r2 = 0.85) given the inherent constraints of predicting yield potential in a particular year. Although the model tended to over-predict N rates, it was able to capture changes in N requirements across the range of conditions tested. The results indicate that the RGDVI-based remote sensing technique can be used to adjust late in-season N rates. Further research is underway to validate the model across years.  

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