Determining In-Season Nitrogen Requirements for Corn Using Aerial Color-Infrared Photography

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Title: Determining In-Season Nitrogen Requirements for Corn Using Aerial Color-Infrared Photography
Author: Sripada, Ravi Prakash
Advisors: Dr. Ronnie W. Heiniger, Committee Co-Chair
Dr. Jeffrey G. White, Committee Co-Chair
Dr. David A. Crouse, Committee Member
Dr. Randy Weisz, Committee Member
Dr. John L. Havlin, Committee Chair
Abstract: Fast, accurate methods to determine in-season corn (Zea mays L.) nitrogen (N) requirements are needed to provide more precise and economical management and potentially decrease groundwater N contamination. The objectives of this study were to: (i) determine if there is a response to in-season N applied to corn at (V7: NV7) and pre-tassel (VT: NVT) under irrigated and non-irrigated conditions; (ii) develop a methodology for predicting in-season N requirement for corn at the V7 and VT stages using aerial color infrared (CIR) photography; (iii) validate the RGDVI-based remote sensing technique for determining in-season N requirements for corn at VT growth stage and to test the robustness of the model across years; (iv) examine the response of corn agronomic parameters (biomass, plant N concentration, and total N uptake) and spectral parameters (near-infrared [NIR], red [R], and green [G]) from CIR measured at the V7 and VT growth stages to changing environments (year), irrigation, and N applied at planting (NPL); and (v) determine the relationships between corn agronomic parameters and spectral parameters that influence the prediction of optimum NV7 and NVT rates. Field studies were conducted for four years over a wide range of soil conditions and water regimes in the North Carolina Coastal Plain. A two-way factorial experimental design was implemented as a split-plot in randomized complete blocks with NPL as the main plot factor and NV7 or NVT as the sub-plot factor. Corn agronomic parameters were measured and aerial CIR photographs were obtained for each site at V7 or VT prior to N application. Significant grain yield responses to NPL and NV7, and NVT were observed. Spectral radiation of corn measured using the Green Difference Vegetation Index (GDVI) relative to high-N reference strips using a linear-plateau model was the best predictor of optimum NVT (R2 = 0.67). Optimum N rates at V7 (NV7) ranged from 0 to 207 kg N ha-1 with a mean of 67 kg N ha-1. Very weak correlations were observed between optimum rates of NV7 and band combinations with significant correlations for relative G, RGDVI, and relative difference vegetation index (RDVI). In the VT validation study, the difference between predicted and observed optimum NVT rates ranged from -30 to 90 kg N ha-1. Overall, the remote sensing technique was successful (r2 = 0.85) in predicting optimum NVT rates despite the inherent constraints of predicting yield potential in any particular year. Although the model tended to over-predict optimum N rates, it was able to capture changes in optimum N rates across the range of conditions tested. Corn spectral parameters measured at V7 and VT also varied with year and NPL. G and NIR were significantly correlated with biomass and total N uptake. Relative indices using G and NIR were related to plant N concentration. The spectral index RGDVI showed consistently significant relationships with corn agronomic parameters measured at VT when analyzed across irrigated and non-irrigated experiments. Lack of adequate N prior to VT resulted in a loss of yield potential that was irreversible, that is, not regained by N additions at VT. Thus adequate N applied earlier in the season is necessary to maintain yield potential through VT. By assessing corn N requirements late in the season during the period of maximum N uptake and applying fertilizer appropriately, application of large amounts of N early in the season when corn uptake is low and leaching potential high might be avoided, and thus minimize groundwater pollution. There exists a potential to improve in-season estimates of N requirements earlier in the season by investigating further into the use of high resolution images.
Date: 2005-04-19
Degree: PhD
Discipline: Soil Science

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