Characterizing Water and Nitrogen Stress in Corn Using Remote Sensing

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nteractions between water and N may impact remote-sensing-based N recommendations. The objectives of this study were to determine the influence of water and N stress on reflectance from a corn (Zea mays L.) crop, and to evaluate the impacts of implementing a remote-sensing-based model on N recommendations. A replicated N and water treatment factorial experiment was conducted in 2002, 2003, and 2004. Yield losses due to water (YLWS) and N (YLNS) stress were determined using the ^sup 13^C discrimination (Δ) approach. Reflectance data (400-1800 nm) collected at three growth stages (V8-V9, V11-VT, and R1-R2) were used to calculate six different remote sensing indices (normalized difference vegetation index [NDVI], green normalized vegetation index, normalized difference water index [NDWI], N reflectance index, and chorophyll green and red edge indices). At the V8-V9 growth stage, increasing the N rate from 0 to 112 kg N ha^sup -1^ decreased reflectance in the blue (485 nm), green (586 nm), and red (661 nm) bands. Nitrogen had an opposite effect in the near-infrared (NIR, 840 nm) band. At the V11-VT growth stage, reflectance in the blue, green, and red bands were lower in fertilized than unfertilized treatments. At the R1-R2 growth stage, YLWS was highly correlated (r = 0.58, P = 0.01) with red reflectance and NDVI (r = -0.61, P = 0.01), while YLNS was correlated with all of the indices except NDVI. A remote sensing model based on YLNS was more accurate at predicting N requirements than models based on yield or yield plus YLWS. These results were attributed to N and water having an additive effect on yield, and similar optimum N rates (100-120 kg N ha^sup -1^) for both moisture regimes.

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Agronomy Journal





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