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Comparison of petiole nitrate concentrations,SPAD chlorophyll readings,and QuickBird satellite imagery in detecting nitrogen status of potato canopies
Authors:Jindong Wu  Dong Wang  Carl J. Rosen  Marvin E. Bauer
Affiliation:1. Department of Forest Resources, University of Minnesota, St. Paul, MN 55108, USA;2. Department of Soil, Water, and Climate, University of Minnesota, St. Paul, MN 55108, USA
Abstract:Nitrogen (N) management is critical in optimizing potato yield and quality and reducing environmental pollution. Six N rates from 34 to 270 kg ha−1, and different timing of N application were used in a 3-year field experiment to contrast SPAD-502 chlorophyll meter and QuickBird satellite imagery data against the conventional petiole sampling technique for assessing canopy N status. Overall treatment variations in SPAD readings were consistent with those in petiole nitrate-nitrogen (NO3-N) concentrations. However, the ability of the SPAD meter to detect treatment differences varied with growth stage and growing season. Severe N deficiency was detected about 1 month after emergence with SPAD readings, but as early as 2 weeks after emergence with petiole NO3-N concentrations. Petiole NO3-N concentrations tended to differentiate more treatment variations than SPAD readings at all growth stages except at hilling. N deficiency was detected with QuickBird image-derived vegetation indices (VIs) at the hilling stage in 2002, but not in 2003. At the post-hilling stage, treatment differences in VI values were minimal and insignificant except very late in the growing season. SPAD meters could be used as an indirect method for detecting N deficiency at the hilling stage when making supplemental N applications, but they are not as sensitive as the petiole sampling method. The sensitivity of QuickBird imagery to canopy N variations needs to be further tested with more pixel data. However, cloud interference and high cost of images could limit the use of QuickBird data in making timely management decisions.
Keywords:Chlorophyll meter   Petiole sampling   Remote sensing   Vegetation index
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