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REMOTE SENSING

Optimization of Crop Canopy Sensor Placement for Measuring Nitrogen Status in Corn

^a Dep. of Agronomy & Horticulture, Univ. of Nebraska, Lincoln, NE 68583
^b Dep. of Biological Systems Engineering, Univ. of Nebraska, Lincoln, NE 68583
^c USDA-ARS, Agroecosystem Mgmt. Res. Unit, Lincoln, NE 68583
^d formerly USDA-ARS, Agroecosystem Mgmt. Res. Unit, Lincoln, NE 68583

^* Corresponding author (droberts2{at}unl.edu).

Active canopy sensors can be used to assess corn (Zea mays L.) N status and direct spatially-variable in-season N application. The goal of this study was to determine optimal sensor spacing for controlling whole- and/or split-boom N application scenarios for a hypothetical 24-row applicator. Sensor readings were collected from 24 consecutive rows at eight cornfields during vegetative growth in 2007 and 2008, and readings were converted to chlorophyll index (CI) values. A base map of measured CI values was created using square pixels equal to row spacing for each site (0.91 or 0.76 m in size). Sensor placement and boom section scenarios were evaluated using MSE (mean squared error) of calculated CI maps vs. the base CI map. Scenarios ranged from one sensor, one variable-rate to 24 sensors, 24 variable-rates for the hypothetical 24-row applicator. The greatest reduction in MSE from the one variable-rate scenario was obtained with 2 to 3 sensors estimating average CI for the entire boom width, unless each row was individually sensed. In every field, more accurate prediction of CI was obtained by averaging sensor readings across the entire 24 rows rather than predicting CI for more than two consecutive rows using only one sensor in each section. Because of the nature of spatial variability in CI, some fields may benefit from an increased number of sensors and/or boom sections equipped with 2 to 3 sensors each.

Abbreviations: CI, chlorophyll index • MSE, mean squared error • NUE, nitrogen use efficiency • NIR, near-infrared

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Received for publication August 30, 2008.