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WHEAT

Quantitative Approaches for Using Color Infrared Photography for Assessing In-Season Nitrogen Status in Winter Wheat

^a USDA-ARS, Air Quality–Plant Growth and Development Research Unit, 3908 Inwood Rd., Raleigh, NC 27603
^b Dep. of Crop Science, North Carolina State Univ., Box 7620, Raleigh, NC 27695-7620
^c Dep. of Crop Science, North Carolina State Univ., Vernon James Res. and Ext. Center, 207 Research Rd., Plymouth, NC 27962

^* Corresponding author (mike_flowers{at}ncsu.edu).

Received for publication February 11, 2002. Due to the timing and rates of N applications in wheat (Triticum aestivum L.), the potential exists for high N loading to the environment. Plant tissue tests offer growers the ability to determine in-season N status, and to optimize N applications and N use efficiency. However, sampling and N analysis can be costly, difficult, and time consuming. Remote sensing may offer a solution to these problems. The objectives of this study were to determine (i) if remote sensing could be used to estimate in-season N status, (ii) if within-field calibration would improve the ability of remote sensing to estimate crop N status, and (iii) if optimum N rates could be estimated using remote sensing. Research was conducted in 1999 to 2001 at eight sites. Two sites had randomized complete block designs with variety, seeding rate, and N rate as treatments. Six sites had a single seeding rate and wheat variety. Biomass was found to influence spectral measurements of in-season N status. A strong relationship between the normalized difference vegetation index (NDVI) and growth stage (GS)-30 whole-plant N concentration (R² = 0.69) and GS-30 N uptake (R² = 0.61) was found. Within-field calibration did not improve the estimation of in-season N status by NDVI. While it was possible to use NDVI to estimate GS-30 N uptake, predicted N fertilizer rates based on N uptake were highly unreliable. However, NDVI reliably predicted GS-30 N fertilizer rates based on whole-plant N concentration for wheat that had mean GS-30 biomass values >1000 kg ha^-1.

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