Assessing Nitrogen Stress in Corn Varieties of Varying Color

Nitrogen (N) is an essential element for crop growth and its accurate assessment in plants is a key to nutrient management. This research was conducted to explore the feasibility of using spectral reflectance to predict nitrogen content in corn plant varieties with varying color. A problem identified by previous researchers is the variability of spectral responses from varieties with different canopy colors. Reflectance was measured for samples from subplots of three different corn varieties (selected for varying leaf color) and 5 N treatments (0, 67.0, 134.1, 201.1, and 268.1 kg/ha). Multivariate statistical analyses (stepwise discriminant analysis, canonical discriminant analysis and principal component analysis) were performed on the spectral responses for each variety and N treatment to determine the ability to predict the actual leaf N content. It was found that the leaf greenness did not have any effect to canopy spectral reflectance to assess nitrogen status. Partial Least Square (PLS) regression, Principal Component Regression (PCR) and Multiple Linear Regression (MLR) were conducted to build prediction models. The standard errors of calibration (SEC) for ear leaf were 0.08%, 0.09%, and 0.0% for PLS, PCR, and MLR, respectively and the standard errors of prediction (SEP) for ear leaf were 0.16%, 0.15%, and 0.20% for PLS, PCR, and MLR, respectively. For the younger leaf samples, the SEC were 0.18%, 0.18%, and 0.0% for PLS, PCR, and MLR, respectively, and the SEP were 0.25%, 0.25%, and 0.90% for PLS, PCR, and MLR, respectively.