Dependency of Cotton Leaf Nitrogen, Chlorophyll, and Reflectance on Nitrogen and Potassium Availability

Abstract

In-season assessment of cotton (Gossypium hirsutum L.) leaf N and K concentration using remote sensing techniques is needed to address spatial and temporal variation of these two nutrients. The objective of this study was to evaluate the effects of varying N and K availability on cotton chlorophyll concentration and detection of leaf N utilizing reflectance properties. Fertilizer N rates of 0, 45, 90, 135, and 180 kg ha−1 in factorial combination with K rates of 0 and 112 kg ha−1 were applied to cotton under field conditions. Recently matured leaves on the main stem were collected at first bloom and peak bloom physiological stages of growth in 1999 and 2000 to determine leaf N and K concentrations, chlorophyll concentration, and spectral reflectance. Nutrient stress anomalies from spectral reflectance data were predicted using partial least-squares regression. Partial least squares yielded a better predictability of leaf N concentration at first bloom and peak bloom when K was adequately supplied. The greatest predictability of leaf N was observed at peak bloom in 2000 with a maximum r2 of 0.77 and minimum standard error of prediction of 2.72. A red-edge shift to longer wavelengths with increased N supply was observed when K was sufficient. Utilization of leaf N concentration sampled at a coarse resolution in combination with timely and appropriate imagery may enhance nutrient management capabilities in precision agriculture so long as other nutrients are not limiting.