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United States Department of Agriculture

Agricultural Research Service


item Frantz, Jonathan
item Pitchay, Dharmalingham
item Ritchie, Glen
item Zhu, Heping

Submitted to: HortScience
Publication Type: Abstract Only
Publication Acceptance Date: 2/15/2005
Publication Date: 7/17/2005
Citation: Frantz, J., Pitchay, D., Ritchie, G., Zhu, H. 2005. Identifying common reflectance properties in diverse ornamental species to non-destructively determine nitrogen statues. Hortscience. 40(4):1060.

Interpretive Summary:

Technical Abstract: Nitrogen (N) is often supplied to plants in excess as a way to minimize the likelihood of encountering N deficiency that would reduce the quality of their plants due to leaf chlorosis. This is not only costly, but can reduce the quality of their plants in other ways as well as predispose the plants to biotic stress such as botrytis. There are several tools that can be used to identify N deficiency in plants, and most are based on the absorption or reflective properties of chlorophyll. While sensitive when plants are experiencing N deficiency, the spectral signals can saturate in ample N thereby making it difficult to discern sufficient and super-optimal N nondestructively. We grew three diverse ornamental species (begonia, Begoniaceae x tuberhybrida ; butterflybush, Buddleja davidii, geranium, Pelargonium x spp) in a broad range of N supplies (1.8 mM to 58 mM) in three separate studies that resulted in a range of 1.8% to 6% tissue N content. Using a spectroradiometer, we measured reflectance from the plants twice over a period of 3 weeks. A first-derivative analysis of the data identified six wavebands that were strongly correlated to both begonia and butterflybush tissue N content (r2 ~ 0.9), and two of these also correlated well to geranium N content. These wavebands did not correlate to chlorophyll peak absorbance, but rather blue, green, red, and far-red edges of known plant pigments. These wavebands hold promise for use as a nondestructive indicator of N status over a much broader range of tissue N content than current sensors can reliably predict.

Last Modified: 10/19/2017
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