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ARS Home » Southeast Area » Houma, Louisiana » Sugarcane Research » Research » Publications at this Location » Publication #173084
Title: Discrimination of Sugarcane Varieties with Pigment Profiles and High Resolution, Hyperspectral Leaf Reflectance Data

Author
item Johnson, Richard
item Viator, Ryan
item Veremis, John
item Richard Jr, Edward
item Zimba, Paul

Submitted to: American Society of Sugar Cane Technologists
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/10/2008
Publication Date: 12/20/2008
Citation: Johnson, Richard M., Viator, Ryan P., Veremis, John C., Richard Jr., Edward P., Zimba, Paul V. 2008. Discrimination of Sugarcane Varieties with Pigment Profiles and High Resolution, Hyperspectral Leaf Reflectance Data. Journal of the American Society of Sugar Cane Technologists. 28:63-75.

Interpretive Summary: In a process that may take 12 or more years, Louisiana sugarcane breeders select the best individuals from crosses made between commercial and near commercial varieties and wild canes. Although the majority of crosses result in hybrids a small percentage are self-fertilized (selfs) because female parents still have the capability of producing some pollen. It is important to be able to detect these selfs from the true hybrids, particularly with crosses involving wild canes as many are classified as noxious weeds and should not be planted in the field. This study investigated the use of leaf reflectance measurements combined with plant pigment measurements as an aid in the identification and separation of seedlings resulting from true crosses (hybrids) from the unwanted selfs, hopefully while they were still in the greenhouse. Seven varieties were selected for reflectance analysis, including five commercial sugarcane varieties, one noble cane, and one wild cane. Differences in reflectance were observed for each variety, with the varieties having a 3-fold difference in reflectance values. Reflectance measurements at 560 and 700 nm and reciprocal reflectance at 700 and 710 nm provided the best discrimination (76%) with single wavelengths while vegetation indices based on multiple wavelengths could improve the varietal discrimination to 81%. When all wavelengths were included in the analysis the varieties were correctly classified in 100% of the cases using plant pigment data and in 89% of the cases using the reflectance data. Finally, in all cases the wild sugarcane was correctly classified. In one case a commercial variety was classified as a wild cane. This method should prove useful in separating sugarcane varieties in the field and in identifying seedlings of undesirable selfs from the more desirable hybrids while they are still in the greenhouse. This will improve selection efficiency and the release of superior varieties from sugarcane varietal development programs.

Technical Abstract: This study reports our evaluation of leaf reflectance and pigment measurements as a potential tool to aid in the identification and delineation of commercial sugarcane, noble cane and wild canes. Seven varieties of sugarcane were selected from the USDA-ARS-SRRC, Sugarcane Research Unit (SRU) breeding program for reflectance analysis, including: five commercial cultivars, one noble cane, and one wild cane. High resolution leaf reflectance data were collected from the third youngest fully open leaf from nine replicates using a fiber optic reflectance meter under natural light conditions. After reflectance measurements were completed the same leaf was sampled for plant pigment analysis by boring a 0.5 cm disc from ca. 10 cm from the leaf tip. The discs were extracted with 100% acetone and analyzed by HPLC. Reflectance data were combined into a 5, 10 or 20-nm interval and with the plant pigment data were statistically analyzed. Differences in reflectance were observed for each variety, with the seven cultivars having ~3-fold difference in reflectance values. Reflectance measurements at 560 and 700 nm and reciprocal reflectance at 700 and 710 nm provided the best discrimination (76%) with single wavelengths while vegetation indices based on multiple wavelengths could improve the varietal discrimination to 81%. Multivariate analysis of leaf reflectance and plant pigment data resulted in a 100% correct classification for plant pigment data and an 89% correct classification for reflectance data. Finally, there were no false negative S. spontaneum species classifications. There was one false positive classification when Ho 95-988 was classified as the S. spontaneum MPTH 97-216.