TITLE:
Examination of Potential Measures of Vine Maturity in Potato
AUTHORS:
Emily Haga, Brooke Weber, Shelley Jansky
KEYWORDS:
Solanum tuberosum; Plant Maturity; Peroxidase; Leaf Chlorophyll; Flower
JOURNAL NAME:
American Journal of Plant Sciences,
Vol.3 No.4,
April
25,
2012
ABSTRACT: Plant maturity is a complex physiological trait routinely evaluated by plant breeders because of its agronomic, economic, and breeding implications. In potato, plant maturity is typically estimated by monitoring vine characteristics. This study investigates several reported measures of vine maturity in potato cultivars, including examination of flower development, leaf chlorophyll content, and leaf peroxidase activity to see what method is most appropriate for maturity classification in temperate production regions. These three measures were evaluated multiple times throughout a single growing season across two locations. The data were analyzed using canonical discriminant and dichotomous tree analyses. Both methods revealed that flower development is not an accurate indicator of maturity, even though it is a common component of maturity screening protocols. Peroxidase measures were slightly better, but the optimal period of time to assess this trait was variable across locations and a considerable time commitment is required to collect and process samples. Monitoring leaf chlorophyll content throughout plant development was most appropriate for predicting vine maturity under these conditions, as this trait showed the most consistency and greatest prediction accuracy (69% - 71%) relative to peroxidase activity and flowering development. Additionally, chlorophyll measurements are a more practical method of measuring maturity due to the ease of data collection. Leaf chlorophyll content best distinguished late cultivars from early-medium and medium-late cultivars. However, it did not separate early-medium from medium-late cultivars. Combining chlorophyll monitoring with peroxidase and flowering measures improved the ability to distinguish among the early-medium and medium-late maturity classes. However, doing so only increased classification accuracy by 3%.