Lesser-known ethnic leafy vegetables Talinum paniculatum grown at tropical ecosystem: Morphological traits and non-destructive estimation of total leaf area per branch - Benyamin Lakitan; Kartika Kartika; Laily Ilman Widuri; Erna Siaga; Lya Nailatul Fadilah (Society for Indonesian Biodiversity, 2021)
Abstract. Lakitan B, Kartika K, Widuri LI, Siaga E, Fadilah LN. 2021. Lesser-known ethnic leafy vegetables Talinum paniculatum grown at tropical ecosystem: Morphological traits and non-destructive estimation of total leaf area per branch. Biodiversitas 22: 4487-4495. Talinum paniculatum known as Java ginseng is an ethnic vegetable in Indonesia that has also been utilized as a medical plant. Young leaves are the primary economic part of T. paniculatum, which can be eaten fresh or cooked. This study was focused on characterizing morphological traits of T. panicultaum and developing a non-destructive yet accurate and reliable model for predicting total area per leaf cluster on each elongated branch per flush growth cycle. The non-destructive approach allows frequent and timely measurements. In addition, the developed model can be used as guidance for deciding the time to harvest for optimum yield. Results indicated that T. paniculatum flourished rapidly under wet tropical conditions, especially if they were propagated using stem cuttings. The plants produced more than 50 branches and more than 800 leaves, or on average produced more than 15 leaves per branch at the age of nine weeks after planting (WAP). The zero-intercept linear model using a combination of two traits of length x width (LW) as a predictor was accurate and reliable for predicting a single leaf area (R2 = 0.997). Meanwhile, the estimation of total area per leaf cluster was more accurate if three traits, i.e., number of leaves, the longest leaf, and the widest leaf in each cluster were used as predictors with the zero-intercept linear regression model (R2 = 0.984). However, the use of a single trait of length (L) and width (W) of the largest leaf within each cluster as a predictor in the power regression model exhibited moderately accurate prediction at the R2 = 0.883 and 0.724, respectively.