Morphological traits and non-destructive leaf area estimation of Moringa oleifera under different stem cutting lengths
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Abstract. Gustiar F, Ria RP, Krhisma NA. 2026. Morphological traits and non-destructive leaf area estimation of Moringa oleifera under different stem cutting lengths. Biodiversitas 27 (5): d270523. https://doi.org/10.13057/biodiv/d270523. Moringa oleifera is widely cultivated for its highly nutritious leaves, making efficient propagation and accurate growth evaluation essential for improving leaf productivity. This study aimed to evaluate the effects of different stem cutting lengths on the morphological traits and early growth of M. oleifera and to develop a non-destructive method for estimating leaf area. The experiment was conducted in a greenhouse using a randomized complete block design with five stem cutting lengths (20, 30, 40, 50, and 60 cm). Leaf area estimation models were developed using measurements of leaf length and width of fully expanded compound leaves using regression analysis. Among the evaluated models, the linear regression based on leaf Length × Width (L × W) produced the highest predictive performance (R² = 0.9636) within the calibration dataset used in this study. Stem cutting length was associated with variation in early growth responses. Cuttings of approximately 40 cm generally showed more favorable early-growth characteristics, including higher survival percentage, larger leaf area, and greater dry biomass accumulation compared with several other treatments. Because survival data were derived from a limited number of plants per treatment, survival responses are interpreted descriptively. Differences in fresh biomass variables were not statistically significant, although variation was observed in leaf and root dry weight. These findings suggest that moderate cutting length may support balanced early establishment of M. oleifera under greenhouse conditions. The developed leaf area model provides a practical tool for repeated growth monitoring without destructive sampling. However, because the model was not validated using an independent dataset, further testing under field conditions and across broader environmental settings is still required before wider application.
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