# Analysis of Marijuana (Cannabis sativa L.) Cuttings: Morphological and Colorimetric Traits as Predictors for Optimization of Vegetative Reproduction

## TL;DR

The study explores how the physical traits of marijuana cuttings affect their success in vegetative propagation, aiming to improve cultivation methods.

## Contribution

The paper identifies specific morphological and colorimetric traits that predict successful rooting in marijuana cuttings.

## Key findings

- Longer cuttings, greater fresh mass, presence of apical meristem, and greener leaves are positively associated with rooting probability.
- Principal Component Analysis confirmed positive correlations between morphological and colorimetric variables.
- The findings support developing efficient and low-cost vegetative propagation protocols for marijuana.

## Abstract

Marijuana (Cannabis sativa L.) has a great economic potential due to its phytotherapeutic properties. Its propagation, however, faces numerous challenges due to the limited availability of standardized technical protocols for the crop. Vegetative propagation represents a, or even the, viable method for multiplying the genetically identical individuals while preserving their phytochemical profile, at lower costs and with shorter production times. This study investigated the morphological and colorimetric attributes associated with vegetative propagation success, aiming to develop sustainable cultivation strategies. Four cutting lengths (5, 10, 15 and 20 cm) were evaluated after 21 days of rooting, considering fresh mass, basal diameter, presence of apical meristem, number of root primordia, root length, and foliar and stem color parameters. Logistic regressions indicated that longer cuttings (p = 0.0101), greater fresh mass (p = 0.073) and the presence of apical meristem (p = 0.065), as well as greener leaves (p = 0.089), were positively associated with rooting probability (p < 0.10). Positive correlations between morphological and colorimetric variables were confirmed by Principal Component Analysis, with the first two principal components explaining 31.2% of the total variance in the dataset. The results provide support for the development of more efficient and low-cost vegetative propagation protocols, promoting uniformity and autonomy in local cutting production of marijuana.