# Influences of organic nitrogen application ratio on oil content in flue-cured tobacco based on field experiments and a random forest model

## TL;DR

This study shows that applying 30% organic nitrogen improves oil content and quality in flue-cured tobacco through better lipid metabolism and leaf properties.

## Contribution

A novel integration of field experiments and a Random Forest model to optimize organic nitrogen application for tobacco quality.

## Key findings

- A 30% organic nitrogen ratio significantly increased leaf oil content and improved leaf physical properties.
- The Random Forest model achieved high predictive accuracy (CV R² = 0.819) using data augmentation and Recursive Feature Elimination.
- Key predictors of oil content included cembratriene-diol, leaf softness, and glandular trichome density.

## Abstract

Tobacco is a key economic crop, with leaf oil content serving as a critical determinant of leaf quality. To address the limited understanding of mechanisms underlying oil content improvement and the decline in flue-cured tobacco quality caused by long-term reliance on chemical fertilizers, this study integrated field experiments with a machine learning approach. Five treatments with varying organic nitrogen ratios (0%, 10%, 20%, 30%, and 40%) were evaluated at a single experimental site in Hengyang, Hunan. Results indicated that a 30% organic nitrogen ratio significantly enhanced the activity of key lipid metabolism enzymes, promoted the accumulation of lipid metabolites (including cembratriene-diol and sucrose esters), increased glandular trichome density, and improved leaf physical properties such as softness, tensile strength, and thickness, ultimately achieving the highest oil content. Using a robust data augmentation strategy and Recursive Feature Elimination, a Random Forest model was constructed to dissect the complex regulatory network. The model achieved a high predictive accuracy (CV R² = 0.819) on the augmented dataset, significantly outperforming the model based on original small-sample data. Feature importance analysis identified petroleum ether extract, cembratriene-diol, leaf softness, reducing sugar, and glandular trichome density as the primary predictors. Significant interactions among these features were also revealed by SHAP dependence plots. These findings provide a theoretical basis for optimizing organic nitrogen application to enhance tobacco leaf oil content and quality in agricultural production.