Hybrid Modeling and Management of Lodging in Subtropical Fiber Flax: Optimizing Plant Nutrition for Sustainable Cultivation

Abstract

Subtropical fiber flax faces significant challenges due to crop lodging, exacerbated by the high N regimes needed for increased fiber production. This study examines impact of balanced P and K applications under varied N regimes on lodging resistance, fiber yield, and quality of flax. Field experiments with 12 NPK treatments revealed that while 120:19:38 kg N, P, K ha−1 achieved highest plant height, stem-diameter, and fresh-weight while increased lodging percentage and reduced fiber quality when P and K were not balanced. Conversely, balanced P and K applications mitigated negative effects of high N, significantly reducing lodging and enhancing fiber yield, quality. Optimal fiber quality, including fiber length and bundle tenacity, was observed with 90:19:38 kg N, P, K ha−1. The novelty of this study lies in integration of stochastic and ML approaches to develop hybrid models for lodging. By combining LASSO with ANN, NLSVR, and RF techniques, hybrid models outperformed individual models in lodging prediction. Specifically, LASSO-NLSVR provided most accurate predictions due to its nonlinear mapping capabilities. Key predictive factors included fiber content, linear density, and tensile strength at 60 days post-sowing, offering valuable insights for breeders and agronomists to improve flax lodging resistance and fiber quality in subtropical climates.

Keywords

• Subtropical fiber flax
• balanced nutrient
• lodging resistance
• fiber quality
• hybrid regression
• 亚热带纤维亚麻