Development of a sensor-based site-specific N topdressing algorithm for a typical leafy vegetable.

Area of Science:

• Agronomy
• Plant Science
• Soil Science

Background:

• Precise nitrogen (N) management in vegetables is crucial for improving N use efficiency.
• Current N fertilizer recommendations are time- and labor-consuming.
• Site-specific management addresses field fertility variations.

Purpose of the Study:

• To establish a site-specific N topdressing algorithm for bok choy (Brassica rapa subsp. chinensis).
• To evaluate the effectiveness of hand-held canopy sensors for N management.
• To identify optimal timing for N application predictions.

Main Methods:

• Field experiments conducted over three years (2014, 2017, 2020) with varying planting densities.
• Utilized a hand-held GreenSeeker canopy sensor to measure NDVI and RVI.
• Assessed five N application rates (0-205 kg N ha⁻¹).

Main Results:

• A strong correlation (R²=0.90) was found between sensor-based vegetation indices and yield potential across densities and years.
• The rosette stage was identified as the earliest reliable predictor for harvest response index.
• Ratio Vegetation Index (RVI) showed a 6.12% improvement over Normalized Difference Vegetation Index (NDVI) in predicting response.

Conclusions:

• Sensor-based N topdressing algorithms show significant potential for bok choy.
• This approach can enhance N use efficiency and contribute to sustainable vegetable production.
• The developed algorithm offers a more efficient alternative to conventional N recommendation methods.