Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice

Area of Science:

• Agricultural Science
• Plant Physiology
• Remote Sensing

Background:

• Canopy chlorophyll density (Chl) is crucial for diagnosing crop growth and nutritional status.
• Accurate assessment of nitrogen (N) status and yield prediction are vital for rice cultivation.

Purpose of the Study:

• To develop Chl-based models for estimating N status and predicting grain yield in rice (Oryza sativa L.).
• To utilize Leaf Area Index (LAI) and upper leaf chlorophyll concentration in these models.

Main Methods:

• Conducted six field experiments across multiple years in East China with varying N rates and rice cultivars.
• Measured SPAD values, LAI, leaf N accumulation (LNA), and plant N accumulation (PNA) from tillering to flowering.
• Developed linear regression models correlating Chl values with N indicators and grain yield, validated with independent data.

Main Results:

• Established significant linear relationships between Chl and N indicators: PNA = (0.092 × Chl) - 1.179 (R² = 0.94) and LNA = (0.052 × Chl) - 0.269 (R² = 0.93).
• Developed a model for yield prediction: normalized yield = (0.601 × normalized Chl) + 0.400 (R² = 0.81).
• Validated the models using independent experimental data, confirming accurate estimation of N status and yield prediction.

Conclusions:

• Canopy chlorophyll density is a reliable indicator for assessing rice nitrogen status.
• Chl-based models effectively predict rice grain yield, aiding in crop management.
• The developed models provide a practical tool for optimizing N fertilization and improving rice production efficiency.