Evaluating crop nitrogen status in maize leaves: A predictive modelling approach using chlorophyll fluorescence...

Area of Science:

• Agricultural Science
• Plant Physiology
• Remote Sensing

Background:

• Chemical fertilizer use has surged, outpacing crop yield increases, highlighting the critical need for precise nitrogen (N) assessment in crops.
• Current methods for N assessment are often labor-intensive, environmentally sensitive, and lack precision.
• Optimizing N fertilization is crucial for enhancing crop yields and mitigating environmental pollution.

Purpose of the Study:

• To develop and validate a model using chlorophyll fluorescence (ChlF) technology for accurate, non-destructive evaluation of leaf nitrogen (N) status in maize.
• To assess the influence of different crop-straw management strategies and N application rates on leaf N content and ChlF parameters.
• To identify key ChlF parameters and complementary leaf characteristics for improving the accuracy of N assessment models.

Main Methods:

• Utilized a long-term experiment with maize hybrid Fumin 985, sampling under two straw management strategies and six N application rates.
• Employed partial least squares regression (PLSR) models incorporating various chlorophyll fluorescence parameters (ChlF) to predict leaf N content (N leaf).
• Applied principal component analysis (PCA) to reduce the dimensionality of ChlF data and compared model performance using different combinations of ChlF and leaf characteristics (total pigment content, leaf dry weight).

Main Results:

• A N application rate of 270 kg ha⁻¹ was found to be sufficient for meeting crop N requirements.
• Leaf N content, total pigment content (TP), and leaf dry weight (DW leaf) significantly correlated with N application rates and influenced OJIP fluorescence dynamics.
• Models incorporating ChlF and TP demonstrated higher accuracy in predicting N leaf compared to models using DW alone. Key ChlF parameters identified include ABS/RC, φ(Eo), ETo/CSm, and δ(Ro)/(1-δ(Ro)).

Conclusions:

• Non-destructive N leaf detection using chlorophyll fluorescence technology is feasible for maize.
• Integrating additional leaf characteristics, such as total pigment content, is essential for enhancing model accuracy.
• The application of this technology at a larger scale requires consideration of local field conditions for efficient N management and sustainable agriculture.