Evaluating the Soil Quality Index Using Three Methods to Assess Soil Fertility

Area of Science:

• Agricultural Science
• Soil Science
• Spectroscopy

Background:

• Soil health is vital for global food security, influencing crop yield and quality.
• Soil quality indices (SQIs) integrate multiple soil properties but traditional lab analysis is costly and time-consuming.
• Developing rapid, non-destructive methods for soil assessment is crucial for practical soil management.

Purpose of the Study:

• To evaluate the efficacy of visible near-infrared (vis-NIR) spectroscopy for predicting key soil fertility indicators.
• To compare different approaches for calculating and predicting Soil Quality Indices (SQIs) using spectral data.
• To establish a cost-effective and environmentally friendly alternative to traditional soil analysis.

Main Methods:

• Focused on predicting seven soil indicators: pH, organic matter (OM), potassium (K), calcium (Ca), magnesium (Mg), available phosphorous (P), and total nitrogen (TN).
• Utilized visible near-infrared (vis-NIR) spectroscopy coupled with the Cubist model for property prediction (R² = 0.35-0.93).
• Compared three SQI calculation methods: measured (SQI_m), predicted (SQI_p), and directly predicted (SQI_dp).

Main Results:

• Vis-NIR spectroscopy accurately predicted individual soil indicators using the Cubist model.
• Direct prediction of SQI (SQI_dp) achieved high accuracy (R² = 0.90).
• Predicting SQI using spectral data (SQI_p) showed lower accuracy (R² = 0.23) compared to direct prediction.

Conclusions:

• Vis-NIR spectroscopy is a viable tool for rapid, non-destructive prediction of soil properties and SQIs.
• Direct prediction of SQI using spectral data offers a more accurate approach than predicting individual properties first.
• This spectroscopic approach provides a cost-effective and sustainable alternative for soil health monitoring.