Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques.

Area of Science:

• Agricultural Science
• Soil Science
• Geoinformatics

Background:

• Assessing soil fertility variability and uncertainty is vital for sustainable agriculture.
• Traditional models often analyze soil parameters individually, neglecting their combined impact and uncertainty.

Purpose of the Study:

• To develop a fuzzy logic and geoinformatics-based approach for simultaneous assessment of multiple soil fertility parameters.
• To integrate fuzzy rules and spatial modeling for enhanced soil fertility classification and nutrient management.

Main Methods:

• A fuzzy logic model with 80 rules was developed to evaluate macro- and micronutrients.
• Geostatistical kriging interpolation was used to create high-resolution soil fertility maps.
• The PUSA Soil Test and Fertilizer Recommendation Meter (STFR) analyzed 250 soil samples.

Main Results:

• Soil fertility parameters including pH, organic carbon, N, P, K, and Fe were identified as critical.
• Fuzzy model-derived fertility scores ranged from 41.55 to 52.60.
• Soil was categorized into low and moderate fertility zones in Jhunjhunu, Rajasthan.

Conclusions:

• The fuzzy-GIS framework improves soil fertility classification and site-specific nutrient recommendations.
• Pearl millet was identified as the most suitable crop based on fertility and yield potential.
• The study supports sustainable crop planning and nutrient management in precision agriculture.