Correlation and regression analysis of key soil fertility parameters in agricultural soils

Abstract

Understanding the interrelationships among soil chemical parameters is essential for optimizing nutrient management and sustaining crop productivity. This study aimed to assess the relationships among key soil fertility indicators pH, electrical conductivity (EC), organic carbon (OC), nitrogen (N), phosphorus (P₂O₅), and potassium (K₂O) using correlation matrices, regression models and scatter plot analysis across 12 treatment. The results revealed a strong positive correlation between pH and EC (r = 0.89), indicating that alkaline soils tend to exhibit higher ionic strength. The strongest correlation was observed between N and K₂O (r = 0.93), suggesting a synergistic relationship possibly driven by co-application of fertilizers or integrated nutrient cycling. Conversely, pH showed strong negative correlations with both N (−0.83) and K₂O (−0.89), implying that higher alkalinity may suppress the availability of these nutrients. Multiple linear regression models confirmed these associations, with adjusted R² values ranging from 0.78 to 0.91, indicating high predictive power for key nutrient parameters. The findings emphasize the importance of co-managing pH, salinity and nutrient inputs to improve soil health and nutrient use efficiency. These insights can support precision agriculture and targeted soil fertility interventions in similar agro-ecological settings.