Modeling the activity ratio of soil potassium using machine learning approach

Desi Nadalia, Arief Hartono, Heru Bagus Pulunggono, Bambang Hendro Trisasongko, Widiatmaka Widiatmaka, Muhammad Fuady Emzir

Abstract

The potassium (K) Quantity-Intensity (Q-I) relationship results in important parameters, including the activity ratio of potassium at equilibrium (AReK), which indicates potassium availability in soil. Experiments to observe soil Q-I K relationship parameters are often complex, time-consuming, and do not include environmental variables. This research aims to model AReK using a machine learning (ML) approach. ML models applied are Random Forest (RF), Cubist, and Support Vector Machine (SVM) as the primary approaches, with Multiple Linear Regression (MLR) serving as a baseline. The dataset was derived from sixty-one observation points in Brebes, Central Java. The predictors were pH, organic carbon, clay, cation exchange capacity (CEC), exchangeable cations (Exc-Ca, Mg, K, Na), water soluble K, available K, K saturation, potential K, non-exchangeable K (NE-K), elevation, and slope. The response variable was the AReK. Variable selection was performed using Pearson correlation to eliminate highly correlated predictors and reduce multicollinearity. Exactly 75% of the data was utilized as the training set and 25% as the test set. Three metrics, i.e., MAE, RMSE, and R², were used in model evaluation. The results showed that the Cubist model could predict AReK with high accuracy (R2=0.9437) and low RMSE (0.5701) and MAE (0.3514). Based on the Cubist model, Exc-K, Exc-Mg, CEC, and Exc-Ca were the most important variables for predicting AReK. This model can be employed to support site-specific fertilizer recommendation strategies. To improve the performance of the model, it is necessary to add other predictor variables (e.g., soil physical properties, clay minerals, rainfall, temperature and soil moisture).

Keywords

Cubist; MLR; Predictor; RF; SVM

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References

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DOI: https://doi.org/10.20961/stjssa.v22i2.100102

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