Prediction of Remaining Productive Life of Oil Palm Plantation Soil Using Support Vector Regression with Permutation-Based Feature Importance Analysis - Zara Yunizar Zainal; Nurdin Nurdin; Zharif Athaya Andarfi (Politeknik Negeri Batam, 2026)
Oil palm (Elaeis guineensis Jacq.) is a strategically vital crop in Southeast Asia, yet progressive soil degradation driven by prolonged monoculture, pathogen pressure, and intensive land use poses a critical threat to long-term plantation sustainability. Existing soil assessment methods deliver static fertility classifications without quantifying the remaining productive lifespan of a given plot. This study introduces Remaining Productive Life (RPL) as a novel regression target defined as the estimated number of years before plantation soil productivity falls below a critical economic threshold. A Support Vector Regression (SVR) model with Radial Basis Function (RBF) kernel, formulated as K(xi, xj) = exp(−γ‖xi − xj‖²) with γ = 0.0303 (= 1/n_features) and regularization parameter λ = 0.5, was applied to a realistic synthetic multi-year dataset comprising 14,400 observations across 800 plantation plots spanning five soil types (Ultisol, Inceptisol, Alfisol, Peat, Oxisol) and the period 2008–2025. Thirty-three soil physicochemical, biological, management, and economic indicators constituted the input feature set. The SVR model achieved R² = 0.9141, MAE = 0.5122 years, RMSE = 1.1026 years, and MAPE = 11.883% on the independent test set, with 92.0% of predictions yielding an absolute error below one year. Permutation-based feature importance analysis identified Degradation Rate (ΔMAE = 0.2864), Plantation Age (0.2560), and Soil Productivity Index (0.2530) as the three dominant predictors, while Ganoderma Risk Index ranked fourth (0.2138), revealing the pivotal contribution of biological soil health to long-term productivity prediction. These findings establish SVR-based RPL estimation as an effective, interpretable framework for precision plantation management and proactive soil sustainability planning.