An Integrated XGBoost-SHAP Framework for XAI-based Analysis of Agroclimatic, Agronomic, and Operational Labor Proxy Affecting Block-Level Oil Palm Productivity

Authors

  • Hafis Ramadhan Muria Kudus University, Indonesia
  • Ahmad Jazuli Muria Kudus University, Indonesia
  • Esti Wijayanti Muria Kudus University, Indonesia
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DOI:

https://doi.org/10.63158/journalisi.v8i4.1770

Keywords:

Block-Level Prediction, CRISP-DM, Explainable AI, Oil Palm Yield Prediction, Precision Agriculture, SHAP, XGBoost

Abstract

Oil palm block-level productivity (Yield Per Hectare/YPH) relies on complex agronomic, climatic, and operational interactions, yet existing predictive models often neglect internal estate conditions. We developed an XGBoost-SHAP framework using 1,351 block-year observations (2018–2024) from a single plantation in Central Kalimantan, Indonesia. Features include plant age, land area, rainfall, and a labor ratio functioning as a land-based workload proxy. The baseline random-split testing achieved R² = 0.6276, MAE = 2.2747 Tons/Ha, RMSE = 3.3303 Tons/Ha, and MAPE = 28.73%. However, evaluating the model under grouped block and time-based validation schemes revealed limited spatial and temporal transferability. SHAP analysis identified plant age (47.51%), land area (14.32%), annual rainfall (10.87%), and labor ratio (8.51%) as primary yield drivers, revealing non-linear interaction patterns. Rather than a fully autonomous tool, this framework provides a transparent interpretive heuristic and decision-support prototype to evaluate block-level operational strategies. By quantifying internal operational variables alongside agronomic factors, this study contributes valuable insights for precision agriculture, suggesting future work validate dashboard heuristics with estate managers and incorporate granular fertilization and harvesting logs to further enhance predictive robustness.

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References

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Published

2026-09-03

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Vol. 8 No. 4 (2026): August

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