An interpretable machine learning approach based on SHAP, Sobol and LIME values for precise estimation of daily soybean crop coefficients
Bibliographic record
Abstract
Increasing water scarcity and climate variability have intensified the need for precise agricultural irrigation management. Accurate estimation of crop coefficients (Kc) is critical for determining crop water requirements, especially in arid and semi-arid regions. However, conventional methods for estimating Kc often rely on generalized plant characteristics, which may not account for local climatic variations. In this study, we address this challenge by predicting the daily crop coefficient for soybean using four machine learning models: Extreme Gradient Boosting (XGBoost), Extra Tree (ET), Random Forest (RF), and CatBoost. These models were trained on meteorological data from Suhaj Governorate, Egypt, spanning 1979-2014. Additionally, SHapley Additive exPlanations (SHAP), Sobol sensitivity analysis, and Local Interpretable Model-agnostic Explanations (LIME) were applied to evaluate model interpretability and consistency with physical processes. Among the models evaluated, the ET model achieved the highest accuracy, with r = 0.96, NSE = 0.93, RMSE = 0.05, and MAE = 0.02. XGBoost and RF also performed well, each obtaining r = 0.96, NSE = 0.92, RMSE = 0.06, and MAE = 0.02. In comparison, CatBoost demonstrated slightly lower accuracy, with r = 0.95, NSE = 0.91, RMSE = 0.06, and MAE = 0.02. SHAP and Sobol analyses consistently identified the antecedent crop coefficient [[Formula: see text]] and solar radiation (Sin) as the most influential variables. LIME results revealed localized variations in predictions, reflecting dynamic crop-climate interactions. This study underscores the importance of integrating interpretable machine learning models to enhance both predictive accuracy and reliability while maintaining alignment with critical physical processes. The proposed framework offers a robust tool for improving daily Kc estimation, thereby supporting more sustainable irrigation practices and climate-resilient agriculture.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".