Performance Evaluation of Publicly Funded Agricultural Research Projects with Light-TabNet

This study focuses on the performance evaluation of publicly funded agricultural research projects in a structured tabular-data setting characterized by small sample size and heterogeneous features. We construct a project-level performance evaluation dataset covering 24 provincial agricultural research institutions in China, with n=280 samples. The target variable is the project self-evaluation score, reflecting overall annual target completion rather than a fixed explicit transformation of the input indicators. To address the limitations of manual evaluation—including subjectivity, poor inter-rater consistency, and potential bias—we propose Light-TabNet, which enhances the model’s fitting capability in small-sample scenarios while preserving interpretability. Interpretability is achieved through sparse decision masks and aggregated feature-attribution analysis, with partial cross-model support from comparison with XGBoost-SHAP rankings. Compared with 13 deep learning and traditional machine learning baselines, Light-TabNet achieves improved accuracy in terms of mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R2) (MAE 4.9765, RMSE 8.8140, R2 0.8891). In a preliminary real-world validation on eight projects from a provincial agricultural research institution, the model’s predicted scores were overall close to ratings provided by a third-party organization, suggesting preliminary practical usefulness in a similar management setting. The results suggest that Light-TabNet can serve as a decision-support tool for the performance evaluation of publicly funded agricultural research projects by providing an objective, traceable, and interpretable quantitative reference.