Nimeshkumar Patel Reviewer
Approved
Relevance and Originality
The study addresses an important topic related to crop yield analysis and agricultural risk assessment. The focus on interpretable statistical models for understanding yield behaviour across multiple crops is relevant for agricultural research and policy planning. The idea of integrating regression models with yield regime classification adds practical value. However, the novelty could be clarified more clearly in relation to previous studies that have already used quantile regression in agricultural analysis.
Methodology
The methodology is based on three regression approaches, Ordinary Least Squares, Quantile Regression, and Robust Regression. This comparative modeling approach is appropriate for agricultural datasets that often contain variability and outliers. The dataset used appears sufficiently large and includes several agronomic and environmental variables. More explanation regarding preprocessing procedures, feature encoding, and model implementation would improve methodological clarity and reproducibility.
Validity and Reliability
The study evaluates model performance using sMAPE, median absolute error, and pinball loss, which are suitable for regression analysis. The results indicate that quantile regression performs better compared with the other models. Nevertheless, the manuscript would benefit from additional validation procedures and discussion about dataset variability to strengthen the reliability of the findings.
Clarity and Structure
The manuscript follows a typical research structure and the overall organization is understandable. Figures and tables support the explanation of the framework. However, the language requires careful editing to improve grammar, readability, and sentence flow.
Results and Analysis
The results demonstrate that quantile regression better captures yield variability and performs more effectively than the baseline models. The yield regime classification provides a useful interpretation of crop yield behaviour and its potential policy implications. A deeper comparison with related studies would further strengthen the analysis.
Nimeshkumar Patel Reviewer