The Prediction of Wheat Yield in the North China Plain by Coupling Crop Model with Machine Learning Algorithms

Round 1

Reviewer 1 Report

This is an interesting study. The authors used the accumulated biomass (AB) simulated by the Agricultural Production Systems sIMulator (APSIM) model, as well as multiple climate indices (e.g., climate suitability indices, extreme climate indices), to predict wheat yield in the North China Plain (NCP). The results showed that the prediction model based on the Random Forest (RF) algorithm outperformed other regression algorithms. The prediction of wheat yield at SM (the period from grain filling to milky stage) based on the RF algorithm can be more accurate. Furthermore, the authors compare various model and accuracy parameters for wheat yield prediction, increasing the validity of the results. The findings of this study have important policy implications and should be disseminated to the scientific community. However, there are a few issues that must be addressed before making a final decision on this paper.

1.      The introduction section, which includes a literature review, requires extensive revision. The introduction currently lacks the key elements of a research article. To begin, the authors must clearly state the research gap, which can be accomplished by effectively positioning the research article within the existing scholarly debate. Second, the authors must explicitly state the study's objectives. Third, a thorough examination literature relevant to existing machine learning and statistical models (or their combinations) for crop output prediction is required. Keeping in view the above points, I am suggesting the following literature for authors to read and include in this paper:

https://doi.org/10.1016/j.scitotenv.2019.02.266

https://doi.org/10.3390/e24101487.

https://doi.org/10.1504/IJMTM.2021.121110

https://doi.org/10.3389/fenvs.2022.944156

https://doi.org/10.3390/w14121832

2.      Why have the authors used data from 2000-2010 only and not used the latest data? What were the limitations? Please describe in methods section.

 

3.      At present, the discussion section merely presents an overview of the study findings, but it does not critically contextualize the study results in light of the extant scholarly debate. As a consequence, the authors are unable to provide us with thick and consistent information about how they are adding to the scholarly debate. Implications for theory and practice are limited. Please improve this section as well.

4.      The section on conclusions is very brief. How do you connect this to potential policy recommendations, your study's contributions to methodological improvements, and other factors? What is the overall significance of your findings?

 

5.      Please acknowledge the limitations of your research and consider expanding on future research directions.

6.      Format of the Tables and Equations in the Materials and Methods section is not consistent.

7.      The abbreviations have been used in the headings without defining them (see 2.3.1 for example). Please correct this.

8.      Please revise the caption of Table 2.

9.      Please double-check all references, both in-text and in the bibliography. Check that the reference format is consistent with the journal style and that all references are current. You can remove some dated references while remaining true to the classics.

10.   Minor language improvements are required throughout the manuscript, so keep this in mind when submitting the revised version.

Author Response

请参阅附件。

Author Response File: Author Response.docx

Reviewer 2 Report

Please, find attached my comments. 

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.docx