-->PONE-D-25-56000-->-->Can a history of crop rotations improve the prediction of soil organic carbon in the Andes? Integrating machine learning multi-annual crop classification as a proxy of soil management-->-->PLOS One

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Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #1: Yes

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-->2. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: N/A

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Reviewer #1: Yes

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Reviewer #1: No

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-->5. Review Comments to the Author

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Reviewer #1: Dear authors

You presented a case study related to the observation of crop rotation and crop classification in the estimation of soil organic carbon. The topic is interesting, and the data—especially the field survey data—are convincing. However, the results may require better explanation. Before proceeding further, please consider the following comments:

1.The survey data covers the period from 2019 to 2022, while the remote sensing data spans from 2022 to 2024. How do you plan to integrate these datasets without introducing errors?

2.You only used data from 2019. Please ensure that the term “historical” is used appropriately.

3.The experiments have distinct purposes: one focuses on remote sensing classification, while the other focuses on soil property estimation. Can you integrate these two aspects more smoothly?

4.The results indicate that excluding crop rotation history yielded the highest performance. This seems contradictory to the rationale stated in the introduction section. Is there any misinterpretation? Do you also consider fallow periods as part of rotation?

5.You need some map to present the results of crop classification, crop rotation and soil property. In this regard, you could remove some figures that are less relevant.

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Reviewer #1: No

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Can a history of crop rotations improve the prediction of soil organic carbon in the Andes? Integrating machine learning multi-annual crop classification as a proxy of soil management

PONE-D-25-56000R1

Dear Dr. Loayza,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

RunGuo Zang

Academic Editor

PLOS One

Additional Editor Comments (optional):

accept

Reviewers' comments: