The manuscript entitled "Farmers' adaptive capacity towards soil salinity effects using hybrid machine learning in the Red River Delta" presents a timely and relevant study that integrates machine learning techniques with socio-economic analysis to assess both the spatial distribution of soil salinity and the adaptive capacity of farmers in a climate-vulnerable region. The study employs a suite of hybrid models combining XGBoost with various optimization algorithms (POA, STO, SOA, PSO, GOA), along with remote sensing data and household surveys, to deliver a comprehensive framework for analyzing the dual dimensions of environmental stress and human response. The article conforms to the journal-specific instructions and the topic fits well with the scope of the journal and proposes an innovative approach. While the article is generally well-structured and balanced, some key methodological details are missing. In particular, key details are missing regarding land use categorization in the study area and the rationale for selecting specific remote sensing indices and optimization algorithms. Additionally, although the findings related to farmers' adaptive capacity are insightful, they are presented largely in isolation from the machine learning analysis, with minimal integration between the two parts of the study. Furthermore, the Discussion section would benefit from a more critical engagement with existing literature, particularly studies that have applied similar optimization algorithms in environmental or agricultural contexts.

Detailed comments on each section are provided below.

Title

The current title may not accurately reflect the study’s output. In the present status, the study does not use machine learning to assess farmer’s adaptive capacity, but rather to predict soil salinization. The title should be reconsidered and rephrased to avoid any misleading interpretations.

Astract

The abstract is complete and gives a clear idea of the content without reading the paper.

Minor comments.

Introduction

Overall, the introduction covers the state of the art and explains the objectives of the study in a complete way. However, several acronyms and abbreviations are introduced here without first presenting their full forms. I recommend carefully reviewing the Introduction, and the manuscript as a whole, for consistency in defining all acronyms upon first use. Minor comments:

L42: Use “posing” instead of “poses”.

L51: Please rephrase “represent an extremely key role”.

L126: This passage would be more suitable for the final remarks (Conclusion) that the Introduction.

Materials and methods

The section is clearly structured into different sub-sections and easy to follow. However, some key information is unclear or missing:

• Model selection and integration: The rationale behind the selection of the specific optimization algorithms (POA, STO, SOA, PSO, GOA) and how they are integrated with the XGBoost model is not clearly explained. It is also not fully clear how these hybrid models contribute to the generation of soil salinity maps. Clarifying this connection would strengthen the methodological transparency.
• Land use consideration: It seems that the modeling process and salinity mapping does not account for different land use types. Applying models across the entire region without filtering by land use could lead to inaccurate interpretations, especially in heterogeneous agricultural landscapes. This should be explicitly addressed.
• Irrigation practices: The manuscript would benefit from including contextual information on irrigation practices in the study area. Specifically, details on the main sources of irrigation, and the distribution of land use types (e.g., paddy fields, rainfed, and irrigated areas) would provide valuable background for understanding the drivers of soil salinity and its spatial variability.
• Farmer interviews: It is recommended that the authors include the full list or at least a representative sample of the questionnaire items used in the household interviews, either within the main text or as supplementary material. Moreover, the socio-economic component of the study is presented independently from the machine learning analysis, with little discussion of how the two are connected. Figure 2 suggests that the selection of interview locations may have been informed by the salinity maps generated through the machine learning models, but this relationship is not clearly explained. Clarifying this linkage would enhance the coherence of the study and highlight the value of integrating spatial and social data.

Minor comments:

L133: Please remove “with the”.

L144: Replace “obtained at” with “reach”.

L165.166: Where are the soil sapling points located exactly?

L185: Please translate “extractés à partir de l’image” into English.

L221: Please define what a Tan commune is.

L223: There is an extra comma “is, often”.

L306 and onwards: Proposed by proposed by Kennedy and Eberhart (1995). Please check the reference style of similar citations throughout the manuscript.

Results

The results are clear and concise. As stated above, there is poor integration between the machine learning analysis and the socio-economic analysis. Minor comments:

L395: What questions are asked in the interviews? (see comment above)

L401: The passage “changing the crop structure” is unclear. Please rephrase.

L472: There is a typo here “the 2soil salinity”.

Discussion

The Discussion section addresses the main findings of the study, particularly the performance of the hybrid XGBoost models and the socio-economic insights from the farmer interviews. However, it falls short in a few critical areas that limit the depth and broader relevance of the study's conclusions:

• Lack of comparative analysis: The discussion would benefit from a more comprehensive comparison with similar studies that have applied enhanced or hybrid XGBoost algorithms (or other machine learning approaches) in soil salinity mapping or related environmental modeling tasks. Including such references would help position the study within the existing body of literature and strengthen its contribution.
• Limited interpretation of spatial variability: While the results highlight the spatial distribution of soil salinity, the discussion does not fully explore the potential environmental, agronomic, or anthropogenic drivers behind the observed variability. Possible contributing factors other than proximity to the coast or rivers should be discussed in more detail to provide context for the spatial patterns.
• Integration between technical and social findings: The Discussion currently treats the machine learning results and socio-economic findings as separate components. A more integrated discussion that connects spatial variability in salinity with local adaptive capacity (e.g., explaining how different levels of salinization impact farmers' strategies or vulnerability) would enhance the coherence and practical relevance of the study.

Minor comments:

L499-509: This paragraphs contains repetitions of already stated concepts. Perhaps it could be shortened.

L586: Please rephrase the sentence.

Conclusions

The conclusions are clear and well-balanced. However, I would recommend clearly stating the future steps to fill the existing gaps.

Soil salinity, which significantly impacts agricultural activities worldwide, is considered one of the major environmental hazards caused by both natural and human-induced processes. This phenomenon has become increasingly severe due to the impacts of climate change, particularly rising sea levels. Therefore, evaluating soil salinity is regarded as a critical task for supporting sustainable agricultural planning. Assessing adaptive capacity is also regarded as a crucial instrument for reducing the impact of soil salinity on local livelihoods. One of the strengths of this article is the integration of physical data, machine learning models, and socio-economic data (through interviews with local populations). As such, this article is relevant and well-aligned with the journal's scope. I accept publishing this article with the condition of major revisions.

Abstract: Although the authors present the objectives, data, and results of the article, I would like to see the inclusion of quantitative results and the significance of the findings.

Introduction: It is necessary to point out the importance of this article. Additionally, it is important to emphasize the role of adaptive capacity in reducing the effects of soil salinity.

Study Area: The reasons for selecting this study area should be explained in more detail, especially the effects of soil salinity on agricultural activities.

Map 1: Please revise Map 1 for better clarity.

Map 2: Similarly, Map 2 should be revised for better quality.

Methodology: This study uses machine learning and optimization algorithms to construct the soil salinity map. However, I do not fully understand how the authors constructed these models. A more detailed explanation is needed.

Interviews with Local Populations: The inclusion of the interview methodology is necessary because adaptive capacity is a key outcome.

Discussion: Although this article clearly discusses the strengths and weaknesses of the machine learning models and also touches on the adaptive capacity of the populations, I believe it would be useful to add the methodology for addressing the effects of soil salinity at the community level.

Extrapolation Issues: In this section, the authors present issues of extrapolation. I would suggest expanding on this point, as it is a challenge not only in soil salinity but also in other types of natural hazards.