Analysis of Land Suitability for Maize Production under Climate Change and Its Mitigation Potential through Crop Residue Management

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The present article dealt with the issue of determining the suitability of land for maize production under climate change and tried to take a forward-looking view to this issue by combining different methods and variables, which is an innovative and good article from this point of view. Of course, there are some ambiguities in different parts, which are mentioned in the text of the article and below:

In my opinion, the abstract is not comprehensive. In particular, it lacks problem statement and results and suggestions.

In the first part of the introduction, especially on the third page, descriptions are mostly related to the research method section.

In the introduction, the reader is more interested in the research problem and stating, its necessity and importance and the purpose of the research.

Page 5 : Please specify which year this map was from?

Table 1: Please specify the year of each data collection in the table.

In the methodology section, the scenarios are not well explained.

Page 6: The fuzzy inference system used and the AHP method and the weight assigned to the criteria and how to assign this weight to the criteria are ambiguous.

The results section is well covered

Page 19- Line 644 : “pH” or “PH”? p shouldn’t this word be capitalized?

Page 21-Line 762 : It would have been better to describe the different scenarios studied and their differences in the form of a table in the methodology section.

Sincerely,

Comments for author File: Comments.pdf

Comments on the Quality of English Language

My native language is not English, but in my opinion, the quality of English is good.

 

Author Response

 

Responses to Reviewer #1's comments

The present article dealt with the issue of determining the suitability of land for maize production under climate change and tried to take a forward-looking view to this issue by combining different methods and variables, which is an innovative and good article from this point of view. Of course, there are some ambiguities in different parts, which are mentioned in the text of the article and below:

In my opinion, the abstract is not comprehensive. In particular, it lacks problem statement and results and suggestions.

Response: We agree with the reviewer. The abstract has been completely revised, and additional content has been included in the manuscript lines 16-30: “Land Suitability Analysis (LSA) under the impact of climate change is a fundamental approach for the design of appropriate land management strategies for sustainable crop production and food security. In this study, the FAO framework was used to assess the impact of climate change on land suitability for maize in Flanders, Belgium. Current LSA revealed the marginal suitability for maize cultivation, characterizing most of the agricultural land in Flanders and identifying precipitation as the most limiting factor for maize suitability. LSA under two climate change scenarios was based on climate projections from several CMIP5 Global Circulation Models, transformed into future land suitability projections and assembled into a multi-model ensemble (MME) of projected suitability changes. The results indicate an average reduction in projected suitability of approximately 7% by 2099 under the high emission scenario. The potential of Soil Improving Cropping System (SICS) to mitigate the impacts of climate change on land suitability was statistically significant under both low and high emission scenarios. This research provides valuable insights into the MME modelling of climate change impacts on land suitability and its associated uncertainty, with the application of SICS as a potential long-term mitigation measure to promote sustainable agricultural practices.”

In the first part of the introduction, especially on the third page, descriptions are mostly related to the research method section.

Response: The section has been reduced in length, but it still maintains its extensive nature as it encompasses the literature review related to the subject.

In the introduction, the reader is more interested in the research problem and stating, its necessity and importance and the purpose of the research.

Response: We agree with the reviewer; the literature review is essential for framing the problem statement and justifying the research's purpose.

Page 5: Please specify which year this map was from? 

Response: The year information was added to map caption.

Table 1: Please specify the year of each data collection in the table. 

Response: Table 1 was amended to provide statistics per region. The relevant info was added to the manuscript in lines 228-231: “The climatic variables of monthly precipitation and monthly average and minimum temperature during the local growing season of maize (May - August), were averaged over a 20-year period (2001-2020) from the available time–series of climatic data”. Additionally, SoilGrids were published in 2016 as modelling output with no specific time-reference.  

In the methodology section, the scenarios are not well explained.

Response: We agree with the reviewer's point that the scenarios are only briefly mentioned. To address this, we have incorporated the following content into the manuscript, specifically in lines 307-314: “The two scenarios differ substantially not only in the projected GHG emissions to 2100, but also in the scenario’s storyline that describes the driving forces behind these emissions. TheRCP8.5 storyline describes a heterogeneous world with a steadily increasing global population, higher energy demands and intensive agricultural production in the absence of policies to limit climate change impacts, whereas RCP4.5 follows a stabilization pathway, assuming that climate policies, such as the current global price on GHG emissions, are fully implemented to achieve limited emissions and associated radiative forcing”    

Page 6: The fuzzy inference system used and the AHP method and the weight assigned to the criteria and how to assign this weight to the criteria are ambiguous.

Response: We agree with the reviewer that the AHP approach is only briefly introduced, and this is because it is thoroughly described in the previous publication by Bilas et al., 2022, doi:10.3390/land11122200. The current work is a continuation of this study; therefore, the process should not be repeatedly detailed here but only referenced.

The results section is well covered.

Response: Thank you for your kind comment.

Page 19- Line 644 : “pH” or “PH”? p shouldn’t this word be capitalized?

Response: The standard symbol is pH. In the manuscript the symbol PH is only used as a variable name to reference pH.

Page 21-Line 762 : It would have been better to describe the different scenarios studied and their differences in the form of a table in the methodology section.

Response: The relevant paragraph has been updated to provide a clearer description of the involved scenarios. The following information was added to the manuscript, specifically in lines 821-828: “Two different scenarios of future climate change were studied, RCP4.5 and RCP8.5, which represent two different future climates based on the projected GHG emissions under different socioeconomic storylines i.e. low and high emissions scenario. The impact of climate change on suitability was assessed through future LSA, using MME of projected suitability changes, developed from GCMs future climate projections. Combined with a scenario of future soil conditions based on the CRM-SICS application for a period of 80 years up to 2099, the findings of this study allow the following conclusions to be drawn:”

Reviewer 2 Report

Comments and Suggestions for Authors

 

GENERAL

The paper is focused on future changes of land suitability for maize under different scenarios of climate change. It is rather easy in lecture, although it requires certain knowledge on methods used by authors.

I note some issues related mainly to (1) SoilGrids as the source of data; (2) insufficient characteristics of agricultural regions; (3) the selection of the layer from 0 to 60cm to analysis; (4) soil limiting factors (lines 430-437); (5) editiorial issues which difficults the interpretation of tables and figures.

(1) The Soil Grids are not very reliable information of soil data, particularly at lower (local) level, as it was proved in France (Richer-de-Forges et al., 2023), Ghana (Maynard et al., 2023) and Zambia (Buenemann et al. 2023). It is clear that Soil Grids and many other sources of soil data are not very reliable for land management. On the other hand, do the inaccuracies and errors found in these data sources invalidate the quality of predictions of future changes of land suitability? I hope not, but the authors could shortly discuss it.

(2) For agronomists and soil scientists it would be very useful to see more detailed information on the regions considered in this study: Sandy loam, Campine and Sandy. What are the differences and similarities regarding not only soil properties, but also climate variables? I strongly recommend to split the table 1 into 2 tables: one of them with soil properties, separately for 3 regions (Sandy loam, Campine and Sandy), and the second table with climatic data of these regions. Additionally, the information regarding 1st and 3rd quantiles is not necessary, in my opinion.

(3) in my opinion the layer considered in soil sutability analysis should be quite ficker, of about 0 to 100cm. For example, the sand underlying the 60cm loam layer still affects land sutability and reduces crop yields, particularly in dry years. Please, justify the selection of the layer 0-60cm for LSA.

(4) Most frequently, sands have lower CEC and SOC than sandy loams, for this reason I would like to know, why CEC limits crop production in Sandy loam region, but not in Sandy region. Additionally, low soil pH is natural in conditions of humid climate (precipitation>evapotranspiration) and intensive nitrogen fertilization which accelerates soil acidification. However, soil acidity (low pH values) is - relatively easily - corrected by liming, which is a commonly used agronomic practice. Consequently, soil acidity is rather limiting factor due to natural water balance and errors in agricultural technology (insufficient liming).

(5) The main editorial issue is splitting of the tables (1, 3 and 6, including their titles/captions) in different pages. It makes interpretation of the data very difficult.

As a consequence, I recommend to publish this paper after major revision of the text.

REFERENCES regarding reliability of sources of soil information

RICHER-de-FORGES, Anne C., et al. "Hand-feel soil texture observations to evaluate the accuracy of digital soil maps for local prediction of soil particle size distribution: A case study in Central France." Pedosphere 33.5 (2023): 731-743.

Maynard, Jonathan J., et al. "Accuracy of regional-to-global soil maps for on-farm decision-making: are soil maps “good enough”?." Soil 9.1 (2023): 277-300.

Buenemann, Michaela, et al. "Errors in soil maps: The need for better on-site estimates and soil map predictions." Plos one 18.1 (2023): e0270176.

Author Response

Responses to Reviewer #2’s comments

GENERAL

The paper is focused on future changes of land suitability for maize under different scenarios of climate change. It is rather easy in lecture, although it requires certain knowledge on methods used by authors.

I note some issues related mainly to (1) SoilGrids as the source of data; (2) insufficient characteristics of agricultural regions; (3) the selection of the layer from 0 to 60cm to analysis; (4) soil limiting factors (lines 430-437); (5) editiorial issues which difficults the interpretation of tables and figures.

(1) The Soil Grids are not very reliable information of soil data, particularly at lower (local) level, as it was proved in France (Richer-de-Forges et al., 2023), Ghana (Maynard et al., 2023) and Zambia (Buenemann et al. 2023). It is clear that Soil Grids and many other sources of soil data are not very reliable for land management. On the other hand, do the inaccuracies and errors found in these data sources invalidate the quality of predictions of future changes of land suitability? I hope not, but the authors could shortly discuss it.

Response:  A new section in chapter 4, titled Data-related implications is dedicated to that discussion. Briefly, although SoilGrids exhibit inaccuracies in certain regions (France), the soil profile density in Flanders area is very high (screenshot from soilgrids.org), making the prediction much more reliable. The mean errors reported from the uncertainty layers of SoilGrids are acceptable, and no significant bias is introduced into the LSA process. The uncertainty concerning the climate variables is also discussed.

The following text was added to the manuscript lines 678-711:

Data-related implications

LSA relies on spatial data on soil, climate and landscape characteristics (Table 1), which have inherent uncertainties, associated with either model prediction errors or in-accurate representations of the physical environment. SoilGrids [ ] is the result of global predictions for standard soil properties based on ML methods, using several remote sensing-based soil covariates, mostly MODIS land products, and an extensive database of soil profiles for training purposes. The distribution of soil profiles greatly affects the accuracy of the derived predictions, as reported by recent study [ ] in Central FR, concerning the ac-curacy of SoilGrids at local level. The study area of Flanders has a very high density coverage of soil profiles (228 profiles per 1000 km2) [ ], with more than 7000 profiles covering the whole of BE. Using the uncertainty layers that accompany SoilGrids predictions [ ], prediction uncertainties were derived for specific soil properties and averaged over the study area. The averaged errors and their standard deviations were 0.56 for PH (sd=0.1), 2.23 cmol/kg for CEC (sd=0.32), 5.58 g/kg for SOC (sd=2.3) and 4.12% for CRFVOL (sd=0.9). The magnitude of these errors together with the very high density of soil profiles in the area, suggest that SoilGrids provided a reliable representation of the soil characteristics for LSA purposes, and that no significant biases were introduced into the process. Concerning climate data, monthly averages for the variables of interest were extracted for the 20-year period of 2001-2020 from the available time-series . TerraClimate uses climatologically assisted interpolation on different global gridded climate datasets, to provide higher spatial resolution (1/24°) climatic variables with lower temporal resolution (monthly), showing a significant improvement in overall mean absolute error compared to the original coarser resolution gridded datasets [ ]. ERA5-Land combines gridded climatic data and observations using physical modelling to describe the evolution of the water and energy cycles over land during long time periods, covering both past and present climatic conditions, with enhanced spatial resolution (1/10°). Validation against independent in-situ observations and global reference datasets has shown the added value of ERA5-Land in describing the hydrological cycle, while highlighting the existence of significant precipitation biases especially in the tropical regions [ ]. To minimize the introduction of bias into the LSA process, climate data on monthly precipitation and minimum temperature were extracted from the TerraClimate time-series, while ERA5-Land was used only for the extraction of monthly average temperature.                                

(2) For agronomists and soil scientists it would be very useful to see more detailed information on the regions considered in this study: Sandy loam, Campine and Sandy. What are the differences and similarities regarding not only soil properties, but also climate variables? I strongly recommend to split the table 1 into 2 tables: one of them with soil properties, separately for 3 regions (Sandy loam, Campine and Sandy), and the second table with climatic data of these regions. Additionally, the information regarding 1st and 3rd quantiles is not necessary, in my opinion.

Response: We agree with the reviewer, no information was provided for the soils in the 3 regions of interest. The following was added to the manuscript lines 189-200: “According to the soil map of the Flemish region, and following the World Reference Base for Soil Resources (WRB) soil classification system [ ], the dominant Reference Soil Groups per agricultural region considered here are: 1) Sandy region: Cambisols, i.e. soils with a moderately developed profile due to the limited age of the soil material and which can be very productive, especially in loess areas, Anthrosols i.e. soils under intensive agricultural use, with substantial additions of mineral and organic fertilizers, and Arenosols or sandy soils with limited soil formation. 2) Campine region: Podzols which are acidic, mostly coarse textured soils with a bleached horizon underlain by an accumulation of organic matter, aluminium and iron, and Anthrosols. 3) Sandy loam region: Luvisols i.e. soils with a subsurface horizon of high activity clay accumulation and high base saturation, Retisols which are soils with a clay-enriched subsoil, and Fluvic Cambisols formed on sediment deposits in alluvial valleys on.” 

Additionally, Table 1 content in line 237, was replaced with basic statistics (min,max,mean,sd) per region.

(3) in my opinion the layer considered in soil sutability analysis should be quite ficker, of about 0 to 100cm. For example, the sand underlying the 60cm loam layer still affects land sutability and reduces crop yields, particularly in dry years. Please, justify the selection of the layer 0-60cm for LSA.

Response: For LSA purposes, the choice of 0-60cm for SoilGrids integration corresponds to the effective rooting depth over an analysis cell, rather than the maximum rooting depth of a single plant. Zhang et al. 2023 (https://doi.org/10.3390/agriculture13040765) focus on this depth for root characteristics for maize. For SoilGrids use in LSA, flattening down the soil profile up to 1m depth might not be realistic for some properties, since e.g. it would artificially decrease CEC.

The following text was added to the manuscript lines 225-231: “For LSA purposes, SoilGrids were pre-processed by combining the first four layers of available information, integrating values over the soil depth up to 60cm which corresponds to the effective rooting depth over an analysis cell to which the crop can take up water and nutrients, rather than the maximum rooting depth of a single plant [].”

 (4) Most frequently, sands have lower CEC and SOC than sandy loams, for this reason I would like to know, why CEC limits crop production in Sandy loam region, but not in Sandy region. Additionally, low soil pH is natural in conditions of humid climate (precipitation>evapotranspiration) and intensive nitrogen fertilization which accelerates soil acidification. However, soil acidity (low pH values) is - relatively easily - corrected by liming, which is a commonly used agronomic practice. Consequently, soil acidity is a limiting factor due to natural water balance and errors in agricultural technology (insufficient liming).

Response: Statistics in Table 1. Indicate that Sandy loam region has lower CEC than Sandy region, in SoilGrids. This is justifiable by the distribution of the dominant Soil Groups in the 3 regions that has been added in lines 189-200: “According to the soil map of the Flemish region…”. For the detailed distribution of Soil Groups see referenced report [].

 (5) The main editorial issue is splitting of the tables (1, 3 and 6, including their titles/captions) in different pages. It makes interpretation of the data very difficult.

As a consequence, I recommend to publish this paper after major revision of the text.

REFERENCES regarding reliability of sources of soil information

RICHER-de-FORGES, Anne C., et al. "Hand-feel soil texture observations to evaluate the accuracy of digital soil maps for local prediction of soil particle size distribution: A case study in Central France." Pedosphere 33.5 (2023): 731-743.

Maynard, Jonathan J., et al. "Accuracy of regional-to-global soil maps for on-farm decision-making: are soil maps “good enough”?." Soil 9.1 (2023): 277-300.

Buenemann, Michaela, et al. "Errors in soil maps: The need for better on-site estimates and soil map predictions." Plos one 18.1 (2023): e0270176.

Response: We agree with the reviewer that long tables are difficult to study. We leave the decision of splitting long tables or not with the copy editor who will do the final formulation of the manuscript.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

 

GENERAL

The authors addressed many of my notes regarding this paper. However some of my doubts regarding this paper persist, and some new doubts appeared  from agronomic and edaphological point of view.

.

(1) SoilGrids as the source of data. The quality of database should increase with number of observations (in this case - soil pits) used for it’s preparation. But it does not always occur and, in case of soil maps (SoilGrids is a kind of soil map, actually), some methods used for their preparation, e. g. method of interpolation may affect negatively their quality, in this case - the agreement of map with current reality in terrain (soil properties).

Why I doubt in quality of SoilGrids in particular case of this paper? The names of agricultural regions - Sandy and Sandy Loam do not agree with average soil properties (Table 1)! Actually the „average” soil of Sandy agricultural region has an texture class of loam according to USDA (slightly modified in WRB) soil texture classification and is, clearly, less sandy than Campina region. Moreover, the „average” soil of Campina region, is more „sandy” than „average” soil of Sandy region, still has a sandy loam soil texture class. The „average” soil of „Sandy loam” region belongs to a soil texture class of loam. What do these names of agricultural regions come from? Are these names traditional and thus reflecting the most common (prevailing) soil properties of these regions? Why these names do not agree with average soil properties?

I see only one method to check the quality of SoilGrids for particular area - Flanders - considered in this paper. The authors should take the geographic coordinates of each soil profile from Lucas database - 165 georeferenced points fo Flanders. Next, the authors should compare the sand, silt and clay content data for these soil profiles from the Lucas project with the sand, silt and clay contents derived from SoilGrids for the exact same sites. Such a comparison will provide information on the actual reliability of SoilGrids for the Flanders.

And what, if these the data derived from SoilGrids are not very reliable? If the modelling method is correct, the paper will still inform about most probable future changes of land suitability for maize in Flanders.

(2) Soil layer considered in the study. Perhaps, I was not sufficiently precise regarding my suggestions regarding soil layer. Actually, according to the study of Ning et al. 2015 (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0121892) the major density of maize roots was found in 0-20cm layer and I would generalize that this refers to humus accumulation layer (A horizon), which is, most frequently, 20-30cm thick, with exception of some Chernozems, Phaeozems etc... The humus accumulation layer is the main source of nutrients for crops, including maize. However, the deeper layers affects the water supply for roots, both in direct and indirect way. For this reason, the authors are considering the layer 0-60cm, and I suggest to consider the layer of 0-100cm. Actually, it is a question of opinion and I respect the idea of authors. However, I have another, even more important doubt. The humus accumulation layer has different properties (soil organic carbon, and frequently texture) than deeper layers (e.g. 30-60cm) in majority of soils. Some but not all, Chernozems, Pheozems, Anthrosols and even Cambisols may be considered as relatively uniform to 60cm of depth, but not Podzols, Luvisols and Retisols. In intensively cultivated soils, the thickness of A layer scarcely exceeds 30cm. For these reason, the soil properties considered in this study should be different for the 0-30cm (A) layer and the 30-60cm layer. Is this truth? Please, clarify.

(3) Another doubt refers to so low, prevailing, Land Suitability for maize (S3) currently and in the future. As agronomist and soil scientist I assess the „average soil” (Table 1) of this study, of each agricultural regionas as moderately suitable (S2), although, the average temperature is not very favourable for this crop (the optimum being between 20 and 30°C, depending on growth stage) and I would assess it as factor more limiting maize yield than precipitation in current thermic conditions. I agree that in future the water will be more important yield limiting factor for maize than temperature. However, it is only, partly subjective opinion. To avoid such doubts, I suggest to provide additional data in this paper,perhaps as supplementary files:

- the critical values of soil, topographic and climate variables for particular classes Land Suitability for maize (S1, S2 and S3);

- current average temperatures in May, June, July and August;

- the predicted values of climatic variables (precipitation and temperatures) for particular climate projections (RCP4.5 and RCP 8.5) in the years 2030, 2050, 2070 and 2080.

The addition of these data would make this paper more comprehensive and understandable for agronomists and farmers.

I recommend major revision of this paper.

DETAILED

Please add citation of [47] and [67] references, please, see below, if I am right:

Line 352: I suppose that authors should add [47] after Yip et al., 2011

Line 742: I suppose that authors should add [67] after Cattiaux et al. 2013.

I hope all the tables and their captions will be put together within one single page before pulication. It is just editorial issue, but important for reading and interpretation of this paper.

Author Response

Responses to reviewers’ comments

Responses to Reviewer #2’s comments – (Round 2)

"The authors addressed many of my notes regarding this paper. However, some of my doubts regarding this paper persist, and some new doubts appeared from agronomic and edaphological point of view.

(1) SoilGrids as the source of data. The quality of database should increase with number of observations (in this case - soil pits) used for it’s preparation. But it does not always occur and, in case of soil maps (SoilGrids is a kind of soil map, actually), some methods used for their preparation, e. g. method of interpolation may affect negatively their quality, in this case - the agreement of map with current reality in terrain (soil properties).

Why I doubt in quality of SoilGrids in particular case of this paper? The names of agricultural regions - Sandy and Sandy Loam do not agree with average soil properties (Table 1)! Actually the „average” soil of Sandy agricultural region has a texture class of loam according to USDA (slightly modified in WRB) soil texture classification and is, clearly, less sandy than Campina region. Moreover, the „average” soil of Campina region, is more „sandy” than „average” soil of Sandy region, still has a sandy loam soil texture class. The „average” soil of „Sandy loam” region belongs to a soil texture class of loam. What do these names of agricultural regions come from? Are these names traditional and thus reflecting the most common (prevailing) soil properties of these regions? Why these names do not agree with average soil properties?

Response: The reviewer has been – justifiably – misled by the names of the agricultural regions. The names 'Sandy,' 'Campine,' and 'Sandy loam' are official names used by local authorities, and as is usually the case with conventional names describing local regions, they have been established historically. Since then, the soils in these regions have undergone significant changes, mainly due to human intervention, resulting in the occurrence of Anthrosols, for example. Nowadays, these names do not fully correspond to the prevailing soils in these areas, but they continue to be in use. Highlighting the inconsistencies between the historical regional names and the current soil conditions in the corresponding areas was beyond the scope of this study.  

I see only one method to check the quality of SoilGrids for particular area - Flanders - considered in this paper. The authors should take the geographic coordinates of each soil profile from Lucas database - 165 georeferenced points of Flanders. Next, the authors should compare the sand, silt and clay content data for these soil profiles from the Lucas project with the sand, silt and clay contents derived from SoilGrids for the exact same sites. Such a comparison will provide information on the actual reliability of SoilGrids for the Flanders.

Response: We utilized SoilGrids data in this study, by emphasizing and commenting on all the aspects that are related to the accurate representation of the soil conditions in the areas of interest. Moreover, we specifically reported on the uncertainty accompanying the predictions of soil properties provided by SoilGrids. For the purposes of our study, the use of SoilGrids does not introduce any significant bias into the results. Additionally, a detailed comparative evaluation of the reliability of SoilGrids is beyond the scope of this study. 

And what, if these the data derived from SoilGrids are not very reliable? If the modelling method is correct, the paper will still inform about most probable future changes of land suitability for maize in Flanders.

Response: It is acknowledged that there exists inherent uncertainty in the utilization of SoilGrids, and this uncertainty is thoroughly reported and discussed in the specified lines (682-695), in accordance with the authors' perspective. An uncertainty assessment of the SoilGrids predictions under consideration is provided for the reader’s assessment.

(2) Soil layer considered in the study. Perhaps, I was not sufficiently precise regarding my suggestions regarding soil layer. Actually, according to the study of Ning et al. 2015 (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0121892) the major density of maize roots was found in 0-20cm layer and I would generalize that this refers to humus accumulation layer (A horizon), which is, most frequently, 20-30cm thick, with exception of some Chernozems, Phaeozems etc... The humus accumulation layer is the main source of nutrients for crops, including maize. However, the deeper layers affect the water supply for roots, both in direct and indirect way. For this reason, the authors are considering the layer 0-60cm, and I suggest to consider the layer of 0-100cm. Actually, it is a question of opinion and I respect the idea of authors. However, I have another, even more important doubt. The humus accumulation layer has different properties (soil organic carbon, and frequently texture) than deeper layers (e.g. 30-60cm) in majority of soils. Some but not all, Chernozems, Pheozems, Anthrosols and even Cambisols may be considered as relatively uniform to 60cm of depth, but not Podzols, Luvisols and Retisols. In intensively cultivated soils, the thickness of A layer scarcely exceeds 30cm. For these reason, the soil properties considered in this study should be different for the 0-30cm (A) layer and the 30-60cm layer. Is this truth? Please, clarify.

Response: After careful examination of the Soil Map of Flanders area (screenshot), we have noticed the occurrence of many different Soil categories, along with their variations, concerning drainage status (Endogleyic, Amphigleyic, Stagnic, …), texture classes (Siltic, Loamic, Arenic, Clayic), chemical fertility (Dystric, Eutric, Calcaric, Salic) and morphologic features (Colluvic, Fluvic, Relocatic, Ruptic, Abruptic, Thaptohistic). All these soil variations occur interchangeably across the landscape, affecting the local conditions in all regions of interest (delineated in the map), resulting in a significant local variability in soil characteristics in the area. For LSA purposes, we have concluded to the integration depth 0-60cm as an approximation value for the effective rooting depth over an analysis cell of 6.25ha. We agree with the reviewer that differentiating on the integration depth according to the occurrence of different soils types, would indeed increase the detail of approximation, but that would probably make much more sense in a finer scale of analysis, with a smaller than 6.25ha cell size.  

(3) Another doubt refers to so low, prevailing, Land Suitability for maize (S3) currently and in the future. As agronomist and soil scientist I assess the „average soil” (Table 1) of this study, of each agricultural regionas as moderately suitable (S2), although, the average temperature is not very favourable for this crop (the optimum being between 20 and 30°C, depending on growth stage) and I would assess it as factor more limiting maize yield than precipitation in current thermic conditions. I agree that in future the water will be more important yield limiting factor for maize than temperature. However, it is only, partly subjective opinion. To avoid such doubts, I suggest providing additional data in this paper, perhaps as supplementary files.

Response: An alternative methodological approach to assess land suitability, different from the one adopted in this study, might involve considering yield as a proxy variable. However, it was a deliberate choice throughout this study to refrain from using yield as an indicator of suitability. This decision was made to mitigate potential biases in the Land Suitability Analysis (LSA) process and to avoid a shift in focus from land to crop (lines 137-140).

- the critical values of soil, topographic and climate variables for particular classes Land Suitability for maize (S1, S2 and S3)

Response: Provided in supplementary material

- current average temperatures in May, June, July and August;

Response: Provided in supplementary material

- the predicted values of climatic variables (precipitation and temperatures) for particular climate projections (RCP4.5 and RCP 8.5) in the years 2030, 2050, 2070 and 2080.

Response: Provided in supplementary material

The addition of these data would make this paper more comprehensive and understandable for agronomists and farmers.

I recommend major revision of this paper.

DETAILED

Please add citation of [47] and [67] references, please, see below, if I am right:

Line 352: I suppose that authors should add [47] after Yip et al., 2011

Response: Done. Thank you.

Line 742: I suppose that authors should add [67] after Cattiaux et al. 2013.

Response: Done. Thank you.

I hope all the tables and their captions will be put together within one single page before publication. It is just editorial issue, but important for reading and interpretation of this paper.

Response: We agree with the reviewer that the manuscript requires careful formulation before publication. As indicated, we trust that the editorial team will attend to this matter.

 

Author Response File: Author Response.docx