The Use of Spatial Interpolation to Improve the Quality of Corn Silage Data in Case of Presence of Extreme or Missing Values

Round 1

Reviewer 1 Report

This is an interesting attempt to analysis and comprehensive study about of use of spatial modelling in corn tillage. I appreciate these types of studies, that use spatial methods to examine the relationship between crops variables and spatial sampling and bias that may underpin local and regional spatial patterns. The authors have also made enormous efforts to accumulate relevant field’s data and design spatial methodologies to interpolation data from five sampling and three replacing methods. However, I consider that there are some methodological shortcomings remaining, which negate many of the conclusions of their analysis (explained in detail below). The manuscript lacks clarity and care with language in some places which I have mentioned under minor comments below. I hope these comments will prove useful for the authors to rethink their analysis and incorporated into the manuscript. I have some general and specific comments that should be addressed by the authors. The manuscript is recommended for publication with major revision. In the next paragraph, I explain the comments.

 

Major points

1. The introduction and discussion should include more details and comparisons on other similar studies in landslides worldwide. There is also general confusion as to which materials and methods and corresponding results are adequate to use by farmers to design correct sampling to estimate productive variables. More details are also needed to describe the analysis of the data so reviewers can ensure their appropriateness for the type of data presented. Additional attention to detail is needed to improve the overall quality of manuscript including the small detail about modeling, data uses, and computation performance.
2. Please see details of others spatial analyses under different conditions because the references used were very local. Some of the literature cited is old and the work will have been superseded by new evidence, much more is now known about spatial analyses
3. Ssince the approach used present a good approximation when the analysis is local, it is appropriate, practical and methodologically correct to do fine work for a specific region? I would like to see more details about the model selection results. It would be interesting if you provide a supplementary materials explain clearly the ranked models and the null model so the reader can have a better clue of the model selection.
4. I hope that the authors can find the time to revise and clarify their main position, results and discussion points, to pull together a paper which may help managers and decision makers utilize a knowledge of potential spatial dynamics to design an integrated management under field conditions. That a spatial modeling would be associated with its conductive variables is pretty clear to see, but it is what we can pull together in a map and modelling framework to improve decision-making on ground using a Gis approach which makes the discipline truly useful.
5. The objectives of the study have not been clearly stated. To what extent is spatial analysis of different sampling methods and replacing missing data under field’s condition? Why was the study undertaken? More background is needed on the problem and to what extent the modelling can assist in prediction spatial data?
6. Similarly, the key outcomes of the study are unclear. What were the study's main findings and what will there be impact be in terms of understanding or predicting the behavior of the productive corn silage and its future geographical spread?
7. This characteristically too-long sentence is a typical example of confusing use of the English language. There are very many similar issues largely associated with grammar and other English language aspects. The text is still wordy sometimes, contains several typos, grammatical errors, and the punctuation is not always adequate. Therefore it needs a thorough language editing either by the Authors or by the language editing service.
8. Associated with the spatial methodologies used I had many questions and believe that the manuscript is not clear
9. The geographic spatial interpolation using kriging approach may be interesting, but the application needs the assumption of mathematical and the principles for its use, but it has its limitations since from its theoretical conception it needs a base of heterogeneity and spatial autocorrelation. It is also a measure of the spatial pattern, but it does not provide information about a certain area and uncertainty.
10. It is argued that the “genetic hotspots” was used. This is quite worrying given that the use of this spatial-genetics tool requires compliance with some principles and specific algorisms, but this part was not identified at work. I was not able to identify that the principles of this method are fulfilled and don’t see the fir to the variograms.
11. Another big question is why other spatial approach was not used? As for the purposes, it would be adequate used other methods and try to compare what was the better.
12. Why wasn’t traditional method for spatial sampling optimization? Example: simulated annealing analysis…

 

Specific comments

Title

My suggestion is change the title, because is very general.

Abstract

Try to be specific and write a paragraph more informative, because is very confused the aim, and the relationship between the approach used. In addition, you can add more information based in data (statistical, among others).

Introduction

Review the reference format used for the Journal see lines 49. Lines 64 to 72  missing references?

Add more information about important aspects (e, j., origin and causes of problem, modeling, methods used, advantage and disadvantage with other spatial approach, among others).

Try to improve the understanding of the importance of sampling and interpolation methods

The aim and hypothesis is necessary improve, because is not clear, in especial the roll of the approach used  

 

Material and Methods

It is necessary to contextualize and clearly explain the variables used, the conditions of the experiment, the factors that were controlled, the characteristics of the production system, such as the identification of productivity variation, among others. The methods are superficially described, omitting basic information that is of utmost importance to guarantee reproducibility, a basic criterion in scientific research.

Is no clear the origin of data. Is necessary add more details about this process (resolution, temporal, and spatial dynamics).

The model processes are a bit more complex, in which a multi-step must be made to improve the capacity of the models and not simply reproduce information of low quality. In addition, when algorithm is used must be considered an exhaustive evaluation of the parameters associated with the performance computational. On the other hand, I suggest that to select the best model approach is necessary incorporate the sources of variation.

How was the sampling performed?

How was the missing data performed?

Result and discussion

Emphasize on explaining what advantages you have when using these spatial analysis strategies and not others.

How did you relate the variability of crop production  and spatial dimensions?

What is the current use of the areas with differential production under landslides?

How are the productivity systems in the area tested?

It is important to highlight the results and that these are incorporated into a management program for producers and what economic implications and profitability indicators represent the use of these practices at the farm level.

Conclusion

The author should improve the conclusion and focus on the most important data of the study. The conclusions presented do not represent the importance of the research work.

Figures and Tables

The quality of the figures and tables also needs attention because there are not stand on. In addition, the manuscript had a lot of tables.

Reference

Review the correct format used by the Journal.

Author Response

Response to Reviewer 1

We would like to thank reviewer for the detailed comments and suggestions for the manuscript. We addressed all the issues mentioned and we tried to make improvements throughout the manuscript. Below, you will find a point-by-point description of how each comment was addressed.

 

Reviewer #1, comment No. 1: This is an interesting attempt to analysis and comprehensive study about of use of spatial modelling in corn tillage. I appreciate these types of studies, that use spatial methods to examine the relationship between crops variables and spatial sampling and bias that may underpin local and regional spatial patterns. The authors have also made enormous efforts to accumulate relevant field’s data and design spatial methodologies to interpolation data from five sampling and three replacing methods. However, I consider that there are some methodological shortcomings remaining, which negate many of the conclusions of their analysis (explained in detail below). The manuscript lacks clarity and care with language in some places which I have mentioned under minor comments below. I hope these comments will prove useful for the authors to rethink their analysis and incorporated into the manuscript. I have some general and specific comments that should be addressed by the authors. The manuscript is recommended for publication with major revision. In the next paragraph, I explain the comments.

Response Reviewer #1, comment No. 1: We would like to thank reviewer for the kind comments on the novelty and complexity of our study. We understand his concern regarding the methodology, and we are willing to add any kind of information needed and shed light to manuscript to get the clarity it should have. Concerning the gridding process, we added a new paragraph at materials and methods giving further information about the creation of grids and the use of variograms. If needed, we can add more info or reports as supplementary material. We would like also to note that due to the size of the data analysis some parts, although written at the beginning, were finally considered less useful for the reader, and this is why it was decided to be removed (i.e. the mathematical background of Kriging). We mainly focused on the results that someone can achieve (reduced data variability and better CV values) and less on how the implementation of an interpolating technique is performed (as it is a well-known process); the findings of our work confirm that the implementation of an interpolating geospatial technique can improve data quality in case of extreme and missing values in experimental data. This case has been confirmed for three different crop parameters (most commonly measured) and in three different plots.

 

Reviewer #1, comment No. 2: The introduction and discussion should include more details and comparisons on other similar studies in landslides worldwide. There is also general confusion as to which materials and methods and corresponding results are adequate to use by farmers to design correct sampling to estimate productive variables. More details are also needed to describe the analysis of the data so reviewers can ensure their appropriateness for the type of data presented. Additional attention to detail is needed to improve the overall quality of manuscript including the small detail about modeling, data uses, and computation performance.

Response Reviewer #1, comment No. 2: We agree with the reviewer that there is a "general confusion" as to which method is better for the farmers or agro-scientists to use but it reflects a general situation where it is not clear which method should be used and with what results. In addition, according to our knowledge, no attempt also has been made yet to estimate the uncertainty of each of the sampling method used for calculating crop metrics. Our effort was to present the situation and offer a way (interpolation) to improve data quality regardless the sampling method used.

 

Reviewer #1, comment No. 3: Please see details of others spatial analyses under different conditions because the references used were very local. Some of the literature cited is old and the work will have been superseded by new evidence, much more is now known about spatial analyses.

Response Reviewer #1, comment No. 3: We added more recent literature concerning works with spatial analysis in Agriculture (27, 29, 30, 31, 32). Thank you for the suggestion.

 

Reviewer #1, comment No. 4: Since the approach used presents a good approximation when the analysis is local, it is appropriate, practical, and methodologically correct to do fine work for a specific region? I would like to see more details about the model selection results. It would be interesting if you provide supplementary materials clearly explain the ranked models and the null model so the reader can have a better clue of the model selection.

Response Reviewer #1, comment No. 4: The proposed approach of applying interpolation to crop field data before the calculation of crop metrics or any further statistical analysis, can be appropriate, practical, and methodologically correct to be used in a larger scale with only one constrain: measurements must be equally distributed in the area. In case of our experiment the measurements derived from harvested plants located in a grid, so equally distributed across the plot area.

 

Reviewer #1, comment No. 5: I hope that the authors can find the time to revise and clarify their main position, results, and discussion points, to pull together a paper which may help managers and decision makers utilize a knowledge of potential spatial dynamics to design an integrated management under field conditions. That a spatial modeling would be associated with its conductive variables is pretty clear to see, but it is what we can pull together in a map and modelling framework to improve decision-making on ground using a GIS approach which makes the discipline truly useful.

Response Reviewer #1, comment No. 5: Our aim is to provide a way to improve crop field data via interpolation by reducing the variability in the field mainly caused by extreme or missing values. In this work we provide a comparison both in mean and CV values between sampling methods, at which missing values are replaced with an average value (total average or row/column average). We hope that our approach will be easily adopted as an important last step before any statistical analysis, or any kind of calculation of crop parameter as metric for larger crop areas. We made improvements throughout the manuscript to improve clarity; last section of introduction regarding the aims of the study was re-written, materials and methods were enriched with more information concerning the experiment and the gridding process, and conclusion section was also re-considered and improved.

 

Reviewer #1, comment No. 6: The objectives of the study have not been clearly stated. To what extent is spatial analysis of different sampling methods and replacing missing data under field’s condition? Why was the study undertaken? More background is needed on the problem and to what extent the modelling can assist in prediction spatial data?

Response Reviewer #1, comment No. 6: We would like to thank reviewer for this comment as it is critical for the clarity of the manuscript as mentioned above. For this reason, the objectives of the study were re-written (at the end of introduction), as follows:

“The objectives of this work are the follows:

- Examine whether an interpolation method can improve field data quality by reducing the expected crop field variability and to what extent can be achieved?

- Examine whether an interpolation method can effectively address the problems of extreme or missing values in data?

The idea behind the objectives set is that if interpolation can improve data quality then the means used for calculating crop metrics can be more dependable, reliable, and representative for the statistical population of each of the crop parameter under consideration.”

In this case, spatial modelling with the form of data interpolation can predict – estimate values at non-sampled locations (where measurements are not available) as it is clearly stated in detail (lines 54-83). Also, in Discussion section (line 349) it is mentioned that the implementation of an interpolation method such as Kriging can lead to an improvement of CV values by 25.2 to 33.7%, which means reduced variability and more reliable data for further statistical analysis.

 

Reviewer #1, comment No. 7: Similarly, the key outcomes of the study are unclear. What were the study's main findings and what will there be impact be in terms of understanding or predicting the behavior of the productive corn silage and its future geographical spread?

Response Reviewer #1, comment No. 7: As mentioned above, in results, discussion and conclusion (lines 392, 398) we stress that by applying an interpolation method to crop data we can manage crop field variability and achieve up to 30% reduction in CV values compared to the other sampling methods that can lead to only up to 15% lower CV values.

We also add a paragraph to emphasize these results:

“In Table 4, the difference in CV values (%) between all measured data and those derived from the sampling methods MC, RT, RC, and RR. It is notable that implementation (I) can achieve up to 30% lower CV values (column: I diff% from M). Compared to interpolation, the other sampling methods (RT, RC, RR) can lead to only up to 15% lower CV values. Therefore, interpolation can achieve twice the efficiency compared to other commonly used methods. “

Managing the field variability by achieving better CV values is our main key outcome that can significantly improve crop field data. However, we agree to the comment that it should also be clear how the adoption of this methodology can affect the productive corn silage in larger areas. For this reason, we added some extra text to support the importance of our findings for future estimates. Conclusion section was also re-written to support the above statement.

 

Reviewer #1, comment No. 8: This characteristically too-long sentence is a typical example of confusing use of the English language. There are very many similar issues largely associated with grammar and other English language aspects. The text is still wordy sometimes, contains several typos, grammatical errors, and the punctuation is not always adequate. Therefore, it needs a thorough language editing either by the Authors or by the language editing service.

Response Reviewer #1, comment No. 8: We made several corrections throughout the manuscript to improve text quality. Thank you for the suggestion.

 

Reviewer #1, comment No. 9: Associated with the spatial methodologies used I had many questions and believe that the manuscript is not clear

Response Reviewer #1, comment No. 9: In material and methods, we added some more info regarding the grid creation and variograms.

More specifically we added the following paragraph:

“Concerning the general info about the gridding process, no trend removal was used, and the automatic Variogram Fitting mode was selected (default setting set by Surfer - the gridding software used), which attempts to find a better set of parameters for the current model. Regarding the values used by the autofit, the fit Criterion was set to "Least Squares" with maximum iterations set to 50, target precision (%) set to 0.0001, and maximum distance set to 1E+38. Concerning the experimental Variogram, the lag size was set to 0.5, number of lags15, direction equals to 0, and tolerance equals to 90. For the Semi-Variogram model: the Variogram Component type selected was linear for all grids created (anisotropy Angle set to 0 and anisotropy Ratio set to 1). Concerning the Kriging parameters and gridding rules, kriging type was set to point (point weight measurements for fresh, dry and ear weight corresponding to plants). Regarding the "search neighborhood", all data were used for the estimation of the values of the new grids.”

 

 

Reviewer #1, comment No. 10: The geographic spatial interpolation using kriging approach may be interesting, but the application needs the assumption of mathematical and the principles for its use, but it has its limitations since from its theoretical conception it needs a base of heterogeneity and spatial autocorrelation. It is also a measure of the spatial pattern, but it does not provide information about a certain area and uncertainty.

Response Reviewer #1, comment No. 10: It is well-known that interpolation is effective, yet it has indeed limitations. Regarding agriculture, Kriging is highly recommended as an effective method to derive yield maps (especially for corn silage yield data) including both spatial and temporal variation in yield. Therefore, we believe that the effectiveness of the method has been enough tested despite its limitations. In case of a plot-level (small scale) analysis concerns about limitations and constraints should be reduced; measurements were taken using a given grid of harvested plants (25x6) in plots (values equally distributed in space) and treated with the same agricultural practices and with almost similar soil characteristics. However, we do share the same concern with the reviewer. Differences in the field variability are confirmed and presented by the contour maps of Kriging. For all the above, we added maps with the standard deviations of Kriging (Figure 3) to examine how accurate was the process of implementing an interpolation method in estimating new data compared to the original set of data.

 

Reviewer #1, comment No. 11: It is argued that the “genetic hotspots” was used. This is quite worrying given that the use of this spatial-genetics tool requires compliance with some principles and specific algorisms, but this part was not identified at work. I was not able to identify that the principles of this method are fulfilled and don’t see the fir to the variograms.

Response Reviewer #1, comment No. 11: Thank you for the comment. In case of crop field data within a plot, we believe that the basic principle of geography concerning spatial autocorrelation is fulfilled; measurements of plants that are closer to each other are more alike or have more similar values than measurements farther apart.  Besides, Kriging as mentioned above it is used for mapping crop field and soil parameters usually following only the rule/constrain that the measurements should be equally distributed in the area.

If needed, we can add supplementary material or even a more detailed section. However, a detailed presentation of all the grid information (materials and methods) would have taken up considerable space that now it is used for the results or other parts of the manuscript. To provide more information about the interpolation, we added some more info about the spatial gridding process and more specifically about the variograms created.

 

Reviewer #1, comment No. 12: Another big question is why other spatial approach was not used? As for the purposes, it would be adequate used other methods and try to compare what was the better. Why wasn’t traditional method for spatial sampling optimization? Example: simulated annealing analysis…

Response Reviewer #1, comment No. 12: Thank you for the comment. It is a nice idea, but maybe out the scope of current work. Our aim is not to find the most effective spatial interpolation technique for improving crop field data quality but to prove that interpolation can successfully manage the issue of extreme and missing value and can lead to more representative means as crop metrics. For this reason, we chosen Kriging as the most used and more effective interpolation method already tested in crop field data. We noted your suggestion for future publications.

 

Reviewer #1, comment No. 13: Title: My suggestion is to change the title, because is very general.

Response Reviewer #1, comment No. 13: Thank you for the suggestion. We tried to find and use a more general title because our goal is not only to just provide results for this case (three types of weights of maze in three different plots), but also to show that this can be an effective new approach of improving data quality by achieving significantly reduced CV values and thus reducing the within field variability. Following the suggestion of the reviewer we changed the title of our study from: “The use of spatial interpolation to increase the quality of corn silage data before the statistical analysis” to “The use of spatial interpolation to improve the quality of corn silage data in case of extreme or missing values”.

 

Reviewer #1, comment No. 14: Abstract - Try to be specific and write a paragraph more informative, because is very confused the aim, and the relationship between the approach used. In addition, you can add more information based in data (statistical, among others).

Response Reviewer #1, comment No. 14: We would like to thank reviewer for the comment; we added a new paragraph and re-write the aims of the study. We noted that the implementation of interpolation can reduce crop field variability (extreme values) and achieve an improvement of Coefficient of Variation (CV) values up to 30% compared to other methods used such as the replacing of missing values by the average of all data or the average of the row or column having an improvement of only up to 15%. Following reviewer’s suggestion, we also re-write some parts of the abstract to improve clearness.

 

Reviewer #1, comment No. 15: Review the reference format used for the Journal see lines 49. Lines 64 to 72 missing references?

Response Reviewer #1, comment No. 15:  Done. We made some minor corrections to [19, 20] references to follow the format suggested by the Journal.

 

Reviewer #1, comment No. 16: Add more information about important aspects (e, j., origin and causes of problem, modeling, methods used, advantage and disadvantage with other spatial approach, among others).

Response Reviewer #1, comment No. 16:  We added more information in Introduction section (lines 80-84).

 

Reviewer #1, comment No. 17: Try to improve the understanding of the importance of sampling and interpolation methods. The aim and hypothesis is necessary improve, because is not clear, in especial the roll of the approach used  

Response Reviewer #1, comment No. 17:  Following the suggestion, the aims of the study were re-written. We also added a small paragraph regarding the main idea of this work that is if interpolation can improve data quality, then estimates can be more accurate and therefore, the calculation of mean values as crop metrics can be more dependable and representative for the statistical population of any crop parameter under consideration.

Reviewer #1, comment No. 18: Material and Methods - It is necessary to contextualize and clearly explain the variables used, the conditions of the experiment, the factors that were controlled, the characteristics of the production system, such as the identification of productivity variation, among others. The methods are superficially described, omitting basic information that is of utmost importance to guarantee reproducibility, a basic criterion in scientific research.

Is no clear the origin of data. Is necessary add more details about this process (resolution, temporal, and spatial dynamics).

The model processes are a bit more complex, in which a multi-step must be made to improve the capacity of the models and not simply reproduce information of low quality. In addition, when algorithm is used must be considered an exhaustive evaluation of the parameters associated with the performance computational. On the other hand, I suggest that to select the best model approach is necessary incorporate the sources of variation.

How was the sampling performed?

How was the missing data performed?

Response Reviewer #1, comment No. 18: Concerning the establishment of the experiment, the materials and methods used, the conditions, and the practices followed, all have been descripted in detail at the first two paragraphs of “Materials and Methods”. Concerning the gridding process and the software used, information is given in the third paragraph. Following the reviewers’ suggestion, we added some more info regarding the grid creation and variograms.

More specifically we added the following paragraph:

“Concerning the general info about the gridding process, no trend removal was used, and the automatic Variogram Fitting mode was selected (default setting by Surfer - the gridding software used), which attempts to find a better set of parameters for the current model. Regarding the values used by the autofit, the fit Criterion was set to "Least Squares" with maximum interations set to 50, target precision (%) set to 0.0001, and maximum distance set to 1E+38. Concerning the experimental Variogram, the lag size was set to 0.5, number of lags15, direction 0, and tolerance 90. For the Semi-Variogram model: the Variogram Compoment type selected was linear for all grids created (anisotropy Angle set to 0 and anisotropy Ratio set to 1). Concerning the Kriging parameters and gridding rules, kriging type was set to point (point weight measurements for fresh, dry and ear weight corresponding to plants). Regarding the "search neightborhood", all data were used for the estimation of the values of the new grids.”

In addition, we added a paragraph that explains how measurements were organized in Excel based on a 25 (rows) x 6 (columns) grid that represents the harvested plants in each plot.

“The silage yield data (measurements for fresh weight, dry weight, ear weight) were finally entered in Excel and organized in one table containing all information available such as the plot and the exact position of each record (column: x dimension, row: y dimension) based on a 25 (rows) x 6 (columns) grid that represents the harvested plants in each plot (Figure 1). For achieving better visual results we used the centers of the squares of the grid of each plot instead of the absolute numbers of rows and columns of the measurements (i.e. for the plant located in the first row and first column, we assigned dimensions x=0.5 and y=0.5 instead of x=1 and y=1)”

We can also provide detailed Gridding Statistics and Cross Validation Reports as supplementary material, if needed.

 

Reviewer #1, comment No. 19: Result and discussion - Emphasize on explaining what advantages you have when using these spatial analysis strategies and not others.

Response Reviewer #1, comment No. 19: We followed the suggestion of the reviewer, and we added a paragraph at the end of introduction to emphasize the advantages of using interpolation as a spatial analysis strategy to improve crop data quality.

“In this case, current work proves that the implementation of an interpolation method can be an effective way to replace missing values based on the values and weights of neighboring existing measurements (according to the principle of spatial analysis). Also, current study can prove that the implementation of interpolation in experimental data regardless the sampling method used can reduce variability caused by extreme values and improve data accuracy and quality by achieving better coefficient of variation (CV) values”.

Also, in discussion (line 419) we attempted to explain how an interpolation method works. We also made some further improvements in the text to emphasize the advantages of applying an interpolation strategy on data.

 

Reviewer #1, comment No. 20: How did you relate the variability of crop production and spatial dimensions?

Response Reviewer #1, comment No. 20: Thank you for this question. I believe you mean “spatial interpolation” instead of “spatial dimensions”. The crop field variability can be assessed via the coefficient of variation (CV) values. The lower the CV values the lower the variability. Interpolation can “manage” field variability in two very useful ways: (a) normalize extreme values by replacing them with other estimated values based on their neighboring values, and (b) estimate values in non-sampled locations (missing values). The ‘spatial dimensions’ of the measurements refer to the number of row and column of each measurement in the grid of each plot. More information added at Materials and Methods section (lines 137 – 142).

 

Reviewer #1, comment No. 21: What is the current use of the areas with differential production under landslides?

Response Reviewer #1, comment No. 21: All measurements for the three types of crop weight (fresh, dry, and ear weight) derived from experimental plots. There is no mention about landslides in the text.

 

Reviewer #1, comment No. 22: How are the productivity systems in the area tested? It is important to highlight the results and that these are incorporated into a management program for producers and what economic implications and profitability indicators represent the use of these practices at the farm level.

Response Reviewer #1, comment No. 22: The metrics used for the productivity of the three plots were based on the measurements for the silage yield (fresh weight – FW, dry weight – DW, and ear weight – EW) of each plant harvested. Calculating more accurate means as crop field metrics via interpolation can lead to more accurate estimates about crop metrics and profitability indicators for producers, and therefore, more reliable data for decision making. We add the above sentence at the end of conclusions section.

 

Reviewer #1, comment No. 23: Conclusion - The author should improve the conclusion and focus on the most important data of the study. The conclusions presented do not represent the importance of the research work.

Response Reviewer #1, comment No. 23: Following the suggestion, we improved and re-write part of the conclusion. Thank you for the advice.

 

Reviewer #1, comment No. 24:  Figures and Tables - The quality of the figures and tables also needs attention because there are not stand on. In addition, the manuscript had a lot of tables.

Response Reviewer #1, comment No. 24: We made every effort to have the tables as detailed (descriptive statistics) as possible; we could move tables to the supplementary section, but the statistics provided are vital for the comparisons between the sampling methods under consideration and the effectiveness of the interpolation.

 

Reviewer #1, comment No. 25:  References - Review the correct format used by the Journal.

Response Reviewer #1, comment No. 25: Done. We made some minor corrections in references to follow the reference format suggested by the journal.

 

Response to general comments:

We have addressed all comments as can be seen in the enclosed list. Based on reviewers’ comments and suggestions, we have made careful modifications to the original manuscript, and carefully proof-read the manuscript to minimize all kind of errors.

We thank the reviewers for the careful and insightful review of our manuscript and for their valuable comments and effort to improve the manuscript.

 

Reviewer 2 Report

1、The kriging method is a common method in data interpolation. Therefore, the innovation of this paper is not very strong. 

2. P1, line 16, "CV" should be given full name in its first use.

3. Accuracy was suggusted to be used as the method's evaluation criterion, not the Coefficient of Variation (CV). 

4. Paper title was suggusted to be modified as "The use of spatial interpolation to increase accuracy of corn silage data before the statistical analysis". 

Author Response

Response to Reviewer 2

We would like to thank reviewer for the detailed comments and suggestions for the manuscript. We addressed all the issues mentioned and we tried to make improvements throughout the manuscript. Below, you will find a point-by-point description of how each comment was addressed.

Reviewer #2, comment No. 1: The kriging method is a common method in data interpolation. Therefore, the innovation of this paper is not very strong. 

Response Reviewer #2, comment No. 1: Kriging has proved to be effective in Agriculture in mapping crop yield and soil parameters data; this is the reason why Kriging was chosen to be used for data interpolation. The innovation of our work is not to prove that Kriging can be effective in crop weights mapping but lies upon the use of Kriging as interpolating geostatistical method to improve data quality by addressing the issues of with-in field variability, such as extreme and missing values. According to the authors’ knowledge, this is the first time that interpolation is used to improve crop data quality by achieving better coefficient of variation (CV) values, and therefore we believe that this is about a very novel idea that can provide more reliable data for calculating mean crop metrics.

Reviewer #2, comment No. 2: P1, line 16, "CV" should be given full name in its first use. Response Reviewer #2, comment No. 2: Done. We followed the suggestion and we replaced “CV” with “coefficient of variation (CV)”.

 

Reviewer #2, comment No. 3: Accuracy was suggested to be used as the method's evaluation criterion, not the Coefficient of Variation (CV). 

Response Reviewer #2, comment No. 3: We used coefficient of variation (CV) values (metric of variability) as a mean to check whether data accuracy/quality can be improved via interpolation.

 

Reviewer #2, comment No. 4: Paper title was suggested to be modified as "The use of spatial interpolation to increase accuracy of corn silage data before the statistical analysis". 

Response Reviewer #2, comment No. 4: Thank you for the suggestion. The term "data quality" is generally used instead of "data accuracy" meaning that the data is accurate without issues. That's why we chose the term "quality" over "accuracy".

Reviewer 3 Report

The study presents a spatial interpolation method useful when data contain missing values. This kind of method leads to much more representative mean crop values. The study confirms the validity of the chosen method.

Line 43: presence 'of' missing values

Lines 73-78: What do you mean with 'alternative'?  Which crop parameter estimation methods have been mainly used? I don't understand if the Kriging method is considered 'alternative'. I think that the relationship among the different estimation methods has to be better explained.

Lines 132-134: Can you explain how the software has been used with the Kriging method?

Line 147: 'excluded' area

Line 154: What do you mean with 'grand mean'?

Line 163: 'pattern'

Line 285: 'with'

Table 1. Can you add the measurement unit (g) ? 

Author Response

Response to Reviewer 3

We would like to thank reviewer for the detailed comments and suggestions for the manuscript. We addressed all the issues mentioned and we tried to make improvements throughout the manuscript. Below, you will find a point-by-point description of how each comment was addressed.

 

Reviewer #3, comment No. 1: The study presents a spatial interpolation method useful when data contain missing values. This kind of method leads to much more representative mean crop values. The study confirms the validity of the chosen method.

Response Reviewer #3, comment No. 1: We would like to thank reviewer for this comment. The findings of our work confirm that the implementation of an interpolating geospatial technique can improve data quality in case of extreme and missing values in experimental data.

 

Reviewer #3, comment No. 2: Line 43: presence 'of' missing values.

Response Reviewer #3, comment No. 2: Done. We would like to thank reviewer for this correction.

 

Reviewer #3, comment No. 3: Lines 73-78: What do you mean with 'alternative'?  Which crop parameter estimation methods have been mainly used? I don't understand if the Kriging method is considered 'alternative'. I think that the relationship among the different estimation methods has to be better explained.

Response Reviewer #3, comment No. 3: Done. We removed the word “alternative” as it may cause misunderstanding.  We originally used the term ‘alternative’ to describe a new approach of providing more accurate data (with reduced variability – lower CV values) for calculating crop metrics via the implementation of an interpolating geostatistical technique. The word ‘alternative’ was used to distinguish our proposal from the commonly used crop parameter estimation method that was just the calculation of a mean based on the available crop data. We would like to thank reviewer for this correction.

 

Reviewer #3, comment No. 4: Lines 132-134: Can you explain how the software has been used with the Kriging method?

Response Reviewer #3, comment No. 4: Done. We added a section describing the gridding process via Golden Software – Surfer software that we used in our analysis. We would like to thank reviewer for this suggestion.

 

Reviewer #3, comment No. 5: Line 147: 'excluded' area.

Response Reviewer #3, comment No. 5: Done. Thank you for the correction.

 

Reviewer #3, comment No. 6: Line 154: What do you mean with 'grand mean'?

Response Reviewer #3, comment No. 6: Done. We replaced “grand” with “total”. Thank you for the correction.

 

Reviewer #3, comment No. 7: Line 163: 'pattern'

Response Reviewer #3, comment No. 7: Done. Thank you for the correction.

 

Reviewer #3, comment No. 8: Line 285: 'with'.

Response Reviewer #3, comment No. 8: Done. Thank you for the correction.

 

Reviewer #3, comment No. 9: Table 1. Can you add the measurement unit (g)? 

Response Reviewer #3, comment No. 9: Done. We added the grams (g) measurement unit in all tables (Table 1, 2, and 3). Thank you for the correction.

 

 

Response to general comments:

We have addressed all comments as can be seen in the enclosed list. Based on reviewers’ comments and suggestions, we have made careful modifications to the original manuscript, and carefully proof-read the manuscript to minimize all kind of errors.

We thank the reviewers for the careful and insightful review of our manuscript and for their valuable comments and effort to improve the manuscript.

 

Round 2

Reviewer 1 Report

Under this new version, I consider that the manuscript can be accepted

Reviewer 2 Report

The implementation of an interpolation method in case of having extreme or missing values in crop data is an effective process on improving their quality and consequently on their reliability.