Determination of Crop Coefficients for Flood-Irrigated Winter Wheat in Southern New Mexico Using Three ETo Estimation Methods
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe minor typos noted on the article should be corrected.
Comments for author File: 
Comments.pdf
Author Response
Comment 1: The minor typos noted in the article should be corrected.
Response 1: Thanks for your suggestions. We agree with all the comments. Therefore, we have revised the text in Lines 10, 13, 26, 106, 313, and 359 according to the noted comments in the manuscript.
Reviewer 2 Report
Comments and Suggestions for AuthorsThe paper “Determination of crop coefficients for flood-irrigated winter wheat in southern New Mexico using three contrasting ETo estimation methods” investigate variations of ETc and growth-stage-specific Kc in a flood-irrigated winter wheat as a forage crop from 2021 to 2023 in the Lower Rio Grande Valley of southern New Mexico, USA, and evaluate the performances of the two temperature-based ETo estimation methods of Hargreaves-Samani and Blaney-Criddle with the widely used Penman-Monteith method. The text and results presented in the paper have copious amount of flaws. So the manuscript is Rejected in its present form
1. Abstract should be revised and shortened by including most important results only
2. There is no novelty of the data
3. Comparison of data with standard methods is missing
4. Recorded metrological data is provided but no advanced analysis used
5. Old and conventional methods are used
6. Title of the paper needs correction as well. The word contrasting does not make any sense
7. Most recent methods should be used for ET estimation
8. Why soil physical properties provided but not used in any analysis
9. Modelling of data should be done for advanced analysis
10. Growing degree days graphs are provided but not used anywhere in the paper
11. New References should be used as most of the reference are old
Comments on the Quality of English LanguageModerate English editing is needed
Author Response
The paper “Determination of crop coefficients for flood-irrigated winter wheat in southern New Mexico using three contrasting ETo estimation methods” investigates variations of ETc and growth-stage-specific Kc in a flood-irrigated winter wheat as a forage crop from 2021 to 2023 in the Lower Rio Grande Valley of southern New Mexico, USA, and evaluate the performances of the two temperature-based ETo estimation methods of Hargreaves-Samani and Blaney-Criddle with the widely used Penman-Monteith method. The text and results presented in the paper have copious amounts of flaws. So the manuscript is Rejected in its present form.
We gratefully acknowledge the great efforts that you made to improve the study. We have tried our best to modify the article according to the specific comments and also provided detailed explanations on the parts that can’t be changed.
Comment 1: Abstract should be revised and shortened by including most important results only
Response 1: Thanks for your suggestions. The abstract has been shortened by deleting some less important results. (Lines 19-21).
Comment 2: There is no novelty of the data
Response 2: Thanks for the concerns. The data collected in this study mainly included soil physical and chemical properties, soil water content, as well as meteorological variables, we understood that most of these parameters seemed to align with the existing knowledge within the previous literature. However, these differences haven’t been reported in the location of our study area, which is located in the Lower Rio Grande Valley of southern New Mexico, a local irrigated agricultural area. This area is in an arid desert climate region, and the soil is classified as Glendale, where the surface has a very clay texture and the layers below are sand. Based on our specific data collection, we have done the sensor calibration, ETo estimation, input sensitivity analysis, and comparison of growth-stage-specific Kc of winter wheat as a forage crop. Finally, we provided an alternative tool to predict ETo in the study area. We hope this paper can provide insights to improve local irrigation water management and serve as a valuable reference for others conducting similar studies.
Comment 3: Comparison of data with standard methods is missing
Response 3: Thanks for the concerns. In this study, we compared the results of the ETo estimation calculated by Hargreaves-Samani and Blaney-Criddle methods to the Penman-Monteith method. The Penman-Monteith method was widely used in ETo estimation and was found to be the most precise method to estimate ETo under a wide range of climatic conditions in previous literature, therefore, we regarded the Penman-Monteith method as a standard method to investigate the comparison of data among the three methods.
Comment 4: Recorded metrological data is provided but no advanced analysis used
Response 4: Thanks for your suggestion. In this study, the meteorological data including precipitation, air temperature, relative humidity, wind speed, and solar radiation were recorded at 15-minute intervals, we did the basic analysis to present the daily value for each variable in Fig.2 to show the general climate characteristic in the study area for the readers. After that, all the recorded meteorological data were mainly used in the calculations of ETo based on the three methods of Hargreaves-Samani, Blaney-Criddle, and Penman-Monteith.
Comment 5: Old and conventional methods are used
Response 5: We agree the comment. The three methods selected in this study estimating the ETo are old and conventional. However, the motivation we selecting these three methods is that we expected this study could provide new insights into ETo estimation with low-cost or less availability of data. The Penman-Monteith method has been reported to be very precise under different environmental conditions, but its application requires several meteorological variables which sometimes are not available. Therefore, we set the Penman-Monteith method as the standard method and chose another two temperature-based methods (Hargreaves-Samani and Blaney-Criddle) which only need temperature as the input. The Hargreaves-Samani method is a well-known temperature-based method and the Blaney-Criddle method was first developed for New Mexico in 1942 to calculate consumptive water use for limited crops, such as alfalfa, cotton as well as deciduous trees in NM Pecos River Valley. Thus, the three methods are very representative and benefit our goal in our study area.
Comment 6: Title of the paper needs correction as well. The word contrasting does not make any sense
Response 6: Thanks for your suggestion. We have revised the title and deleted the word ‘contrasting’ in Line 3.
Comment 7: Most recent methods should be used for ET estimation
Response 7: Thanks for your suggestion. In this study, three old and conventional methods and the water balance method were selected to estimate ETo and ETc, respectively with only meteorological data and soil water content data are available. The result of sensitivity analysis also told readers under what conditions each method is most suitable. For example, the Hargreaves-Samani method is accurate in ETo estimation while the researcher only has a temperature sensor in his field. This enables very low-cost estimation of ETo. The Blaney-Criddle method is less sensitive to wind, it is beneficial to use this method when the wind sensor is not available. All these results were not documented in the previous literature and could be a valuable reference for site-specific water management. However, in our future study, we will use more recent and advanced methods such as machine learning algorithms in ETo estimation and if possible, the advantages and disadvantages, and scope of applicability of different methods will be compared.
Comment 8: Why soil physical properties provided but not used in any analysis
Response 8: This is a good question. The soil physical properties provided in this study are basic information of soil type and soil hydraulic characteristics in the study area, which help readers to better understand the results presented in 3.1 temporal variations in soil water content and deep percolation. Since we didn’t employ any water or fertilizer treatments for the study site, the basic properties of soil were provided as auxiliary materials in this study.
Comment 9: Modelling of data should be done for advanced analysis
Response 9: Thanks for your suggestion. As we have explained the reason why we chose the old methods in Comment 5 and Comment 6, we will conduct the advanced analysis and use the most recent method to model our obtained data in our future study. This could increase our contribution to the field.
Comment 10: Growing degree days graphs are provided but not used anywhere in the paper
Response 10: Thanks for your suggestion. The results of growing degree days were used in the Discussion section when we explained why the Kc values for mid-season first decreased, then gradually increased during the period from February 16 to March 8 in the 22-23 season (Lines 471-478). And we also added an explanation to clear this doubt in Lines 277-279.
Comment 11: New References should be used as most of the references are old
Response 11: Thanks for your suggestion. We have added some of the most updated references in the manuscript (Lines 437, 673-678).
Reviewer 3 Report
Comments and Suggestions for AuthorsSee attached file
Comments for author File: Comments.pdf
Author Response
Specific Comments
Line 244-How do you derive Rn in the Penman-Monteith Equation? Is it measured, or is it calculated? If calculated, this explains your Julian day dependence, which comes in later in your analysis. This should be stated when introducing the equation.
Response 1: Thanks for your suggestion. The Rn in the Penman-Monteith Equation was calculated using the equations based on Allen et al. (1998), which is Julian day dependence. We have stated this when introducing the equation as suggested (Line 245-246).
Line 265- How did you derive n/N? Was it measured, and if so, from what?
Response 2: This is a good question. n/N was not measured in this study. We derived n/N from the Weather Spark website for the climate in Las Cruces (https://weatherspark.com/y/3287/Average-Weather-in-Las-Cruces-New-Mexico-United-States-Year-Round). In Las Cruces, the average percentage of the sky covered by clouds experiences mild seasonal variation over the year. Therefore, we averaged the fractions of clearer hours for 12 months and derived n/N = 0.7 in this area. We have noted it in Lines 266-267.
Line 269-You calculate Ra, right? According to the publication you reference, it's calculated using only angles and the day of the year, leading to a dependence on a Julian day. This should be clearly stated in the text.
Response 3: Yes, we calculated Ra based on a set of equations in Awal et al. (2020). It is Julian day dependence. We have revised and clearly stated this in the text (Line 271).
Line 273-276-You introduce GGD but don’t explain how GDD will factor into your calculations. Please add a discussion here.
Response 4: Thanks for your suggestion. The results of growing degree days were used in the Discussion section when we explained why the Kc values for mid-season first decreased, then gradually increased during the period from February 16 to March 8 in the 22-23 season (Lines 432-437). And we also added an explanation to clear this doubt in Lines 277-279.
Line 304-can you present a figure of a set of figures that show the distribution of ETo from your three models? Visually this will help the reader see the dependence of each model on the variable. -Also, when you discuss the sensitivity rankings of your input parameters, state that these will be summarized later, as presented in Fig 5.
Response 5: Yes. The figures showing the distribution of ETo from three methods have been added as Fig. 3. The statement has been added in Lines 314-315.
Specific Comments Figures
Fig 4. Consider alternative color schemes for the lines to make them friendly to color-blind readers.
Response 6: Thanks for your suggestion. We have revised the color schemes for the lines in Fig. 5 to make them friendly to color-blind readers.
Fig 5. I really liked this result. It should be mentioned when you first introduce Crystal Ball. Also, I think the results of this sensitivity analysis should be addressed in your conclusions to strengthen this paper's contribution.
Response 7: Thanks for your suggestion. We have revised and addressed this result in Lines 301-303 when first introducing Crystal Ball. The results of this sensitivity analysis have been addressed in conclusion (Lines 491-503).
Fig 6. Consider alternative color schemes for the lines to make them friendly to color-blind readers.
Response 8: Thanks for your suggestion. We have revised the color schemes for the lines in Fig. 7 to make them friendly to color-blind readers.
Fig 7. Initial Looks at Figure 7 make it difficult to understand the difference between the GGD and accumulated GGD. Consider alternative markings. For example, placing the dots accumulated GGD on the line makes it appear that there is a thin line and a bold line. If this had been the case of a thin and bold line and the legend showed accumulated GGD in bold, it would have been easier to understand.
Response 9: Thanks for your suggestion. We have revised and placed the daily GDD short dash-dot and the accumulated GDD bold line to make it easier to understand. The revision is shown in Fig. 8.
General Comments:
This paper was very interesting to read. It demonstrates a high level of thoroughness in several critical aspects, including data collection, sensor calibration, input sensitivity analysis, and a study of the resulting measurements. The authors have gone to great lengths to document their methods meticulously, and the data analysis appears sound. I find the conclusions of the paper lacking, which leaves me with concerns regarding the paper's novelty and overall contribution to the field. I suggest the authors do a careful rewrite of the conclusions of the paper.
However, the differences they identify between the three ETo estimation methods seem to align with existing knowledge within the literature. While these differences may not have been documented in the exact context or location studied by the authors, they do not seem to offer new insights that significantly advance the understanding within the field. However, given the substantial effort that has gone into the study, particularly in areas like sensor calibration and sensitivity analysis, I see a contribution in their methodological rigor. The paper could serve as a valuable reference for others conducting similar studies, particularly its detailed data collection and analysis approach.
I appreciate the authors' sensitivity analysis study and see it as a significant strength of this paper. However, I believe that this strength is not fully discussed in the conclusions. The study shows that the Penman-Monteith, Hargreaves-Samani, and Blaney-Criddle methods are all sensitive to different input parameters. As a reader, I would greatly benefit from a discussion of how these different sensitivities would impact the estimation under varying conditions. There are periods when one of the methods significantly diverges from the Penman-Monteith calculations. Discussions of the conditions that are happening at each of these events would be of value to the reader, and greatly strengthen the paper.
This leads me to another area I found lacking in the paper. The authors analyze these three different methods, but I don’t feel that I was told why I should be interested in these temperature-based methods as a reader. Other than Penman-Monteith taking more meteorological inputs, tell me what is the motivation behind this study and the end user. I believe it is likely the ease of taking temperature measurements or the low cost of such sensors, but this is never told to me. Also, given the sensitivity analyses you conducted, tell me why I would select to use a method other than Penman-Monteith. For example, the Hargreaves and Samani method does fairly well, and I only need to have a temperature sensor in my field. This enables very low-cost estimation of ETo, which either benefits areas with lower financial resources or measures spatial variability at low cost. Blaney-Criddle is less sensitive to wind, so if I don’t have a wind sensor, it could be beneficial to use this method.
Response 10: Thank you for your review of this paper. We are very grateful for the encouragement from you and gratefully acknowledge the great efforts that you made to improve this study. The entire manuscript has been revised carefully and we made all the changes in detail in response to the comments. As suggested, we rewrote the conclusion of this paper to strengthen the contribution of this paper by discussing the results of global sensitivity analysis (Lines 491-503) and also added a discussion of how those sensitivities impact the applications of three different methods under various conditions (Lines 405-414, 424-439).
Round 2
Reviewer 3 Report
Comments and Suggestions for AuthorsThe edits create a floating statement in the abstract, "in both growing seasons." I believe these words should be removed.
The cloud-free fraction is reasonable for Las Cruces, New Mexico. The NASA facility outside Lac Cruces, at the edge of Las Cruces, operates multiple cloud camera systems that could be used to calculate this metric carefully if needed in the future.
The authors have addressed my concerns.
Author Response
Comment 1: The edits create a floating statement in the abstract, "in both growing seasons." I believe these words should be removed.
Response 1: Thanks for the reminder. The floating statement “in both growing seasons” has been removed (Line 22).
Comment 2: The cloud-free fraction is reasonable for Las Cruces, New Mexico. The NASA facility outside Lac Cruces, at the edge of Las Cruces, operates multiple cloud camera systems that could be used to calculate this metric carefully if needed in the future.
Response 2: Thanks for the information. It’s helpful to improve the accuracy of our ETo estimation using the Blaney-Criddle method. We will use it in our future calculations.
