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Article
Peer-Review Record

A Daily Reference Crop Evapotranspiration Forecasting Model Based on Improved Informer

Agriculture 2025, 15(9), 933; https://doi.org/10.3390/agriculture15090933
by Junrui Pan 1, Long Yu 1,*, Bo Zhou 2 and Junhong Zhao 3
Reviewer 2: Anonymous
Reviewer 3: Anonymous
Agriculture 2025, 15(9), 933; https://doi.org/10.3390/agriculture15090933
Submission received: 3 March 2025 / Revised: 9 April 2025 / Accepted: 22 April 2025 / Published: 25 April 2025
(This article belongs to the Section Agricultural Water Management)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors
  1. Figure 3 are too many and too small graphs, not so sure what they try to show in this waya.a The pictures are better combined for 2 or more variables per graph
  2. Axis labels and titles for figure 4 are too small
  3. Figures 4 and 5 show the very same data but different way of visualization, how about we chose one to be presented in the manuscript
  4. If possible, please shorten the explanation on steps for deep learning models especially lstm and informer, except the one you modified or you implemented for your research.
  5. Table 2 is written in a way that can be misunderstood, Initially I thought as there are 7 different scenarios (S1-S7) undefined, each one has an additional parameter added., it turned out that S1 is a scenario with radiation input, sunshine duration, max temperature plus soil temperature and soon. The table should be rearranged in a clearer way such as using columns, rows and headers.
  6. Figure 12, what if you change the graph style into symbols only for data, and solid line or the predicted value.
  7. Actually by understanding theory behind penman-monteith equation we can figure which parameters are representing: thermal energy (sensible and latent), energy losses (soil and air warming), aerodynamic and the ‘container’(the atmosphere). By that understanding, I did a much simpler study long time ago with multilayer perceptron ANN and it worked although not as precise as the one presented here. The point is, it will be great if the authors add comparation of their best scenario parameters to the penman-monteith approach. This is strongly suggested but not obligatory, the paper presents more in deep learning technique than the agriculture.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The work is very interesting. Some suggestions and questions were included in the text.

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,

The subject analyzed by you is a topical one. There are a few aspects that need to be improved. These are:

  • Write the full name for LSTM, then use the acronym, as you have done in other situations. Check that all acronyms are written as the full name first.
  • Convert from acres to square meters for the area of ​​the analyzed area.
  • For the annual average values ​​of meteorological parameters, please mention the time period for which they are calculated.
  • Usually the soil depth is taken as 20 - 100 cm. Why did you use 28 - 100 cm?
  • Below the top left image in figure 1 is written - Figure number: GS (2024) 0650. Why?
  • Put the cartographic scales on all the maps that make up figure 1.
  • In Figure 4, you need to enlarge the writing on the Ox axis. It is not visible and cannot be read easily.

Your study is technical, more about computational language and not practical applicability. It is a worked material, but from my point of view it lacks this aspect of agricultural applicability. 

I wish you success in your endeavor.

Sincerely,

Comments on the Quality of English Language

...

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,

I appreciate your effort to improve the material. It's alright!

All the best,

 

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