Using Ground and UAV Vegetation Indexes for the Selection of Fungal-Resistant Bread Wheat Varieties

Round 1

Reviewer 1 Report

This article investigated the climate characteristics of five different locations in northern Spain, tested 40 different wheat varieties at different locations, designed a comparative experiment to determine the acceptance of antifungal agents, discussed the significant differences in wheat and vegetation indices between different months and locations, demonstrated preferred phenotype analysis indicators: GA, CSI, TGIveg. The article also discussed the resistance of wheat to fungi under different environmental conditions. While the article is of some significance., it has the following issues:

 

1. The article's title misleads readers into thinking that it aims to screen out antifungal wheat varieties from 40 different wheat varieties.

2. The article's experimental design feels overwhelming, and it is recommended to supplement it with a flowchart to provide clarity.

3. The paper suggests that there is a significant difference in wheat flavonoids between the regions of Ejea de los Caballeros and Elorz, but the inconsistent planting time may also be a source of difference, rather than the effect of rainfall and temperature on fungal resistance.

4. The article aims to discuss the resistance of wheat to fungi under different environmental conditions but provides only a brief explanation in the discussion portion and does not explain how water and temperature affect wheat resistance to fungi. Besides, the significance of the previously reported climate characteristics is not fully reflected.

5. The article's conclusion that ground NDVI has a higher correlation with yield than aerial drone NDVI seems trivial and does not provide significant insights.

 

Please carefully edit the entire document!

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

The authors conducted interesting research. 

In particular, it is very impressive between the five regions of Spain was studied for wheat.

Unfortunately, the paper is overall too verbose

First, summarize the abstract more compactly.

It seems unnecessary list the results here in detail.

The passion of the authors is felt in material and methods.

Nevertheless, I would like to summarize this part as well. 

Please check the rest of the title including 286 line title.

There is no need to explain the definition (2.4. part)

The tables in result are too listed. Please find a  way to combine.

 In the discussion, it is recommend to reinforce the contents by including the contents of more preceding studies.

The study design and results are judged to be sufficient value.

Overall content is sufficient to convey the author`s intention.

But it need to change more readable.

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

This work presented an application of multispectral sensors as non-destructive methods for phenotyping different wheat cultivars under abiotic stress. Some indicators, including GA, TGI and CSI, were identified as valuable indicators for reflecting the influence of fungicide treatments on different wheat cultivars. The research was interesting and of great importance for plant breeding. But the method is not innovative, and much of the authors’ description is confusing. There are some points that need substantial improvement:

1. The studies were all based on the data collected from the experimental year of 2021, and were not verified in other years. The generalizability of the conclusions reached still needs to be considered.

2. The specific type of fungicide used in this study and how did the authors add the fungicide were not well explained in Section 2.3.

3. The authors did not seem to have given a detailed description of the fungal classes involved.

4. How many measurements were performed to determine the chemical components, such as the protein content and flavonoids? Three replicates for each sample? Different aliquots of the same samples were measured and, then, the values were averaged? Please provide some more details about this important point. In addition, there is large error in detecting protein content with NIRS (Section 2.4.6).

5. I recommend adding a paragraph of “Practical Application”. Indeed, although the approach has been well described, its practical application should be described more in depth. Can this study be used to test the resistance of different wheat cultivars to different fungal disease?

6. Table 6, correct P-value of ‘0.0.321’ for site Briviesca.

Extensive editing of English language required。

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

All the problems raised last time have been resolved.

Author Response

As no additional comment really needs to be made, we thank the reviewer for the valuable contributions towards the manuscript in the previous round of revisions.

Reviewer 3 Report

After careful revision by the authors, the quality of this manuscript has been significantly improved. The new title and abstract provide a better understanding of the purpose of this paper to assess the impact of fungal disease on wheat through the efficient acquisition and analysis of phenotypic information. The supplementary description of the Materials and Methods section more clearly shows the details involved in carrying out the experiment, so that the readers can reproduce it. Furthermore, in the Discussion section, the influence of climatic conditions on wheat resistance to fungal diseases is better explained. I would like to give some minor suggestion to the authors: (1) This revised manuscript is very hard to read; (2) The presentation of Table 1 and 2 could be improved; (3) In 2.4.1 section, the authors used non-standard methods to measure chemical composition in leaves, which was inherently subject to error. 

Minor editing of English language required

Author Response

Dear Reviewer 3,

We thank you for your valuable insights and constructive comments towards the improvement of our manuscript submission to MDPI Drones.

We provide here the detailed documentation of our changes to the manuscript. Our comments in response are in Arial, italic and the comment of the reviewer shown in Bold.

 

Comments and Suggestions for Authors

This revised manuscript is very hard to read

 The entire document has been reread by the team and corrected for minor errors and typos throughout as well as other minor aspects that might affect the general readability.

 A native English speaker has also gone through all the text after all the changes were by the first author and the co-author team.

The presentation of Table 1 and 2 could be improved

We also feel that these Table bring little to the manuscript and are over simple for presentation of this information, so we have decided to keep the details in the text and integrate this information into the summary Figure 3. Thus, the Figure 3 flow chart, which contains all the information previously shown by Tables 1 and 2, has taken the place of Tables 1 and 2.

Previous references in other paragraphs have simply replaced mention of Tables 1 and 2 to indicate Figure 3 instead, thus keeping the changes minimal overall while solving the issue raised by the reviewer.

(see Figure in attachement letter)

Figure 3. Flow chart describing the experimental design of the various trials from sites across Northern Spain. The experiment was conducted in five locations located in Tordómar, Briviesca, Elorz, Sos del Rey Católico and Ejea de los Caballeros. Sowing date was staggered between the different sites as indicated at the top. At each location 160 plots were divided into 4 blocks with 3 treated and 1 untreated with fungicide. Ground and UAV RGB and NVDI data were captured during 5 visits to each site. Dualex was measured on visits 3 and 4 and stable isotope samples were collected of the grains during the last visit 5. Final yield and protein content were assessed in situ using a combine harvester with an integrated NIRS (near infrared spectrometer).

In 2.4.1 section, the authors used non-standard methods to measure chemical composition in leaves, which was inherently subject to error. 

We agree that the standard chemical procedures to analyze leaf pigments were not conducted and that different procedures and sensors may have different errors. However, the use of leaf sensors for this kind of tasks is very common in the literature and the usual procedure when phenotyping under field conditions. There are several reasons:

 

1. This is much more high throughput procedure than taking leaf samples in the field, for further analysis in the lab. Consider that for each site and visit we were measuring near 200 samples, and we have used this sensor for two visits of all five sites.
2. Taking leaf samples in the field, means `preserving them under low temperature (liquid N₂ or at least an icebox until reaching the lab. Our field sampling campaign implied continuous working during 5 days, outside the lab. In summary, logistics did not support taking samples in the field to further running chemical analyses.
3. The readings of the leaf sensors are pigment values on an area basis. Therefore, they take into account the thickness/density of the leaf. These kinds of analyses have more sense than the values of pigment content on a fresh weight basis (the usual way to express the results in the lab unless a plug borer of the known area is used to sample leaf laminas).
4. Furthermore, this is a much more advanced version compared to previous leaf sensors, for example SPAD, which gave pigment content in arbitrary units with non-linear response functions. The Dualex is both calibrated to area units and has a linear response function. Besides the error is documented and quite small, it is not so important since the point here is to be able to differentiate between treatments.

More details have been provided in this section that we feel should be very informative to the reader. Also, a second paragraph with new additional references has added to the section 2.4.1. Dualex, to describe and demonstrate the Dualex's effectiveness, and precision in quantifying leaf pigments.

Text Before

“The Dualex is an optical portable sensor (Force-A, Orsay, France) for determining flavonoids, anthocyanins, and chlorophyll levels in leaves. It enables non-destructive and real-time measurements to be made [26]. Furthermore, the nitrogen balance index (NBI) was determined, which is the chlorophyll/flavonoids ratio which informs on the nitrogen and carbon allocation [27]. The Dualex uses a UV excitation beam at 357 nm, which corresponds to the maximum absorption for flavonoids, as well as a green LED for anthocyanins, a red reference beam at 650 nm, which corresponds to chlorophyll absorption, and two other near-infrared references. The measurement was repeated five times for each plot, resulting in a representative measurement of the various pigments on both sides of the plot. In each case the last fully expanded leaf was measured. “

 Text after

“The Dualex is an optical portable leaf clip sensor (Force-A, Orsay, France) for determining flavonoids, anthocyanins, and chlorophyll levels in leaves that was used for the additional assessment of leaf chlorophyll (Chl), epidermal flavonoids (Flav), and nitrogen balance of the study sites during the 3^rd and 4th visits (Figure 3). The Dualex offers the advantages of high throughput, non-destructive and real-time measurements to be made of these relevant physiological crop components and is factory calibrated to provide leaf Chl content in µg cm^-2 units with a linear response function and a documented error of 2.4 and 3.4% compared to chemical composition analyses and for Chl and Flav, respectively [26]. Furthermore, the Dualex determines the Nitrogen Balance Index (NBI), which is the chlorophyll/flavonoids ratio and informs on nitrogen and carbon allocation [27].

The Dualex manages high precision and linear response functions using a unique combination of a UV excitation beam at 357 nm, which corresponds to the maximum absorption for flavonoids, as well as a green LED for anthocyanins, a red reference beam at 650 nm, which corresponds to chlorophyll absorption, and two other near-infrared reference wavelengths (710 nm and 850 nm) [28, 29]. The Dualex displayed high accuracy in Chl content assessment when specifically tested on wheat in a study that assessed fluorescence techniques for estimations of phenolic compounds positively [28]. As a field device, it adjusts the level of fluorescence caused by a reference red light to the level of fluorescence caused by UV light to provide quick measurements from attached leaves in field conditions, is water resistant, stores data digitally, and has an integrated GPS sensor for both location and accurate time stamping. It has furthermore been used to estimate soil nitrogen and wheat productivity estimations [30].

The measurements with the Dualex sensor were performed on the leaf adaxial side and repeated five times for each plot in order to sample a representative measurement of the various pigments on both sides of the plot. In each case the last fully expanded leaf was measured.“

The bibliography section was revised in accordance with any new citations added to the paper and was written in the manner specified by the journal.

Comments on the Quality of English Language

Minor editing of English language required.

As mentioned before, the entire document has been reread by the team and corrected for minor errors and typos throughout as well as other minor aspects that might affect the general readability.

 A native English speaker has also gone through all the text after all the changes were by the first author and the co-author team.

Author Response File: Author Response.docx