Finding Phenotypic Biomarkers for Drought Tolerance in Solanum tuberosum
Round 1
Reviewer 1 Report
The article entitled “Finding phenotypic biomarkers for drought tolerance in Solanum tuberosum” is of interest to the special issue. The main goal of the manuscript was identifying phenotypic tolerance traits that could facilitate the screening of large populations of Solanum tuberosum in the field. Since yield selection of tolerant plants in arid environments can take decades, this topic is relevant by proposing marker-assisted selection to speed up the process.
This manuscript focuses on phenotypic biomarkers for drought tolerance in potatoes like tuber number, size grading, weight, and starch content. Additionally, shoot was phenotyped with two IR-laser-scanners mounted on an automobile Fieldscan system. The measurements were done every 4 hours, thus yielding six images per plant and day. From images were estimated the features plant height (PH), total leaf area (A3), projected leaf area (A2), leaf area index (LAI), digital biomass (DB), leaf inclination (LI), leaf angle (LA), and light penetration depth (LPD) for each plant and each time point.
Drought stress was applied as long-term drought stress, named ss, from the early vegetative phase to harvest, early drought stress, named sc, from the early vegetative phase to flowering (interval 2) and late drought stress, named cs, from flowering to harvest (interval 3).
According to the results, early stress mainly affected tuber numbers, while late stress mainly tuber size. Late stress reduced tuber starch yields more than early stress. The tuber filling phase is deemed to be the phase that is most sensitive to drought. In relation to secondary phenotypic traits, the analysis of variance on the growth curve parameters suggested the maximum and the turning point of the leaf area and of the leaf area index were the best criteria for tolerance prediction. The authors found a relationship between leaf area growth and drought tolerance.
The authors also found that early stress treatments can be a proxy for long-term stress in drought tolerance screens if there is no genetic variation for stress memory or recovery in the study population.
The manuscript is well structured and written in a clear manner. The length of the reference list is enough for the manuscript and the references included are relevant and recent.
Overall, the manuscript is well structured and provides sufficient information. However, I also have few concerns about the manuscript as follow:
Tittle
Comment 1: The scientific name should be italicized.
Introduction
Comment 2: Replace “word” by “world” (line 41)
Comment 3: Replace “parts” by “regions” (line 41)
Comment 4: Avoid
Comment 5: Rewrite the reference in Vancouver style (line 78)
Comment 6: After the first author followed by et al. you should include the reference number. Khan et al [#] (line 113).
Materials and Methods
Comment 7: Reorder the series name of Figure 1 as they are mentioned in the figure caption (cc, sc, cs and ss).
Results
Comment 8: Replace “produces” by “produced”
Discussion
Comment 9: Rewrite the reference in Vancouver style (line 609)
Comment 10: Delete parenthesis in [38]) (lines 613)
Author Response
Response to Review Report Reviewer 1
Tittle
Comment 1: The scientific name should be italicized.
done
Introduction
Comment 2: Replace “word” by “world” (line 41)
done
Comment 3: Replace “parts” by “regions” (line 41)
done
Comment 4: Avoid
We need further information on what to avoid
Comment 5: Rewrite the reference in Vancouver style (line 78)
Done. We apologize.
Comment 6: After the first author followed by et al. you should include the reference number. Khan et al [#] (line 113).
done
Materials and Methods
Comment 7: Reorder the series name of Figure 1 as they are mentioned in the figure caption (cc, sc, cs and ss).
done
Results
Comment 8: Replace “produces” by “produced”
done
Discussion
Comment 9: Rewrite the reference in Vancouver style (line 609)
done
Comment 10: Delete parenthesis in [38]) (lines 613)
done
Reviewer 2 Report
This study is good. But I have many notes
- In line 234- Why have they used analysis of nonlinear regression without the use of linear first?
- Why you used the Spearman correlation in this study? (It is used in non-biological sciences)?
- What is the aim of the analysis of logistic regression parameters as (ANOVA) in Table 2?
- Table 1 is unclear - what do you mean DF(error) and how was it calculated?
- What is the aim of the analysis of logistic regression parameters as (ANOVA) in Table 2?
- Table 3 is unclear - how do you calculate treatment itself [DRYMp (ss) with Phenotype (ss)]?
-Replace r square with R square in all manuscript.
Author Response
This study is good. But I have many notes
- In line 234- Why have they used analysis of nonlinear regression without the use of linear first?
We have used linear regression to analyze the growth response in both treatment intervals. Linear regression resulted in non-random error distribution with maximum deviations at the beginning and the end of the regression interval (see L237 to 239). Thus, we concluded that linear regression analysis is not a suitable method to model the growth curves.
- Why you used the Spearman correlation in this study? (It is used in non-biological sciences)?
We decided to use Spearman correlation instead of Pearson correlation as we did not expect a strictly linear relationship between phenotypic features and drought tolerance, but rather a saturation type curve. By calculating the correlation between the ranks, we reduced the effect of the curve shape on the result. Furthermore, Spearman correlation is less sensitive to outliers than Pearson correlation.
- What is the aim of the analysis of logistic regression parameters as (ANOVA) in Table 2?
We analyzed the effect of year, genotype and treatment on the regression parameters. A significant effect of genotype informs us, whether there is genetic variation for the parameter (see old manuscript L 448 to 455). Without genetic variation, the parameter is not useful as a marker. The F statistic on the effect of year and treatment tells us, how much the parameter is affected by the environment. The lower the effect, the better the marker, as it would be the same each year and independent of the plant water status. (See old manuscript L 430 to 436). We have added two sentences to make the approach clearer (see new manuscript: L 433 to L 436).
- Table 1 is unclear - what do you mean DF(error) and how was it calculated?
DF(error) is the degree of freedom for the error in the general linear model. It is calculated as the number of observations minus the degrees of freedom for the effects estimated in the ANOVA.
- What is the aim of the analysis of logistic regression parameters as (ANOVA) in Table 2?
See above
- Table 3 is unclear - how do you calculate treatment itself [DRYMp (ss) with Phenotype (ss)]?
We have added a cross-reference to the section in material and methods, in which the modelling is explained. Furthermore, we added three sentences (L 561 to 566) to explain the content of table 3.
--Replace r square with R square in all manuscript.
done
