Evaluation of Morpho-Physiological Traits in Rice Genotypes for Adaptation under Irrigated and Water-Limited Environments
, Adel M. Ghoneim 1, Hafeez Ur Rehman 2,*, Mohamed M. Shehab 1, Mohamed I. Ghazy 1, Ahmed S. El-Iraqi 3, Abdelwahed E. Mohamed 3, Muhammad Waqas 4,5, Noraziyah Abd Aziz Shamsudin 6 and Yaning Chen 5,*Round 1
Reviewer 1 Report
Please check again the text and at point 3.3 the word Hybrid2 and Hybrid 2 are both present, same in tables, please check
Author Response
Reviewer 1
Open Review
(x) I would not like to sign my review report
( ) I would like to sign my review report
English language and style
( ) Extensive editing of English language and style required
( ) Moderate English changes required
(x) English language and style are fine/minor spell check required
( ) I don't feel qualified to judge about the English language and style
Yes Can be improved Must be improved Not applicable
Does the introduction provide sufficient background and include all relevant references?
(x) ( ) ( ) ( )
Are all the cited references relevant to the research?
(x) ( ) ( ) ( )
Is the research design appropriate?
(x) ( ) ( ) ( )
Are the methods adequately described?
(x) ( ) ( ) ( )
Are the results clearly presented?
(x) ( ) ( ) ( )
Are the conclusions supported by the results?
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Comments and Suggestions for Authors
Please check again the text and at point 3.3 the word Hybrid2 and Hybrid 2 are both present, same in tables, please check
Re: Thank you, the suggestion have been incorporated.
Reviewer 2 Report
The manuscript deals with the evaluation of 17 genotypes of rice cultivated under irrigated ad limited water conditions. Several morphological, phenological, physiological and agronomic traits were evaluated on all genotypes in both water conditions.
The mansucript is of great interest and the topic is current.
The used methods of measurement are current.
Nevertheless there are manys concerns in this manuscript.
1- the results obtained were not discussed in the right way. Indeed there are more 30 days of difference in heading date, which can be considered as substantial difference. This difference is the driving force behind the results achieved. As an example, Egyptian Yasmine (late genotype) presents the lowest stomatal conductance and therefore the lowest dry matter production.
This probably due to the lack of literature background.
See for example: Monneveux et al. (2006) https://doi.org/10.1016/j.plantsci.2005.12.008
2-Moreover, statistical analyses are not correct. in the principal component analysis, the traits used should be independents. This not the case. DTH influence all other traits which driving force therefore all traits are oriented in the same way (Figure 3). In addition, in this figure, yead and its components are used together inducing more errors.
In fact, in order to compare genotypes, it is necessary to use DTH as a covariable in ANOVA and generate adjusted means (to overcome the effect of precocity). These adujsted means should be usd for PCA without yield. This latter can be added as an additional trait in the PCA to give indicative information
Author Response
Reviewer 2
Open Review
(x) I would not like to sign my review report
( ) I would like to sign my review report
English language and style
( ) Extensive editing of English language and style required
( ) Moderate English changes required
(x) English language and style are fine/minor spell check required
( ) I don't feel qualified to judge about the English language and style
Yes Can be improved Must be improved Not applicable
Does the introduction provide sufficient background and include all relevant references?
( ) (x) ( ) ( )
Are all the cited references relevant to the research?
( ) ( ) (x) ( )
Is the research design appropriate?
(x) ( ) ( ) ( )
Are the methods adequately described?
( ) ( ) (x) ( )
Are the results clearly presented?
( ) ( ) (x) ( )
Are the conclusions supported by the results?
( ) ( ) (x) ( )
Comments and Suggestions for Authors
The manuscript deals with the evaluation of 17 genotypes of rice cultivated under irrigated ad limited water conditions. Several morphological, phenological, physiological and agronomic traits were evaluated on all genotypes in both water conditions.
The mansucript is of great interest and the topic is current.
The used methods of measurement are current.
Nevertheless there are manys concerns in this manuscript.
1- the results obtained were not discussed in the right way. Indeed there are more 30 days of difference in heading date, which can be considered as substantial difference. This difference is the driving force behind the results achieved. As an example, Egyptian Yasmine (late genotype) presents the lowest stomatal conductance and therefore the lowest dry matter production.
This probably due to the lack of literature background.
See for example: Monneveux et al. (2006) https://doi.org/10.1016/j.plantsci.2005.12.008
Re: Thank you for your suggestion, we did not find difference of 30 days in days to heading. And the reported genotype usually maintained the SC under both NS and DS condition.
2-Moreover, statistical analyses are not correct. in the principal component analysis, the traits used should be independents. This not the case. DTH influence all other traits which driving force therefore all traits are oriented in the same way (Figure 3). In addition, in this figure, yead and its components are used together inducing more errors.
In fact, in order to compare genotypes, it is necessary to use DTH as a covariable in ANOVA and generate adjusted means (to overcome the effect of precocity). These adujsted means should be used for PCA without yield. This latter can be added as an additional trait in the PCA to give indicative information.
Re: Statistical analysis has been revised, PCA has been replaced with cluster analysis. All data were re-analyzed while considering DTH as co-variable in ANOVA and later adjusted means were used for further analysis in pearson’s and cluster analysis.
Reviewer 3 Report
A review for multi-variate analysis reveals morpho-physiological adaptive traits in rice genotypes for irrigated and water-limited environments
The author tried to evaluate 17 different genotypes of rice under irrigated and non-irrigated conditions. They collected some morphological and physiological data to see the response of genotypes under these two different conditions. The experiment was conducted for two years. The design was a split-plot with three field replications. Irrigation was the main plot. The authors have collected good data and the result may be relevant for a future breeding program.
However, correct data analysis is required, please consult a statistician. There is a data analysis method for split-plot design. Please refer to Ayele et al 2020, “Responses of Upland Cotton (Gossypium hirsutum L.) Lines to Irrigated and Rainfed Conditions of Texas High Plains’. The SAS PROC MIXED procedure may be a good way to analyze this data. For example, In Table 1, year, blocks, irrigation type, and genotypes have been used as factors, which makes the analysis more complicated. Why is replication used as a fixed factor? It can be used as a random factor. Please consult a statscian if the year can be used as a random factor or not. Genotype, irrigation type, and maybe a year.
PCA is not the right fit for this data set. From the result presented in the main body, correlation analysis and PCA analysis showed a similar concept. Please remove PCA from these analyses.
The way the author did the correlation analysis was confusing. I would use regular multivariate correlation analysis, which can be done using software including JMP software within a few minutes, instead of the graphic method. Please run correlation analysis using selected traits with purpose. Don’t push the whole traits into correlation analysis.
A few points,
Line 1: Title. Multi-variate analysis reveals morpho-physiological adaptive traits in rice genotypes for irrigated and water-limited environments.
A multi-variate analysis is one of the statistical methods that you could prove your work with. Nothing to do with the title in this case. Please modify the title.
Line 31: yield reduction (YR). You cannot make abbreviations for infrequently used words.
Line 104 Table 1.
- Redo the analysis
- Revert the sources of variation into Y-axis
Line 105. Please do not use ns = for Not significant, leave it out. Put a star on a significant variable only.
In Tables 2 and 3, morphological and agronomic traits were mixed. For example, why is PH under Table 2?
Both Tables 2 and 3 are so messy. Please use landscape using the standard table formatting. Why do you need these two tables at all?
Line 150, The PCA value (PC1 = 57 %) here does not match with the PCA value in the Abstract. The first two components of the PCA analysis contributed 72.89% variability in the rice genotypes for yield traits under NS and DS, respectively (in Abstract).
Line 167 Figure 2; Too many traits participated in the correlation analysis. please reduce it.
Figure 3 is unreadable. This figure should be removed
Author Response
Reviewer 3
Open Review
(x) I would not like to sign my review report
( ) I would like to sign my review report
English language and style
( ) Extensive editing of English language and style required
( ) Moderate English changes required
(x) English language and style are fine/minor spell check required
( ) I don't feel qualified to judge about the English language and style
Yes Can be improved Must be improved Not applicable
Does the introduction provide sufficient background and include all relevant references?
(x) ( ) ( ) ( )
Are all the cited references relevant to the research?
( ) (x) ( ) ( )
Is the research design appropriate?
(x) ( ) ( ) ( )
Are the methods adequately described?
( ) ( ) (x) ( )
Are the results clearly presented?
( ) ( ) (x) ( )
Are the conclusions supported by the results?
( ) (x) ( ) ( )
Comments and Suggestions for Authors
A review for multi-variate analysis reveals morpho-physiological adaptive traits in rice genotypes for irrigated and water-limited environments
The author tried to evaluate 17 different genotypes of rice under irrigated and non-irrigated conditions. They collected some morphological and physiological data to see the response of genotypes under these two different conditions. The experiment was conducted for two years. The design was a split-plot with three field replications. Irrigation was the main plot. The authors have collected good data and the result may be relevant for a future breeding program.
However, correct data analysis is required, please consult a statistician. There is a data analysis method for split-plot design. Please refer to Ayele et al 2020, “Responses of Upland Cotton (Gossypium hirsutum L.) Lines to Irrigated and Rainfed Conditions of Texas High Plains’. The SAS PROC MIXED procedure may be a good way to analyze this data. For example, In Table 1, year, blocks, irrigation type, and genotypes have been used as factors, which makes the analysis more complicated. Why is replication used as a fixed factor? It can be used as a random factor. Please consult a Statesian if the year can be used as a random factor or not. Genotype, irrigation type, and maybe a year.
PCA is not the right fit for this data set. From the result presented in the main body, correlation analysis and PCA analysis showed a similar concept. Please remove PCA from these analyses.
The way the author did the correlation analysis was confusing. I would use regular multivariate correlation analysis, which can be done using software including JMP software within a few minutes, instead of the graphic method. Please run correlation analysis using selected traits with purpose. Don’t push the whole traits into correlation analysis.
Re: Thank you for your suggestion, all the changes have been incorporated.
A few points,
Line 1: Title. Multi-variate analysis reveals morpho-physiological adaptive traits in rice genotypes for irrigated and water-limited environments.
A multi-variate analysis is one of the statistical methods that you could prove your work with. Nothing to do with the title in this case. Please modify the title.
Re: title has been modified
Line 31: yield reduction (YR). You cannot make abbreviations for infrequently used words.
Re: Incorporated
Line 104 Table 1.
Redo the analysis
Revert the sources of variation into Y-axis
Re: incorporated.
Line 105. Please do not use ns = for Not significant, leave it out. Put a star on a significant variable only.
Re: Incorporated
In Tables 2 and 3, morphological and agronomic traits were mixed. For example, why is PH under Table 2?
Re: it is morphological traits as well, therefore kept under Table 2.
Both Tables 2 and 3 are so messy. Please use landscape using the standard table formatting. Why do you need these two tables at all?
Re: Now the tables have been fitted with column width and are illustrative.
Line 150, The PCA value (PC1 = 57 %) here does not match with the PCA value in the Abstract. The first two components of the PCA analysis contributed 72.89% variability in the rice genotypes for yield traits under NS and DS, respectively (in Abstract).
Re: PCA analysis has been replaced with cluster analysis and all suggested changes has been made.
Line 167 Figure 2; Too many traits participated in the correlation analysis. please reduce it.
Figure 3 is unreadable. This figure should be removed
Re: Figure removed and replaced with table showing Pearson correlation.
