Leaf Area Estimation of Yellow Oleander Thevetia peruviana (Pers.) K. Schum Using a Non-Destructive Allometric Model
, Alfredo Jarma-Orozco, Juan de Dios Jaraba-Navas, Yirlis Yadeth Pineda-Rodriguez and Marcelo F. Pompelli *Round 1
Reviewer 1 Report (Previous Reviewer 3)
Comments and Suggestions for AuthorsThe alometry of flowers, fruits, and seeds interferes with the accuracy of leaf area estimation, as highlighted in the title.
It is suggested that an explanation or implementation of the proposed model be presented.
Comments for author File: 
Comments.pdf
Author Response
in attachment
Author Response File: Author Response.pdf
Reviewer 2 Report (New Reviewer)
Comments and Suggestions for AuthorsAn interesting article. Likely to be of interest to the researcher in this field. The text is well-written and easy to follow. However, I recommend highlighting the names of the statistical tests used.
I have some comments and corrections listed below.
On line 21, the LW expression in the regression model is not clarified. In the abstract, the authors do not mention the length and width of the leaf, so LW is not given. In the paper, the authors use L, W, or product LW as an explanatory variable in the approximation. The presentation of the model needs to be improved.
Line 119: The formulation of the null hypothesis H0: beta_1=beta_2=beta_n, is not accompanied by a reference to the regression models. The current presentation of the null hypothesis gives the impression that it is a test of the equality of the means of the three samples (ANOVA). It is necessary to improve the presentation of H0, and test statistics.
line 164: remove the * symbol between ln and the right parenthesis
line 173: what characteristic does the tolerated error of 15% refer to?
Table 3: improper use of the symbol x for the product, I recommend removing it
Table 4: on the left side of the equation is the estimate \hat{Y}, then on the right side of the equation there must be estimates \hat{beta}_1 and \hat{beta}_2, on the right side there should be no error \epsilon_i
Line 336: citations to the F-test and test statistics are missing. What are the assumptions for using this test? There were assumptions fulfilled?
Line 433: What estimate bias is being discussed?
Author Response
in attachment
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report (New Reviewer)
Comments and Suggestions for AuthorsThe authors were insufficiently responsive to my comments from the first review. In the text of the article,
I still miss the mention of the models on line 126, for which they formulate the null hypothesis H0: beta_1=beta_2=beta_n. This is completely incomprehensible to the reader. Essentially, before testing, you need to create a model, then estimate the parameters and then test the hypothesis. The presentation of the model and hypothesis in the article is very superficial. Moreover, this theoretical presentation is not sufficiently cited in the numerical study. This leads to the fact that the reader would not be able to perform a similar calculation and test without studying other publications.
The answer to line 170 is completely wrong. A logarithm is a function and has an argument. Again, I ask the authors to remove the symbol for the multiplication between ln and the expression (SQE/n).
Answer to line 173. The common name is type one error also referred to as alpha significance level. It is usually chosen as alpha=5%. Therefore, it is necessary to recalculate, because 15 percent is an unacceptable error.
Table 4: on the left side of the equation is the estimate \hat{Y}, then on the right side of the equation there must be estimates \hat{beta}_1 and \hat{beta}_2, on the right side there should be no error \epsilon_i
the new version is still not correct
Fix as follows:
Stochastic model & Estimate & RMSE & AIC\\
Y = beta0 + beta1 × LW + É›i & Ŷ = 0.284 + 0.766 × (LW) & 0.429 & 2764.085\\
Y = beta0 × LW^beta1 + É›i & Ŷ = 0.914 × (LW)^0.939 & 0.425 & 2764.742\\
Note: deterministic model is Y =beta0 + beta1 × LW
Another version of Tab 4 is:
Deterministic model & Estimate & RMSE & AIC\\
Y = beta0 + beta1 × LW & Ŷ = 0.284 + 0.766 × (LW) & 0.429 & 2764.085\\
Y = beta0 × LW^beta1 & Ŷ = 0.914 × (LW)^0.939 & 0.425 & 2764.742\\
Line 336: citations to the F-test and test statistics are missing. Write the model and the null hypothesis. What are the assumptions for using this test? There were assumptions fulfilled? The answer is still missing in the article.
Line 433: What estimate bias is being discussed? The answer is still missing in the article.
Author Response
in attachment
Author Response File: Author Response.pdf
This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe equtions needs to be written in equation editors. avoid * for multiplication. Statistical analysis should be improved.
Comments on the Quality of English Languageneeds improvement
Reviewer 2 Report
Comments and Suggestions for AuthorsMy major concerns are that this study is to describe a reliable and accurate leaf area estimation of Thevetia peruviana and quantify the concentration of thevetin A and thevetin B in leaves, flowers, and seeds, and in its summary and conclusions the authors linked their observations with “the aesthetics of our public squares through a non-careful landscaping project, which includes the placement of ornamental plants. While these plants add to the area's visual appeal, the city must take great care in selecting non-poisonous varieties to ensure the safety of residents and visitors. Choosing ornamental plants responsibly not only enhances the aesthetic ap peal of the city, but also protects against the potential risks associated with plant poisoning, in line with our commitment to creating a welcoming and safe public space for all.”. it’s absurd and unacceptable! The authors should focused their attention on the ecological implication of their observations!
Reviewer 3 Report
Comments and Suggestions for AuthorsThe use of allometry in this study must be supported by appropriate references.
It is preferable to focus on leaf area dealing with quantification of Thevetin A and Thevetin rather than estimate leaf area using a prediction model.
Comments for author File: Comments.pdf
