Prediction of Subcutaneous Fat Thickness (SFT) in Pantaneiro Lambs: A Model Based on Adipometer and Body Measurements for Android Application

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The article is devoted to the current area of research – digitalization of agriculture. Its implementation will optimize the processes of farming, including the rearing of animals, and minimize the human factor. Despite the relevance of the study, there are comments:

1. The abstract does not fully reflect the results of the research. 1-2 suggestions should be added about the unique product and its effectiveness in comparison with existing methods.

2. The introduction does not sufficiently provide an overview of the current state of the topic under study. It is necessary to indicate how effective it is to use mobile applications when assessing the condition of animals for slaughter in comparison with standard methods. It is also necessary to provide more data on the development of mobile applications for data or related purposes. Indicate the prevalence of the breed being studied in the country and in the world to indicate the relevance of the research being conducted.

3. In Materials and Methods, it is necessary to provide more information about the principle of operation of the mobile application. If any analogue was taken for the development of this application, this must be indicated.

4. There is no item about the developed mobile application in the results and discussions.

5. In the conclusions, you indicate that when developing and implementing updates for the application, the application can be used for other breeds. It is important to reflect in the introduction why this could not be done for existing mobile applications. 

6. Arrange the References in accordance with the requirements of the journal. Also, you must specify the DOI.

7. Most of the cited articles are over 5 years old. It is necessary to add at least 10 sources (scientific articles) that have been published in the last 5 years.

General recommendation: The article should be significantly expanded in volume, and the uniqueness of the developed application and its relevance should be clearly reflected.
The article can be published in the journal after significant changes.

Author Response

The article is devoted to the current area of research – digitalization of agriculture. Its implementation will optimize the processes of farming, including the rearing of animals, and minimize the human factor. Despite the relevance of the study, there are comments:

1. The abstract does not fully reflect the results of the research. 1-2 suggestions should be added about the unique product and its effectiveness in comparison with existing methods.

Thank you for pointing this out. We agree with this comment. Therefore, we have revised the abstract to better reflect the research findings by including suggestions that highlight the uniqueness of the “Slaughter Point Lambs” app and its effectiveness in comparison with existing approaches. This revision can be found on page 1, lines 27–30 of the revised manuscript.

2. The introduction does not sufficiently provide an overview of the current state of the topic under study. It is necessary to indicate how effective it is to use mobile applications when assessing the condition of animals for slaughter in comparison with standard methods. It is also necessary to provide more data on the development of mobile applications for data or related purposes. Indicate the prevalence of the breed being studied in the country and in the world to indicate the relevance of the research being conducted.

Accordingly, we have revised the Introduction to provide a more comprehensive overview of the current state of research on mobile applications for livestock evaluation.

Specifically:

• On page 1, line 38, we added Reference 1
• Lines 56–58 were revised to improve the discussion on recent advances in monitoring systems, and References line 59, 9–11 were added.
• On line 60, Reference 12 was inserted to support the argument on precision livestock farming.
• Lines 66–69 were revised to clarify the transition between technological approaches and challenges.
• Lines 70–75 were expanded to include further details, and Reference 17 was included on line 73; line 75, reference 18 added.

Thank you for your observation. We understand the importance of presenting the prevalence of the studied breed as a way to contextualize the relevance of the research. However, in this particular study, we intentionally chose not to emphasize the specific breed (Pantaneira sheep) in order to avoid characterizing the work as breed-specific. Our primary objective was to evaluate phenotypic and physiological variables related to carcass traits and fat deposition, aiming to develop predictive tools that can potentially be applied across different breeds.

The Pantaneira sheep were used as a biological model due to their diverse range of tissue deposition patterns. This intra-breed variability, encompassing different biotypes, made it a suitable population for building generalizable models. By avoiding breed-centered discussions, we sought to reinforce the broader applicability of our findings and tools.

 

3. In Materials and Methods, it is necessary to provide more information about the principle of operation of the mobile application. If any analogue was taken for the development of this application, this must be indicated.

On page 2, line 82, added reference 19

On page 5, lines 191–197, we incorporated updated literature and further clarified the methodology,

4. There is no item about the developed mobile application in the results and discussions.

Results: page 7, Lines 264–275 – Paragraph Addition: developed of the Application

Discussion: page 9, lines 335 -369. Added reference 34 line 364.

5. In the conclusions, you indicate that when developing and implementing updates for the application, the application can be used for other breeds. It is important to reflect in the introduction why this could not be done for existing mobile applications. 

Yes, we agree with the reviewer’s observation.

6. Arrange the References in accordance with the requirements of the journal. Also, you must specify the DOI.

Yes, we agree with the reviewer’s observation

7. Most of the cited articles are over 5 years old. It is necessary to add at least 10 sources (scientific articles) that have been published in the last 5 years.

we have added eight references published within the last five years

Reviewer 2 Report

Comments and Suggestions for Authors

To me is a very good paper, I woulds only suggest to use more recent refernces specialy in topics such as Artifitial Intelligence and Its use in Predictive Applications develompent for Cel Phones

Author Response

Comments:

To me is a very good paper, I woulds only suggest to use more recent refernces specialy in topics such as Artifitial Intelligence and Its use in Predictive Applications develompent for Cel Phones

R1: Thank you for pointing this out. we have added 8 references published within the last five years

Reviewer 3 Report

Comments and Suggestions for Authors

This paper aims to develop an Android application to assist in determining the ideal slaughter point of Pantanal lambs. The study takes lambs as the research objects and uses parameters such as live-weight measurements, body condition scores, and skinfold thickness to construct equations for predicting subcutaneous fat thickness (SFT). Through multivariate and univariate analyses, the validity of variables is determined, appropriate prediction equations are screened out and integrated into the application to provide producers with real-time data-driven decision-making tools.

1.The experiment only used 45 lambs, and the relatively small sample size may have what impacts on the accuracy and generality of the model? Are there plans to expand the sample size for further verification?

2.The Prime Med Digital DG + skinfold thickness gauge is used to measure skinfold thickness. How is the measurement accuracy and reliability of this instrument ensured? Please supplement the key parameters of this device.

3.In the regression model, the equation based on body weight (BW) and lumbar skinfold thickness (LST) has a high coefficient of determination (R² = 55.44%). However, in practical applications, measuring lumbar skinfold thickness may have certain difficulties. Can the practicality be improved by adjusting variables or improving the model?

4.Cluster analysis shows that the lambs are divided into two different groups. Is this grouping related to the growth stage, physiological state, or other factors of the lambs?

Author Response

1.The experiment only used 45 lambs, and the relatively small sample size may have what impacts on the accuracy and generality of the model? Are there plans to expand the sample size for further verification?

R1:Although the number of 45 lambs may appear relatively small, it is quite representative when considering local breeds, whose population sizes are typically limited. Moreover, this experiment was conducted as a pilot study, with the initial objective of establishing a preliminary model and validating the methodologies. We have clear plans to expand the sample size in future studies, including more animals and flocks, which will allow for greater generalization and refinement of the model.

2.The Prime Med Digital DG + skinfold thickness gauge is used to measure skinfold thickness. How is the measurement accuracy and reliability of this instrument ensured? Please supplement the key parameters of this device.

R2:The measurements using the Prime Med Digital DG device were performed on the skin of lambs, based on the assumption that the device’s sensitivity would be similar to that observed in humans. This assumption was tested within the experiment and is an integral part of the study. Had the device not been sensitive enough to detect differences between animals and assist in estimating accumulated fat, the development of the final predictive model presented would not have been possible.

3.In the regression model, the equation based on body weight (BW) and lumbar skinfold thickness (LST) has a high coefficient of determination (R² = 55.44%). However, in practical applications, measuring lumbar skinfold thickness may have certain difficulties. Can the practicality be improved by adjusting variables or improving the model?

R3: The model can and should be adjusted as more data are collected, since measurements using the Prime Med Digital DG device are more practical and straightforward, requiring less expertise compared to the more qualitative assessment via body condition scoring.

4.Cluster analysis shows that the lambs are divided into two different groups. Is this grouping related to the growth stage, physiological state, or other factors of the lambs?

R4: As described on line 289, the clusters were likely formed due to physiological differences in tissue growth, given that the evaluation range spanned from 15 to 42 kg of body weight. This broad range was intentionally chosen to increase the robustness of the model. Additionally, it is important to highlight that the experimental animals belonged to a native breed, which until recently was naturally selected, resulting in considerable phenotypic diversity within the population. Therefore, it is not uncommon to find young animals of similar age and weight, raised under the same feeding system, but exhibiting different body condition scores. This indicates that they are at different physiological stages, with animals having higher body condition representing the more precocious individuals. Such variation influences growth variables, muscle composition, and the rate of fat accumulation, which is reflected in the formation of distinct groups.