Estimating Herbaceous Aboveground Biomass Using an Indirect Method Based on the Herbaceous Layer Characteristics

Round 1

Reviewer 1 Report (Previous Reviewer 1)

Comments and Suggestions for Authors

The main objective of the study was to “to develop a method for estimating herbaceous biomass availability based on height and/or vegetation coverage as input data”. This is outside my main research focus, but a quick search of literature suggests that it has relevance. For example, Chieppa et al. (2020) reported cover was a useful surrogate to estimate biomass for several plant species, while the addition of height improved model fits in some circumstances. Chieppa et al. also discusses several related studies in Introduction and Chieppa et al. is cited by several related studies (e.g., Nafus et al., 2009; Monzingo et al., 2022). Seems some comparisons and discussions of these and related studies should be added. Overall, the manuscript seemed reasonably well written and could be a useful resource to assess ecosystem functioning, etc.

 

Chieppa et al. 2020. Allometric Estimates of Aboveground Biomass Using Cover and Height Are Improved by Increasing Specificity of Plant Functional Groups in Eastern Australian Rangelands. Rangeland Ecology and Management, 73(3). https://doi.org/10.1016/j.rama.2020.01.009

 

Monzingo et al. 2022. Factors influencing predictions of understory vegetation biomass from visual cover estimates. Wildlife Society Bulletin 46(3)

 

Nafus et al. 2009. Multispecies allometric models predict grass biomass in semidesert rangeland. Rangeland Ecology & Management 62(1)

 

Author Response

Review 1

The main objective of the study was to “to develop a method for estimating herbaceous biomass availability based on height and/or vegetation coverage as input data”. This is outside my main research focus, but a quick search of literature suggests that it has relevance. For example, Chieppa et al. (2020) reported cover was a useful surrogate to estimate biomass for several plant species, while the addition of height improved model fits in some circumstances. Chieppa et al. also discusses several related studies in Introduction and Chieppa et al. is cited by several related studies (e.g., Nafus et al., 2009; Monzingo et al., 2022). Seems some comparisons and discussions of these and related studies should be added. Overall, the manuscript seemed reasonably well written and could be a useful resource to assess ecosystem functioning, etc.

Chieppa et al. 2020. Allometric Estimates of Aboveground Biomass Using Cover and Height Are Improved by Increasing Specificity of Plant Functional Groups in Eastern Australian Rangelands. Rangeland Ecology and Management, 73(3). https://doi.org/10.1016/j.rama.2020.01.009

Reply: the document is used in the discussion section

Monzingo et al. 2022. Factors influencing predictions of understory vegetation biomass from visual cover estimates. Wildlife Society Bulletin 46(3)

Reply: I think that this document can be skipped because the subject is a bit different

Nafus et al. 2009. Multispecies allometric models predict grass biomass in semidesert rangeland. Rangeland Ecology & Management 62(1)

Reviewer 2 Report (Previous Reviewer 2)

Comments and Suggestions for Authors

The topic of the article has potential scientific value and practical application prospect. Indirect estimation of herbaceous biomass by non-destructive methods in semi-arid regions is of great significance for forage resource management and animal husbandry. Different from traditional measurement methods, this paper develops a non-destructive measurement method based on the height and coverage of herbaceous layer, which saves time and is more convenient. The data covered 2017-2019 and were compared in the fenced area and the livestock area. The method of data collection and analysis was relatively scientific, which ensured the reliability of the results. The prediction ability of the model was verified by several indexes, indicating that plant height * FVC = volume index was more accurate in predicting herbaceous biomass. The findings could help better plan and manage forage resources.

However, the paper is not deep enough in the selection of methods and the discussion of results. It is suggested to add discussion of similar studies to highlight the innovation and contribution of this study. It is pointed out that the prediction effect of the model is significantly different between the grazing area and the fenced area, and the dynamic change of the biomass of the herbaceous layer is affected by grazing activities, which leads to the low prediction accuracy of the model in the grazing area. This indicates that the model has limited applicability under different management conditions and needs to be calibrated according to specific environmental conditions. In addition, despite the high coefficient of determination (R²) of the model, in some cases (especially at high biomass), the model's predictions deviate from the actual measurements. The model based on height has a large error in predicting high biomass, while the model based on volume index is relatively more accurate, but it still has some limitations in predicting ability under extreme conditions.

The model mainly relied on dry weight, plant height and cover to make predictions, but did not take into account other important factors that may affect herbaceous biomass, such as rainfall and soil type. These factors may have significant effects on biomass dynamics in different years and locations, and ignoring these factors may reduce the applicability of the model in different time and space conditions. In addition, the data sources and application scope of the model are limited, and its application in other climate zones or ecosystems has not been verified. The application in grazing areas also has limitations. In areas with high grazing pressure, plants tend to grow short and dispersed, which is different from the plant growth pattern in non-grazing areas, thus affecting the prediction accuracy of the model. These limitations may affect the universality and predictive power of the model. It is suggested that future studies expand the dataset, include more influencing factors, and validate the model under different environmental conditions.

In terms of diagrams, the presentation of Figures 2 and 5 can be further optimized. Figure 2 recommends using thicker lines and enhanced color contrast to improve clarity, while using different colors to represent the relationship between height, coverage and volume index, and ensuring that the axes are clearly labeled and include the necessary units, such as "herbaceous biomass (kg DM/ha)", "vegetation height (cm)", "coverage (%)". In addition, the legend should clearly identify the meaning of each regression line or data point, and explain outliers and key data points. The timeline in Figure 5 can be labeled with a finer time scale (such as by month), distinguished between measured and predicted values by using different line styles, and confidence intervals or standard deviations can be added to visualize the uncertainty of the model's predictions. If data from grazing areas are available, they can be compared with fenced area data to better show how models differ under different management conditions. In addition, important time points (such as the beginning and end of the rainy season) can also be marked in the graph.

In Conclusion and Discussion section, it is recommended to summarize the main findings of the research, emphasizing the uniqueness of the research method and its advantages in estimating herbaceous biomass. At the same time, the potential of research results in practical applications should be discussed in more detail. Although the limitations of the model were mentioned in the discussion, such as the impact of different pasture conditions on model predictions, it is possible to explore in more detail the specific effects of these limitations on prediction results and how to improve these deficiencies in the future. Should be paid attention to the impact of external factors such as rainfall and soil types the sections.

By improving the above issues, the scientific and practical aspects of the article will be further enhanced.

Comments on the Quality of English Language

The language is clear and easy to understand, making the manuscript easy to comprehend. The manuscript has no significant grammar or spelling errors, thus improving overall readability. Variable sentence structures contribute to the fluency of manuscripts. Overall, the quality of the English is very good, only minor modifications are needed.

Author Response

Review 2

1. The topic of the article has potential scientific value and practical application prospect. Indirect estimation of herbaceous biomass by non-destructive methods in semi-arid regions is of great significance for forage resource management and animal husbandry. Different from traditional measurement methods, this paper develops a non-destructive measurement method based on the height and coverage of herbaceous layer, which saves time and is more convenient. The data covered 2017-2019 and were compared in the fenced area and the livestock area. The method of data collection and analysis was relatively scientific, which ensured the reliability of the results. The prediction ability of the model was verified by several indexes, indicating that plant height * FVC = volume index was more accurate in predicting herbaceous biomass. The findings could help better plan and manage forage resources.

 

2. However, the paper is not deep enough in the selection of methods and the discussion of results. It is suggested to add discussion of similar studies to highlight the innovation and contribution of this study. It is pointed out that the prediction effect of the model is significantly different between the grazing area and the fenced area, and the dynamic change of the biomass of the herbaceous layer is affected by grazing activities, which leads to the low prediction accuracy of the model in the grazing area. This indicates that the model has limited applicability under different management conditions and needs to be calibrated according to specific environmental conditions. In addition, despite the high coefficient of determination (R²) of the model, in some cases (especially at high biomass), the model's predictions deviate from the actual measurements. The model based on height has a large error in predicting high biomass, while the model based on volume index is relatively more accurate, but it still has some limitations in predicting ability under extreme conditions.

 

Reply: the discussion section has been improved.

Since the model allows biomass prediction with a high coefficient of correlation between measured and predicted biomass in the grazed and ungrazed areas, I do not consider the difference in prediction accuracy as a limit for the model. The prediction is possible in both cases with a quite good coefficient of correlation.

 

3. The model mainly relied on dry weight, plant height and cover to make predictions, but did not take into account other important factors that may affect herbaceous biomass, such as rainfall and soil type. These factors may have significant effects on biomass dynamics in different years and locations, and ignoring these factors may reduce the applicability of the model in different time and space conditions. In addition, the data sources and application scope of the model are limited, and its application in other climate zones or ecosystems has not been verified. The application in grazing areas also has limitations. In areas with high grazing pressure, plants tend to grow short and dispersed, which is different from the plant growth pattern in non-grazing areas, thus affecting the prediction accuracy of the model. These limitations may affect the universality and predictive power of the model. It is suggested that future studies expand the dataset, include more influencing factors, and validate the model under different environmental conditions.

 

Reply: of course, several factors may affect the herbaceous biomass but are not taken into account in this study. But, what we are trying to do is to find a simple way to estimate the germinated herbaceous biomass amount at a precise date during the growth period. It means, that once the plants have germinated, what physical parameter could be simply measured to estimate the biomass amount through a simple calculation? For example, even if the soil type is important in biomass production, it seems to us easier to measure the coverage and the plant height than the soil characteristics. Also, performing the model in a grazed and a fenced side allow to see how the model behaves in these situations. On the other hand, this model was already tested in a temperate area. So one of our objectives is to test it in a different climate region. It is a way to make the method more universal.

 

We provided a few additions in the introduction, discussion, and conclusion parts.

 

We strongly agree with you on extending the data for future studies.

 

4. In terms of diagrams, the presentation of Figures 2 and 5 can be further optimized. Figure 2 recommends using thicker lines and enhanced color contrast to improve clarity, while using different colors to represent the relationship between height, coverage and volume index, and ensuring that the axes are clearly labeled and include the necessary units, such as "herbaceous biomass (kg DM/ha)", "vegetation height (cm)", "coverage (%)". In addition, the legend should clearly identify the meaning of each regression line or data point, and explain outliers and key data points. The timeline in Figure 5 can be labeled with a finer time scale (such as by month), distinguished between measured and predicted values by using different line styles, and confidence intervals or standard deviations can be added to visualize the uncertainty of the model's predictions. If data from grazing areas are available, they can be compared with fenced area data to better show how models differ under different management conditions. In addition, important time points (such as the beginning and end of the rainy season) can also be marked in the graph.

 

Reply: the way we want figures to be arranged suits better with the chosen axis’s labels. If we add more details like “herbaceous biomass (kg DM.ha-1) the labels text will be difficult to read.

 

5. In Conclusion and Discussion section, it is recommended to summarize the main findings of the research, emphasizing the uniqueness of the research method and its advantages in estimating herbaceous biomass. At the same time, the potential of research results in practical applications should be discussed in more detail. Although the limitations of the model were mentioned in the discussion, such as the impact of different pasture conditions on model predictions, it is possible to explore in more detail the specific effects of these limitations on prediction results and how to improve these deficiencies in the future. Should be paid attention to the impact of external factors such as rainfall and soil types the sections.

 

Reply: some modifications have been added to the conclusion section to better show the innovative nature of the method and its advantages

 

6. Comments on the Quality of English Language
7. The language is clear and easy to understand, making the manuscript easy to comprehend. The manuscript has no significant grammar or spelling errors, thus improving overall readability. Variable sentence structures contribute to the fluency of manuscripts. Overall, the quality of the English is very good, only minor modifications are needed.

 

Reply: some changes have been made overall to improve again the writing

Author Response File: Author Response.pdf

Reviewer 3 Report (Previous Reviewer 3)

Comments and Suggestions for Authors

Please see the attachment.

Comments for author File: Comments.zip

Comments on the Quality of English Language

Moderate editing of English language required.

Some grammatical mistake and format error were detected.

Author Response

Review 3

It is important to monitor herbaceous biomass without destroying it, and it is very meaningful to predict biomass with non-destructive parameters such as height and vegetation coverage as well as volume index that used in this manuscript. However, the serious defect is that the vegetation coverage was visually estimated instead of accurate measurement. In other words, the prediction results in this manuscript with visually estimated coverage is good, how about the results with the coverage that estimated by another person? The accuracy of visual estimation is difficult to confirm. Regardless of remote sensing techniques, vegetation coverage can be observed with instruments. The specific comments are as below:

 

1. “The vegetation coverage was visually estimated as the percentage of ground covered by the herbaceous plants”, are you sure the vegetation coverage was visually estimated? and then the visually estimated index was used to predict the biomass? How do you quantify the accuracy?

 

Reply: various studies have tested different ways to estimate accurately the plant coverage. We based on the following statement: “The visual estimation of the ground coverage is generally well correlated with that obtained based on photos (correlation coefficient between visual estimates and photos equal to 0.94) and can therefore be used in the field (Büchi et al., 2016)”

 

2. Page 3 Line 107-108, “obtained after drying the harvested fresh biomass in an oven”, please add the drying temperature of the oven and the dry time duration. It is only the above ground biomass, right?

 

Reply: the temperature and the time duration are added. “aboveground” is added to the title for more precisions

 

3. The quality of Figure 2 is a little poor, please provide the original picture with high DPI. The same problem for Figure 3, 5 and 6.

 

Reply: the image resolution was changed to the highest resolution in the entire document

 

4. Line143 “Comparison of the models between grazed and fenced sites”, What kind of models used here, was it introduced in Methods and Materials? Ws it the line regression in Figure 2?

 

Reply: more details have been added in the Data analysis section

 

5. Line 152, grammatical mistake, it should be “ closer to measured ones” or “ measured biomass”.

 

Reply: done

 

6. In addition to the simple research outlook, the main results of this manuscript should be listed .n conclusion part. For example, “Various factors are involved in biomass production “, this view point should be discussed in discussion part instead of listing in Conclusion.

 

 

7. Some format error occurred, eg Line 91 and line 4.

 

Reply: done with Line 91, but I don’t see any format error in Line 4

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report (Previous Reviewer 1)

Comments and Suggestions for Authors

No new comments. Authors have adequately addressed comments.

Author Response

We want to thanks the reviewers

Reviewer 2 Report (Previous Reviewer 2)

Comments and Suggestions for Authors

The paper has been carefully modified and could be accepted.

Author Response

We want to thank the review

Reviewer 3 Report (Previous Reviewer 3)

Comments and Suggestions for Authors

Dear authors,

The revised manuscript shows significant improvement, I appreciate the effort that has gone into this work, while, a major concern remains regarding the use of visual estimation to assess vegetation coverage. While a study published in 2016 indicated a fitting quality of 0.94 for coverage obtained by photos and visually estimated ones, I still believe that for parameters used in subsequent yield calculations, precise measurements are essential. Relying on visual estimation may introduce errors that could accumulate and compromise the scientific rigor of the yield predictions. I recommend considering more accurate methods for assessing vegetation cover to enhance the reliability of the results and their implications for yield estimation. Actually, whether the vegetation coverage was visually obtained by photo method, they themselves are also low in accuracy. Instruments for measuring vegetation cover are widely available now, it would be a good choice. I am so sorry for this.

Comments on the Quality of English Language

 Minor editing of English language required.

Author Response

Dear reviewer,

Indeed, cover by photography  could be more accurate. However , visual estimation of cover is done in many studies and as we state some study compare both method and find similar results.

We add one sentence in the discussion “The cover is visually estimate. One improvement could be to used photography but it requires more equipment and software skills”

Indeed , this method is developed in test in underdeveloped countries with low access to equipment and  the goal is to develop a quick method that can be used  without any equipment’s and low computer skills and knowledge. We did not have any photograph for these 3 of data  and only visual cover.

Sincerely the authors

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 Authors

The main objective of the study was to “develop a non-destructive method for estimating herbaceous biomass for the Sahelian rangelands based on measurements of its height and coverage”. This may have value to researchers, but many related studies have been done using satellite or aerial or picture images. The only closely related studies reported however, are a thesis and a study in a journal not indexed Web of Science or other leading indexing services and not in English (Buchi et al., 2016; Diatta, 2021). Before further reviewing this current study, background on how it relates to other important studies and how the results compare to things like NDVI or similar studies may be needed. Perhaps a comparison to NDVI would be useful??

Büchi, L., Mouly, P., Amossé, C., Bally, C., Wendling, M., & Charles, R. (2016). Méthode non destructive d’estimation de la biomasse de couverts végétaux. Recherche Agronomique Suisse, 7(3), 136–143.

Diatta, O. (2021). Dynamique saisonnière et interannuelle de la strate herbacée des parcours sahéliens du Sénégal (crz-dahra, nord- Sénégal). Thèse de doctorat, Université Cheikh Anta Diop, 105 p.

Reviewer 2 Report

Comments and Suggestions for Authors

Line 29: “height” is improper

Line 47-56: add corresponding introductions and examples;

Line 60: describe in detail the purpose of your research;

Line 75: inspection units like m², ha^-1

Line 115-117: Please check carefully;

Line 131-132: “Figure”, “Table” abbreviation or not?

Line 232-249: Please check carefully;

Line 177: Please check the reasonableness of the content of your discussion;

Line 129: Please embellish your all diagrams and tables

Line 255: Please check carefully your references

Please double-check the formatting of the full article and consider enriching the content of your article, including basic introductions to concepts, etc.

It is recommended that you revise the content carefully, otherwise your results will hardly convince the reader

Comments for author File: Comments.pdf

Comments on the Quality of English Language

no

Reviewer 3 Report

Comments and Suggestions for Authors

Please see the attachment.

Comments for author File: Comments.zip

Comments on the Quality of English Language

Moderate editing of English language required