Comprehensive Evaluation and Screening of Autumn-Sown Oat (Avena sativa L.) Germplasm in Different Agropastoral Regions

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Zhang et al screened nine autumn-sown oat genotypes across 6-site-years for their suitability as alternative dual-purpose crop to spring sown oats. The manuscript is well organized, with clear messages and easy to understand style. It fits well within the scope of Agronomy journal and will appeal to agronomists, and growers seeking scalable, low-risk innovations. However, while the scope is commendable, the manuscript falls short in its scientific framing, analytical depth, and editorial quality. A revision is required to improve clarity, rigor, and interpretive quality.

Abstract:

1. The context of the knowledge gaps the study is intended to address is not clearly defined in the first few sentences, rather just emphasizing the importance of oats and their role in animal husbandry, which could apply to any region or study. Systematic evaluation of oat germplasm does not address any scientific problem expect the evaluation is aimed at identifying alternative genotype to those currently in used. Suggest explicitly articulating the specific knowledge gap this study addresses.
2. The proposition that autumn sown oats have longer growth cycle than spring-sown oats in L17-18 is not evaluated in the current manuscript as no phenological measurement was reported. Suggest rephrasing this to indicate the single time measurement of dry matter and quality parameters.
3. L22: …parameters during the filling stage.” What filling? Do you mean grain filling ? or if referencing to growth scale e.g. (Zadok), provide the Zadok stage number.

Introduction

1. I do not think this study achieve objective one ‘comprehensively assess the adaptability of different oat varieties/lines”. Adaptability is about the ability of a crop variety/genotype to respond positively to changes in production conditions with the values of relative adaptability often determine by the regression of the trait (yield) of the tested crop over the average yield of compared crops from several environments. Rather, this study only evaluated the genotypes for their suitability to the 4-locations and 2-years environments. As the full GxE was not explored or analysed, I will recommend rephrasing or removing this objective. This applies to objective two, there was no stability analysis and therefore cannot infer which genotype was the most stable.

Materials & methods

1. The four locations have the same soil types? No description of soil characteristics at any of the sites.
2. L86-88 …. Agricultural University based on previous screening efforts in autumn-sown areas of China (provide reference)
3. What does DEM in legend of Figure 1b represent? Figures should be standalone with clear caption
4. L111...Throughout the trial, other field management practices adhered to local protocols. What are these? Provide reference or outline what was specifically done.
5. L115.” What filling? Do you mean grain filling ? or if referencing to growth scale e.g. (Zadok), provide the Zadok stage number.
6. L118…a 9.60 m2 area of mature forage from the central region… This contradicts above statement of at “filling” stage. What is a mature forage?
7. There is no justification for choice of one-way ANOVA. You have multi-site-years experiments but choose to do one-way ANOVA and then in the result section, e.g. Tables 1-4, you are reporting main and interaction effects of genotype, location, and year. This is completely wrong.
8. The SEM analysis is under-explained, with no mention of residuals, model fit beyond CFI, or assumptions. While use of SEM is commendable, the path model equations and assumptions are not clearly described for a general audience. Consider a brief explanation of model rationale.

Results:

This section is hard to follow. Provide a more balanced comparison. Consider including stability analysis (e.g., AMMI or GGE biplot) to demonstrate  specific performance across environments with less bias.

Discussion:

The Discussion mostly repeats results. It should be more interpretive. Other genotypes are barely discussed beyond comparisons with WC109. There is no discussion of trade-offs, risks, or limitations associated with WC109 — e.g., pest resistance, seed availability, or adaptability under extreme conditions.

Language

The manuscript contains numerous grammatical and stylistic errors. Examples include:

1. "The The experiment was conducted..." L104
2. "...livestock facing the predicament of 'full in summer, fat in autumn, thin in winter and dead in spring'" – overly colloquial. L59

Author Response

We appreciate you very much for the constructive comments and for highlighting the contributions of our manuscript. Each suggested revision and comment was accurately incorporated and considered. We have now revised the manuscript according to these valuable suggestions and provide our point-by-point responses. We have made every effort to ensure that our revisions and responses address all of the remaining concerns.

Comments to the Author:

Zhang et al screened nine autumn-sown oat genotypes across 6-site-years for their suitability as alternative dual-purpose crop to spring-sown oats. The manuscript is well organized, with clear messages and easy to understand style. It fits well within the scope of Agronomy journal and will appeal to agronomists, and growers seeking scalable, low-risk innovations. However, while the scope is commendable, the manuscript falls short in its scientific framing, analytical depth, and editorial quality. A revision is required to improve clarity, rigor, and interpretive quality

RESPONSE: We are deeply grateful to these positive and constructive remarks about our study. Specific revisions and responses to each comment are provided in detail below.

 

Abstract

1. The context of the knowledge gaps the study is intended to address is not clearly defined in the first few sentences, rather just emphasizing the importance of oats and their role in animal husbandry, which could apply to any region or study. Systematic evaluation of oat germplasm does not address any scientific problem expect the evaluation is aimed at identifying alternative genotypes to those currently in used. Suggest explicitly articulating the specific knowledge gap this study addresses.

RESPONSE: Thanks for raising this point, as we realized that our presentation was not sufficiently clear. As suggested, we have checked the manuscript carefully and made improvements on the places that might have not been well presented. Please kindly review all revisions from the attached manuscript text file “marked-up version”. Specific revisions and changes we have made according to your comments are as follows [L16-20]:

In light of current global challenges of climate change, over exploitation of natural resources, and increasing demand for livestock products, the exploration of excellent forage crop resources holds great potential for development. Therefore, selecting forage crops that are high-yield, high-quality, and have excellent resistance to pests and diseases can greatly promote the development of the livestock industry.

 

2. The proposition that autumn sown oats have a longer growth cycle than spring-sown oats in L17-18 is not evaluated in the current manuscript, as no phenological measurement was reported. Suggest rephrasing this to indicate the single time measurement of dry matter and quality parameters.

RESPONSE: Thank you for making this valuable suggestion. We agree with the reviewers and add the growth period of autumn-sown oats to the manuscript (Table 1). At the same time, we also mentioned in the literature review section that spring-sown oats have a shorter growth period [L59-62]:

In China southwestern region, the conventional agricultural practice involves sowing in late spring and harvesting in early autumn during the frost-free period (90-125 d), with subsequent fallow periods in late autumn and winter ^[11].

Table 1. Growth periods of nine oat genotypes in four locations (WJ, CZ, GY and XC) from 2022-2024.

Genotypes	Growth stage (d)
	2023WJ	2023CZ	2023GY	2023XC	2024WJ	2024CZ	2024GY	2024XC
Longyan 3	178	172	180	166	180	176	185	162
Intimidator	172	170	183	167	176	172	183	170
WC109	160	156	171	154	166	167	176	161
WC130	176	170	182	162	175	171	179	167
WC179	170	169	180	161	174	166	181	169
WC283	164	161	179	159	169	161	173	158
WC286	176	173	188	170	177	170	182	167
WC291	169	164	174	161	171	164	177	166
WC299	166	159	169	154	170	162	172	157

 

3. L22: …parameters during the filling stage.” What filling? Do you mean grain filling ? or if referencing to growth scale e.g. (Zadok), provide the Zadok stage number.

RESPONSE: Sorry for the unclear statement on this point. We have now explicated the filling stage in the revised manuscript [L24-27]:

We conducted two growing seasons (2022-2024) field experiment across four locations to evaluate nine oat genotypes for growth phenotypes, forage productivity and nutritional quality through 11 agronomic traits and nutritional parameters during the filling stage (Zadok’s 75).

 

Introduction

1. I do not think this study achieve objective one ‘comprehensively assess the adaptability of different oat varieties/lines”. Adaptability is about the ability of a crop variety/genotype to respond positively to changes in production conditions with the values of relative adaptability often determine by the regression of the trait (yield) of the tested crop over the average yield of compared crops from several environments. Rather, this study only evaluated the genotypes for their suitability to the 4-locations and 2-years environments. As the full GxE was not explored or analysed, I will recommend rephrasing or removing this objective. This applies to objective two, there was no stability analysis and therefore cannot infer which genotype was the most stable.

RESPONSE: We appreciate your constructive feedback regarding the clarity and accuracy of our objectives. You are correct that the full GxE interaction was not fully explored in our study, and we acknowledge the importance of accurately representing our research scope. Therefore, we've revised the results section to better reflect our goals. Specifically, we added an assessment of high yield and stable yield to make the results we screened more representative.

 

Materials and methods

1. The four locations have the same soil types? No description of soil characteristics at any of the sites.

RESPONSE: We apologize for not being able to explain it clearly. We have supplemented soil nutrient data (Table 2) for each setting [L116-119]:

Before sowing, soil samples from 0-20 cm were collected using a five-point sampling method (Table 2). The determination of soil nutrients was conducted in accordance with the methods of soil agrochemical analysis (https://www.docin.com/p-2219784431.html).

Table 2. Determination of soil nutrient content in 0-20 cm before sowing in four locations (WJ, CZ, GY and XC) during the 2022-2024 growing season.

Soil Nutrient	2023WJ	2023CZ	2023GY	2023XC	2024WJ	2024CZ	2024GY	2024XC
Origin matter (g/kg)	32.31	28.54	29.20	28.40	32.49	28.62	29.31	28.44
Total nitrogen (g/kg)	1.56	1.48	1.49	1.45	1.62	1.48	1.54	1.46
Available nitrogen (mg/kg)	105.88	100.29	98.28	97.86	106.03	100.30	98.33	97.93
Available phosphorus (mg/kg)	15.53	12.24	12.01	11.46	15.57	12.31	12.03	11.55
Available potassium (mg/kg)	148.32	150.26	119.37	115.53	148.33	150.34	119.42	115.55
pH	6.48	7.12	6.84	7.43	6.46	7.14	6.83	7.45

 

2. L86-88 …. Agricultural University based on previous screening efforts in autumn-sown areas of China (provide reference)

RESPONSE: We thank you for pointing this out. Relevant supporting literature has been added to the new manuscript [L98-101]:

The remaining seven lines ('WC109', 'WC130', 'WC179', 'WC283', 'WC286', 'WC291', 'WC299') were selected from Sichuan Agricultural University based on previous screening efforts in autumn-sown areas of China [23].

3. What does DEM in legend of Figure 1b represent? Figures should be standalone with clear caption

RESPONSE: Sorry for the unclear description of the SEM. In the revised manuscript, we have already provided relevant explanations for DEM [L123-124]:

Digital Elevation Model (DEM) is a data set that digitally represents the terrain of the Earth's surface and records the elevation values at each location.

4. ..Throughout the trial, other field management practices adhered to local protocols. What are these? Provide reference or outline what was specifically done.

RESPONSE: We apologize for not being able to explain it clearly. We have already added to that section [L135-138]:

During the experimental period, other field management practices adhered to local protocols. No additional fertilization or irrigation was applied. All test plots were kept weed-free by manual hoeing. Chemicals are used to control pests and diseases when necessary.

5. ” What filling? Do you mean grain filling ? or if referencing to growth scale e.g. (Zadok), provide the Zadok stage number.

RESPONSE: Sorry for the unclear statement on this point. We have now explicated the filling stage in the revised manuscript [L141]:

All agronomic characteristics were assessed at the filling stage (Zadok’s 75) ^[24].

6. L118…a 9.60 m² area of mature forage from the central region… This contradicts above statement of at “filling” stage. What is a mature forage?

RESPONSE: Thanks for raising this point as we realized that our presentation was not sufficiently clear. The mature forage we want to express is the forage that we mow during the filling stage (Zadok’s 75).We've made changes as follows [L148-149]:

The yield was determined by manually harvesting a 9.60 m² area of forage (Zadok’s 75) from the central region of each 15 m² plot.

7. There is no justification for choice of one-way ANOVA. You have multi-site-years experiments but choose to do one-way ANOVA and then in the result section, e.g. Tables 1-4, you are reporting main and interaction effects of genotype, location, and year. This is completely wrong.

RESPONSE: Sorry for the unclear description on how to deal with the statistical methods when interaction analysis of genotype, environment, and year. In the data analysis, we performed a one-way ANOVA to see the differences in each trait of the nine materials in the same place, and the interaction of the three factors was to show the degree to which each trait was affected by it.

We have now revised description about the statistical methods to avoid misunderstanding [L174-175]:

All data was subjected to one-way and three-way (genotype × year × location) analysis of variance (ANOVA) and presented as mean ± standard deviation.

8. The SEM analysis is under-explained, with no mention of residuals, model fit beyond CFI, or assumptions. While use of SEM is commendable, the path model equations and assumptions are not clearly described for a general audience. Consider a brief explanation of model rationale

RESPONSE: We apologize for not being able to explain it clearly. In our resubmitted manuscript, SEM has been explained [L181-185]:

Subsequently, we simplified the model by eliminating non-significant paths and selecting the model with a chi-square to degrees of freedom ratio of less than 2.0, root mean square error of approximation of less than 0.05, comparative fit index of greater than 0.9, and a P-value of greater than 0.05.

 

Results

This section is hard to follow. Provide a more balanced comparison. Consider including stability analysis (e.g., AMMI or GGE biplot) to demonstrate specific performance across environments with less bias.

RESPONSE: We appreciate your constructive feedback regarding the clarity and accuracy of our results. In the newly submitted manuscript, we used the AMMI model to analyze the stable yield of each genotype, refining our results [L329-345]:

3.7. The AMMI model was employed to evaluate the yield stability and high-yield potential of the tested genotypes

In this study, the AMMI biplot was constructed with average yield on the x-axis and IPCA1 (interaction principal component axis 1) on the y-axis (Figure 9). A horizontal line was drawn at IPCA1 = 0, and a vertical line was drawn at the mean yield of all genotypes. Horizontally, genotypes with higher x-axis values have higher yields. Vertically, genotypes closer to the horizontal line have more stable yields. Line 'WC109' had a significantly higher yield than the control, while the yield difference between 'WC283' and the control was not significant. The other five genotypes had yields lower than the control. The genotypes with better yield stability than the control were 'WC286', 'WC179', 'WC130', 'WC283', and 'WC109'. Therefore, 'WC109' was identified as the high-yielding and stable genotype.

In the horizontal direction, the locations are more dispersed than the genotypes, indicating that the variation among locations is much greater than that among genotypes. Genotypes located above or below the horizontal line have positive interactions with the locations on the same side, meaning that the four high-yielding materials ('WC283', 'WC109','Longyan 3', and 'Intimidator') have the best adaptability in WJ.

And please see "Figure 9. Biplot of AMMI model." in our revised manuscript.

 

Discussion

The Discussion mostly repeats results. It should be more interpretive. Other genotypes are barely discussed beyond comparisons with WC109. There is no discussion of trade-offs, risks, or limitations associated with WC109 — e.g., pest resistance, seed availability, or adaptability under extreme conditions.

RESPONSE: We thank you for pointing this out. The corresponding modifications have been made in the discussion section ( [L357-359], [L376-379],[L419-424] ):

PH and TN are critical determinants of forage yield, constituting key breeding targets for plant breeders [31]. The present study observed a range of 112.3 to 160.8 cm for PH and 4.7 to 6.5 for TN across nine genotypes. Additionally, our research found significant differences in PH among the same genotypes under different environments and among different genotypes under the same environment, indicating that PH is influenced by both. Our research findings are consistent with those of Dinkale [32] and Tulu [33], who both reported that the agronomic and nutritional values of the same oat variety vary across different locations. Notably, compared with the 2022-2023 growing season, all nine oat lines showed higher PH and TN in 2023-2024, which in turn increased yields. This may be due to an increase in soil nutrient content.

In this study, the SLR of the nine oat genotypes ranged from 66.60% to 77.90%, with 'WC179' having the lowest ratio and 'WC299' the highest. Line 'WC299' exhibited the lowest plant height and fewer tillers but had a higher stem diameter, a plant architecture that confers better resistance to lodging, thereby enabling it to maintain a favorable growth status across various environments. The variation in SLR under different environments is quite large, indicating that it is affected by soil fertility and temperature conditions.

Furthermore, climate conditions exhibit regional diversity, substantially influencing the selection of oat genotypes. Temperature, precipitation, and photoperiod are key climatic factors that directly effects the growth cycle, biomass accumulation, and yield of oats [48-50]. In this study, the nine oat genotypes did not show any susceptibility to pests and diseases across different environments, indicating their high resistance. Moreover, the yield variation of the same genotype was relatively small across different years in the same environment, demonstrating their stability in production.

 

Language

The manuscript contains numerous grammatical and stylistic errors. Examples include:

1. The The experiment was conducted..." L104

RESPONSE: Thanks for your careful checks. In our newly submitted manuscript, revisions have been made [L128-129].

The experiment was conducted using a completely randomized block design with four replications.

 

2. "...livestock facing the predicament of 'full in summer, fat in autumn, thin in winter and dead in spring'" – overly colloquial. L59

RESPONSE: We are deeply thankful to the reviewer for raising this point as we realized that our description requires adjustment [L63-66]:

In this region, the seasonal imbalance of forage supply - especially the feed shortage during the winter and spring seasons (from November to April of the following year) - has become a key bottleneck restricting the sustainable development of the livestock industry.

Reviewer 2 Report

Comments and Suggestions for Authors

This well-written manuscript is of regional/local value. The SEM analysis is especially valuable (Figure 6).

1. Explain the market for oats for livestock in China better.
2. Give more details about oats cultivars and lines, e.g., time (in days) from sowing to milk maturity, average plant length, resistance to pests, etc.
3. Figure 1a: what is DEM?
4. How did you keep the planned seed density, depth, and row spacing if seeds were planted by hand?
5. ln 176: stem: leaf ratio; in M&M, describe how it was calculated?
6. How does your research cover all the variability in oats germplasm in China? What were the initial key characteristics of the oat lines you chose for study?

Author Response

Comments and Suggestions for Authors
This well-written manuscript is of regional/local value. The SEM analysis is especially valuable (Figure 6).

RESPONSE: We are deeply grateful to these positive and constructive remarks about our study. Specific revisions and responses to each comment are provided in detail below.

 

1. Explain the market for oats for livestock in China better.

RESPONSE: We thank reviewer for pointing this out. Combined with China's current industry policies and market demand, this issue is addressed in our new submission [L76-83]:

The demand for oats continues to rise due to their high nutritional value, good palatability and easy digestion and absorption (https://www.renrendoc.com/paper/400944282.html). Furthermore, Oat planting techniques are being continuously improved, resulting in significant increases in yield and quality. Meanwhile, oat processing technologies are also being refined, leading to a growing variety of products and enhanced market competitiveness (https://max.book118.com./html/2025/0112/623211215011024.shtm).

2. Give more details about oats cultivars and lines, e.g., time (in days) from sowing to milk maturity, average plant length, resistance to pests, etc

RESPONSE: Sorry for the unclear description of the oats cultivars and lines. Forage fertility and disease resistance are important factors to consider. We have now listed the growth periods of forage oat genotypes in eight environments in the revised manuscript, supplemented with disease resistance in the materials section as supporting data [Table 1] in the revised draft follows as [L101-103]:

Table 1. Growth periods of nine oat genotypes in four locations (WJ, CZ, GY and XC) from 2022-2024.

Genotypes	Growth stage (d)
	2023WJ	2023CZ	2023GY	2023XC	2024WJ	2024CZ	2024GY	2024XC
Longyan 3	178	172	180	166	180	176	185	162
Intimidator	172	170	183	167	176	172	183	170
WC109	160	156	171	154	166	167	176	161
WC130	176	170	182	162	175	171	179	167
WC179	170	169	180	161	174	166	181	169
WC283	164	161	179	159	169	161	173	158
WC286	176	173	188	170	177	170	182	167
WC291	169	164	174	161	171	164	177	166
WC299	166	159	169	154	170	162	172	157

 

The growth period of each genotype in different environments is shown in Table 1. Based on our initial screening, all the selected genotypes showed high disease resistance potential and good lodging resistance.

 

3. Figure 1a: What is DEM?

RESPONSE: Sorry for the unclear description of the SEM. In the revised manuscript, we have already provided relevant explanations for DEM [L123-124]:

Digital Elevation Model (DEM) is a dataset that digitally represents the terrain of the Earth's surface and records the elevation values at each location.

 

4. How did you keep the planned seed density, depth, and row spacing if seeds were planted by hand?

RESPONSE: We thank you for pointing this out. Based on the row length and spacing of each plot, we manually used a furrow opener with a depth of 4 cm to create furrows. We calculated the required seed amount per row according to the hundred-grain weight of each material, bagged the seeds separately, and then sowed them by hand.

 

5. ln 176: stem: leaf ratio; in M&M, describe how it was calculated?

RESPONSE: Thank you for making this valuable suggestion. We have provided a detailed description of it in the methods section of the revised manuscript [L143-148]:

Ten plants were randomly selected from the central part of each plot to measure PH, TN, and SLR. PH was measured using a tape measure from the base to the top of the plant. The TN was counted directly. After measuring PH and TN, the plants were cut at ground level, and their stems and leaves were separated. The dry weights of the stems and leaves were calculated after drying to obtain the SLR.

6. How does your research cover all the variability in oats germplasm in China? What were the initial key characteristics of the oat lines you chose for study?

RESPONSE: Thank you for your valuable comments. We have carefully considered your questions and provided detailed responses below:

This study focuses on the autumn-sown oat region in China and employs an ecoregional-phenotypic screening system to maximize the coverage of all variability within the autumn-sown forage oat germplasm in China. Currently, there are relatively few forage oat varieties suitable for autumn sowing in China, and autumn-sown oats are often rotated with a series of crops such as rice, maize, and potatoes, etc. Therefore, we selected materials based on growth period, strain composition of the plant, yield, nutritional components, as well as resistance to diseases, pests, and frost to develop new oat lines.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Authors have address most of comments and the MS can be accepted in its current format. My only concern is still the SEM. There was no explanation of why SEM. Jumping from descriptive and variance analysis to SEM is too much of leap given the sample size. 

Author Response

We appreciate the reviewers very much for the constructive comments and highlighting the contributions of our manuscript. Each suggested revision and comment was accurately incorporated and considered. We have now revised the manuscript according to these valuable suggestions and provide our point-by-point responses. We have made every effort to ensure that our revisions and responses address all of the remaining concerns.

 

Reviewer 1:
Comments and Suggestions for Authors

 

1. Authors have address most of comments and the MS can be accepted in its current format. My only concern is still the SEM. There was no explanation of why SEM. Jumping from descriptive and variance analysis to SEM is too much of leap given the sample size.

RESPONSE: Thank you for your valuable comments on our manuscript. We have carefully considered your feedback and made appropriate revisions. Below is our response to your concerns regarding the structural equation model (SEM):

Firstly, we chose SEM for its ability to comprehensively capture the complex relationships between variables, which is crucial for our research question. SEM allows us to evaluate both direct and indirect effects among multiple variables, offering deeper insights. Secondly, the data used for the SEM analysis were collected from eight experimental sites, nine materials, and four replicates for each material. After conducting a difference analysis, we performed a correlation analysis to explain the direct effects between variables, and then used SEM to further refine the direct and indirect effects among the variables. The results of the two analyses are consistent, which also confirms the reliability of the SEM.

To further clarify, we have added a brief explanation in the methods section, detailing our rationale for selecting SEM and its suitability for our research framework. We hope these additions will help you better understand our methodology. [L178-183]:

In order to more clearly demonstrate the direct and indirect effects among multiple variables, we conducted a structural equation model (SEM) analysis using the data collected from eight experimental sites, nine materials, and four replicates for each material. This analysis aimed to reflect how growth performance and nutritional composition influence forage yield and feeding value.

Author Response File: Author Response.pdf