Critical Nitrogen Dilution Curve for Diagnosing Nitrogen Status of Cotton and Its Implications for Nitrogen Management in Cotton–Rape Rotation System

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This article describes a fertiliser response experiment, repeated over two years. The trial, in itself, seems competently run, if inadequately described here.

However, as far as I can work out, the experimental design is inadequate to establish a critical nitrogen dilution curve. A critical nitrogen dilution curve is intended to show how the critical nitrogen concentration changes (decreases) as the crop grows and matures, so that farmers can choose a critical nitrogen concentration appropriate to the stage of the crop when they have taken samples for analysis. It requires multiple sampling dates, beginning in the vegetative stage, early enough that farmers might remediate an N deficiency with top-dressing. Also, it is normal to specify a tissue to be sampled, that is easier for farmers to sample than “whole plant including roots”.

In the methods (Line 109), it is unclear whether two or three sampling dates are used. This confusion is possibly due to a typo where, in Line 109, “bolling stage” should read “flowering stage”? Otherwise, “bolling stage, 15 days after flowering” must be interpreted as a single sampling date. It is also unclear whether the final harvest is at the boll opening stage or some time later when bolls are dried out (typically this is 5-8 weeks after boll opening). Figures 1 and 2 give no “boll opening stage”, suggesting that the final harvest (at which reproductive and vegetative parts were separately analysed) was taken at the boll opening stage. No total plant biomass or N absorption is given for this sampling time, which would allow a critical N concentration to be estimated on the same basis as the earlier samplings, although Section 2.3.1 states that whole plants were sampled at this time. In any case, earlier sampling dates would be preferable for this work to be agronomically valuable.

Further confusing the picture, the Abstract (Lines 19-20) uses different wording: “Cotton dry matter accumulation and nitrogen concentration were measured at the flowering and boll stage, peak boll stage, and boll opening stage.” Is that three or four samplings? It further suggests that the final harvest was at the boll opening stage, making yield measurements unrepresentative of actual agronomic yield at crop maturity.

Section 2.4.1 describes the calculation of critical N concentration (N_c) but fails to specify that this is done separately for each of the sampling dates. The subsequent description of the use of the critical nitrogen dilution curve model does not specify what data are used in this model. As far as I can see, only the N_c for each sampling time (two or possibly three data points) are available for this, making a non-linear curve fit dubious. The 15 data points in Figure 3a are therefore somewhat mysterious. Nothing in the text identifies what they are.

The mystery of this data is exemplified in the statement (Line 216) that “The nitrogen concentration in cotton plants showed a decreasing trend with increasing dry matter accumulation (Fig. 3a).” Clearly, in Figure 1, at any sampling date, the opposite trend is observed – and this is fundamental to the calculation of a critical N concentration. That the N concentration in a plant (at any level of N nutrition) tends to fall over time as the plant grows is a relationship unexplored by the presentation of data up to this point. That the “critical nitrogen concentration dilution curve” refers to the critical concentration, derived from all the data at each sampling time, is not explained. The data in Figure 3a remain unidentifiable to me.

The validation of this curve in Figure 3b is also confused. The comparison line should be a y=x line, not a best-fit linear regression (otherwise, there would be a calibration error, even if the predictiveness of the model was high). The % signs should be removed in all except nRMSE (which is the RMSE as a percentage of the overall mean). That the values on axes happen to represent percentage N content is immaterial – at this point, they are merely numbers. In any case, this analysis tells us no more than the statistics from the non-linear regression in Figure 3a (where R² gives the proportion of variation explained by the model, near equivalent to 1-nRMSE).

At no point is there an explicit explanation of how the 2022 data were used to validate the curve established on 2021 data, as inferred in the abstract (Line 24). No comparison is given of curves derived from 2021 data and 2022 data.

The nitrogen nutrient index (NNI) tabulated in Table 1 belies the claim that the critical N depletion curve established “effectively diagnoses nitrogen status” (line 33): in 2021, it would not have identified the N60 treatment as being N-deficient at any sampling stage, although clearly it was. The standard errors for at least the 2022 data seem unreasonably low, given the much greater relative errors in the primary data shown in Figures 1 and 2.

Below are further editorial notes:

The English language composition is excellent – I wonder if an AI translation tool was used, as occasionally a word is chosen that seems unexpected. For example, L 99 “community” (not sure what meaning was intended there); L 102 and 104 “ditch” (I would have expected “furrow” – a ditch implying a larger excavation); L 177 “population” – do you mean whole plants?; L 183-184 “nutritional” should be “vegetative”?; similarly L 205 and L 213 “nutrient” should be “vegetative”; L 233 “were” should be “are”.

Also, occasionally the imperative is used where convention demands the passive voice:
L 152: “Calculate the nitrogen nutrition index (NNI) for each treatment” should be “The nitrogen nutrition index (NNI) for each treatment was calculated”;
L 172: “Use Excel 2010 for data organization” should be “Excel 2010 was used for data organization”

Abstract: L 34: yield misspelled.

Section 2.2: There is no mention of buffer areas between plots. Were there buffers, or was the entire area of the plot, including plants adjacent to differently-fertiliised plants, harvested for the determination of yield? (Line 122 says “complete harvest” implying up to the boundary of the plot, unless a buffer zone has been previously specified.) Also, were the same treatments applied to the same plots in consecutive years, or was there a different experimental area in each year?

L 166-167: “n” and “0” should be subscript, as given in L 169-170. Calculation of NI (the nitrogen absorption rate) is not described.

L 190: “The results of Figure 2” should begin a new paragraph, below Figure 1.

L 201-202: The line break should be removed, since the following paragraph is clearly part of the caption for Figure 1. In both Figure 1 and Figure 2, it would be better to use the actual N application rates on the x-axis, as in Figure 5, rather than the treatment label N0, N60 etc. Then, the explanation of those labels can be removed from captions and the charts become more self-explanatory.

L 208: Figure 2 caption incorrectly states “Cotton biomass” when the figure depicts nitrogen absorption.

L 246: “This demonstrated that 246 NNI could be used to predict cotton yield.” Given the large difference in yield between the two years of the trial, this seems like an overly bold statement.

In Figure 4, for an R² to be meaningful, the data from each plot should be plotted, not treatment means.

L 260: “The results in Table 2 showed that within the N0 to N180 range, seed cotton yield and boll density gradually increased with higher nitrogen (N) application rates.” The table shows no such increase in boll density in 2021.Line 263 and 727 “bolls per plant” are not in the table.

L 290: “These findings demonstrated that excessive nitrogen application (>240 kg ha⁻¹) diminishes the nitrogen-enhancing potential of straw incorporation.” How can the data say anything about the nitrogen-enhancing potential of straw incorporation, when no treatment compared with and without straw application?

 

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript presents objectives that aim to solve a practical problem in the Jiangxi region, China, cotton production. The aim is to verify the critical concentration of nitrogen and its effects on the development and productivity of cotton, through doses of N (urea) after incorporation of straw rich in N, P and K. It also aims to verify the efficiency of fertilization and reduce costs with nitrogen fertilization. The experiment was carried out in two agricultural years.

The Introduction item clearly presents the problem and proposes a very well-founded solution.

The objectives are three, well-defined, clear and concise.

The methodology was properly described and complete to achieve the proposed objectives.

No Results item, small questions were presented, easily solvable.

The Discussion item is well done and the connections improved in the results and discussion.

Small considerations were presented in the text of the manuscript.

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear All,

The submitted manuscript presents a two-year field study aimed at developing and validating a critical nitrogen concentration (CNC) dilution curve for cotton grown under straw incorporation in a cotton–rape rotation system. It explores the diagnostic capability of this model through nitrogen nutrition index (NNI) assessments and examines the impact of nitrogen (N) rates on biomass production, yield components, and nitrogen use efficiency (NUE).

The study addresses a highly relevant issue, precision N management in dual cropping systems, and applies an analytical framework increasingly employed in agronomy. The methodology is mostly appropriate and supported by a large dataset. The CNC dilution model achieved strong predictive power, and the conclusion that 120 kg N ha-1 is optimal is both practically and scientifically valuable. However, the manuscript contains issues that weaken its clarity and impact, including insufficient mechanistic rationale for key experimental choices (e.g., N rates), vague hypothesis definition, and lack of soil health data that would strengthen ecological interpretations.

Strong Points

1. Robust experimental design: The two-year, multi-treatment field experiment is well-replicated and conducted under realistic agronomic conditions. The nitrogen gradient is appropriate for field-scale analysis.

2. Applied relevance: The manuscript addresses an urgent practical issue in cotton–rape rotation systems, optimizing nitrogen management under straw incorporation, which is highly relevant to sustainable agriculture in the Yangtze River Basin.

3. CNC model development and validation: The critical N dilution curve is methodologically well-constructed and statistically validated using independent-year data, strengthening its applicability.

4. Integration with NNI and NUE: The study effectively combines CNC-based diagnosis with yield and N-use efficiency outcomes, offering a multidimensional framework for N recommendation.

5. Clear data presentation: Tables and figures are informative, well-organized, and mostly self-explanatory, aiding result interpretation.

6. Strong empirical evidence: High R² values in CNC modeling and consistent yield responses across treatments lend credibility to the conclusions.

Weaker Aspects

1. Lack of clearly stated hypothesis and rationale: The manuscript lacks an explicit mechanistic hypothesis and does not adequately explain the rationale behind selecting specific N rates.

2. Insufficient soil ecological characterization: Soil health and biological indicators, which are critical under straw incorporation systems, are not reported or discussed.

3. Limited physiological interpretation: The discussion occasionally lacks depth in explaining why certain N levels influence biomass partitioning or NUE metrics, reducing the mechanistic strength of findings.

4. Terminology and unit inconsistencies: Non-SI units (e.g., P₂O₅, K₂O) and macronutrient concentrations (% instead of g kg⁻¹) are used inconsistently throughout the manuscript, which must be corrected for compliance.

5. Correlation ≠ Causation in Discussion: Correlation results (e.g., between NNI and yield) are at times interpreted as causal relationships without sufficient physiological explanation or limitations discussed.

6. Language and style issues: Several minor English issues (e.g., awkward phrases, unclear transitions) reduce readability and should be addressed during revision.

So, I have annotated below (and along the manuscript) an attempt to clarify certain ideas, but the authors should examine my suggested wording changes carefully to be sure that I have not misinterpreted what they wanted to say.

Major Comments

1. Scientific Hypothesis and Rationale

NOTICE - Issue: The rationale behind selecting specific nitrogen application rates is not adequately grounded in prior regional data or national recommendations. Similarly, the central hypothesis is not explicitly formulated.
- Action Required: In the final paragraph of the Introduction (Lines 77–83), clearly define the working hypothesis and rationale as:

“We hypothesize that under straw incorporation, cotton exhibits a dilution trend in nitrogen concentration with biomass accumulation, and that an optimal NNI range exists for diagnosing sufficiency. Therefore, we aimed to establish and validate a CNC model and identify nitrogen rates optimizing yield and NUE.”

Additionally, the nitrogen gradient (0–240 kg N ha⁻¹) needs scientific justification, ideally from regional agronomic bulletins or prior Jiangxi field trials. Please insert references to local official standards or published works to support this choice.

2. Soil Characterization and Ecological Relevance

NOTICE - Issue: While some soil chemical properties are described, soil biological indicators (e.g., microbial activity, SOM turnover, soil respiration) and physical structure (e.g., bulk density) are omitted. These are essential for understanding the mechanisms through which straw incorporation alters N availability.
- Action Required: Add to Section 2.1 a sentence such as:

“To complement the chemical profile, future studies should include microbial biomass N, mineralization potential, and soil enzyme activities, as these are closely linked to the N release rate from decomposed straw.”

3. Dilution Curve Assumptions and Model Evaluation

NOTICE - Issue: The CNC model is built using 2021 data and validated in 2022, which is commendable. However, the biological interpretation of the coefficients a and b (3.5774 and -0.42) is not discussed in relation to plant physiology or environmental response.
- Action Required: In the Discussion (Lines 329–339), add:

“The relatively lower a and b coefficients suggest a more gradual dilution rate, which may reflect sustained N mineralization from incorporated straw and improved N retention in the rhizosphere, delaying depletion.”

4. Yield-NUE Tradeoff Interpretation

NOTICE - Issue: The interpretation that 120 kg N ha⁻¹ is optimal is numerically supported, but the biological explanation is limited. The relationship between NUE metrics and yield is correlative, not mechanistically unpacked.
- Action Required: Deepen the explanation in Section 4.2:

“Although NUEa and NPFP decline beyond 60 kg N ha⁻¹, yield continues to rise until 180 kg N ha⁻¹, likely due to sufficient N enabling sustained boll retention and sink filling. However, diminishing returns and the onset of luxury uptake (NNI > 1.1) suggest inefficiency and ecological loss beyond 120 kg N ha⁻¹.”

5. Terminology, Units, and Compliance

NOTICE - Issue: Use of non-SI units (e.g., “P2O5”, “K2O”) violates journal standards. Macronutrient concentrations in plant tissues are not consistently reported in g kg⁻¹, as required.
- Action Required: Revise all occurrences (e.g., Table 1, lines 249–254) to:

• Replace “%” for plant N concentration with “g kg⁻¹”

• Replace “P₂O₅” and “K₂O” with elemental P and K, unless quoting fertilizer grades.

Minor Comments and Technical Corrections

Page	Line	Issue	Recommendation
1	34	“yiel,”	Typo: should be “yield”
2	45–50	Straw incorporation benefits	Add quantification (e.g., % increase in SOM or N mineralization)
3	94	“replicated thrice”	Use “with three replications”
3	102	Use of “P₂O₅ and K₂O”	Replace with elemental P and K in kg ha⁻¹
4	138–149	RMSE explanation	Provide a reference or citation here
6	175–199	Biomass discussion	Clarify why N120 is sufficient for reproductive organ biomass
8	259–275	Yield plateau	Use the term “yield plateaued” explicitly
9	282–292	NUE terms	Define NUEp and NUEi when first introduced
10	302–310	Correlation	Use “strong positive correlation” and provide biological interpretation
12	374–386	NUE vs Yield	Discuss trade-off clearly and mention possible N leaching risk

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.pdf