| 20 May 2026
Estimating attainable soil organic carbon and farm-level limiting factors across Australia’s grain-growing regions
Abstract. Soil Organic Carbon (SOC) is a determining factor of soil health and agricultural crop productivity. The SOC level has generally declined since European settlement in Australia, primarily due to the clearing of native vegetation for agricultural purposes. To enhance soil health and crop yield, it is necessary to determine the attainable SOC potential and site-specific soil constraints that limit SOC build-up. To achieve this, we applied a Boundary Line Analysis (BLA) model to a dataset consisting of 1,782 soil sample sites, collected from 72 farms across the grain-growing regions of Australia. Laboratory measured soil properties, including clay content, electrical conductivity (EC), cation exchange capacity (CEC), pH, exchangeable sodium percentage (ESP), and silt+clay from both the topsoil (0–30 cm) and subsoil (30–60 cm) were used as predictors in two separate BLA model to assess attainable SOC levels in the topsoil (0–30 cm), representing topsoil-driven and subsoil-driven constraints, respectively. Upper-envelope functions between SOC and each predictor variable were derived using a locally estimated scatterplot smoothing (LOESS) model in the BLA framework. A separate BLA was performed for different rainfall regions across Australia to reflect differences in attainable SOC. Digital soil maps for each soil property for a case study farm in the Wimmera, Victoria, were used to illustrate how the BLA can be used to identify limiting factors at the within-field level. Next, we applied the developed BLA models to that case study farm to determine the attainable SOC at the farm scale across six key soil properties. The results of the entire regional BLA model revealed that the attainable SOC in the topsoil varied from 0.98 % (mean minimum) to 1.39 % (mean maximum), in contrast to a mean actual (measured) SOC of 0.68 % (SD = 0.37; IQR = 0.45–0.82) across the region. This signifies a sequestration potential of 0.3–0.71 % in the uppermost soil layer, depending on rainfall zones and management practices. In addition, digital maps of the case study farm generated by applying BLA-derived models to spatial layers of soil predictors determine the attainable SOC, show its spatial pattern, and identify the most limiting factors across soil depths. The resulting map also illustrates the potential SOC gain after ameliorating the constraints. This BLA model is reproducible and can be readily applied across the 72 individual farms in Australia (if they have Digital Soil Maps) to quantify attainable SOC and identify site-specific soil constraints. Thus, providing a rapid and effective decision-support tool for farmers and farm managers to implement site-specific soil and agronomic management, thereby improving soil carbon levels.
In this manuscript, the authors used a boundary line approach to define attainable soil organic carbon (SOC) content based on several explanatory variables that could limit SOC storage. They developed boundary line models in southern Australia and applied them at the farm level to illustrate their use. The authors then produced an interesting map of limiting factors for the farm. The methodology is sound, and the manuscript deserves to be published with a few adaptations. Below are my comments and recommendations for improving the manuscript.
The progression is very slow in the Materials and Methods and Results sections. The authors are encouraged to adopt a sharper writing style by presenting the methodology synthetically at the beginning of the Materials and Methods section then detailing the different steps, making better use of Fig. 3, and avoiding repetitions. This would also solve the problem of information in the Materials and Methods section being mixed (for example, BLA and DSM are briefly presented in Section 2.1 Study site and dataset). The description of the subsoil model results is especially lengthy (BLA figures, attainable topsoil SOC, and limiting factor map). Adopting a broader perspective would be more efficient, by highlighting the differences compared to the topsoil models. Additionally, many sentences in the Results section read more like discussions.
The discussion section could be expanded:
Because of the LOESS method used to adjust boundary lines, no equations are shared with the readers. How do you plan to disseminate the results?
Please, see also the following minor comments:
In the introduction, some of the phrasings are too abrupt: “boosts soil water holding capacity”, “stop soil loss”.
l. 54: Why do you add “although SOC dynamics rely on carbon inputs and losses from the system”.
l. 60: Remove “are highly varied”.
l. 61-62: Please rephrase or make a better link between site-specific factors and soil-forming factors.
l. 86-87: Please rephrase “determines the SOC for a given property”.
l. 106-107: Please rephrase “under a condition when a parameter”.
l. 117-125: I don't think it is useful for the understanding of the manuscript.
l. 139: Avoid using abbreviations ESP and EC here.
l. 140-141: Rather group this with your objective (ii).
l. 144: Add if SOC is a concentration or a stock.
Fig. 1: Maybe you need to add the source of the image and of the agroecological zones in the figure caption. Add the meaning of the red and white symbols in the legend or figure caption. You can also consider removing this figure because the different regions are not used then, and only showing the outline of the entire grain growing zone in Figure 2 (then it will be quicker to say that you have stratified your dataset with the rainfall classification).
l. 160-162: Rephrase to better fit a Material and Methods section (not a Discussion section).
l. 161: “and the same commercial laboratory was used”, add that it is for soil analysis.
Title 2.2 could be simplified.
Caption of Fig. 2: Remove or move “Sites span six rainfall regimes, demonstrating the wide climatic range represented in the dataset” to section 2.2.
Table 1: Move the column “Rainfall region” to the left. Column “Classification ID” is not useful.
l. 212: Rephrase “Subsoil is about processes that control plant growth and so impact on biomass production, which relates to biomass inputs into the topsoil”.
l. 220: End with “:”.
l. 228-230: Theoretically, your kernel density approach should be enough to remove outliers. Why not here? (It is just curiosity, no modification required)
l. 240: Not useful.
l. 247: and is time-consuming.
l. 256: "but retained in the data cloud”. Remove, “data cloud” not used then.
l. 253-263: Could you please explain this more directly?
l. 302-303: “The above-mentioned steps are significant as they involve identifying the most limiting factors influencing the SOC accumulation and stabilisation for the case study farm”. Remove?
Fig. 3 is crowded. You could reduce the information to a minimum and split it between a development and test part. Refer to Fig. 3 in the text.
l. 364: Better explain how you computed a boxplot from the BLA models (actually, I don't understand how “the measured SOC demonstrated narrower variability than attainable SOC in all soil properties” in l. 367-368).
l. 405-407: And also due to correlations between topsoil and subsoil properties.
l. 413-414: “The summer 350-650 mm region showed soil containing 40-50 % clay produced a moderate level of attainable SOC (1.25 %)”. Two verbs, please rephrase.
l. 484-485: “From Figure 8, distinct spatial variability is evident throughout the case study farm, emphasising regions with differing soil properties”. Please rephrase.
l. 493: “Silt+clay represents the summation of clay and silt content”. Can be removed.
l. 495: Replace “firm” by “farm”.
l. 496: Vertosol and soil description can be moved to section 2.1.
l. 501: You are moving from the present to the past tense.
l. 505: Rephrase “Textural qualities”.
Caption of Fig. 5: Add definitions for CCC, NNSE and RMSE to understand the table without reading the text.
l. 523-526: Can be removed.
l. 530: Refer to Fig. 9.
Fig. 10: Homogenize “Min SOCatt” and “SOCatt” in the figure and caption (I guess it is the same).
l. 614-617: Please shorten.
Fig. 12. Title of panel a, above the map: is it topsoil or subsoil?
l. 655-656: “Jien et al. (2025) also demonstrated that the BLA estimated SOC sequestration potential was 2.1 times higher than that of quantile regression, reinforcing the suitability of the BLA model in determining SOC”. I would not give so much importance to this 2.1 ratio because this ratio highly depends on the arbitrary choices Jien et al. (2025) made for both methods (i.e., which quantile is used). You would find another ratio with your dataset. The main advantage of your approach is rather the ability to adjust nonlinear functions.
l. 660-664: “In contrast, the attainable topsoil SOC derived from the subsoil-based BLA model was calculated to be 1.01 % (mean minimum) -1.39% (mean maximum), indicating a further sequestration potential of 0.33-0.71%. These findings highlight…”. It is not a “further sequestration potential”, it is another way to calculate it, isn't it? Please, clarify.
l. 689-688: “Across all rainfall regions, attainable SOC is found to be higher in the topsoil compared to the model built on the subsurface soil in all soil properties. This is expected as topsoil receives direct organic matter such as plant residue, litterfall, organic compost and microbial activity”. You did not use subsoil for this reason, I don't understand why you give such an explanation.
Quadratic curve: when this term is employed alone, we cannot know whether the parabola opens upward or downward.
Homogenize the use of the terms SOC storage, sequestration, accumulation and buildup throughout the manuscript.
Consider producing a single figure with panels for the different limiting factor maps.