the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 01 Sep 2025
A spatially explicit dataset of agriculture liming across the contiguous United States
Abstract. Agricultural lime has historically been applied to croplands in the United States to counteract soil acidification and enhance soil fertility, with important consequences for crop productivity and Earth’s carbon cycle. Previous work on agricultural liming has largely focused on either region-specific case studies or national-level estimates of total application rates, leaving a major gap in understanding the spatial variability in lime application. This study addresses this gap by presenting the first spatially explicit dataset of agricultural lime application across the contiguous United States. The dataset comprises state-level data for 1930–1950 and a more detailed county-level dataset for 1954–1987, enabling comprehensive spatial-temporal analyses at multiple scales. Counties in the Midwest region exhibited the highest total amounts of lime applied in the latter half of the twentieth century, reflecting intensive agricultural activity. These counties were characterized by higher overall lime application rates (amount of lime applied per unit of limed area each year) but relatively lower liming frequency (ratio of limed area to total agricultural land area each year). In contrast, counties in the southeastern coastal region exhibited lower lime application rates per unit of limed area but more frequent lime applications. We used a machine learning framework, to elucidate key environmental and agricultural drivers of lime application. Our results show that the total amount of lime applied, as well as the application rate and frequency, are strongly associated with regional climatic conditions and soil properties. However, we also found evidence that agricultural management practices (such as crop production, fertilizer use, and soil pH recommendations) played a key role in shaping liming applications. Spatiotemporal integration of the data product results in a revised national estimate of total lime application, with a range of 15–25 million tons (Mt) per year. This study establishes a critical observational baseline for assessing the potential of agricultural lime application as a climate mitigation strategy and highlights the need for further research into its long-term environmental impacts.
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Status: final response (author comments only)
- RC1: 'Comment on essd-2025-411', Anonymous Referee #1, 08 Oct 2025
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RC2: 'Comment on essd-2025-411', Anonymous Referee #2, 14 Nov 2025
This manuscript compiles state-level (1930–1950) and county-level (1954–1987) liming statistics for the United States, linearly interpolates missing census years, and uses Random Forests to identify correlates of lime mass, “rate,” and “frequency.” While a spatially explicit collation of historical liming could, in principle, be useful, the paper in its current form is methodologically shallow, limited to spatiotemporal gap-filling, and weak in terms of application potential.
There are several fundamental limitations: (1) a serious temporal mismatch between historical lime application data (1978 to 1987) and modern environmental covariates (e.g., SSURGO soils), which undermines the validity of inferred relationships between lime use and soil or climate drivers; (2) The county-level resolution and the historical time span from the 1930s to the 1990s substantially limit the general applicability of the dataset, particularly given that lime is not routinely applied like nutrients (e.g., N and P) and is inherently difficult to predict. As a result, these historical records have limited relevance for understanding or informing current agricultural production.; and (3) an ambitious framing around climate mitigation that is not matched by quantitative assessment, particularly in light of the steep decline in liming since the 1970s. In addition, the manuscript provides limited insight beyond basic interpolation and visualization of known spatial gradients, and the proposed drivers (e.g., corn yield) are not well justified, particularly given the lack of consideration of land-use history. Taken together, the analysis is predominantly descriptive, lacks robustness, and does not yet meet the standards for a data-rich, interpretable, and conceptually well-founded contribution.
Major Comments
The central contribution is presented as “the first spatially explicit dataset” of U.S. liming. However, the core analysis consists of (i) linear interpolation and extrapolation of census statistics between reporting years, (ii) county-level summarization of largely static environmental rasters, and (iii) a Random Forest variable-importance analysis. Overall, the conceptual and methodological novelty is modest, and the manuscript currently reads more as a spatial gap-filling exercise than as a substantively new data product or process study.
Temporal scope, interpolation, and data pairing: The final dataset comprises state-level records for 1930–1950 and county-level records for 1954–1987. Missing county values between irregular census years are filled by linear interpolation or extrapolation. There is a substantial temporal mismatch between lime application data (1950s–1980s) and the environmental covariates used to interpret spatial patterns. Soil properties derived from SSURGO represent relatively recent conditions and are essentially static “one-time” variables, whereas crop yields and climatic variables are treated as time-series. The manuscript does not clearly explain how these differing temporal resolutions are reconciled when identifying “key drivers” of lime application. Using present-day soil data to explain liming patterns several decades earlier is problematic and undermines the validity of inferred relationships between lime use and soil factors. Given these limitations, the resulting dataset primarily reflects historical conditions of lime use. While this has historical value, its relevance for contemporary management and climate mitigation applications is limited and should be framed accordingly.
Climate-mitigation framing and lack of quantitative support: Lines 53–55 state that the study aims to “assess the potential of agricultural lime application as a climate mitigation strategy.” In its current form, the manuscript does not provide sufficient evidence or quantitative analysis to support this claim. The Introduction briefly discusses enhanced carbonate weathering and potential CO₂ removal, but there is no explicit evaluation of carbon budgets, the fraction of lime weathered by strong versus carbonic acids, or the net CO₂ impact of liming across regions. Furthermore, national lime application has already declined sharply since its peak in the 1970s, as documented both in this dataset and by West & McBride (2005). Given that lime is not routinely required once soils reach a suitable pH for crop growth, it is not clear that historical liming patterns, especially where current use is low, represent a high-impact pathway for future climate mitigation. The climate-mitigation framing is therefore overly ambitious and should either be substantially toned down or supported by a dedicated geochemical and carbon-accounting analysis.
Choice and interpretation of drivers (soil properties, crop yield, climate): the manuscript treats soil properties as static variables while crop yield and climate are time-varying. This mismatch raises questions about how the model disentangles spatial from temporal patterns when identifying key drivers. The role of crop yield, especially corn yield, as a major driver of lime application is not convincingly justified. Lime is primarily applied to correct soil acidity, not primarily to boost the yield of a specific crop, unless clear evidence is provided that different crops require systematically different lime rates or have distinct acidity thresholds.
The claim that liming has “historically occurred almost exclusively in the eastern half of the United States, which is characterized by more acidic soils” (Lines 135–137) is an oversimplification. In addition to soil acidity, the historical distribution of cropland is a major determinant: agricultural land has been heavily concentrated in the eastern U.S., especially prior to the 1960s. Thus, soil pH, cropland extent, and land-use history should be jointly and quantitatively assessed rather than attributing liming primarily to acidity alone. Similarly, the concluding statement that spatial variation in lime application is shaped by “agricultural practices, climate, and soil properties” (Lines 137–138) is too vague. The conclusion should clearly state the specific key predictors identified by the analysis—for example, precipitation and corn yield (though the latter requires further justification)—rather than restating broad categories.
Predictability of lime use and suitability of metrics and predictors: lime is not a plant nutrient like nitrogen or phosphorus and is not applied in a regular, predictable manner. It is typically used episodically, when soil acidity becomes sufficiently severe to limit productivity. This makes liming inherently harder to predict than nutrient application. The manuscript defines a “frequency” metric that is effectively the fraction of cropland area treated with lime in a given year, but then interprets this as a temporal rate (e.g., “once every N years”). The metric should be described and used as lime-treated area (or fraction) rather than as a true application frequency per field. Converting treated fraction into an implied interval assumes uniform and repeated application across the entire cropland base, which is unrealistic given the episodic nature of liming.
In Section 3.2, the emergence of corn yield as a key predictor of lime use is not adequately explained. Given the strong decline of liming after the 1970s (Figure 9) and the central role of soil acidity, the identified “key factors” may not be truly causal or robust predictors, especially in the absence of explicit soil pH reconstructions or crop-specific liming recommendations. The additional restriction to counties with >10% cropland by area also needs stronger justification in interpreting factor importance.
Cropland expansion and total cultivated area across the conterminous U.S. (CONUS) reached their maximum in the 1970s, which largely explains the observed peak in lime application during that period. Once cropland expansion stabilized and soils in many regions were sufficiently ameliorated, lime use declined, as reflected in both this dataset and the trends reported by West & McBride (2005).
In the Discussion (e.g., Lines 638–639), the manuscript appears to suggest that lime and fertilizer follow similar trajectories. However, the figures indicate that fertilizer use remains relatively stable while lime application decreases substantially after the 1980s. This inconsistency should be acknowledged and discussed explicitly. Lime use is arguably a more meaningful indicator of long-term soil management and pH adjustment than fertilizer use, and the contrasting dynamics of these two inputs deserve clearer treatment.
Additional Analyses Suggestions:
1.Clarify the “frequency” metric.
The term “frequency” should be avoided. The metric currently represents the fraction of cropland area treated with lime in a given year and should be named and interpreted accordingly as lime-treated % area.
2.Crop-specific and grid-level liming (if possible).
If crop-specific lime application rates and treated areas are available, combining them with pixel-level land-use datasets would enable estimation of grid-level liming rates or amounts. This would add an important spatial dimension and significantly enhance the dataset’s utility.
3.Link liming trends to land-use change.
In the southeastern U.S., declines in lime application appear to coincide with reductions in annual limed area (Figure 5) during the 1980s. It would be valuable to explicitly examine whether these patterns reflect land-use transitions (e.g., cropland abandonment or shifts to less acid-sensitive systems) and to discuss this in the context of long-term agricultural change.
4.Potential extension: estimating current liming needs.
If soil acidity data or crop-specific soil pH thresholds could be compiled, there may be an opportunity to estimate contemporary lime requirements to sustain current crop production. Such an analysis would provide more direct and policy-relevant implications than focusing solely on historical liming patterns.
Minor Comments
1.Line 215: The reference to “Fig. 4” appears out of order. Please ensure that figures are cited in the correct numerical sequence throughout the manuscript.
2.Units:
Line 296: Use a consistent standard format for units, e.g. “t yr⁻¹” rather than “t/yr,” and apply this convention uniformly.
Lines 315–316: The text states “exceeding 6 Mt lime year⁻¹,” but Figure 3 appears to show a maximum below 6 Mt. This discrepancy should be corrected. Also, choose either “yr⁻¹” or “/yr” and use it consistently to avoid confusion.
3.Green Revolution explanation
Lines 310–313: The link between the Green Revolution and increased lime use is not clearly articulated. As written, it does not convincingly explain why Green Revolution technologies would directly drive liming. This section should be clarified or revised.
4.Assumption of uniform long-term liming
Lines 380–382: The assumption that lime is applied uniformly . Liming is episodic and contingent on soil pH.
5.Lines 382–385: The large variation in lime use is likely more strongly related to underlying soil pH patterns and historical land expansion than can be resolved at the county level with the current dataset. This limitation should be acknowledged more clearly.
Citation: https://doi.org/10.5194/essd-2025-411-RC2
Data sets
Agricultural lime application across the contiguous United States, 1930–1987 Samuel Shou-En Tsao, Tim Jesper Surhoff, Giuseppe Amatulli, Peter A. Raymond https://zenodo.org/records/15758275?preview=1&token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjdiN2VkNDkwLTE1YjctNGNiZC1iMjY2LTBlOGMzZjkzYTZmZSIsImRhdGEiOnt9LCJyYW5kb20iOiJjZTFmNTA4ZDliZGU4NDQzOGNlMDkzMTYxNjJkMDQxNSJ9.2yChJg8txya_OZzIo6nmgFAjVj-haakBfMtFcLrDukLXaZxKVM19i-DrUZ3kKuI443l4z1KjREqlKiOlHbJ42g
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This manuscript describes a methodology that created a CONUS scale dataset related to agricultural lime application (mass, area, and rate) from 1930 to 1987 based on state and county-level census of agriculture datasets. Further, the authors analyze some of the environmental predictors of lime application using statistical methods. Overall, this study fills an important gap in better understanding the spatio-temporal variation in agricultural lime application and the authors write in a clear and organized manner. Therefore, I recommend acceptance with minor revisions.
My main concern is related to the scope of ESSD. This is my first review for this journal so I am not sure how the scope is enforced but the statistical analysis aimed at determining the main drivers/predictors of lime application seemed like it could be interpreted as outside the scope. In particular, the journal website states: "Any interpretation of data is outside the scope of regular articles". It appears that many currently published articles could reasonably violate a strict interpretation of this scope statement. But I raise this issue because the statistical analysis presented is entirely separate from the development of the dataset. It is interesting and the methods are sound, but it doesn't directly contribute to the production of the dataset. Therefore, I would appreciate more guidance from the editors to determine whether that part of the manuscript is out of scope or not.
Another broader point that I suggest the authors make is related to the data availability at the county scale. As the authors note, the county-scale data is no longer available beyond the year 1987. In the interest of better scientific understanding and more open public datasets, I would recommend that the authors highlight the need to restart the collection of this specific county-scale dataset by the USDA. Otherwise, researchers are severely limited as we move forward into the future, especially as liming is potentially seen as a carbon sequestration practice. A good citation to include here would be Rissing et al. (2023) (https://doi.org/10.1038/s43016-023-00711-2) who discuss the importance of USDA data collection policies and the need to include datasets that better help us manage our agricultural landscape in a more sustainable manner.
Specific comments:
Line 53: What time period is associated with this range? Please specify.
Lines 104-106: Need a citation to support this sentence.
Lines 106-108: This is confusing. You just said that the historical consensus has been that carbonate amendments are net CO2 sources but now you're citing sources that argue that carbonate amendments "contribute substantially to carbon...sequestration"?
Line 122: "where it can be marketed". I would avoid unnecessarily inserting the idea that this research is essential to develop a carbon market. I recommend just stating something like "it will be important to develop a better understanding of when and where it will likely be a robust and reliable carbon removal practice." or similar
Lines 135-139: This paragraph is not appropriate at the end of the introduction because it discusses the results. Suggest removing or moving to discussion/conclusion section.
Lines 174-177: This description of the pH recommendations is confusing. Where exactly is this information coming from? How exactly were these pH recommendations estimated? Please provide some context on how those recommendations are determined by agronomists.
Line 259: Please add that this was done using Python.
Line 463: Don't you have this total ag land area data compiled and available to better determine this?
Line 524: suggest replacing "cause" with "driver"
Technical comments:
Lines 85-87: Suggest rewording because "depends on the pH of the soil" and "Depending on soil pH" sounds repetitive. Also, "through equilibration of the carbonic acid system" tripped me up while reading and I'm not sure that it's necessary here.
Line 114: remove the "a" after "high"
Line 149: change "years" to "year"
Line 338: replace "which" with "with"
Line 400: I would prefer the font size to be uniform across this figure.
Line 462: missing end parentheses
Line 535: Please be consistent with capitalizing regions like "Southeastern"