| 12 Feb 2026
Agricultural Land Management Practices in the Conterminous United States from 1980–2023
Abstract. Agricultural land management practices affect environmental variables including greenhouse gas emissions, carbon and nitrogen cycles, soil profiles, water quality, and air pollution. To better understand agricultural land management practices, we compiled a land management history for the conterminous United States (CONUS) from 1980–2023. We used the National Resources Inventory as the basis for our sample-based approach to impute planting and harvest dates, synthetic N fertilizer and manure N and C application rates and timing, tillage systems and intensity, and cover crop adoption. We aggregated the imputations to a 0.25-degree grid and compiled a comprehensive dataset detailing the management practices used on agricultural lands across CONUS. From 1980–2023, we found trends towards later planting dates for cotton and spring grains and trends towards earlier planting dates for soybeans. Synthetic N fertilizer rates increased steadily from 1980–2000 and then stabilized from 2000–2023, while manure N amendments were low between 1980 and 2000 and then increased rapidly from 2000–2023. Generally, there were increases in no-till and reduced-till systems across CONUS, with more notable increases in central and eastern regions. Cover crop adoption increased across CONUS from 1980–2023, with the highest level of adoption occurring in the Northeast. The results from this product align with previously published histories, although our work provides a more comprehensive representation of cropland management practices than previous works. Our dataset is available on Dryad under public domain license (Hoskovec et al., 2025) and can be used to inform studies of agricultural lands that evaluate processes such as greenhouse gas emissions, environmental impacts, and food production.
General Comments:
The manuscript “Agricultural Land Management Practices in the Conterminous United States from 1980–2023” presents a grid-based crop management dataset derived from a data fusion and modeling approach. The authors provide visualizations that illustrate spatial and temporal patterns across multiple management variables produced in this study. The manuscript also describes the novelty of the dataset and includes comparisons with existing products. Overall, this work represents a timely contribution with clear potential value for modelers and a broad range of stakeholders. I offer several minor suggestions below that may further improve the clarity and accessibility of the manuscript for a wider audience.
Specific Comments:
L2: The manuscript did mention applications for GHG and C accounting quite a few times, but there was not much mention of water quality or air pollution, despite their inclusion in the Abstract. I suggest that the authors mention these applications in both the Introduction and the Discussion sections. It may be particularly helpful to add a short paragraph that lists example use cases of this dataset, which would clarify its broader applicability and help a wider audience appreciate its full value.
L49-50. The historical agricultural management data is very important for model spinup and making historical model simulations for counterfactual analysis. Consider mentioning these applications in the Introduction and/or Discussion sections.
L57-60. The language in this section gives the impression that the work primarily focuses on gap filling. However, the study also includes evaluation results, as described in the Appendix, and quality control steps are probably included as well. I suggest revising the language to better reflect the full range of activities conducted in this study. It may also be beneficial to highlight elements of novelty here rather than waiting until the Discussion section.
L65-77. Based on my understanding, the original NRI data are not openly accessible to the broader research community. If this is correct, an important contribution of this work lies in making information derived from this valuable dataset available through a gridded product. I suggest emphasizing this aspect.
Figure 1. Is it necessary to include both Figures 1 and 2 in the main manuscript? They are not mentioned frequently in the manuscript, and the results are not often aggregated to these units. Consider moving to SI.
L85. For audience less familiar with NRI and various agricultural management data product, it might be helpful to mention whether the following data products are completely independent from NRI or has partial overlaps. There are many products discussed in this section and a flowchart or a table (mentioning existing products and what novelty this study brings) might be helpful to illustrate the methodology.
L169-170. Later on, the manuscript also mentioned manure animal type, which is important information and should probably be mentioned here as well. What about fertilizer types? Is it possible to include such information in the dataset as well and if not, what is the main reason for the exclusion/ challenge?
L175-177. With this calculation method, is it possible to encounter area double counting when both manure and synthetic fertilizers? The ARMS data does include %area receiving both (even though it’s a small value).
L183. Assume the N availability data is assigned with animal type and manure form for the subsequent calculation?
L200. Would it be accurate to say that the dataset of this study is more focused on crops, and therefore is not suited for the modeling of pastureland or vegetables?
L220-221. The availability of crop-specific dataset from ARMS is highly dependent upon the time period. Is it possible to show some statistics about how much gap filling needs to be done for each time period? The gap-filling aspect can also influence conclusions drawn on temporal trends.
Table 1. Is it possible to connect these levels with typical tillage practice names or expand the “Example” column for the broader audience who is interested in using this dataset?
L283-292. Would something like CDL be helpful since crop types are identified in time? Or is CDL already embedded in one of the source datasets here? If not, I wonder if that data could be used for some sort of validation.
L294. Explain why 0.25 degree is selected as the final grid size.
L296-298. It is not fully clear what the six imputations correspond to – is it for calculating the quantiles mentioned in the next paragraph?
L305. Explain more about the “data suppression” procedure and whether there is follow up interpolations.
L306. Point out corresponding “Appendix” for the Method section, and consider adding some languages related to validation/ evaluation, as some of the results in the Appendix seems quite important.
Figure 3. The right edge of the (f) figure was cut off.
Figure 8. Could this temporal trend be explained?
Figure 10. Could there be a bit more explanation in the caption to make it stand alone. For example, consider mentioning that the tillage intensity increases from A to K.
Figure 11. Consider modifying the color scheme or value range associated with the legend – the distinction of color in these four figures is pretty minor.
L374-377. It would be great if the authors could also mention main differences in methodology in addition to the similarity. Otherwise, it would be expected that the results are similar given the underlying datasets and methodology.