the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Jul 2026
Spatial patterns of global land management intensity are influenced by socioeconomic, biophysical and behavioural factors
Abstract. Land systems are increasingly influenced not only by land-use change but also by land management intensity. However, there limitations exist in data and in systematic understanding of management intensity and how it is shaped by socioeconomic, biophysical and human behaviour. We develop a global dataset of land management intensity for 2020 at 0.01 ° spatial resolution, distinguishing unmanaged, very extensive, extensive, and intensive management across cropland, pasture, and forest systems. Intensive management occupies about 22 % of global managed land, while extensive and very extensive management dominate (78 %). Intensive cropland management accounts for around 44 % of cropland area, but intensive pasture is limited to 10 % of pasture area, and intensive forest systems to only 5 % of forest area, revealing distinct sectoral contrasts. Management intensity is highly heterogeneous, with intensive cropland concentrated in North America, Europe, and South and East Asia, while extensive management dominates in Africa and Latin America. Five countries account for nearly half of global intensive cropland. Income, market access, population density, and aridity influences cropland management intensity, whereas pastures and forests show more complex relationships. Comparison with global land decision-making types shows spatial consistency (68 %), suggesting that land management intensity is influenced by land-user behaviour.
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Status: final response (author comments only)
- RC1: 'Comment on essd-2026-482', Anonymous Referee #1, 17 Jul 2026
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RC2: 'Comment on essd-2026-482', Anonymous Referee #2, 30 Jul 2026
In this study the authors develop a harmonized global dataset of land management intensity for 2020 at 0.01 ° spatial resolution, distinguishing unmanaged, very extensive, extensive, and intensive management across cropland, pasture, and forest systems. They also link the spatial patterns of land management intensity with socioeconomic and biophysical variables and with land-use decision making types. The developed dataset and the conducted analysis can facilitate the global land system analysis and understanding of the drivers and behavioural influences of land management intensity.
Here are some comments regarding the methodology.
2.2 Mapping crop types and management intensity onto agriculture land
It is not clear how to identify rainfed areas, and how to differentiate rainfed and irrigation areas.
2.3 Mapping Pasture Management Intensity
Pasture management intensity was classified into three intensity categories based on the N application rates: very extensive (< 50 kg N ha-1 yr-1), extensive (50-100 kg N ha-1 yr-1), and intensive (≥ 100 kg N ha-1 yr-1). The authors need to justify these thresholds. Although a sensitivity analysis of a range of thresholds is provided, it is still necessary to explain how to determine the thresholds and why such values are selected.
The unit: superscript is necessary for “-1”.
2.5: Photovoltaic (PV) Farm Mapping
PV cell allocation was restricted to non-water, non-urban, and non-forest areas and allocated by ranking candidate cells by descending PV fraction and allocating PV cells until the total PV area was achieved using latitude-adjusted cell areas. According to previous research, a large number of PV farms are located in water, urban, and forest areas. The assumption of non-water, non-urban, and non-forest areas is not valid and may produce a large uncertainty for this step.
How PV farm mapping contributes to the production of land management intensity dataset is not clearly explained. It seems that the definition of land management intensity does not involve the factor of PV farms.
Multinomial logistic regression model
It is not clear how the model was trained. How many samples were used to train the model? How were the samples collected? Did the authors use cross-validation to evaluate the model performance?
It would also be helpful to understand the model performance by adding other commonly used metrics for binary classification, such as AUC, F1-score, recall, and precision.
Citation: https://doi.org/10.5194/essd-2026-482-RC2
Data sets
Global Gridded Land Management Dataset at 0.01-degrees Ankita Saxena, Calum Brown, Karina Winkler, Almut Arneth, and Mark Rounsevell https://zenodo.org/records/18249970
Interactive computing environment
Global Land Management Ankita Saxena, Calum Brown, Karina Winkler, Almut Arneth, and Mark Rounsevell https://ee-ankitasaxena03as.projects.earthengine.app/view/global-land-use-management
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Review Comments
Title of Manuscript: Spatial patterns of global land management intensity are influenced by socioeconomic, biophysical and behavioural factors
Manuscript ID: essd-2026-482
General Observations
The manuscript presents novel research investigating the influence of socioeconomic, biophysical, and behavioural characteristics on the spatial distribution of land management intensity. The authors have developed a global dataset of land management intensity for 2020 at a spatial resolution of 0.01°, comprising four management classes: unmanaged, very extensive, extensive, and intensive. These classes are mapped globally across cropland, pasture, and forest systems.
The study concludes that income, market access, population density, and aridity influence cropland management intensity, whereas pasture and forest systems exhibit more complex relationships. The results are further compared with global land decision-making types, demonstrating a spatial consistency of 68%. Based on this comparison, the authors conclude that land management intensity is also influenced by land-user behaviour.
The study addresses an important research gap in land system science regarding land management intensity. It also provides a foundational global dataset of land management intensities and their spatial distribution, thereby creating opportunities for further research into the socioeconomic, biophysical, and behavioural factors influencing land-use and land-change decisions. The topic is timely and highly relevant, and the resulting dataset could become an important resource for global land-system modelling and land-management policy analysis.
The study is promising, original, and likely to be of interest to a broad range of researchers, particularly because it links the socioeconomic, biophysical, and behavioural characteristics of decision-makers with land management intensity and, more broadly, with land-use change. In my opinion, the following minor revisions should be considered before the manuscript is accepted for publication in ESSD.
Comments and Suggestions
Recommendation
The study addresses an important research gap in land system science by examining how land management intensity is influenced by decision-makers' socioeconomic, biophysical, and behavioural characteristics. It also provides a potentially valuable global dataset that may support further research in land-system modelling, land-use change, and land-management policy analysis.
The manuscript currently requires only minor improvements, mainly related to language and grammatical editing, clearer presentation of the originality of the work, and a stronger explanation of how the present findings and dataset may support future research. These issues can be adequately addressed in a revised manuscript.
Therefore, I recommend a Minor Revision before the manuscript is considered for publication.