the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 May 2025
A high-resolution gridded dataset of livestock distribution on the Mongolian Plateau (2000–2020)
Abstract. Accurate quantification of the geospatial distribution of livestock in pastoral regions is important for assessing and maintaining grassland ecological security and sustainable development. Statistical livestock data based on static and macro-level administrative units cannot characterize the fine-scale distribution of livestock across mobile geographic spaces. This study proposed a livestock spatial mapping framework that combined livestock inventory statistics of soum/banner counties with multi-source data (e.g., land cover, population, topography, and climate, etc.) using the Random Forest model (RF). A series of high-resolution gridded spatial distribution datasets of total livestock, sheep & goats, and large livestock (cattle, horses, and camels) densities at five-year intervals were obtained for the Mongolian Plateau from 2000 to 2020. The fitting accuracy of this dataset with statistical data (R²>0.85) is significantly better than that of the existing Gridded Livestock of the World (GLW) series dataset, and the spatial distribution is more accurate and detailed. At the same time, it also compensates for the lack of spatial information of large livestock such as camels in the GLW. This approach enables coarse-grained administrative division data transforming into high-resolution spatial gridded data, by solving the key problems of low spatial resolution, missing local details, and the spatial fusion of different data sources. Based on the acquired high-precision spatial distribution data of livestock density, it can be fused and analyzed with other geographic environment data, which is of great value for the ecological environment protection of grassland in nomadic grassland areas. Gridded livestock density datasets are freely available at https://doi.org/10.6084/m9.figshare.28695728 (Liu and Wang, 2025).
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RC1: 'Comment on essd-2025-256', Anonymous Referee #1, 03 Jun 2025
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This paper presents a spatial mapping framework for livestock distribution by integrating soum/banner-level livestock inventory statistics with multi-source data using the Random Forest model. The study is well-designed, the manuscript is clearly written and logically structured. However, several issues should be addressed to further enhance the clarity, comprehensiveness, and practical value of the research.
- The spatial resolution of the results should be explicitly stated in the abstract. Additionally, the study does not produce annual livestock distribution maps. Is this limitation due to the availability of annual statistical data?
- Regarding Figure 1, a land cover map might be more informative than a terrain map in helping readers understand the context of the study. For Figure 3, the axes should be clearly labeled with appropriate units and descriptions.
- In Table 1, the authors mention using both MCD12Q1 and GLC_FCS30D as land cover datasets. However, the manuscript lacks a clear explanation of how MCD12Q1 was utilized. Could the authors clarify its specific role? Moreover, if both datasets were used, how were potential inconsistencies or conflicts between them resolved?
- What role does land cover data play in this study? Is it used as an input feature for model training, or as a mask to constrain the spatial extent of livestock distribution? Given that most livestock in the study area are likely found in grassland areas, was this considered in the mapping process?
- In Figure 9, there appears to be a decline in livestock distribution in the northwestern part of the study area around 2005. What could be the reason for this change? Was it due to policy, climate, land use changes, or other factors?
- In the discussion section, the authors primarily focus on analyzing the spatiotemporal patterns of the resulting dataset. However, for a data-oriented journal, I believe greater emphasis should be placed on discussing the data construction methodology and potential sources of uncertainty associated with the dataset.
Citation: https://doi.org/10.5194/essd-2025-256-RC1 -
RC2: 'Comment on essd-2025-256', Anonymous Referee #2, 07 Jun 2025
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This study presents a valuable contribution to the field of grassland ecology and sustainable livestock management by generating a 1-km resolution gridded dataset of livestock distribution across the Mongolian Plateau from 2000 to 2020. The work addresses a critical gap in existing datasets, such as the Gridded Livestock of the World (GLW), by integrating multi-source remote sensing data (e.g., land cover, climate, and socioeconomic variables) with statistical livestock inventories using a Random Forest (RF) model. The dataset notably improves spatial resolution and includes large livestock species (e.g., camels) overlooked in prior global datasets. The research is scientifically significant for informing grassland conservation, overgrazing mitigation, and policy-making in mobile pastoral systems. However, the methodology adopted by the authors may lead to considerable uncertainty in the results. In addition, the manuscript needs to add some necessary details to the description of the method.
1. The RF model’s hyperparameter optimization (e.g., n_estimators, max_depth) is briefly mentioned but lacks details on cross-validation procedures or sensitivity analyses. Documenting the iterative process (e.g., grid/random search) and reporting optimal parameters is critical for reproducibility. It is recommended to add relevant descriptions.
2. The study relies on 436 administrative units annually, potentially oversimplifying spatial heterogeneity in complex landscapes (e.g., the Gobi Desert vs. the steppe). Stratified sampling or spatially explicit validation (e.g., hotspot analysis) is needed to ensure model robustness across diverse ecosystems.
3. This effort develops livestock data from 2000 to 2020, but unfortunately with 5-year intervals rather than continuous annual time series, which would lose some time variation information. Since yearly time series of livestock numbers are available for the different boroughs (https://www.1212.mn/), it is recommended to develop a continuous time series for the study period.
4. The underlying data of livestock numbers used in this work are from statistical yearbooks or the Bureau of Statistics, and considering that there is some uncertainty in these statistics, the uncertainty of the results of this paper should be discussed.
5. The work focuses on the spatial distribution and change characteristics of the number of livestock. It is suggested to introduce the temporal changes in the number of different livestock during the study period so that readers can more easily understand the spatial and temporal dynamics of the livestock industry in the region.
Citation: https://doi.org/10.5194/essd-2025-256-RC2 -
CC1: 'Comment on essd-2025-256', Yuanzhi Yao, 11 Jun 2025
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There are many errors in the codes they shared in the links and do not align with the description in the muascript.
Such as, they said they used RandomizedSearchCV method in line 243, but they used GridSearchCV methods in their codes.
I think the authots need to carefully check their codes to match the description in the manuscripts.
Citation: https://doi.org/10.5194/essd-2025-256-CC1
Data sets
Gridded_livestock_mongolian_plateau_2000_2020 Yaping Liu and Juanle Wang https://doi.org/10.6084/m9.figshare.28695728
Model code and software
Gridded_livestock_mongolian_plateau_2000_2020 Yaping Liu and Juanle Wang https://doi.org/10.6084/m9.figshare.28695728
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