the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Jun 2025
CLRD-GLPS: A Long-term Seasonal Dataset of Ruminant Livestock Distribution in China's Grazing Production Systems (2000–2021) Using Stacking-based Interpretable Machine Learning
Abstract. Understanding the spatial-temporal distribution of grazing livestock is crucial for assessing livestock system sustainability, managing animal diseases, mitigating climate change risks, and controlling greenhouse gas emissions. In China, grazing ruminants are predominantly distributed across vast grasslands in semi-humid and alpine regions. However, existing gridded livestock distribution datasets fail to distinguish between grazing and other livestock production systems and do not simultaneously account for long-term and seasonal dynamics. This study introduces CLRD-GLPS, a comprehensive dataset mapping China's ruminant livestock distribution in grazing livestock production systems from 2000 to 2021. Our approach addresses limitations in existing datasets by integrating interpretable machine learning methods to segment grazing livestock from total livestock populations and generate seasonal grazing pastures with dynamic grazing suitability masks. We developed a stacking-based ensemble methodology that enhances predictive performance while providing insights into distribution mechanisms. The stacking ensemble models demonstrate robust performance through 5-fold cross-validation, with R² values ranging from 0.909 to 0.967 for cattle and 0.874 to 0.914 for sheep and goats. Validation results demonstrated the high accuracy of CLRD-GLPS across multiple spatial scales. At the county level, it strongly agreed with census data, effectively capturing grazing livestock distribution. City-level validation confirmed strong agreement (R² = 0.691–0.881), while grid-level validation using independent observations yielded R² = 0.79, further confirming the accuracy of fine-resolution predictions. The CLRD-GLPS dataset provides essential information for understanding grazing ruminant dynamics and developing targeted livestock management policies. Furthermore, our methodological framework offers a template for creating similar livestock distribution datasets for other regions and livestock production systems.
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Status: open (until 11 Jul 2025)
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RC1: 'Comment on essd-2025-263', Venkatesh Kolluru, 18 Jun 2025
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General Comments
This manuscript presents the development of CLRD-GLPS, a long-term seasonal dataset mapping ruminant livestock distribution in China's grazing production systems from 2000-2021. While the work addresses an important gap in livestock distribution modeling by distinguishing grazing from non-grazing systems and incorporating seasonal dynamics, several methodological concerns and limitations significantly impact the reliability and broader applicability of the findings.
Major Concerns
Why were sheep and goats considered as one variable? I could see that the China Statistical Yearbook data has data separately for sheep and goats. I suggest that authors segregate and model them separately. Also, the hog population is considerably higher, and their consumption is equivalent to that of cattle or sheep combined. I could also see that the data is available for these. Why haven’t these been considered in the study, as the same approach can be tested for other livestock types?
I haven't understood the logic behind the usage of livestock proportion instead of directly using the livestock density as a response variable, as it is a spatial prediction problem. I strongly suggest that authors rerun the models by using the livestock numbers or densities instead of proportions.
The downscaling approach from provincial to county level presents fundamental scaling issues that are not adequately addressed. The assumption that environmental predictors can accurately capture the complex socioeconomic and management factors that determine grazing livestock distribution at local scales is questionable. The authors rely primarily on biophysical variables like grassland proportion and NDVI, but ignore critical factors such as market access, infrastructure development, labor availability, policy implementation variations, and local pastoral traditions that significantly influence livestock distribution patterns. This oversimplification may lead to substantial prediction errors, particularly in regions where human management decisions override environmental suitability.
The temporal aspects of the methodology are also problematic. While the authors distinguish between seasonal pasture types, the approach fails to capture the dynamic nature of pastoral systems where livestock move between different grazing areas throughout the year. The static assignment of livestock to pasture types doesn't reflect the reality of mobile pastoralism, where animals may utilize multiple pasture types within a single year. Additionally, the use of annual livestock census data averaged across seasonal patterns may mask important temporal variations in grazing intensity and distribution.
How many data points were used to develop the RF model in section 2.2.3? Since you have 29 provinces, do you have just 29 points per year? So, in total, did the authors use approximately 600 points for RF model training and validation? These are relatively few data points considering the size of the study area. Also, how were the values assigned for each province? Did the authors consider provincial centers to assign particular livestock numbers? How can the authors be sure that the considered coordinates actually have livestock presence?
Additionally, these livestock numbers are absolute measurements, while model-derived values are relative measurements. For example, if in 2020 for a particular province X, they recorded 500 cattle per sq.km, these are real measurements. If you want to train an ML model, you should train one specific model for each year so that it doesn't assign random cattle densities depending on the predictor variables across different temporal periods.
Similarly, for which coordinate or location was the county-level livestock number assigned? Did you consider any large grazing LPS areas or the centroid of the shapefile? These location details are important since coordinate-specific reflectance values of predictor variables were used for developing machine learning models. If you have assigned a particular high cattle density value to a coordinate in a province that has low grassland height or biomass, the model learns those corresponding reflectance patterns and assigns all high cattle values to lower biomass regions when predicting at the gridded scale.
There are several uncertainties associated with the current methods, including the two-step downscaling procedure from province to county and then county to grid, which introduces substantial amounts of uncertainty at every step and in every model considered. Though the model outputs show some accuracies, these cannot be considered robust unless validated with reference data at appropriate spatial scales.
Section 2.2.4 - Have I misinterpreted this? The methodology appears to systematically double-count livestock in year-round pastures. When calculating density for warm-season models, they divide the total county livestock by the combined area of warm-season AND year-round pastures, then do the same for cold-season models. This means the same livestock numbers are being attributed to overlapping areas, which violates basic principles of spatial density calculations. How do the authors justify this mathematical approach when it essentially inflates livestock presence in year-round pastures? Since the density calculations are based on these questionable assumptions, how can the authors validate whether their gridded predictions actually reflect real livestock distribution patterns? The fundamental flaws in density calculation methodology may propagate through the entire modeling framework, making validation results unreliable.
Also, the methodology uses annual livestock census data but applies it to seasonal pasture models. In reality, livestock numbers and their distribution change throughout the year due to births, deaths, sales, and seasonal movements. How can annual static numbers accurately represent the dynamic seasonal distribution of livestock across different pasture types?
Lines 291-296 - More details are needed on how the dasymetric mapping method is implemented. Generally, in livestock studies, ground truth or census data will be used to correct the predicted livestock densities in this method. However, in your study, even these county-level numbers are derived from the RF model, which are substantially biased due to model uncertainties. How can we consider these as robust? Authors might have to perform any error propagation analysis to understand the uncertainty propagation from input data, predictor variables, and models in this multi-stage framework.
Lines 360-361 - I could see that the authors conducted some change analysis in section 3.2. Is this linear trend analysis? I suggest that authors conduct Mann-Kendall and Sen’s slope analysis, which are non-parametric, and report the observed trends instead of simple change analysis. Also, these details should be provided in the methods discussion. Moreover, just report the changes in counties that have significant results (based on p-value) of increase or decrease instead of reporting non-significant changes, which are not important.
Figure 5 - Why were these plotted on a logarithmic scale? The ranges are not in the range of millions. Converting them to a logarithmic scale suppresses the point deviations, and hence it is suggested to plot in the true scale. Also, why were the values for cattle negative? This is the issue with the log scale. Please change these.
Section 3.4.2 - Have authors corrected their county-level predictions using the dasymetric mapping method? Or did they just use them for validation?
The current discussion sounds like an extension of results rather than a discussion. The discussion should discuss their results in conjunction with other studies, highlighting their findings. The authors should expand their discussion to include a comparative analysis of existing gridded datasets developed for other regions, particularly Central Asian and Mongolian countries with livestock-based economies, examining how their methodologies and spatial resolutions compare to the current approach. Additionally, the manuscript would benefit from discussing how livestock distribution patterns and grazing systems in China differ from those in neighboring pastoral economies and the implications for dataset applicability across different pastoral systems. The authors should clearly articulate the limitations of the current approach and assess whether the proposed methodology can be generalized to other regions with different livestock management practices, topography, and data availability, while better highlighting the specific innovations and improvements this dataset offers over existing products, particularly in terms of spatial accuracy, temporal resolution, or integration of novel data sources.
The methodology raises several concerns regarding data validation and representativeness. How do the authors confirm that these livestock numbers are exclusively from grazing LPS systems? Do the authors have information about livestock counts from other LPS units to ensure proper segregation? The authors rely on only 74 counties from the Grassland Ecological Protection Subsidy Program for validation, which represents a very limited sample size across China's diverse pastoral regions. This small validation dataset may not adequately capture the heterogeneity of grazing systems, environmental conditions, and management practices found across the country. Furthermore, there is no discussion of how representative these 74 counties are of the broader pastoral landscape, potentially introducing significant bias in model evaluation.
I could clearly see that RF or CB performed comparably well to the stacking ensemble. If there isn’t much drastic difference in the accuracies, why should someone adopt these ensemble algorithms? Also, the stacking ensemble methodology implementation lacks sufficient theoretical justification for its application to livestock distribution modeling. The authors claim superior performance over individual models (4.2% improvement in R² for cattle, 6.2% for year-round pastures), but these improvements are modest and may not justify the increased computational complexity. More critically, the interpretability claims are overstated - while SHAP values provide feature importance, the ensemble approach actually reduces interpretability compared to simpler models by obscuring individual model contributions and creating a more complex decision boundary. I personally suggest that authors just rely on the RF or CB model, as it has been proven robust from several similar studies and even from this study.
Different ML models showed different variables as top predictors. So, which one is correct, or which one should someone take as standard? These should also be discussed and concluded in the study.
The validation approach using standard conversion factors (1 cattle = 5 sheep units) oversimplifies the complex relationships between different livestock species and their grazing impacts. This conversion ignores regional variations in animal size, breed differences, and actual grazing behavior that can vary significantly across China's diverse pastoral systems. The assumption that this standardized conversion adequately represents local grazing pressure is likely flawed, particularly in regions with distinct pastoral traditions or environmental constraints.
The statistical validation approaches contain several limitations that affect interpretation. The county-level validation shows very high R² values (0.990-0.999), which is expected given that county census data served as constraints in the dasymetric mapping process. This represents circular validation rather than independent verification. The city-level validation provides more meaningful assessment (R² = 0.71-0.92), but the grid-level validation uses only 65 observation points across the entire study region, which is insufficient for robust spatial validation of a 1km resolution dataset covering such a large area.
Minor Comments
What is this CLRD-GLPS? It’s not properly abbreviated, and it is also highly suggested to remove acronyms from the title.
Citations needed for Line 39
Lines 40-41 - These are decade-old. Update the statistics with recent findings.
Lines 54-56 - Seems incomplete. Read and rephrase the sentence. Also, discuss about the recent version GLW4 instead of GLW3 - https://data.amerigeoss.org/dataset/9d1e149b-d63f-4213-978b-317a8eb42d02.
Lines 57-59 - Are the sentence and citations correct? Have previous researchers used GLW datasets in an ML framework for developing high-resolution datasets? Check the sentence and the associated citations again.
Lines 59-62 – Split them into two sentences
Line 62 – Abbreviate GDGI on its first mention
Line 76 – relies --> relying
Figs A7-A12 – SAHP --> SHAP
I noted that the livestock census data availability is incomplete, with the authors noting that "data in some provinces of some years can't be found" (line 135), yet there is insufficient discussion of how many data points are missing or how missing data patterns might bias the results or affect model training.
Citation: https://doi.org/10.5194/essd-2025-263-RC1
Data sets
CLRD-GLPS: A Long-term Seasonal Dataset of Ruminant Livestock Distribution in China's Grazing Production Systems (2000-2021) Using Stacking-based Interpretable Machine Learning Ning Zhan, Tao Ye, Mario Herrero, Jian Peng, Weihang Liu, Heng Ma https://doi.org/10.5281/zenodo.15347430
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