A high-resolution gridded grazing dataset of grassland ecosystem on the Qinghai–Tibet Plateau in 1982–2015

Nan, 孟楠 Meng; Wang, Lijing; Qi, Wenchao; Dai, Xiujuan J.; Li, Zuzheng; Yang, Yanzheng; Li, Ruonan; Ma, Jinfeng; Zheng, Hua

doi:10.1038/s41597-023-01970-1

Cited by 19 publications

(16 citation statements)

References 62 publications

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“…The spatial heterogeneity of resources and environmental factors have a significant impact on the distribution of livestock. Hence, based on the existing literature, we identified and obtained 13 SES factors that may potentially influence LSK D distribution 21 , 24 , 35 – 37 . The variables with global extents were selected to facilitate the replication of the current study in other geographic regions (Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

“…Environmental variables, such as precipitation, temperature, solar radiation, and vapor pressure deficit, were included to reflect climatological differences across regions. These environmental variables directly affect vegetation growth and indirectly impact livestock distribution by determining feed and water availability in a given area 36 , 37 , 48 . Additionally, the distance to settlements and water bodies was included as high-productive forage close to residential areas, and water bodies are more convenient for herders to allow livestock to graze 21 , 37 .…”

Section: Methodsmentioning

confidence: 99%

“…These environmental variables directly affect vegetation growth and indirectly impact livestock distribution by determining feed and water availability in a given area 36 , 37 , 48 . Additionally, the distance to settlements and water bodies was included as high-productive forage close to residential areas, and water bodies are more convenient for herders to allow livestock to graze 21 , 37 . Elevation was selected as it affects biotic variables, such as plant species composition and production, affecting livestock grazing distribution and movement 49 , 50 .…”

Section: Methodsmentioning

confidence: 99%

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Gridded livestock density database and spatial trends for Kazakhstan

Kolluru,

John,

Saraf

et al. 2023

Sci Data

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Livestock rearing is a major source of livelihood for food and income in dryland Asia. Increasing livestock density (LSKD) affects ecosystem structure and function, amplifies the effects of climate change, and facilitates disease transmission. Significant knowledge and data gaps regarding their density, spatial distribution, and changes over time exist but have not been explored beyond the county level. This is especially true regarding the unavailability of high-resolution gridded livestock data. Hence, we developed a gridded LSKD database of horses and small ruminants (i.e., sheep & goats) at high-resolution (1 km) for Kazakhstan (KZ) from 2000–2019 using vegetation proxies, climatic, socioeconomic, topographic, and proximity forcing variables through a random forest (RF) regression modeling. We found high-density livestock hotspots in the south-central and southeastern regions, whereas medium-density clusters in the northern and northwestern regions of KZ. Interestingly, population density, proximity to settlements, nighttime lights, and temperature contributed to the efficient downscaling of district-level censuses to gridded estimates. This database will benefit stakeholders, the research community, land managers, and policymakers at regional and national levels.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Gridded livestock density database and spatial trends for Kazakhstan

Kolluru,

John,

Saraf

et al. 2023

Sci Data

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“…Li et al established the relationship between various environmental factors (such as land, terrain, climate, and socioeconomic factors) and livestock density using the RF, and constructed a spatial distribution map of cattle and sheep in western China at a 1 km resolution 20 . Some scholars have realized the acquisition of livestock density gridded datasets in Qinghai-Tibet Plateau and Kazakhstan based on the RF, which can bridge the gap in the dynamic monitoring of the spatial distribution of livestock density in the region over a long period 21 , 22 . Cheng et al used RF to downgrade reared pigs from the administrative level to 30 × 30 arcsec (about 131 km) resolution 23 .…”

Section: Introductionmentioning

confidence: 99%

Mapping livestock density distribution in the Selenge River Basin of Mongolia using random forest

Liu,

Wang,

Yang

et al. 2024

Sci Rep

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Mapping dynamically distributed livestock in the vast steppe area based on statistical data collected by administrative units is very difficult as it is limited by the quality of statistical data and local geographical environment factors. While, spatial mapping of livestock gridded data is critical and necessary for animal husbandry management, which can be easily integrated and analyzed with other natural environment data. Facing this challenge, this study introduces a spatialization method using random forest (RF) in the Selenge River Basin, which is the main animal husbandry region in Mongolia. A spatialized model was constructed based on the RF to obtain high-resolution gridded distribution data of total livestock, sheep & goats, cattle, and horses. The contribution of factors influencing the spatial distribution of livestock was quantitatively analyzed. The predicted results showed that (1) it has high livestock densities in the southwestern regions and low in the northern regions of the Selenge River Basin; (2) the sheep & goats density was mainly concentrated in 0–125 sheep/km2, and the high-density area was mainly distributed in Khuvsgul, Arkhangai, Bulgan and part soums of Orkhon; (3) horses and cattle density were concentrated in 0–25 head/km2, mainly distributed in the southwest and central parts of the basin, with few high-density areas. This indicates that the RF simulation results effectively depict the characteristics of Selenge River Basin. Further study supported by Geodetector showed human activity was the main driver of livestock distribution in the basin. This study is expected to provide fundamental support for the precise regulation of animal husbandry in the Mongolian Plateau or other large steppe regions worldwide.

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“…used machine learning algorithms to produce gridded livestock distribution data at 1 km resolution for 2000-2015 in western China at five year interval, based on county-level livestock census and 13 factors including NDVI, topography, climate, and population density . A contribution from Meng et al (2023) brought forth annual longer time-series grazing maps using a random forest model, integrating climate, soil, NDVI, water distance, and settlement density to decompose county-level livestock census data to a 0.083° (≈10 km at the equator) grid for 1982-2015 (Meng et al, 2023). Similarly, Zhan et al (2023) also used a random forest algorithm to combine eleven influence factors to provide a winter and summer grazing density map at a 500 m resolution for 2020.…”

Section: Introductionmentioning

confidence: 99%

Annual high-resolution grazing intensity maps on the Qinghai-Tibet Plateau from 1990 to 2020

Zhou,

Niu,

et al. 2023

Preprint

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Abstract. Grazing activities constitute the paramount challenge to grassland conservation over the Qinghai-Tibet Plateau (QTP), underscoring the urgency for obtaining detailed extent, patterns, and trends of grazing information to access efficient grassland management and sustainable development. Here, to inform these issues, we provided the first annual Gridded Dataset of Grazing Intensity maps (GDGI) with a resolution of 100 meters from 1990 to 2020 for the QTP. Five most commonly used machine learning algorithms were leveraged to develop livestock spatialization model, which spatially disaggregate the livestock census data at the county level into a detailed 100 m× 100 m grid, based on seven key predictors from terrain, climate, land cover and socioeconomic factors. Among these algorithms, the extreme trees (ET) model performed the best in representing the complex nonlinear relationship between various environmental factors and livestock intensity, with an average absolute error of just 0.081 SU/hm2, a rate outperforming the other models by 21.58 %~414.60 %. By using the ET model, we further generated the GDGI dataset for the QTP to reveal the spatio-temporal heterogeneity and variation in grazing intensities. The GDGI indicates grazing intensity decreased from 1990 to 2001 period, and fluctuated thereafter. Encouragingly, comparing with other open-access datasets for grazing distribution on the QTP, the GDGI has the highest accuracy, with the determinant coefficient (R2) exceed 0.8. Given its high resolution, recentness and robustness, we believe that the GDGI can significantly enhance understanding of the substantial threats to grasslands emanating from overgrazing activities. Furthermore, the GDGI product holds considerable potential as a foundational source for research, facilitating rational utilization of grasslands, refined environmental impact assessments, and the sustainable development of animal husbandry. The GDGI product developed in this study is available at https://figshare.com/s/ad2bbc7117a56d4fd88d (Zhou et al., 2023).

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A high-resolution gridded grazing dataset of grassland ecosystem on the Qinghai–Tibet Plateau in 1982–2015

Cited by 19 publications

References 62 publications

Gridded livestock density database and spatial trends for Kazakhstan

Gridded livestock density database and spatial trends for Kazakhstan

Mapping livestock density distribution in the Selenge River Basin of Mongolia using random forest

Annual high-resolution grazing intensity maps on the Qinghai-Tibet Plateau from 1990 to 2020

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