TY - JOUR
T1 - Spatiotemporal variability in drivers of grassland degradation and recovery under economic transformation in Mongolia
AU - Li, Ting
AU - Li, Sha
AU - Li, Pengfei
AU - Huang, Jing
AU - Wang, Juanle
AU - Ochir, Altansukh
AU - Yang, Meihuan
AU - Wang, Tao
AU - Shun Chan, Faith Ka
N1 - Publisher Copyright:
© 2025 Elsevier Ltd
PY - 2025/10
Y1 - 2025/10
N2 - Over the past few decades, grasslands in countries traditionally reliant on livestock industry have faced dual pressures from climate change and economic transformation driven by international market demands. However, understanding on the spatiotemporal variability of grassland change drivers remains insufficient, hindering the formulation of effective strategies for mitigating grassland degradation. This study conducted a comparative analysis on grassland degradation and recovery in Mongolia over two time periods (2000–2010 and 2000–2020) by integrating the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Net Primary Productivity (NPP). The spatial livestock density across the country was derived by downscaling the Global Gridded Livestock dataset. Based on this, a Bayesian Belief Network (BBN) model incorporating twelve driving factors was developed and integrated with Geographic Detector to jointly identify the spatiotemporal heterogeneity of grassland change drivers. The results indicated that grasslands in Mongolia have gradually shifted from non-significant degradation to non-significant recovery, with restored areas covering 19.89 % of the country's land area by 2020. Precipitation, evapotranspiration, and temperature were the primary drivers of grassland change in both periods, with their combined contributions being 74.66 % in 2010 and 66.76 % in 2020. However, nitrogen dioxide (NO2) concentration and the human footprint have exerted greater impacts on grassland dynamics than livestock grazing under the economic transformation, which weakened grassland recovery in northern regions of the country. Additionally, expanding transportation in southern provinces and nomadic activities in the western regions are expected to exacerbate grassland degradation in Mongolia. This study provides insights into preventing grassland degradation risks during economic transformation in traditional livestock countries.
AB - Over the past few decades, grasslands in countries traditionally reliant on livestock industry have faced dual pressures from climate change and economic transformation driven by international market demands. However, understanding on the spatiotemporal variability of grassland change drivers remains insufficient, hindering the formulation of effective strategies for mitigating grassland degradation. This study conducted a comparative analysis on grassland degradation and recovery in Mongolia over two time periods (2000–2010 and 2000–2020) by integrating the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Net Primary Productivity (NPP). The spatial livestock density across the country was derived by downscaling the Global Gridded Livestock dataset. Based on this, a Bayesian Belief Network (BBN) model incorporating twelve driving factors was developed and integrated with Geographic Detector to jointly identify the spatiotemporal heterogeneity of grassland change drivers. The results indicated that grasslands in Mongolia have gradually shifted from non-significant degradation to non-significant recovery, with restored areas covering 19.89 % of the country's land area by 2020. Precipitation, evapotranspiration, and temperature were the primary drivers of grassland change in both periods, with their combined contributions being 74.66 % in 2010 and 66.76 % in 2020. However, nitrogen dioxide (NO2) concentration and the human footprint have exerted greater impacts on grassland dynamics than livestock grazing under the economic transformation, which weakened grassland recovery in northern regions of the country. Additionally, expanding transportation in southern provinces and nomadic activities in the western regions are expected to exacerbate grassland degradation in Mongolia. This study provides insights into preventing grassland degradation risks during economic transformation in traditional livestock countries.
KW - Bayesian belief network
KW - Driving forces
KW - Grassland dynamics
KW - Livestock industry
KW - Mongolia
UR - https://www.scopus.com/pages/publications/105013756895
U2 - 10.1016/j.jenvman.2025.127097
DO - 10.1016/j.jenvman.2025.127097
M3 - Article
C2 - 40850258
AN - SCOPUS:105013756895
SN - 0301-4797
VL - 393
JO - Journal of Environmental Management
JF - Journal of Environmental Management
M1 - 127097
ER -