2022
DOI: 10.1016/j.scitotenv.2022.157562
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Optimal ranges of social-environmental drivers and their impacts on vegetation dynamics in Kazakhstan

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Cited by 47 publications
(16 citation statements)
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References 89 publications
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“…This was due to the significant time it takes for rainfall to convert into sub-surface moisture and contribute to vegetation growth (Zhang et al 2012, Zhou et al 2015. These inter-seasonal relationships are in agreement with previous studies that have attempted to analyze the influence of seasonal rainfall on vegetation in semi-arid ecosystems (Wen et al 2019, Shi et al 2021, Venkatesh et al 2022.…”
Section: Joint Interaction Of Seasonal Rainfall and Winter Percent Sn...supporting
confidence: 89%
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“…This was due to the significant time it takes for rainfall to convert into sub-surface moisture and contribute to vegetation growth (Zhang et al 2012, Zhou et al 2015. These inter-seasonal relationships are in agreement with previous studies that have attempted to analyze the influence of seasonal rainfall on vegetation in semi-arid ecosystems (Wen et al 2019, Shi et al 2021, Venkatesh et al 2022.…”
Section: Joint Interaction Of Seasonal Rainfall and Winter Percent Sn...supporting
confidence: 89%
“…Consequently, Asian drylands are identified as landuse and climate change hotspots that are vulnerable to ecological and environmental degradation (de Beurs et al 2018). However, little is known about long-term vegetation changes, grassland degradation and drivers associated with human-environmental interactions (Abel et al 2021, Venkatesh et al 2022. Investigating vegetation dynamics and the underlying drivers is crucial for preventing further degradation and restoring degraded grassland ecosystems (Meyfroidt et al 2016).…”
Section: Introductionmentioning
confidence: 99%
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“…The GD requires that the continuous independent variable data are discretized into categorical variables before computation . This study employed the R package “GD” (version 4.2.3) to discretize the continuous independent variable data using equal breaks, quantile breaks, natural breaks, and geometric breaks . The classification method and interval number that maximized the q -value of the input continuous independent variable were selected as the optimal discretization method …”
Section: Materials and Methodsmentioning
confidence: 99%
“…In addition to climate change, socio-economic factors such as population pressure and income levels are also closely related to the S vpt changes (Venkatesh et al, 2022). The results indicate that under the low-emission Earth's Future 10.1029/2022EF003395 scenario, the S vpt in sparsely populated areas with lower income generally presents a positive trend, that is because the region is subjected to less human disturbance (Ge et al, 2021), and growth is mainly driven by the positive effect of climatic factors (Philippon et al, 2014).…”
Section: Potential Relationship Between Socio-economy and S Vptmentioning
confidence: 99%