2021
DOI: 10.1002/ldr.3976
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Multiple trajectories of grassland fragmentation, degradation, and recovery in Russia's steppes

Abstract: Over the 20th century, the Eurasian steppes underwent drastic land-cover changes.Much progress was made studying cropland expansion and the post-1990 (i.e., post-Soviet) agricultural land abandonment in Eurasia. However, the alteration of steppe landscapes may include other disturbances, such as oil and gas development, formal and informal roads and garbage dumps, which were not systematically documented.Considering the example of the steppe Orenburg Province in Russia, we reconstructed agricultural land-cover… Show more

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Cited by 16 publications
(5 citation statements)
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References 99 publications
(147 reference statements)
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“…The relative importance of the seven factors affecting vegetation vulnerability to drought was identified using the boosted regression trees (BRT) model, which was widely used in related studies (Jiang et al, 2019b;Li et al, 2020a;Prishchepov et al, 2021). The BRT is a machine learning method that uses regression and boosting, and randomness is utilized to improve its predictive performance (Elith et al, 2008).…”
Section: Boosted Regression Treesmentioning
confidence: 99%
“…The relative importance of the seven factors affecting vegetation vulnerability to drought was identified using the boosted regression trees (BRT) model, which was widely used in related studies (Jiang et al, 2019b;Li et al, 2020a;Prishchepov et al, 2021). The BRT is a machine learning method that uses regression and boosting, and randomness is utilized to improve its predictive performance (Elith et al, 2008).…”
Section: Boosted Regression Treesmentioning
confidence: 99%
“…We also found the vegetation browning in higher latitudes in Central Siberia Highlands with a mainly positive CRTP ( Figure 6 C). This phenomenon well corresponded to the degradation of grasslands 52 and boreal forests 53 in many regions of Asia during recent decades caused by drought and overgrazing. 53 , 54 …”
Section: Discussionmentioning
confidence: 60%
“…First, considering the underlying nonlinearities and interactions among the rural livelihood, land use decisions, and family traits (Kuang et al, 2019), we used a non‐parametric boosted regression tree to analyze the influences on the agricultural income of rural households arising from a series of external factors, with special attention to land management intensity. The use of boosted regression trees has grown in popularity recently as it is viewed as a powerful multivariate analysis tool in the land system science domain due to its capability to reveal complex nonlinearities and handle large datasets that may have outliers (Ma et al, 2020; Prishchepov et al, 2021). Aside from the metrics of agricultural input, a set of survey variables that characterize the household, namely, farm size, education, and health, were selected as the predictors in the model.…”
Section: Methodsmentioning
confidence: 99%