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Subsoil organic carbon turnover is dominantly controlled by soil properties in grasslands across China
Catena ( IF 5.4 ) Pub Date : 2021-08-11 , DOI: 10.1016/j.catena.2021.105654
Yuehong Shi 1 , Xiaolu Tang 2, 3 , Peng Yu 1 , Li Xu 4 , Guo Chen 2, 3 , Longxi Cao 2, 3 , Ci Song 5 , Chunju Cai 6 , Jingji Li 2, 3
Affiliation  

Soil organic carbon turnover time (τ, year) is an important indicator of soil carbon stability and a major factor determining soil carbon sequestration capacity. Many previous studies have investigated τ in the topsoil layers, but little is known about subsoil τ and its environmental drivers. We estimated subsoil τ (the ratio of subsoil organic carbon (OC) stock to net primary production) using field observations from the published literatures and employed regression analysis and the structural equation model (SEM) to identify the effects of climate, soil, and vegetation variables on subsoil τ in China’s grasslands. The results showed that the average subsoil τ over 0.2–1 m varied greatly from 5.5 to 702.2 years, with a mean (±standard deviation) of 118.5 ± 97.8 years. Subsoil τ varied significantly among different grassland types, with 164.0 ± 112.0 years for alpine meadow, 107.0 ± 47.9 years for alpine steppe, 177.0 ± 143.0 years for temperate desert steppe, 96.6 ± 88.7 years for temperate meadow steppe, and 101.0 ± 75.9 years for temperate typical steppe. Subsoil τ was significantly negatively correlated (p < 0.05) with normalized difference vegetation index, leaf area index, and gross primary production. Mean annual (air) temperature and precipitation had a negative impact on τ, indicating a faster turnover of soil carbon with a changing climate. Based on SEM, soil properties mainly drove subsoil τ, challenging our current understanding and providing evidence that different factors control topsoil and subsoil τ. In this sense, different environmental factors should be considered to reliably predict soil carbon dynamics at the top of 1 m and subsoils in biogeochemical models or Earth system models at regional or global scales.

更新日期:2021-08-11
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