Abstract

Soil organic carbon (SOC) is an important component of the global carbon (C) cycle, while tillage has been shown to significantly impact SOC stocks, its impact on the different SOC fractions is not well understood at global level, making it difficult to predict the behavior of SOC over time. Using data from 95 studies on a global level, we investigated the changes of various SOC fractions in 0-400 mm [dissolved organic C (DOC), particulate organic C (POC), easily oxidizable organic C (EOC), microbial biomass C (MBC), and mineral-associated organic C (MOC)] with NT application. This study also quantified the roles of different environmental and agronomic factors (e.g., climatic conditions, soil texture and nutrient conditions, study duration, and cropping intensity) in the changes of these SOC fractions. Compared to conventional tillage (CT), NT and reduced tillage (RT; tillage systems that are less intensive, with fewer trips in the field than CT) increased SOC concentrations, which were 7.4% higher in NT than in RT (percentage change based on weighted response ratio). Compared with CT, NT significantly increased DOC (17.6%), POC (11.7%), EOC (14.8%), MBC (33.1%), and MOC concentration (16.0%). Furthermore, POC, MBC, and MOC concentrations were 8.2, 34.1, and 4.4% higher in NT than in RT, respectively. Importantly, the increased SOC concentrations under NT were significantly correlated with MBC (R2 = 0.62) and POC (R2 = 0.55). Soil depth and mean annual temperature were the dominant factors affecting the response ratios of SOC fractions (DOC, EOC, MBC, MOC, and POC) under NT, followed by the duration of NT application. Our results suggest that SOC fractions, especially those driving soil biological activities (rather than total SOC), should be considered when evaluating the effects of conservation tillage practices on SOC sequestration. We highlight the development of site-specific strategies to effectively manage C stocks and to enhance local parameterization while developing biogeochemical and Earth system models.

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