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Sci Total Environ ; : 174686, 2024 Jul 09.
Article in English | MEDLINE | ID: mdl-38992360

ABSTRACT

Soil net nitrogen mineralization (Nmin), a microbial-mediated conversion of organic to inorganic N, is critical for grassland productivity and biogeochemical cycling. Enhanced atmospheric N deposition has been shown to substantially increase both plant and soil N content, leading to a major change in Nmin. However, the mechanisms underlying microbial properties, particularly microbial functional genes, which drive the response of Nmin to elevated N deposition are still being discussed. Besides, it is still uncertain whether the relative importance of plant carbon (C) input, microbial properties, and mineral protection in regulating Nmin under continuous N addition would vary with the soil depth. Here, based on a 13-year multi-level field N addition experiment conducted in a typical grassland on the Loess Plateau, we elucidated how N-induced changes in plant C input, soil physicochemical properties, mineral properties, soil microbial community, and the soil Nmin rate (Rmin)-related functional genes drove the responses of Rmin to N addition in the topsoil and subsoil. The results showed that Rmin increased significantly in both topsoil and subsoil with increasing rates of N addition. Such a response was mainly dominated by the rate of soil nitrification. Structural equation modeling (SEM) revealed that a combination of microbial properties (functional genes and diversity) and mineral properties regulated the response of Rmin to N addition at both soil depths, thus leading to changes in the soil N availability. More importantly, the regulatory impacts of microbial and mineral properties on Rmin were depth-dependent: the influences of microbial properties weakened with soil depth, whereas the effects of mineral protection enhanced with soil depth. Collectively, these results highlight the need to incorporate the effects of differential microbial and mineral properties on Rmin at different soil depths into the Earth system models to better predict soil N cycling under further scenarios of N deposition.

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