Mendeley Data

Although the individual effects of biological and abiotic factors on aggregate stability are well understood, the mechanisms of their synergistic interactions under changing environmental conditions remain poorly understood. This significantly limits the precise regulation of aggregate stability. Therefore, when available evidence is synthesized, the following hypotheses are proposed: i) The stabilization mechanism of macroaggregates is dominated by both biotic and abiotic factors, and the contribution of biotic factors may be greater than that of abiotic factors for cultivated land. ii) The stabilization mechanism of microaggregates is dominated by nonbiological factors.
Single-factor analysis of variance (LSD at P < 0.05) was conducted using SPSS 16.0 (IBM SPSS Software, Armonk, NY, USA) to determine statistically significant changes in the MBC and MBN, TOC and its components (POC and MAOC), and aggregate stability indicators (RSI and RMI) between the growing and nongrowing seasons. A partial least squares structural equation model (PLS-SEM) was used in SmartPLS 4.1 software (SmartPLS GmbH, Germany) to fit the paths and determine the relative contributions of various factors to the stability of aggregates of different particle sizes during the growing and nongrowing seasons. P-values, T-statistics and variance inflation factor (VIF) were employed to assess the model suitability. Before fitting the structural equation model, we conducted Pearson correlation analysis on various influencing factors and stability indicators of aggregates of different fraction sizes in SPSS 16.0, extracted the factors related to them, and then fitted the structural equation model. After all nonsignificant indicators were excluded, the best-fitting model was obtained.