关键词: 地下滴灌, BIOLOG, 基础呼吸, 微生物生物量, 土壤酶

Abstract: Soil organic nitrogen mineralization (SONM) is a critical N cycling process. Quantitative prediction of SONM is essential for assessing soil N supply capacity. In this study, four different mineralization models (i.e., effective accumulated temperature, hyperbolic, One-pool and Two-pool exponential models) were selected to fit SONM process in nine typical paddy soils in Dongting Lake region. The relationships between model parameters and soil organic nitrogen components were analyzed using correlation and path analysis. Results showed that SONM rate substantially varied with soil subtypes. The cumulative mineralized N measured throughout the whole incubation periods generally ranked as Fluvisols > Gleysols > Cambisols. Basing on the coefficients of determination (R²), root meansquare error estimations and model fitted parameter values, all  the four models could effectively simulate the SONM process, of which Twopool exponential model consistently had the best fitting effect, followed by hyperbolic, effective accumulated temperature and the One-pool exponential model. Pearson correlation analysis showed that model fitted SONM rate constants were insignificantly (P>0.05) correlated with soil organic nitrogen components due to their weak variability. On the contrary, all model parameters of characterizing SONM intensity or potential had significant positive correlations with amino acid N and ammonium N (r=0.755-0.950, P<0.05). Furthermore, amino acid N was the primary organic N component directly affecting soil mineralizable N capacity. Our results suggested that the Two-pool exponential model could simulate SONM process more effectively than others as it could reveal the mineralization processes of soil active and slow pools of mineralizable N simultaneously. Thus, Two-pool exponential model could improve the validity and accuracy of SONM prediction by assigning fixed values for the mineralization rate constants of soil active and slow mineralizable organic N pools.

Key words: subsurface drip irrigation, BIOLOG, basal respiration, microbial biomass, soil enzymes.