2.5. Statistical analyses
All soil spectra were smoothed and normalized before spectral analysis in MATLAB R2020b (The Math Works, Natick, USA). A one-way analysis of variance (ANOVA) was used to analyze the effects of various treatments on grain yield, NAE, changes in soil properties, soil NH4+-N and NO3-N contents, and plant N content. Differences between treatments were determined by comparing their means using the least significant difference (LSD) at the 0.05 probability level. A two-way ANOVA was applied to evaluate the main and interactive effects of crop rotation and fertilization on grain yield, NAE, changes in soil properties, soil NH4+-N and NO3-N contents, and plant N content. A pairwise samples test was conducted to compare the significant changes in soil properties and characteristic spectral bands after various treatments. Principal component analysis (PCA) was performed to illustrate the internal structure of spectra. A two-way permutational multivariate analysis of variance (PERMANOVA) was applied to evaluate the effect of crop rotation, fertilization, and their interaction on the changes in spectral structures. Regressions between spectral data and SOC, TN, and POXC were built by the partial least squares regression (PLSR) model. The five-fold cross-validation was performed to obtain the optimal number of latent variables in the PLSR model. The variable importance in projections of spectral bands in the PLSR model was used for identifying the great changes in molecular structure for soil organic matter and TN. Structural equation modeling (SEM) was applied to determine the direct and indirect effects of selected variables on grain yield and NAE based on known correlations. The chi-square,P -value, root-mean-square error of approximation (RMSEA), and good fit index (GFI) were used to evaluate the model fitness. All data processing, statistical analysis, and visualization were implemented in R 4.1.0 software (R Development Core Team, 2018).