Canoco 5

The changes in soil biochemical characteristics and rice grain yields as affected by fertilization treatment were examined using the ANOVA method in Statistics 8.1 (Analytical Software Tallahassee, FL, United States). First, the data were subjected to standard tests to ensure they met the assumptions of normality. Furthermore, percentages data were arcsine transformed before analysis to normalize the variables. Tukey’s post hoc test was performed to compare means for the variables where the effects of treatments were significant. A Venn diagram was used to illustrate the number of related and distinctive OTUs in the samples and determine the similarity and coincide in the number of OTUs between the samples. Microbial alpha diversity indices, including Simpson’s and Shannon indices, were calculated using MOTHUR (Schloss et al., 2009 (link)). Species richness rarefaction curves were plotted against the series of data using the MicrobiomeAnalyst (Dhariwal et al., 2017 (link)). RDA was used to analyze the relationship strength between soil traits and soil bacterial diversity using the software package CANOCO5 (Microcomputer Power, Ithaca, NY, United States).

Three-way analysis of variance (ANOVA) was used for the analysis of barley genotype, [CO₂] and light intensity effects and their interactions. Fisher’s LSD ANOVA post-hoc test (p = 0.05) was used to analyze significant differences between means. Statistical analyses were conducted using the software STATISTICA 12 (StatSoft, Tulsa, CA, USA). The bar graphs representing means with standard errors were developed with the software SigmaPlot 11.0 (Systat Software, San Jose, CA, USA). The redundancy analysis (RDA) and biplot of RDA results were set up in the software CANOCO 5 (Microcomputer Power, Ithaca, NY, USA) [82 ].

The trimmed paired end reads were imported into the CLC Genomics Workbench version 11.01 and were processed using the CLC Microbial Genomics Module version 2.5.1 (CLC bio, Arhus, Denmark). The paired end reads were merged into one high quality representative sequence using CLC default parameters (Mismatch cost = 1, Minimum score = 40, Gap Cost = 4, Maximum unaligned end mismatches = 5). The sequences were then clustered into operational taxonomic unit (OTUs) at 97% identity threshold, followed by taxonomic annotation using SILVA database v132 (released on Dec 13, 2017) (44 (link)). Multivariate redundancy analysis (RDA) was employed to identify microbial signatures in different time-points or treatment groups using CANOCO 5 (Microcomputer Power, Ithaca, NY, USA) according to manufacturer’s instructions (45 ). Additionally, the linear discriminant analysis effect size (LEfSe) algorithm (46 (link)) was used to characterize the microbial differences between groups. Principal coordinate analysis (squared Bray Curtis distance) was employed to assess microbiota maturation in colon (day29 and day+3) by comparing to the relatively ‘mature’ post-weaning time-point (day+19) in rectal swabs.

The Shapiro−Wilk and Tukey’s honestly significant difference (HSD) tests (α = 0.05) were used to examine the normality and homoscedasticity of the residuals. Tukey’s HSD test (α = 0.05) was used to compare means. SAS 9.2 (SAS Institute, Inc., Cary, North Carolina, USA) was used to conduct statistical analyses using one-way analysis of variance (ANOVA). Origin Pro 2021 (Norhtampton, MA, USA) was used to evaluate correlations between belowground root morphological features, soil pH, biomass and N accumulation in the intercropping system. To detect significant changes between treatments, the international standard software CANOCO 5 and CanoDraw (Microcomputer Power, Ithaca, USA) were used for ranking procedures, and a linear model was chosen for redundancy analysis (RDA).

Statistics 8.1 analytical software was used to determine the analysis of variance among the treatments for each variable. Alpha diversity of bacteria and fungi including Simpson, Shannon, Chao1, and ACE indices was calculated using QIIME software (v1.8.0). Rarefaction curves of the species richness were plotted against the number of sequences, and the analysis of the dominant phyla was done using the Microbiome Analyst (Dhariwal et al., 2017 (link)). Redundancy analysis (RDA) was performed using the software package CANOCO5 (Microcomputer Power, Ithaca, United States) to measure soil properties and soil microbial diversity relationship. R (3.2) software was used to conduct correlation analysis among treatments for soil microbial abundance, soil properties, and grain yield. SmartPls3 software was used to analyze consistence multi-group analysis (MGAc) among the treatments for all attributes.

Microsoft Excel 2019 (Microsoft, Redmond, WA, USA) and Origin 8.5 software (Origin Lab, Northampton, MA, USA) were used for data processing and graph plotting, respectively. A one-way analysis of variance (ANOVA) was conducted with all data performed as mean (n = 3) followed by standard deviation. Further the least significant difference (LSD) test was adopted for multiple comparisons (p < 0.05 and p < 0.01) using SPSS Statistics 22 (IBM SPSS Statistics, Chicago, IL, USA) software. Pearson’s correlation coefficient (r) was used to test the correlation between variables and principal component analysis (PCA) was used to assess the relationships among all parameters in CANOCO 5 (Microcomputer Power, Ithaca, NY, USA).

Significant difference level of nifH gene abundance was measured by one-way ANOVA using SPSS version 19.0 (IBM Inc., USA). All T-RFLP profile fragments were included for principal component analysis (PCA) using CANOCO 5 (Microcomputer Power, Ithaca, NY) to examine the diazotrophic community structure. Shannon diversity was calculated based on T-RFLP profiles using R (version 3.2.2).