30-Month Pot Experiment: Biochar Alters Soil Potassium Forms, Soil Properties and Soil Fungal Diversity and Composition in Acidic Soil of Southern China

Abstract

Biochar has a significant impact on improving soil, nutrient supply, and soil microbial amounts. However, the impacts of biochar on soil fungi and the soil environment after 30 months of cultivation experiments are rarely reported. We studied the potential role of peanut shell biochar (0% and 2%) in the soil properties and the soil fungal communities after 30 months of biochar application under different soil potassium (K) levels (100%, 80%, 60%, 0% K fertilizer). We found that biochar had a promoting effect on soil K after 30 months of its application, such as the available K, water-soluble K, exchangeable K, and non-exchangeable K; and increments were 125.78%, 124.39%, 126.01%, and 26.63% under biochar and K fertilizer treatment, respectively, compared to control treatment. Our data revealed that p_Ascomycota and p_Basidiomycota were the dominant populations in the soil, and their sub-levels showed different relationships with the soil properties. The relationships between c_sordariomycetes and its sub-level taxa with soil properties showed a significant positive correlation. However, c_Dothideomycetes and its sub-group demonstrated a negative correlation with soil properties. Moreover, soil enzyme activity, especially related to the soil C cycle, was the most significant indicator that affected the community and structure of fungi through structural equation modeling (SEM) and redundancy analysis (RDA). This work emphasized that biochar plays an important role in improving soil quality, controlling soil nutrients, and regulating fungal diversity and community composition after 30 months of biochar application.

Keywords: biochar treatment; fungal community; potassium levels; soil properties.

Figure 1

Water-soluble K (A), available K (B), exchangeable K (C), and non-exchangeable K (D) during the incubation under different biochar and K fertilizer treatments. (Sampling time: 6 months, 12 months, 18 months, 24 months, 30 months). Different lowercase letter indicate significant differences according to Duncan’s test (p < 0.05).

Figure 2

Panel shows heatmaps of Spearman’s rank correlation coefficients of fungal alpha-diversity metrics with soil physicochemical properties for the entire set of soil samples (All) and subsets of different soil depths (K0, K100, CK0, and CK100). *** Correlation is significant at the 0.001 level. ** Correlation is significant at the 0.01 level. Blue indicates a negative correlation, and red indicates a positive correlation.

Figure 3

Two groups of comparison based on phylum and genus levels with K fertilizer and biochar addition. (A,B) and (C,D) represent the statistical results at phylum and genus levels, respectively. p < 0.05 marked as *, p < 0.01 marked as **.

Figure 4

Panel (A) shows the heatmap of Spearman’s rank correlation coefficients between relative abundances of major fungal taxa from phyla to genera (dominance of the relative abundance on average) and soil properties. *** Correlation is significant at the 0.001 level. ** Correlation is significant at the 0.01 level. * Correlation is significant at the 0.05 level. Blue indicates negative correlation, and red indicates positive correlation. Panels (B) shows the stepwise multiple regression (SMR) showing the total explanation rate of environmental variables to the richness of each species. *** Correlation is significant at the 0.001 level. ** Correlation is significant at the 0.01 level. * Correlation is significant at the 0.05 level.

Figure 5

Results of redundancy analysis of acidic soils. (A,B) represent soil chemical properties on phylum and genus levels. (C,D) represent soil enzyme activity on phylum and genus levels.

Figure 6

The structural equation modeling (SEM) shows the influences and effects of biochar and K fertilizer on soil properties, soil K components, soil enzyme activity, and soil fungi. The numbers above the arrows denote the standardized path coefficients. The red and yellow arrows indicate positive and negative effects, respectively (A). The standardized regression and influence effects of the structural equation modeling (SEM) (B). Stars denote significance at p < 0.05 and p < 0.01 probability levels (* and **, respectively).