Enhancing Morchella Mushroom Yield and Quality Through the Amendment of Soil Physicochemical Properties and Microbial Community with Wood Ash

Abstract

Morchella mushroom is a nutritionally rich and rare edible fungus. The traditional cultivation model, which relies on expanding the cultivation area to meet market demand, is no longer sufficient to address the rapidly growing market demand. Enhancing the yield and quality of Morchella without increasing the cultivation area is an intractable challenge in the development of the Morchella mushroom industry. Against this backdrop, this study investigates the effects of different amounts of wood ash (WA) application on the yield and quality of Morchella, and conducts an in-depth analysis in conjunction with soil physicochemical properties and microbial communities. The results indicate that the application of WA improves both the yield and quality of Morchella, with the highest yield increase observed in the WA2 treatment (4000 kg/hm²), which showed a 118.36% increase compared to the control group (CK). The application of WA also modified the physicochemical properties of the soil, significantly improving the integrated fertility index of the soil (IFI, p < 0.05). The soil microbial community structure was altered by the addition of WA. Redundancy analysis (RDA) revealed that pH and total potassium (TK) were the main environmental factors influencing the bacterial community, while pH, TK, and total nitrogen (TN) were the main factors influencing the fungal community structure. In addition, bacterial community diversity tended to increase with higher WA application rates, whereas fungal community diversity generally showed a decreasing trend. Furthermore, the relative abundance of beneficial microbial communities, such as Acidobacteriota, which promote the growth of Morchella, increased with higher WA application, while the relative abundance of detrimental microbial communities, such as Xanthomonadaceae, decreased. Partial least squares path model (PLS-PM) analysis of external factors affecting Morchella yield and quality indicated that WA application can alter soil physicochemical properties and soil microbial communities, thereby improving Morchella yield and quality. Among these factors, soil fertility was identified as the most important determinant of Morchella yield and quality.

Keywords: Morchella yield; microbial community; soil fertility; wood ash.

Figure 1

Yield (A), morphology (B), and nutrient composition (C–F) of the fruiting bodies of Morchella in different groups of WA additions. Different lowercase letters indicate significant differences (p < 0.05) in the same indicator under different vegetation types. * (0.01 ≤ p < 0.05), ** (0.001 ≤ p < 0.01), *** (p < 0.001).

Figure 2

Variation in soil physicochemical properties. (A–I) shows the variation curves of integrated fertility index (IFI), pH, organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkaline-hydrolysed nitrogen (AN), available phosphorus (AP), and rapidly available potassium (AK), respectively. Different lowercase letters indicate significant differences (p < 0.05) in the same indicator under different vegetation types.

Figure 3

Microbial community diversity in different groups of WA additions. (A) The Goods_coverage index of the bacterial community. (B,C) The Shannon index and Simpson index of the bacterial community. (D) The Goods_coverage index of the fungal community. (E,F) The Shannon index and Simpson index of the fungal community. (G,H) NMDS analysis of bacterial community and fungal community. * (0.01 ≤ p < 0.05), ** (0.001 ≤ p < 0.01).

Figure 4

β-diversity analysis of bacterial communities. (A,B) Bacterial community composition at the phylum and family level in different treatment groups. (C,D) LEfSe elucidation of bacterial communities at the phylum and family level.

Figure 5

β-diversity analysis of fungal communities. (A,B) Fungal community composition at the phylum and family level in different treatment groups. (C,D) LEfSe elucidation of fungal communities at the phylum and family level.

Figure 6

Combined RDA and Mantel tests analysis of bacterial (A) and fungal (B) communities. (A) the numerical labels represent bacterial taxa, specifically, a1: Proteobacteria, a2: Acidobacteriota, a3: Actinobacteriota, a4: Bacteroidota, a5: Gemmatimonadota, a6: Chloroflexi, a7: Methylomirabilota, a8: Myxococcota, a9: Vicinamibacteraceae, a10: Enterobacteriaceae, a11: Oxalobacteraceae, a12: Pseudomonadaceae, a13: Gemmatimonadaceae, a14: Comamonadaceae, a15: Sphingomonadaceae, a16: Sphingobacteriaceae, a17: Nitrosomonadaceae, a18: Xanthomonadaceae, a19: Micrococcaceae, a20: Flavobacteriaceae, a21: Microbacteriaceae, a22: Rhizobiaceae, a23: Clostridiaceae, a24: Caulobacteraceae, and a25: Pyrinomonadaceae. (B) the numerical labels represent fungal taxa, specifically, b1: Ascomycota, b2: Mortierellomycota, b3: Fungi_phy_Incertae_sedis, b4: Basidiomycota, b5: Nectriaceae, b6: Chaetomiaceae, b7: Bombardiaceae, b8: Mortierellaceae, b9: Fungi_fam_Incertae_sedis, b10: Aspergillaceae, b11: Lasiosphaeriaceae, b12: Plectosphaerellaceae, b13: Pseudeurotiaceae, b14: Sordariales_fam_Incertae_sedis, b15: Helotiaceae, and b16: Microascaceae.

Figure 7

Mantel correlation analysis based on bacterial communities. 1: IFI, 2: BD, 3: SMC, 4: pH, 5: SOM, 6: TN, 7: TP, 8: TK, 9: AN, 10: AP, 11: AK, 12: Proteobacteria, 13: Acidobacteriota, 14: Actinobacteriota, 15: Bacteroidota, 16: Gemmatimonadota, 17: Chloroflexi, 18: Methylomirabilota, 19: Xyxococcota, 20: Vicinamibacteraceae, 21: Enterobacteriaceae, 22: Oxalobacteraceae, 23: Pseudomonadaceae, 24: Gemmatimonadaceae, 25: Comamonadaceae, 26: Sphingomonadaceae, 27: Sphingobacteriaceae, 28: Nitrosomonadaceae, 29: Xanthomonadaceae, 30: Micrococcaceae, 31: Flavobacteriaceae, 32: Microbacteriaceae, 33: Rhizobiaceae, 34: Clostridiaceae, 35: Caulobacteraceae, 36: Pyrinomonadaceae, 37: bacterial Shannon diversity index, 38: bacterial Simpson diversity index. * (0.01 ≤ p < 0.05), ** (0.001 ≤ p < 0.01), *** (p < 0.001).

Figure 8

Mantel correlation analysis based on fungal communities. 1: IFI, 2: BD, 3: SMC, 4: pH, 5: SOM, 6: TN, 7: TP, 8: TK, 9: AN, 10: AP, 11: AK, 12: Ascomycota, 13: Mortierellomycota, 14: Fungi_phy_Incertae_sedis, 15: Basidiomycota, 16: Nectriaceae, 17: Chaetomiaceae, 18: Bombardiaceae, 19: Mortierellaceae, 20: Fungi_fam_Incertae_sedis, 21: Aspergillaceae, 22: Lasiosphaeriaceae, 23: Plectosphaerellaceae, 24: Pseudeurotiaceae, 25: Sordariales_fam_Incertae_sedis, 26: Helotiaceae, 27: Microascaceae, 28: fungal Shannon diversity index, 29: fungal Simpson diversity index. * (0.01 ≤ p < 0.05), ** (0.001 ≤ p < 0.01), *** (p < 0.001).

Figure 9

PLA-PM analysis of the main factors of quality and yield enhancement.