Microorganisms · 2025
Differential Effects of Biogas Slurry Topdressing on Winter Wheat (Triticum aestivum L.) Soil Enzyme–Microbe Interactions
Yin, D., Wang, B., Qin, J., Liu, W., Niu, X., Chen, D., Zhu, J., Zhang, F.
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Differential Effects of Biogas Slurry Topdressing on Winter …
Abstract
This study explored how top-dressed biogas slurry at winter wheat’s (Triticum aestivum L.) jointing stage (JS) and grain-filling period (GP) affects soil enzyme–microbe interactions, aiming to address nutrient supply–crop demand mismatches. A field experiment with five treatments (water [CK], chemical fertilizer [CF], and three biogas slurry topdressing regimes [S1–S3]) was conducted. Soil samples (0–20 cm) were collected at JS, flowering stage (FS), GP, and reaping period (RP) to analyze soil properties (total nitrogen [TN], available phosphorus [AP], available potassium [AK], soil organic matter [SOM], ammonium nitrogen [AN], pH), enzyme activities (urease [UE], neutral phosphatase [NP], sucrase [SC], catalase [CAT]), and microbial community abundance (via Illumina NovaSeq sequencing). Results showed biogas slurry altered enzyme activities, microbial structure (e.g., Actinomycetota, Ascomycota), and their interactions by regulating soil properties. JS application boosted Pseudomonadota and UE activity, GP application increased Ascomycota and CAT activity, and S3 had the most complex enzyme–microbe network, enhancing nutrient cycling. The analysis indicated that UE activity was strongly and positively correlated with several bacterial phyla (e.g., Planctomycetota, Verrucomicrobiota) (p < 0.01) and fungal phyla (e.g., Ascomycota) (p < 0.01).
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This study explored how top-dressed biogas slurry at winter wheat’s (Triticum aestivum L.) jointing stage (JS) and grain-filling period (GP) affects soil enzyme–microbe interactions, aiming to address nutrient supply–crop demand mismatches. A field experiment with five treatments (water [CK], chemical fertilizer [CF], and three biogas slurry topdressing regimes [S1–S3]) was conducted. Soil samples (0–20 cm) were collected at JS, flowering stage (FS), GP, and reaping period (RP) to analyze soil properties (total nitrogen [TN], available phosphorus [AP], available potassium [AK], soil organic matter [SOM], ammonium nitrogen [AN], pH), enzyme activities (urease [UE], neutral phosphatase [NP], sucrase [SC], catalase [CAT]), and microbial community abundance (via Illumina NovaSeq sequencing). Results showed biogas slurry altered enzyme activities, microbial structure (e.g., Actinomycetota, Ascomycota), and their interactions by regulating soil properties. JS application boosted Pseudomonadota and UE activity, GP application increased Ascomycota and CAT activity, and S3 had the most complex enzyme–microbe network, enhancing nutrient cycling. The analysis indicated that UE activity was strongly and positively correlated with several bacterial phyla (e.g., Planctomycetota, Verrucomicrobiota) (p < 0.01) and fungal phyla (e.g., Ascomycota) (p < 0.01).
This study explored how top-dressed biogas slurry at winter wheat’s (Triticum aestivum L.) jointing stage (JS) and grain-filling period (GP) affects soil enzyme–microbe interactions, aiming to address nutrient supply–crop demand mismatches. A field experiment with five treatments (water [CK], chemical fertilizer [CF], and three biogas slurry topdressing regimes [S1–S3]) was conducted. Soil samples (0–20 cm) were collected at JS, flowering stage (FS), GP, and reaping period (RP) to analyze soil properties (total nitrogen [TN], available phosphorus [AP], available potassium [AK], soil organic matter [SOM], ammonium nitrogen [AN], pH), enzyme activities (urease [UE], neutral phosphatase [NP], sucrase [SC], catalase [CAT]), and microbial community abundance (via Illumina NovaSeq sequencing). Results showed biogas slurry altered enzyme activities, microbial structure (e.g., Actinomycetota, Ascomycota), and their interactions by regulating soil properties. JS application boosted Pseudomonadota and UE activity, GP application increased Ascomycota and CAT activity, and S3 had the most complex enzyme–microbe network, enhancing nutrient cycling. The analysis indicated that UE activity was strongly and positively correlated with several bacterial phyla (e.g., Planctomycetota, Verrucomicrobiota) (p < 0.01) and fungal phyla (e.g., Ascomycota) (p < 0.01).
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