4.7 Article

Extracellular Enzyme Patterns Provide New Insights Regarding Nitrogen Transformation Induced by Alkaline Amendment of Acidic Soil

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AGRONOMY-BASEL
卷 12, 期 12, 页码 -

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MDPI
DOI: 10.3390/agronomy12123015

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nitrification; mineralization; extracellular enzyme activity; alkaline amendment; acidic soil

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This study examines the effects of alkaline materials on nitrogen transformation in acidic soils. The results show that the addition of CSMP significantly increases soil pH and enhances net nitrogen mineralization, nitrification, and ammonification rates. Machine learning analysis reveals that soil pH is the best predictor for nitrogen mineralization and ammonification, while nitrate is the best predictor for nitrification.
Nitrogen (N) availability is generally a limiting factor in highly acidic soil, which could be improved by amending these soils with alkaline materials. Soil extracellular enzyme activity (EEA) plays an important role in N transformation; a current knowledge gap is how this occurs in acidic soils amended with alkaline material. The present 45-day incubation experiment was designed to examine the effects of different amounts of alkaline materials (urea and/or calcium-silicon-magnesium-potassium fertilizer (CSMP)) on N transformation. The results show that soil pH significantly increased after the CSMP amendment (1.2 units) and increased soil net N mineralization (R-min), net nitrification (R-nit) rates, and net ammonification (R-amo) rates. CSMP amendment changed the different soil EEA but with differing or opposing effects, e.g., R-nit was positively correlated with the activities of L-leucine aminopeptidase, beta-xylosidase, alpha-glucosidase, and N-acetyl-beta-glucosaminidase but negatively correlated with beta-1, 4-glucosidase and beta-cellobiosidase. A machine learning analysis indicated that the best predictor for R-min and R-amo was soil pH, and for R-nit, it was nitrate. The results of the present study improve our understanding of N availability in acidic soils amended with materials to control soil pH. Such knowledge could lead to more bespoke nutrient management planning at the field scale, leading to better agronomic and environmental outcomes.

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