Effects of nitrogen fertilization and bioenergy crop species on central tendency and spatial heterogeneity of soil glycosidase activities

Bioenergy crops such as switchgrass (SG) and gamagrass (GG) can reduce fossil fuel use, and their yields are often enhanced by nitrogen fertilization. While many studies emphasize aboveground yields, belowground processes, including soil extracellular enzyme activities that reflect microbial nutrient acquisition and soil health, are also impacted by N fertilization. Extracellular glycosidases (AG, BG, BX, CBH) mediate decomposition of labile soil organic carbon and are informative indicators of soil C cycling. However, how N fertilization influences not only the mean activities but also the spatial heterogeneity of these enzymes in bioenergy cropping systems, and whether effects differ between SG and GG, remain insufficiently understood. The study tests three hypotheses: (1) N fertilization increases the central tendency (plot-mean) of all glycosidases with no differences between SG and GG due to both crops’ extensive root systems; (2) in previously unfertilized soils, N fertilization restructures spatial heterogeneity of AG, BG, BX, CBH and their sum (Cacq); (3) N effects on central tendency and spatial heterogeneity vary by enzyme type because of distinct enzyme characteristics.

Prior work shows heterogeneous responses of glycosidases to N addition depending on enzyme type, depth, and ecosystem. For example, in acidic forest topsoil N reduced BG but not AG, BX, or CBH, while alpine grassland topsoil showed no response. Deeper soil layers exhibited reductions in AG, BG, BX but not CBH. Meta-analyses indicate variable yet sometimes positive effects of N on glycosidases and C acquisition enzymes. Cropping systems also influence enzyme activities; higher AG and BG were reported in meadow or oat systems versus lower in corn/soybean, and BG varied among sorghum, cotton, and was enhanced across systems by N in some studies. Spatial heterogeneity of soil enzymes occurs from centimeter to kilometer scales, sometimes similar across scales. Spatial patterns can depend on plant root morphology, soil physical structure, and microbial community composition (e.g., saprotrophic basidiomycetes influencing BG and CBH). Spatial heterogeneity tends to be more evident in grasslands and forests than in agricultural soils. Effects of N on spatial patterns are rarely studied; a Mediterranean shrubland showed high N deposition tended to homogenize enzyme spatial patterns, while other work suggests N can increase spatial variability of microbial biomass, with effects contingent on site fertility. Overall, links between microbial biomass and enzyme spatial patterns are not always direct, warranting direct observations of enzyme spatial structure under N fertilization in bioenergy crops.

Study site and design: A bioenergy crop fertilization experiment was established in 2011 at Tennessee State University’s AREC, Nashville, TN (warm humid temperate climate, mean annual temperature 15.1 °C, precipitation ~1200 mm). Soils are Armour silt loam (Ultic Hapludalfs), pH ~5.97, organic matter ~2.4%. Treatments in a randomized block design included two crops (Alamo switchgrass and gamagrass) and three N levels: NN (0), LN (84 kg N ha⁻¹ yr⁻¹ as urea), HN (168 kg N ha⁻¹ yr⁻¹ as urea), with four replicate 3 × 6 m plots per treatment in the broader experiment. Fertilizer was applied manually in June/July after cutting. For the present spatial study, 12 plots were sampled (2 crops × 3 N × 2 replicates). Sampling: On June 6, 2015, 24 soil cores per plot (0–15 cm) were collected using a spatially explicit clustered random design with assigned x,y coordinates, totaling 288 cores. Samples were transported on ice; subsamples stored at 4 °C (chemistry) and −20 °C (enzymes). Pre-assay processing included sieving to 2 mm and removal of visible roots/rocks. Moisture and water pH (1:5) were measured. Enzyme assays: Activities of α-glucosidase (AG), β-glucosidase (BG), β-xylosidase (BX), and cellobiohydrolase (CBH) were quantified via fluorometric microplate assays using 4-methylumbelliferyl (MUB) substrates (MUB-α-D-glucopyranoside, MUB-β-D-glucopyranoside, MUB-β-D-xylopyranoside, MUB-β-D-cellobioside) at 200 mmol L⁻¹. Soil suspensions: 1.0 g soil homogenized in 50 mM acetate buffer (pH 5), final volume 125 mL. Plates incubated at 20 °C for 18–24 h depending on enzyme. Reference standard 10 μM MUB and quench controls included. Reactions stopped with 1.0 M NaOH (10 μL per well). Fluorescence was read at Ex 365 nm/Em 460 nm. Activities expressed as μmol g⁻¹ soil h⁻¹. Repeated tests yielded 2–8% CV per measurement. Statistical analyses: Descriptive statistics (means, frequency distributions), within-plot variances, and coefficients of variation (CV) were computed. Two-way ANOVA on plot means tested main and interactive effects of N and crop (P < 0.05). Variance homogeneity was assessed by Cochran’s C test; log transforms applied where needed. Sample size requirements (SSR) per plot were calculated for specified relative error γ using CI = X ± t0.975·s/√N and ln(N) = 2·ln(t0.975·CV) − 2·γ, enabling comparison of plot-level variation under a 10% relative error. Geostatistics: (1) Trend surface analysis (second-order polynomial of x,y; parameters β1–β5) to detect linear/nonlinear spatial trends (P < 0.05). (2) Moran’s I correlograms on TSA residuals to assess spatial autocorrelation over distances 0–5.5 m at 0.25 m increments; significant if outside 95% CI. (3) Spatial interpolation maps produced via inverse distance weighting (IDW; exponent 2.0) due to modest within-plot sample size (n = 24); cross-validation performed in ArcGIS 10.6.

- ANOVA results: Significant fertilization effect only for BX (P = 0.0077). Significant crop effects for BG (P = 0.0099), BX (P = 0.0233), and Cacg (sum of AG+BG+BX+CBH; P = 0.0178). Interaction (fertilization × crop) significant for BX (P = 0.0337). AG and CBH main effects not significant; CBH crop effect marginal (P = 0.0558). - Nitrogen effects on means: In switchgrass (SG), N fertilization elevated BX by 14% (LN) and 44% (HN) relative to NN. Other enzymes in SG and all enzymes in GG showed no significant N effect in ANOVA, though frequency distributions in SG shifted toward higher values with fertilization. - Crop species differences: Relative to SG, gamagrass (GG) exhibited higher activities by approximately 15% (BX), 31% (BG), 32% (Cacg), and 39% (CBH); AG showed no consistent crop difference. - Within-plot variability: Enzyme CVs ranged 15–47%. CVs for AG, CBH, and Cacg tended to be higher in SG than GG. Plots exceeding 40% CV were more frequent in SG (e.g., CBH in 4 of 6 SG plots) than in GG. Cochran’s C tests indicated fertilization generally did not change plot-level variance except AG in SG where HN had the highest variance. - Sample size requirements (10% relative error): SSRs were generally higher for NN than LN/HN in both crops (except CBH in GG). SSRs were larger in SG than GG under the same error. Extremes: 123 samples required for CBH under NN in SG vs 14 samples for BG under HN in GG. - Spatial structure: Trend surface analysis detected few significant linear/nonlinear trends per plot; about half of plots showed none. In SG, no significant surface trends occurred in HN plots; more trends appeared in LN than NN for AG and BG. In GG, NN plots generally lacked trends; more trends appeared in LN/HN for BG, CBH, and Cac. Moran’s I analyses showed more frequent significant spatial autocorrelations in fertilized plots (LN, HN) compared to NN for several enzymes (BG, BX, CBH in SG; AG, BX, CBH in GG), with significant distances ranging ~0.5–5.25 m and both positive and negative autocorrelation observed. - Spatial maps (IDW): GG displayed generally higher activity ranges than SG across enzymes and treatments. In SG, fertilization (particularly HN) produced stronger contrasts (hotspots) within plots, especially for BX. - Correlations: SOC correlated positively with several glycosidases (e.g., BG, CBH), whereas microbial biomass C (MBC) showed no significant correlations with glycosidases, suggesting substrate availability (SOC) rather than biomass abundance drives enzyme patterns. - Overall: N fertilization elevated central tendency and spatial heterogeneity of glycosidase activities, with effects contingent on crop (SG vs GG) and enzyme type (notably BX for means; BG in SG and CBH in GG for spatial heterogeneity).

The study addressed whether N fertilization and crop species modulate both mean levels and spatial heterogeneity of soil glycosidases in bioenergy crops. The significant increase of BX with N in SG, the higher enzyme activities in GG than SG for most enzymes, and the more pronounced spatial structuring under fertilization collectively support that N inputs can enhance both central tendency and spatial variability, but these responses are enzyme- and crop-dependent. Mechanistically, BX responsiveness may be linked to shifts in microbial community composition (e.g., Gram-negative bacteria) and increased SOC under N fertilization, enhancing substrate availability and enzyme production; by contrast, other enzymes (e.g., CBH) may be constrained by different microbial groups or resource dynamics. The decoupling between enzyme activities and microbial biomass (MBC) underscores that substrate availability (SOC) and nutrient hotspots induced by manual fertilizer application likely drive spatial patterns more than biomass abundance. Crop-specific differences (GG > SG for BG, BX, CBH, Cacg) may reflect distinct root chemistry and nutrient acquisition strategies that alter microbial substrate availability and enzyme investment. Spatial analyses indicate that fertilization can restructure within-plot spatial autocorrelation and produce more evident hotspots, especially in SG for BG and in GG for CBH. These findings refine our understanding of how N fertilization shapes belowground function in bioenergy systems and emphasize the importance of considering spatial heterogeneity alongside mean responses.

Nitrogen fertilization in bioenergy croplands increased the mean activity (central tendency) and strengthened spatial heterogeneity of soil glycosidases in topsoil, with responses differing by enzyme and crop. In switchgrass, BX increased by 14–44% under N, while gamagrass exhibited higher overall glycosidase activities than switchgrass (except AG). Fertilization consistently restructured spatial heterogeneity, notably for BG in SG and CBH in GG. These enzyme-specific, crop-dependent responses likely reflect interactions among plant roots, soil properties, and microbial community strategies governed by substrate availability. Future work should target specific enzymes within defined bioenergy cropping contexts, incorporate multi-depth and multi-season assessments, and optimize sampling designs to capture spatial variability with sufficient statistical power.

- The spatial analysis used two replicate plots per treatment (12 plots total), limiting power to detect treatment effects and increasing plot-to-plot variability. - Within-plot sample size was modest (n = 24), necessitating IDW interpolation rather than variogram-based kriging and yielding enzyme-specific sample size requirements for desired precision. - Only topsoil (0–15 cm) and a single sampling date (June 6, 2015) at one site were assessed, limiting temporal and vertical generalizability. - Manual fertilizer application may introduce uneven nutrient distribution, potentially amplifying spatial heterogeneity. - The uniform sampling effort across enzymes resulted in differing statistical precision (error terms) due to enzyme-specific variability. - Study focused on two perennial grasses at a single location; extrapolation to other crops, soils, or climates should be cautious.