Introduction

There is a growing need for the development of alternative bioenergy sources to reduce the environmental impacts of anthropogenic activities [1]. Miscanthus × giganteus (giant miscanthus) is a C4 perennial grass that has gained interest as a bioenergy feedstock due to its high biomass production, low fertilizer input costs, and low environmental impact [1, 2]. The high biomass productivity of Miscanthus make it a promising candidate for production on marginal lands in the southeastern USA [3]. However, to develop Miscanthus as a suitable bioenergy feedstock requires knowledge on its biomass composition which can impact bioenergy conversion process efficiency [1, 4]. Miscanthus yields may vary considerably with environmental conditions, management practices, and harvest time. Studies have highlighted the importance of water availability and N fertilization particularly for Miscanthus grown on soils characterized by poor water and nutrient holding capacities [5]. While much effort has been focused on evaluating Miscanthus yield and aboveground biomass quality responses to different management practices [1, 5], understanding the variability in the chemical composition of different Miscanthus aboveground tissues (leaves vs. stems) has not received as much attention.

Studying the variability in the chemical composition of Miscanthus leaves and stems is important for several reasons: (1) The nutrient composition of biofuel raw materials can impact biomass energy chains because elements released during the feedstock combustion process can limit the effectiveness of conversion and can cause serious problems to power plants [6,7,8] and (2) knowing the chemical composition of harvested material left in the field can help producers understand feedstock management impacts to soil quality and nutrient status [9]. This information can facilitate a reduction in biomass conversion and fertilizer costs, and therefore maximize overall profitability. The result would then be a more economically sustainable biofuel production system.

Some important chemical properties of feedstock are its elemental contents including carbon (C), nitrogen (N), calcium (Ca), potassium (K), magnesium (Mg), phosphorus (P), sulfur (S), and silicon (Si). These nutrients are vital for plant growth, reproduction, and defense [10]. However, inorganic nutrients in feedstock biomass can cause serious problems to power plants through the accumulation of unwanted deposits on the surfaces of processing equipment. Miscanthus stems have been shown to have a lower inorganic nutrient content compared to leaves [6], resulting in enhanced fuel quality. The ratios between K, Ca, and Si should also be considered when evaluating biofuel quality because biofuels containing high Si or Ca in association with alkali metals such as K can result in degraded fuel quality and fouling and corrosion on reactor walls [6, 8].

In addition, Miscanthus can be useful in promoting the accumulation of soil organic matter in the topsoil due to the recycling of plant biomass [9]. For this reason, another important component of biomass quality is the composition of the tissue water extractable organic matter (WEOM). The WEOM derived from fresh or decomposing crop residues plays a critical role in soil nutrient availability and organic matter accumulation [11,12,13]. Once incorporated into the soil, plant WEOM merges with dissolved organic matter from soil fauna to create WEOM pools easily degraded by microbes [11, 14], resulting in C loss via CO2 respiration or available for leaching downward through the soil profile. The WEOM pool contains many kinds of structures such as carbohydrates, amino acids, phenolic compounds including lignin monomers and dimers, carboxylic acids, sterols, and heterocyclic N-containing compounds [15]. In a recent survey of surface soils (0–15 cm) from 124 sites across North America, Liptzin et al. [13] reported the effect of soil health practices on N indicators was lower for N pools such as water extractable organic N (WEON) and higher for microbially regulated processes such as potentially mineralizable N (PMN). Thus, understanding crop-derived WEOM composition can help inform on its behavior in agricultural soils.

The objectives of this study were to (1) describe the chemical characteristics of Miscanthus × giganteus aboveground tissues (leaves and stems) and (2) find relationships between Miscanthus tissue composition and soil characteristics at different depths. Miscanthus tissues were characterized using a published dataset including tissue C, N, macronutrient concentrations, and the composition of the tissue WEOM assessed using optical indices derived from absorbance and fluorescence measurements [16]. Soil chemistry measurements included total and mobile C and N (0–60 cm). Tissue and soil chemistry were modeled using multivariate adaptive regression splines (MARS) and linear mixed models (LMMs). The hypothesis was that the chemical composition of the Miscanthus aboveground tissues would be different between leaves and stems and that these differences would result in varying relationships between tissue and soil chemistry. Specifically, the leaves were expected to be higher in nutrients and WEOM associated with processes such as photosynthesis and pest defense mechanisms (e.g., ATP, cuticular waxes and precursors, lipids and sulfolipids, proteins, enzymes, and soluble carbohydrates). The WEOM of stems was expected to contain more structural materials (e.g., cellulose, hemi-cellulose, and lignin) along with smaller contents (at any point in time) of leaf-produced water-soluble compounds in the phloem being transported to support belowground structures and metabolism.

Materials and Methods

Site Description and Biomass Sample Collection

This study was conducted in a 13.84 ha field in TyTy, Georgia (GA), USA (31.44° N, 83.59° W), situated in the Southeastern Plains ecoregion (Fig. 1). The mean annual precipitation in the area is 1200 mm, and the mean annual temperature is 19 °C with the coldest temperatures occurring in January (11 °C) and the warmest in July (27 °C) [17]. Soils are well drained acidic loamy sands characterized by a sandy topsoil that extends about 40 cm and is underlain by a clay sublayer. The soils are predominantly classified as Tifton loamy sand with smaller areas of Stilson loamy sand, Carnegie sandy loam, and Alapaha loamy sand (Web Soil Survey https://websoilsurvey.sc.egov.usda.gov).

Fig. 1
figure 1

Map of the study site in TyTy, Georgia including the Miscanthus x giganteus aboveground tissue and soil core sampling locations

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In preparation for planting, the field was harrowed, and beds were laid on 90 cm spacing. In the spring of 2012, Miscanthus × giganteus (IL clone) rhizomes of ~ 20 cm in length were planted on 30 cm spacings in 91.4 cm rows to meet a target population of 35,800 plants per hectare. After initial establishment, irrigation and pesticides were not used and crops were maintained under minimal management. Inorganic fertilizer was applied based on University of Georgia Extension Service recommendations (aesl.ces.uga.edu; Table 1). Because the soils in this region are sandy and have low nutrient holding capacity, inorganic fertilizer was applied at rates needed to maximize Miscanthus production potential. Gypsum was applied to increase soil water infiltration and to supplement soil Ca. The Miscanthus was harvested once a year in 2013 and 2014. In 2015 and 2016, Miscanthus was harvested twice a year after reaching reproductive senescence (approximately 50 % of the field with seed heads), once in early June and once in October (Table 1). As part of a study identifying relationships between environmental variables and insect distributions in this same Miscanthus field [18], circular plots (r = 8 m) were marked and the Miscanthus was harvested from the surrounding areas before the 2016 October harvest. Miscanthus aboveground biomass was collected in October 2016 (Table 1) along a 15-m transect centered on each circular plot (n = 24; Fig. 1). One plant was collected every ~ 3.75 m, providing five sub-samples per circular plot. The plant tissue sampling and preparation have previously been described [19].

Table 1 Summary of the land management activities that took place at the research site during the study period in 2016
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Analysis of the Tissue Nutrient and WEOM Data

The chemical composition of the Miscanthus leaves and stems described in the present paper, including the C, N, and macronutrient contents, and the WEOM characteristics are based on the analysis of a published dataset [16]. The Miscanthus tissue chemical data was used to calculate the leaf-to-stem ratio of the most abundant nutrient components (detected in the entire sample set and at mean tissue concentrations ≥ 400 mg kg−1) including Ca, K, Mg, P, sulfur (S), and Si. The leaf-to-stem ratio of the tissue C and N was also calculated. To assess the feedstock quality of Miscanthus leaves and stems, several stoichiometric ratios were calculated including the Si:K and Ca:K ratios as well as the C:N, C:P, and N:P ratios [8, 20].

The tissue WEOM was characterized by its optical properties [16, 19]. Specifically, the absorbance and fluorescence excitation emission matrix (EEM) spectral data of the leaf and stem WEOM were used to obtain information about its chemical structure and composition [21, 22]. Two absorbance indices, the E2:E3 ratio and the slope ratio (SR) are inversely related to the relative molecular weight of WEOM [23]. The fluorescence index (FI) is used to distinguish WEOM derived from terrestrial sources (plants and soil; low values) vs. microbial sources (bacteria and algae; high values) [24]. The FI of non-degraded grass leachates has been reported to be ~ 1.4 [25]. The humification index (HIX) is used as an indicator of the degree of humification and molecular complexity of the WEOM [24]. The freshness index (β:α) is used to distinguish recently produced and bio-labile material from decomposed material [24].

The EEMs of 284 Miscanthus leaf and stem WEOM samples were used to develop a Parallel Factor (PARAFAC) analysis model. PARAFAC is a multiway statistical method used to decompose a suite of EEMs into scores and loading vectors that estimate the relative intensity of the fluorescing chemical components [26]. The wavelength ranges used for this purpose were 250–450 nm and 300–520 nm for the excitation and emission, respectively. Obvious residual peaks were not found after fitting the samples to this model which was validated using split-half analysis (98.7 % pass) and the analysis of residuals and loadings [26]. PARAFAC analysis was performed with a specifically written PARAFAC algorithm using Solo (Eigenvector Research Inc., version 9.0). The loading scores of the resulting PARAFAC model were used to estimate the relative contribution of each fluorescing component for each WEOM sample.

Soil Core Collection and Processing

Soil cores were collected in December 2016 (n = 55; Fig. 1) using a tractor mounted Giddings probe fitted with a clear plastic sleeve inside the sampling tube (Giddings Machine Company; 55 cm diameter core). Each core was plugged with paper towels to minimize disturbance of the soil surface layer, capped, transported to the lab, and stored at 4 °C until processed. Cores were sub-sampled by depth increment (0–15, 15–30, 30–45, and 45–60 cm) for analysis. Fresh soil was pushed through a 2-mm sieve, and sub-samples were taken for determination of water content (dried 24 h at 105 °C) and chloroform extraction. Soil passing the sieve was air-dried for 48 h followed by the disruption of clods with a wooden rolling pin. Roots were removed by hand, and the remaining sample was again sieved (2 mm). The sieved material was dried (24 h at 105 °C), and the mass remaining above the sieve was recorded as gravel. Particle size analyses were determined (n = 43) by the hydrometer method.

Oven-dried (60 °C) and ball-milled soil subsamples (~ 50 mg) were analyzed for total C (TC) and total N (TN) content on a Vario EL III combustion C-N analyzer (Elementar Americas Inc.). To obtain the mobile C and mobile N pools, fresh soil subsamples (~ 20 g) were placed in 100 mL beakers to which 100 µL ethanol-free chloroform was added. To ensure even chloroform distribution throughout the sample, the beakers were placed in a vacuum dessicator with moist paper towels and a beaker containing 50 mL of ethanol-free chloroform. The dessicator was evacuated three times to create a chloroform atmosphere and closed after the third evacuation. Samples were kept in the dessicator for 7 days at room temperature. The samples were then extracted with 100 mL of aqueous 0.5 M potassium sulfate (K2SO4) by shaking for 60 min on a reciprocal shaker at 180 rpm. The extracts and extractant solution blanks were passed through a 0.7-µm GF/F filter (Whatman) and analyzed for TC and TN on a Shimadzu TOC-VCPN analyzer with an ASI-V autosampler and a TNM-1 N module (Shimadzu Scientific Instruments). The soil chloroform extractant includes ammonium, nitrate, WEOM (water extractable organic carbon [WEOC] and nitrogen [WEON]), microbial biomass pools, and dissolved organic matter sorbed to soil particle surfaces, and is herein taken as an estimate of potentially mineralizable C and N (PMC and PMN) [13, 27, 28]. As these pools are subject to leaching and mineralization, the terms mobile C and mobile N will be used when referring to the combined pools. The soil TC, TN, and mobile C and N were multiplied by the soil bulk density to compute bulk soil concentrations (g−1 soil) and molar ratios (C:N, mobile C:N), respectively.

Statistical Analysis and Modeling

All statistical analyses were performed in R version 4.2.3 (R Core Team 2023), and the graphics were generated using the “ggplot2” R package [29]. Histogram plots and the Shapiro–Wilk and Kolmogorov–Smirnov tests for normality showed normal distributions for tissue nutrients and WEOM optical properties, and for soil chemistry. Site data were pooled to run unpaired t-tests to determine differences between tissue chemistry and WEOM properties from stems and leaves. One-way ANOVAs and Tukey’s honest significant difference post hoc tests were used to compare soil texture and chemistry among depths. A principal component analysis (PCA) was used to examine tissue composition using the “prcomp” function in the FactoMineR package, which uses singular value decomposition to examine covariances and correlations between the observations. The factoextra package [30] was used to evaluate the eigenvalues to determine the highest percentages of variance retained by each principal component. The cos2 and contribution values were used to determine the variables with the highest representation quality and contribution to each principal component.

Tissue composition (including the complete suite of macro- and micronutrients reported in [16]) and soil chemistry were modeled using MARS [31], a non-parametric, machine learning approach that accounts for linear and non-linear interactions between explanatory variables. MARS has been used to determine system behaviors and relationships of soil–plant dynamics [32]. To determine the most important soil variables that may be associated with the Miscanthus tissue composition, the nutrients and WEOM optical properties from leaves (n = 31) and stems (n = 31) were modeled as a function of soil chemistry (TC, TN, mobile C, and mobile N) from each depth increment (0–15, 15–30, 30–45, and 45–60 cm). The four soil variables had a variance inflation factor (VIF) < 5 and met the acceptable threshold for the degree of multicollinearity across the independent variables [33]. The total number of possible significant models was \((31+31)\bullet 4 = 248\).

The tissue nutrient-specific MARS models were built using the R package “earth” [34]. The MARS models are formed algorithmically through a multi-step process. A parent model is built through a forward pass that overfits the data, selecting all potential hinge functions given all variables, to reach the lowest possible residual sum of squares (RSS). A backward pass then iteratively prunes the parent model, deleting terms based on complexity penalty in which too many parameters cause the model to be too stiff to be useful for future predictions. A suite of model subsets is therefore formed, and a final model is chosen based on the lowest generalized cross validation (GCV) value:

$$GCV=\frac{RSS}{N{\left(1-\frac{n}{N}\right)}^{2}}$$
(1)

where \(N\) is the number of observations and \(n\) is the effective number of parameters

$$n=(H+1)+ \sigma \bullet H$$
(2)

where \(H\) is the number of hinge-function knots and \(\sigma\) is a penalty per knot (a value equal to 2 is appropriate when only additive terms are used in the model) [34]. The final resulting MARS model only consists of statistically significant input variables (Fig. S1).

The relative importance of each soil input variable for predicting Miscanthus tissue chemistry was determined by using the suite of the model subsets created by the MARS algorithm’s backward pass. This process involved removing a variable of interest from the model subsets and then calculating the sum of GCV changes across model subsets. This calculation was conducted for each variable, and the variable of maximum importance was identified as the variable that caused the largest overall summed GCV increase. For the remaining variables, their summed GCV changes were then normalized to the overall most important variable, which was set to a maximum value of 100.

Based on the MARS model and variable importance results, soil mobile C and mobile N were chosen to examine significant relationships with C, Ca, K, Mg, N, P, S, and Si from the Miscanthus leaves and stems using a series of LMMs. These nutrients were among the most explanatory variables determined by the PCA analysis and have been reported to influence the health, growth, and quality of Miscanthus [6, 7]. The LMMs were generated using the “lme4” R package [35]. All variables used for the LMMS were log transformed because the explanatory and response variables ranged over several orders of magnitude. The Akaike information criterion (AIC) values from each model were used to calculate a second-order bias correction estimator (AICC). The best fit models were chosen based on the AICC values and quality checks (normality of residuals, homogeneity, VIF, and the normality of random effects) observed using the “easystats” R package (Fig. S2) [36]. The “report” package [37] was used to summarize the model results and identify the significant relationships between variables (alpha = 0.05; Table S1). Additional LMMs were used to examine the relationships between Miscanthus tissue biomass (as dry weight) [16] and the selected tissue nutrients and soil chemistry.

Results

Tissue Nutrient Composition and Characteristics of the Water Extractable Organic Matter

Carbon dominated the tissue composition in both the Miscanthus leaves and stems (Table S2). After C, the most abundant nutrients in the Miscanthus leaves were N > K > Ca > P > Mg and K > N > Ca > P > Mg in the stems (Table S2). Significant relationships were observed between some of these tissue nutrients and biomass (as leaf and stem dry weight; Table S3). For instance, a positive relationship was observed between the tissue N concentration and biomass for both the leaves (p < 0.01) and stems (p < 0.05). However, for all the nutrients measured, the Miscanthus leaves contained significantly higher concentrations compared to the stems (p < 0.001), except for C which was measured at similar concentrations in the leaves and stems (Fig. 2). The mean Si:K ratios of the Miscanthus leaves and stems were 0.0935 ± 0.0288 and 0.0560 ± 0.0235, and the mean Ca:K ratios were 0.445 ± 0.112 and 0.145 ± 0.0351, respectively (Table 2). The Si:K and Ca:K ratios were significantly higher in the Miscanthus leaves compared to the stems (p < 0.001). The elevated N and P contents of the Miscanthus leaves compared to the stems (Fig. 2) resulted in lower C:N and C:P ratios of the former compared to the latter (p < 0.001; Table 2). The mean N:P ratio of the Miscanthus leaves (9.08 ± 1.53) was significantly higher than the N:P ratio of the stems (4.65 ± 2.10; p < 0.001; Table 2). A positive and significant correlation was found between the leaf N:P ratio and leaf biomass (p < 0.001; Table S4) but not for stem biomass.

Fig. 2
figure 2

Miscanthus leaf-to-stem ratios for the nutrients analyzed including C and N. The bars represent the mean leaf-to-stem ratios and the error bars represent standard error. The dashed line at a ratio value of one represents equal nutrient concentrations in the leaves and stems

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Table 2 Miscanthus tissue nutrient stoichiometric ratios and WEOM optical properties from leaves and stems reported as mean ± standard deviation. WEOM optical indices are summarized from [16]
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The mean spectral E2:E3 ratio of the Miscanthus leaf WEOM (3.19 ± 0.370) was significantly lower than the E2:E3 ratio of the stem WEOM (4.76 ± 2.41, p < 0.001; Table 2). The mean SR values of the Miscanthus leaves (0.593 ± 0.124) and stems (0.561 ± 0.125) were not statistically different (Table 2). The mean FI of the Miscanthus leaves (1.33 ± 0.106) was significantly lower than the stems (1.43 ± 0.212, p < 0.001), and the mean HIX of the leaves (0.332 ± 0.0908) was significantly higher than the stems (0.246 ± 0.147, p < 0.001; Table 2). The β:α was significantly higher in the leaves (0.519 ± 0.146) compared to the stems (0.465 ± 0.158, p < 0.05; Table 2).

PARAFAC modeling of the Miscanthus leaf and stem optical data resulted in three fluorescing components including one humic-like and two protein-like components (Fig. 3). Component 1 (C1) showed spectral characteristics similar to the amino acid tyrosine as was previously reported in wetland grass leachates [21] and fresh crop WEOM [22]. This protein-like component was more abundant in the Miscanthus stem (34.3 ± 9.02 %) compared to the leaf WEOM (29.5 ± 6.84 %, p < 0.01; Table 2). Component 2 (C2) was identified as a humic-like component [38] and was more abundant in the Miscanthus leaves (42.1 ± 9.35 %) compared to the stems (31.7 ± 13.4 %, p < 0.001; Table 2). Finally, component 3 (C3) was identified as another fresh-like component (tryptophan-like) which is known to originate from aromatic amino acids and from polyphenols contained in plants [39]. As in the other fresh-like component identified in this study, C3 was more abundant in the Miscanthus stems (34.0 ± 10.5 %) compared to the leaves (28.4 ± 6.51 %, p < 0.001; Table 2).

Fig. 3
figure 3

Excitation (dashed line) and emission (solid line) spectral loadings of the three-component PARAFAC model of the Miscanthus WEOM. Values are excitation and emission maxima for two fresh-like components (protein-like components 1 and 3) and one humic-like component (component 2)

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The PCA plot (Fig. 4) shows the within-field variation in Miscanthus tissue chemical characteristics with clear separation between leaves and stems. Dimension 1 of the PCA explained 56.1 % of the variability in tissue chemistry with highest contributions from the tissue concentrations of Ca, S, N, Mg, and Si. The clear separation between the Miscanthus leaf and stem tissues on the PCA plot is due to the much higher concentration of these nutrients in the leaves compared to the stems (Fig. 2). Dimension 2 of the PCA explained 24.5 % of the variability in tissue chemistry and the highest contributing factors to this variability were the tissue WEOM optical properties. Specifically, this variation was driven by the WEOM composition such as the presence of recalcitrant, humic-like material (C2, HIX) vs. labile, fresh-like material (C1, C3, β:α; Fig. 4).

Fig. 4
figure 4

Principal component analysis (PCA) plot showing correlations between Miscanthus tissue types (leaves and stems) and measured tissue nutrients and WEOM

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Soil Texture and Chemical Characteristics

The soil characteristics summarized below (Table 3 and S5) describe soils (0–60 cm) collected in 2016 after 5 years of Miscanthus production (Table 1). The percent gravel ranged from 9.69 to 12.7 % and was not different throughout the soil profile. The mean percentage of sand was highest in the top 30 cm of soil (69.5 ± 7.02 %) and was dominated by coarse, medium, and fine sand particle sizes. The percentage of silt was low (~ 6 %) throughout the soil profile, and the percentage of clay in the 30–45- and 45–60-cm depth increments was significantly higher than their preceding increment (p < 0.05; Table 3).

Table 3 Soil texture and chemistry reported as mean ± standard deviation for each depth increment
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The mean soil TC was highest at the surface (0–15 cm, 13,327 ± 2,737 kg ha−1), decreased by 15 % in the 15–30-cm increment (11,387 ± 3,005 kg ha−1), and decreased by more than 30 % over the 30–45-cm (7,630 ± 2,973 kg ha−1) and the 45–60-cm increments (5,246 ± 2,562 kg ha−1, p < 0.05; Table 3). Similarly, the mean soil TN was highest at the surface (841 ± 160 kg ha−1) and decreased by 12 and 14 % in the 15–30-cm (739 ± 170 kg ha−1) and 30–45-cm (637 ± 170 kg ha−1) increments, but the slight decrease moving to the 45–60-cm increment was not significant (591 ± 150 kg ha−1; Table 3). The greater C loss rate relative to N across depths resulted in significant narrowing of the soil C:N ratio in the 30–45-cm (12.0 ± 3.56) and 45–60-cm depth increments (8.82 ± 3.41, p < 0.05; Table 3). Soil mobile C and mobile N content were higher at the soil surface (0–15 cm, 166 ± 52.9 kg ha−1 and 24.3 ± 8.14 kg ha−1, respectively) and decreased down the soil profile (Table 3). The soil mobile C decreased in the 15–30-cm (138 ± 50.0 kg ha−1) and the 45–60-cm (127 ± 49.2 kg ha−1) increments, while the soil mobile N decreased in the 15–30-cm (14.6 ± 5.58 kg ha−1) and the 30–45-cm increments (11.6 ± 4.51 kg ha−1, p < 0.05; Table 3). At the soil surface (0–15 cm), the mobile C content had a positive and significant relationship with both the Miscanthus leaf and stem biomass (p < 0.05; Table S6). The opposite was true for the surface soil mobile N content which had a negative, significant relationship with leaf and stem biomass (p < 0.05; Table S6). The soil mobile C:N ratio was lowest in the top 15 cm (6.99 ± 1.65) and increased in the 15–30-cm (9.81 ± 2.69) and the 30–45-cm increments (13.4 ± 3.07, p < 0.05; Table 3). However, even at a depth of 60 cm, the mobile C and N represented 2.4 and 1.6% of the total C and N respectively.

Examination of soil chemistry across the four depth increments sampled here (Table 3) indicated that (1) TC declined by 15 %, 33 %, and 31 % relative to each preceding increment; (2) a TN decline of 12 % and 14 % was significant only between the 0–15 and 15–30-cm increments; (3) the bulk soil C:N ratio narrowed with each increment from 15 to 60 cm; (4) mobile C decline was significant only between the 0–15 and 15–30 increments; (5) mobile N declined through the top three increments; (6) the mobile C:N ratio widened through the top three increments; (7) the ratio of mobile C to total C widened with increasing depth; and (8) the ratio of mobile N to total N narrowed with increasing depth.

Relationships Between Miscanthus Tissue Composition and Soil Characteristics

Results of the MARS models and variable importance showed different soil variables for each soil depth increment that may associate with Miscanthus leaf and stem tissue chemistry (Fig. 5). The two most important variables in the 0–15-cm increment were the soil TC and mobile C contents. Soil mobile C and mobile N were the most important variables in the 15–30-cm increment. In the 30–45-cm increment, soil mobile C was the most important variable associated with Miscanthus leaf tissue chemistry, while soil mobile N was the most important variable associated with stem tissue chemistry. Soil TC was the most important variable in the 45–60-cm increment (Fig. 5). Based on these variable importance results, LMMs were used to further investigate the relationships between the mobile C and mobile N at each soil depth increment and the Miscanthus tissue concentrations of C, Ca, K, Mg, N, P, S, and Si (Tables 4 and S1). These nutrients are important for feedstock quality and contributed to the variation in tissue chemistry between the leaves and stems (Fig. 4). In addition, these nutrients may be important for Miscanthus biomass production as suggested by the positive and significant relationships observed between leaf and stem biomass and the concentrations of certain tissue nutrients (e.g., K and N in the leaves (p < 0.01) and C, Ca, K, Mg, N, and Si in the stems (p < 0.01 and p < 0.05); Table S3). Furthermore, the soil mobile C and mobile N pools showed significant relationships with the Miscanthus leaf and stem biomass (Table S6). The mobile C in the 0–15-cm soil increment had significant and positive relationships with leaf and stem biomass (p < 0.05) while the 45–60-cm increment had a significant and negative relationship only with the stem biomass (p < 0.05; Table S6). The direction of significant relationships of the mobile N pool with leaf and stem biomass was generally opposite to those of mobile C. The relationship of mobile N in the 0–15-cm soil increment with leaf and stem biomass was significant and negative, while the relationship was significant and positive for the stem biomass in the 45–60-cm increment (p < 0.05; Table S6).

Fig. 5
figure 5

Aggregated normalized generalized cross validation (GCV) values showing the most important soil chemistry variables (total C, total N, mobile C, and mobile N) for each soil depth increment that may associate with Miscanthus tissue chemistry in the leaves and stems. For easier cross-model comparisons since not all models were formed using the same number of subsets, these results are standardized to the maximum number of subsets that a variable appeared in, which was then summed across models

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Table 4 Linear mixed models containing nutrients from Miscanthus leaves (n = 24) and stems (n = 24) as dependent variables, mobile C or N at each soil depth as the independent variables, and field as a random effect (1|Field). Data were log-transformed because variables ranged over several orders of magnitude. Values represent the beta regression coefficients from each model (degrees of freedom = 13)
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The mobile C in the 0–15-cm soil depth had a significant and positive relationship with C concentrations in the leaves (p < 0.05; Table 4 and S1). The mobile C at 45–60-cm soil depth had a significant and negative relationship with both the leaf and stem C (p < 0.05; Table 4 and S1). The LMMs performed with the log-transformed biomass data showed that C concentrations in the stems had a significant and negative relationship with stem biomass (p < 0.01; Table S3). Finally, the soil mobile N at 0–15 cm had a significant and positive relationship with Si concentrations in the stems (Table 4 and S1). Si concentrations in the stems had a positive and significant relationship with stem biomass (p < 0.05; Table S3).

Discussion

Chemical Characterization of Miscanthus Leaf and Stem Tissues

The most abundant nutrients in the Miscanthus leaves were N > K > Ca > P > Mg and K > N > Ca > P > Mg in the stems (Table S2). These nutrients have been reported to dominate Miscanthus aboveground tissue composition [6, 7] and in this study were found to have significant relationships with leaf and stem biomass (Table S3). For instance, a positive relationship was observed between the tissue N concentration and biomass for both the leaves and stems. Miscanthus is typically harvested in the winter after senescence, allowing aboveground nutrient translocation to roots [40]. Because the crop for tissue analyses was harvested in October prior to senescence (Table 1), it is likely that nutrient concentrations from the June pre-senescence harvest would be similar. Thus, elevated nutrient concentrations in Miscanthus leaves harvested before senescence may contribute to degraded biofuel quality compared to stems [6, 8].

The Si:K and Ca:K ratio values observed for the Miscanthus leaves and stems (Table 2) were much lower than those reported in the literature [8], likely due to the wet digestion procedure used, which did not include hydrofluoric acid to decompose silicates into a colloidal form. The higher ratios observed in the leaves compared to the stems indicate the leaves have significantly higher Si and Ca concentrations (Fig. 2, Table S2) which can result in the potential degradation of biofuel quality (higher nutrient content) compared to the stems. Agricultural practices aimed at reducing the leaf component at harvest or promoting the translocation of nutrients from above- to belowground tissues (e.g., delaying the harvest), may improve the suitability of Miscanthus biofuels for combustion plants [8].

The mean C:N and C:P ratios of the Miscanthus leaves were lower compared to the stems (Table 2) and were lower than values reported in other studies [9, 20]. Because the tissues analyzed in this study were sampled in the fall, it is likely that nutrient translocation to the belowground tissues had not yet occurred [40]. These results suggest that if Miscanthus is harvested after nutrient re-translocation from above- to belowground tissues, it can potentially contribute significant amounts of N and P to the soil in addition to further increasing feedstock quality for biofuels. The N:P ratio was higher for the leaves and fell within the range reported for cereal, legume, and oilseed crops [41]. Crop N:P ratios have been used to understand the relationship between crop nutrient uptake and maximum crop yields [41] and in this study were positively correlated to leaf biomass (Table S4).

The significantly lower E2:E3 ratio of the leaves compared to the stems (Table 2) indicates a relatively higher abundance of soluble high molecular weight structures (> 1,000 Da) in the leaf WEOM [22]. The elevated E2:E3 ratio of the stem WEOM suggests that low molecular weight structures are more easily solubilized from the stems. In the stems, the high molecular weight aromatic and phenolic structures are associated with the lignin macro-molecule [42] which was not solubilized under the extraction procedure used in this study. The elevated E2:E3 ratio of the stem WEOM could also be due to the greater abundance of low molecular weight structures, such as sucrose, used for C allocation for stem growth [43]. The SR values observed for the Miscanthus leaves and stems (Table 2) are comparable to fresh plant and grass leachate values of ~ 0.6 [25].

Although the mean FI of the Miscanthus leaves was significantly lower than the stems (Table 2), both values are characteristic of plant derived WEOM [25]. The HIX, which increases with the degree of humification and molecular complexity, was characteristic of WEOM from fresh (undecomposed) crop residues [22]. The relatively low HIX values of both the leaf and stem (Table 2) WEOM may be due to the presence of polysaccharides and other weakly chromophoric biomolecules in the Miscanthus tissues. However, the significantly higher HIX values of the leaves compared to the stems suggests the leaf WEOM contains more aromatic and molecularly complex structures (carboxylic and phenolic structures) compared to the stem WEOM (sucrose and other structural precursors). The β:α, which increases with the amount of recently produced and unaltered WEOM (e.g., amino acids, neutral sugars), was significantly higher in the leaves compared to the stems (Table 2). These β:α values are similar to what has been reported for fresh plant leachates before photolytic and biological degradation [24].

The two protein-like PARAFAC components have similar spectral characteristics to the fluorescing amino acids tyrosine, tryptophan, and phenylalanine [24], but also to other non-protein compounds such as fresh polyphenols present in plant leachates [39]. For this reason, the protein-like components have been described as reflective of fresh-like organic matter [24]. The greater relative abundance of fresh-like fluorescing components in the stem compared to the leaf WEOM may be due to the assimilation and transport of N and recently produced organic structures from the shoots and roots to the stem [43]. The humic-like component C2, which can include structures such as polyphenols, highly aromatic and highly carboxylated compounds, and highly unsaturated aliphatics [44], was more abundant in the leaves (Table 2).

The nutrient and WEOM composition were different between the Miscanthus leaves and stems (Fig. 4) with the former characterized by elevated nutrient concentrations and dominated by large, aromatic structures and humic-like fluorescing compounds while the latter characterized by lower nutrient concentrations and dominated by small structures and fresh-like fluorescing compounds. If Miscanthus leaves are not harvested but left as residues on the soil surface, they may contribute molecularly complex WEOM structures to soil organic matter. Fallen Miscanthus leaves can represent an average annual input of 1.4 Mg C ha−1 and 16 kg N ha−1 [9]. Thus, understanding the differences in Miscanthus leaf and stem WEOM composition may lead to the development of management practices aimed at enhancing C allocation for improved feedstock quality and can provide information on crop residue inputs to agricultural soils. Within a crop field, biomass characteristics can vary with growing conditions such as soil type and chemistry [45] and with management practices such as fertilization and harvest timing [6, 7]. While it appears that the variability in Miscanthus tissue chemistry within a bioenergy field is largely driven by the tissue nutrient and WEOM composition (Fig. 4), factors related to the growing environment could be impacting this variability. In the next section, the relationships between Miscanthus tissue and soil chemistry are explored.

Soil Characteristics and Relationships to Miscanthus Tissue Chemistry

The soil TC, TN, mobile C, and mobile N were highest at the surface and decreased with soil depth (Table 3). Interestingly, the mobile C and mobile N still represented a small percentage of the TC and TN at a depth of 60 cm. The PMN pool measured by the anaerobic N mineralization assay for plant-available N is highly correlated with N measured by chloroform fumigation-incubation and largely measures microbial biomass N [28]. Furthermore, in keeping with the observations of Franzluebbers et al. [27] that dissolved organic C and N pools rendered from chloroform fumigation incubation without subtraction of a control are robustly related to active soil pools, this study suggests that pools rendered via chloroform fumigation-extraction can also be taken as indicating the presence of active pools deeper in the soil profile.

As mentioned in the Introduction, Liptzin et al. [13] reported that the effect of soil health practices on N indicators was lower for N pools such as WEON and higher for microbially regulated processes such as PMN, while also being puzzled over the lack of effects on soil and water-extractable C:N ratios. Taken together with the conceptual model that chloroform extracts are representative of labile C and N pools that are mobile through the soil profile, and considering the soil profile as a vertical system of organismal communities wherein substrates are supplied by leaching and biological or mechanical perturbation, these patterns may help address the concerns of [13]. In such a system, the shallow soil zones would contain a complex mix of plant materials having wide ranges of C:N ratios as well as the highest levels of perturbation. Although microbial community activity in soil also affects hydrologic flow [46], reductions in perturbation with increasing depth would also increase the reliance of soil communities on eluviation of soluble or colloidal C and N substrates via matrix flow. In this study, the bulk soil TC, TN, and C:N patterns observed with depth support continuous C loss via respiration or leaching and conservation of N as a macronutrient. The lack of mobile C change with depth in combination with the very narrow range of mobile C to TC ratios (0.0121–0.0242), stabilization of mobile N content, and widening of the mobile C:N ratio suggests that microbial communities are strongly regulating C and N cycling processes and that microbial community composition tends toward greater N use efficiency as substrates become increasingly processed. This hypothesis of microbial community driven C and N regulation also fits with the observations that soil organic C and potential C mineralization can be used as surrogates for N indicators in surface soil health assessments and that the signal of soil health N indicators is stronger for microbially regulated processes such as PMN than for N pool indicators such as TN, WEON, and autoclavable citrate extractable protein [13]. Chloroform-extractable C and N, like PMC, more effectively captures the microbial component of active organic C and N than does WEOM.

The MARS models indicated that mobile C and mobile N were the top variables associated with Miscanthus leaf and stem tissue chemistry (Fig. 5). The LMMs identified relationships between the soil mobile C and the Miscanthus tissue concentrations of C, and Si (Tables 4 and S1). While cause effect attribution cannot be determined from the limited scope of metrics addressed in this study, the relationships with these active soil C and N pools may play important roles in supplying plant-available N (negative relationship between the soil mobile N in the 0–15 cm increment and the leaf and stem biomass; Table S6) and regulating C and energy exchange between plant root exudates and soil microbial communities (positive relationships between leaf and stem biomass and mobile C in the 0–15- and 30–45-cm increments, and negative relationship between stem biomass in the 45–60-cm increment; Table S6). The dominance of small and fresh-like structures in the stem WEOM may also be linked to the organic matter exchanges between plant roots and soil.

While this study provides information on the relationships between Miscanthus tissue chemistry and soil C and N pools, a more detailed soil chemical analysis (including K, Mg, P, S, and Si contents) may provide additional insight on the influence of soil on the chemical characteristics of crop tissues. Additionally, this type of analysis can be done on larger datasets to identify management effects (fertilization and harvest time) on the relationships between crop tissue composition and soil characteristics. Finally, the results of this study suggest that more detailed experiments examining the relationships between individual components of plant biomass (leaves, stems, seeds, etc.) and mobile C and N pools throughout the soil profile may be critical to understanding the complex interactions between plants and soil and the capacity for soil to store and cycle C and nutrients.

Conclusions

Understanding the variation in the chemical composition of biofuel feedstock materials is important for developing effective biomass energy chains and for managing nutrient inputs to soils. In this study, Miscanthus biomass quality was found to be different between leaves and stems. The higher nutrient content, higher Si:K and Ca:K ratios, and lower C:N and C:P ratios of the Miscanthus leaves compared to the stems indicates that including leaves can result in reduced biofuel feedstock quality compared to stems alone. Furthermore, if Miscanthus leaves are not removed from the field after harvest, they may contribute humified and molecularly complex WEOM to the underlying soil. The soil chloroform extractable pool can serve as an indicator of the proportion of nutrients and C actively cycling through microbial biomass at any point in time. Characterizing crop residue WEOM inputs to soils can help us better understand the potential of soils to retain nutrients within the rhizosphere and respire versus sequester C. Although the objective of this study was not to test the effects of fertilization and harvest time on tissue chemistry, these results provide information that promote nutrient management practices (e.g., for crop nutrient allocation for increased biofuel quality) and incorporating crop residues into agricultural soils.