Biomass maturity as a key driver of green manure decomposition and soil nutrient dynamics

Abstract

Green manuring plays a vital role in enhancing soil fertility and organic matter dynamics in sustainable cropping systems, yet its effectiveness largely depends on the biochemical quality and maturity of the incorporated biomass. To elucidate the influence of crop type and development stage of buried plant biomass on soil traits, a 17-week pot experiment was conducted using safflower (Carthamus tinctorius), radish (Raphanus sativus), and white mustard (Sinapis alba) plants, harvested at two maturity stages: after 11 weeks (biomass A) and 12.5 weeks (biomass B) of cultivation. Biomass maturity exerted a stronger influence than crop identity on plant composition (fat, NDF, cellulose, hemicellulose), amount of residual plant biomass, and on soil properties (β-glucosidase, urease, pH). The more matured biomass (B) contained higher carbohydrates and fat contents, and despite higher recalcitrance indices, it decomposed more completely, resulting in lower residue retention. Crop-specific responses of soil traits were the most distinct at the early harvest stage: radish biomass A enhanced soil enzyme activities and total N, whereas white mustard, particularly biomass B, promoted soil P, Mg, and K availability alongside elevated N-acetyl-β-D-glucosaminidase activity. Safflower decomposed more slowly, indicating potential for longer-term carbon stabilization but limited immediate fertility gains. Overall, harvest timing emerged as a critical determinant in tailoring green manures for both nutrient cycling efficiency and soil carbon stabilization.

Introduction

Green manuring is a widely adopted practice in sustainable and organic agriculture for maintaining soil fertility (Frøseth et al., 2014; Garcia-Franco et al., 2015), improving soil structure (Ma et al., 2024; Antosh et al., 2022), and enhancing carbon sequestration (Singh et al., 2023; Zhang et al., 2025). Green manuring particularly through the incorporation of cover crops or catch crops into soil at the green or early flowering stage (Singh et al., 2023), is a widely applied practice in organic farming. It delivers both economic benefits (Antosh et al., 2022) and ecological services that enhance soil fertility and sustainable production. By incorporating fresh plant biomass into soil, green manures provide easily decomposable organic substrates that stimulate microbial activity, nutrient mineralization, and aggregation processes. Major reported effects hitherto include improved biomass production (Antosh et al., 2022), enhancement of soil physical, chemical (Adetunji et al., 2020) and biological properties (dos Santos Cordeiro et al., 2021; Feng et al., 2021), higher soil organic matter (SOM) quality as well as C (Bogužas et al., 2015) and N contents (Liu et al., 2020), reduced nutrient leaching (Couëdel et al., 2018a), and improved nutrient availability (Su et al., 2022).

However, the benefits of green manuring depend largely on the biochemical quality and maturity of the biomass at incorporation, which govern its decomposition rate and subsequent effects on nutrient availability and soil organic carbon (SOC) stabilization. For instance, the efficacy of green manuring depends on processes following biomass incorporation through tillage (Hu et al., 2023; Li et al., 2024; Zhang et al., 2023). Incorporated biomass residues release nutrients through mineralization and transformation (Zhang et al., 2023; Sharma et al., 2000; Liu et al., 2008; Xie et al., 2022), with release dynamics influenced by residue stoichiometry, particularly the C:N ratio (Ranaivoson et al., 2022). These processes are accompanied by enhanced enzymatic activity (Xu W. et al., 2023; Piotrowska-Dlugosz and Wilczewski, 2020; Xu J. et al., 2023), which further improves nutrient availability (Xu W. et al., 2023; Solangi et al., 2019; Dong et al., 2021) and short-term use efficiency (Muhammad et al., 2022; Wang et al., 2025). Consequently, green manuring often leads to higher crop yields (Zhang et al., 2023; Sharma et al., 2000; Xie et al., 2022; Gautam et al., 2021).

The decomposition dynamics of green manures are closely linked to their chemical composition, including the proportions of carbohydrates, lignin, cellulose, hemicellulose, and fat (Sharma et al., 2000; Liu et al., 2008). A key factor shaping these outcomes is the maturity stage of the incorporated biomass (Sandhu et al., 2022). Younger residues typically contain easily degradable lipids, proteins, and non-structural carbohydrates (Feng, 2022; Jenkinson, 1965; Gunnarsson et al., 2008), whereas more mature residues are enriched in cellulose, hemicellulose (Henriksen and Breland, 1999), and lignin (Jensen et al., 2005; Moore et al., 1999), conferring stability and recalcitrance (Hadas et al., 2004). Therefore, identifying the optimal harvest stage that balances nutrient release and carbon stabilization remains a major challenge in optimizing green manuring systems. Despite the recognized importance of maturation stage at harvesting for biomass quality, most studies on green manures have emphasized species selection or management frequency (Singh et al., 2023; Antosh et al., 2022; Zhang et al., 2023; Sharma et al., 2000; Liu et al., 2008; Xie et al., 2022), while the influence of biomass maturity, particularly in short-duration crops remains poorly quantified. A deeper understanding of how subtle differences in crop maturity alter biochemical composition, decomposition patterns, and soil responses is essential to refine green manure management for both short-term fertility improvement and long-term carbon stabilization.

To address this research gap, we conducted a 17-week pot experiment using three green manure crops—safflower (Carthamus tinctorius), radish (Raphanus sativus), and white mustard (Sinapis alba), harvested at two maturity stages (11 and 12.5 weeks). The objectives were to (i) evaluate how crop type and maturity stage affect biomass composition and decomposition, (ii) assess their impacts on soil enzyme activities and nutrient availability, and (iii) identify the optimal maturity stage for maximizing both soil fertility and carbon stabilization potential of green manures.

Materials and methods

The crop biomass acquirement

Incorporated plants were applied as aboveground biomass: radish (Raphanus sativus L.), safflower (Carthamus tinctorius L.), and white mustard (Sinapis alba L.) were harvested from fields near Nová Ves, Brno (49°06′25.7″ N, 16°18′15.3″ E) after 77 days (11 weeks; biomass A) or 87 days (12.5 weeks; biomass B; Table 1). Plants were cut at ground level, oven-dried at 60 °C to constant weight, analysed (details in the section Plant and soil analyses) and stored.

Soil preparation and experimental design

The experiment was conducted at Agricultural Research, Ltd. (Troubsko, Czech Republic; 49°17′49.8″ N, 16°49′13.0″ E). Arable topsoil (0–15 cm) was collected from fields managed conventionally in a cereal–rapeseed–pea rotation. The site is moderately warm and dry (mean annual temperature 9.0 °C, precipitation 526 mm). Geologically, it is underlain by loess/loess loam of the Bohemian Massif. The soil was classified as a Haplic Luvisol (WRB) with silty clay loam texture (USDA). The primery chemical properties were: total C 14.0 g·kg⁻¹, total N 1.60 g·kg⁻¹, P 0.10 g·kg⁻¹, S 0.15 g·kg⁻¹, Ca 3.26 g·kg⁻¹, Mg 0.24 g·kg⁻¹, K 0.23 g·kg⁻¹; pH (CaCl₂) 7.3. Soil was homogenized and sieved (2 mm, Retsch GmbH, Germany).

The trial was conducted in rectangular boxes (57 × 37 cm; 10 kg soil each). Two semipermeable polyamide mesh bags (Uhelon 130 T, 42 nm pore size, SILK and PROGRESS, Czech Republic), each containing 20 g of dry biomass (equivalent to 2 g·kg⁻¹ soil), were placed in each box and covered with 2 cm soil layer (Supplementary Figure S1). All treatments (Table 2) were arranged in triplicate (n = 3).

The experiment was conducted from 16 February to 16 June 2022 (120 days). Environmental conditions were maintained at 9/16 °C (night/day), and each box was watered weekly with 1.5 L of distilled water to simulate alternating dry and moist periods. Urea was applied at a rate of 3.44 g per box on days 15 and 35 after incubation began. This meant than soil was fertilized with urea (46% N, AGRO CS a.s., Czech Republic) at dose of 0.688 g N·kg⁻¹ (150 kg N·ha⁻¹), applied in two splits.

During sampling at the end of the trial, soil was gently removed, mesh bags collected, and soil sampled from the 0–2 mm layer beneath each bag. Residual biomass (RB; g·bag⁻¹) was oven-dried (65 °C) and weighed.

Plant and soil analyses

Plant biomass

Fibre fractions were determined as acid-detergent fibre (ADF) and neutral-detergent fibre (NDF) (ANKOM, 2006), using ANKOM 220 fibre analyser (ANKOM Technology, USA). Acid-detergent lignin (ADL) was measured according to ISO 13906:2008 (ISO_13906, 2008). Hemicellulose was calculated as NDF – ADF and cellulose as ADF – ADL (Rinne et al., 1997). Crude protein was determined by the Kjeldahl method using Kjeltec™ 2300 Analyzer (FOSS, Denmark) (AOAC, 2023) and calculated with equation: protein content = total N × 6.25. Crude fibre was determined by two-step hydrolysis (H₂SO₄ followed by KOH) with ash correction. Reducing sugars were measured by the Luff–Schoorl method (Pomeranz and Meloan, 1994; Marrubini et al., 2017). Ash was determined according to (ISO_6865, 2000). Fat content was determined gravimetrically using the Soxhlet extraction with petroleum ether (Garcı́a-Ayuso and Luque de Castro, 1999).

Soil

Fresh soil was sieved (2 mm; Retsch, Germany) and soil subsamples were processed differently according to which analyses they were used for. Air-dried subsamples were analysed for pH(CaCl₂) ISO 10390:2005 (ISO_10390, 2005), oxidizable carbon (Cox), determined by sulfochromic oxidation ISO_14235 (1998), and total nitrogen (TN) ISO 13878:1998, measured by dry combustion (ISO_13878, 1998), using a Vario Macro Cube (Elementar Analysensysteme GmbH, Langenselbold, Germany). The Cox_N ratio was calculated from Cox and TN. Soil phosphorus (P), magnesium (Mg) and potassium (K) were quantified in the soil extract, prepared according to the method based on Mehlich III reagent (Mehlich, 2008), using the Agilent 7700x ICP-MS featuring the Octopole Reaction System (Agilent Technologies, Japan).

Soil enzyme assays

Sieved soil (<2 mm) was used fresh or stored at 4 °C for dehydrogenase activity (DHA) determined by the TTC method (Doi and Ranamukhaarachchi, 2009) expressed as µg TPF·g⁻¹·h⁻¹. Freeze-dried soil was analysed for β-glucosidase (GLU; C-mineralising), urease (Ure; N-mineralising), N-acetyl-β-D-glucosaminidase (NAG; N-mineralising), phosphatase (Phos; P-mineralising), and arylsulfatase (ARS; S-mineralising) following (ISO_20130, 2018). Activities were measured spectrophotometrically at λ = 405 nm (PNP assays) or 650 nm (urease), expressed as nmol PNP·g⁻¹·min^-1 (enzymes) or nmol NH₃·g⁻¹·min⁻¹ (urease).

Stoichiometric metrics

Nutrient acquisition ratios were calculated according to Cui et al. (2022), using the equations for C (Equation 1) and N (Equation 2).

Microbial resource limitation was evaluated by vector analysis according to Moorhead et al. (2016), using the Equations 3, 4:where ARCTG2 denotes the arctangent.

Statistic analyses

All data (plant biomass and quality traits, soil chemical and biological parameters) were analysed in R v4.3.1 (R_Core_Team, 2023). Pearson’s correlations were used to characterise relationships, interpreted as moderate (0.5–0.7), high (0.7–0.9), or very high (>0.9) (Hinkle et al., 2003). Figures were created using ggplot2 0.5–0.7 = moderate, 0.7–0.9 = high, and >0.9 = very high correlation. Figures were created using ggplot2 package (Wickham, 2016).

One-way and two-way ANOVA with Type I sums of squares were applied at α = 0.05 (Zar, 1984), using the FactoMineR (Lê et al., 2008) and Factoextra (Kassambara and Mundt, 2020) packages. Tukey’s HSD (α = 0.05) was used for post hoc comparisons. Results are presented as standard error of the mean (±SEM) (Faraway, 2014).

Model assumptions were tested at α = 0.05. Normality of residuals was evaluated with Kolmogorov–Smirnov and Anderson–Darling tests (nortest package). Homoscedasticity was assessed by Bartlett’s and Fligner–Killeen tests. Diagnostic plots included residuals vs. fitted values, Q–Q plots, Cook’s distance, and leverage plots (Faraway, 2014; Pekár and Brabec, 2016). Distributional shape was further tested using D’Agostino’s K-squared (skewness) and Anscombe–Glynn (kurtosis) tests (moments package (Komsta and Novomestky, 2022).

Results

Biodegradation and properties of plant biomass

Chemical and nutritional traits of plant biomass, which determine decomposition rates, varied significantly with both crop species and harvest time (one-way ANOVA; partial eta-squared; Supplementary Table S1; Figures 1A–H).

FIGURE 1

Carbon content (C, % DM) was lower in radish compared to safflower and white mustard, and also differed between RadA and RadB (Figure 1A). Nitrogen content (N, % DM) did not vary among crop species but was higher in RadB than in RadA (Figure 1B). Ash content (% DM) was highest in radish for both harvests; however, later harvest increased Ash in both radish and safflower (Figure 1C).

Carbohydrates (CH, % DM) in biomass A showed a decreasing trend from radish (highest) to white mustard (lowest), whereas in biomass B radish and white mustard were comparable, and both SafB and WmuB increased with ripening time (Figure 1D). Fat (% DM) content showed the strongest contrast between harvest stages in white mustard, with the lowest values in WmuA and the highest in WmuB (Figure 1E).

Neutral detergent fibre (NDF) and acid detergent fibre (ADF, % DM) decreased in radish and safflower biomass B compared to biomass A. In contrast, WmuB had significantly higher NDF, ADF, and acid detergent lignin (ADL, % DM) than the other crops at the later harvest (Figures 1F–H). ADL in biomass A was highest in SafA but declined in SafB, while RadB and WmuB were higher than RadA and WmuA (Figure 1H).

Similar to NDF, ADF, and ADL, crude fibre (CF) and cellulose (CEL, % DM) values were highest in WmuB (Figures 1I,J). In biomass A, CEL was reduced in SafA. For safflower, CF and CEL decreased from SafA to SafB, while hemicellulose (HCEL, % DM) showed the opposite trend: highest in SafA but reduced in SafB compared to the other crops (Figure 1K).

The ratio of ADL to nitrogenous compounds (ADL_NC), an indicator of digestibility, closely followed ADL patterns. Values increased from biomass A to biomass B in radish and white mustard, but decreased in safflower (SafB < SafA; Figure 1L).

Residual biomass (RB, % DM) was significantly higher in SafA than in the other two crops, indicating slower decomposition of safflower. However, RB values were comparable across crops in biomass B (Figure 1M).

Soil properties

Soil pH did not differ among variants with various incorporated crops, neither in earlier (biomass A) or later maturity stage (biomass B). However, biomass B amendments generally lowered pH compared with unamended soil (Figure 2C). In particular, RadB- and WmuB-amended soils had lower pH than RadA and WmuA, respectively (Figure 2A).

FIGURE 2

Soil contents of oxidizable organic carbon (Cox), total carbon (TC), total nitrogen (TN), and their ratio (Cox/TN) were unchanged across crops within both maturity stages (Figures 3A–I). By contrast, total phosphorus (P) and potassium (K) increased in soils amended with biomass A, showing a trend from lower values in RadA to higher values in WmuA, all exceeding the unamended control (Figures 4B,H). Biomass B amendments also elevated P, K, and Mg contents relative to the control, except for P in SafB and Mg in RadB (Figures 4C,F,I). Within each maturity stage, Mg did not differ among crops (Figure 4D). P and K were highest in WmuA soils, while P was highest in WmuB soils, compared with RadA (P, K) and SafB (P) (Figures 4A,G).

FIGURE 3

FIGURE 4

Enzyme activities responded variably to biomass incorporation. β-glucosidase (GLU) activity increased in RadA- and SafA-amended soils compared with the control (Figure 5B), but showed no differences among crop treatments within each maturity stage (Figure 5A). Phosphatase (Phos) activity was higher in RadA and SafB than in the control (Figures 5E,F). Across maturity stages, biomass A amendments generally enhanced GLU (all crops) and Phos (radish and white mustard) compared to biomass B (Figure 5A).

FIGURE 5

Urease (Ure) activity increased in SafA but decreased in RadB compared with the control (Figures 6B,C). Differences among crops were also evident: WmuA and RadB had lower Ure activity than the other two crops at the same maturity stage (Figure 6A). N-acetyl-β-D-glucosaminidase (NAG) and arylsulfatase (ARS) increased in RadA relative to the control (Figures 6E,H). Biomass B amendments further raised NAG in WmuB and ARS in RadB and SafB (Figures 6F,I). Among crop comparisons, WmuA decreased ARS relative to RadA, whereas WmuB increased NAG compared to RadB and SafB (Figures 6D,G).

FIGURE 6

Enzymatic stoichiometry also shifted with crop type and maturity. The soil carbon acquisition ratio (Cacq.) and vector length (C limitation index) remained unchanged with biomass A but increased under RadB compared with other crops (Figures 7C,I). Within biomass A, WmuA showed higher Cacq. and vector length than SafA, while RadB had the highest values among biomass B treatments (Figures 7A,G).

FIGURE 7

Nitrogen acquisition ratio (Nacq.) and vector angle (N limitation index) showed opposite patterns. RadB decreased Nacq. but increased vector angle relative to the control (Figures 7F,L). Within biomass A, WmuA had lower Nacq. and higher angle than SafA. Conversely, WmuB had the highest Nacq. and lowest angle among biomass B treatments. Across crops, Nacq. declined while vector angle increased with longer maturation (biomass A vs. B; Figures 7D,J).

The relations among plant and soil properties

Pearson’s correlation analysis (Supplementary Figure S2A revealed several significant relationships. Plant C content correlated negatively with Ash content (p ≤ 0.001, r = −0.89) and with the soil Vector index (p ≤ 0.01, r = −0.65), but positively with soil urease (Ure) activity and N acquisition ratio (Nacq.) (p ≤ 0.001, r = 0.74 and 0.71, respectively). Conversely, Ash content correlated negatively with Ure and Nacq. (p ≤ 0.001, r = −0.80 and −0.73). Acid detergent fibre (ADF) correlated positively with Nacq. (p ≤ 0.01, r = 0.63), while neutral detergent fibre (NDF) and residual biomass (RB) correlated positively with soil pH (p ≤ 0.01, r = 0.61; p ≤ 0.001, r = 0.72). Acid detergent lignin (ADL) correlated negatively with β-glucosidase (GLU; p ≤ 0.01, r = −0.60). Residual biomass correlated positively with hemicellulose (HCEL; p ≤ 0.001, r = 0.83). Among soil parameters, phosphatase (Phos) correlated negatively with P, K, and Mg contents (p ≤ 0.01, r = −0.65, −0.70, −0.73).

When analysed separately for biomass A (Supplementary Figure S2B), RB correlated positively with NDF, HCEL, and ADL (p ≤ 0.001, r = 0.87, 0.93, 0.85), and negatively with ADF, cellulose (CEL), and Ash (p ≤ 0.001, r = −0.81, −0.88, −0.95).

Discussion

Crop-specific quality and maturity stage impacts plant biomass properties

The results of residual biomass quantification, soil trait and plant quality determination indicated that both crop choice and, more decisively, harvest timing are critical determinants of biomass quality and subsequent soil responses. Early-harvested biomass provides more labile substrates that promote larger decomposition, whereas later-harvested biomass, particularly of white mustard, is more resistant and may contribute to long-term soil organic matter stabilization. Specifically, residual biomass (RB; Figure 1M) indicated more extensive biodegradation of all crops (77%–87% mass loss), except safflower biomass A (SafA), which decomposed less (62% loss). This lower degradability of SafA was associated with higher NDF, ADL, hemicellulose, and ADL:NC ratios, and reduced cellulose and Ash. High lignin content (Jensen et al., 2005), hemicellulose (Henriksen and Breland, 1999), and elevated ADL:nitrogen ratios (Moore et al., 1999) are generally linked with structural stability and decomposition resistance. Conversely, high protein, fat, and soluble carbohydrates support easier degradation (Heal et al., 1997; Aditya and Wolfram, 2020).

Interestingly, the most degraded biomasses such as RadA and WmuB did not show higher nitrogenous compound contents, challenging the assumption that N-rich biomass always decomposes easily. Instead, both were rich in fat, fibre, and cellulose. Although these parameters cause moderately recalcitrant decay (Henriksen and Breland, 1999; Zoghlami and Paës, 2019), cruciferous crops (radish, white mustard) tended to degrade more easily, consistent with previous observations (Kucerik et al., 2024). By contrast, safflower decomposed less easily, despite relatively low ADL and fibre fractions in SafB, highlighting inconsistency with previous reports of safflower green manure contributing substantially to soil C (Kucerik et al., 2024).

As aforementioned, decomposition was shaped by both crop type and maturity stage (two-way ANOVA, Supplementary Table S1). In some traits (e.g., fat, NDF, hemicellulose), harvest stage had a stronger effect than crop identity. Correlation analysis confirmed that RB was positively associated with hemicellulose and NDF, and negatively with lignin, cellulose, and Ash (Supplementary Figure S2). This suggests that decomposition depends not only on the abundance of resistant polymers, but also on the specific balance between moderately degradable (cellulose, hemicellulose) and resistant (lignin, Ash) compounds.

These results indicate a fundamental difference between crucifers and safflower: cruciferous residues appear more degradable despite moderately recalcitrant fibre fractions, possibly due to their biochemical composition (e.g., glucosinolates and associated breakdown products), while safflower shows unexpectedly slow decomposition even at lower ADL contents. Such a decoupling between bulk ADL and decay rate is consistent with evidence that lignin chemistry and phenolic cross-linking (e.g., ferulate-mediated linkages) can physically and chemically protect labile carbohydrates, thereby constraining decomposition independent of ADL concentration (Hatfield et al., 2017; Talbot et al., 2012).

Therefore, the results imply that cruciferous crops appear more degradable, making them better suited for short-term nutrient release, while safflower may stabilize soil C. A combination of cruciferous residues with safflower could therefore balance rapid nutrient turnover with longer-term carbon sequestration, a strategy that could be agronomically exploited for soil fertility management.

Impact on soil pH

Previous studies often report that green manuring raises soil pH (Xu W. et al., 2023; Wang et al., 2024). In this experiment, crop type did not significantly affect pH, but biomass B amendments slightly decreased pH compared to the control. This acidifying effect was linked to altered Mg:K ratios: unamended soil had Mg:K ≈ 2.4, while biomass B-amended soils ranged from 1.4 to 1.8. Such shifts in cation ratios can influence soil buffering capacity and mineral availability (Jalali, 2008).

Although the magnitude of pH change was small, it appeared that even minor acidification may accumulate under repeated applications of green manures, especially when combined with shifts in Mg:K balance. This could alter root nutrient uptake and rhizosphere buffering, implying that crop maturity selection could serve as a management tool to fine-tune soil reaction for target crops.

Impacts on soil carbon, mineral content and transformation

Stoichiometric indicators revealed that radish at later harvest (RadB) intensified microbial C limitation (higher C acquisition and vector length) but reduced N acquisition, accompanied by greater N limitation (higher vector angle). In contrast, white mustard at later harvest (WmuB) promoted microbial N acquisition and alleviated N limitation. These findings suggest that the maturity stage of incorporated biomass is a decisive factor also in regulating soil nutrient availability and microbial resource allocation. Early incorporation enhances labile nutrient cycling, while later incorporation, particularly of white mustard, supports more balanced nutrient acquisition and may favour longer-term soil fertility.

In general, green manures are linked to higher soil C (Patra et al., 2023), however, in this study, no significant changes in oxidizable C were detected. Radish (lower plant C) degraded faster, whereas safflower (higher plant C) decomposed slowly, reducing its contribution to soil C enrichment. This illustrates a trade-off: C-rich biomass may not enrich soil if its decomposition is too slow to release labile substrates (Tamura et al., 2017; Gleixner et al., 2001). In general, the length of the experiment was too short to consider markable changes in total C contents that would be likely observed in similar medium- to long-termed trial.

Enzyme responses supported this view. β-glucosidase (GLU) activity was unchanged under biomass B but increased under RadA and SafA, coinciding with higher cellulose + hemicellulose contents and carbohydrate levels. Higher hemicellulose:ADL ratios in early harvests (A) compared to later harvests (B) likely explained these effects (Liu et al., 2025). Thus, enzyme activity was driven more by substrate quality than crop type alone.

C acquisition (Cacq.) and microbial C limitation (Vector length) responded only to biomass B, and most strongly in RadB. This biomass had high HCEL:ADL and Ash but low plant C and C:N, promoting greater microbial demand and C turnover. In contrast, soil P, K, and Mg generally increased with biomass amendments, consistent with other reports for P (Zhang et al., 2023; Kataria et al., 2024; Khan et al., 2025), potassium and magnesium (Solangi et al., 2019; Kucerik et al., 2024; Ma et al., 2021).

Interestingly, higher phosphatase activity (Phos) occurred in RadA and SafB, both associated with lower soil P content. This suggests microbial upregulation of P-mining under limitation, corroborated by higher vector angles (>45°) aligning with the notion about mutual relation between reduced availability of P and induced Phos (functioning also vice-versa (Bargaz et al., 2012; Janes-Bassett et al., 2022)).

Altogether, these results show that the maturity stage determines whether soil C inputs stimulate microbial activity (labile C cycling, possible priming of SOM) or contribute to persistent organic matter pools (slow decomposition, reduced turnover). The consistent link between stoichiometric indices and enzyme assays highlights that integrating these approaches is essential to reveal “hidden” nutrient limitations and microbial allocation trade-offs.

Impacts on soil enzymes and related elements

Correlation analysis confirmed strong links between plant biomass traits and soil enzyme dynamics. High C content in plant material was associated with increased urease activity and microbial N acquisition, while high Ash content showed the opposite trend, indicating that mineral-rich biomass may partially suppress N cycling. Fibre quality traits also played a key role: hemicellulose and lignin were strongly associated with residual biomass, suggesting that structural compounds drive decomposition resistance, while lignin negatively correlated with β-glucosidase, reflecting reduced microbial C acquisition from recalcitrant residues. Among soil properties, higher P, K, and Mg contents were linked with lower phosphatase activity, implying nutrient feedback regulation.

Contrary to some studies (Lyu et al., 2023; Wang et al., 2025), biomass incorporation did not significantly change soil TN or C:N ratios in this study. However, enzyme activities indicated shifts in N cycling. Urease activity increased under SafA, while WmuA and RadB showed reduced values compared to other treatments. This suggests that safflower may sustain N mineralization despite its slower decomposition.

N-acetyl-β-D-glucosaminidase (NAG), linked to fungal chitin turnover, increased in RadA and WmuB relative to the control. Crucifer biomass maturity negatively affected NAG, as seen in lower activity under SafB versus SafA, aligning with reports that later maturity reduces protein degradation (Kohn and Allen, 1995; Kejun et al., 2011). Arylsulfatase (ARS) also increased with biomass amendments, consistent with earlier findings (Tejada et al., 2008; Elfstrand et al., 2007). However, WmuA showed the lowest ARS, despite white mustard being rich in S-containing glucosinolates (Couëdel et al., 2018b). This discrepancy likely reflects that glucosinolate degradation is mediated by enzymes like myrosinase (Al-Turki and Dick, 2003; Huber et al., 1983) rather than ARS.

Taken together, these enzyme responses indicate that microbial N and S cycling can shift without parallel changes in bulk TN, highlighting the importance of process-level indicators. While safflower decomposes slowly, it still supports steady N turnover through urease activity. Crucifer residues, in contrast, showed maturity-dependent differences in protein- and chitin-degrading pathways, with direct implications for the timing of incorporation to optimize N release. Moreover, the interaction between glucosinolate-rich residues and sulfur enzymes suggests that biochemical specificity of plant metabolites must be considered when predicting S dynamics under green manuring.

Conclusion

The results suggest that although residue quality indicators such as lignin, cellulose, and C:N ratio are commonly used to predict decomposition rates, they do not always capture the variability associated with different crop types and maturity stages. For cruciferous residues, larger degradation has often been reported despite moderately recalcitrant fibre fractions, suggesting that secondary metabolites such as glucosinolates may play a role in enhancing microbial breakdown. In contrast, oilseed crops such as safflower have been described as more resistant due to structural complexity and phenolic cross-linking in their cell walls, which can decouple decomposition rates from bulk lignin content. Our findings reinforce and expand upon these observations, showing that safflower residues decomposed less extensively than cruciferous biomass even at lower ADL contents, highlighting the importance of polymer interactions and cell wall architecture beyond simple lignin abundance. Moreover, by comparing early- and late-harvested biomass, this study provides novel evidence that maturity stage can override crop identity in shaping residue quality and subsequent soil responses, an aspect largely overlooked in previous green manure studies. This highlights the need to integrate biochemical residue traits with stoichiometric and enzymatic assays to better predict residue–soil interactions and their implications for nutrient cycling and soil carbon dynamics.

References

• 1

  AdetunjiA. T.NcubeB.MulidziR.LewuF. B. (2020). Management impact and benefit of cover crops on soil quality: a review. Soil Tillage Res.204, 104717. 10.1016/j.still.2020.104717

  • CrossRef
  • Google Scholar

• 2

  AdityaR.WolframY. M. (2020). Chemical compounds and decomposition process from four species leaf litter as a source of organic matter soil in anggori education forest, Manokwari. J. Sylva Indonesiana3, 60–67. 10.32734/jsi.v3i02.2848

  • CrossRef
  • Google Scholar

• 3

  Al-TurkiA.DickW. (2003). Myrosinase Activity in Soil. Soil Sci. Soc. Am. J. - SSSAJ67, 139–145. 10.2136/sssaj2003.1390

  • CrossRef
  • Google Scholar

• 4

  ANKOM (2006). ANKOM technology.

  • Google Scholar

• 5

  AntoshE.LiebigM. A.ArcherD. W.LucianoR. (2022). Cover crop interseeding effects on aboveground biomass and corn grain yield in western North Dakota. Crop, Forage and Turfgrass Manag.8, e20148. 10.1002/cft2.20148

  • CrossRef
  • Google Scholar

• 6

  AOAC (2023). Association of official analytical chemists. Oxford University Press. 10.1093/9780197610145.003.2944

  • CrossRef
  • Google Scholar

• 7

  BargazA.FaghireM.AbdiN.FarissiM.SifiB.DrevonJ.-J.et al (2012). Low soil phosphorus availability increases acid phosphatases activities and affects P partitioning in nodules, seeds and rhizosphere of Phaseolus vulgaris. Agriculture2, 139–153. 10.3390/agriculture2020139

  • CrossRef
  • Google Scholar

• 8

  BogužasV.MikučionienėR.ŠlepetienėA.SinkevičienėA.FeizaV.SteponavičienėV.et al (2015). Long-term effect of tillage systems, straw and green manure combinations on soil organic matter. Zemdirbyste-Agriculture102, 243–250. 10.13080/z-a.2015.102.031

  • CrossRef
  • Google Scholar

• 9

  CouëdelA.AllettoL.JustesÉ. (2018a). Crucifer-legume cover crop mixtures provide effective sulphate catch crop and sulphur green manure services. Plant Soil426, 61–76. 10.1007/s11104-018-3615-8

  • CrossRef
  • Google Scholar

• 10

  CouëdelA.AllettoL.KirkegaardJ.JustesÉ. (2018b). Crucifer glucosinolate production in legume-crucifer cover crop mixtures. Eur. J. Agron.96, 22–33. 10.1016/j.eja.2018.02.007

  • CrossRef
  • Google Scholar

• 11

  CuiJ.ZhangS.WangX.XuX.AiC.LiangG.et al (2022). Enzymatic stoichiometry reveals phosphorus limitation-induced changes in the soil bacterial communities and element cycling: evidence from a long-term field experiment. Geoderma426, 116124. 10.1016/j.geoderma.2022.116124

  • CrossRef
  • Google Scholar

• 12

  DoiR.RanamukhaarachchiS. L. (2009). Soil dehydrogenase in a land degradation-rehabilitation gradient: observations from a savanna site with a wet/dry seasonal cycle. Rev. Biol. Trop.57, 223–234. 10.15517/rbt.v57i1-2.11317

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 13

  DongN.HuG.ZhangY.QiJ.ChenY.HaoY. (2021). Effects of green-manure and tillage management on soil microbial community composition, nutrients and tree growth in a walnut orchard. Sci. Rep.11, 16882. 10.1038/s41598-021-96472-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 14

  dos Santos CordeiroC. F.EcherF. R.AraujoF. F. (2021). Cover crops impact crops yields by improving microbiological activity and fertility in sandy soil. J. Soil Sci. Plant Nutr.21, 1968–1977. 10.1007/s42729-021-00494-0

  • CrossRef
  • Google Scholar

• 15

  ElfstrandS.BåthB.MårtenssonA. (2007). Influence of various forms of green manure amendment on soil microbial community composition, enzyme activity and nutrient levels in leek. Appl. Soil Ecol.36, 70–82. 10.1016/j.apsoil.2006.11.001

  • CrossRef
  • Google Scholar

• 16

  FarawayR. J. J. (2014). Linear models with R. Boca Raton, FL, USA: Chapman and Hall/CRC Press.

  • Google Scholar

• 17

  FengX. (2022). Plant-derived macromolecules in the soil. 79–115. 10.1002/9781119480419.ch4

  • CrossRef
  • Google Scholar

• 18

  FengH.SekaranU.WangT.KumarS. (2021). On-farm assessment of cover cropping effects on soil C and N pools, enzyme activities, and microbial community structure. J. Agric. Sci.159, 216–226. 10.1017/s002185962100040x

  • CrossRef
  • Google Scholar

• 19

  FrøsethR. B.BakkenA. K.BlekenM. A.RileyH.PommerescheR.Thorup-KristensenK.et al (2014). Effects of green manure herbage management and its digestate from biogas production on barley yield, N recovery, soil structure and earthworm populations. Eur. J. Agron.52, 90–102. 10.1016/j.eja.2013.10.006

  • CrossRef
  • Google Scholar

• 20

  Garcia-FrancoN.AlbaladejoJ.AlmagroM.Martínez-MenaM. (2015). Beneficial effects of reduced tillage and green manure on soil aggregation and stabilization of organic carbon in a mediterranean agroecosystem. Soil Tillage Res.153, 66–75. 10.1016/j.still.2015.05.010

  • CrossRef
  • Google Scholar

• 21

  Garcı́a-AyusoL. E.Luque de CastroM. D. (1999). A multivariate study of the performance of a microwave-assisted soxhlet extractor for olive seeds. Anal. Chim. Acta382, 309–316. 10.1016/s0003-2670(98)00795-8

  • CrossRef
  • Google Scholar

• 22

  GautamR.ShriwastavC. P.LamichhaneS.BaralB. R. (2021). The residual effect of pre-rice green manuring on a succeeding wheat crop (Triticum aestivum L.) in the rice-wheat cropping system in Banke, Nepal. Int. J. Agron.2021, 10, 4142743. 10.1155/2021/4142743

  • CrossRef
  • Google Scholar

• 23

  GleixnerG.CzimczikC. J.KramerC.LühkerB.SchmidtM. W. I. (2001). in Global biogeochemical cycles in the climate system. Editors SchulzeE.-D.HeimannM.HarrisonS.HollandE.LloydJ.PrenticeI. C. (San Diego: Academic Press), 201–215. 10.1016/B978-012631260-7/50017-0

  • CrossRef
  • Google Scholar

• 24

  GunnarssonS.MarstorpH.DahlinA. S.WitterE. (2008). Influence of non-cellulose structural carbohydrate composition on plant material decomposition in soil. Biol. Fertil. Soils45, 27–36. 10.1007/s00374-008-0303-5

  • CrossRef
  • Google Scholar

• 25

  HadasA.KautskyL.GoekM.Erman KaraE. (2004). Rates of decomposition of plant residues and available nitrogen in soil, related to residue composition through simulation of carbon and nitrogen turnover. Soil Biol. Biochem.36, 255–266. 10.1016/j.soilbio.2003.09.012

  • CrossRef
  • Google Scholar

• 26

  HatfieldR. D.RancourD. M.MaritaJ. M. (2017). Grass cell walls: a story of cross-linking. Front. Plant Sci.7. 10.3389/fpls.2016.02056

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 27

  HealO. W.AndersonJ. M.SwiftM. J. (1997). “Plant litter quality and decomposition: an historical overview,” in Driven by Nature: Plant Litter Quality and Decomposition. CadischG.GillerK. E. (Wallingford; CAB International), 3–30.

  • Google Scholar

• 28

  HenriksenT. M.BrelandT. A. (1999). Evaluation of criteria for describing crop residue degradability in a model of carbon and nitrogen turnover in soil. Soil Biol. Biochem.31, 1135–1149. 10.1016/s0038-0717(99)00031-0

  • CrossRef
  • Google Scholar

• 29

  HinkleD. E.WiersmaW.JursS. G. (2003). Applied statistics for the behavioral sciences. 5th ed. Boston, Mass.: Houghton Mifflin.

  • Google Scholar

• 30

  HuZ.ZhaoQ.ZhangX.NingX.LiangH.CaoW. (2023). Winter green manure decreases subsoil nitrate accumulation and increases n use efficiencies of maize production in North China Plain. Plants-Basel12 (2), 311. 10.3390/plants12020311

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 31

  HuberJ.KranzG.KreibichG.BeiningK.KrügerM.WeissbachF. (1983). Microbiological degradation of glucosinolates in defatted rapeseed meal. Food/Nahr.27, 257–263. 10.1002/food.19830270319

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 32

  ISO_10390 (2005). International Organization for Standardization. Geneva, Switzerland.

  • Google Scholar

• 33

  ISO_13878 (1998). International Organization for Standardization. Geneva, Switzerland.

  • Google Scholar

• 34

  ISO_13906 (2008). International Organization for Standardization. Geneva, Switzerland.

  • Google Scholar

• 35

  ISO_14235 (1998). International Organization for Standardization. Geneva, Switzerland.

  • Google Scholar

• 36

  ISO_20130 (2018). International Organization for Standardization. Geneva, Switzerland.

  • Google Scholar

• 37

  ISO_6865 (2000). International Organization for Standardization. Geneva, Switzerland.

  • Google Scholar

• 38

  JalaliM. (2008). Effect of sodium and magnesium on kinetics of potassium release in some calcareous soils of Western Iran. Geoderma145, 207–215. 10.1016/j.geoderma.2008.03.005

  • CrossRef
  • Google Scholar

• 39

  Janes-BassettV.BlackwellM. S. A.BlairG.DaviesJ.HaygarthP. M.MezeliM. M.et al (2022). A meta-analysis of phosphatase activity in agricultural settings in response to phosphorus deficiency. Soil Biol. Biochem.165, 108537. 10.1016/j.soilbio.2021.108537

  • CrossRef
  • Google Scholar

• 40

  JenkinsonD. S. (1965). Studies on the decomposition of plant material in soil. i.t: losses of carbon from ¹⁴ c labelled ryegrass incubated with soil in the field. J. Soil Sci.16, 104–115. 10.1111/j.1365-2389.1965.tb01424.x

  • CrossRef
  • Google Scholar

• 41

  JensenL. S.SaloT.PalmasonF.BrelandT. A.HenriksenT. M.StenbergB.et al (2005). Influence of biochemical quality on C and N mineralisation from a broad variety of plant materials in soil. Plant Soil273, 307–326. 10.1007/s11104-004-8128-y

  • CrossRef
  • Google Scholar

• 42

  KassambaraA.MundtF. (2020). CRAN.R-project.

  • Google Scholar

• 43

  KatariaP.SinghJ.SinghG.MaviM. S.SillanpääM.Al-FarrajS. (2024). Residual phosphorus availability after soil application of different organic waste in varying soil P status soils. Heliyon10, e25732. 10.1016/j.heliyon.2024.e25732

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 44

  KejunS.JuntaoZ.YingC.ZongwenL.LinR.CongL. (2011). Accelerating the degradation of green plant waste with chemical decomposition agents. J. Environ. Manag.92, 2708–2713. 10.1016/j.jenvman.2011.06.011

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 45

  KhanA. A.AzeemI.HuiJ.ChenY.YuanY.ShahT.et al (2025). Non-leguminous green manures improve labile phosphorus availability and crop yield in agroecosystems: a global meta-analysis. Soil Tillage Res.248, 106430. 10.1016/j.still.2024.106430

  • CrossRef
  • Google Scholar

• 46

  KohnR. A.AllenM. S. (1995). Effect of plant maturity and preservation method on in vitro protein degradation of forages. J. Dairy Sci.78, 1544–1551. 10.3168/jds.s0022-0302(95)76776-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 47

  KomstaL.NovomestkyF. (2022). CRAN.R-project.

  • Google Scholar

• 48

  KucerikJ.BrtnickyM.MustafaA.HammerschmiedtT.KintlA.SobotkovaJ.et al (2024). Utilization of diversified cover crops as green manure-enhanced soil organic carbon, nutrient transformation, microbial activity, and maize growth. Agronomy-Basel14, 2001. 10.3390/agronomy14092001

  • CrossRef
  • Google Scholar

• 49

  LêS.JosseJ.HussonF. (2008). FactoMineR: An R package for multivariate analysis. J. Stat. Softw.25 (1), 1–18. 10.18637/jss.v025.i01

  • CrossRef
  • Google Scholar

• 50

  LiP.JiaL.ChenQ.ZhangH.DengJ.LuJ.et al (2024). Adaptive evaluation for agricultural sustainability of different fertilizer management options for a green manure-maize rotation system: impacts on crop yield, soil biochemical properties and organic carbon fractions. Sci. Total Environ.908, 168170. 10.1016/j.scitotenv.2023.168170

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 51

  LiuJ. X.PengS. J.Faivre-VuillinB.XuZ. H.ZhangD. Q.ZhouG. Y. (2008). Erigeron annuus (L.) Pers., as a green manure for ameliorating soil exposed to acid rain in Southern China. J. Soils Sediments8, 452–460. 10.1007/s11368-008-0041-1

  • CrossRef
  • Google Scholar

• 52

  LiuA.KuY. S.ContadorC. A.LamH. M. (2020). The impacts of domestication and agricultural practices on Legume nutrient acquisition through symbiosis with Rhizobia and arbuscular mycorrhizal fungi. Front. Genet.11, 583954. 10.3389/fgene.2020.583954

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 53

  LiuX.TangS.TouC.ChenJ.WanekW.KuzyakovY.et al (2025). Opposite roles of plant quality and soil exoenzymes in regulation of litter carbon transfer to fungal and bacterial necromass. Soil Biol. Biochem.210, 109940. 10.1016/j.soilbio.2025.109940

  • CrossRef
  • Google Scholar

• 54

  LyuH.LiY.WangY.WangP.ShangY.YangX.et al (2023). Drive soil nitrogen transformation and improve crop nitrogen absorption and utilization - a review of green manure applications. Front. Plant Sci.14, 1305600. 10.3389/fpls.2023.1305600

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 55

  MaD.YinL.JuW.LiX.LiuX.DengX.et al (2021). Meta-analysis of green manure effects on soil properties and crop yield in northern China. Field crop. Res.266, 108146. 10.1016/j.fcr.2021.108146

  • CrossRef
  • Google Scholar

• 56

  MaZ.LiangT.FuH.MaQ.ChangD.ZhangJ.et al (2024). Long-term green manuring increases soil carbon sequestration via decreasing qCO2 caused by lower microbial phosphorus limitation in a dry land field. Agric. Ecosyst. and Environ.374, 109142. 10.1016/j.agee.2024.109142

  • CrossRef
  • Google Scholar

• 57

  MarrubiniG.PapettiA.GenoriniE.UlriciA. (2017). Determination of the sugar content in commercial plant milks by near infrared spectroscopy and luff-schoorl total glucose titration. Food Anal. Methods10, 1556–1567. 10.1007/s12161-016-0713-1

  • CrossRef
  • Google Scholar

• 58

  MehlichA. (2008). Mehlich 3 soil test extractant: a modification of mehlich 2 extractant. Commun. Soil Sci. Plant Analysis15 (12), 1409–1416. 10.1080/00103628409367568

  • CrossRef
  • Google Scholar

• 59

  MooreT.TrofymowJ.TaylorB.PrescottC.CamiréC.DuscheneL.et al (1999). Litter decomposition rates in Canadian forests. Glob. Change Biol.5, 75–82. 10.1046/j.1365-2486.1998.00224.x

  • CrossRef
  • Google Scholar

• 60

  MoorheadD. L.SinsabaughR. L.HillB. H.WeintraubM. N. (2016). Vector analysis of ecoenzyme activities reveal constraints on coupled C, N and P dynamics. Soil Biol. Biochem.93, 1–7. 10.1016/j.soilbio.2015.10.019

  • CrossRef
  • Google Scholar

• 61

  MuhammadG.KhanS.KhanM. A.AnjumJ.AlizaiN. A.KamranA.et al (2022). Green manuring for increasing nitrogen use efficiency and growth performance of wheat. J. Appl. Res. Plant Sci.3, 177–186. 10.38211/joarps.2022.3.1.22

  • CrossRef
  • Google Scholar

• 62

  PatraA.SinghR. P.KunduM.KumarG.MalkaniP.SinghB.et al (2023). Millet production in india: challenges and opportunities.

  • Google Scholar

• 63

  PekárS.BrabecM. (2016). Modern analysis of biological data. Generalized linear models in RBrno, Czech Republic: Masaryk University Press.

  • Google Scholar

• 64

  Piotrowska-DlugoszA.WilczewskiE. (2020). Influence of field pea (Pisum sativumL.) as catch crop cultivated for green manure on soil phosphorus and P-cycling enzyme activity. Arch. Agron. Soil Sci.66, 1570–1582. 10.1080/03650340.2020.1715950

  • CrossRef
  • Google Scholar

• 65

  PomeranzY.MeloanC. E. (1994). Food analysis, 625–677. 10.1007/978-1-4615-6998-5_36

  • CrossRef
  • Google Scholar

• 66

  R_Core_Team (2023). R Foundation for Statistical Computing. Vienna, Austria.

  • Google Scholar

• 67

  RanaivosonL.FalconnierG. N.AffholderF.LerouxL.AutfrayP.MullerB.et al (2022). Can green manure contribute to sustainable intensification of rainfed rice production in Madagascar?Field crop. Res.289, 108711. 10.1016/j.fcr.2022.108711

  • CrossRef
  • Google Scholar

• 68

  RinneM.JaakkolaS.HuhtanenP. (1997). Grass maturity effects on cattle fed silage-based diets. 1. Organic matter digestion, rumen fermentation and nitrogen utilization. Animal Feed Sci. Technol.67, 1–17. 10.1016/s0377-8401(96)01141-8

  • CrossRef
  • Google Scholar

• 69

  SandhuO. S.JatM. L.GuptaR. K.ThindH. S.SidhuH. S.SinghY. (2022). Influence of residue type and method of placement on dynamics of decomposition and nitrogen release in maize-wheat-mungbean cropping on permanent raised beds: a litterbag study. Sustainability14, 864. 10.3390/su14020864

  • CrossRef
  • Google Scholar

• 70

  SharmaA. R.GhoshA.CyclN. (2000). Effect of green manuring with Sesbania aculeata and nitrogen fertilization on the performance of direct-seeded flood-prone lowland rice. Nutr. Cycl. Agroecosyst.57 (2), 141–153. 10.1023/a:1009863100224

  • CrossRef
  • Google Scholar

• 71

  SinghD.DeviK.PA.BahadurR.KumarN.DeviO.et al (2023). Int. J. Environ. Clim. Change13, 39–45. 10.9734/IJECC/2023/v13i113142

  • CrossRef
  • Google Scholar

• 72

  SolangiF.BaiJ. S.GaoS. J.YangL.ZhouG. P.CaoW. D. (2019). Improved accumulation capabilities of phosphorus and potassium in green manures and its relationship with soil properties and enzymatic activities. Agronomy-Basel9 (11), 708. 10.3390/agronomy9110708

  • CrossRef
  • Google Scholar

• 73

  SuG.ZhaoR.WangY.YangY. a.WuX.WangJ.et al (2022). Green manure return strategies to improve soil properties and Spring maize productivity under nitrogen reduction in the North China Plain. Agronomy-Basel12, 2734. 10.3390/agronomy12112734

  • CrossRef
  • Google Scholar

• 74

  TalbotJ. M.YelleD. J.NowickJ.TresederK. K. (2012). Litter decay rates are determined by lignin chemistry. Biogeochemistry108, 279–295. 10.1007/s10533-011-9599-6

  • CrossRef
  • Google Scholar

• 75

  TamuraM.SuseelaV.SimpsonM.PowellB.TharayilN. (2017). Plant litter chemistry alters the content and composition of organic carbon associated with soil mineral and aggregate fractions in invaded ecosystems. Glob. Change Biology23, 4002–4018. 10.1111/gcb.13751

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 76

  TejadaM.GonzalezJ. L.Garcia-MartinezA. M.ParradoJ. (2008). Effects of different green manures on soil biological properties and maize yield. Bioresour. Technology99, 1758–1767. 10.1016/j.biortech.2007.03.052

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 77

  WangH.ZhongL.LiuJ.LiuX.XueW.LiuX.et al (2024). Systematic analysis of the effects of different green manure crop rotations on soil nutrient dynamics and bacterial community structure in the Taihu Lake Region, Jiangsu. Agriculture14, 1017. 10.3390/agriculture14071017

  • CrossRef
  • Google Scholar

• 78

  WangG.HuF.LiH.YinW.FanZ.FanH.et al (2025). Optimal green manure application reduces nitrogen losses, enhances wheat grain yield, and improves nitrogen use efficiency. J. Integr. Agric.10.1016/j.jia.2025.04.001

  • CrossRef
  • Google Scholar

• 79

  WickhamH. (2016). Springer-Verlag.

  • Google Scholar

• 80

  XieZ. J.ShahF. R.ZhouC. H. (2022). Combining rice straw biochar with leguminous cover crop as green manure and mineral fertilizer enhances soil microbial biomass and rice yield in South China. Front. Plant Sci.13, 778738. 10.3389/fpls.2022.778738

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 81

  XuW.YangQ.JiangY.YuJ.LiJ.LiuW.et al (2023a). Improving soil pH, nutrient concentrations, and enzyme activities by green manure returning in young and mature rubber plantation on Hainan island, China. Plant Soil495, 341–358. 10.1007/s11104-023-06327-6

  • CrossRef
  • Google Scholar

• 82

  XuJ.SiL. L.ZhangX.CaoK.WangJ. (2023b). Various green manure-fertilizer combinations affect the soil microbial community and function in immature red soil. Front. Microbiol.14, 1255056. 10.3389/fmicb.2023.1255056

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 83

  ZarJ. H. (1984). Biostatistical analysis. 2nd ed. Englewood Cliffs, New Jersey, USA: Prentice-Hall, Inc.

  • Google Scholar

• 84

  ZhangJ.NieJ.CaoW.GaoY.LuY.LiaoY. (2023). Long-term green manuring to substitute partial chemical fertilizer simultaneously improving crop productivity and soil quality in a double-rice cropping system. Eur. J. Agron.142, 126641. 10.1016/j.eja.2022.126641

  • CrossRef
  • Google Scholar

• 85

  ZhangJ.HeW.WeiZ.ChenY.GaoW. (2025). Integrating green manure and fertilizer reduction strategies to enhance soil carbon sequestration and crop yield: evidence from a two-season pot experiment. Front. Sustain. Food Syst.8, 1514409. 10.3389/fsufs.2024.1514409

  • CrossRef
  • Google Scholar

• 86

  ZoghlamiA.PaësG. (2019). Lignocellulosic biomass: understanding recalcitrance and predicting hydrolysis. Front. Chem.7, 874. 10.3389/fchem.2019.00874

  • Pubmed Abstract
  • CrossRef
  • Google Scholar