Introduction

Crop residues, the aboveground plant materials left in the field after harvest, represent a substantial source of organic inputs to agricultural soils. Globally, an estimated 3.8 billion metric tons of crop residues are produced annually, with Asia contributing 47%, followed by the Americas (29%), Europe (16%), Africa (6%) and Oceania (2%)1. In India alone, around 500 million metric tons of crop residues are generated each year, of which cereals account for the majority (352 Mt), followed by residues from fibers, oilseeds, pulses and sugarcane2. Efficient recycling of these residues is vital, not only for maintaining soil productivity and health but also for minimizing environmental degradation and ensuring long-term agricultural sustainability3.

Residue incorporation plays a pivotal role in regulating the physical, chemical and biological properties of soil4,5. However, the rate and extent of residue decomposition are highly variable and depend on both residue characteristics and soil conditions. Factors such as residue type, quality (chemical composition), treatment method and timing strongly influence decomposition kinetics6,7. While plant residues largely share similar biochemical constituents, including cellulose, hemicellulose, lignin, proteins and phenolics, their relative proportions markedly affect microbial colonization, enzyme induction and nutrient mineralization rates8,9.

According to the initial litter quality hypothesis, residue decomposition is largely determined by its initial chemical makeup, particularly the concentrations and ratios of labile and recalcitrant compounds10. Labile fractions like proteins and polysaccharides degrade rapidly, while recalcitrant polymers such as lignin resist decomposition11. However, residue quality is dynamic and shifts during decomposition, depending on the selective degradation of its constituent compounds12. Therefore, understanding how biochemical composition drives temporal changes in decomposition is critical for improving residue management practices.

Traditionally, the carbon-to-nitrogen (C: N) ratio has been used to predict decomposition rates13. However, this index alone is insufficient. Ratios such as lignin/N and polyphenol/N, along with the absolute contents of cellulose, hemicellulose and lignin, provide a more comprehensive understanding of residue decomposition behaviour14,15. Despite this, very few studies have comprehensively tracked the temporal transformation of these biochemical constituents in different crop residues under controlled conditions. This limitation is particularly evident for residues with contrasting C: N ratios and lignocellulosic profiles, such as legume versus cereal or stalk residues, which follow distinct decomposition pathways.

Furthermore, while past studies have examined carbon mineralization or bulk nutrient release, there remains a significant knowledge gap regarding how microbial populations and lignocellulolytic enzyme activities (e.g., cellulase, xylanase, laccase, lignin peroxidase) respond dynamically to residue composition during decomposition. A mechanistic understanding of how these biological drivers interact with biochemical fractions over time is lacking, especially in the context of in situ residue transformation. Despite the global importance of residue decomposition in nutrient cycling and soil health, few studies have characterized the synchronized biochemical (lignin, cellulose, hemicellulose, protein, phenol), enzymatic and microbial changes across crop residues with differing C: N and lignin contents under uniform incubation conditions.

To address the research gap, we hypothesized that the biochemical composition and diversity of crop residues, specifically differences in lignin, cellulose, hemicellulose, protein, phenol, nitrogen content and C: N ratio would substantially influence their decomposition dynamics, as well as associated microbial and enzymatic responses, at different stages of incubation. Therefore, the objective of this study was to systematically investigate the decomposition behaviour of nine commonly used crop residues under controlled incubation, focusing on (i) the temporal transformation of lignocellulosic and protein fractions, (ii) changes in microbial populations (bacteria, fungi, actinomycetes) and (iii) the activity of key lignocellulolytic enzymes (cellulase, xylanase, laccase and lignin peroxidase). By linking residue quality with microbial and enzymatic responses, this study aims to provide actionable insights for residue management strategies that enhance nutrient release and soil health.

Materials and methods

Soils and crop residues

The soils and crop residues used for the incubations were collected from the B block of the Student Farm (17.3142 °N, 78.4237 °E, 542.6 m sea level), Professor Jayashankar Telangana Agricultural University, College of Agriculture, Rajendranagar, which is located in the Ranga Reddy district of Telangana, India. The soil, derived from loamy loess, is classified as an Entisol (Typic Quartzipsamments).

Soils (0–15 cm) were sampled in early October after kharif crop harvest. After crop residues and debris were removed, the soil samples were sieved through a 2 mm mesh and subsequently divided into two separate subsamples. One subsample was used to conduct the incubation experiment. Another subsample was air-dried at room temperature and used for chemical analyses. A portion of the air-dried samples was used for soil pH, EC and total N (TN) measurements, whereas the other part was ground (0.5 mm sieve) prior to SOC analysis. The soil used contained 5.7 g kg− 1 organic C and 0.06% TN and the pH and EC were 7.68 and 0.48, respectively.

Crop residues were collected during harvest in early October. Five replicate samples were collected per crop type. All crop residues were combined for each replicate and air-dried. A portion of each sample was then oven-dried at 60 °C, ground to 2 mm and stored in a desiccator for the incubation experiment.

Incubation experiment

The incubation study was conducted under controlled laboratory conditions at the Department of Soil Science, Professor Jayashankar Telangana Agricultural University, Rajendranagar, Hyderabad. The experiment was maintained at a constant temperature of 28 ± 2 °C and 60% of soil water-holding capacity throughout the 120-day incubation period. All trays were kept in a temperature-controlled room to avoid fluctuations caused by sunlight or air movement.

The experiment followed a completely randomized design (CRD) with nine treatments and three replications: maize stover (T1), rice straw (T2), cotton stalks (T3), redgram stalks (T4), greengram residue (T5), blackgram residue (T6), sunhemp residue (T7), soybean residue (T8), and sorghum stover (T9). Residue decomposition and the transformation of lignocellulose, total phenols, and proteins were monitored using the litterbag method16.

Each treatment was established in a plastic tray (30 × 25 × 10 cm) containing 1 kg of sieved (2 mm) soil. Six nylon litter bags (5 × 5 cm, 1 mm mesh), each containing 10 g of oven-dried crop residue, were evenly buried horizontally at a depth of 5 cm and spaced approximately 3–4 cm apart to prevent contact and cross-contamination. Moisture was maintained by weighing trays weekly and adding deionized water as required to restore field capacity. Soil temperature and relative humidity, monitored using a digital soil thermometer and hygrometer, showed only minor variations (± 1 °C and ± 3% RH).

At each sampling interval (15, 30, 45, 60, 90, and 120 days after incubation), one litter bag per replicate (three in total per treatment) was carefully retrieved. To minimize disturbance, only the soil directly above the selected bag was removed, leaving the remaining bags intact for subsequent sampling. Retrieved bags were gently cleaned of adhering soil, placed in labelled paper envelopes, and transported to the laboratory for biochemical and enzymatic analyses.

Using six bags per tray enabled sequential time-series sampling under a consistent soil microenvironment, maintaining uniform decomposition conditions across intervals. In contrast, using single bags in separate trays would have introduced variability in soil moisture, aeration, and microbial activity. The adopted design thus ensured consistent soil–residue interactions and reduced experimental error associated with between-tray differences.

Analytical methods

Chemical analyses

Air-dried soil samples were used to determine the soil pH and EC in a 1:2.5 (w/v) mixture of soil and water17. The organic carbon in the soil samples was analyzed via wet chromic acid digestion as outlined by Walkley and Black18,19.

The chemical composition of the crop residues was determined on the initial crop residues for N, P, K, micronutrients, total carbon and total nitrogen. For the determination of phosphorus (P), potassium (K) and micronutrients, 1 gram of crop residue was digested with 12 mL of a diacid mixture consisting of nitric acid and perchloric acid at a 9:4 ratio. The samples were left overnight for pre-digestion, followed by digestion on a hotplate. After digestion, the mixture was filtered and aliquots of the filtrate were used for analyzing P, K and micronutrient concentrations20. Phosphorus was estimated via a spectrophotometer (Elico SL 177) by vanado molybdate method and potassium was estimated via a flame photometer (Elico CL 361)21. Manganese (Mn), iron (Fe), copper (Cu) and zinc (Zn) contents were estimated via an atomic absorption spectrophotometer (Elico SL 177) by22. The total carbon content was determined via a muffle furnace by Allen et al.23.

Biochemical analysis

The biochemical composition of the crop residues was determined on the initial crop residues (undecomposed) and on the decomposed residues after 15, 30, 45, 60, 90 and 120 days of decomposition. Proximate analysis was conducted on 1 g of 2 mm ground crop residue to determine hemicellulose, cellulose and lignin contents, following the sequential fiber fractionation procedure using the standard acid detergent system method24,25. Measurements were performed in duplicate. The total phenol content in the crop residues was determined via the Folin‒Ciocalteu method25,26. The protein content of the crop residue was assessed by multiplying the total nitrogen content, determined through the Kjeldahl digestion and distillation method, by a factor of 6.2527.

Biological properties

The specific lignocellulolytic enzyme activities, namely cellulase, xylanase, laccase and lignin peroxidase were estimated from soil samples using standard spectrophotometric protocols. Cellulase activity (µg glucose g− 1 soil h− 1) was determined using carboxymethyl cellulose as substrate and the released glucose was quantified by the DNS method25,28. Xylanase activity (µg xylose g− 1 soil h− 1) was measured using birchwood xylan as substrate and quantified following the DNS protocol29,30. Laccase activity (µmol ABTS oxidized g− 1 soil h− 1) was assessed using ABTS as substrate with absorbance recorded at 420 nm30,31. Lignin peroxidase activity (µmol veratraldehyde formed g− 1 soil h− 1) was estimated using veratryl alcohol, monitoring veratraldehyde production at 310 nm25,32. All enzyme activities were expressed on an oven dry soil weight basis and performed in triplicate.

Soil microbial populations were estimated using the serial dilution pour plate method (1 g soil in 10 mL of 0.85% saline, diluted up to 106). Bacteria, fungi and actinomycetes were cultured on nutrient agar, rosebengal agar and actinomycete isolation agar, respectively33,34,35. Plates were incubated at 37 °C for 24 h (bacteria), 25 °C for 72 h (fungi) and 30 °C for 48 h (actinomycetes). Colony counts were expressed as CFU g−1 dry soil using the formula of Schmidt and Cadwell36.

$$\:\text{N}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{b}\text{a}\text{c}\text{t}\text{e}\text{r}\text{i}\text{a}\:\text{o}\text{r}\:\text{f}\text{u}\text{n}\text{g}\text{i}\:\text{o}\text{r}\:\text{a}\text{c}\text{t}\text{i}\text{n}\text{o}\text{m}\text{y}\text{c}\text{e}\text{t}\text{e}\text{s}\:\text{p}\text{e}\text{r}\:\text{g}\:\text{s}\text{o}\text{i}\text{l}\:=\:\frac{No.of\:cfu\:\times\:dilution}{Dry\:weight\:of\:1g\:moist\:soil\:\times\:\:aliquot\:taken}$$

Statistical analysis

Statistical analysis was performed using one-way analysis of variance (ANOVA) under a completely randomized design with nine treatments and three replications, following the procedure of Panse and Sukhatme37. The assumptions of ANOVA (normal distribution and homogeneity of variances) were tested and met, and no data points were excluded as outliers. Treatment means were compared using Tukey’s honest significant difference (HSD) test at p < 0.05. The variability of the data was expressed as the mean ± standard error (SE) of three replicates. Pearson’s correlation analysis and principal component analysis (PCA) were performed in Python with the Matplotlib and Seaborn library and RASINS software (v1.2.0)38 to examine relationships and multivariate patterns among decomposition variables.

Results

Characterization of crop residues

Biochemical composition

The initial biochemical compositions of the various residues are summarized in Table 1. There were considerable differences in the biochemical compositions of the residues. Redgram stalks (T4) presented the highest cellulose (44.10%), hemicellulose (26.32%), lignin (22.16%), total phenol content (232.68 mg GAE/100 g), lignin: N ratio (31.66) and phenol: N ratio (0.33), whereas sunhemp residues (T7) presented the highest protein (16.25%) and the lowest cellulose (30.96%), hemicellulose (19.96%), lignin (4.20%), total phenol content (103.73 mg GAE/100 g), lignin: N ratio (1.62) and phenol: N ratio (0.04).

Nutrient composition

The initial nutrient composition of the crop residues is presented in Table 2, revealing notable variability among residues. Sunhemp residue (T7) recorded the highest nitrogen content (2.60%), while redgram stalks (T4) had the lowest (0.70%). Phosphorus content ranged from 0.27% in sunhemp (T7) to 0.14% in rice straw (T2) and potassium varied from 1.70% in sunhemp (T7) to 0.71% in cotton stalks (T3). Total carbon content was highest in soybean residue (T9: 49.20%) and lowest in rice straw (T2: 44.72%). The C: N ratio ranged widely, from 66.78 in redgram stalks (T4) to 18.52 in sunhemp residue (T7), indicating differences in residue decomposability. Among micronutrients, maize stover (T1) showed the highest manganese content (53.00 mg kg− 1), while greengram residue (T5) had the lowest (22.00 mg kg− 1). Iron content was greatest in sunhemp (T7: 180.80 mg kg− 1) and lowest in redgram stalks (T4: 115.30 mg kg− 1). Copper content ranged from 18.75 mg kg− 1 in greengram (T5) to 4.00 mg kg− 1 in rice straw (T2), whereas zinc was highest in maize stover (T1: 41.00 mg kg− 1) and lowest in sunhemp (T7:15.75 mg kg− 1).

Table 1 Biochemical composition of different crop residues.
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Table 2 Nutrient composition of different crop residues.
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Degradation characteristics of residue biochemical components

Lignin degradation

The impact of crop residue type on lignin content (%) was statistically significant (p < 0.05) throughout the decomposition period at 0, 15, 30, 45, 60, 90 and 120 days after incorporation (DAI) (Table 3). Lignin content declined progressively over time, with values ranging from 4.20 to 22.16% at 0 DAI, decreasing to 2.39 to 16.36% by 120 DAI. During the early decomposition phases (0–15, 15–30 and 30–45 DAI), sunhemp residue (T7) exhibited the greatest lignin reduction (5.50%, 6.90% and 8.80%, respectively), while redgram stalks (T4) showed the least reduction (1.50%, 1.90% and 2.80%). Between 45 and 60 DAI, the highest decrease was observed in greengram residue (T5: 13.50%), whereas redgram stalks (T4) remained the least decomposed (4.10%). From 60 to 90 DAI, soybean residue (T8) had the maximum reduction (11.31%) and cotton stalks (T3) the minimum (7.90%). Interestingly, in the final phase (90–120 DAI), redgram stalks (T4) recorded the highest lignin loss (10.50%), while sunhemp residue (T7) had the lowest (7.62%) (Figure S1). Overall, over the entire 120-day period, sunhemp residue (T7) showed the greatest cumulative lignin degradation (42.47%), while redgram stalks (T4) were the most resistant, with the lowest reduction (26.15%).

Cellulose degradation

The effect of crop residue type on cellulose content (%) was statistically significant (p < 0.05) across all sampling intervals at 0, 15, 30, 45, 60, 90 and 120 days after incorporation (DAI) (Table 4). A consistent decline in cellulose content was observed over time, ranging from 30.96 to 44.10% at 0 DAI and reducing to 14.77 to 27.10% by 120 DAI. In the early decomposition stages (0–15, 15–30 and 30–45 DAI), sunhemp residue (T7) exhibited the highest cellulose loss (6.54%, 8.15% and 11.05%), whereas redgram stalks (T4) showed the lowest reduction (2.50%, 3.15% and 5.05%). Between 45 and 60 DAI, the greengram residue (T5) recorded the highest degradation (16.00%), while redgram stalks (T4) remained the most resistant (6.60%). From 60 to 90 DAI, soybean residue (T8) showed the greatest reduction (15.75%) and cotton stalks (T3) the least (12.15%). During the final interval (90–120 DAI), redgram stalks (T4) experienced the highest decrease (16.00%), while sunhemp residue (T7) had the lowest (13.00%) (Figure S2). Cumulatively, over the 120-day incubation, sunhemp residue (T7) recorded the greatest overall reduction in cellulose content (52.29%), whereas redgram stalks (T4) demonstrated the least total loss (38.55%), reflecting their comparatively recalcitrant nature.

Table 3 Temporal changes in lignin content (%) of crop residues during incubation following residue incorporation.
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Table 4 Temporal changes in cellulose content (%) of crop residues during incubation following residue incorporation.
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Hemicellulose degradation

The influence of crop residue type on hemicellulose content (%) was statistically significant (p < 0.05) across all decomposition intervals at 0, 15, 30, 45, 60, 90 and 120 days after incorporation (DAI) (Table 5). Hemicellulose content steadily declined over time, ranging from 26.32 to 19.96% at 0 DAI and decreasing to 8.83 to 14.19% at 120 DAI. During the initial phases (0–15, 15–30 and 30–45 DAI), sunhemp residue (T7) exhibited the greatest hemicellulose reduction (7.35%, 9.15% and 12.55%), while redgram stalks (T4) showed the least decrease (3.25%, 4.15% and 6.55%). Between 45 and 60 DAI, the greengram residue (T5) recorded the highest reduction (17.75%), in contrast to redgram stalks (T4), which had the lowest (8.35%). From 60 to 90 DAI, soybean residue (T8) showed the highest loss (15.65%) and cotton stalks (T3) the lowest (14.65%). In the final stage (90–120 DAI), redgram stalks (T4) presented the greatest decline (20.00%), while sunhemp residue (T4) had the smallest reduction (14.00%) (Figure S3). Over the full incubation period (0–120 DAI), sunhemp residue (T7) showed the maximum cumulative hemicellulose loss (55.76%), whereas redgram stalks (T4) recorded the minimum (38.55%), reflecting differences in residue degradability.

Protein degradation

The effect of crop residue type on protein content (%) was statistically significant (p < 0.05) throughout the decomposition period at 0, 15, 30, 45, 60, 90 and 120 days after incorporation (DAI) (Table 6). Protein content declined progressively across treatments, ranging from 4.38 to 16.25% at 0 DAI and reducing to 1.95 to 6.05% by 120 DAI. During the early phases (0–15, 15–30 and 30–45 DAI), sunhemp residue (T7) showed the highest reduction in protein content (8.52%, 10.65% and 15.05%), while redgram stalks (T4) exhibited the lowest (4.50%, 5.65% and 9.05%). Between 45 and 60 DAI, greengram residue (T5) had the maximum protein loss (20.51%) compared to redgram stalks (T4), which again recorded the least reduction (11.10%). From 60 to 90 DAI, soybean residue (T8) showed the greatest decline (18.91%) and cotton stalks (T3) the lowest (17.93%). In the final stage (90–120 DAI), redgram stalks (T4) showed the highest reduction (24.50%), while sunhemp residue (T7) had the lowest (17.81%) (Figure S4). Over the entire incubation period, sunhemp residue (T7) exhibited the highest cumulative protein loss (62.78%), whereas redgram stalks (T4) showed the lowest overall reduction (55.11%), indicating slower protein mineralization in more recalcitrant residues.

Table 5 Temporal changes in hemicellulose content (%) of crop residues during incubation following residue incorporation.
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Table 6 Temporal changes in protein content (%) of crop residues during incubation following residue incorporation.
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Total phenol dynamics

The variation in total phenol content (mg GAE 100 g− 1) among crop residue types was statistically significant (p < 0.05) throughout the decomposition period at 0, 15, 30, 45, 60, 90 and 120 days after incorporation (DAI) (Table 7). Total phenol levels declined initially until 30 DAI, followed by a gradual increase across all treatments through 120 DAI. At 0 DAI, total phenol content ranged from 103.73 to 232.68 mg GAE 100 g− 1, which increased to 124.99 to 311.00 mg GAE 100 g− 1 by 120 DAI. During the early phase (0–30 DAI), sunhemp residue (T7) exhibited the highest reduction in phenol content (6.0% and 7.4% at 0–15 and 15–30 DAI, respectively), while redgram stalks (T4) showed the lowest decline (2.0% and 2.4%). Between 30 and 45 DAI, phenol accumulation was greatest in sunhemp (7.48%) and lowest in redgram stalks (1.82%). From 45 to 60 DAI, the greengram residue (T5) recorded the highest phenol increase (11.48%), whereas redgram stalks (T4) showed the least change (2.67%). During 60–90 DAI, redgram stalks (T4) exhibited the highest increase (9.46%) and sunhemp residue (T7) the lowest (8.12%). In the final phase (90–120 DAI), redgram stalks (T4) again showed the most pronounced increase (22.12%), compared to sunhemp residue (T7), which had the smallest rise (7.68%) (Figure S5). Cumulatively, over the 120-day incubation, sunhemp residue (T7) exhibited the lowest overall increase in phenol content (20.49%), while redgram stalks (T4) recorded the highest (33.65%), suggesting delayed phenol transformation in residues with greater lignin content.

Lignocellulolytic enzyme dynamics

Lignin peroxidase activity

Soil lignin peroxidase activity (µmol veratraldehyde g− 1 h− 1) varied significantly (p < 0.05) across crop residue treatments throughout the incubation period, assessed at 15, 30, 45, 60, 90 and 120 days after incorporation (DAI) (Table 8). Enzyme activity ranged from 3.10 to 9.40 µmol veratraldehyde g− 1 h− 1 across all time points. During the early stages (15–45 DAI), sunhemp residue (T7) showed the highest lignin peroxidase activity, recording 5.20, 6.10 and 7.40 µmol veratraldehyde g− 1 h− 1 at 15, 30 and 45 DAI, respectively. In contrast, redgram stalks (T4) exhibited the lowest activity during the same period (3.10, 3.80 and 4.70 µmol veratraldehyde g− 1 h− 1). At 60 DAI, the highest activity was observed under greengram residue (T5: 8.80 µmol veratraldehyde g− 1 h− 1), while redgram stalks (T4) remained the least responsive (6.10 µmol veratraldehyde g− 1 h− 1). At 90 DAI, soybean residue (T8) showed peak activity (9.30 µmol veratraldehyde g− 1 h− 1) and cotton stalks (T3) recorded the lowest (6.40 µmol veratraldehyde g− 1 h− 1). Interestingly, in the final stage (120 DAI), redgram stalks (T4) exhibited the highest lignin peroxidase activity (9.40 µmol veratraldehyde g− 1 h− 1), while sunhemp (T7) had the lowest (5.70 µmol veratraldehyde g− 1 h− 1).

Table 7 Temporal changes in total phenol content (mg GAE/100 g) of crop residues during incubation following residue incorporation.
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Table 8 Effect of different crop residues on soil lignin peroxidase activity (µmol veratraldehyde g− 1 h− 1) during incubation.
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Cellulase activity

Soil cellulase activity (µg glucose g− 1 h− 1) differed significantly (p < 0.05) across crop residue treatments and decomposition intervals at 15, 30, 45, 60, 90 and 120 days after incorporation (DAI) (Table 9). Cellulase activity ranged from 16.80 to 35.50 µg glucose g− 1 h− 1 across all time points. In the early phase (15–45 DAI), sunhemp residue (T7) showed the highest cellulase activity, with values of 23.80, 26.60 and 30.60 µg glucose g− 1 h− 1, respectively. Redgram stalks (T4) recorded the lowest activity during this period (16.80, 18.40 and 20.60 µg glucose g− 1 h− 1). At 60 DAI, the peak cellulase activity was observed in greengram residue (T5) at 35.50 µg glucose g− 1 h− 1, while redgram stalks (T4) remained lowest (22.80 µg glucose g− 1 h− 1). By 90 DAI, soybean (T8) and blackgram (T6) showed the highest enzyme activities (34.20 and 33.60 µg glucose g− 1 h− 1), whereas cotton stalks (T3) and sorghum stover (T9) exhibited the lowest (26.50 and 27.10 µg glucose g− 1 h− 1). At the final stage (120 DAI), redgram stalks (T4) recorded the highest cellulase activity (33.50 µg glucose g− 1 h− 1), while sunhemp residue (T7) had declined to the lowest level (22.34 µg glucose g− 1 h− 1). This indicates an early stimulation of cellulase production under more labile residues like sunhemp and delayed but sustained activity under more recalcitrant substrates like redgram stalks.

Xylanase activity

Soil xylanase activity (µg xylose g− 1 h− 1) was significantly influenced by crop residue type across all decomposition stages (p < 0.05), measured at 15, 30, 45, 60, 90 and 120 days after incorporation (DAI) (Table 10). Activity values ranged from 83.30 to 154.30 µg xylose g− 1 h− 1 across all intervals. During the early phase (15–45 DAI), sunhemp residue (T7) recorded the highest xylanase activity, with values of 105.90, 127.10 and 144.20 µg xylose g− 1 h− 1, respectively, while redgram stalks (T4) consistently exhibited the lowest activity (83.30, 89.60 and 100.20 µg xylose g− 1 h− 1). At 60 DAI, maximum activity was observed in greengram (T5) and sunhemp residues, recording 154.30 and 149.90 µg xylose g− 1 h− 1, respectively. Meanwhile, redgram stalks (T4) again presented the lowest (110.80 µg xylose g− 1 h− 1). At 90 DAI, xylanase activity peaked in maize stover (T1), rice straw (T2) and cotton stalks (T3) 135.80, 134.20 and 131.20 µg xylose g− 1 h− 1), while sunhemp (T7) showed a noticeable decline to 113.10 µg xylose g− 1 h− 1. By 120 DAI, the highest activity was found in redgram stalks (T4) and maize stover (T1) (152.80 and 151.20 µg xylose g− 1 h− 1), whereas sunhemp residue (T7) showed the lowest value (108.80 µg xylose g− 1 h− 1).

Table 9 Effect of different crop residues on soil cellulase activity (µg glucose g− 1 h− 1) during incubation.
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Table 10 Effect of different crop residues on soil Xylanase activity (µg xylose g− 1 h− 1) during incubation.
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Laccase activity

Soil laccase activity (µmol ABTS oxidized g− 1 h− 1) differed significantly (p < 0.05) among crop residue treatments across all decomposition intervals at 15, 30, 45, 60, 90 and 120 days after incorporation (DAI) (Table 11). Laccase activity ranged from 3.90 to 12.10 µmol ABTS oxidized g− 1 h− 1 across all intervals. During the initial phase (15–45 DAI), sunhemp residue (T7) consistently exhibited the highest laccase activity, with values of 5.70, 6.50 and 7.80 µmol ABTS oxidized g− 1 h− 1, respectively. Redgram stalks (T4) showed the lowest activity in this period, ranging from 3.90, 4.30 and 5.10 µmol ABTS oxidized g− 1 h− 1. At 60 DAI, peak activity was recorded in greengram residue (T5) at 9.40 µmol ABTS oxidized g− 1 h− 1, whereas redgram stalks (T4) 6.50 µmol ABTS oxidized g− 1 h− 1. From 90 to 120 DAI, a shift in activity patterns was observed. At 90 DAI, redgram stalks (T4) showed the highest laccase activity (10.20 µmol ABTS oxidized g− 1 h− 1), contrasting with sunhemp (T7) which declined to 7.50 µmol ABTS oxidized g− 1 h− 1. At 120 DAI, redgram stalks (T4) maintained the highest value (12.10 µmol ABTS oxidized g− 1 h− 1), while sunhemp (T7) and other legume residues like greengram (T5: 6.90 µmol ABTS oxidized g− 1 h− 1) and blackgram (T6: 7.10 µmol ABTS oxidized g− 1 h− 1) recorded lower activities.

Microbial population dynamics

Bacterial population dynamics

Bacterial populations (×106 CFU g− 1 soil) were significantly influenced (p < 0.05) by the type of crop residue throughout the incubation period at 30, 60 and 120 DAI (Fig. 1). At 30 DAI, the highest bacterial counts were recorded under sunhemp (T7), greengram (T5) and blackgram (T6) (59.50, 56.30 and 55.70 × 106 CFU g− 1 respectively), while redgram stalks (T4) had the lowest (40.60 × 106 CFU g− 1). By 60 DAI, sunhemp residue (T7) again exhibited the highest bacterial population (74.20 × 106 CFU g− 1), followed by greengram (T5: 70.50 × 106 CFU g− 1), while redgram stalks (T4) remained lowest (51.00 × 106 CFU g− 1). Interestingly, at 120 DAI, bacterial populations declined in early-labile residues like sunhemp residue (T7: 46.80 × 106 CFU g− 1), while redgram stalks (T4: 64.10 × 106 CFU g− 1) showed a late increase, indicating delayed microbial proliferation. These findings suggest that labile residues stimulate early bacterial growth, whereas recalcitrant residues support prolonged microbial activity.

Table 11 Effect of different crop residues on soil laccase activity (µmol ABTS oxidized g− 1 h− 1) during incubation.
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Fig. 1
Fig. 1
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Effect of crop residue decomposition on bacterial population (×106 CFU g− 1 soil) at 30, 60 and 120 days after incorporation (DAI). Tukey’s Honestly Significant Difference (HSD) test was used for mean comparison. Treatments sharing the same letter within each timepoint are not significantly different at p < 0.05. See treatment details in incubation experiment section.

Actinomycetes population dynamics

Actinomycetes populations (×105 CFU g− 1 soil) also exhibited significant variation across treatments and time points (p < 0.05) (Fig. 2). At 30 DAI, maximum counts were observed under sunhemp (T7: 27.30 × 105 CFU g− 1), greengram (T5: 26.50 × 105 CFU g− 1) and blackgram (T6: 26.10 × 105 CFU g− 1) residues. These legume-based treatments maintained the highest actinomycete populations at 60 DAI, with sunhemp (T7: 31.10 × 105 CFU g− 1) and greengram (T5: 29.80 × 105 CFU g− 1) significantly exceeding stalk- and cereal-based residues. However, by 120 DAI, the highest actinomycete activity shifted to redgram stalks (T4: 23.60 × 105 CFU g− 1), while counts declined in earlier high-performing treatments like sunhemp (T7: 17.60 × 105 CFU g− 1) and greengram (T5: 18.00 × 105 CFU g− 1) residue. This indicates a residue-specific temporal shift, where labile inputs promote early actinomycete proliferation, while lignin-rich substrates sustain microbial activity in later stages.

Fungal population dynamics

Fungal populations (×104 CFU g− 1 soil) were significantly affected by residue type at all sampling intervals (p < 0.05) (Fig. 3). At 30 DAI, the highest fungal counts were observed in sunhemp (T7: 21.60 × 104 CFU g− 1), greengram (T5: 20.80 × 104 CFU g− 1) and blackgram (T6: 20.50 × 104 CFU g− 1), with the lowest in redgram stalks (T4: 10.90 × 104 CFU g− 1). This trend persisted at 60 DAI, where sunhemp residue (T7) again showed the highest fungal activity (23.20 × 104 CFU g− 1) and redgram stalks (T4) the lowest (13.00 × 104 CFU g− 1). By 120 DAI, however, redgram stalks (T4: 16.00 × 104 CFU g− 1) showed the highest fungal count, while sunhemp (T7:11.40 × 104 CFU g− 1) exhibited the lowest, reflecting an early depletion of easily available carbon in labile residues. Overall, fungal proliferation was rapid in early stages under legume residues, but more sustained under recalcitrant materials like redgram in later stages.

Principal component analysis (PCA)

Principal Component Analysis (PCA) was conducted to explore the multivariate relationships among crop residue treatments and key decomposition indicators, including enzyme activities (cellulase, xylanase, laccase, lignin peroxidase), microbial populations (bacteria, fungi, actinomycetes) and residue biochemical properties (lignin, cellulose, hemicellulose, protein, phenols) across the 120-day incubation period (Fig. 4).

Fig. 2
Fig. 2
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Effect of crop residue decomposition on Actinomycetes population (×105 CFU g− 1 soil) at 30, 60 and 120 days after incorporation (DAI). Tukey’s Honestly Significant Difference (HSD) test was used for mean comparison. Treatments sharing the same letter within each timepoint are not significantly different at p < 0.05. See treatment details in incubation experiment section.

Fig. 3
Fig. 3
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Effect of crop residue decomposition on Fungi population (×104 CFU g− 1 soil) at 30, 60 and 120 days after incorporation (DAI). Tukey’s Honestly Significant Difference (HSD) test was used for mean comparison. Treatments sharing the same letter within each timepoint are not significantly different at p < 0.05. See treatment details in incubation experiment section.

The first two principal components, PC1 (90.33%) and PC2 (3.98%), cumulatively explained 94.31% of the total variance. PC1 primarily captured the variability associated with decomposition dynamics and enzyme activity, whereas PC2 accounted for secondary differences related to microbial succession and biochemical traits. The treatments exhibited distinct clustering patterns. T7 (Sunhemp), T5 (Greengram), T6 (Blackgram) and T8 (Soybean) were positioned on the negative axis of PC1 and PC2 and were strongly associated with early-stage enzyme activities (15–45 DAI) and higher bacterial and fungal populations. These residues likely favoured rapid microbial colonization and enzymatic turnover due to their rich labile carbon and nitrogen fractions. In contrast, T1 (Maize stover), T2 (Rice straw) and T4 (Redgram stalks) were located on the positive side of PC1, correlating with sustained microbial populations at 90–120 DAI and high lignin content across time points. These treatments were characterized by slower decomposition and a higher proportion of recalcitrant carbon compounds, which supported microbial activity over longer durations. T3 (Cotton stalks) exhibited an intermediate position in the biplot, indicating moderate enzymatic and microbial responses and suggesting a balanced decomposition trajectory between labile and recalcitrant residue types.

Fig. 4
Fig. 4
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Principal Component Analysis (PCA) biplot showing relationships among crop residue treatments (T1–T9) and lignocellulolytic enzyme activities, microbial populations and biochemical traits across different days after incorporation (DAI). Figure was generated using RASINS software, version 1.2.0. URLs: https://www.raisins.live/.

Discussion

The decomposition of crop residues is largely governed by the structural complexity and chemical composition of their constituent polymers. In this study, lignin, cellulose, hemicellulose and phenolic compounds showed residue-specific degradation patterns that closely aligned with corresponding changes in enzyme activities namely lignin peroxidase, cellulase, xylanase and laccase, respectively. The temporal dynamics of these enzymes reflect both substrate availability and microbial adaptation, determining the rate and extent of organic matter turnover under incubation conditions.

Lignin and lignin peroxidase

Lignin degradation progressed more slowly than other biochemical components, reflecting its inherently recalcitrant structure. As a complex, water-insoluble, three-dimensional aromatic polymer with non-hydrolysable ether linkages, lignin is highly resistant to microbial enzymatic attack39,40. Nevertheless, sunhemp residue (T7) exhibited the most substantial lignin reduction (42.47%) over 120 days, which corresponded with an early and pronounced increase in lignin peroxidase activity, peaking at 7.40 µmol veratraldehyde g− 1 h− 1 by 45 DAI. This efficient degradation is likely due to a higher abundance of syringyl units and a favourable syringyl/vanillyl (S/V) ratio in sunhemp lignin, which results in more linear and less condensed polymer structures that are more susceptible to oxidative cleavage41. Additionally, its low total phenol content (103.73 mg GAE 100 g− 1), high nitrogen concentration (2.60%), low lignin: N ratio (1.62) and phenol: N ratio (39.90) likely enhanced microbial colonization and enzyme synthesis.

In contrast, redgram stalks (T4) recorded the lowest lignin loss (26.15%) and the weakest early-stage lignin peroxidase activity. This can be attributed to their high lignin content (22.16%), elevated phenol concentration (232.68 mg GAE 100 g− 1), lignin: N ratio (31.66), phenol: N ratio (332.40) and the lowest nitrogen content (0.70%), which collectively restrict microbial activity during the initial stages. These observations are supported by strong negative correlation between lignin and nitrogen (r = − 0.95***) and positive correlations between lignin and phenols (r = 0.886***), lignin and lignin: N ratio (r = 0.96***) and lignin and Phenol: N ratio (0.969***) as illustrated in Fig. 5. Moreover, lignin and lignin peroxidase activity were negatively correlated in the early phase (r = − 0.732***), but this relationship turned strongly positive in later stages (r = 0.824***), indicating a temporal shift in enzyme expression as substrate availability and microbial adaptation progressed. The elevated phenolic content may have also inhibited ligninolytic enzymes, while the lower S/V ratio in redgram lignin likely contributed to greater structural complexity and enzymatic resistance40,42.

These findings are in agreement with Liu et al.43, who reported that lignin-degrading enzymes are typically upregulated under conditions of nitrogen limitation or advanced substrate recalcitrance. Likewise, Zhong et al.44 demonstrated that the long-term incorporation of wheat straw alongside fertilizers increased lignin peroxidase activity by 24%, emphasizing the stimulatory effect of carbon-rich substrates on ligninolytic enzyme production.

Fig. 5
Fig. 5
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Clustering heatmap of biochemical parameters, enzymatic activities and microbial populations during crop residue decomposition. Clustering heatmap showing relationships among treatments, lignin, cellulose, hemicellulose, total phenols, microbial populations (bacteria, fungi, actinomycetes) and enzyme activities (laccase, lignin peroxidase, cellulase, xylanase) at different decomposition stages (Initial, 30 and 120 DAI). Figures were generated using Python (version 3.11.5, Anaconda distribution) with Matplotlib library (version 3.7.2) and seaborn library (version 0.12.2), executed in a local Jupyter Notebook environment.

Cellulose and cellulase

Cellulose decomposition progressed more rapidly than lignin, particularly in residues with minimal lignin shielding and greater nitrogen availability. Among the treatments, sunhemp (T7), greengram (T5) and blackgram (T6) showed the most substantial cellulose degradation, with sunhemp showed a 52.29% reduction by 120 DAI. This trend corresponded with early peaks in cellulase activity, particularly in greengram (T5: 35.50 µg glucose g− 1 h− 1 at 60 DAI). These residues were characterized by abundant labile carbon, low cellulose crystallinity, reduced lignin content and low lignin: N ratio, which improved microbial accessibility and enzymatic breakdown. A significant positive correlation between lignin and cellulose content (r = 0.896***) and cellulose and lignin: N ratio (r = 0.892***) supports the role of lignin in restricting early cellulose degradation by limiting enzyme access. Additionally, the sunhemp residue (T7), which had a low C: N ratio (18.52), low lignin: N ratio (1.62), and phenol: N ratio (39.90), was conducive to early microbial proliferation and enzyme production, as evidenced by the strong positive correlations between the C: N ratio and cellulase activity (r = 0.976***), lignin: N ratio and cellulase activity (r = 0.902***), and phenol: N ratio and cellulase activity (r = 0.932***) (Table S1).

In contrast, redgram stalks (T4) exhibited delayed cellulose loss (38.55%) and low initial cellulase activity, which peaked only at 120 DAI (33.50 µg glucose g− 1 h− 1). The semi-crystalline structure of cellulose, coupled with tight lignin–cellulose complexes, high C: N ratio, lignin: N ratio, phenol: N ratio and low nitrogen content, impeded early microbial colonization and delayed enzymatic activity. This was supported by the negative correlations between cellulose content and nitrogen (r = − 0.823***) suggesting that nitrogen limitation may restrict early microbial activity. Moreover, cellulase activity showed a negative relationship with cellulose content in early stages (r = − 0.717***), transitioning to a strong positive correlation at later stages (r = 0.927***), indicating a time-dependent induction of enzymatic activity as microbial adaptation progressed.

These observations are consistent with the findings of Chen et al.45 and Zheng et al.46, who reported that substrate recalcitrance and nitrogen deficiency delay microbial enzyme expression. Jat et al.47 similarly reported that zero tillage with residue retention improved cellulase activity by promoting microbial transformation through enhanced residue quality and soil conditions.

Hemicellulose and Xylanase

Hemicellulose degraded more rapidly than cellulose, which is consistent with its amorphous, highly branched structure and lower degree of polymerization, making it more accessible to enzymatic attack48. Among the residues, sunhemp (T7) showed the greatest hemicellulose loss (55.76%), accompanied by early and elevated xylanase activity (144.20 µg xylose g− 1 h− 1 at 45 DAI). This rapid decomposition can be attributed to reduced lignin cross-linking, low phenolic interference and greater xylan accessibility, all of which facilitated fungal colonization and enzymatic action. These effects were strongly supported by the positive early-stage correlations between nitrogen content and fungal population (r = 0.890***) and xylanase activity (r = 0.945***) (Figure S6). However, at later stages, both relationships turned negative (r = − 0.872*** and − 0.912***, respectively), indicating that nutrient-rich residues tend to support early degradation, while xylanase activity declines as hemicellulose is depleted.

In contrast, redgram stalks (T4) displayed slower hemicellulose degradation (46.08%) and low early xylanase activity due to their high lignin–hemicellulose bonding and elevated ester-linked phenol content, which restrict enzymatic access. However, xylanase activity in redgram gradually increased, reaching its peak (152.80 µg xylose g− 1 h− 1) only at 120 DAI. This trend was confirmed by a negative correlation between hemicellulose and xylanase at early stages (r = − 0.773***), which later shifted to positive (r = 0.924***), indicating a delayed but significant enzymatic response as structural barriers diminished. These findings are consistent with Panigrahi et al.49 and Ostby et al.50, who reported similar lag-phase enzyme dynamics in residues with high lignin and low nitrogen content. Likewise, Sharma et al.25 reported that crop residues with lower C: N ratios showed higher xylanase activity, especially when combined with microbial consortia, further emphasizing the role of residue quality and microbial enhancement in regulating hemicellulolytic processes.

Protein decomposition

Protein degradation progressed more rapidly than that of structural carbohydrates and was predominantly governed by the nitrogen content, lignin concentration and phenolic load of the residues. Among the treatments, sunhemp (T7) exhibited the greatest protein reduction (62.78%), followed closely by greengram (61.91%) and blackgram (60.55%). This accelerated breakdown is attributed to the combination of high nitrogen availability and lower levels of lignin, total phenolics, lignin: N ratio, phenol: N ratio which collectively promoted faster nitrogen mineralization. Strong statistical correlations further supported these relationships, with protein degradation showing a positive association with nitrogen content (r = 0.985***) and negative correlations with C: N ratio (r = − 0.969***), lignin (r = − 0.956***), total phenol content (r = − 0.792***) lignin: N ratio (r = − 0.939***) and phenol: N ratio (r= − 0.959***). These findings align with the results of Subaedah and Aladin51, who reported that nitrogen-rich residues low in lignin and polyphenols facilitated more efficient nitrogen release.

By contrast, residues with higher C: N ratios such as redgram stalks (T4) and sorghum stover (T9) demonstrated delayed protein mineralization, with the highest degradation occurring near 120 DAI. This pattern supports the conceptual model proposed by52, which suggests that microbial communities initially target easily degradable substrates such as sugars and amino acids before shifting toward the slower decomposition of structural proteins and lignified material. The decline in protein breakdown beyond 60 DAI in legume-based residues likely reflect this transition, where microbial access to nitrogen becomes increasingly limited due to the progressive exhaustion of labile compounds and the persistence of more recalcitrant substrate fractions.

Total phenol content and laccase

Total phenol content exhibited a biphasic trend, declining during the initial stages (0–30 DAI) and subsequently increasing up to 120 DAI. This pattern is likely attributed to the release of bound phenolic intermediates during lignin depolymerization. A strong positive correlation between total phenol and lignin content (r = 0.907***) further supports their interconnected dynamics. The activity of laccase, a key enzyme responsible for phenol oxidation, closely mirrored this trend.

Among treatments, sunhemp residue (T7) recorded the highest early-stage laccase activity (7.80 µmol ABTS oxidized g− 1 h− 1 at 45 DAI), which may be linked to its lower total phenol content, simpler phenolic composition and higher concentrations of low-molecular-weight phenols that readily stimulate laccase expression. In contrast, redgram stalks (T4) exhibited low initial laccase activity, which steadily increased and peaked at 12.10 µmol ABTS oxidized g− 1 h− 1 by 120 DAI. The delayed laccase response is likely due to the gradual solubilization of complex bound phenolics and the presence of enzyme-inhibiting polyphenols in early decomposition phases. This is further corroborated by a negative correlation between total phenol content and laccase activity in the early phase (r = − 0.793***), which later turned strongly positive (r = 0.934***) during the advanced stages of lignin breakdown.

These observations are consistent with Sharma et al.30, who reported that laccase activity increases only after sufficient release of aromatic lignin derivatives during later stages of decomposition. Similar findings were also noted by Kunnika and Pranee53, Navas et al.54 and Blanquez et al.55, who observed that phenolics in crop residues predominantly exist in bound forms that become mobilized as decomposition progresses. Supporting this trend, Chen et al.56 reported higher xylanase activity in long-term crop residue incorporation compared to short-term treatments, emphasizing the cumulative effect of residue addition on sustained enzyme induction over time.

Microbial population dynamics

Soil microbial communities responded distinctly to the biochemical composition of incorporated crop residues. Leguminous residues such as sunhemp (T7), greengram (T5) and blackgram (T6), characterized by low C: N ratios, lignin: N ratio, phenol: N ratio, high nitrogen and lower lignin and phenol contents, promoted early and higher bacterial, fungal and actinomycete populations at 30 and 60 DAI. In contrast, recalcitrant residues like redgram stalks (T4) supported delayed but sustained microbial proliferation, with peak populations observed at 120 DAI. This temporal shift is attributed to the rapid depletion of labile carbon in legume residues and the gradual breakdown of complex polymers in stalk-based residues. Microbial populations showed strong negative correlations with C: N ratio at early stages (bacteria: r = − 0.857***, fungi: − 0.848***, actinomycetes: − 0.803***) and positive correlations at later stages (r = 0.937***, 0.943***, 0.739***), indicating that as the C: N ratio narrows, microbial activity increases. Similarly, lignin content also exhibited a negative correlation in the early phase (r = − 0.882***, − 0.877***, − 0.829***) and a positive relationship in the later phase (r = 0.942***, 0.949***, 0.753***), highlighting its role in sustaining microbial activity over extended decomposition. Likewise, phenol: N ratio also exhibited a negative correlation in the early phase (r = − 0.864***, − 0.796***, − 0.840***) and a positive relationship in the later phase (r = 0.912***, 0.921***, 0.917***).

These results corroborate earlier findings that residue retention enhances microbial abundance by improving carbon inputs and modifying the soil microenvironment57,58. The elevated fungal and actinomycete populations observed at later stages align with the degradation of complex substrates such as lignin and cellulose59,60. Similarly, Reddy et al.61 reported increased microbial counts at tasselling and harvest stages with residue incorporation. Collectively, the findings emphasize that residue biochemical quality regulates microbial succession, with labile residues favouring early colonizers and recalcitrant residues sustaining long-term microbial activity.

Across all biochemical fractions, sunhemp residue (T7) consistently exhibited the highest degradation, enzyme activity and microbial population dynamics owing to its high nitrogen content, low lignin, phenol, lignin: N ratio and phenol: N ratio simplified lignin structure. In contrast, redgram stalks (T4) showed the lowest decomposition, delayed enzymatic responses and lesser microbial population due to high C: N ratio, lignin: N ratio, phenol: N ratio, lignin and phenol concentrations, complex lignin architecture and low nitrogen availability. These results are consistent with previous findings by Singh et al.32 and Panigrahi et al.49, who emphasized the combined role of residue chemistry and microbial succession in regulating enzymatic breakdown of plant polymers. Ultimately, the strong correlation between residue quality, enzyme induction timing and decomposition rate confirms the central role of biochemical composition in residue management strategies aimed at enhancing nutrient cycling in soil systems.

Principal component analysis (PCA)

The PCA results clearly demonstrate that residue decomposition is primarily driven by biochemical composition, particularly the balance between labile and recalcitrant fractions. Legume residues such as sunhemp (T7), greengram (T5), blackgram (T6) and soybean (T8) clustered with early-stage enzymatic activities (cellulase, xylanase, laccase, lignin peroxidase) and high microbial populations, reflecting rapid breakdown of soluble carbon, proteins and simple phenolics. In contrast, more recalcitrant residues like maize stover (T1), rice straw (T2) and redgram stalks (T4) aligned with late-stage microbial activity and lignin content, indicating delayed yet sustained degradation due to higher lignocellulose and C: N ratios. Cotton stalks (T3) showed an intermediate response, suggesting a balanced decomposition pathway. These patterns support residue-specific microbial succession, where microbial and enzymatic responses vary with residue quality and stage of decay19,62,63,64. Overall, the PCA highlights that residue chemistry shapes both the pace and functional trajectory of decomposition, with implications for optimizing residue management to boost soil biological activity and nutrient turnover.

Collectively, these results highlight that soil biochemical properties including enzyme activities, microbial population dynamics, and nutrient availability, play a central role in regulating the mechanism of straw degradation. High nitrogen availability, low C: N ratios, and lower phenolic/lignin content create a favorable biochemical environment that accelerates microbial colonization and induces specific enzymes (cellulase, xylanase, lignin peroxidase, laccase), facilitating rapid breakdown of labile and structural components25,52. Conversely, residues with high lignin and phenolic content or high C: N ratios create a more recalcitrant biochemical environment, delaying enzyme induction and microbial activity, thereby slowing degradation30,57. This mechanistic link between residue quality and soil biochemical properties underscores the importance of considering both substrate chemistry and soil biological responses in residue management strategies aimed at enhancing organic matter turnover and nutrient cycling.

Conclusions

This incubation study provides strong evidence that the decomposition dynamics of crop residues are primarily governed by their biochemical composition. Residues rich in labile components and low lignin content and narrow C: N ratios, lignin: N ratio, phenol: N ratio such as sunhemp (T7), greengram (T5) and blackgram (T6) underwent rapid degradation, driven by elevated microbial populations and enhanced activities of key enzymes, including cellulase, xylanase, laccase and lignin peroxidase, particularly during early stages. In contrast, residues with high lignin content and wide C: N ratios, lignin: N ratio, phenol: N ratio, such as redgram stalks (T4), maize stover (T1) and rice straw (T2) decomposed more slowly, supporting sustained microbial activity over a longer period due to the gradual breakdown of recalcitrant compounds. Correlation and principal component analyses further confirmed that residue quality strongly influenced the timing and magnitude of microbial and enzymatic responses, indicating residue-specific patterns of microbial succession and enzyme induction. From a management perspective, our findings suggest that high C: N ratio residues (maize, rice, redgram) should be incorporated at least 90 days before planting, whereas low C: N ratio residues (legumes) can be incorporated 30 days before planting to optimize nutrient release and minimize nitrogen immobilization. Furthermore, mixing high and low C: N ratio residues can promote a balanced and continuous decomposition process, improving synchronization between residue breakdown and nutrient availability while reducing the need for residue burning. However, a key limitation of this study is that all observations were derived from a single soil type under controlled incubation conditions. Because soil properties including texture, mineralogy, pH, nutrient status, and baseline microbial communities strongly regulate residue decomposition and enzyme activities, the extent to which these findings can be extrapolated to other soil environments remains uncertain. Therefore, caution is warranted when generalizing these results beyond the studied soil. Future studies conducted across multiple locations, soil types, and field conditions are essential to validate the broader applicability of these findings and to refine residue management recommendations for diverse agroecosystems.