Soil biochemical quality indices can capture transitional changes of tillage and residue regime in lateritic soils

Abstract

Aiming to evaluate the sustainability and ecological viability of the rice-wheat system, field experiment was conducted at experimental farms of IIT Kharagpur, West Bengal, India in 2022–24 to assess how Conservation Agriculture (CA) based practices influence soil biochemical changes throughout the growth stages of rice and wheat. Under this experiment, the impact of varying tillage treatments (conventional tillage vs. zero tillage) and residue management practices (incorporated vs. retained) on soil biochemical properties in a rice-wheat cropping system was evaluated during the first year transitional period of adaptation of CA. This experiment was laid out in a randomized block design (RBD) with eight treatments among which rice residue retention, rice residue incorporation treatments along with no residue application were adopted over different crop establishment practices viz. conventional tillage (CT) and zero tillage (ZT). Key parameters such as soil organic carbon, soil microbial biomass carbon, enzyme activities (including dehydrogenase, phosphatase, and urease) were monitored during this transitional period of CA adaptation and compared with CT practices. The results showed that ZT practices, coupled with residue retention (RR), significantly enhanced Walkley black carbon (WBC) content at top 15 cm of soil by 0.33% at this transitional period compared to conventional tillage where WBC content was reduced by 1.55% from initial values. Enzyme activities, particularly dehydrogenase and phosphatase, were higher under zero till and residue retention treatments, indicating improved microbial activity and nutrient cycling. Soil respiration rates were also higher in zero till plots, suggesting an increased microbial turnover and organic matter decomposition. In contrast, conventional tillage with incorporated residue exhibited higher soil compaction, reducing microbial activity and overall biochemical quality indices. The findings highlight that conservation tillage, specifically zero till systems with residue retention, offers substantial benefits for improving soil biochemical health and sustainability in lateritic soils. This system can potentially enhance carbon sequestration, promote soil microbial biodiversity, and improve nutrient cycling, making it a viable strategy for sustainable agriculture in tropical and subtropical regions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-025-07212-3.

Introduction

The lateritic soils, widely distributed across tropical and subtropical regions globally, represent a critical challenge due to their inherently poor fertility and vulnerability to degradation [1]. These soils, characterized by high acidity, low organic matter, and a prevalence of aluminum and iron oxides, are particularly prone to nutrient leaching and erosion. In regions where intensive agricultural practices dominate, the deterioration of lateritic soils is further exacerbated by deforestation, overgrazing, and unsustainable cultivation methods [2, 3]. This degradation compromises soil structure, reduces water-holding capacity, and limits plant-available nutrients, significantly affecting crop productivity and food security. Globally, such soil vulnerability poses a threat to agricultural sustainability, necessitating immediate interventions [4, 5]. Conservation Agriculture (CA), incorporating practices like zero tillage (ZT) and residue retention (RR), offers a viable strategy to rehabilitate these fragile soils. CA mitigates erosion, enhances soil organic carbon, and fosters microbial diversity and resilience against climate-induced stresses [6, 7]. By adopting CA practices, restoring lateritic soil health can play a pivotal role in sustaining global food production and environmental quality [8, 9].

The decline in soil fertility, particularly in red-lateritic soils, is primarily driven by inefficient land use, improper residue management, and the overexploitation of soil and water resources [10–12]. These soils, characterized by poor fertility and low organic matter, are further degraded by intensive tillage practices, such as wet tillage (puddling) and dry tillage, commonly adopted to conserve soil moisture and nutrients [13, 14]. While these conventional management strategies are integral to the rice-wheat system (RWS), their prolonged use has been linked to soil organic carbon depletion, declining productivity, and system yield stagnation [15]. Sustainable practices, e.g. residue retention instead of burning, reduced tillage intensity, and zero tillage systems, have shown potential globally to counteract these challenges by improving soil physical properties and promoting resource conservation [16, 17]. Adopting such practices in red-lateritic soils offers a crucial pathway to enhancing soil health, boosting system productivity, and ensuring environmental sustainability [18].

Soil quality may be defined as the capacity of soil to perform functions within natural and managed ecosystem boundaries and to counteract positively with surrounding ecosystems [19]. The soil quality is generally measured by assessing changes in its attributes, or in other words called indicators. Amongst these indicators, effective indicators can be screened as they forecast minute changes in the ecosystem early [20]. Traditionally, physical and chemical indicators are used to characterize soil quality. Soil organic matter (SOM) is essential to every aspect of soil quality, influencing structure, water dynamics, nutrient availability, and biodiversity, making it a key soil quality indicator [21]. However, SOM changes occur gradually, so short-term shifts in soil quality are typically assessed through the biologically active components of SOM, such as microbial biomass and enzyme activities [22].

Active participation of such soil enzymes acts as a biological catalyst for mineralization of SOM and reported as critical indictors of soil quality as it directly involves nutrient release for plant and microbial growth [23, 24]. One of such enzymes include the activity of dehydrogenase, reported to be present only in living cells [25]and can consequently be regarded as a direct indicator of soil microbial activity as it is sensitive to soil management [26]. Four commonly used single soil quality indexes are microbial quotient (microbial C to total organic C ratio), specific enzyme activities (enzyme activity to microbial biomass ratio), metabolic index (MI: dehydrogenase activity to extractable organic C ratio) and metabolic quotient (qCO2: basal respiration to microbial biomass ratio) [27]. The use of single enzyme or individual biochemical properties are not relevant measures of soil quality because they can change with different seasons and regions [28, 29]. Therefore, the characterization of soil quality indicators requires the selection of soil properties that are readily sensitive to changes in management practices [30]. Previously, over 40 years several scientists made attempts to develop soil quality indices based on biological properties mostly by empirical methods. Stefanic et al. (1984) coined the term biological activity index and developed it in a more general and simplified expression combining soil dehydrogenase and catalase activity. Beck [31] developed an enzyme number index by comparing five different soil enzymes, whereas Perucci [32], developed another index “hydrolyzing coefficient” by calculating amount of fluorescein acetate hydrolyzed by soil. Jimenez et al., [33] established soil quality index through selection of most appropriate variables viz. arylsulphatase, acid phosphomonoesterase, β-glucosidase and dehydrogenase as an indicator of S, P, C cycles and microbial activity respectively. Dick [34] and Gil-Sotres [23] criticized using physico-chemical parameters as soil quality indicators because they lack reference values, show inconsistent responses to soil degradation, and fluctuate seasonally and regionally. To overcome these issues, Bloem et al., [35] developed biochemical based quality index requiring no reference data and resistant to seasonal and site variations. Nath et al., [36] also developed a soil quality index for a long-term CA system that included seven enzymatic parameters viz. fluorescein diacetate, urease, acid and alkaline phosphatases, β-glucosidase, dehydrogenase and protease and biological parameters such as microbial biomass C and N, soil respiration.

As a result, a biochemical soil quality index is often used to capture these short-term changes more effectively than an overall soil quality index [37]. So, several recent studies include biochemical indicators as they respond more quickly to external perturbations to the system [38–40]. Soil enzyme activities are considered the most sensitive parameters in determining biochemical soil quality. They are directly involved in the soil microbial properties, making it easy to evaluate and respond rapidly to soil management [20, 28]. These soil enzymes are mainly generated from microbial activities on SOM and are classified into intracellular and extracellular enzymes. Among them, dehydrogenase (oxidoreductase), acid and alkaline phosphatase, and urease (hydrolase) are enzymes that have been extensively studied in soil [41]. These enzyme activities are frequently more sensitive to soil management practices (tillage and fertilization) than physical and chemical indicators [42]. As enzyme kinetics is directly related to the rhizospheric microenvironment (temperature, pH) therefore, seasonal [20, 43, 44] and temporal [45, 46] variations in enzyme activities are reported, which indirectly impact microbial proliferation [47].

Strong evidence indicates that long-term conservation agriculture (CA) practices significantly influence soil enzyme activities across various crop growth stages [48, 49]. Therefore, biochemical soil quality indicators a regarded as essential tools for validating such changes [50]. Studies have demonstrated that ZT with residue retention positively impacts crop biomass and soil biochemical properties in mid- and long-term CA systems [38, 51]. The integration of ZT with RR enhances soil organic carbon (OC)/Walkley-Black Carbon (WBC) sequestration and stabilization, which subsequently affects soil biochemical attributes, including C cycling, microbial biomass, enzyme activities, and soil biota [52].

It is reported that soil management practices under CA showed significant impact on different pools of SOC [53, 54]. According to variation in the decomposition rate, SOC pools can be subdivided of three distinct forms viz. stable or recalcitrant, intermediate, and labile. Stable forms are dominant fractions of TOC, consists of humified fraction and transforming much slower over decades to century [53]. Labile form is the small fraction of TOC and decomposed relatively quickly within months to years [54]. As a consequence, labile SOC has a much significance on short-term C turnover and availability of nutrients in the terrestrial agro-ecosystem as compared to stable SOC pool [55]. Total OC content in soil has been analytically estimated in lab using Walkley-black methods. However, WBC values represent mainly recalcitrant or passive forms of OC. Therefore, WBC and total carbon have limitations as an overall soil quality indicator [56, 57]. Conversely, labile OC or active pool contributed significantly to priming effect of soil and are more prone to change earlier as compared to recalcitrant pool under short-term agricultural soil management, suggesting the higher potential of labile OC (POXC and MBC) as soil quality index in highly degraded red lateritic soil [58, 59]. CA techniques have been shown to enhance soil carbon storage by 0.4–0.6 tons per hectare annually [60]. Measuring the labile carbon components (POXC and MBC) may provide a more responsive evaluation method of biochemical soil quality index and better indicate how tillage practices and crop residue management affect overall soil health [57, 61].

Although these management practices emphasize sustainable soil carbon management, changes in stable parameters such as SOM or WBC are challenging to detect in the early stages of adoption due to their inherent stability. The current study investigates soil carbon pools alongside WBC and total carbon (TC), integrating sensitive biological and enzymatic properties to address this. By incorporating microbial biomass carbon (MBC), labile carbon fractions, and enzymatic activities, the study develops quality indices capable of capturing subtle, early-stage benefits of improved management practices. These indices offer a robust framework for assessing the impacts of CA practices on soil health and productivity, even in the initial years of adoption. As plant growth and functioning are intricately linked to soil ecology and microbial activity, recognizing the fluctuation patterns of soil biochemical variables during crop growth is critical for evaluating the nutrient-supplying capacity of the ecosystem [62]. Furthermore, limited knowledge exists about the variations in soil enzyme activities across rice and wheat growth stages in response to different tillage and residue management practices under red soil conditions. This study addresses these gaps by assessing how CA-based practices influence soil biochemical changes throughout the growth stages of rice and wheat, aiming to evaluate the sustainability and ecological viability of the rice-wheat system.

Materials and methods

Experimental details

The field experiment was conducted at research farm of the Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, India (22°19′ N, 87°19′ E, 71 m above mean sea level) during 2022-24. The climate of the region is classified as humid and subtropical. The prevailing weather conditions during the experimental period are depicted in Fig. 1. The experimental site had acidic (pH 5.5) red lateritic soil taxonomically grouped under ‘Haplustalf’ with a sandy loam texture. The initial soil conditions before the experiment are given in Table 1. Rice variety ‘Saurabh’ and wheat variety ‘HD 2629’ were used for sowing. Wheat seeds (Certified Class) were sourced from Borlaug Institute for South Asia, Samastipur, Bihar, India and rice seeds (Truthfully Labelled Seed Class) were sourced from local seed selling outlets (Midnapore Market, West Midnapore, India). Rice and wheat were grown annually during Jul-Oct (rainy season) and Nov-Mar (winter season), respectively. After the wheat harvest, all plots were left fallow until pre-sowing irrigation was applied for the upcoming rice crop each year in the first week of June. The experiment was laid out in a randomized block design (RBD) with eight treatments replicated thrice. Treatment details are given in Table 2. The dimension of each plot was 5 m × 6 m.

Fig. 1.

Prevailing weather conditions during the experimental period

Table 1.

Initial soil conditions at experimental site

Property	Value obtained	Method employed
Textural analysis		Hydrometer method (Bouyoucos,1962) [63]
Sand (%)	62.6
Silt (%)	23.8
Clay (%)	13.6
Textural class	Sandy loam
pH (1:2.5 soil water suspension)	5.25	Glass electrode pH meter [64]
Electrical conductivity (mS m−1) (1:2.5 soil water suspension	0.04	Electrical conductivity meter [64]
Walkley-Black Organic carbon (%)	0.36	Walkley and Black method (1934)[65]
Available nitrogen (kg N ha−1)	177.6	Alkaline KMnO4 method (Subbiah and Asija, 1956) [66]
Available phosphorus (kg P ha−1)	13.8	Bray no. 1 extraction method [67]
Available potassium (kg K ha−1)	67.5	Neutral normal ammonium acetate method [64]

Table 2.

Treatment details

Treatment symbol	Treatment details
TPR -CTW	Transplanted rice- Conventional tilled wheat
TPR- CTW (RI)	Transplanted rice - Conventional tilled wheat with rice residue incorporation
TPR-ZTW (RR)	Transplanted rice - Zero-tilled wheat with rice residue retention
TPRAWD – CTW	Transplanted rice with alternate wetting & drying - Conventional tilled wheat
TPRAWD - ZTW (RR)	Transplanted rice with alternate wetting & drying - Zero-tilled wheat with rice residue retention
ZTDSR-CTW (RI)	Zero-till Direct seeded rice- Conventionally tilled wheat with rice residue incorporation
ZTDSR-ZTW (RR)	Zero-till Direct seeded rice- Zero-tilled wheat with rice residue retention
ZTDSR – CTW	Zero-till Direct seeded rice - Conventionally tilled wheat

Note: TPR: Puddled Transplanted Rice, CTW: Conventionally Tilled Wheat, ZTW: Zero-tilled Wheat, TPRAWD: TPR with Alternate Wetting and Drying, ZTDSR: Zero-tilled Direct Seeded Rice, RI: Residue incorporation and RR: Residue retention

Crop establishment and management

For the puddled transplanted rice (TPR) treatment, soil preparation involved two rounds of shallow ploughing, followed by puddling with a power tiller in standing water (10 cm) and levelling. Rice was transplanted manually, using 30 days old seedlings with a spacing of 15 cm×20 cm with 2–3 seedlings hill⁻¹. The zero-tilled (ZT) direct-seeded rice (DSR) was sown using a zero-till fertilizer-cum-seed drill with rows spaced 20 cm apart. A seed rate of 60 kg ha⁻¹ was used for DSR. Rice was harvested manually at 10–15 cm above the ground in the second week of October. For residue retention (RR) treatments, standing residues of about 45 cm in height were kept in the field after harvesting. For plots designated for residue incorporation (RI), leftover residues were incorporated into the soil using a rotary cultivator during land preparation. For CT wheat plots, two cross ploughings with a cultivator and two ploughings with a rotary cultivator were done. Wheat was sown with a seed rate of 125 kg ha⁻¹ and 20 cm row-to-row spacings. Recommended fertilizer dose (RDF) of 120:60:60 kg N, P₂O_5, K₂O ha⁻¹ and 150:60:40 kg N, P₂O_5, K₂O ha⁻¹ were applied for rice and wheat, respectively, in the form of urea, single superphosphate (SSP), and muriate of potash (MOP), respectively following standard schedules uniformly throughout the experiment [68]. The N fertilizer was applied in three split dosages with 150 kg N ha⁻¹ during wheat season with top dressing just before irrigation as recommended by Gill et al., [69]. Half N and a complete dose of P₂O₅ and K₂O were applied as basal. The remaining half of N was top-dressed at critical growth stages viz. tillering (25 days) and panicle initiation (45 days) stages after crop establishment as followed by Singh et al., [70] for South-Asian Indo-Gangetic region.

Irrigation management in case of rice involved maintaining a 7 cm depth surface pond for a period of two weeks after transplanting in case of TPR. Thereafter irrigation was applied with 7 cm standing water until hairline cracks appeared on the soil surface for both TPR and DSR plots. For TPR-AWD plots, a perforated tube was placed in the soil to monitor the water level and determine when to irrigate. Flooding conditions were maintained from the day of transplantation to 20 DAT and from panicle initiation to the flowering stage. The fields were re-irrigated (3 cm depth) for rest of the growing period when the water levels in the pipe dropped to 15 cm below the soil surface (soil water tension −20 kPa). Wheat received four irrigations at important growth stages viz., crown root initiation (21 DAS), tillering (35–40 DAS), flowering (50–55 DAS) and grain filling (70–75 DAS).

Soil sampling and analysis

A total of 288 soil samples were collected from eight treatments with three replications, two depths (0–15 and 15–30 cm) at three different growth stages (Active tillering: 30–45 days, flowering: 90–95 days and harvest: 115–120 days) of rice and wheat during two crop growth cycles. The composite soil samples were collected with a tube auger from three randomly selected positions within each plot. Half of the processed sample was immediately stored at 4 °C to assay enzymatic properties. The remaining samples were processed by air drying, grinding and passing through a 2 mm sieve.

Acid (ACP) and alkaline phosphatase (ALP) activity were measured based on the determination of p-nitrophenol released after incubation of soil with p-nitrophenyl phosphate at pH 6.5 and 11 respectively at 37℃ [71] and activity was expressed as g p-nitrophenol g⁻¹ of dry soil. Dehydrogenase activity (DHA) was measured based on the reduction of triphenyl tetrazolium chloride to triphenyl formazan by soil at 30 °C for 24 h [71] and the activity was expressed in mg TPF g⁻¹ dry soil d⁻¹ [72]. Urease activity was measured by determining the NH₄⁺ released from soil incubation with tris hydroxymethyl aminomethane (THAM), urea solution and toluene at 37 °C in steam distillation method. The Urease was expressed as µg NH₄⁺ g⁻¹ of dry soil [73].

Soil available N (Av. N) was determined by the alkaline permanganate method [74] in an automatic nitrogen analyzer (Make: Borosil, India). Available P (Av. P) was determined spectrophotometrically (Make: Shimadzu, Japan) by Bray method using as Bray-1 extractant (0.025 N hydrochloric acid and 0.03 N ammonium fluoride) [75]. Available K (Av. K) was determined by 1 N NH₄OAc-extractable K method by [76] in flame photometer (Make: Systronics, India). Total carbon (TC) was measured directly, while acid digested soil sample was used for the analysis of Total Organic carbon (TOC) by dry combustion method [77] using a TOC analyzer (Make: Analytic Jena, Germany). Total inorganic carbon (TIC) was then calculated by subtracting TOC from total carbon (TC) (Eq. 1).

1

Labile carbon pool viz. microbial biomass carbon (MBC) was determined by chloroform fumigation and extraction method using 0.5M K₂SO₄ extractant [78]. Walkley-Black organic carbon (WBC) was estimated using potassium dichromate oxidation [65]. After the determination of WBC in fumigated and nonfumigated extracts, MBC was calculated with Eq. 2.

2

Where, WBC_F and WBC_NF are Walkley-Black Carbon (%) in fumigated sample and nonfumigated sample, respectively. C.F. is a conversion factor (0.41).

The permanganate oxidizable organic carbon (POXC) was estimated using Eq. 3 according to a method described by [79].

3

Where, C is the initial solution conc. of KMnO₄ (0.02 mol L⁻¹); a and b are intercepts and slope of the std. curve, A is the absorbance of the sample at 550 nm, and W is the weight of air-dried soil sample (kg). Other soil parameters like pH, electrical conductivity (EC), bulk density (BD) were measured using standard protocols [80].

System yield

The grain yield of rice and wheat was reported at 12% moisture content. The system yield was expressed as rice equivalent yield (REY). The REY was calculated using Eq. 4, and rice and wheat prices were taken from the minimum support price (MSP) declared by the Government of India [20, 81].

4

Biochemical quality index

For the development of the biochemical quality index (BCQI), the REY was taken as the management goal for biochemical soil quality assessment. The soil quality indicators were selected following the soil management and assessment framework [82, 83]. The data were reduced to a minimum dataset (MDS) using principal component analysis [83, 84]. Before conducting the PCA an exploratory Pearson’s correlation analysis was conducted to know about the presence of multicollinearity (Supplementary Figure S1). The variables with high factor loadings were assumed to be variables that best represent system attributes [84]. Only variables with factor loadings within ± 20% of the highest factor loadings in each PC were considered MDS for the calculation of BCQI. After determination of MDS indicators, each of MDS variables was scored based on the performance of the soil function. Each variable was transformed or standardized to a value between 0 (least favourable soil function) and 1 (most favourable soil function) scoring functions [82]. Once transformed, the MDS variables for each observation were weighted using the PCA results to get the BCQI value (Eq. 5).

5

Statistical analysis

The ANOVA method for multiyear RCBD was used to analyze all the data using Fisher’s LSD at p < 0.05 [85]. Tuckey’s HSD method was employed as pos-hoc test for mean separation. For quality index calculation, PCA was performed on standardized data to reduce dimensionality, retaining components with eigenvalues > 1. Scree plots and cumulative variance explained guided component selection, and biplots visualized variable relationships. Backward regression identified significant predictors by iteratively removing variables with p > 0.05, ensuring model stability through VIF analysis and evaluating performance via adjusted R² and AIC. Hierarchical clustering, using standardized data, computed distance matrices and formed clusters with linkage methods, validated by silhouette or elbow methods. Analyses were performed in R software [86] and SPSS [87].

Results

Soil physical and chemical properties

Bulk density in the top soil layer (0–15 cm) was recorded to be highest for TPR AWD-CTW and TPR-CTW (RI), which were significantly higher than CA treatments. Adopting ZT practices and RR led to a reduction in BD. A comparable trend was also noted in the subsoil layer (15–30 cm), as presented in Fig. 2. The soil available N content at rice and wheat harvest stages was significantly higher in 0–15 cm to the tune of 20 and 47%, respectively, in ZTDSR-ZTW (RR) compared to conventionally tilled residue removed plots (Fig. 3). Similarly, available P was reported to be highest under ZTDSR-ZTW (RR) (33.07 kg ha⁻¹) and was significantly greater (2.5 and 1.8 times) than available P recorded with TPR-CTW (13.17 kg ha⁻¹) and TPR(AWD)-CTW (18.73 kg ha⁻¹), respectively in 0–15 cm soil layer (Fig. 4). Available K was found to be lowest under TPR-CTW (38.85 kg ha⁻¹), which was 53% lower than ZTDSR-ZTW (RR) (82.85 kg ha⁻¹) (Fig. 5). Plots under residue retention were reported to contain higher TOC in 0–15 cm depth (across the range of 4.58 to 4.61 g kg⁻¹) than residue removed plots (4.46 to 4.48 g kg⁻¹) after one year of cropping cycle. The TOC values were reported to be significantly higher (3.13 and 3.45%) in ZTDSR-ZTW (RR) than TPR-CTW in 0–15 cm depth of rice and wheat harvested soil respectively (Tables 3 and 4). Likewise, TOC values were showed resemblance in trends for both rice and wheat in 15–30 cm depth. The TIC content in 0–15 cm depth of rice harvested soils was reported to be highest in ZTDSR-ZTW (RR) (1.33 g kg⁻¹) and lowest in TPR-CTW (0.50 g kg⁻¹).

Fig. 2.

Effect of different establishment and residue management regimes on bulk density. (a) soil sampled after rice harvest (b) soil sampled after wheat harvest. Note: The columns sharing any common small cap letters under same series demark no significant differences (p > 0.05) and vice-versa

Fig. 3.

Effect of different establishment and residue management regimes on available nitrogen. (a) soil sampled after rice harvest (b) soil sampled after wheat harvest. Note: The columns sharing any common small cap letters under same series demark no significant differences (p > 0.05) and vice-versa

Fig. 4.

Effect of different establishment and residue management regimes on available phosphorus. (a) soil sampled after rice harvest (b) soil sampled after wheat harvest. Note: The columns sharing any common small cap letters under same series demark no significant differences (p > 0.05) and vice-versa

Fig. 5.

Effect of different establishment and residue management regimes on available potassium. (a) soil sampled after rice harvest (b) soil sampled after wheat harvest. Note: The columns sharing any common small cap letters under same series demark no significant differences (p > 0.05) and vice-versa

Table 3.

Effect of different establishment and residue regimes on total organic carbon (TOC)

Treatments	TOC (g kg−1)
	After Rice		After Wheat
	0–15 cm	15–30 cm	0–15 cm	15–30 cm
TPR -CTW	4.46 ± 0.03d	3.76 ± 0.03b	4.46 ± 0.01d	3.76 ± 0.02e
TPR- CTW (RI)	4.54 ± 0.07bc	3.82 ± 0.05ab	4.54 ± 0.07bc	3.82 ± 0.01b
TPR-ZTW (RR)	4.53 ± 0.06bc	3.79 ± 0.03ab	4.49 ± 0.02cd	3.79 ± 0.0 cd
TPRAWD - CTW	4.54 ± 0.02abc	3.81 ± 0.01ab	4.53 ± 0.01c	3.81 ± 0.01bc
TPRAWD- ZTW (RR)	4.58 ± 0.02ab	3.83 ± 0.02a	4.58 ± 0.01ab	3.83 ± 0.02b
ZTDSR-CTW (RI)	4.54 ± 0.03bc	3.79 ± 0.03ab	4.53 ± 0.02c	3.79 ± 0.01de
ZTDSR-ZTW (RR)	4.61 ± 0.02a	3.85 ± 0.07a	4.61 ± 0.01a	3.86 ± 0.02a
ZTDSR - CTW	4.48 ± 0.01cd	3.78 ± 0.08ab	4.48 ± 0.01d	3.78 ± 0.02cd

Note: TPR: Puddled Transplanted Rice, CTW: Conventionally Tilled Wheat, ZTW: Zero-tilled Wheat, TPRAWD: TPR with Alternate Wetting and Drying, ZTDSR: Zero-tilled Direct Seeded Rice, RI: Residue incorporation and RR: Residue retention; The mean values sharing any common small cap letters under demark no significant differences in between (p > 0.05) and vice-versa

Table 4.

Effect of different establishment and residue regimes on total inorganic carbon (TIC)

Treatments	TIC (g kg−1)
	After Rice		After Wheat
	0–15 cm	15–30 cm	0–15 cm	15–30 cm
TPR -CTW	0.50 ± 0.26c	0.40 ± 0.20abc	0.79 ± 0.26e	0.38 ± 0.19b
TPR- CTW (RI)	0.80 ± 0.25bc	0.22 ± 0.07cd	1.14c ± 0.15de	0.21 ± 0.11c
TPR-ZTW (RR)	1.20 ± 0.26ab	0.32b ± 0.04cd	1.63 ± 0.28ab	0.34 ± 0.06b
TPRAWD - CTW	1.13 ± 0.30ab	0.50 ± 0.35ab	1.25 ± 0.24bcd	0.51 ± 0.32a
TPRAWD- ZTW (RR)	1.20 ± 0.17ab	0.20 ± 0.10cd	1.72 ± 0.25a	0.2 ± 0.13c
ZTDSR-CTW (RI)	0.90 ± 0.26bc	0.15 ± 0.03d	1.26 ± 0.30bc	0.13 ± 0.04d
ZTDSR-ZTW (RR)	1.33 ± 0.42a	0.60 ± 0.10a	1.58 ± 0.58ab	0.50 ± 0.22a
ZTDSR - CTW	0.53 ± 0.15c	0.47 ± 0.06ab	0.83 ± 0.11de	0.48 ± 0.04b

Note: TPR: Puddled Transplanted Rice, CTW: Conventionally Tilled Wheat, ZTW: Zero-tilled Wheat, TPRAWD: TPR with Alternate Wetting and Drying, ZTDSR: Zero-tilled Direct Seeded Rice, RI: Residue incorporation and RR: Residue retention; The mean values sharing any common small cap letters under demark no significant differences in between (p > 0.05) and vice-versa

Soil carbon pools

A significant impact of CA treatments on initial soil C pools was recorded. Results indicated that the values of MBC varied from 95.3 to 132.8 mg kg⁻¹ at tillering stage, 115.1 to 156.2 mg kg⁻¹ at flowering and 95.9 to 134.2 mg kg⁻¹ at harvest stage of rice for 0–15 cm depth (Table 5). The MBC content started increasing after sowing and reached its highest value during the season at the flowering stage, then gradually decreased towards maturity. At the flowering stage, MBC content was increased on an average of 1.7 and 1.2 times than at the tillering stage in the surface (0–15 cm) and subsurface (15–30 cm) soil layers, respectively, for all the treatments.

Table 5.

Effect of different establishment and residue regimes on soil carbon pools at 0–15 cm depth after rice harvest

Treatments	MBC (mg kg−1)			WBC (g kg−1)			POXC (mg kg−1)
	Tillering	Flowering	Harvest	Tillering	Flowering	Harvest	Tillering	Flowering	Harvest
TPR -CTW	95.3 ± 1.69f	115.1 ± 2.66g	95.9 ± 0.71e	3.39 ± 0.03b	3.39 ± 0.03e	3.38 ± 0.02b	107.8 ± 1.00ef	111.5 ± 0.80f	111.9 ± 1.08f
TPR- CTW (RI)	105.6 ± 1.00d	121.1 ± 2.48e	106.5 ± 1.07c	3.39 ± 0.03b	3.39 ± 0.01e	3.39 ± 0.02ab	109.6 ± 0.38de	114.9 ± 0.61de	118.2 ± 2.85d
TPR-ZTW (RR)	107.7 ± 0.83c	127.5 ± 0.65c	108.6 ± 2.36c	3.41 ± 0.02ab	3.40 ± 0.01de	3.40 ± 0.03ab	110.6 ± 0.96d	121.1 ± 2.09d	122.4 ± 4.44cd
TPRAWD - CTW	95.6 ± 0.93f	115.4 ± 1.02g	98.3 ± 3.89de	3.40 ± 0.01ab	3.41 ± 0.01cde	3.40 ± 0.02ab	104.9 ± 2.16f	113.9 ± 1.28de	112.7 ± 0.42ef
TPRAWD- ZTW (RR)	124.3 ± 0.77b	141.3 ± 1.52b	125.5 ± 2.68b	3.41 ± 0.03ab	3.41 ± 0.02bcd	3.41 ± 0.03ab	131.8 ± 0.28b	134.9 ± 0.26b	135.6 ± 1.03b
ZTDSR-CTW (RI)	105.4 ± 1.16d	125.0 ± 1.05d	108.9 ± 5.76c	3.42 ± 0.02a	3.42 ± 0.02ab	3.43 ± 0.02a	118.6 ± 0.84c	123.8 ± 0.79c	123.4 ± 14.48c
ZTDSR-ZTW (RR)	132.8 ± 2.14a	156.2 ± 1.27a	134.2 ± 0.67a	3.42 ± 0.02a	3.43 ± 0.01a	3.43 ± 0.02a	134.9 ± 3.44a	138.9 ± 1.54a	139.2 ± 1.13a
ZTDSR - CTW	98.2 ± 0.60e	117.3 ± 0.42f	100.1 ± 3.62d	3.42 ± 0.02e	3.42 ± 0.01abc	3.43 ± 0.02a	110.8 ± 1.37de	113.3 ± 1.24e	116.6 ± 2.23e

Note: TPR: Puddled Transplanted Rice, CTW: Conventionally Tilled Wheat, ZTW: Zero-tilled Wheat, TPRAWD: TPR with Alternate Wetting and Drying, ZTDSR: Zero-tilled Direct Seeded Rice, RI: Residue incorporation and RR: Residue retention; The mean values sharing any common small cap letters under demark no significant differences in between (p > 0.05) and vice-versa

The highest MBC content was observed in ZTDSR-ZTW (RR) plots for rice and wheat seasons. MBC contents were significantly higher in surface soil of ZTDSR-ZTW (RR) than TPR-CTW in the tune of 39, 36 and 40% for three growth stages of rice, respectively, and for wheat, 39, 31 and 37% higher MBC were recorded for three growth stages of wheat, respectively (Tables 5 and 6). Throughout the crop growth cycles, average MBC content was 25% higher in TPRAWD-ZTW(RR) and 2.5% higher in TPRAWD-CTW than TPR-CTW (Table 5). The MBC content was, in general, higher in surface (0–15 cm) soil than in sub-surface soil (15–30 cm) (Tables 7 and 8). After one year of cropping cycle, the change in WBC under TPRAWD-ZTW(RR) was reported to be 0.33% and 0.09% higher than initial value in 0–15 and 15–30 cm soil respectively. On the other hand, plots under TPR-CTW were reported to be 1.55% and 0.47% lower WBC as compared to initial value in 0–15 and 15–30 cm soil (Table 5). A similar trend was also observed in the wheat season (Table 6).

Table 6.

Effect of different establishment and residue regimes on soil carbon pools at 0–15 cm depth after wheat harvest

Treatments	MBC (mg kg−1)			WBC (g kg−1)			POXC (mg kg−1)
	Tillering	Flowering	Harvest	Tillering	Flowering	Harvest	Tillering	Flowering	Harvest
TPR -CTW	99.26 ± 0.71d	123.58 ± 3.23d	102.70 ± 1.75d	3.38 ± 0.02c	3.37 ± 0.01d	3.37 ± 0.02c	112.61 ± 1.98f	114.54 ± 1.92e	114.65 ± 1.89e
TPR- CTW (RI)	109.89 ± 1.07c	134.21 ± 1.71c	113.33 ± 1.02c	3.39 ± 0.01c	3.39 ± 0.02cd	3.39 ± 0.02bc	118.88 ± 2.08e	120.92 ± 2.04d	121.06 ± 2.03de
TPR-ZTW (RR)	110.95 ± 2.36c	135.27 ± 2.35c	114.39 ± 2.28c	3.39 ± 0.01bc	3.40 ± 0.02bc	3.40 ± 0.04abc	126.06 ± 1.58d	130.84 ± 2.04c	130.88 ± 1.97c
TPRAWD - CTW	101.64 ± 3.89d	125.96 ± 8.24d	105.08 ± 3.06d	3.39 ± 0.01c	3.39 ± 0.01cd	3.40 ± 0.02abc	113.50 ± 1.03f	118.28 ± 1.04d	118.33 ± 0.98d
TPRAWD- ZTW (RR)	129.84 ± 2.68b	154.16 ± 3.49b	133.29 ± 2.83b	3.40 ± 0.02bc	3.41 ± 0.02abc	3.41 ± 0.02abc	136.60 ± 1.45b	140.17 ± 1.35b	140.24 ± 1.66b
ZTDSR-CTW (RI)	112.31 ± 5.76c	136.63 ± 3.96c	115.76 ± 9.54c	3.43 ± 0.01a	3.42 ± 0.01ab	3.43 ± 0.02ab	130.58 ± 2.30c	142.77 ± 3.67b	143.01 ± 3.37b
ZTDSR-ZTW (RR)	137.98 ± 0.67a	162.30 ± 1.36a	141.42 ± 2.33a	3.43 ± 0.02a	3.43 ± 0.02a	3.43 ± 0.02a	140.87 ± 0.87a	151.97 ± 1.38a	152.25 ± 1.64a
ZTDSR - CTW	108.98 ± 0.82c	133.30 ± 2.19c	112.42 ± 0.76c	3.42 ± 0.01ab	3.42 ± 0.01ab	3.43 ± 0.01ab	117.74 ± 0.41e	121.31 ± 1.08d	121.59 ± 1.37d

Note: TPR: Puddled Transplanted Rice, CTW: Conventionally Tilled Wheat, ZTW: Zero-tilled Wheat, TPRAWD: TPR with Alternate Wetting and Drying, ZTDSR: Zero-tilled Direct Seeded Rice, RI: Residue incorporation and RR: Residue retention; The mean values sharing any common small cap letters under demark no significant differences in between (p > 0.05) and vice-versa

Table 7.

Effect of different establishment and residue regimes on soil carbon pools at 15–30 cm depth after rice harvest

Treatments	MBC (mg kg−1)			WBC (g kg−1)			POXC (mg kg−1)
	Tillering	Flowering	Harvest	Tillering	Flowering	Harvest	Tillering	Flowering	Harvest
TPR -CTW	53.8 ± 2.15f	62.3 ± 0.90f	55.9 ± 0.58g	2.87 ± 0.02a	2.87 ± 0.03a	2.87 ± 0.01a	101.9 ± 1.04f	104.2 ± 1.01f	104.3 ± 0.55f
TPR- CTW (RI)	60.8 ± 1.76d	70.0 ± 1.03d	63.7 ± 1.07d	2.87 ± 0.03a	2.87 ± 0.02a	2.87 ± 0.02a	102.5 ± 1.79f	105.5 ± 2.03 f	105.6 ± 1.78f
TPR-ZTW (RR)	73.7 ± 0.97b	82.8 ± 2.12b	76.5 ± 0.74b	2.87 ± 0.01a	2.87 ± 0.01a	2.87 ± 0.02a	110.3 ± 0.58c	113.0 ± 0.58 c	113.0 ± 0.57c
TPRAWD - CTW	53.6 ± 0.76f	62.8 ± 1.83f	56.5 ± 0.38f	2.87 ± 0.01a	2.87 ± 0.03a	2.87 ± 0.03a	104.6 ± 0.64e	106.9 ± 0.64e	106.9 ± 0.25e
TPRAWD- ZTW (RR)	75.3 ± 0.77b	84.4 ± 1.66b	78.1 ± 0.26b	2.87 ± 0.02a	2.87 ± 0.02a	2.87 ± 0.02a	114.0 ± 1.31b	119.2 ± 1.31b	119.3 ± 0.39b
ZTDSR-CTW (RI)	65.8 ± 0.85c	75.0 ± 2.62c	68.7 ± 1.12c	2.88 ± 0.02a	2.88 ± 0.02a	2.88 ± 0.01a	105.1 ± 1.50d	109.8 ± 1.51d	111.9 ± 1.22d
ZTDSR-ZTW (RR)	81.1 ± 3.02a	90.3 ± 2.42a	83.9 ± 1.42a	2.88 ± 0.03a	2.88 ± 0.02a	2.88 ± 0.02a	114.6 ± 1.95a	120.9 ± 1.96a	121.0 ± 1.31a
ZTDSR - CTW	56.7 ± 1.96e	63.6 ± 1.16e	57.3 ± 0.60e	2.88 ± 0.02a	2.88 ± 0.02a	2.88 ± 0.03a	104.9 ± 0.56e	106.8 ± 0.64e	108.3 ± 1.28e

Note: TPR: Puddled Transplanted Rice, CTW: Conventionally Tilled Wheat, ZTW: Zero-tilled Wheat, TPRAWD: TPR with Alternate Wetting and Drying, ZTDSR: Zero-tilled Direct Seeded Rice, RI: Residue incorporation and RR: Residue retention; The mean values sharing any common small cap letters under demark no significant differences in between (p > 0.05) and vice-versa

Table 8.

Effect of different establishment and residue regimes on soil carbon pools at 15–30 cm depth after wheat harvest

Treatments	MBC (mg kg−1)			WBC (g kg−1)			POXC (mg kg−1)
	Tillering	Flowering	Harvest	Tillering	Flowering	Harvest	Tillering	Flowering	Harvest
TPR -CTW	57.4 ± 0.81f	74.0 ± 1.98f	59.9 ± 0.51f	2.87 ± 0.01a	2.87 ± 0.02a	2.87 ± 0.02a	105.6 ± 1.04f	104.3 ± 1.04f	107.7 ± 0.54f
TPR- CTW (RI)	65.2 ± 2.68d	81.9 ± 4.68d	67.7 ± 2.85d	2.87 ± 0.01a	2.76 ± 0.02a	2.87 ± 0.02a	107.5 ± 1.79e	105.5 ± 1.78e	109.5 ± 1.78e
TPR-ZTW (RR)	78.2 ± 0.77b	94.8 ± 2.65b	80.7 ± 1.25c	2.87 ± 0.03a	3.23 ± 0.01a	2.87 ± 0.01a	115.6 ± 0.58c	113.0 ± 0.58 c	114.5 ± 1.01c
TPRAWD - CTW	58.86 ± 0.86ef	75.4 ± 2.75ef	61.3 ± 1.30ef	2.87 ± 0.01a	3.00 ± 0.01a	2.87 ± 0.01a	108.6 ± 0.64e	106.9 ± 0.64e	110.5 ± 2.14se
TPRAWD- ZTW (RR)	81.5 ± 1.08ab	98.1 ± 1.70ab	83.9 ± 1.70b	2.87 ± 0.04a	3.27 ± 0.04a	2.87 ± 0.01a	121.4 ± 1.31b	119.2 ± 1.16b	123.4 ± 2.07b
ZTDSR-CTW (RI)	73.6 ± 4.53c	90.2 ± 4.51c	76.1 ± 5.17d	2.88 ± 0.02a	2.92 ± 0.03a	2.88 ± 0.01a	112.4 ± 1.50d	111.9 ± 1.25d	114.4 ± 2.78d
ZTDSR-ZTW (RR)	84.6 ± 0.95a	101.2 ± 3.02a	87.1 ± 1.17a	2.88 ± 0.02a	3.52 ± 0.01a	2.88 ± 0.01a	124.6 ± 1.95a	120.9 ± 1.95a	126.6 ± 1.11a
ZTDSR - CTW	60.8 ± 0.49e	77.4 ± 1.68e	68.2 ± 0.76e	2.88 ± 0.03a	2.70 ± 0.01a	2.88 ± 0.03a	106.6 ± 0.56f	108.3 ± 1.75f	107.6 ± 4.23f

Note: TPR: Puddled Transplanted Rice, CTW: Conventionally Tilled Wheat, ZTW: Zero-tilled Wheat, TPRAWD: TPR with Alternate Wetting and Drying, ZTDSR: Zero-tilled Direct Seeded Rice, RI: Residue incorporation and RR: Residue retention; The mean values sharing any common small cap letters under demark no significant differences in between (p > 0.05) and vice-versa

The POXC content significantly increased by an average of 25 and 23% (in 0–15 cm depth); 13 and 12% (in 15–30 cm depth) in all the growth stages for ZTDSR-ZTW (RR) and TPRAWD-ZTW(RR), respectively, as compared to TPRAWD-CTW (Tables 5, 6, 7 and 8). The POXC content increased gradually from tillering to flowering, reaching the highest value at the harvest stage for both crops.

Soil biochemical activities

A significant influence of short-term CA-based practices was found in the biochemical soil quality parameters. The soil enzyme activities viz. DHA, ACP, ALP, and urease were higher under ZTDSR-ZTW (RR) than other treatments at all rice and wheat growth stages (Figs. 6, 7, 8 and 9). The enzyme activities were higher at the flowering stage than tillering and maturity/harvest stages for rice and wheat seasons.

Fig. 6.

Effect of different establishment and residue management regimes on soil dehydrogenase activity across crop growth stages. (a) 0–15 cm soil sampled after rice harvest; (b)15–30 cm soil sampled after rice harvest; (c) 0–15 cm soil sampled after wheat harvest; (b)15–30 cm soil sampled after wheat harvest. Note: The columns sharing any common small cap letters under same series demark no significant differences (p > 0.05) and vice-versa

Fig. 7.

Effect of different establishment and residue management regimes on soil urease activity across crop growth stages. (a) 0–15 cm soil sampled after rice harvest; (b)15–30 cm soil sampled after rice harvest; (c) 0–15 cm soil sampled after wheat harvest; (b)15–30 cm soil sampled after wheat harvest. Note: The columns sharing any common small cap letters under same series demark no significant differences (p > 0.05) and vice-versa

Fig. 8.

Effect of different establishment and residue management regimes on soil acid phosphatase activity across crop growth stages. (a) 0–15 cm soil sampled after rice harvest; (b)15–30 cm soil sampled after rice harvest; (c) 0–15 cm soil sampled after wheat harvest; (b)15–30 cm soil sampled after wheat harvest. Note: The columns sharing any common small cap letters under same series demark no significant differences (p > 0.05) and vice-versa

Fig. 9.

Effect of different establishment and residue management regimes on soil urease activity across crop growth stages. (a) 0-15 cm soil sampled after rice harvest; (b)15-30 cm soil sampled after rice harvest; (c) 0-15 cm soil sampled after wheat harvest; (b)15-30 cm soil sampled after wheat harvest. Note: The columns sharing any common small cap letters under same series demark no significant differences (p >0.05) and vice-versa

During the rice growing season, the DHA activities ranged from 120.7 to 127.5 µg TPF g⁻¹ soil 24 h⁻¹ at tillering stage, 127.0 to 139.3 µg TPF g⁻¹ soil 24 h⁻¹ at flowering stage and 126.2 to 130.9 µg TPF g⁻¹ soil 24 h⁻¹ at harvest stage at 0–15 cm soil depth across the treatments (Fig. 6a). The plots under treatment ZTDSR-ZTW (RR) showed 6 and 10% higher DHA activity than TPR-CTW and TPR-CTW (RI), respectively, at flowering stage of rice (at 0–15 cm depth). Similarly, at that same soil depth (0–15 cm), DHA activity was 10 and 12% higher in ZTDSR-ZTW (RR) than TPR-CTW and TPR-CTW (RI), respectively, at flowering stage of wheat (Fig. 6c). Similar trends were recorded for the 15–30 cm soil layer (Fig. 6d). Among Zero-tilled plots, DHA activity under residue retained treatment [ZTDSR-ZTW (RR)] significantly increased by an average of 7 and 5% as compared to residue removed treatment (ZTDSR-CTW) and residue incorporated treatment [ZTDSR-CTW (RI)], respectively. The ACP activity varied from 106.5 to 134.1 µg p-nitrophenol g⁻¹ soil h⁻¹ at tillering stage, 119.2 to 182.9 µg p-nitrophenol g⁻¹ soil h⁻¹ at flowering stage, 115.2 to 170 µg p-nitrophenol g⁻¹ soil h⁻¹ at harvest stage during the rice season (0–15 cm depth) (Fig. 7). The higher ACP activity was recorded at 0–15 cm soil layer across the treatments compared to 15–30 cm soil depth. From rice to wheat season, ACP activity was recorded to be increased by 40–55% at tillering stage, 20–60% at flowering stage and 9–45% at maturity stage at the 0–15 cm soil layer (Fig. 7). Likewise, ALP activity was highest at the flowering stage of rice and wheat seasons for both soil layers (0–15 and 15–30 cm), followed by harvest and tillering stages (Fig. 8). At flowering and maturity stages of rice, 0–15 cm soil layers of ZTDSR-ZTW(RR) were recorded with 32 and 39% higher ALP activity and 24 and 35% higher ACP activity than TPR-CTW, respectively (Fig. 7–8). Urease activity varied from 93.9 to 121.9 µg NH₄⁺ g⁻¹ at tillering stage, 130.5 to 185.1 µg NH₄⁺ g⁻¹ at flowering stage, 86.3 to 127.2 µg NH₄⁺ g⁻¹ at maturity stage of rice (at 0–15 cm depth) across the treatments (Fig. 9). In wheat growing season, urease activity varied from 90.2 to 141.7 µg NH₄⁺ g⁻¹ at tillering stage, 146.7 to 189.5 µg NH₄⁺ g⁻¹ at flowering stage, 65.1 to 111.2 µg NH₄⁺ g⁻¹ at maturity stage at 0–15 cm soil depth. Urease activity was lowest under TPR – CTW in all stages of rice and wheat seasons. Urease under TPR – CTW plots were 6 and 11% lower at flowering and harvest stages of rice and 3 and 23% lower at flowering and harvest stages of wheat as compared to residue incorporated plots [TPR- CTW (RI)] (Fig. 9). Among different rice establishment methods, AWD treatments showed non-significant effect over TPR treatments for all enzymes viz. DHA, ACP, ALP, and urease at 0–15 and 15–30 cm soil depth. However, AWD with residue retention [TPRAWD- ZTW (RR)] performed better than AWD without residue (TPRAWD – CTW).

Biochemical quality index

After rice harvest, 0–15 cm soil properties were subjected to PCA, and principal components PC1, PC2 and PC3 together captured a significant portion (83.07%) of the total variability present in the soil parameters recorded at 0–15 cm at rice harvest, with PC1, PC2 and PC3 individually contributing around 63.99, 10.92 and 8.16% variability, respectively (Table 9). Urease activity, TIC, and WBC were the top contributors in PC1, PC2 and PC3, respectively. The factors POXC, MBC, DH, ACP, ALP, Urease were loaded in PC1, whereas TIC and WBC were loaded in PC2 and PC3, respectively (Table 9). For 15–30 cm soil properties after rice harvest, the PC1, PC2 and PC3 together captured 77.35% of the total variability, with PC1, PC2 and PC3 individually capturing 54.88, 12.82 and 9.64%, respectively (Table 9). Parameters such as POXC, MBC, ACP, ALP and Urease were loaded in PC1; WBC was loaded in PC2 and Av. N loaded in PC3. The MBC, WBC and Av. N scored highest factor loadings in PC1, PC2 and PC3, respectively.

Table 9.

Result of PCA on soil quality parameters recorded from 0–15 cm and 15–30 cm soil layers after harvesting of rice

Statistics/Variable	After Rice Harvest
	0–15 cm Soil Layer			15–30 cm Soil Layer
	PC1	PC2	PC3	PC1	PC2	PC3
Eigen values	7.68	1.31	0.98	6.58	1.54	1.16
% of variance	63.99	10.92	8.16	54.88	12.82	9.64
Cumulative % of variance	63.99	74.91	83.07	54.88	67.71	77.35
Eigen vectors
Walkley Black C	0.243	0.031	0.432	0.053	0.482	0.243
Permanganate Oxidizable C	0.806	0.027	0.049	0.902	0.002	0.000
Microbial biomass C	0.898	0.182	0.042	0.948	0.001	0.009
Dehydrogenase activity	0.776	0.062	0.028	0.857	0.000	0.008
Acid Phosphatase activity	0.902	0.024	0.016	0.942	0.000	0.000
Alkaline Phosphatase activity	0.859	0.024	0.016	0.917	0.008	0.000
Urease activity	0.931	0.012	0.024	0.819	0.073	0.000
Available N	0.341	0.307	0.121	0.106	0.002	0.722
Available P	0.689	0.056	0.035	0.628	0.008	0.007
Available K	0.211	0.335	0.187	0.102	0.335	0.027
Total Organic C	0.607	0.074	0.009	0.181	0.188	0.120
Total Inorganic C	0.372	0.444	0.001	0.130	0.438	0.032

Bold letters show the values that are loaded for the corresponding PC. Underlined values are highest within a particular PC

After harvesting of wheat, for 0–15 cm soil properties, PC1 (61.17%), PC2 (12.18%) and PC3 (8.86%) together captured 82.21% of the total variability in the dataset of 0–15 cm wheat soil (Table 10). Except WBC, TOC, TIC and Av. N, K, all the biochemical variables were loaded in PC1, whereas Av. N and Av. K were loaded in PC2 and PC3, respectively. DHA, Av. N and Av. K scored highest factor loadings in PC1, PC2 and PC3, respectively (Table 10). Similarly, for 15–30 cm soil properties after wheat harvest, PC1, PC2 and PC3 captured 80.6% of the total variability in the dataset, respectively. Parameters such as POXC, MBC, ACP, DH and Urease were loaded in PC1; TOC was loaded in PC2 and WBC loaded in PC3. MBC, TOC And WBC scored highest factor loadings in PC1, PC2 and PC3, respectively (Table 10).

Table 10.

Result of PCA on soil quality parameters recorded from 0–15 cm and 15–30 cm soil layers after harvesting of wheat

Statistics/Variable	After Wheat Harvest
	0–15 cm Soil Layer			15–30 cm Soil Layer
	PC1	PC2	PC3	PC1	PC2	PC3
Eigen values	7.95	1.58	1.15	5.94	2.05	1.31
% of variance	61.17	12.18	8.86	49.50	17.05	10.95
Cumulative % of variance	61.17	73.35	82.21	49.50	66.55	77.50
Eigen vectors
Walkley Black C	0.612	0.117	0.053	0.054	0.003	0.499
Permanganate Oxidizable C	0.835	0.067	0.031	0.880	0.011	0.020
Microbial biomass C	0.839	0.001	0.067	0.917	0.002	0.011
Dehydrogenase activity	0.856	0.011	0.000	0.866	0.031	0.021
Acid Phosphatase activity	0.733	0.020	0.092	0.848	0.018	0.001
Alkaline Phosphatase activity	0.415	0.320	0.011	0.548	0.245	0.042
Urease activity	0.834	0.014	0.126	0.818	0.076	0.015
Available N	0.133	0.754	0.000	0.264	0.047	0.291
Available P	0.695	0.008	0.112	0.684	0.007	0.032
Available K	0.189	0.013	0.606	0.034	0.493	0.203
Total Organic C	0.635	0.005	0.015	0.024	0.786	0.025
Total Inorganic C	0.479	0.146	0.019	0.000	0.324	0.152

Bold letters show the values that are loaded for the corresponding PC. Underlined values are highest within a particular PC

The REY was used as a management goal for developing BCQI. The values of BCQI varied from 0.57 to 0.75 and 0.58 to 0.72 in the 0–15 and 15–30 cm, respectively, after harvesting of rice, while BCQI varied from 0.53 to 0.78 and 0.66–0.80 for 0–15 and 15–30 cm soil depth, respectively, after wheat harvest. The highest values of BCQIs were recorded in the ZTDSR-ZTW(RR) treatment, and the lowest values were recorded in TPR-CTW (Fig. 10). To validate the BCQIs with REYs, regression studies were run for four different BCQIs and the highest R² (0.66) was recorded with BCQI developed with soil parameters recorded from 15 to 30 cm after harvesting of wheat. It was followed by BCQI developed with 15–30 cm soil properties after rice harvest with an R² value of 0.61, while BCQIs developed with subsoil (0–15 cm) parameters recorded after rice and wheat did not perform well (Fig. 11). The PC regression with PCs developed with parameters for calculating BCQI (wheat, 15–30 cm) and REYs resulted in an R² of 0.687 and is given by Eq. 6.

Fig. 10.

Effect of different establishment and residue management treatments on developed soil biochemical quality indices BCQIs. Note: The columns sharing any common small cap letters under same series demark no significant differences (p > 0.05) and vice-versa

Fig. 11.

Regression lines between soil biochemical quality indices (BCQIs) and rice equivalent yield (REY). (a) BCQI (Rice harvest, 0–15 cm), (b) BCQI (Wheat harvest, 0–15 cm), (c) BCQI (Rice harvest, 15–30 cm), (d) BCQI (Wheat harvest, 15–30 cm)

6

A backward regression analysis was used with individual parameters to calculate BCQI (wheat, 15–30 cm) to predict REY. The final model included the independent variables POXC and MBC. The regression equation derived from the analysis resulted in an R² of 0.601, is given by Eq. 7.

7

Suggesting the key parameters to be POXC and MBC for soil biochemical quality index at 15–30 cm depth after wheat harvest.

Though all of the developed BCQIs showed significant treatment differences, demonstrating their ability to identify conservation agriculture-based practices in the early stages, BCQI (Rice, 0–15 cm) failed to distinguish between TPR-CTW (RI) and ZTDSR-CTW pair. Also unsupervised clustering based on this indicator showed a big difference between TPR-ZTW (RR) and ZTDSR-CTW (RR) with TPRAWD-ZTW(RR) and ZTDSR-ZTW(RR). BCQI (Rice, 15–30 cm) succeed to group RR, RI and no residue treatments together. BCQI (Wheat, 15–30 cm) also correctly grouped conservation-based RR regimes together. Maximum clustering distance was noted between TPR-CTW, TPRAWD-CTW, TPR-CTW (RI) cluster with TPRAWD-ZTW (RR), ZTDSR-ZTW (RR) and TPR-ZTW (RR) cluster (Fig. 12). Hence, all the developed Biochemical Quality Indices (BCQIs) were effective in detecting differences among treatments, highlighting their usefulness in identifying conservation agriculture practices at the transition phases.

Fig. 12.

Dendrogram of hierarchical clustering of different establishment and residue management treatments based on loaded parameters for development of respective soil biochemical quality indices (BCQIs). (a) BCQI (Rice harvest, 0–15 cm), (b) BCQI (Wheat harvest, 0–15 cm), (c) BCQI (Rice harvest, 15–30 cm), (d) BCQI (Wheat harvest, 15–30 cm)

Discussion

The reason behind the reduction in the bulk density in the zero-tilled residue retained and incorporated plots may be the improved microbial activity, which influences root exudation and soil aggregate formation [88]. Many short-term experiments agreed that the continuous input of residues and live roots of the previous crop in the zero-tilled plots for multiple years might be responsible for enhanced soil aggregation with rise in aggregate associated organic carbon (SOC) content and improved soil porosity, thereby reducing compaction measured by bulk density [89, 90]. Therefore, there might be a connection between soil aggregate stabilization and residue retention. Residues protect and mitigate the impact of external forces like erosions [91, 92]. Decomposed crop residues also provide an additional influx of SOM, which contains molecules like polysaccharides, organic acids and microbial byproducts that bind soil particles to form micro- and macro-aggregates [93]. These organic substances are also regarded as substrates for soil enzymes and feed for microbial proliferation [94]. The labile pools of SOC, i.e. POXC and MBC, are considered the most sensitive to different residue management practices, specifically at the early stages of management regime changes [95]. The decomposition rate of residue biomass depends on the quantity of MBC [96]. The MBC is also considered the most consistent and frequently used biochemical attribute as it influences the C and N biogeochemical cycles and microbial demand for nutrients [97]. The MBC increased from sowing to flowering stage and decreased gradually at harvest, possibly due to the unavailability of substrate – C for enzymatic degradation and declined rhizodeposition at crop maturity [98]. The highest value of MBC at flowering stage of rice and wheat can be ascribed to higher root exudation and vigorous growth stage. The current findings are also consistent with the observations of [17, 20].

MBC is significantly affected by tillage practices and sampling time as well. The current study reported higher MBC in the topsoil layer under zero-tilled residue-retained plots than conventionally tilled residue-removed plots. Several long-term experimentations in the tropical and subtropical soils also agree with current findings [99–101]. Such continuous supplementation of labile C in the upper soil layer from decomposed crop residues serves as an energy source of microorganisms that helps to build the equilibrium for proper functioning and sustainability of terrestrial ecosystems [102, 103]. The higher WBC was found in zero-tilled compared with conventionally tilled plots. This might be due to less mechanical disturbances in soil under ZT and higher SOC oxidation associated with conventionally tilled plots. Another explanation might be the slower decomposition rate under ZT, which primarily protects the soil from physical disruptive force and leads to higher WBC content [104, 105].

Additionally, CT affected the hyphal growth of fungi and microbial bio-diversity, resulting in higher WBC content. Higher POXC content was recorded under ZT plots, and residue retention over CT was recorded with removed plots. This might be due to more humification by microorganisms and stabilization of organic C, which is associated with lesser disturbance [106–109]. Tillage practices and straw management are key factors in modifying WBC levels in agricultural soils. In the study, surface retention of rice residues on zero-tillage plots [ZTDSR-ZTW (RR)] significantly increased WBC content in the 0–15 cm layer of wheat harvest soil compared to conventional tillage without residues (TPR-CTW) and zero tillage without residues (ZTDSR-CTW). Conventional tillage disrupts the soil by blending soil particles with crop residues, which can initially enhance microbial activity and lead to increased CO₂ emissions into the atmosphere [110]. On the contrary, zero tillage may help slow carbon oxidation, promoting higher WBC levels ([111, 112]), while conventional tillage breaks up soil aggregates, exposing stored organic matter to microbial activity, which speeds up decomposition [113], leading to lower WBC. WBC levels peaked during the wheat harvest, likely due to more significant carbon input from sources such as root matter and decomposed rice residues, compared to earlier growth stages [114].

Moreover, the ZTDSR-ZTW (RR) treatment improved available N and Bray-P levels relative to ZTDSR-CTW and TPR-CTW without residue. The higher N availability in ZTDSR-ZTW (RR) may be due to reduced N losses from volatilization, denitrification, and leaching, as opposed to conventional tillage. Our findings also support that applying straw as a surface mulch or incorporating it into the soil increased available N in the top 0–15 cm layer compared to removing straw. The rise in available P in the ZTDSR-ZTW (RR) treatment is likely due to the additional phosphorus from decomposed crop residues, expanding the phosphorus pool in the soil [115]. These results align with earlier studies by [91] and [92], which found that phosphorus tends to build up in the surface layers under ZT with RR, compared to conventional tillage with residue removal.

All enzyme activities are reported to be higher at the flowering stage of rice and wheat in both soil layers. These can be ascribed to greater root exudation at this stage, influencing the secretion of all the enzymes [109]. The enhanced dehydrogenase and phosphatase activity were observed in the wheat flowering stage, which has been described due to the higher rate of residue decomposition in the favored temperature at the summer and rainy seasons [116] and greater availability of nutrients required for microbial metabolism [20]. This explanation is corroborated by the findings of [98] and [110]. Zero tillage with residue retention (ZT with RR) encourages soil carbon build-up and enhances soil fertility [117]. The present study showed dehydrogenase activity as an important contributor to soil BCQI which is consistent with the findings of Nath et al., [36]. Thus, adding crop residues to zero tillage practices proves to be essential for boosting soil enzyme activity [46]. Dehydrogenase enzyme activity, in particular, is a reliable indicator of viable microbial activity [45] and can be used to assess the short-term impact of land management on soil quality [95]. Surface soils showed more significant variability among treatments than subsurface soils, highlighting that the effects of management practices are more pronounced in the topsoil. This may be due to direct interactions of residues, tillage, and microbial activity with the surface layer. Treatments with residue retention and zero tillage, viz. TPRAWD- ZTW (RR), ZTDSR-CTW (RI), ZTDSR-ZTW (RR), and ZTDSR – CTW showed positive effects on soil quality indicators. These practices likely enhance microbial activity, nutrient cycling, and carbon sequestration. These results were supported by the findings of [116, 118–120] and [121]. Hasimi et al., [122] reported that zero tillage led to a notably higher percentage of macroaggregates (> 2 mm) compared to conventional tillage. The increased input of crop biomass at the soil surface and living roots of the retained residues under ZT undergo decomposition that leads to the breakdown of components such as proteins, cellulose, and lignin, which are essential for forming soil aggregates by facilitating adsorption processes [123]. It also encourages the development of hyphal network at the root and more root exudations that associated with larger soil aggregates and enhances their stability [124].

AWD-treated plots were observed to have higher MBC and WBC than conventionally transplanted rice plots. This result is consistent with the findings of [125] and [110] and inconsistent with the findings of [103] and [104–107]. Alternate wetting and drying change the soil moisture regime, leading to the death of microorganisms that release intra-cellular osmolytes, disengaging enzymatic activity from cellular metabolism [127, 128]. Long-term waterlogging and the return of rice residues preserve soil carbon and induce microbial proliferation in soil [129–132]. Therefore, short-term drying stress followed by rewetting during AWD does not affect the biomass characteristics. In the case of AWD, periodic drying disrupts the anaerobic conditions in the soil, inhibiting the activity of methanogenic bacteria responsible for methane production, which is a significant contributor to GHG emissions. In addition to reducing methane emissions, AWD can improve root health by promoting aeration, which supports better nutrient uptake and more vigorous plant growth [133]. Adopting AWD can also benefit farmers economically by lowering water pumping and irrigation costs [134]. Moreover, AWD aligns with global climate goals by mitigating GHG emissions contributing to sustainable agricultural practices [135–137]. The current research has indicated that when coupled with other conservation practices, such as residue retention and zero-tillage, AWD further improves soil quality and resilience, enhancing the long-term productivity of rice-based cropping systems.

The study developed sensitive BCQIs for lateritic soils, which can detect early changes in degraded soils which is consistent with findings of [34, 138] in contrast to traditional SOC-centered soil quality evaluation methods such as [56], which are dependent upon more stable SOC parameters and are merely responsive to initial changes [139]. The BCQIs for lateritic soil could be a future success to address the seasonal variation of enzymatic activities with an interpretation of discriminating between transient effects of short-term conservation agriculture due to crop establishment and residue retention practices [20, 109]. Thus, parameters like urease activity, MBC, POXC, Av. N, and Urease activity were found to be the dominant contributors across depths and crops. Supplementary figure S1 shows the correlation between enzymatic parameters. It was observed that carbon pools such as WBC, POXC and MBC were positively correlated with enzymatic parameters such as urease, ALP, DH having correlation coefficient ranging between 0.76 and 0.93. These were consistent with the findings of the studies of [57, 140, 141]. These studies also reported a positive correlation between MBC and POXC and as well as DH (r = 0.96) [57]. Similarly, Mir et al., [140] in their work, reported a positive correlation between WBC, MBC and POXC (r = 0.86).

Regression analysis validated BCQIs against REYs, with the most potent model derived from wheat 15–30 cm soil properties, emphasizing MBC, POXC as critical predictors. BCQIs developed with subsoil parameters showed less sensitivity than topsoil parameters, which may be explained by higher biological activities in the topsoil layers [24, 109, 142]. The study found deeper soil parameters resist short-term changes in the managemental regimes. Hierarchical clustering highlighted BCQI (wheat, 15–30 cm) as the most effective in distinguishing conservation practices from conventional, clustering residue-retained and residue-incorporated treatments separately. These improvements directly address the structural and nutrient constraints of lateritic soils. The higher BCQI values in ZTDSR-ZTW-RR treatments reflect the sensitivity of biochemical indicators to early management shifts, emphasizing their utility in monitoring soil quality transitions. Integrating the formulation of a biochemical quality index into practical monitoring and management decisions enhances the present study’s relevance by demonstrating how biochemical indicators can be used to enhance soil health. The biochemical quality index-built from sensitive attributes such as microbial biomass and enzyme activities offers early and precise detection of soil changes in response to management practices. This allows for timely interventions, helping land managers and farmers make decisions to maintain or improve soil quality and productivity.

Conclusions

The current study demonstrated that biochemical quality indices based on soil biochemical parameters are highly sensitive to transitional phases of tillage and residue management in lateritic soils, offering valuable insights into their dynamic response to changes in agronomic practices. Specific shifts in soil enzyme activities, carbon pools, and nutrient availability were observed, highlighting the critical role of tillage intensity and residue placement in influencing soil biochemical stability and resilience. The use of multivariate approaches, such as clustering and principal component analysis, demonstrated the ability to identify dominant factors driving soil biochemical dynamics, offering a data-driven method to simplify complex soil quality assessments. The current study highlighted that the soil biochemical quality index developed from soil parameters recorded from the subsoil layers (15–30 cm) after the annual cropping cycle was the best to relate with yield parameters and classify the treatments according to changes in the management regimes during the transitional phases. Among several other parameters, microbial biomass carbon, permanganate oxidizable organic carbon were found to be most important. The findings underline the vulnerability of lateritic soils to degradation under inappropriate tillage-residue regimes while showcasing their potential for speedy recovery and enhancement under optimized management practices. Despite the promising findings, the study has limitations. The short-term evaluation restricts the interpretation of long-term resilience or degradation trends in lateritic soils. Moreover, the inclusion of soil microbiological parameters may further strengthen the robustness and sensitivity of developed quality indices.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Investigation, Formal analysis and Writing-Original Draft: S. Rana; Supervision: D.K. Swain, Writing-Original Draft and Visualization: P. Bhattachariya & A. Roy; Project administration, Conceptualization: S. Samanta; Conceptualization, Funding acquisition, Writing - Review & Editing: M. F. Seleiman, N. Ali; Conceptualization, Project administration, Funding acquisition and Supervision: P. Dey.

Funding

Ongoing Research Funding Program, (ORF-2025-751), King Saud University, Riyadh, Saudi Arabia and Varaha ClimateAg Pvt. Ltd. (R/PR/2022–2023/512), Indian Institute of Technology Kharagpur, India.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Clinical trial number

Not applicable.

Competing interests

The authors declare no competing interests.