This study was conducted at two sites of IGP, i.e., Taraori, Karnal, India (Lat. 29°48’ N and Long. 76°55′ E; called Karnal hereinafter) and Borlaug Institute of South Asia (BISA) farm, Samastipur, India (Lat. 25°57’ N and Long. 85°40′ E; called Samastipur hereinafter; Figure 1). In both the sites, the trial was initiated in 2012 (June in Karnal and November in Samastipur) on a well level (0–1% slope) having loam and silty loam soil texture, respectively. In Karnal, the climate is semiarid with hot, dry to wet summers (May–October) and cool, dry winters (November–April) having 24°C average annual temperature and 670 mm mean annual rainfall, of which 75–80% is received usually during southwest monsoon (July to September). Karnal is classified as humid subtropical, dry winter climate (Classification: Cwa). In Samastipur, the climate is monsoon-influenced humid subtropical climate, characterized by hot and humid summers and cold winters with an average annual temperature and rainfall of 25.5°C and 1,200 mm, respectively, 70% of which is received between July to September. The dominant soils at the study sites in the Indo-Gangetic Plains, specifically in Karnal and Samastipur, are characterized as Anthraquic Haplustepts and Oxyaquic Haplustepts, respectively. These soils feature low organic carbon content and an alkaline reaction. The texture of Karnal’s soil is loam, while Samastipur’s soil is silty loam. Such soil properties are crucial for understanding the impacts of conservation agriculture practices on soil health and carbon sequestration in these regions.

The experiment’s design using a randomized block with six treatments in Karnal (K1-K6) and four in Samastipur (S1-S4) was chosen to effectively account for the inherent variability within and across these contrasting agro-ecological sites. This methodological approach allows for a nuanced examination of CA practices’ impacts on soil carbon sequestration by accommodating site-specific conditions such as soil type, climate, and cropping systems. The plot size in both, Karnal and Samastipur, was same as 350 m², with three replications in each site (Figure S1). Both sites’ treatments were based on conventional and conservation agriculture systems of rice, wheat, and maize-based cropping systems (Tables 1, 2). In addition, a mustard-based cropping system in Samastipur seems more promising and suitable in this climatic region. The diversified cropping systems were designed to include various cereals and legumes with different C:N ratios to study their effect on soil carbon and nitrogen dynamics. The sowing and harvesting time of all crops in other treatments is depicted in Figures 2a,b. The crops in different treatments were grown according to the recommended agronomic practices. Figure 3 illustrates the innovative cropping systems employed on permanent raised beds (PB) in two distinct regions: Karnal and Samastipur. In Karnal, the cropping system of maize-wheat-mungbean is adopted, showcasing a diversification strategy aimed at optimizing crop yields and improving soil health through varied crop rotations. Samastipur experiments with both maize-wheat-mungbean and maize-mustard-mungbean systems, reflecting an effort to tailor cropping patterns to local agro-ecological conditions while enhancing sustainability.

Rice and wheat were harvested and threshed at crop maturity by a combined harvester. Mustard, maize and mungbean were harvested manually. In mungbean, pods were picked manually and the biomass was retained in the plots. For grain and biomass yield, crops were harvested manually from randomly selected 4 m × 2.7 m quadrate from three places within each field. The crop yield was recorded at 12–14% moisture content, while biomass yield was calculated after drying in an oven at 50°C for 72 h. The mustard yield was converted to wheat equivalent yield (WEY) to compare the treatments using Equation 1. System yield (SY) was calculated on rice equivalent yield (REY) basis for wheat, maize, mustard and mungbean grain yield by using Equation 2.

Where, MSP is the Minimum Support Price set by Govt. of India; INR is the India National Rupee (rice: 1,360 ± 85.4, wheat: 1,547 ± 135.0, maize: 1,297 ± 71.8, mustard: 3,440 ± 405.3 and mungbean: 4,950 ± 447.6).

After the crop harvest, the total biomass yield of roots and stubble was estimated using 0.5 m × 0.5 m quadrate, throwing randomly three places within each plot and extrapolated to a hectare basis at 0–15 cm soil depth. After 5 years of experimentation, samples from each crop were taken for total carbon (C) and nitrogen (N) in the crop residue, stubble and root by dry combustion method with an NC analyzer (Vario Max CHN, Elementar). The average total C and N in the crop residue, stubble and root at both sites are presented in Supplementary Tables S1, S2.

After 3 and 5 years of experimentation (in 2015 and 2017 for Karnal and 2016 and 2018 for Samastipur), composite soil samples (five randomly selected points within each plot) were collected from two depths (0–5 cm and 5–15 cm) with a soil augur of 5 cm internal diameter.

The selection of two soil depths (0–5 cm and 5–15 cm) for sampling in this study is strategic, aimed at capturing the most significant changes in carbon (C) and nitrogen (N) content within the plough layer, which is generally up to 15 cm. This depth aligns with standard ploughing practices and encompasses the majority of root activity, ensuring that the assessment of soil health and nutrient dynamics is both relevant and comprehensive. The methodology for soil sampling, involving composite samples from five random points within each plot, and the subsequent analysis, including the use of an improved chromic acid digestion method for total organic carbon (TOC) determination, is designed to provide a detailed understanding of soil carbon and nitrogen changes attributable to conservation agriculture practices over the 3 and 5-year experimentation periods. To quantify the overall changes in C and N content up to plough layer (0–15 cm), Equation 3 was used that quantifies the weighted mean of the soil C and the soil N for the layer 0–15 cm. The soil samples were air-dried and passed through a 2 mm sieve for analysis. As the soil samples of both sites contain a substantial amount of carbonates, total organic carbon (TOC) was analyzed by an improved chromic acid digestion method (Tyurin, 1966). Soil bulk density (BD) was determined with a core sampler and cores of 5 cm height and 5 cm diameter (Blake and Hartge, 1986).

POM fractionation provides insights into the soil’s ability to sequester carbon and maintain fertility, highlighting the effectiveness of CA practices in enhancing soil health. Additionally, understanding POM dynamics allows for assessing soil organic matter turnover and stability, critical factors in evaluating the sustainability of agricultural systems.

Twenty grams of air-dried and sieved soil (2 mm), five glass beads and 50 mL of distilled water were put in a 100 mL plastic bottle and shaken for 16 h at 20°C in an end-over-end shaker at 40 rotations per minute to ensure physical fractionation of soil organic matter (Koutika et al., 2017). The soil was wet-sieved to separate the suspension into three fractions: 2,000–250 μm, 250–53 μm and 0–53 μm. The following fractions were collected: coarse (cPOM, 2,000–250 μm) and fine POM fractions (fPOM, 250–53 μm) fractions and the organo-mineral fraction (OMF, < 53 μm). All fractions were dried at 65°C and weighed. The recovery of fractionation (sum of all fraction mass divided by soil mass) was 1.0047. Proportional soil mass (%) was calculated by using Equation 4

The C in cPOM fraction was analyzed by dry combustion method with the NC analyzer, whereas C in fPOM and OMF fractions was analyzed by Tyurin method to avoid overestimation as these fractions contain carbonates. Total N in all the fractions were analyzed by NC analyzer.

The size of the TOC and TN in each POM fraction was calculated by multiplying their respective TOC and TN concentration with soil mass (mentioned as SM in equation) in each depth (0–5 cm and 5–15 cm) and mass of soil in each fraction using Equation (5).

Where, soil mass in Mg ha − 1 (depth 0–0.05 m) = BD (Mg m⁻³) × 500; Soil mass in Mg ha − 1 (depth 0.05–0.15 m) = BD (Mg m⁻³) × 1,000. TOC stock and TN stock up to plough layer (0–15 cm) were simply adding stock in 0–5 cm and 5–15 cm.

The modified procedure for determining permanganate oxidizable carbon (POXC) is adapted from Culman et al. (2012). Briefly, 2.5 g of air-dried soil were dispersed in 20 mL of KMnO₄ solution (0.02 mol L⁻¹), with shaking on a reciprocating shaker (120 strokes minute⁻¹) for exactly 2 min. After shaking, the soil solution was allowed to settle for 10 min, then 1 mL of the supernatant was transferred into a 50 mL centrifuge tube and 49 mL of deionized water was added. A spectrophotometer read the absorbance of the diluted supernatant at 550 nm. The change in the concentration of KMnO₄ was used to estimate the amount of oxidized C. Sample POXC was calculated as in Weil et al. (2003) using Equation 6

Where 0.02 mol L⁻¹ is the initial concentration of the KMnO₄ reactant, a and b are the intercept and slope of the standard curve, respectively, Abs is the sample absorbance, 9,000 mg C mol⁻¹ is the amount of C (0.75 mol) oxidized by 1 mol of MnO₄, changing Mn⁷⁺ to Mn⁴⁺, and 0.0025 kg soil is the amount of soil reacted with KMnO_4. The POXC stock was calculated by multiplying the respective POXC concentration with BD and soil depth.

The analysis of variance (ANOVA) was carried out separately for both locations to determine the statistically significant differences among the treatments in both sites using SPSS (17.0) statistical package (SPSS Inc.). Tukey’s post hoc test was used for multiple comparisons among the treatments (p < 0.05). Pearson correlation coefficient was used to evaluate the relationship between the carbon inputs and soil properties.

After 5 years, significant yield differences emerged among tillage and residue management treatments in both sites (Table 3). In Karnal, maize in K5 and K6 (PB) outperformed ZT (K3, K4) and CT (K1, K2). Wheat and mungbean yields favored CA practices, particularly in ZT and PB systems. System yield was notably higher in treatments incorporating crop residue and mungbean (K2, K4, K6), with K6 leading (20.2% higher than K1). In Samastipur, rice yields in S1 and S2 were comparable, but PB treatments (S3, S4) yielded lower for maize compared to CT (S1). Wheat yields showed no significant difference. The highest system yields were in S2 (15.0 Mg ha⁻¹) and S4 (13.7 Mg ha⁻¹), with ZT (S2) achieving a 23.0% increase over CT (S1). Plant-derived C input data are detailed in Supplementary Table S3.

In Karnal, significant differences in TOC stocks at 0–5 cm depth were observed among treatments in both 2015 and 2017, with K4 and K6 showing the highest stocks, 17 and 11% higher than K1, respectively, in 2015; and 17 and 20% higher than K1 in 2017 (Table 4). TN stocks differed significantly in 2015 but not in 2017, with K6 (≈21% and ≈13% in 2015 and 2017 respectively) and K4 (≈12 and ≈15% in 2015 and 2017, respectively) showing the highest increases over K1. At 5–15 cm depth, TOC stocks showed significant change in 2015 and 2017 (8–11% (2015) and 5–10% (2017) in K4 to K6 over K1). While TN stocks differed in K4 (23%) in 2015 and, K4 (42%) and K6 (37%) in 2017 (Table 4).

TOC stocks in the 0–15 cm layer significantly differed in 2015 and 2017, with K6 and K4 substantially higher by 8 to 13% than K1. TN stocks showed significant differences in K4 and K6 2015 (18 and 9%) and 2017 (31 and 27%).

In Samastipur, TOC stocks at 0–5 cm depth differed significantly in both sampling periods, with the highest in S2 (24%) and S3 (26%) in 2016 and S2 (20%) in 2018 showing significantly higher stocks than others (Table 5). TN stocks at 0–5 cm showed significant differences in all the treatments (S2 to S4) over S1. At 5–15 cm depth, TOC and TN stocks in 2016 and 2018 showed no significant changes. Overall, TOC stocks at 0–15 cm depth varied significantly, with the highest values recorded in S3 (11%) in 2016 and 2018 (8%), while TN stocks showed improvement in 2016 with highest level in S2 (28%) and S4 (30%) but there was no change or variation in 2018.

In Karnal, the POXC content and stock exhibited no significant differences among the treatments in 2015 at all the soil depths (0–5 cm, 5–15 cm, and 0–15 cm; Table 6). In 2017, POXC stock was 37.0% higher in K4 than in K1 at 0–5 cm soil depth. No significant differences were observed among the treatments in 2017 at 5–15 cm depth. In 0–15 cm depth, POXC content and stock showed a similar trend as 0–5 cm depth, with the highest value in K4, which was 32.5% higher than K1 after the 2018 sampling. In Samastipur, POXC stock at 0–5 cm depth recorded no significant difference in 2016, whereas, in 2018, S2 showed 93.8% higher POXC stock than S1 at 0–5 cm depth (Table 7).

A Pearson’s correlation matrix indicated varying correlations between TOC stock, POXC stock, carbon input, and POM fractions across depths and times (Table 10). Table 10 shows the comparison of data at the same location. Initially, no significant correlation was found between carbon stock and cPOM fraction after 3 years. However, after 5 years, a notable correlation emerged (r = 0.63 and r = 0.76) at 0–5 cm depth in both sites. In Karnal, a significant correlation was found between soil carbon stock and total carbon input after both 3 and 5 years (r = 0.48 and r = 0.55) at 0–5 cm depth, while in Samastipur, this was observed only after 3 years (r = 0.78). No correlations except for carbon input were noted at 5–15 cm depth. At 0–15 cm depth, significant correlations between carbon stock and cPOM emerged after 5 years in Karnal (r = 0.57), with soil carbon stock significantly associated with total carbon input in both sites over time, except after 3 years in Samastipur.

Our study highlights the significant influence of tillage and crop establishment methods, residue recycling/incorporation, and legume integration on crop grain yield and system yield (SY), as seen in the substantial SY increase in Karnal for the maize-wheat-mungbean rotation under permanent beds (PB). This finding aligns with the combined effects of yield increments across these crops, improved soil moisture and temperature regimes, enhanced aeration and light availability, and better soil physicochemical properties (Choudhary et al., 2018; Kumar et al., 2018; Choudhury et al., 2014; Parihar et al., 2016; Jat et al., 2014; Nandan et al., 2019; Parihar et al., 2018; Samal et al., 2017). Additionally, the increased nutrient availability and effective recycling of crop residues contribute to an enhanced total organic carbon (TOC) stock, which facilitates better soil aggregation (Blanco-Canqui and Lal, 2007; Jat H. S. et al., 2018; Chaudhary et al., 2022; Mishra et al., 2018; Kumar et al., 2019; Samal et al., 2017). In Samastipur, the treatments S2 (ZT-DSR and ZT-wheat with mungbean) and S4 (PB-maize and PB-mustard with mungbean (MM)) exhibited comparable SY, significantly higher than S3 (PB-maize and PB-wheat with mungbean) and S1 (CT-rice and CT-wheat), highlighting the benefits of conservation agriculture (CA) practices. The higher SY achieved through ZT and PB with the integration of mungbean in RW and MM systems is consistent with previous findings in the IGP region, reinforcing the value of CA systems in sustainable agriculture (Jat et al., 2019; Kumar et al., 2018). The lower SY observed in the poorest-performing treatments (K1 in Karnal and S1 in Samastipur) can be attributed to suboptimal soil and crop management practices, particularly the rapid decomposition of organic matter (Powlson et al., 2014; Singh et al., 2015; Stewart et al., 2007).

Exploring carbon stocking and lability in short-term trials of CA-based management practices over 3 and 5 years is crucial for identifying optimal practices for specific tropical soil sites. In the loamy soils of Karnal and the silty loams of Samastipur, the lability of TOC plays a vital role in carbon sequestration and stabilization. Our analysis of TOC and POXC stocks, particularly under permanent raised bed systems with maize-wheat-mungbean rotations and full residue retention at 0–15 cm soil depth, reveals the beneficial impacts of reduced tillage and enhanced crop residue turnover on carbon stocks (Tables 6–9). CT systems, conversely, may expedite organic matter decomposition through aggregate disruption and increased microbial exposure (Jat M. L. et al., 2018). Notably, after 5 years, K6 in Karnal exhibited the highest carbon stock increase at 72.3% over K1 at 0–5 cm depth, underscoring the efficiency of CA practices in enhancing soil carbon levels (Table 9). Similarly, in Samastipur, S2 and S3 treatments demonstrated significant carbon stock increases after 3 years, with S2 showing the most substantial rise after 5 years (Table 5). This suggests that ZT and PB practices, especially when including crop rotations with legumes like mungbean, significantly contribute to soil organic carbon accumulation, corroborating findings by Govaerts et al. (2007), Meurer et al. (2018), and Parihar et al. (2018). Our results align with the broader literature, indicating that CA practices, particularly those involving diverse crop rotations and residue management, are crucial for enhancing soil carbon sequestration in tropical soils (Awale et al., 2017; Bongiorno et al., 2019; Moharana et al., 2012).

Additionally, the observed increase in total organic carbon (TOC) across all soil layers under conventional management (K1 and S1) over 2 years may be explained by several mechanisms (Awale et al., 2017). Although conventional tillage (CT) typically accelerates organic matter decomposition by disrupting soil aggregates and enhancing microbial activity, it can also lead to short-term increases in TOC, particularly when crop residues are incorporated into the soil or when fertilization promotes greater biomass production (Rahman et al., 2021). In the short-term timeframe of this study, organic inputs from crop residues under CT may temporarily increase TOC before decomposition rates surpass carbon accumulation. Additionally, the lability of TOC, influenced by site-specific soil properties such as the loamy soils of Karnal and silty loams of Samastipur, may contribute to transient carbon sequestration even in conventional systems (Dey et al., 2020). This underscores the need for long-term evaluations to fully assess the impacts of tillage practices on carbon stabilization in tropical soils.

The study’s examination of particulate organic matter (POM) fractionation and carbon stabilization across different conservation agriculture (CA) management practices provides compelling evidence of the pivotal role of short-term agronomic strategies in enhancing soil carbon stocks. Specifically, the significant increase in coarse particulate organic matter (cPOM) at the 0–5 cm soil depth within treatments K4 and K6 in Karnal, as observed in both 2015 and 2017, underscores the synergistic effects of reduced tillage and diverse crop residue management on soil structure and carbon dynamics (Table 6). This finding is particularly noteworthy as it highlights how strategic residue management and tillage reduction can lead to enhanced carbon sequestration by promoting the formation of macroaggregates, which serve as protective niches for organic carbon, thereby reducing its susceptibility to microbial decomposition.

In Samastipur, the superior performance of S2 and S4 treatments regarding cPOM accumulation further corroborates the efficacy of integrating legumes such as mungbean into cropping systems. This integration not only diversifies the cropping system but also contributes to a more complex soil organic matter composition through the addition of legume-derived residues, which are known for their rapid decomposition and nutrient release, enhancing soil fertility and structure (Benbi and Senapati, 2010; Parihar et al., 2018). Such practices, indicative of CAB34’s principles, foster a conducive environment for sustainable soil health management by improving the soil’s physical, chemical, and biological properties.

Moreover, the observed correlations between carbon stock and cPOM over time (Table 10) suggest a dynamic and responsive soil system under CA management, with implications for long-term soil health and productivity. The data indicate that as CA practices mature, their impact on soil carbon stocks becomes increasingly pronounced, highlighting the importance of time in assessing the outcomes of agricultural management practices on soil carbon dynamics.

This study’s findings align with and extend the work of Govaerts et al. (2007), Meurer et al. (2018), and others, situating them within the broader discourse on soil carbon sequestration and the sustainability of agricultural systems (Rahman et al., 2021). By demonstrating the potential of CA practices to influence soil carbon stocks in the short term significantly, this research contributes valuable insights into the mechanisms through which agricultural management practices can be optimized to enhance carbon sequestration, soil health, and, ultimately, the sustainability of farming systems. This underscores the critical need for continued research in this area, particularly long-term studies that can further elucidate soil carbon dynamics under different agricultural management practices and their implications for addressing global challenges such as climate change and food security.

Pearson’s correlation matrix revealed that soil carbon stock was significantly and positively correlated with cPOM and carbon input from crop residues in both sites, mainly after 5 years of experimentation. The correlation is robust at 0–5 cm and weaker at 0–15 cm soil depth. Such a reduction in correlation might be due to no significant correlation in 5–15 cm soil depth. The results indicate that soil carbon stock build-up is strongly influenced by the continuous flow of crop residues into the system (Choudhury et al., 2018), especially the cPOM fraction. Barreto et al. (2010) have observed that the more significant variation in the labile fraction (cPOM) would be a consistent indicator that would monitor the variation in soil quality under different management practices. However, the period of experimentation is crucial for identifying such changes in CA-based management practices. The results of the present study support earlier findings (Moharana et al., 2012; Somasundaram et al., 2018; Choudhary et al., 2018). Thus, it can be stated by the final analysis that cPOM helps in understanding soil carbon dynamics under different climate regimes.

Additionally, study was done at two site in two different states sharing different agro-climatic zones and ecology, so these practices and findings are applicable to similar regions with similar climatic conditions following rice-wheat cropping systems. Apart from this, study is short term but longer term studies will also be rewarding based on the findings of the study and significance of the practices.