The present study was conducted at Agronomy Research Farm, Punjab Agricultural University (PAU), Ludhiana, India. The farm is located 247 m above mean sea level (MSL) at coordinates 30° 54′ N and 75° 48′ E. The location experiences semi-arid, sub-tropical climates and is classified under India’s Trans-Gangetic agroclimatic zone weather conditions. The maximum temperatures were 42.1°C and 40.0°C during 2019–2020 and 2020–2021. The mean relative humidity during the cropping season ranged from 30.50% to 86.92% and 34.00% to 85.14% during the crop season of 2019–2020– and 2020–2021, respectively. During summer season, a maximum temperature that ranged 32.9°C–42.1°C and a minimum temperature that ranged 11.1°C –27.6°C were recorded in the summer season of 2020, whereas during 2021 it was 23.4°C–39.4°C and 15.8°C– 26.6°C, respectively. Total rainfall received during the crop season was 72.4 mm and 126.8 mm during 2020 and 2021, respectively. The experimental soil was sandy loam in texture (76.5% sand, 16.3% silt, and 7.2% clay) and low in nitrogen (181.9 kg ha^-1) and soil organic carbon (0.37%), medium in accessible potassium (208.6 kg ha^-1) and phosphorus (21.2 kg ha^-1), and neutral in reactions (pH 7.31).

The field trials were established in a randomized complete block design (RCBD) with four replications of each of the six cropping system treatments, which varied in tillage intensity, cropping system, and residue management. Each experimental plot measured 18 m × 10.5 m ( Table 1 ). The study was conducted over two cropping years from 2019–2020 to 2020–2021.

Crop residues of rice, maize, soybean, wheat, and mungbean were managed differently based on the treatments listed in Table 2 . In treatments R-W-SM (R0)- S1, M-W-SM (R0)- S3, and S-W-SM (R0)- S5, the crop residues were removed, while in R-W-SM (R+)- S2, M-W-SM (R+)- S4, and S-W-SM (R+)- S6 the residues were retained. Harvesting operations for all crops in the experimental plots were conducted using a combine harvester integrated with the advanced Super SMS (straw management system) ( Figure 2 ). This innovative technology incorporates a chopper and spreader, working in tandem to finely chop the straw and ensure its uniform distribution over a wider area, effectively acting as mulch. Harvesting was carried out at varying clearances from the ground level: 30–40 cm for rice, 10–15 cm for wheat, 125 cm for maize, 15–20 cm for soybean, and 20–25 cm for mungbean in treatments S2, S4, and S6. As per treatments, the crop residue load was calculated and mentioned in Table 2 .

S1: Intensive tillage for rice–wheat–mung rotation with high NPK rates (105–125 N kg/ha), broadcast application, continuous flooding for rice, and flood irrigation for other crops. S2: Rice under intensive tillage, wheat with Happy Seeder, mung zero-tillage on residues. Reduced NPK (84–100 N kg/ha) with Green Seeker precision management and irrigation at -20 to -40 kPa matric potential. S3: Conventional tillage for maize–wheat–moong rotation with comprehensive NPK fertilization (125 N kg/ha for maize/wheat), broadcast application, and furrow/flood irrigation at -40 to -50 kPa. S4: Permanent bed system with residue retention for all crops. Reduced wheat seeding (75 kg/ha), lower NPK rates (100 N kg/ha), Green Seeker precision management, and furrow irrigation throughout, S5: Conventional tillage for soybean–wheat–moong rotation. Soybean at 62.5 kg/ha with reduced nitrogen (31.25 kg/ha) due to N-fixation, standard wheat fertilization, and mixed furrow/flood irrigation. S6: Permanent beds with full residue retention across soybean–wheat– mungbean rotation. Reduced wheat seeding (75 kg/ha), precision nitrogen management via Green Seeker, and consistent furrow irrigation for optimal water use efficiency ( Table 3 ).

Baseline soil samples were drawn from two soil depths of 0 to 7.5 and 7.5 to 15 cm using an auger of 5 cm in diameter prior to the initiation of the experiments. The soil bulk density was calculated as per standard protocol suggested by Chopra and Kanwar (1991). A double ring infiltrometer was used to measure the infiltration rate (Bouwer, 1986), which determines the rate at which water level recedes or the rate at which water is withdrawn from a supply source to maintain a constant head of water on the soil surface. The soil analysis for available NPK followed the standard method described in Jackson (1967) ( Table 4 ). The determination of Fe, Mn, Zn, and Cu was carried out with DTPA (pH 7.3) extractant using an atomic absorption spectrophotometer (AAS Varian AAS-FS 240 model) (Arora, 2018).

The data from both experimental years were analyzed using two-way analysis of variance (ANOVA) in Statistical Analysis Software v9.4 (SAS Institute Inc. SAS/STAT® 9.4., 2013). Treatment effects on soil properties were evaluated through biplot and loading plot analyses using principal component analysis (PCA) with OriginPro software. A Pearson correlation matrix was constructed to assess the relationships between the measured soil variables. Tukey’s HSD test was used to compare the treatment means at 5% level of significance.

Bulk density (BD) was not significantly influenced by CA scenarios at 0–7.5 and 7.5–15 cm depth ( Figure 1 ). In general, the BD of the upper 0–7.5 cm layer was lesser compared with the lower layer of soil (7.5–15 cm).

Puddled transplanted rice (PTR) recorded a higher BD compared with conservation wheat and maize. Scenarios S2, S4, and S6 recorded a lower BD by 2.22%, 3.70%, and 4.44%, respectively, in the 0–7. 5-cm soil layer and 0.70%, 1.41%, and 4.25% in the lower layer at 7.5–15-cm soil depth, respectively, compared with S1 (1.35 and 1.41 g cm^-3). The infiltration rate was significantly higher by 45.68% under S6 over the S1 treatment. Moreover, residue retention significantly influenced the infiltration rate over no residue applied under the respective crop establishment techniques. The highest infiltration rate was noted under S6 (3.38 cm h^-1) followed by S4 and S2 (3.00 and 2.64 cm h^-1), whereas the lowest infiltration rate was noted under S1 and S3 (2.32 and 2.45 cm h^-1, respectively).

The CA-based practices did not significantly influence soil pH and electrical conductivity at 0–7.5 and 7.5–15 cm of soil depth ( Table 5 ). The maximum soil organic carbon (SOC) was recorded under S6 (0.50% to 0.52%), followed by S4 and S2 (0.49% to 0.51% and 0.48% to 0.50%, respectively), whereas minimum organic carbon was recorded under S1 (0.38% to 0.40%) at 0–7. 5-cm depth in both years. The top layer was found to have the highest SOC value (0–7.5 cm); following that, as soil depth increased, the SOC content dropped significantly across all scenarios ( Table 5 ). After completion of the experiment, SOC was significantly higher by 31.5%, 28.9%, and 26.3% under S6, S4, and S2 compared with S1 (0.38%) at the upper most layers (0–7.5 cm) in the first year. Similarly, SOC was significantly higher by 30.0%, 27.5%, and 25.0% at 0–7. 5-cm depth under S6, S4, and S2 than S1 in the second year, but SOC remained unchanged at a – lower depth (7.5–15 cm) compared with S1.

The data depicted in Table 6 show that enhanced CA-based management techniques have a big impact on soil nutrient availability, specifically primary nutrients. Throughout the cropping cycles, conservation agriculture (CA) practices resulted in higher levels of available primary nutrients in the soil. The higher available N, P, and K was recorded under S6 (259.9, 30.50, and 230.1 kg ha^-1), followed by S4 (250.2, 28.4, and 221.4), whereas minimum available N, P, and K was recorded under S1 (199.8, 23.30, and 215.4 kg ha^-1) at 0–7. 5-cm depth. S6 and S4 resulted in a higher availability of N, P, and K in comparison with the rest of the scenarios. After 2 years of the study, the farmer’s practice (S1, S3, and S5) resulted into statistically less available N (199.8 kg ha^-1), available P (23.30 kg ha^-1), and available K (215.4 kg ha^-1) content in S1 at 0–7. 5-cm depth in comparison with the rest of the scenarios ( Table 6 ). The available N, P, and K were statistically higher by 30.0%, 30.9%, and 6.82% under S6 over the S1 treatment. Moreover, residue retention statistically affected the available primary nutrient over no residue applied under the respective crop establishment techniques.

The data shown in Table 7 reveal that improved management practices had a significant effect on the micro- nutrient content in the soil. Zn, Fe, Mn, and Cu with ranges of 1.90–2.10, 9.30–12.96, 8.40–9.10, and 0.43–0.70 mg/kg, respectively. The results show that S6 had the highest content of Zn, Fe, Mn, and Cu compared with the other treatments. S6 recorded the highest values of 2.10 mg kg^-1 for Zn, 12.96 mg kg^-1 for Fe, 9.10 mg kg^-1 for Mn, and 0.70 mg kg^-1 for Cu. This indicates that residue retention in S6 resulted in a higher micro-nutrient availability in the soil. Similarly, S4 also showed relatively higher micro-nutrient levels compared with S1, S2, S3, and S5. These results suggest that residue retention in the cropping systems contributes to improved soil micro-nutrient levels, particularly in the case of S6 and SS4.

The basal soil respiration varied from 16.2 to 23.2 μg g^-1 soil 24 h^-1. The maximum soil respiration was recorded under S6 (22.8 and 23.2 μg g^-1 soil 24 h^-1), followed by S4 and S2 (18.6 –19.1 μg g^-1 soil 24 h^-1), whereas minimum soil respiration was recorded under S1 and S3 (16.8 and 17.0 μg g^-1 soil 24 h^-1). The sequence of soil respiration of the different scenarios was S6 > S4 > S2, and the lowest was recorded in S1 (16.2 and 16.4 μg g^-1 soil 24 h^-1) and the highest was in S6 ( Table 8 ).

Pearson’s correlation analysis was employed to assess the influence of CA-based cropping system on the properties of soil in reference with post-harvest soil fertility ( Figure 3 ). A highly significant and positive relationship (R ² = 0.79) was found between soil pH and EC, whereas the EC of soil showed a high correlation with DTPA Cu (R ² = 0.64). Highly significant and maximum values of R ² were found for APA and DTPA Mn (0.99). Additionally, DTPA Zn, Fe, and Cu micro-nutrients were found to be more correlated with DHA activity in the soil.

The principal component analysis (PCA) reduced the six experimental treatments into two independent components (eigenvalues > 1), which together accounted for 92.8% of the total variation among the variables. The first principal component (PC1) explained 80.0% of the variation, with the highest significant contribution from DTPA-extractable Zn. The second principal component (PC2) accounted for 12.8% of the variation and had the greatest loadings for soil pH. A third component (PC3), contributing 3.43% of the variation, showed significant negative loadings for total microbial biomass carbon. The 3D PCA graphs for macro-nutrients, micro-nutrients, and biological properties depicted the relative positions of observations corresponding to each treatment, highlighting the interactions between the two main components ( Figure 4 ).

The bulk density decreased as a result of residue from crop retention with zero tillage and permanent bed; this effect was most apparent in the top soil layer. Among the scenarios studied, the combination of PTR followed by zero-till wheat, permanent bed maize, and permanent bed soybean (S2, S4, and S6) caused the bulk density of the soil to drop. Notably, the double legume-based cropping system with residue retention (S6) exhibited the lowest soil bulk density (Gogoi et al., 2018; Raj et al., 2023). This could be attributed to higher presence of organic C on the soil surface, resulting in improved soil aggregates and creation of more pore space, consequently leading to a lower bulk density in scenarios where crop residues were retained (Lynch et al., 2022; Musto et al., 2023; Yadvinder-Singh et al., 2022). As is frequently documented in the IGP region, the layering of various management approaches had a minor effect on soil bulk density, suggesting the presence of subsurface compaction in the rice–wheat–mungbean, maize–wheat–mungbean, and soybean–wheat–mungbean cropping systems (Chandra, 2011; Govaerts et al., 2006; Roldan et al., 2005).

Agronomic practices such as tillage, crop residue management, and changes in cropping systems can significantly impact soil infiltration rates (Dev et al., 2023; Indoria et al., 2020). Our study focused on intensifying cropping systems with mungbean under CA (S2, S4, and S6), which resulted in a substantial improvement in infiltration rates compared with CT. The higher infiltration observed in CA treatments can be attributed to three main factors. Firstly, the retention of crop residue protects the soil from the impact of raindrops, preventing displacement of surface aggregates and clogging of large pores (Fernández et al., 2017; Gómez-Paccard et al., 2015). Secondly, CA practices promote larger and more continuous pores through increased biological activity, creating root channels and macropore networks (de Moraes et al., 2016; Patra et al., 2023). Lastly, CA practices facilitate the accumulation of soil organic matter, contributing to macropore formation (Bhattacharyya et al., 2008; Gathala et al., 2011; Jat et al., 2013; Kahlon et al., 2013; Kumar et al., 2022). Similar results were found in northwest India, where CA-based systems like maize–wheat–mungbean and rice–wheat–mungbean demonstrated better infiltration rate and cumulative infiltration compared with the rice–wheat system under CT (Jat et al., 2018; Singh et al., 2014).

After 2 years of field study assessment, SOC was significantly higher by 26%–31% under zero tillage and permanent bed CA treatments including S2, S4, and S6 because C tends to accumulate in less disturbed soils (Francaviglia et al., 2023; He et al., 2023). In Sc6, the SOC content increased by 31.6% due to retention of crop residue of zero cycle crops (wheat and mungbean), year-round soil cover with zero tillage, preventing direct sunlight exposure and oxidation of organic matter. Compared to rice residue retention (S2), the inclusion of double legume crops in S6 reduced the C/N ratio, leading to increased SOC content. In S2, the high rice residue load (14.2 t ha^-1) and higher C/N ratio along with high silica and lignin content resulted in slower mineralization and subsequently low SOC (Choudhary et al., 2018; Das et al., 2013; Naorem et al., 2023; Qi et al., 2023; Balota et al., 2004). The decomposition of crop residues releases different organic compounds and increases microbial activity as binding agents, which cements the smaller particles into larger macro-aggregates (Sharma et al., 2025).

The incorporation of leguminous green manure has been reported to favor the net C buildup in soil, which is considered as an important indicator of C sequestration in soil (Six and Paustian, 2014; Sharma et al., 2019). The inclusion of legumes in rice–wheat systems releases root exudates, improves the soil biological activity, and thereby causes a higher C concentration in soil macro-aggregates (Sharma et al., 2021). Changes in SOC associated with different tillage practices can significantly affect the N content. Conventional tillage often leads to greater N losses due to repeated soil disturbance, enhanced leaching, and increased mineralization (Lal, 1997; Cui et al., 2023). The increase in SOC in the absence of tillage might be due to the deep penetration of wheat roots and the reduced oxidation of in situ organic matter (Modak et al., 2020). Crop residue retention or assimilation improved the productivity of intensified irrigated agriculture systems and increased the organic C (Modak et al., 2020; Sarkar and Kar, 2006). Reduced tillage and residue retention slow down the pace at which soil organic matter (SOM) breaks down, which causes SOC to rise over time (Gwenzi et al., 2009). It has been revealed that labile C derived from crop residues is first incorporated into labile C pools and subsequently accumulates and becomes stable or recalcitrant C in soils. Furthermore, the rice residue mulch may have improved the soil structure by protecting SOM through aggregation against microbial degradation and the reduced rate of SOC decomposition (Diekow et al., 2005; Gong et al., 2009). Moreover, an increase in SOC fractions with CA-based cropping systems caused slower SOC decomposition compared with CT and residue removal because of the reduction in soil disturbance and protection within aggregates and changes in the soil microbial environment under various tillage practices (Salve et al., 2012; Chen et al., 2009). In addition a large amount of rice residue addition provided C source which, upon decomposition, ultimately became part of SOC. These findings reinforce that changes in soil organic carbon (SOC) following tillage are largely attributed to active carbon pools, owing to their high turnover and sensitivity to disturbance (Culman et al., 2010). Additionally, the significant increase in SOC under scenario S6 might be due to the increase in annual C input and variations in organic matter quality, thus modifying the liability of C to change to an oxidized form (Ladha et al., 2004, 2003).

The fertility of the soil and nutrient preservation are enhanced by conservation agriculture techniques like permanent bed (S4 and S6) and zero tillage (Anil et al., 2022; Chaudhary et al., 2019; Parihar et al., 2018; Palm et al., 2014). Zero tillage systems (S2, S4, and S6) slow down soil organic matter mineralization, increase soil N reserves, and enhance microbial activity compared with conventional tillage (S1, S3, and S5) (Acosta-Martínez et al., 2004; Thapa et al., 2023; Pisante et al., 2015). Double legume cropping systems (S6) contribute to a higher mineral N content due to the chelation of inorganic P and increased SOM (Jangir et al., 2021; Kumar et al., 2023; Mutuku et al., 2020). The higher N and phosphorus (P) availability is typically observed in the surface layers of soil under zero and minimum tillage systems than CT. The accumulation of available P is primarily due to its limited mobility within the soil profile, as previously documented (Nze Memiaghe et al., 2022). Similarly, the increased availability of available K and available P under conservation and organic management practices may be attributed to the reduced fixation and enhanced solubilization of fixed forms. This is often facilitated by the presence of organic acids and the mineralization of added organic manures, as reported in earlier studies (Elayarajan et al., 2015; Meena et al., 2019; Mahanta and Rai, 2008 et al., 2010). Moreover, CA-based practices also enhance the available K content by chelating nutrients with organic matter, resulting in higher soil nutrient availability (Jat et al., 2021; Lv et al., 2023).

In comparison with conventional tillage scenario (S1), the conservation agriculture scenario (S6) showed higher levels of DTPA Zn, Fe, Mn, and Cu by 10.5%, 39.5%, 8.3%, and 62.8%, respectively. The retention of crop residues on the soil surface in CA positively influences micro-nutrient availability, likely due to the mixing of previous crop residues (Mhlanga et al., 2022; Yadav et al., 2022), which increases the presence of labile C after the decomposition of residues from previous years. In order to preserve the availability of micro-nutrients in the soils of this area, conservation tillage in conjunction with organic nutrient management may be a viable strategy. Nevertheless, their phyto-availability in soils may be reduced in the future due to their removal from crop biomass with continued cropping. Thus, regular soil testing may aid in determining their depletion in soil so that suitable corrective action can be taken (Khoshgoftarmanesh et al., 2010). In a similar vein, Jayaraman et al. (2021) found that, in Central Indian vertisols, the available Fe content was comparatively greater under no-till scenarios than conventional tillage. Under conservation agriculture, the decomposition of fresh crop residues releases organic tissue-bound micro-nutrients and natural chelating agents like citric acid and humic acids, thereby enhancing micro-nutrient availability in the soil (Chaudhary et al., 2019). The lowest micro-nutrient levels were observed in the conventional tillage scenario (S1). Similar positive effects of conservation agriculture on micro-nutrient content have been reported by other researchers as well (Chaudhary et al., 2019; Das et al., 2018; Kharia et al., 2017; Yadav S. L. et al., 2022; Yadav M. et al., 2022).

In the conservation agriculture scenario (S6), the total microbial population was 233% and 232% higher than CT (S1) in the first and second year, respectively, which is likely due to increased organic C addition and availability of food sources for microbial growth (Gupta et al., 2020; Yadav et al., 2023). Furthermore, in S6, a double legume crop was included in the cropping system, which resulted in the deposition of root exudates in soil, serving as a nutrient source for soil microbes (Hazra et al., 2020b). Conservation tillage techniques enhance fungal and bacterial populations, while the preservation of crop residues further stimulates microbial activity (Kumawat et al., 2022). The presence of cover crop residues as substrate enhances microbial diversity, C content, mineralization rate, soil respiration, and enzyme secretion (Choudhary et al., 2018; Dasila et al., 2023; Ghimire et al., 2014; Helgason et al., 2009). Additionally, minimum soil disturbance in conservation-based practices provides a suitable environment for microbes by moderating soil moisture and temperature than the CT practices (Choudhary et al., 2018; Saikia et al., 2019). An additional benefit for improved microbial growth was the retention of crop residue and the addition of organic manure and mineral fertilizers, which sped up nutrient mineralization and improved nutrient availability (Kiboi et al., 2021). The continued retention of crop waste above the soil surface may be caused by this impact since it increases the accessibility of labile carbon created by the breakdown of residues during the previous year (Piper, 2019). A high content of SOC is beneficial to the growth of microorganisms with active metabolic processes, which, in turn, leads to the accumulation of soil enzymes. In the present study, the increased availability of substrate (green manure and crop residues) and a favorable habitat for microbial communities seem to be responsible for higher enzyme activities. Furthermore, DHA and APA activities were linked to increased microbial activity, especially microbial biomass carbon and microbial biomass nitrogen through the release of organic compounds, contributing to a positive rhizosphere effect (Dasila et al., 2023; Roldán et al., 2005). In addition, DHA activity increased under CA-based cropping system, which supplied continuous substrates for microbial proliferation, along with improved SOC, which increased adsorption sites and supplied energy to micro-organisms throughout the decomposition process to improve enzyme activity (Sharma et al., 2025). Under CA-based practices, the increase in bacterial and fungal population was due to minimum soil disturbance, which plays a major role in the initial phases of decomposition of organic C compounds and degrades cellulolytic material through ligno-cellulytic enzyme activity (Sharma and Singh, 2023).

The majority of the calculated variables were maximum with scenario S6, followed closely by S4 ( Figure 4 ). The main influential variables for PC1 and PC2 were different available nitrogen, available K, available phosphorus, copper, manganese, zinc, iron, total microbial count, DHA enzyme, APA, and BSR with scenario S6, followed by S4 and S2 ( Figure 4 ). The clustering of SOC with microbial activity under conservation agriculture (CA) creates synergistic effects that enhance both climate adaptation through improved water retention and soil structure stability and mitigation through increased carbon sequestration rates (Powlson et al., 2014). This implies that the continuous addition of C sources by previous crop residues raised more soil C pools and hydrolytic enzymatic activities, along with the activity of microbes, the accessibility of different communities of microbes in the soil, the availability of nutrients, and rhizodeposition. The majority of variables under study—microbial biomass, C respiration, and basal soil respiration—were more strongly conjugated in organically managed soils than in inorganically managed soils (Araújo et al., 2008).