Soil quality indices and critical limits of indicators under long-term rice-based agroecosystems in subtropical India

1 Introduction

Soil quality refers to the ability of soil to perform its function and to provide ecosystem services that support productivity, sustainability, and environmental safety (1). However, soil quality in intensively cultivated regions globally has declined continuously as a result of declining soil organic carbon levels, soil erosion, improper and unbalanced nutrient application, reduced soil biodiversity, and soil compaction (2), which are the most significant threats to agricultural sustainability (3). Soil quality degradation leads to declines in ecosystem services and soil functions. Assessing soil quality is therefore essential for sustainable agriculture development.

Rice-based agroecosystems dominate the subtropical regions of South Asia (4); however, their productivity has shown signs of stagnation or decline in recent years mainly due to continuous alternation between anaerobic and aerobic soil conditions, which is considered a primary driver of changes in the physico-chemical and biological characteristics of soils (5, 6). Puddling and beushening are commonly practiced by farmers to optimize the production of kharif rice. Continuous puddling and beushening under wet soil conditions progressively degrade soil quality by damaging soil structure and reducing its stability (7–10). Continuous flooding and inadequate drainage conditions adversely affect nutrient use efficiency, contribute to the deterioration of soil fertility (11), and alter soil biodiversity (12). After harvesting kharif rice, many farmers burn crop residues in the field, resulting in poor microbial biomass accumulation, a decline in organic carbon (OC) (13, 14), and nutrient imbalances arising from deficiencies of nitrogen, phosphorus, boron, and zinc (7, 9). All these soil conditions reduce the yield of subsequent crops. Following rice, farmers commonly grow wheat, chickpea, or lathyrus or leave fields fallow. For the cultivation of wheat, intensive ploughing is practiced. However, unreliable water resources and injudicious and imbalanced use of major nutrients are responsible for poor wheat productivity. Many farmers leave fields fallow after rice due to a lack of resources and irrigation facilities (14). Lathyrus is grown in a paira (relay) cropping system using residual soil moisture and nutrients; therefore, its productivity is very low. These conditions can adversely affect the chemical, physical, and biological characteristics of the soils, ultimately leading to a decline in overall soil quality in the region (9, 14).

Direct measurement of soil quality is often difficult (15) due to its complex and dynamic nature. Therefore, soil quality is assessed through various measurable indicators that reflect essential soil functions. Identifying the most responsive soil attributes is essential for selecting minimum data set (MDS) indicators, which can support the formulation of effective management strategies for sustaining soil quality. The integration of MDS indicators enables the development of a soil quality index (SQI). Several approaches have been proposed for evaluating soil quality; however, the SQI derived from principal component analysis (PCA) is currently the most widely adopted method (16–19), due to its quantitative reliability, flexibility, and simplicity in interpretation and application (20).

India has a variety of soils, each with its own potential and limitations, of which Inceptisols are the dominant soil order, while Vertisols are the fourth major soil order in India (21). In our previous study (9), MDS indicators were developed by combining samples from both soil types within the same study area and agroecosystems. However, different management practices significantly influence the ecosystem services of these soils, leading to differences in soil quality between soil orders. Consequently, a common set of MDS indicators may not adequately represent the functional variability among different soil types. Hence, the present investigation aims to develop separate MDS indicators for Vertisols and Inceptisols under identical rice-based agroecosystems using the same sampling framework. As an extension of our previous study, the present investigation also aims to establish separate SQI and critical limits of indicators for both soil types.

Soil quality and sustainable agricultural productivity in tropical India are challenged by several interconnected biophysical and socioeconomic factors, including inadequate irrigation facilities, ecosystem degradation, and limited access to resources for sustainable soil management. These challenges, together with food insecurity and rural socioeconomic vulnerability, pose significant constraints to maintaining long-term agricultural productivity and meeting the food demands of an increasing global population. Under such circumstances, soil quality may not be enhanced but must be sustained against degradation while meeting production targets. Although several studies have evaluated soil quality in non-rice cropping systems and for single soil orders (17, 19, 22–25), research focused specifically on developing separate MDS indicators for different soils, establishing soil-order-specific critical limits, and evaluating them under long-term farmer-managed field conditions remains limited both globally and in India (15, 18, 26, 27). Moreover, the majority of existing studies have been conducted under experimental conditions, which often fail to capture the variability and management practices in farmers’ fields. Consequently, there is a lack of field-based assessments under real farming conditions (28), particularly in dominant rice-based agroecosystems across major soil types in subtropical regions of India. The novelty of this study lies in the development of soil-order-specific MDS for Vertisols and Inceptisols. Unlike generalized soil quality assessment approaches, this study establishes separate critical limit values for key soil quality indicators for each soil order, enabling more accurate and region-specific evaluation of soil health. Furthermore, the study is based on long-term farmer-managed rice-based cropping systems, ensuring that the findings reflect realistic field conditions and management practices. This integrated approach provides a robust framework for soil-quality assessment and supports site-specific soil management strategies for sustainable crop production.

Our hypothesis was that different soil orders respond differently to long-term rice-based management, resulting in different MDS indicators and different critical limits for both soil types. Therefore, the present study was undertaken to assess soil quality and to derive the most sensitive physical, chemical, and biological soil properties for inclusion in an MDS of soil quality indicators. This study also aimed to define critical limits for SQI and MDS indicators for long-term rice-based agroecosystems of Vertisols and Inceptisols under real farmers’ field conditions in subtropical India. The relationship between SQI and system yield was also examined. The findings on soil quality evaluation and the identification of MDS indicators across the two soil orders may provide useful insights for similar soils and agro-climatic zones in other parts of the world.

2 Materials and methods

2.1 Site descriptions

The study was carried out in Balod District, located in the state of Chhattisgarh, India, covering an area of approximately 3,572 km². Geographically, the district extends from 20°24′ to 21°03′ N latitude and from 80°47′ to 81°31′ E longitude, with an average elevation of about 324 m above mean sea level. Agriculture is the predominant land use, accounting for nearly 50% of the total geographical area. The district falls within the Eastern Plateau agro-climatic zone of India. Agricultural activities in the region are largely rainfed, with an average annual rainfall of around 1,200 mm. The southwest monsoon season, extending from mid-June to September, contributes approximately 88% of the annual precipitation, while the remaining 12% is received during the winter period from December to February. The area is characterized by an ustic soil moisture regime and a hyperthermic soil temperature regime. Summer temperatures generally range from 30 °C to 42 °C, whereas winter temperatures vary between 0 °C and 25 °C. The soils of the region are predominantly classified as Typic Haplusterts and Typic Haplustepts. The soils are mostly deep, loamy, non-calcareous, and neutral to slightly acidic in reaction. The growing period in the region extends from about 150 to 180 days (29).

2.2 Soil survey and sampling

A survey conducted in Balod district identified two dominant soil orders: Vertisols with a clay loam texture and Inceptisols with a sandy clay loam texture (30). Under farmers’ field conditions, four important rice-based agroecosystems were selected for detailed investigation: rice-wheat (RW), rice-chickpea (RC), rice-lathyrus (RL), and rice-fallow (RF). Rice is cultivated during the autumn (kharif) season, from July to November, which largely depends on the southwest monsoon with direct-seeded rice (DSR) method being predominantly practiced. Transplanting is practiced in a small portion of the district where water resources are available before the onset of the monsoon. The subsequent crops in the winter (rabi) season (November to February) are wheat, chickpea, and lathyrus. Medium-duration rice varieties (120–125 days) are commonly cultivated under continuously flooded conditions throughout the crop growth period. Owing to the favorable rainfall-driven ecosystem, kharif rice achieves relatively high productivity (5.5 Mg ha^-1). In comparison, rabi crops exhibit relatively lower productivity, with average yields of 3.2 Mg ha^-1 for wheat, 1.0 Mg ha^-1 for chickpea, and 5.5 q ha^-1 for lathyrus. On average, farmers use inorganic fertilizers as recommended for the district by Indira Gandhi Krishi Vishwavidyalaya, Raipur (31). Most farmers remove crop residues from the field and use them mostly for cattle feed. Some farmers practice in situ burning of crop residues in the field after harvest.

Stratified random sampling was adopted to ensure adequate representation of different cropping systems and soil types across the study area. A total of 10% of the district’s villages were selected to capture spatial variability. Within the selected villages, sampling was carried out using a factorial design based on two factors: (i) cropping system and (ii) soil type. The selected fields had been under the respective cropping systems continuously since 2000. It was also made sure that uniform nutrient, irrigation, and residue management practices were followed by the farmers of the sampled fields. The fields used the DSR system for rice, received recommended doses of fertilizers, were rainfed with limited irrigation during the rabi season, and had crop residues removed; these are the most common agronomic practices in the district. After harvest of the cropping system, at each selected site, surface soil samples collected from different spots were thoroughly mixed together to prepare a composite sample (Figure 1). Since the effective root zone of the crops is confined to a soil depth of 0–15 cm, soil data from the surface layer were considered for SQI development. Altogether, 140 geo-tagged soil samples were collected 5–7 days after crop harvest during March–April 2017. A total of four rice-based cropping systems were identified in Vertisols, from which 80 soil samples were obtained, comprising 20 samples from each system. Similarly, in Inceptisols, three cropping systems were considered and 60 samples were collected, comprising 20 samples from each system.

2.3 Soil analysis

After collection from the study area, a subsample of each soil sample was stored at 5 °C under refrigerated conditions for the analysis of biological properties. The second portion of each soil sample was air-dried in the shade, gently processed, and sieved through a 2 mm mesh before soil chemical analysis. The remaining portion of the soil sample was used for the determination of soil physical properties. All analyses were conducted in accordance with standard protocols.

Soil texture was determined using the international pipette method (32). Bulk density (BD) and particle density (PD) were measured according to Method No. 39 described in USDA Handbook No. 60 (33), and these measurements were subsequently used to calculate soil porosity. Water-holding capacity was assessed using the Keen–Raczkowski box technique (32), whereas saturated hydraulic conductivity (HC) was estimated using the constant-head method (34). Aggregate stability, expressed as mean weight diameter (MWD), was evaluated using Yoder’s modified wet-sieving procedure (35). Soil moisture retention at −33 kPa (field capacity, FC) and −1500 kPa (wilting point, WP) was determined with a pressure plate apparatus (36).

For chemical analyses, soil pH and electrical conductivity (EC) were measured in a 1:2.5 soil-to-water suspension following established protocols (33). Soil organic carbon was determined by the Walkley and Black wet oxidation method using potassium dichromate in the presence of sulfuric acid, followed by back-titration with ferrous ammonium sulfate (37), while cation exchange capacity (CEC) was determined using the neutral 1 N ammonium acetate (NH₄OAc, pH 7.0) saturation method, followed by estimation of the displaced ammonium (33). Available nitrogen (AN) was determined by the alkaline potassium permanganate method, in which soil was distilled with alkaline KMnO₄ and the released ammonia was absorbed in boric acid and titrated with standard sulfuric acid (38). Available phosphorus (AP) was extracted using 0.5 M sodium bicarbonate (NaHCO₃, pH 8.5) by the Olsen method, and the phosphorus concentration in the extract was determined using a spectrophotometer (39). Available potassium (AK) was extracted with neutral 1 N ammonium acetate (NH₄OAc, pH 7.0), and the potassium concentration in the extract was determined using a flame photometer (40). Available sulfur (AS) was extracted with 0.15% CaCl₂, and sulfate-sulfur in the extract was determined turbidimetrically by measuring the barium sulfate suspension using a spectrophotometer (41).

Micronutrients including iron (Fe), manganese (Mn), copper (Cu), and zinc (Zn) were extracted with a diethylenetriaminepentaacetic acid (DTPA)–CaCl₂ solution at pH 7.3 (42) and quantified using an atomic absorption spectrophotometer. Available boron (B) was estimated using the hot water-soluble boron method (43). Biological properties of the soil, such as microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) (44), potentially mineralizable carbon and nitrogen (45), dehydrogenase activity (46), acid and alkaline phosphatase activities (47), and urease activity (48) were also evaluated.

2.4 Sample sufficiency test

The suitability of the dataset (Table 1) for principal component analysis (PCA) was examined using the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy (49). This test evaluates whether the variables included are appropriate for PCA. According to Kaiser’s guidelines, a KMO value above 0.5 is considered acceptable; values below this threshold suggest the need for additional data collection or reconsideration of variable selection. More specifically, values from 0.5 to 0.7 are considered average, values from 0.7 to 0.8 indicate good adequacy, values from 0.8 to 0.9 indicate very good adequacy, and values exceeding 0.9 are considered excellent. In this study, KMO values varied from 0.80 to 0.90, placing them in the very good category. Hence, the sample size was sufficient to proceed with PCA.

Table 1. Sample sufficiency test for Vertisols and Inceptisols.

2.5 Selection of MDS indicators and evaluation of soil quality

A standard principal component analysis was performed on 32 soil physical, chemical, and biological attributes using IBM SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, USA). Before analysis, all variables were standardized through centering and scaling procedures to ensure a mean of zero and a variance of one. The correlation matrix was subsequently used for eigenvalue decomposition. Although normality is not a prerequisite for PCA, the distribution of each variable was assessed using the Shapiro–Wilk test (50). The test results revealed no significant deviation from normality (p > 0.05).

Principal components (PCs) with higher eigenvalues were considered more effective in explaining the variability within the dataset. Therefore, only PCs with eigenvalues ≥1 were retained for identifying indicators for the minimum data set (MDS) (51). To improve interpretability, varimax rotation was applied to maximize the relationships between soil properties and the extracted PCs. Variables exhibiting high factor loadings within each PC were selected as potential MDS indicators. In cases where multiple variables within the same PC showed high loadings, a multiple correlation analysis was conducted to eliminate redundancy among the selected variables. If these highly weighted variables were not significantly correlated, all were retained as MDS indicators. Conversely, when significant correlations existed, only the variable with the highest factor loading was selected to represent that PC.

The selected MDS indicators were subsequently transformed into standardized scores using a linear scoring approach. To facilitate comparison, the indicators were normalized to dimensionless values ranging from 0 to 1. Depending on their contribution to soil functioning, indicators were scored according to either a “more is better” or a “less is better” criterion. Under the “more is better” approach, individual observations were divided by the maximum observed value, assigning the highest value a score of 1. Under the “less is better” approach, the minimum observed value was divided by each individual observation, thereby assigning the lowest value a score of 1.

In the present study, dehydrogenase activity (DHA), mean weight diameter (MWD), organic carbon (OC), available phosphorus (AP), zinc (Zn), cation exchange capacity (CEC), alkaline phosphatase activity (AlPA), and hydraulic conductivity (HC) were evaluated using the “more is better” scoring method, whereas bulk density (BD) was assessed using the “less is better” criterion.

Following the scoring process, the selected MDS indicators were integrated to calculate the soil quality index (SQI) using the weighted additive soil quality index (WTD-SQI) approach. Higher SQI values indicated better overall soil quality. The scores assigned to each MDS indicator were weighted based on PCA-derived weighting factors. The weight of each PC was calculated by dividing the percentage variance explained by that component by the cumulative variance explained by all retained PCs (eigenvalue ≥1). Finally, the overall SQI was determined by summing the weighted scores of all selected MDS indicators according to the following Equation 1:

(1) SQI =∑i=1nwisi

Si represents the score of the i^th indicator; Wi denotes the weighting factor assigned to the corresponding PC.

2.6 Establishment of critical limits

The critical limits of MDS indicators represent the threshold ranges necessary to ensure optimal soil functioning and sustain soil health for long-term crop productivity. The relative yield (RY) of rice was computed by dividing the rice equivalent yield (REY) of an individual site by the maximum REY obtained under optimum input conditions and multiplying the result by 100. To establish these critical limits, regression analyses were conducted between the RY and the values of individual MDS indicators. Within the regression equation, RY values of 40% and 80% were selected to identify the lower and upper critical limits of the respective indicators (52). The values of MDS indicators associated with RY ≥80% were considered adequate because this level is assumed to correspond to maximum economic efficiency of production (52), consistent with critical-limit concepts for soil nutrients proposed by Cate and Nelson (53). Indicator values corresponding to RY between 41% and 79% were classified as moderate, whereas those associated with RY ≤40% were classified as low (52).

2.7 Rice equivalent yield and relative yield of rice

For each experimental site, the mean yield of rice, wheat, chickpea, and lathyrus was computed using data collected over a 20-year period under farmers’ field conditions. The REY was computed using the following (Equation 2):

(2) REY (Mg ha−1)=Yrc×PrcPr

where Yrc represents the companion crop yield (Mg ha^-1), Prc denotes the market price of the companion crop (USD kg^-1), and Pr refers to the market price of rice (USD kg^-1). Wheat, chickpea, and lathyrus were used as companion crops for calculating the REY of RW, RC, and RL, respectively. The minimum support price of each crop for the year 2025–2026 (54) was used to calculate REY.

The RY (Equation 3) of rice was calculated as 100 times the ratio of the REY at a given site to the maximum REY obtained under adequate but non-excessive input conditions.

(3) RY (%)=REY of particular site×100Maximum REY attainable

2.8 Statistical analysis

Differences in soil physical, chemical, and biological attributes and SQI across various agroecosystems were assessed using analysis of variance (ANOVA), followed by Tukey’s honestly significant difference (HSD) test at a significance level of p = 0.05 (55), using R software (56). Before PCA, dataset sufficiency was evaluated using the KMO test (49). Before PCA, data normality was evaluated using the Shapiro–Wilk test (50), while homogeneity of variance was verified using Bartlett’s test (57). Multivariate analysis was carried out using IBM SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, USA) to identify the PCs. Pearson correlation analysis was performed between different soil attributes and SQI for both soil types. The linear regression analysis was conducted between SQI and REY of rice-based agroecosystems to validate SQI. Simple regression analysis was employed to establish the critical limits of MDS indicators and the SQI by relating them to RY. Data visualization and graphical analyses were carried out using R (56) and Origin software (OriginLab Corporation, Northampton, MA, USA).

3 Results

3.1 Soil physical attributes

Particle size distribution exhibited significant variation under different rice-based agroecosystems in both soil types (Table 2). In Vertisols, sand fraction was significantly higher under rice-fallow (RF) (167.6 g kg^-1), followed by rice-wheat (RW) (157.0 g kg^-1), rice-chickpea (RC) (155.5 g kg^-1), and rice-lathyrus (RL) (155.1 g kg^-1). Silt content was highest under RF (273.7 g kg^-1), at par with RC and RL, and lowest under RW (243.2 g kg^-1). Conversely, clay content was maximum under RW (599.9 g kg^-1) and minimum under RF (558.7 g kg^-1). Bulk density (BD) was maximum under RW (1.40 Mg m^-3) and minimum under RC (1.35 Mg m^-3). In contrast, particle density (PD) did not differ significantly across cropping systems. Total porosity showed a reverse trend to BD and was significantly higher under RC (48.91%) and lowest under RW (47.66%).

Table 2. Soil physical attributes for different rice-based agroecosystems in Vertisols and Inceptisols.

1. For each soil type (Vertisols and Inceptisols), mean values within a row carrying different lowercase letters indicate significant differences among cropping systems based on Tukey’s multiple comparison test at p ≤ 0.05; 2. n denotes the number of soil samples analyzed; 3. BD, bulk density; PD, particle density; FC, field capacity; WP, wilting point; WHC, water-holding capacity; HC, hydraulic conductivity; MWD, mean weight diameter.

Field capacity (FC) and wilting point (WP) moisture contents were highest under the RC system (32.43% and 21.45%, respectively), statistically at par with the RF system, while the lowest values were recorded under the RW system (27.74% and 16.54%, respectively). Similarly, the water holding capacity (WHC) was highest in RC (51.86%) and lowest in RW (44.86%). Hydraulic conductivity (HC) exhibited trends parallel to WHC of soils. The maximum and minimum mean weight diameter (MWD) values were recorded under RC (0.86 mm) and RW (0.71 mm) systems, respectively.

In Inceptisols (Table 2), sand fraction was highest under the RW (585.5 g kg^-1) and lowest under the RF (497.5 g kg^-1). Conversely, silt and clay contents were highest under RL (215.7 and 286.9 g kg^-1) and lowest under RW (137 and 277.3 g kg^-1). The RW system recorded the highest BD (1.41 Mg m^-3), whereas the lowest was observed under the RL system (1.37 Mg m^-3). Total porosity showed the opposite trend, being highest in RL (47.95%) and lowest in RW (47.05%). Soil moisture contents at FC and WP were highest under RL (28.82% and 24.53%, respectively) and lowest under RW (24.53% and 14.25%, respectively). Similarly, WHC was significantly higher under RL (34.25%), followed by RF (31.43%), and lowest under RW (26.61%). The soil HC was highest under RL (0.98 cm h^-1) and lowest under RW (0.91 cm h^-1), respectively. Soil aggregation, expressed as MWD, was highest under RL (0.77 mm) and lowest under RF (0.70 mm).

3.2 Soil chemical attributes

In Vertisols, soil pH ranged from neutral to slightly alkaline, with the lowest value under RC (7.12), at par with RL and RF, and the highest under RW (7.56) (Table 3). Soil electrical conductivity (EC) remained within safe limits. OC levels ranged from low to medium across agroecosystems, with the highest value under RC (5.81 g kg^-1) and the lowest under RW (4.68 g kg^-1).

Table 3. Soil chemical attributes for different rice-based agroecosystems in Vertisols and Inceptisols.

1. For each soil type (Vertisols and Inceptisols), mean values within a row carrying different lowercase letters indicate significant differences among cropping systems based on Tukey’s multiple comparison test at p ≤ 0.05; 2. n denotes the number of soil samples analyzed; 3. EC, electrical conductivity; OC, organic carbon; CEC, cation exchange capacity; AN, available nitrogen; AP, available phosphorus; AK, available potassium; AS, available sulfur; Fe, iron; Mn, manganese; Cu, copper; Zn, zinc; B, boron.

The highest CEC was observed in RC [50.03 cmol (p+) kg^-1], followed by RL and RF, whereas the lowest value was recorded under RW [45.22 cmol (p+) kg^-1]. Available nitrogen (AN) status in the soil was low, with the highest value recorded in RC (246.95 kg ha^-1) and the lowest in RW (227.65 kg ha^-1). Soil available phosphorus (AP) remained at a medium level, with the highest value recorded under RC (18.94 kg ha^-1) and the lowest under RW (15.07 kg ha^-1). Soil available K (AK) was in the high category, with the highest value under RC (439.90 kg ha^-1), at par with RL and RF, and the lowest under RW (412.65 kg ha^-1). Available sulfur (AS) status in the soil was low, with the highest value recorded under RC (17.56 kg ha^-1), while the lowest was recorded under RW (14.87 kg ha^-1). Soil micronutrients (Fe, Mn, Cu, Zn, and B) were present at adequate levels, with the highest levels recorded under RC and the lowest under the RF system.

In Inceptisols, soil pH was acidic, with the lowest value under RL (5.94) and the highest under RW (6.20) (Table 3). Soil EC remained within safe limits. Soil OC was classified as low across all systems; however, RL recorded the highest OC (4.89 g kg^-1), followed by RF, whereas the lowest was recorded under RW (3.79 g kg^-1). Similarly, CEC was significantly higher in RL [34.97 cmol (p⁺) kg^-1], followed by RF, while the lowest value was recorded in RW [28.35 cmol (p⁺) kg^-1]. AN was classified as low; however, it was highest under RL (232.90 kg ha^-1) and lowest under RF (210.50 kg ha^-1). Soil AP was at a medium level, with the highest value recorded in RL (17.02 kg ha^-1) and the lowest in RW (12.51 kg ha^-1). Similarly, soil AK was in the medium-to-high range, with the highest value under RL (352.10 kg ha^-1) and the lowest under RW (298.90 kg ha^-1). Soil AS remained low across all systems, following a pattern similar to that observed in Vertisols, with the highest value under RL (14.32 kg ha^-1) and the lowest under RW (11.74 kg ha^-1). Adequate levels of soil micronutrients (Fe, Mn, Cu, Zn, and B) were observed across systems; however, they varied significantly, with the highest and lowest levels recorded under the RL and RW systems, respectively.

3.3 Soil biological attributes

Soil biological attributes varied significantly among agroecosystems in both soil types. In Vertisols, the highest microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) were recorded under RC (816.94 and 237.61 ppm), at par with RL and RF. The lowest MBC and MBN values were observed under the RW system (787.83 and 219.21 ppm, respectively) (Table 4). Similarly, the RC system recorded the highest potentially mineralizable carbon (PMC) and potentially mineralizable nitrogen (PMN) values (437.66 and 192.89 ppm, respectively), at par with RL and RF. The lowest PMC and PMN values were recorded under the RW system (407.98 and 173.00 ppm, respectively).

Table 4. Soil biological attributes for rice-based agroecosystems in Vertisols and Inceptisols.

1. For each soil type (Vertisols and Inceptisols), mean values within a row carrying different lowercase letters indicate significant differences among cropping systems based on Tukey’s multiple comparison test at p ≤ 0.05; 2. n denotes the number of soil samples analyzed; 3. MBC, microbial biomass carbon; MBN, microbial biomass nitrogen; PMC, potentially mineralizable carbon; PMN, potentially mineralizable nitrogen; APA, acid phosphatase activity; AlPA, alkaline phosphatase activity; UA, urease activity; DHA, dehydrogenase activity; TPF, triphenyl formazan.

Further, the RC system recorded the highest acid phosphatase (APA) and alkaline phosphatase (AlPA) activities (91.82 and 303.67 µg p-nitrophenol g^-1 d^-1, respectively), whereas the lowest values were observed under RW (86.73 and 277.68 µg p-nitrophenol g^-1 d^-1, respectively). Similarly, soil urease activity (UA) and dehydrogenase activity (DHA) were highest under RC and lowest under RW.

The highest soil MBC and MBN values in Inceptisols were recorded under RL (783.42 and 193.68 ppm, respectively), whereas RW exhibited the lowest values (742.23 and 176.79 ppm, respectively) (Table 4). Soil PMC and PMN values were highest under RL (422.76 and 167.64 ppm, respectively) and lowest under RW (399.60 and 142.49 ppm, respectively). The RL system recorded the highest APA and AlPA activities, whereas the lowest activities were observed under RW; APA values were 79.38 and 74.21 µg p-nitrophenol g-1 d-1 under RL and RW, respectively, and AlPA values were 267.23 and 231.39 µg p-nitrophenol g^-1 d^{-1 under RL and RW,}respectively. ^Soil UA and DHA were highest under RL and lowest under the RW system.

3.4 Minimum data set indicators

After obtaining satisfactory results from the sampling sufficiency test, PCA was conducted on the full dataset to derive MDS indicators for the two soil types. The MDS indicators were selected based on higher factor loadings and correlations among the variables.

3.4.1 Minimum data set indicators in Vertisols

In Vertisols, the first six principal components (PCs) with eigenvalues ≥1 explained 75.26% of the cumulative variability in the dataset across four different rice-based agroecosystems (Table 5). Soil attributes with high factor loadings within each PC were considered for inclusion in the MDS. Based on higher factor loadings, OC, Mn, MBC, PMC, APA, AlPA, UA, and DHA were selected from PC1. The selected soil attributes were significantly correlated with each other (p< 0.05; Figure 2a). Therefore, DHA was retained as the representative key soil quality indicator because it showed the highest factor loading. In PC2, silt content, BD, WP, and CEC were selected based on higher factor loadings, and after correlation analysis (Figure 2a), only BD was retained as a representative MDS indicator. Similarly, clay content, MWD, AN, and AK were selected from PC3, and after correlation analysis, MWD was retained as an MDS indicator. Soil OC, AP, and Zn were retained as MDS indicators from PC4, PC5, and PC6, respectively, as they were the only variables that exhibited high loadings within their respective PCs. Thus, the final indicators retained in the MDS for Vertisols were DHA, BD, MWD, OC, AP, and Zn. These MDS indicators were further used for calculation of SQI.

Table 5. Principal component analysis showing cumulative variance and rotated component matrix of variables across the first six PCs in Vertisols.

Factor loadings shown in bold were treated as highly weighted, and the underlined variables were selected for the MDS. PC, principal component; MDS, minimum data set; BD, bulk density; FC, field capacity; WP, wilting point; HC, hydraulic conductivity; MWD, mean weight diameter; EC, electrical conductivity; OC, organic carbon; CEC, cation exchange capacity; AN, available nitrogen; AP, available phosphorus; AK, available potassium; AS, available sulfur; Fe, iron; Mn, manganese; Cu, copper; Zn, zinc; MBC, microbial biomass carbon; MBN, microbial biomass nitrogen; PMC, potentially mineralizable carbon; PMN, potentially mineralizable nitrogen; APA, acid phosphatase activity; AlPA, alkaline phosphatase activity; UA, urease activity; DHA, dehydrogenase activity.

To establish the relationship between rice-based agroecosystems and soil attributes, a PCA biplot (Figure 3a) was constructed using the first two PCs, which together accounted for 50.94% of the total variability. The variation explained by PC1 (35.96%) was associated with higher OC, MWD, porosity, WHC, microbial and enzyme activities, and improved nutrient availability. In comparison, PC2 (14.99%) was characterized by higher BD along with variations in clay and sand fractions. The biplot analysis showed that RC and RL were positively associated with PC1, while RW and, to a lesser extent, RF were aligned toward the negative side of PC2.

3.4.2 Minimum data set indicators in Inceptisols

For Inceptisols, the first six PCs with eigenvalues ≥1 accounted for 73.59% of the total variability observed within the dataset (Table 6). In PC1, the soil attributes OC, AS, Fe, Mn, MBC, and Cu exhibited high factor loadings. Following multivariate correlation analysis among these variables (Figure 2b), soil OC, which exhibited the highest factor loading, was selected as a representative indicator in the MDS.

Table 6. Principal component analysis showing cumulative variance and rotated component matrix of variables across the first six PCs in Inceptisols.

Factor loadings shown in bold were treated as highly weighted, and the underlined variables were selected for the MDS. PC, principal component; MDS, minimum data set; BD, bulk density; PD, particle density; FC, field capacity; WP, wilting point; WHC, water-holding capacity; HC, hydraulic conductivity; MWD, mean weight diameter; OC, organic carbon; CEC, cation exchange capacity; AN, available nitrogen; AP, available phosphorus; AK, available potassium; AS, available sulfur; Fe, iron; Mn, manganese; Cu, copper; Zn, zinc; MBC, microbial biomass carbon; MBN, microbial biomass nitrogen; PMC, potentially mineralizable carbon; PMN, potentially mineralizable nitrogen; APA, acid phosphatase activity; AlPA, alkaline phosphatase activity; UA, urease activity; DHA, dehydrogenase activity.

Similarly, the variables with high loadings identified from PC2 were FC, WP, and CEC, while those identified from PC3 were AN, AP, Zn, AlPA, and UA. Finally, CEC from PC2 and AlPA from PC3 were retained based on their highest factor loadings and the correlation matrix (Figure 2b) and were considered MDS indicators. In PC4, AP, MBN, and DHA showed strong factor loadings. Following significant intercorrelation analysis, DHA was identified as the key soil quality indicator. In PC5, HC and AK were highly weighted variables; however, only HC was retained as an MDS indicator due to its higher factor loading and positive correlation with AK. As soil Zn was the only variable with a high factor loading in PC6, it was directly considered a key MDS indicator. The final retained MDS indicators for Inceptisols were OC, CEC, AlPA, DHA, HC, and Zn.

The first two PCs were used to prepare a PCA biplot (Figure 3b) illustrating the relationship between rice-based agroecosystems and soil properties; together, these PCs explained 44.01% of the total variability. The factor loadings in PC1 were dominated by OC, nutrient availability, enzymatic activities, improved HC, WHC, and aggregation properties, while PC2 was dominated by higher BD and sand content. RL, followed by RF, was largely distributed on the positive side of PC1, indicating superior soil quality. In contrast, RW systems were separated by PC2, showing comparatively poorer soil quality.

3.5 Calculated soil quality index

The SQI in Vertisols differed significantly among the four rice-based agroecosystems, ranging from 0.73 to 0.85 (Figure 4a). The highest SQI value was observed under RC (0.85), which was significantly higher than under the RF and RW systems (p< 0.05), whereas it remained statistically at par with RL (0.81). Similarly, the RL system exhibited a significantly higher SQI than RW (p< 0.05), while being statistically similar to RF (0.77). Further, the SQI of RF was at par with RW (0.73). The lowest SQI was recorded under RW. In Inceptisols, the SQI varied significantly among the rice-based agroecosystems, ranging from 0.64 to 0.78 (Figure 4b). RL recorded the highest SQI (0.78), which was significantly higher than the values observed under RF and RW (p< 0.05). Similarly, RF exhibited a significantly higher SQI (0.71) than RW (p< 0.05). RW consistently recorded the lowest SQI (0.64). Vertisols sustained better soil quality than Inceptisols.

3.6 Contribution of MDS indicators to SQI

In Vertisols, among 32 evaluated soil chemical, physical, and biological attributes, the final MDS indicators retained were DHA, BD, MWD, OC, AP, and Zn. Among these, DHA (48%) contributed the most to the SQI, followed by BD (20%), MWD (12%), OC (7%), AP (7%), and Zn (6%) (Figure 5a). In Inceptisols, the final retained MDS indicators from 32 soil attributes included OC, CEC, AlPA, DHA, HC, and Zn. Among these, OC contributed the most (26%) to the SQI, followed by CEC (23%), AlPA (18%), DHA (14%), HC (11%), and Zn (6%) (Figure 5b).

3.7 Validation of SQI

The relationship between SQI and crop yield was evaluated using rice-equivalent yield (REY) under different rice-based agroecosystems across both soil types (Figure 6). In Vertisols, the SQI showed a strong and significant correlation with REY under the RC system (R² = 0.91), followed by RL (R² = 0.89), RF (R² = 0.88), and RW (R² = 0.82). Similarly, in Inceptisols, the strongest correlation between SQI and REY was observed under the RL system (R² = 0.89), followed by RW (R² = 0.86) and RF (R² = 0.85) (Figure 6).

3.8 Establishment of critical limits of soil quality indicators

In Vertisols, the relationship between DHA and RY demonstrated that DHA levels of 25 and 42.5 µg triphenyl formazan (TPF) g^-1 d^-1 were required to attain 40% and 80% of the potential maximum yield, respectively. BD exhibited lower and upper critical limit values of 1.33 and 1.44 Mg m^-3 (Figure 7). The lower and upper critical limits of MWD were 0.62 and 0.84 mm, respectively, whereas the corresponding critical limits for OC were 4.2 and 5.8 g kg^-1. Similarly, the attainment of 40% and 80% of the maximum achievable yield corresponded to AP levels of 14.5 and 22.2 kg ha^-1, respectively, while the associated critical limits for Zn were 0.34 and 0.78 mg kg^-1 (Figure 7). For Inceptisols, the regression relationships between RY and MDS indicators revealed that OC levels of 3.6 and 5.25 g kg^-1, and CEC values of 26 and 42 cmol (p⁺) kg^-1, represented the lower and upper critical limits corresponding to 40% and 80% RY, respectively. The lower and upper critical limits for AlPA were 238 and 290 µg p-nitrophenol g⁻¹ d⁻¹, respectively, whereas the corresponding limits for DHA were 20 and 36 µg TPF g⁻¹ d⁻¹. Similarly, HC values of 0.80 and 0.97 cm h^-1 and Zn concentrations of 0.68 and 1.28 mg kg^-1 represented the lower and upper critical limits corresponding to 40% and 80% RY, respectively (Figure 8).

3.9 Critical limits of SQI

To achieve 40% and 80% RY, the lower and upper critical limits of SQI were 0.72 and 0.86, respectively, for Vertisols (Figure 9a), whereas the corresponding values in Inceptisols were 0.66 and 0.78 (Figure 9b). This result indicates that achieving 80% RY requires comparatively higher SQI values in Vertisols than in Inceptisols.

3.10 Soil quality indicator interpretation

The individual values of key MDS indicators were interpreted based on the fitted response models developed as a function of RY. Critical limits of indicators were categorized into three interpretative classes following the approach of Lopes et al. (52): low (<40% RY), moderate (40%–80% RY), and adequate (>80% RY). The corresponding class limits for each key indicator for both soil types are presented in Table 7.

Table 7. Interpretative classes of selected MDS indicators for both soil types as a function of relative yield (RY).

Relative yield (RY) was interpreted as low (<40% RY), moderate (40%–80% RY), and adequate (>80% RY). MDS, minimum data set; DHA, dehydrogenase activity; BD, bulk density; MWD, mean weight diameter; OC, organic carbon; AP, available phosphorus; Zn, zinc; CEC, cation exchange capacity; AlPA, alkaline phosphatase activity; HC, hydraulic conductivity; TPF, triphenyl formazan.

4 Discussion

Soil quality attributes (soil chemical, physical, and biological properties) varied significantly among different rice-based agroecosystems in both soil types. This may be attributed to differences in agronomic practices adopted by farmers, including cropping patterns, crop establishment methods, tillage intensity, fertilizer application, quality and quantity of organic manure inputs, weed control, irrigation scheduling, and residue management. Similar observations were reported in our earlier study, wherein distinct rice-based agroecosystems with contrasting management practices significantly influenced soil quality attributes (14).

Long-term rice-based agroecosystems significantly influenced soil quality; among them, the rice-legume (RC and RL) systems maintained higher SQI than RW or RF in both soil types. In Vertisols, the RC system exhibited the highest soil quality, whereas in Inceptisols, the RL system was more effective, which is a manifestation of the complementary effects of legume inclusion, its rooting behavior, and residue quality on soil processes (58). The inclusion of chickpea alleviated post-puddling compaction and enhanced aggregate stability (59) through higher root biomass, higher root diameter density, and higher organic matter inputs, thereby moderating the swell-shrink behavior of smectitic clays. In Inceptisols, lathyrus contributed to improved soil aggregation by root-induced structural stabilization. Chemically, legume inclusion increased biological N fixation, while the rapid decomposition of crop residues with a narrow C:N ratio enhanced OC stocks and the availability of N and P through rhizosphere-mediated nutrient mobilization (60). Biologically, continuous inputs of labile carbon from chickpea and lathyrus roots and residues stimulated microbial biomass and enzyme activities (59), accelerating nutrient mineralization. In contrast, the current RW system experienced higher compaction and nutrient mining, while the RF system lacked post-rice organic carbon inputs, resulting in poor soil quality. Thus, the integration of legumes into rice-based systems effectively sustained long-term soil quality in subtropical agroecosystems (9, 58, 59).

The MDS indicators represent the most sensitive soil attributes that strongly influence soil functions and the productivity of rice-based agroecosystems. In Vertisols, of the 32 soil attributes, only 6 were retained as MDS indicators through PCA, representing all three pillars of soil quality (i.e., chemical, physical, and biological). Among these, DHA showed the highest contribution (48%) to SQI, followed by BD (20%), MWD (12%), OC (7%), AP (7%), and Zn (6%).

The dominant contribution of DHA indicates its high sensitivity and strong variability across management practices. It reflects the oxidative activity of living organisms and regulates organic matter decomposition, hence serving as a robust indicator of soil biological functioning (18). In subtropical Vertisols, rice is predominantly cultivated under puddled and beushening conditions, which create a temporary anaerobic soil environment. Following rice harvest, rabi crops are grown on residual soil moisture under aerobic conditions. This seasonal shift between anaerobic and aerobic environments strongly affects microbial metabolism. The strong sensitivity of DHA to variation in soil moisture regime and cropping systems highlights its suitability as a reliable indicator of soil quality. Previous studies by Tarafder et al. (61) Bangre et al. (62), and Chanu et al. (18) recognized DHA as a sensitive MDS indicator in rice-based systems. The critical limit values of DHA corresponding to 40% and 80% of the maximum possible yield were 25.0 and 42.5 µg TPF g^-1 d^-1, respectively. Based on the regression equation (R² = 0.85) between DHA and RY, approximately 45% of the studied soil samples were in the adequate class, indicating sufficient microbial activity to support good crop yields without immediate intervention. The remaining soils have lower DHA, suggesting a need for practices like organic amendments, residue incorporation, or balanced fertilization to improve soil biological activity. Our observation is similar to those reported earlier by Basak et al. (28) and Biswas et al. (63) under rice-based systems.

After DHA, BD was identified as the MDS indicator with the second-highest weighting. BD strongly affects soil aggregation and structural stability by regulating pore continuity, aeration, root proliferation, microbial activity, and overall crop performance (64). Long-term puddling and beushening under kharif rice increase soil compaction and consequently raise BD (14, 65). As a result, ploughing after rice often generates large soil clods, which adversely affect seed-soil contact and germination of rabi crops. Given its strong influence on soil physical attributes and crop productivity, BD was therefore included as a key indicator in the MDS. Hemmati et al. (66) and Biswas et al. (63) also reported BD as a key indicator of soil quality in rice-based systems. BD values of 1.33 and 1.44 Mg m^-3 were identified as the lower and upper critical limits required to achieve 40% and 80% of the maximum relative yield, respectively. The lower limit of BD was very similar to that observed under the RC system (1.34 Mg m^-3). The diversification of rice-based agroecosystems with legumes helps to sustain a lower soil BD conducive to achieving maximum system productivity.

MWD emerged as the third most important indicator in the MDS. Higher BD accelerates aggregate breakdown during puddling and wet-dry cycles, thereby reducing the MWD of aggregates. This poor aggregation limits water retention and movement, nutrient availability, and rooting depth of subsequent rabi crops. Consequently, for rice-based systems in Vertisols, MWD was retained as a key MDS indicator. 15 Biswas et al. (63) also reported MWD as a prominent MDS indicator for rice-based systems in the Indo-Gangetic Plain. The importance of MWD as a component of the MDS under rice-wheat systems has also been documented by Ghosh et al. (10) in the Indo-Gangetic Plains. MWD values of 0.62 and 0.84 mm represented the lower and upper critical limits required to achieve 40% and 80% of the maximum attainable RY, respectively. The upper limit of MWD was very close to that observed under RC (0.86 mm) and RL (0.82 mm) systems. This observation is supported by a strong correlation (R² = 0.88) between MWD and RY (%), indicating that rice-legume cropping may help sustain better soil aggregation and structural stability. In contrast, RW and RF systems require improvement in MWD through better management practices to maintain soil quality.

Among the chemical attributes OC, AP, and Zn emerged as key soil quality indicators. OC is considered a critical soil quality indicator owing to its influence on soil physical, chemical, and biological properties. Long-term continuous rice cropping with imbalanced fertilization significantly depletes OC, particularly under farmers’ field conditions in the subtropical region (13, 67). OC was therefore included as a key indicator in the MDS. Ghosh et al. (10) and Theresa et al. (6) also identified OC as a prominent MDS indicator for rice-based agroecosystems. The upper critical limit of OC was 5.8 g kg^-1 of soil, corresponding to 80% of the maximum possible RY. This limit was achieved under the RC system, likely due to higher root biomass contribution and the enhanced carbon sequestration capacity associated with chickpea cultivation (14). However, Biswas et al. (63) observed a higher upper limit of OC (7.7 g kg^-1) for rice-based systems in the Indo-Gangetic Plain. The relatively lower upper critical limit observed in the present study may be due to inherent soil and physiographic differences in the Central Indian Plateau, particularly the Eastern Plateau region, where clay-rich soils have different organic carbon stabilization and turnover mechanisms from Indo-Gangetic Plain soils.

Available P (AP) was retained as an important soil quality indicator. Continuous application of P fertilizer, such as diammonium phosphate (DAP), has resulted in the build-up of soil P in Indian Vertisols (13). This accumulated P governs soil quality and crop productivity. Das et al. (7) observed AP as a key MDS indicator for Indo-Gangetic plain rice-based systems in India. The importance of AP as a soil quality indicator under different crop and land management systems has been well documented by Reza et al. (23). The AP concentrations of 14.5 and 22.2 kg ha^-1 were identified as the lower and upper critical limits, corresponding to 40% and 80% of the maximum RY, respectively. Since all samples belonged to the moderate fertility class (12–25 kg ha^-1), the observed accumulation of AP is likely to be an important factor governing soil quality and crop productivity in the study area (68).

Zn is a key factor in soil quality, particularly in rice-based agroecosystems (69). Under typical paddy field conditions, Zn availability is often limited by an anaerobic soil environment, where reduced redox potential promotes the formation of insoluble Zn compounds. Consequently, rice cultivation under flooded conditions in Vertisols often results in persistent Zn deficiency, making it an important soil quality indicator (70). Similarly, Biswas et al. (15) and Theresa et al. (6) reported Zn as an MDS indicator under rice-based agroecosystems. Zn levels of 0.34 and 0.78 mg kg^-1 were identified as the lower and upper critical limits, representing 40% and 80% of the maximum attainable RY, respectively. However, none of the studied cropping systems attained the upper limit of Zn, indicating Zn deficiency under farmers’ field conditions (71). These results highlight Zn as a major yield-limiting micronutrient under rice-dominated agroecosystems.

In Inceptisols, OC, CEC, AlPA, DHA, HC, and Zn were identified as key indicators from 32 evaluated soil attributes. OC, DHA, and Zn were the soil attributes that emerged as key indicators in both soil types. Their consistent selection reflects that they are less influenced by soil type and more responsive to management-induced changes, making them reliable indicators for soil functioning across contrasting soils. Similarly, Fan et al. (72) demonstrated that among 876 studies on MDS selection, OC was the most widely selected indicator, followed by Zn and DHA across a wide range of soils and cropping systems worldwide. The upper critical limits of OC and DHA were lower in Inceptisols (5.25 g kg^-1 and 36 µg TPF g^-1 d^-1, respectively) than in Vertisols (5.8 g kg^-1 and 42.5 µg TPF g^-1 d^-1, respectively). This difference may be attributed to the lower clay content and reduced potential of Inceptisols to stabilize organic carbon (30). Vertisols, however, are dominated by smectite clay minerals with a high surface area and strong organo-mineral complexes, which favor greater carbon stabilization and support higher microbial activity (73, 74), resulting in higher upper critical limits of OC and DHA (14). However, the upper critical limit of Zn was higher in Inceptisols (1.28 ppm) than in Vertisols (0.78 ppm).

CEC was considered the second most important key indicator. In Inceptisols, CEC is strongly influenced by OC and clay fractions, making it highly responsive to management-induced changes. Under anaerobic paddy-field conditions, fluctuations in redox potential and nutrient transformations enhance the importance of CEC in buffering nutrient losses through leaching and fixation (75, 76). Similarly, Damiba et al. (16) identified CEC as a key MDS indicator and reported its greatest contribution to SQI. The critical limit values for CEC ranged from 26 to 42 cmol (p⁺) kg^-1 soil, representing the lower and upper critical limits, respectively. However, the optimum CEC level was not achieved by any of the studied cropping systems. This is supported by Basak et al. (28), who reported that the optimum CEC level was not achieved under any rice-based system in Inceptisols.

AlPA is a key soil enzyme governing P availability and overall soil biological functioning. The prolonged submergence during rice cultivation suppresses microbial activity and enzymatic mineralization of organic P, leading to reduced AlPA and limited P availability. In contrast, aerobic rabi conditions stimulate microbial proliferation and reactive enzyme systems, resulting in a marked increase in AlPA and improved P availability. This highlights the importance of AlPA as a sensitive soil quality indicator. Consistent with the present findings, phosphate activity has been recognized as an important MDS indicator in rice-based systems by Chanu et al. (18) and Theresa et al. (6). The critical limits for AlPA were 238 and 290 µg p-nitrophenol g^-1 d^-1, representing the lower and upper limits required to achieve 40% and 80% of maximum RY, respectively. The strong regression relationship between AlPA and RY (R² = 0.83) highlights the significant role of enzymatic activity in regulating nutrient availability and crop productivity. Moreover, more than 50% of the samples fell within the upper limit, indicating that a majority of fields possess adequate biological activity, which is conducive to achieving higher system productivity.

HC was retained as a key indicator in the MDS because it sensitively reflects changes in soil physical structure and water transmission induced by management practices. The continuous beushening and puddling during rice cultivation disturb soil aggregates and reduce microporosity, leading to lower HC (77). In contrast, aerobic rabi crops partially restore soil structure and permeability. In Inceptisols, which have moderate texture and structural stability, HC effectively captures the cumulative response of beushening and puddling on soil water movement, aeration, and root development (78, 79). Similarly, Das et al. (7) reported HC as an important MDS indicator under rice-based systems in Inceptisols. The critical limit values for HC ranged from 0.80 to 0.97 cm h^-1, with the lower and upper critical limits corresponding to 40% and 80% of the maximum RY, respectively. The upper limit was attained under the RL system (0.98 mm h⁻¹), indicating favorable conditions for crop growth. The significant relationship between HC and RY (R² = 0.76) highlights its importance in influencing productivity. Furthermore, the classification shows that nearly 50% of the samples fall within the adequate range, suggesting moderate scope for improvement in HC for RW and RF to enhance crop yield.

Different sets of MDS indicators were identified for the two soil types, reflecting the inherent differences in their pedogenic characteristics, mineralogy, and nutrient dynamics. In Vertisols, soil quality was primarily governed by structural stability and biological activity, whereas in Inceptisols, nutrient retention and fertility-related properties played a greater role. These findings demonstrate that soil quality is governed by different controlling factors depending on soil order; therefore, a universal set of indicators may not adequately capture soil quality across contrasting soils. The observed variation in MDS indicators highlights the importance of developing soil-order-specific soil quality assessment frameworks to improve the accuracy of soil quality evaluation and support targeted soil management strategies.

When comparing the two soil types, Vertisols (SQI = 0.79) sustained better soil quality than Inceptisols (SQI = 0.71), possibly due to differences in soil characteristics and mineralogical composition. In the study region, Vertisols are deep soils dominated by smectitic clay (30), which favor better root zone water availability and high cation exchange capacity, thereby enhancing nutrient retention and supply relative to Inceptisols. The high clay content further promotes the stabilization of organic carbon through the formation of clay-humus complexes. This results in higher microbial biomass, higher enzyme activity, and efficient nutrient cycling. Consequently, Vertisols had a higher upper critical limit of SQI (0.86) than Inceptisols (0.78) for achieving 80% RY, consistent with higher yield potential (9, 14). The strong and significant relationships between SQI and RY in Vertisols (R² = 0.90) and Inceptisols (R² = 0.89) indicate that SQI strongly influenced crop productivity across the two soil types. The slopes of the relationships (Vertisols: 304.47; Inceptisols: 297.37) indicate that a 0.1-unit increase in SQI results in approximate increases of 30.4% and 29.7% in RY of the cropping system under Vertisols and Inceptisols, respectively.

5 Conclusions

The present study concluded that long-term rice-based agroecosystems significantly influenced soil chemical, physical, and biological attributes, consequently affecting overall soil quality. Among them, RC, followed by RL, optimized the soil attributes (viz., soil chemical, physical, and biological properties), resulting in sustained improvement in soil quality across both soil types. Particularly in Vertisols, the present study recommends the adoption of the RC cropping system, followed by the RL system. Furthermore, the RF system maintained comparatively better soil quality than the intensive RW system. The study identified distinct MDS indicators for Vertisols and Inceptisols, and critical limits for these indicators were established, providing soil-specific management strategies for sustaining soil quality. These findings highlight that biological indicators were more influential in Vertisols, whereas chemical indicators were more important in Inceptisols. The establishment of lower and upper limits for key soil indicators and SQI for rice-based agroecosystems across both soil types in the subtropical region will help develop soil-specific optimum management strategies to sustain soil quality and long-term productivity under farmers’ field conditions. The outcomes of this study support progress toward the Sustainable Development Goals (SDGs), with particular relevance to SDG 2 (Zero Hunger) through enhanced sustainability and SDG 15 (Life on Land) through improved soil resource management.

6 Limitations

The study aimed to provide a systematic framework for soil quality evaluations and to identify critical limits of MDS indicators in rice-based agroecosystems of subtropical India for two different soil orders using soil samples collected from the 0–15 cm surface layer. The present study is subject to certain limitations that can be addressed in future research, including sampling depth, single-time sampling, and validation of the critical limits of soil quality indicators under varied agro-ecological conditions. Sampling in deeper layers or soil profile studies may help to capture subsurface processes influencing long-term soil quality. Seasonal sampling may also help capture interannual variability. The acceptability and robustness of the critical limits may be enhanced by replicating and validating the study in diverse agroecosystems and translating the limits into actionable soil health management strategies using participatory approaches involving farmers and extension agencies.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsoil.2026.1918756/full#supplementary-material

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Footnotes

Edited by: Martina Mazzon, University of Bologna, Italy

Reviewed by: Narendra Kumar Lenka, Indian Institute of Soil Science (ICAR), India

Bholanath Saha, Bihar Agricultural University, India

Urjashi Bhattacharya, The Neotia University, India

Krisanu Golui, Chaudhary Charan Singh Haryana Agricultural University, India