Soil Quality Index as Affected by Integrated Nutrient Management in the Himalayan Foothills

Abstract

Soil quality assessment serves as an index for appraising soil sustainability under varied soil management approaches. Our current investigation was oriented to establish a minimum data set (MDS) of soil quality indicators through the selection of apt scoring functions for each indicator, thus evaluating soil quality in the Himalayan foothills. The experiment was conducted during two consecutive years, viz. 2016 and 2017, and comprised of 13 treatments encompassing different combinations of chemical fertilizers, organic manure, and biofertilizers, viz. (i) the control, (ii) 20 kg P + PSB (Phosphorus solubilizing bacteria), (iii) 20 kg P + PSB + Rhizobium, (iv) 20 kg P + PSB + Rhizobium+ FYM, (v) 20 kg P + 0.5 kg Mo + PSB, (vi) 20 kg P + 0.5 kg Mo + PSB + Rhizobium, (vii) 20 kg P + 0.5 kg Mo + PSB + Rhizobium + FYM, (viii) 40 kg@ P + PSB, (ix) 40 kg P + PSB + Rhizobium, (x) 40 kg P + PSB + Rhizobium+ FYM, (xi) 40 kg P + 0.5 kg Mo + PSB, (xii) 40 kg P + 0.5 kg Mo + PSB + Rhizobium, and (xiii) 40 kg P + 0.5 kg Mo + PSB + Rhizobium + FYM. Evaluating the physical, chemical, and biological indicators, the integrated module of organic and inorganic fertilization reflected a significant improvement in soil characteristics such as the water holding capacity, available nitrogen, phosphorus, potassium, and molybdenum, different carbon fractions and soil biological characteristics encircling microbial biomass carbon (MBC), and total bacterial and fungal count. A principal component analysis (PCA) was executed for the reduction of multidimensional data ensued by scoring through the transformation of selected indicators. The soil quality index (SQI) established for different treatments exhibited a variation of 0.105 to 0.398, while the magnitude of share pertaining to key soil quality indicators for influencing soil quality index encircled the water holding capacity (WHC), the dehydrogenase activity (DHA), the total bacteria count, and the available P. The treatments that received an integrated nutrient package exhibited a higher SQI (T10—0.398; T13—0.372; T7—0.307) in comparison to the control treatment (T1—0.105). An enhanced soil quality index put forth for all organic treatments reflected an edge of any conjunctive package of reduced synthetic fertilizers with prime involvement of organic fertilizers over the sole application of inorganic fertilizers.

1. Introduction

The soil—an intricate heterogeneous natural resource—has received a lofty cognizance in the circumference of research and policy making, directing to its proficiency to accomplish myriads of functions. Agricultural amplification, through the exploitation of high-yielding varieties, rigorous irrigation, and the total reliance on synthetic fertilizers paved the way to the green revolution, apart from autonomy in food production, in the majority of countries. Soils are a substantial part of agricultural productivity, biogeochemical cycling, energy cycling, climate regulation and resilience, suppression of disease and pests, and detoxification of contaminants along with the endowment of below deck crew and aboveground biodiversity (Figure 1). However, enduring anthropogenic activities pose grave threats to soil ecosystems, in view of fluctuations in land use patterns, contamination of soil and aquatic ecosystems, land degradation, and desertification, directing to the loss of the multifunctional edge of soils for the provision of ecosystem services [1,2].

Currently, the attainment of sustainable development, interwoven around healthy soil is strongly affiliated with the maintenance of soil quality [3]. Soil quality signifies the ability of soil to perform within ecosystem confinements to assure the sustenance of biological productivity and to maintain environmental quality along with the promotion of floral and faunal health [4]. In addition, soil quality speaks of productivity concerns in line with the sustainability notion, offering an indispensable platform for developing nations. Agricultural intensification has posed a serious threat to soil quality. The deteriorating soil quality is apparent from different indices comprising organic carbon depletion, the arrival of multi-nutrient deficiencies, and a decline in bio-driven activities [5,6]. Imbalanced nutrient application, phosphate build-up, and persistent imprints of agrochemicals in intensively cultivated soils comprise the driving forces for worsening soil quality. Concerns regarding the long-lasting sustainability of ecosystem services have deteriorated the environment, owing to both excessive and sub-optimal nutrient application [7]. Overuse of inorganic fertilizers in developed countries has stemmed from soil degradation and contaminated water sources. In contrast, the climate change-induced vagaries, population explosion, and constraints in per capita land availability with the inclusion of a drop in the adoption of conventional soil management approaches in developing countries have further aggravated soil fertility [8]. Taking into consideration the aforesaid aspects, agricultural intensification for the forthcoming proliferation of crop yields is a requisite, but not at the expense of environmental sustainability. For this compliance, integrated nutrient management offers a promising toolkit as backed by the FAO, the success of it being reflected in the preceding two decades under different crop production systems [9].

The bold label of integrated nutrient management (INM) encircles the exploitation of synthetic fertilizers, the introduction of legumes, organic manures, adoption of biofertilizers, and crop residues as chief modules. INM is recognized for improving all characteristics of molecular absorption of macro as well as micronutrients, apart from synchronizing the crop nutrient demands and alleviating the nutrient deficit stresses without jeopardizing environmental and food safety [10]. The INM ensures the balanced nutrition to crops along with the drop in antagonistic imprints that graft from nutrient imbalances as well as hidden deficiencies [11]. The consequences of INM are more conspicuous in circumstances prevailing under rainfed production in comparison to irrigated conditions.

Organic manure application in collaboration with mineral fertilization has been recognized for improvement in soil organic carbon (SOC) content, nutrient accessibility, MBC, and activities of dehydrogenase and alkaline phosphatase [12]. Voluminous research opined greater values of SQI in soils that are recipients of an integrated nutrient application along with distinct resource conservation techniques when compared to imbalanced ones [13]. Legumes are recognized for their substantial part in the maintenance of soil fertility, owing to their N-fixing ability [14]. The benefits delivered by legumes in view of soil quality enhancement comprise improvisation of soil natural matter, nutrient cycling, soil porosity, soil structure, drop in soil pH, and diversification of soil biodiversity along with the suppression of pests and diseases [15]. The inclusion of legumes poses an indelible impression on different soil health indicators which concurrently shakes various soil ecosystem services.

Consequently, evaluating the status of soil quality is authoritative in crafting healthier soil management approaches that would in turn boost crop productivity while assuring environmental sustainability. Apart from this, it facilitates decision makers to assess the management practices that ensure the sustainability of soil quality taking into consideration the pros and cons of organic and inorganic fertilization. Alongside this backdrop, the current exploration was backed by the fact that soil quality indicators encircling different properties exhibit sensitivity to different organic and inorganic fertilization approaches. Therefore, the study was designed with the following objectives: (1) to frame a minimum data set (MDS) of different soil quality indicators via the selection of apt scoring indices for each indicator, and (2) to assess the quality of soils that are the recipients of different INM treatments to acquire the ace soil management approaches using SQI. These objectives are further backed by the element that there would be variation in the overall soil quality index in integrated nutrient management and a prominent one shall be secluded given soil quality concern, posing its imprints on soil health in the Shiwalik foothills.

2. Materials and Methods

2.1. Experimental Site

The experiment was performed during 2016 and 2017 at the Advanced Centre for Rainfed Agriculture at Rakh Dhiansar, SKUAST-Jammu (32°39′ N latitude and 74°58′ E longitude, elevation 332 m above sea level). The climate is characterized by hot and dry summers with a subsequent hot and humid monsoon, exhibiting an annual rainfall of about 1100 mm of which 75 to 80% occurs during the monsoon season. The temperature often rises to as high as 45 °C in the months May–June. The examined soils follow the Entisol order possessing sandy loam texture, a low content of organic matter, are nutrient poor with high erodability [16], and the water retention capacity of the cultivated soils is 21.9–40.5% [17]. A substantial cultivable area exists under cereals, for instance maize, wheat, and bajra grown as rainfed crops. Through varied natural vegetation, the area mostly entails scrub forests, chir pine forests, and moist and dry deciduous trees.

2.2. Treatments Details

Thirteen treatments consisting of chemical fertilizers and amendments (FYM and biofertilizers) at different levels were tested in a randomized block design thrice with a plot size of 3.5 m × 2.0 m. The inoculation of requisite seeds was done with Rhizobium culture @ 25 g kg⁻¹ seed and PSB @ 5 g kg⁻¹ seed, 2–3 h prior to sowing and then dried in the shade. The farmyard manure (FYM @ 2 t ha⁻¹) was incorporated seven days prior to sowing. The sowing of black gram (vr. uttara) was done in lines at a spacing of 30 cm × 10 cm. The treatments comprised (i) T1: the control, (ii) T2: 20 kg P + PSB (Phosphorus solubilizing bacteria), (iii) T3: 20 kg P + PSB + Rhizobium, (iv) T4: 20 kg P + PSB + Rhizobium+ FYM, (v) T5: 20 kg P+ 0.5 kg Mo + PSB, (vi) T6: 20 kg P+ 0.5 kg Mo + PSB + Rhizobium, (vii) T7: 20 kg P + 0.5 kg Mo + PSB + Rhizobium + FYM, (viii) T8: 40 kg@ P + PSB, (ix) T9: 40 kg P + PSB + Rhizobium, (x) T10: 40 kg P + PSB + Rhizobium+ FYM, (xi) T11: 40 kg P + 0.5 kg Mo + PSB, (xii) T12: 40 kg P + 0.5 kg Mo + PSB + Rhizobium, and (xiii) T13: 40 kg P + 0.5 kg Mo + PSB + Rhizobium + FYM, where P (P₂O₅) represents diammonium phosphate and Mo represents ammonium molybdate. The whole quantity of P and Mo was applied as per treatment details.

2.3. Collection and Preparation of Soil Samples

Soil samples were collected from each replicated plot and sieving was performed via a 2 mm mesh sieve and instantly preserved in a refrigerator at 4 °C using plastic bags for the stabilization of microbiological activity with subsequent analysis for biological characteristics. The remaining fraction of the sample being air-dried in the shade was ground in a wooden mortar and pestle to pass via a 2 mm sieve in view of estimating different soil chemical characteristics along with carbon pools.

2.4. Soil Analysis

The measurement of soil pH was performed in the suspension of soil–water (1:2.5) with a glass electrode pH meter [18]. The suspension liquid of 1:2.5 soil–water was analyzed for electrical conductivity using a Solubridge conductivity meter at 25 °C, and bulk density was determined by the core sampler method as described by Piper [19]. The mechanical analysis of soil samples was performed by the Bouyoucos hydrometric method [20]. WHC was estimated by the Keen–Rackzowski box method [21]. The Eijkelkamp wet sieving apparatus was employed to assess the aggregate stability (WSA: water-stable aggregates) of experimental soil. The measurement of the steady state infiltration rate was carried out using a double ring infiltrometer. The observations were noted at 5, 10, 15, 30, and 1 h intervals until the procurement of a constant steady state rate [22].

2.5. Soil Chemical Properties

An estimation of available nitrogen was carried out following the modified Kjeldahl method outlined by Subbiah and Asija [23]. The determination of available phosphorus was performed in line with the procedure outlined by Olsen et al. [24]. Available potassium was estimated by the ammonium acetate method [18]. Acid ammonium oxalate extractant was employed for the available molybdenum (Mo) determination with the colorimetric method [25].

2.6. Soil Organic Carbon Fractions

The organic carbon content of soil samples was analyzed following the rapid titration method [26]. The quantification of potassium permanganate oxidizable carbon using 333 mM of KMnO₄ or Labile carbon (LC) of soil samples was performed as per the procedure outlined by Blair et al. [27]. Particulate organic carbon (POC) was estimated in accordance with the mechanical dispersion and separation procedure as defined by Camberdella and Elliott [28]. Microbial biomass carbon (MBC) of the concerned soil samples was assessed with the employment of the fumigation extraction method in fresher incubated soil samples at 27 °C [29].

2.7. Soil Biological Properties

An assessment of the microbial population (bacteria and fungi) in soil samples was carried out by standard serial dilution and pour plate technique [30]. This inoculation of 1 mL tenfold serially diluted samples was done onto an appropriate culture medium (nutrient agar for bacterial and potato Dextrose agar for fungal count), followed by its incubation at 28 ± 2 °C for 3 days. The observed colonies were calculated and elucidated as colony forming units per gram (cfu g⁻¹ soil) of soil. The soil dehydrogenase activity was estimated in accordance with the procedure of Cassida et al. [31]. Concisely, 4 g of moist soil was treated with a 3 percent solution of Triphenyl Tetrazolium chloride with subsequent incubation of resultant suspension at 28 °C for 24 h. Determination of acquired triphenyl formazan (TPF) was done at 485 nm, and the observations were portrayed as μg TPF g⁻¹ soil 24 h⁻¹.

2.8. Soil Quality Assessment

The soil quality reflects the soil’s capacity to function within natural or managed ecosystem boundaries in addition to sustaining plant productivity while reducing soil degradation [4]. Being a complex functional concept, soil quality cannot be measured directly in the field or laboratory [32], but can only be inferred from soil properties [33], whereby a wide array of soil characteristics or indicators have been acknowledged to estimate soil quality. Soil quality index (SQI) connotes to a measurable soil parameter that influences the capacity of soil to perform a specific function [34]. The SQI approach has been employed as a quantitative tool to establish affiliation among soil health comprising soil’s physical, chemical, and biological properties and management plan [35]. The soil quality index was worked out by considering critical soil quality indicators as detailed by Bhardwaj et al. [36]. Different soil characteristics encircling physical, chemical, and biological factors were accounted as indicators for SQI estimation which were condensed to a minimum data set (MDS) with a principal component analysis (PCA) over an array of univariate and multivariate statistical techniques. Employment of a one-way analysis of variance (ANOVA) was done for the identification of indicators exhibiting significant treatment differences. Only variables possessing a significant difference (p < 0.05) amid appraised treatments were selected for the subsequent phase of MDS formation. Ensuing statistical analysis, the variables which differed among the treatment means were imperiled to a principal component analysis employing SPSS software. Principal components which received higher Eigenvalues (>1) and elucidated at least 5% of the data variation along with the variables exhibiting higher factor loading were picked as apt representative indicators. Transformation of soil quality parameter values into a unitless score (between 0 to 1) was done using a linear transformation. Under a particular PC, only the variable exhibiting the highest factor loading was retained for estimation of SQI. Every principal component elucidated a definite extent of variation (%) in the data set. The ratio of this percentage to the total percentage of variation elucidated by whole principal components having Eigenvectors greater than 1, furnished the weighted aspect for variable elected for a specified principal component. Since some indicators have a great influence than others on soil quality, the scores were multiplied by a weighing factor before taking their averages. The MDS variables pertaining to each observation were weighted with PCA results. The final formula for computing soil quality index (SQI):

$\mathrm{SQI}={{\displaystyle \sum }}_{i=0}^{n}Wi\times Si$

where, Wi reflects PC weighting factor

Si reflects indicator score.

The assumption under consideration encompasses that a higher index score connotes healthier soil quality or better enactment of soil function.

2.9. Statistical Analysis

Statistical analyses of the data sets comprised an analysis of variance pertinent to a randomized block design using the least significant difference as outlined by Gomez and Gomez [37]. A comparison of treatment means was done at a 5% level of significance. The principal component analysis was carried out using SPSS for the development of the SQI.

3. Results

3.1. Influence of Different Treatments on Mean Values of Soil Physical Quality Indicators

The mean values pertaining to various soil physical quality indicators are given in Table 1. Among different treatments, the mean values of bulk density exhibited a variation of 1.48–1.56 Mg m⁻³ and followed a decreasing trend with the incorporation of FYM. The application of FYM in collaboration with phosphorus, molybdenum, and biofertilizers significantly lowered the bulk density in comparison to the control and other treatments. The lower bulk density values could be indebted to the enhanced buildup of organic carbon content in the soil, ensued by a higher pore space and therefore a lower bulk density [38]. The average values of soil porosity and water holding capacity (WHC) exhibited a range of 41.83 to 44.76% and 30.56 to 35.31%, respectively, where the plots which received both organic and chemical fertilization possessed the highest value (Table 1). The varied response of soil water holding capacity could be ascribed to the addition of farmyard manure, and inorganic fertilization besides biofertilizers including phosphorus solubilizing bacteria and Rhizobium, which communally heightened the soil’s organic carbon [35,39]. Datt et al. [40] illustrated an improvement regarding water holding capacity in response to the introduction of farmyard manure along with inorganic and biofertilizers, accounting it for a healthier environment for root proliferation, amended soil structure—apart from improved water-stable aggregates—and moisture retention capacity as an upshot of the total count of storage pores. The mean values for the infiltration rate and water-stable aggregates (WSA) exhibited a variation of 0.85–0.87 cm h⁻¹ and 43.20–44.90%, respectively, where treatments receiving an integrated nutrient package shared higher magnitude in contrast to the control. Improved soil aggregation in addition to structural stability and enhanced macro-aggregate carbon content has been put forth in response to the integrated nutrient management practice, owing it to the introduction of organic sources which pays for improved resistance of aggregates to the slaking action of water [41].

3.2. Influence of Different Treatments on Mean Values of Soil Chemical Quality Indicators

The soil reaction and electrical conductivity (EC) showed no significant changes under various treatments (Figure 2). However, the highest value of soil pH was noted under treatment T1 (6.66) and the lowest under T10 (6.44). In addition, soil pH decreased under treatments T4, T7, T10, and T13 owing to the FYM incorporation. The electrical conductivity was marked highest under T1 and T8 (0.26) and lowest under T4 (0.21). The decline in pH of the soil with the addition of manures only, or in conjunction with synthetic fertilizers, could be attributed to the formation of organic acids in the course of organic matter decomposition [42]. A slight decline in soil pH upon FYM addition has also been put forth by Chandra et al. [43]. These findings are in line with the observations put forward by Shah et al. [44].

The mean values of various soil chemical quality indicators presented in Figure 3 revealed that available nitrogen (N) content exhibited a variation of 93.36 to 120.84 kg ha⁻¹ across different treatments. The maximum nitrogen content was noted under treatment T13 ensued by T12, even though the lowest nitrogen content was marked in T1 (the control). The significant impact on available nitrogen content could be attributed to amplified fixation of nitrogen through the application of P in conjunction with Rhizobium inoculation [45]. Additionally, an upsurge in contents of available N might be ascribed to the significant correlation existing between P and nitrogen fixation [46]. Mo is recognized for its incredible part in symbiotic nitrogen fixation [47], consequently, Mo-treated soil illustrated a significant difference in available nitrogen content. The positive imprints of the INM module on available nitrogen content have also been put forward by Dotaniya et al. [48] and the increments might also be ascribed to the incorporation of farmyard manure in addition to the conversion of organically occluded N to inorganic pools [49].

The evaluated treatments exhibited a significant (p < 0.05) impact on available phosphorus (P) content exhibiting a range of 8.46–14.73 kg ha⁻¹. As a result, significantly higher contents of available P were documented in treatments receiving integrated doses of organics and inorganics. The available phosphorus content of soil amplified in response to INM module (T13—14.73), in contrast to the control (T1—8.46), accounting it to phosphorus solubilization in debt of organic acid production from organic manures, drop-in phosphorus fixation due to chelation of P fixing cations such as Al, Mn, Zn, Cu and Ca, Mg, and heightened microbial activity [50]. Molybdate (MoO₄²⁻) ions are additionally recognized to furnish phosphate ions (PO₄³⁻) from soil colloidal complex executing the mechanism of anion exchange. In addition, the increased accessibility of soil phosphorus could be accredited to seed inoculation with PSB that aids in liberating native phosphorus, apart from shielding the fixation of freshly supplemented phosphorus [51]. The stimulation of soil microbial activity due to phosphorus solubilizing bacteria and amplified availability of N, P, and K after decaying of their bodies in soil has also been documented [52].

The available potassium (K) exhibited a significant difference (p < 0.05) over evaluated treatments, exhibiting an average variation from 111.59 to 118.01 kg ha⁻¹. A higher content of available K (118.01 kg ha⁻¹) was observed under integrated doses of chemical and organic amended plots comprising treatment T13 elucidating a considerable impact of farmyard manure on the accessibility of K in soils. The pronounced increase might be accounted to positive imprints of farmyard manure whose aftermaths stemmed in the reduction of K fixation besides potassium release owed to the clay-organic matter interaction [53]. The available Mo exhibited an extensive variation under observed treatments in contrast to the control. The availability of Mo under various treatments considerably improved with the application of P and Mo. A surge in Mo availability might be accredited to the release of MoO₄²⁻ ions from the exchange complex of soil in replacement for OH⁻ ions in the soil solution [54]. Consistent liming of soils along with phosphorus fertilization has been marked to perk up the accessibility of molybdenum to plants [55].

3.3. Influence of Different Treatments on Mean Values of Soil Carbon Fractions

The mean values of various soil carbon fractions are depicted in Figure 4. The average values on microbial biomass carbon portrayed a distinct difference across different treatments, revealing a range of 30.25 to 34.9 mg kg⁻¹. T13 invariably illustrated a higher content of MBC over other treatments, while the control plot exhibited a lower content of MBC. The application of an integrated module comprising phosphorus, molybdenum, and farmyard manure (FYM) stemmed in significantly higher soil MBC over the rest of the treatments. The elevated contents of microbial biomass carbon in soil could be accredited to different factors like improved soil aggregation and enhanced soil moisture retention along with elevated contents of soil organic carbon [56]. The FYM remediated plots deliver a steady source of organic carbon for the sustenance of the microbial community which results in enhanced microbial biomass carbon [57]. Iqbal et al. [58] reported a significant difference across different treatments where the integrated package of organic manure and chemical fertilizers considerably heightened the MBC content of the soil in contrast to sole chemical fertilization. The mean POC varied widely among various treatments from 258.9 to 272.0 g/m². Consistent with the MBC trend, the integrated application of organics along with inorganics ensued in significantly higher POC over the control. The application of FYM along with inorganic fertilizers stemmed in a significant positive buildup of particulate organic carbon. The elevated contents of POC in T13 could be in debt of the integrated module application that augmented the below deck crop biomass, ensuring the amplified contribution of exogenous sources of organic matter into the soil, which ultimately directs to an upsurge in POC content [59]. The root litter being biochemically recalcitrant might have accounted for the enriched contents of POC in soil, contingent on the production of root biomass [60].

The KMnO₄ oxidizable C signifying labile carbon fraction in soil exhibited a range of 989 to 1161 mg kg⁻¹, where the treatment (T13) portrayed significantly higher content compared to sole inorganic fertilization. Carbon supplementation through the FYM application in combination with chemical fertilization (T13, T10, T7, and T4) marked premier accretion of KMnO₄ oxidizable C, whereas the control witnessed the lowest value. Labile organic carbon is recognized for improved accessibility to microorganisms in the soil, exhibiting a rapid turnover rate apart from posing direct imprints on the nutrient furnishing potential of soil. Increased variations regarding labile carbon in response to the application of integrated package, characterized a higher sensitivity of respective soil organic carbon pool to fluctuations backed by the manure and fertilizer application [61]. Higher turnover rates of root biomass in reaction to the application of the integrated module might additionally have accounted for an amplified surge in this pool in parallel to other treatments [62]. The mean oxidizable carbon (SOC) content of experimental soils marked a variation of 2.47 to 2.58 g kg⁻¹. A substantial quantity of crop residues, as well as the leftover root biomass carbon in the soil, could have accounted for increased contents of soil organic carbon apart from the C supplementation through FYM application [63].

3.4. Influence of Different Treatments on Mean Values of Soil Dehydrogenase Enzyme Activity and Total Microbial Count

The glance of data portrayed in Table 2 illustrates a higher activity of dehydrogenase (26.88 μg TPF g⁻¹ soil 24 h⁻¹) in treatment (T13) followed by (T10) (26.65 μg TPF g⁻¹ soil 24 h⁻¹) and the lowest activity (23.29 μg TPF g⁻¹ soil 24 h⁻¹) under treatment T1. All treatments exhibited significant consequences in comparison to the control. In the current study, the inclusion of legumes has been recognized for a significant increase in enzyme activities, viz. dehydrogenase, owing to it the greater nutrient retention which in turn elevates dehydrogenase activity [64]. Given the amplified nitrogen supply ensured by legume crops, the narrowed down ratio of C:N stimulates the mineralization of added as well as native carbon stocks in the soil. The hasty decomposition of soil organic matter assures the delivery of nutrients, rendering them accessible to micro-organisms for protoplasm synthesis. In addition, the introduction of farmyard manure in combination with inorganic and bio-fertilizers might have encouraged the microbial activity to exploit the intrinsic soil organic carbon pools, which serve as substrates for dehydrogenase enzyme [65]. Dehydrogenase activity serves as a proficient index of microbial metabolic activity, and the soils which received organic fertilization, either solely or in combination with mineral fertilization, exhibited greater values of basal respiration as well as dehydrogenase activity in contrast to the control plots or soils entertaining mineral fertilization only [66].

The observations acquired during the experimentation illustrated a significant variation across all treatments regarding the total bacterial population. The maximum bacterial population (13.82 × 10⁴ cfu g⁻¹ soil) was perceived under treatment (T4) ensued by T7 (13.58), T10 (13.23), and T13 (13.01), however, the lowest (11.04 × 10⁴ cfu g⁻¹ soil) being marked under the control soils. The augmented bacterial population in FYM-recipient plots might be attributed to the organic manure application which offers ample biomass as a substrate for microbes and aids in amplification of the microbial population in the soil. The amplified bacterial population in the soil might be additionally accredited to the supplemental application of nutrients through FYM incorporation which augmented the native biomass, and root exudates and eventually offers carbon as well as energy to soil micro-organisms driving toward the proliferation of the bacterial population [67]. Furthermore, the addition of 20 kg P ha⁻¹ marked a significantly elevated count of bacteria in comparison to 40 kg P ha⁻¹, reflecting that the applied 40 kg P ha⁻¹ acted detrimentally, as the microbial count declined considerably over 20 kg P ha⁻¹ which was marked to be a safe limit for maximum microbial build-up in the black gram. In general, it was apparent from the current investigation that the application of phosphorus has upgraded the microbial population, paying it to the assured energy provision along with components aimed at synthesis of new cells and thereby, favoring the proliferation of these microbes [68].

Across the treatments, the fungal population oscillated around a mean of 7.01 to 10.07 × 10² cfu g⁻¹ soil. The fungal population showed a more pronounced response to the combined treatments including the incorporation of FYM while the population significantly decreased in fertilized treatments [69]. The application of 40 kg P ha⁻¹ posed detrimental imprints as evident from the significant drop in microbial population over 20 kg P ha⁻¹ which was elucidated as a safe limit for the adequate build-up of microbial biomass in the black gram. Wang et al. [70] elucidated a significant fluctuation in bacteria and fungi in response to different fertilization regimes, whereby the application of inorganic fertilizers (CF treatment) considerably reduced the bacterial abundance while enriching the fungal diversity in comparison to the control (CK treatment). The wide variations in bacterial as well as fungal communities across different treatments encircle organic additions and zero organic addition treatments owing to fluctuations in soil organic carbon content and accessible phosphorus, keeping up the assertion that nutrient and soil carbon accessibility constitutes the chief aspects driving the soil microbial population [71].

3.5. Soil Quality Index

For the assessment of the soil quality index, the data were imperiled to a principal component analysis (PCA). The factor loading/Eigenvectors pertinent to significant soil quality indicators acquired from the principal component analysis are given in Table 3. Soil pH, electrical conductivity, infiltration rate, and aggregate stability (WSA) exhibited no significant difference across the evaluated treatments. As inferred from the data in Table 3, the water holding capacity (WHC, 0.911), dehydrogenase enzyme (DHA, 0.905), total bacterial count (0.358), and available P (0.746) acquired the highest loading factors under PC-1, PC-2, PC-3, and PC-4, respectively and as such these four variables were retained as a minimum data set for evolving SQI.

Ensuing the determination of MDS indicators, all MDS variables were scored individually, in view of the enactment of soil function, while keeping in consideration the deviation of values across various treatments (Table 4). Transformation of an individual variable was performed into a unitless score (between 0 to 1) employing linear transformation. When transformed, the MDS variables pertinent to respective observations were weighted with the use of the PCA findings. The extent of the variability described by PCI, PCII, PCIII, and PCIV was 67.99, 16.21, 9.86, and 5.94, respectively (Table 3). The ratio of this percentage to the total percentage of variation elucidated by whole principal components having Eigenvectors greater than 1, furnished the weighted aspect for the variable elected for a specified principal component (Table 3). Calculated weightage for PC-1, PC-2, PC-3, and PC-4 was marked as 0.680, 0.162, 0.099, and 0.059, respectively. Subsequent to scoring and weighting, the determined values were subjected to an additive model, and eventually an aggregate score signifying the soil quality state was assessed and the evaluated numerical soil quality index was attained for each treatment. The soil quality index (SQI) established for different treatments exhibited a variation of 0.105 to 0.398 as presented in Table 4 and followed the order as: T10 >T13 > T7> T4 > T12 > T9 > T6 > T11 > T8 > T5 > T3 > T2 > T1. An enhanced soil quality index has been put forth for all treatments which received organic fertilization, reflecting an edge of the employment of any conjunction package of reduced synthetic fertilizers with prime involvement of organic fertilizers over sole application of inorganic fertilizers regarding farmland productivity [72]. The positive imprints of organic fertilization for improvisation of soil quality agree with the inferences put forward by Lazcano et al. [73] and Li et al. [74].

4. Conclusions

The integrated module of organic and inorganic fertilization significantly improved soil characteristics such as water holding capacity, accessibility of N, P, K, and Mo along with carbon fractions, soil biological properties encircling MBC, and total bacterial and fungal count. The evaluated fertilizer treatments exhibited a variation of 0.105 to 0.398 with regard to soil quality index. The treatments which received an integrated nutrient package exhibited a higher SQI where T10 was a recipient of a combination of 40 kg P + PSB @ 5 g kg⁻¹ seed + Rhizobium @ 25 g kg⁻¹ seed + FYM @ 2 t ha⁻¹ and exhibited a soil quality index of 0.398, followed by T13 (SQI-0.372), which was a recipient of an integrated application of 40 kg P + 0.5 kg Mo + PSB + Rhizobium + FYM and T7 (SQI-0.307), receiving 20 kg P + 0.5 kg Mo + PSB + Rhizobium + FYM in comparison to the control treatment (T1-0.105), illustrating and integrated nutrient management (INM) as an optimal nutrient management approach as well as an ecologically sound technology to ensure soil quality enhancement as well as sustainability. Soil properties such as water holding capacity (WHC), dehydrogenase activity (DHA), total bacterial count and available P took the highest magnitude of share as key soil quality indicators to reflect fluctuations in the soil quality index (SQI). These indicators offer their potential to be exploited for the real-time surveillance of soil quality fluctuations in the upcoming epoch, under different management practices. In the examined fertilization treatments, a conjunctive package of nutrient management unveiled the best performance regarding the soil quality index, thereby, underscoring the proficiency of the applied integrated module of organic plus inorganic nutrient sources in rainfed Entisols of the Shiwalik foothills. Consequently, the present investigation offers an integrated nutrient module as a promising approach to tackle the constraints of low fertilizer response as well as the forthcoming challenges of food security, without compromising the ecosystem health.

Author Contributions

Conceptualization, M.P.S., final draft preparation, T.I.S. and A.M.S., formal analysis, M.I.J., F.J.W., M.I.B. and A.P.R., writing—review and editing N.A.K., S.A.B., A.B., L.A., A.M.A., A.R.M. and A.H.L. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data can be shared upon reasonable request to the author.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. Seven functions of soil.

Figure 2. Effect of different treatments on soil pH and electrical conductivity. Mean values exhibiting same letter do not differ significantly (p < 0.05).

Figure 3. Effect of different treatments on soil available nutrients. Mean values with different letters differ significantly (p < 0.05).

Figure 4. Effect of different treatments on various soil carbon fractions. Mean values with different letters differ significantly (p < 0.05).

Figure 4. Effect of different treatments on various soil carbon fractions. Mean values with different letters differ significantly (p < 0.05).

Table 1

Effect of different organic and inorganic treatments on mean value of soil physical properties.

Treatments	Bulk Density (mg m−3)	WHC (%)	Soil Porosity (%)	Infiltration Rate (cm h−1)	Total WSA (%)
T1	1.54 d	30.56 k	42.51 g	0.85 a	43.20 a
T2	1.56 a	30.66 i	41.83 l	0.85 a	43.55 a
T3	1.53 e	30.75 f	42.56 f	0.85 a	43.65 a
T4	1.48 i	35.27 b	44.76 a	0.87 a	44.85 a
T5	1.55 bc	30.65 i	42.14 j	0.85 a	43.50 a
T6	1.53 e	30.79 e	42.88 e	0.85 a	43.70 a
T7	1.49 h	35.31 a	44.38 b	0.87 a	44.75 a
T8	1.55 bc	30.61 j	42.14 j	0.85 a	43.70 a
T9	1.54 cd	30.73 g	42.29 h	0.85 a	43.85 a
T10	1.51 f	35.06 d	43.64 d	0.87 a	44.90 a
T11	1.55 bc	30.60 j	41.94 k	0.85 a	43.50 a
T12	1.55 b	30.71 h	42.20 i	0.85 a	43.75 a
T13	1.50 g	35.10 c	44.01 c	0.87 a	44.80 a

Means with different superscripts differ significantly (p < 0.05).

Table 2

Effect of different organic and inorganic treatments on mean value of soil biological properties.

Treatments	Dehydrogenase Activity(μg TPF g−1 Soil 24 h−1)	Bacterial Population(×104 cfu g−1 Soil)	Fungal Population(×102 cfu g−1 Soil)
T1	23.29 m	11.04 m	7.01 l
T2	24.04 l	11.78 h	7.98 g
T3	24.56 j	12.19 e	8.39 e
T4	25.85 d	13.82 a	10.07 a
T5	24.24 k	11.56 j	7.76 i
T6	24.92 h	11.96 f	8.11 f
T7	26.10 c	13.58 b	9.84 b
T8	24.70 i	11.40 k	7.59 j
T9	25.14 f	11.93 g	8.12 f
T10	26.65 b	13.23 c	9.66 c
T11	24.97 g	11.26 l	7.43 k
T12	25.36 e	11.70 i	7.86 h
T13	26.88 a	13.01 d	9.53 d

Means with different superscripts differ significantly (p < 0.05).

Table 3

Factor loading/Eigenvectors of significant soil quality indicators from PCA.

	Principal Component I	Principal Component II	Principal Component III	Principal Component IV
Eigen value	13.57	4.66	2.57	1.43
Percentage of variance explained	67.99	16.21	9.86	5.94
Cumulative percentage	67.99	84.20	94.06	100
Weighting	0.680	0.162	0.099	0.059
Eigenvectors
WHC	0.911	0.085	0.236	−0.189
Porosity	0.787	0.077	0.288	0.298
Bacteria	0.421	0.480	0.358	0.218
Fungi	0.811	0.092	−0.107	−0.092
DHA	0.132	0.905	0.296	0.075
Labile carbon	0.383	−0.264	0.295	0.337
POC	0.647	0.167	0.137	0.043
MBC	0.839	−0.341	−0.068	0.164
Bulk density	−0.681	−0.201	0.186	−0.362
Av. N	0.814	−0.354	0.290	0.190
Av. P	0.735	0.158	−0.337	0.746
Av. Mo	0.624	0.704	−0.031	−0.293
Av. K	0.165	0.297	0.140	0.459

Table 4

Score (S), weight (W) and soil quality index (SQI) values of selected minimum data set (MDS) variables for each treatment.

Treatment	WHC		Bacteria		DHA		Available P		SQI
	S	W	S	W	S	W	S	W
T1	0.068	0.680	0.162	0.162	0.189	0.099	0.241	0.059	0.105
T2	0.107	0.680	0.237	0.162	0.113	0.099	0.390	0.059	0.145
T3	0.104	0.680	0.445	0.162	0.057	0.099	0.207	0.059	0.161
T4	0.347	0.680	0.284	0.162	0.047	0.099	0.212	0.059	0.299
T5	0.129	0.680	0.352	0.162	0.187	0.099	0.062	0.059	0.167
T6	0.127	0.680	0.558	0.162	0.238	0.099	0.216	0.059	0.213
T7	0.232	0.680	0.686	0.162	0.152	0.099	0.384	0.059	0.307
T8	0.219	0.680	0.076	0.162	0.191	0.099	0.103	0.059	0.186
T9	0.301	0.680	0.059	0.162	0.012	0.099	0.045	0.059	0.218
T10	0.343	0.680	0.531	0.162	0.571	0.099	0.375	0.059	0.398
T11	0.187	0.680	0.334	0.162	0.041	0.099	0.284	0.059	0.202
T12	0.081	0.680	0.536	0.162	1.000	0.099	0.432	0.059	0.266
T13	0.295	0.680	1.000	0.162	0.030	0.099	0.117	0.059	0.372

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