Different Organic and Inorganic Sources of Plant Nutrients Influence Soil Health, Leading to Improve the Productivity and Profitability of a Fourteen-Year Long-Term Rice–Potato–Groundnut Cropping Pattern

Abstract

This study aims to evaluate the impact of long-term integrated nutrient management (INM) treatments on soil properties, crop productivity and the soil quality index (SQI) in scented rice–potato–groundnut cropping system. A comprehensive evaluation of soil quality indicators was carried out using four different SQI calculation methods: linear additive (SQI_LA), nonlinear additive (SQI_NLA), linear weighting (SQI_LW) and nonlinear weighting (SQI_NLW). Among all the treatments, the INM approach ($T_1$), which combines 50% recommended NPK from inorganic fertilizer with 50% N from FYM, resulted in the maximum yield for all three crops. Within the organic nutrient management treatments ($T_2$–$T_5$), treatment $T_5$, which involved 33% of the recommended nitrogen from various organic sources combined with beneficial microorganisms, showed significantly higher yield. Of the 17 soil quality attributes in the present study, soil organic C (SOC), available N, available S and total soil bacteria count were identified as the most sensitive indicators influencing soil quality. The plots treated solely with organic inputs with biofertilizers yielded the highest SQI values (SQI_LA—4.01; SQI_LW—0.71; SQI_NLA—3.04; SQI_NLW—0.51), closely followed by the INM-treated plots (SQI_LA—3.66; SQI_LW—0.66; SQI_NLA—2.82; SQI_NLW—0.47), and both the treatments significantly outperformed other treatments. All four SQIs showed a high correlation with the system of rice equivalent yield (SREY) calculated from all three crops; however, there are significant differences. The weighted linear (SQI_LW) method proved to be the most sensitive and reliable method for modelling soil quality and predicting crop productivity. The exclusion of biofertilizers from different organic nutrient management options led to 13.36% decline in the SQI value, highlighting the critical role of biofertilizers in maintaining soil health in the scented rice–potato–groundnut system.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

1. Introduction

The exponential surge in population has led to a reduction in the available cultivation area, primarily due to nonagricultural activities, creating a formidable challenge for farming [1, 2]. To counter this, conventional overreliance on inorganic fertilizers has emerged in agricultural practices, negatively impacting environmental health, crop yield quality and farmers’ economic well-being in the long run. The unscientific and excessive use of chemical fertilizers results in depleted soil nutrient levels, causing stagnation or decline in crop yield trends and posing threats to food safety and quality [3–5]. To maintain productivity, farmers generally apply high amount of chemical fertilizers which increases their expenses and leads to soil water and air pollution [4, 5]. Thus, developing eco-friendly strategies and agricultural technologies to conserve food safety and security and enable adequate employment and income generation and long-term results-based decisions are priorities.

In this context, organic farming has gained attention for its potential to yield qualitative produce while maintaining an eco-friendly approach. However, its perceived low productivity has diminished its large-scale adoption as a sustainable alternative to conventional inorganic fertilizer-based agricultural practices [6, 7]. A single source of fertilizer, inorganic or organic, cannot adequately address the interconnected components of yield, economics and the environment. Consequently, there is a growing need for an efficient blend of inorganic and organic fertilizers in farming systems.

Repeated and judicious nutrient management practices over the long term can significantly influence soil properties, subsequently impacting soil health and modulating crop productivity [8]. Investigating the repercussions of these prolonged practices on soil dynamics and crop yield is essential for understanding the sustainability and resilience of agricultural systems [9]. Long-term experiments are pivotal in capturing trends and changes that manifest over time, providing a conclusive understanding of sustained effects on soil health and productivity [9].

In the context of cropping system-based studies, the significance becomes even more pronounced. Understanding the interplay between different crops within a system is crucial for devising holistic and sustainable soil management strategies [10, 11], offering insights into the intricate relationships, synergies and trade-offs between crops. Such insights are invaluable for making informed decisions to increase overall productivity and sustainability in agricultural systems.

Despite previous research, a critical gap remains in evaluating how long-term nutrient management influences integrated soil quality in intensive cropping systems, especially those that incorporate legume-based rotations. Most of the previous studies evaluated only the short-term effects of the application of different organics on different soil indicators and lacked an evaluation of their long-term impact on the integrated soil quality for an intensive cropping sequence. The genesis of these research questions lies in recognizing the challenges posed by monocropping, imbalanced fertilizer use and the exclusion of legumes in the rice–wheat system, as highlighted by Saha et al. [12]. Our hypothesis is that integrated nutrient management (INM) involving organic and inorganic inputs enhances soil quality and crop productivity in the long term, particularly when assessed within a diversified cropping system context.

Furthermore, establishing a soil quality index (SQI) is a vital tool for interpreting data from diverse soil parameters and understanding the overall impact on system productivity, emphasizing the importance of a multidimensional approach to evaluating soil quality. The calculation of SQI generally follows a three-step process: (i) indicator selection—where a minimum dataset (MDS) is identified through statistical tools such as principal component analysis (PCA); (ii) scoring and normalization of individual indicators using linear or nonlinear scoring methods; and (iii) integration and indexing through additive or weighted summation techniques [13, 14]. This multidimensional approach helps interpret the collective impact of long-term practices on soil function and productivity.

In summary, our study seeks to address critical gaps in existing knowledge by conducting a long-term investigation into the effects of nutrient management practices on an intensive and unique cropping system. Through a cropping system-based approach and a comprehensive evaluation of soil quality indicators, we aim to contribute valuable insights that can inform sustainable agricultural practices in the Indo-Gangetic plains, particularly in the Lower-Gangetic plains of Eastern India.

2. Materials and Methods

2.1. Experimental Setup and Site Description

A fourteen-year-long experiment focussing on the rice–potato–groundnut cropping system commenced in 2004–2005 at the Central Research Farm of Bidhan Chandra Krishi Viswavidyalaya, Kalyani, West Bengal, India. The cropping system is the predominant agricultural practice in this region. Rice serves as the staple food, while potato inclusion in the rice-based sequence has gained popularity in West Bengal due to its high yield and economic returns. Additionally, incorporating legumes like groundnut enhances soil fertility and maximizes energy output. Consequently, this cropping pattern is widely adopted as a sustainable and economically viable system in the region. The site, situated in Gangetic alluvium with sandy clay loam texture, is located at 22°58′20″N latitude and 88°30′11″E longitude, approximately 9.75 m above sea level. The zone has a hot, humid subtropical climate with an average annual rainfall of approximately 1500 mm. The experimental field is hyperthermic (Aeric Haplaquept, US Soil Taxonomy, Soil Survey Staff 2003) and silty clay with a pH 7.1, ECe (dS m⁻¹) of 0.12, organic carbon (%) of 0.61, soil available nitrogen (kg ha⁻¹) of 219.71, available phosphorus (kg ha⁻¹) of 32.27 and available potassium (kg ha⁻¹) of 129.21.

2.2. Experimental Design and Treatments

The experiment utilized a randomized block design (RBD) with seven treatments, including six different nutrient management practices and one untreated fallow land as control ($T_1$ to $T_7$) (Table 1). Each treatment replicated three times.

Table 1

Fertilizer applications of the different treatments for the scented rice–potato–groundnut in the long-term fertilization experiment.

Treatments	Cropping system—scented rice–potato–groundnut
$T_1$	50% recommended NPK in inorganic form + 50% recommended N from FYM + 10 kg·ha−1 ZnSO4·7H2O
$T_2$	33% of recommended N each from FYM, vermicompost and neem cake
$T_3$	$T_2$ + Dhaincha as green manure
$T_4$	50% N from FYM + Azospirillum (4.5 kg culture ha−1) and PSB (4.5 kg culture ha−1)
$T_5$	$T_2$ + use of biofertilizers same as $T_4$
$T_6$	100% recommended NPK in inorganic form + 10 kg·ha−1 ZnSO4·7H2O
$T_7$ (fallow land)	Undisturbed fallow land closely situated with the experimental site; no cultivation was done in this plot since the initiation of the experiment

2.3. Experimental Description

The experiment has been ongoing for the last fourteen (14) years. The present investigation was carried out over three consecutive years during the twelfth (2015–16), thirteenth (2016–17) and fourteenth (2017–18) crop cycles, in the same field without disturbing the experimental layout. The size of each plot was 5m × 3 m. The rice–potato–groundnut crop sequence was selected for the study and practised in all the treatment plots. Rice (Oryza sativa L.) is grown during the rainy (kharif) season as scented rice (var. Gobindobhog); potato (Solanum tuberosum) (var. Kufri Jyoti) is grown in the winter (rabi) season; and groundnut (Arachis hypogaea) (var. TAG-24) is grown in the summer season sequentially in a single crop calendar year at the experimental site. The experiment consisted of six (6) different nutrient management practices with one fallow land (Table 1). The fallow land was closely situated with the experimental site and remained undisturbed throughout the 14-year experiment. No cultivation was performed in this plot since the initiation of the experiment. The fallow land used in the experiment was used as a reference to compare the aggradation or degradation of soil health under different nutrient management practices. In fact, the fallow plot was used for assessing the effects of different soil treatments on soil quality, nutrients and carbon stocks in this long-term experiment. Among those six different nutrients management practices, 4 consisted of different sources of organic nutrients ($T_2$, $T_3$, $T_4$ and $T_5$), one consisted of INM (organic + inorganic) ($T_1$), and the other consisted of purely inorganic nutrients ($T_6$) (Table 1). In this experiment, farmyard manure (FYM), vermicompost (VC), neem cake (NC) and Dhaincha green manure (GM) were added as organic sources of nutrients, and the nitrogen contents (on a dry weight basis) of FYM, VC and NC were 0.57, 1.04 and 5.11, respectively. Azospirillum (4.5 kg culture ha⁻¹) and phosphate-solubilizing bacteria (PSB) (4.5 kg culture ha⁻¹) were used as biofertilizers. N-, P-, K- and Zn-containing fertilizers were used as inorganic fertilizers, and the sources used were urea for nitrogen; SSP for phosphorus; and MOP for potassium at the following recommended doses (N:P₂O₅:K₂O): 80:40:40 for rice, 200:150:150:150 for potato; and 20:60:40 for groundnut. All the organic manures were applied before the final land preparation for the transplantation of rice and the sowing of potato tubers and groundnut seeds. In the case of inorganic fertilizers, full quantities of phosphorus (P) and potash (K) were applied as basal fertilizers to all the crops, and nitrogen (N) was applied in split doses. However, no fertilization was performed on fallow land.

2.4. Yield Attributes, Biometric Observations and Economic Analysis

Every year from 2015 to 2018, the scented rice, potato and groundnut crops were harvested from each plot’s net earmarked portion, weighed after proper processing and converted to t·ha⁻¹. All three experimental crops were meticulously assessed for yield-related parameters, such as productive tiller count, panicle length, panicle weight and mature grains per panicle. Dry matter production from both straw and grain was quantified. To address economic disparities and facilitate fair scientific comparisons of productivity and economics across different nutrient management practices, a conversion method was employed to transform organic yield into equivalent yield (EY). This was accomplished via the following formula:$$\begin{array}{}\text{(1)}& \text{EY}\text{of\hspace{0.17em}crop\hspace{0.17em}}\mathrm{a}=Y_a\times \frac{P_a}{P_b},\end{array}$$Here, EY represents the equivalent yield of rice, potato or groundnut (in tons per hectare). $Y_a$ denotes the organic yield of the crops (rice, potato or groundnut) achieved through purely organic farming (in tons per hectare). $P_a$ represents the premium market price of the organic yield of the corresponding crops. Moreover, $P_b$ represents the average market price of the conventionally produced yield of the corresponding crop (rice, potato or groundnut). This formula ensures a standardized basis for comparison while avoiding any economic disparities. The rice EYs of potato and groundnut were subsequently calculated via the following formula:$$\begin{array}{}\text{(2)}& \text{Rice\hspace{0.17em}equivalent\hspace{0.17em}yield}\text{of\hspace{0.17em}crop\hspace{0.17em}}x\mathrm{t}{\text{ha}}^{-1}=\frac{Y_{x}t{ha}^{-1}\times P_{x}t{ha}^{-1}}{P_{y}t{ha}^{-1}},\end{array}$$where x represents potato or groundnut, $Y_x$ represents the yield of groundnut or potato in t·ha⁻¹, $P_x$ represents the price of groundnut or potato per ton and $P_y$ represents the price of rice per ton. Similarly, the cost of cultivation (COC) was calculated via standard methods.

2.5. Soil Sampling and Analysis

The composite soil samples were collected from a depth of 0 to 15 cm of surface soil from each plot after being collected randomly in a zigzag manner. Then, the air-dried soils were ground, passed through a 2-mm sieve and kept in polyethylene bags with proper labels for chemical analysis. Another portion of the fresh soil was immediately taken to the laboratory and used for soil biological analyses, which included the estimation of total bacteria, total phosphate-solubilizing microorganisms, total cellulose-decomposing bacteria (CDB), total fungi, as well as acid and alkaline phosphatase activities. The samples were kept undisturbed in polyethylene bags at 4°C in a refrigerator with proper labelling for the biological analysis.

The soil pH and electrical conductivity (EC) were determined using standard analytical methods [15]. Soil organic carbon (SOC) was analysed via the Walkley and Black method, whereas soil macronutrients, viz. nitrogen (N), phosphorus (P) and potassium (K) were determined via the alkaline potassium permanganate method [16], Olsen method [17] and flame photometric method [15], respectively.

Soil microorganisms (bacteria and fungi) were counted via the standard plate count method with appropriate media following the serial dilution technique [18, 19]. Acid and alkaline phosphatase activities of soil were estimated spectrophotometrically by the method described by Tabatabai and Bremner [20].

Available Zn, Fe and Mn in the soil samples were extracted using a diethylene-triamine-penta-acetic-acid (DTPA) solution (soil:solution-1:2, v/v), and the concentrations of these micronutrients in the extract were determined using an atomic absorption spectrophotometer [21].

The SOC stock was estimated by multiplying the SOC concentration (%) by the bulk density (Mg m⁻³) and depth (m) of sampling via the following formula [22]:$$\begin{array}{}\text{(3)}& \text{SOC\hspace{0.17em}stock}\text{Mg\hspace{0.17em}}\mathrm{C}{\text{ha}}^{-1}=\text{SOC}\%\times \text{soil\hspace{0.17em}depth}\mathrm{m}\times \text{bulk\hspace{0.17em}density}\text{Mg\hspace{0.17em}}{\mathrm{m}}^{-3}\times 100.\end{array}$$

2.6. Computing the SQI

In accordance with the Raiesi [23] process, the SQI assessment was carried out through following four steps such as (i) statement of the management goal, (ii) selection of the MDS from the total dataset (TDS), (iii) performance-based scoring of the MDS indicators and (iv) integration of the indicator scores into a comparable SQI is included.

The SREY was considered the management goal for evaluating treatment-wise soil quality to better understand the effects of different fertilization strategies on yield attributes. To build the MDS through PCA, all 17 significant soil metrics, including physical, chemical and biological aspects, were chosen [24–26]. The primary purpose of PCA is to minimize the dimensionality of the TDS, which is composed of many intercorrelated variables, without significantly sacrificing the amount of information. According to Wander and Bolllero [27], PCs with high eigenvalues of 1.0 account for at least 5% of the variation in the dataset, and PCs with cumulative variations greater than 85% are retained. Varimax rotation was performed to maximize the correlation between PCs and soil characteristics by disrupting the variance [28]. Within each principal component (PC), variables with high factor loadings (> 0.70) were retained for further analysis. Pearson’s correlation coefficients were calculated to evaluate intervariable relationships. Among significantly correlated variables, the one with the highest cumulative correlation was selected for inclusion in the MDS [29], with priority also given to globally recognized soil quality indicators.

The linear scoring function (LSF) and nonlinear scoring function (NLSF) were used to assign soil variables determined by MDS unitless scores ranging from 0 to 1 based on their contributions to soil functions [23, 30–32]. The factors were ranked according to their relative influence on soil quality via the ‘more is better’ or ‘less is better’ technique, which measures each observed value as a ratio to the highest observed value, with the highest value receiving a score of 1 (Equation (4)) or lowest value receiving 1 (Equation (5)), respectively, and the others receiving scores of less than 1.$$\begin{array}{}\text{(4)}& \text{LSF}=\frac{X}{X_{\max }}.\end{array}$$

For the ‘less is better’ approach, each lowest observed value is divided by each observation such that the lowest value receives a score of 1 and the rest receive a score of < 1 (equation (2)).$$\begin{array}{}\text{(5)}& \text{LSF}=\frac{X_{\min }}{X},\end{array}$$where X represents the observed value of each indicator and $X_{\min }$ and $X_{\max }$ represent the highest and lowest observed values of each indicator, respectively.

For the NLSF, soil indicators are transformed via the following sigmoidal curve equation [23, 33, 34]:$$\begin{array}{}\text{(6)}& \text{NLSF}=\frac{\mathrm{a}}{1+{X_i/X_{imean}}^{\mathrm{b}}},\end{array}$$where a = maximum value equal to 1, $X_i$ = value of the soil indicator I, $X_{\text{imean}}$ = the average value of the soil indicator i. b = the equation was set at −2.5 for ‘more is better’ (upper sigmoidal curve), which corresponds to the equation’s highest value of 1, and +2.5 for ‘less is better’ (lower sigmoidal curve), which corresponds to the equation’s average value of the soil indicator I [23, 34–36]. The idea of ‘midpoint is optimum’ refers to quality indicators with a threshold that maximizes soil productivity. These indicators reflect a Gaussian curve, where ‘higher is better’ up to a threshold value and ‘lower is better’ above the threshold [24, 30]. Two alternative methods were used to determine the SQI for the indexing technique: (i) an additive index and (ii) a weighted index. The additive index was calculated by adding the MDS scores for each treatment that had been altered. While the transformed MDS of each PC after scoring is multiplied by the respective weight factor of that PC, the weight index is calculated by deriving the weight factor, which is the ratio of the total percentage variance from each PC to the percent cumulative variance [31, 37]. Finally, the weighted variable cores were summarized to develop the SQI [24].$$\begin{array}{}\text{(7)}& \text{SQI}={\displaystyle \sum }_{i=1}^n\text{WiSi},\end{array}$$where Wi is the weighting factor and Si is the score of each selected indicator.

Finally, additive SQI calculation through LSF and NLSF methods was calculated, which were denoted as SQI_LA and SQI_NLA, and weighted SQI through LSF and NLSF methods was finally calculated , which were denoted as SQI_LW and SQI_NLW.

2.7. Statistical Analysis

A RBD with three replications with 3-year datasets was used for statistical analysis. The data were analysed via statistical analysis software (SPSS software, 19.0; SPSS Institution Ltd., Chicago, IL, USA). One-way analysis of variance (ANOVA), followed by Duncan’s multiple-range test (DMRT), was performed to determine the treatment effects at the 0.05 level of probability outlined by Gomez and Gomez in 1984. The PCA and clustering of the independent variables were performed via R 3.5.2 (R Development Core Team, 2020) software.

3. Results

3.1. Crop Yields as Influenced by Different Sources of Organic and Inorganic Fertilizers

Research has examined how organic, inorganic and INM methods influence the yields of rice, potato and groundnuts over a span of three consecutive years (Table 2).

Table 2

Impact of various long-term nutrient management practices on rice grain yield, potato tuber yield and groundnut pod yield.

Treatment

Rice grain yield (t·ha−1)

Potato tuber yield (t·ha−1)

Groundnut pod yield (t·ha−1)

2016

2017

2018

Pooled

2016

2017

2018

Pooled

2016

2017

2018

Pooled

$T_1$

1.76a

1.78a

1.80a

1.78a

16.50a

16.80a

16.92a

16.74a

1.68a

1.72a

1.83a

1.74a

$T_2$

1.42d

1.62c

1.63b

1.56bc

12.37c

12.80e

12.89d

12.68c

1.21cd

1.35c

1.40c

1.32c

$T_3$

1.53c

1.57d

1.64b

1.58bc

13.12b

13.43d

13.89c

13.48bc

1.25cd

1.36c

1.41c

1.34c

$T_4$

1.44cd

1.53e

1.58b

1.52bc

10.85d

11.33f

10.62e

10.94d

1.19d

1.33c

1.38c

1.3c

$T_5$

1.65b

1.65b

1.66ab

1.65ab

13.52b

14.30c

15.00b

14.27b

1.31c

1.58ab

1.61b

1.50b

$T_6$

1.46cd

1.47f

1.42c

1.45c

16.25a

16.40b

15.42b

16.02a

1.43b

1.49bc

1.44c

1.45bc

SEm (±)

0.02

0.00

0.03

0.02

0.10

0.06

0.11

0.10

0.02

0.03

0.03

0.03

CD ($p\le 0.05$)

0.07

0.01

0.09

0.06

0.31

0.18

0.36

0.27

0.07

0.11

0.08

0.08

Note:$T_1$—50% recommended NPK in inorganic form + 50% recommended N from FYM + 10 kg·ha⁻¹ ZnSO₄, $T_2$—33% of recommended N each from FYM, vermicompost and neem cake, $T_3$—$T_2$ + Dhaincha as green manure, $T_4$—50% N from FYM + Azospirillum (4.5 kg culture ha⁻¹) and PSB (4.5 kg culture ha⁻¹), $T_5$—$T_2$ + use of biofertilizers same as $T_4$, $T_6$—100% recommended NPK in inorganic form + 10 kg·ha⁻¹ ZnSO₄, $T_7$—fallow; numbers followed by different letters are significantly different at $p\le 0.05$ according to Duncan’s multiple-range test (DMRT).

The INM approach ($T_1$), which combines 50% recommended NPK from inorganic fertilizer with 50% N from the FYM, consistently resulted in the highest yield for all three crops (rice, 1.78 t·ha⁻¹; potato tuber, 16.74 t·ha⁻¹; and groundnut, 1.74 t·ha⁻¹) (Table 2). The treatment $T_5$, which combined FYM, VC and NC with beneficial microbes, resulted in a rice yield statistically comparable to that of $T_1$. In contrast, the fully inorganic treatment $T_6$ yielded potato tuber yield statistically similar to $T_1$. However, for groundnut, the pod yield under $T_1$ was significantly higher than all other treatments. The inorganic nutrient management method ($T_6$), where 100% of the recommended NPK was applied from inorganic fertilizers, produced the lowest rice yield (1.45 t·ha⁻¹) but ranked second in potato tuber yield (16.02 t·ha⁻¹) and third in groundnut pod yield (1.45 t·ha⁻¹) (Table 2).

Among the different organic nutrient management treatments ($T_2$ to $T_5$), treatment $T_5$ produced significantly greater yields of all three crops (rice, 1.65 t·ha⁻¹; potato tuber, 14.27 t·ha⁻¹; and groundnut, 1.50 t·ha⁻¹). Conversely, treatment $T_4$, where 50% N from FYM combined with beneficial microorganisms was used, had the lowest yield among the organic management approaches for the three crops (rice, 1.52 t·ha⁻¹; potato tuber, 10.62 t·ha⁻¹; and groundnut, 1.30 t·ha⁻¹).

3.2. Yield Economics as Influenced by Different Sources of Organic and Inorganic Fertilizers

In addition to examining crop yields, economic analysis also considers the profitability associated with various nutrient management practices. When the original yields were converted to EYs, which factor in the higher prices for organic products, significant variations were observed in the SREY, system gross return (GR) and benefit cost ratio (BCR) across the different treatments (Table 3).

Table 3

System rice equivalent yield (SREY), system cost of cultivation (COC), system gross return (GR) and benefit cost ratio (BCR) under different nutrient management practices.

Treatment	SREY (t·ha−1)				System COC (× 103 $·ha−1)
	2016	2017	2018	Pooled	2016	2017	2018	Pooled
$T_1$	12.78b	13.13b	13.67c	13.20ab	1.77c	1.82c	1.92c	1.84c
$T_2$	12.00c	12.81c	13.49c	12.76bc	1.98b	2.09b	2.23b	2.10b
$T_3$	12.68b	13.16b	14.19b	13.34ab	1.98b	2.09b	2.23b	2.10b
$T_4$	11.04d	11.76e	11.91d	11.57d	1.59d	1.64d	1.72d	1.65d
$T_5$	13.19a	14.26a	15.38a	14.28a	2.02a	2.13a	2.28a	2.14a
$T_6$	11.93c	12.22d	11.87d	12.00cd	1.76c	1.81c	1.89c	1.82c
SEm (±)	0.06	0.07	0.10	0.07	0.01	0.01	0.02	0.01
CD ($p\le 0.05$)	0.18	0.21	0.31	0.22	0.03	0.03	0.04	0.03
Treatment	System GR ($\times \mathbf{1}{\mathbf{0}}^{\mathbf{3}}\text{\hspace{0.17em}\$\hspace{0.17em}}\mathbf{h}{\mathbf{a}}^{-\mathbf{1}}$)				Benefit cost ratio (BCR)
	2016	2017	2018	Pooled	2016	2017	2018	Pooled
$T_1$	3.74b	3.98b	4.21c	3.98ab	2.11a	2.19a	2.20a	2.17a
$T_2$	3.51c	3.88c	4.15c	3.85bc	1.77f	1.86e	1.86d	1.83e
$T_3$	3.71b	3.98b	4.36b	4.02ab	1.87e	1.90 d	1.95c	1.91d
$T_4$	3.23d	3.55e	3.67d	3.49c	2.03b	2.16b	2.13b	2.11b
$T_5$	3.86a	4.32a	4.73a	4.30a	1.91d	2.03c	2.08b	2.00c
$T_6$	3.49c	3.70d	3.65d	3.61c	1.98c	2.04c	1.93c	1.99c
SEm (±)	0.02	0.02	0.03	0.01	0.01	0.01	0.02	0.01
CD ($p\le 0.05$)	0.05	0.06	0.09	0.04	0.03	0.03	0.05	0.02

Note:$T_1$—50% recommended NPK in inorganic form + 50% recommended N from FYM + 10 kg·ha⁻¹ ZnSO₄, $T_2$—33% of recommended N each from FYM, vermicompost and neem cake, $T_3$—$T_2$ + Dhaincha as green manure, $T_4$—50% N from FYM + Azospirillum (4.5 kg culture ha⁻¹) and PSB (4.5 kg culture ha⁻¹), $T_5$—$T_2$ + use of biofertilizers same as $T_4$, $T_6$—100% recommended NPK in inorganic form + 10 kg·ha⁻¹ ZnSO₄, $T_7$—fallow; numbers followed by different letters are significantly different at $p\le 0.05$ according to Duncan’s multiple-range test (DMRT).

The SREY ranged from 11.57 to 14.28 t ha⁻¹ among the treatments, whereas the GR varied between 3.61 and 4.30$ ha⁻¹ across the treatments. The organic treatment $T_5$ presented the highest SREY (14.28 t ha⁻¹) and GR (4.30 $ ha⁻¹), followed by the organic treatment $T_3$ (SREY—13.34 t·ha⁻¹ and GR—4.02 $ ha⁻¹) and the INM treatment $T_1$ (SREY—13.20 t ha⁻¹ and GR—3.98 $ ha⁻¹). There was no significant difference in the SREY or GR values among the top three treatments. The sequence for the system COC values was as follows: $T_5$ > $T_2$ = $T_3$ > $T_1$ > $T_6$ > $T_4$. The BCR was highest in the INM treatment at $T_1$ (2.16), followed by the treatments containing organic sources of nutrients, $T_4$ (2.12) and $T_5$ (2.01). In comparison, the inorganic nutrient management treatment $T_6$ resulted in 9.1%, 9.3% and 8.3% lower SREY, GR and BCR values than did $T_1$, respectively (Table 3).

3.3. Soil Quality as Influenced by Different Sources of Organic and Inorganic Fertilizers

Table 4 presents 3 years of pooled data on the soil chemical properties. The results indicate that all the treatments maintained a near-neutral pH, with fallow land ($T_7$) having the closest value to the neutral soil pH (7.12), followed by $T_6$ (7.33), which was the sole inorganic treatment. The EC value did not significantly differ across the treatments. The full organic treatments ($T_2$–$T_5$) outperformed the other treatments in terms of the soil SOC and SOC stocks. The highest and lowest SOC contents occurred under only organic treatment $T_3$ (0.96%) and fallow land, that is, $T_7$ (0.60%). There were no significant differences in the SOC and SOC stock values among the organic treatments. Additionally, all the organic ($T_2$–$T_5$), integrated ($T_1$) and purely inorganic ($T_6$) treatments presented higher soil available N, P, K and S contents than did the fallow land ($T_7$). The organic treatment $T_5$ (FYM + VC + NC + BF) resulted in the highest soil N, P and K contents (297, 61.6, 168.3 kg·ha⁻¹), followed closely by the $T_3$ treatment (FYM + VC + NC + GM), with soil nutrient contents of 287.4 kg·ha⁻¹ for N, 61.3 kg·ha⁻¹ for P and 164.4 kg·ha⁻¹ for K (Table 4).

Table 4

Impact of various long-term nutrient management practices on soil characteristics (pooled data from 3 years).

Treatment	pHw	ECw	N	P	K	SOC	SOC stock	S	Fe
$T_1$	7.50b	0.12	266.9b	50.3b	146.6a	0.81b	21.4b	18.6b	22.8a
$T_2$	7.57ab	0.12	265.9b	52.6b	152.0a	0.94a	24.7a	8.5f	22.5a
$T_3$	7.65a	0.12	287.4ab	61.3a	164.4a	0.96a	24.9a	7.4g	13.2bc
$T_4$	7.64ab	0.10	237.5c	45.7c	142.3a	0.90a	23.7a	11.4d	14.3b
$T_5$	7.54ab	0.12	297.0a	61.6a	168.3a	0.95a	24.8a	14.8c	15.2b
$T_6$	7.33c	0.11	225.8cd	40.8 d	132.0a	0.65c	17.6c	21.8a	22.1a
SEm (±)	7.12d	0.12	212.1d	32.3e	128.7a	0.60d	16.2c	9.4e	11.0c
CD ($p\le 0.05$)	0.15	NS	42.17	3.69	NS	0.07	1.78	0.27	2.36
Treatment	Mn	Zn	TSB	PSB	CDB	STF	Acid phtse	Alk phtse
$T_1$	17.2c	1.23a	14.5d	6.8d	5.3d	19.8a	94.1e	160.1e
$T_2$	27.2a	0.70bc	16.4c	13.0b	8.5bc	14.1b	105.4c	180.5a
$T_3$	26.4a	0.65cd	13.3d	8.3c	9.0b	12.8b	106.8b	161.9d
$T_4$	25.7ab	0.60d	20.5b	14.3b	7.8c	10.5c	103.7d	167.6c
$T_5$	27.0a	0.73b	24.0a	16.1a	10.2a	19.5a	114.1a	175.4b
$T_6$	23.6b	1.26a	11.3e	4.8e	3.9e	9.6c	90.6f	153.6f
SEm (±)	14.8c	0.71b	10.2e	6.1de	5.1d	7.6d	80.8g	112.7g
CD (p ≤ 0.05)	2.46	0.05	1.35	1.38	1.07	1.50	0.71	0.73

Note: N, P, K and S—available N, P, K and S (kg ha⁻¹); SOC—oxidizable organic C (%); Fe, Mn and Zn—DTPA-extractable Fe, Mn and Zn (mg kg⁻¹); PSB—total soil phosphate-solubilizing bacteria (× 10⁵ CFU); CDB—total cellulose-decomposing bacteria (× 10⁴ CFU); STF—total soil fungi (× 10⁴ CFU); acid phtse and alk phtse—acid and alkaline phosphatase activities of soil (μg p-nitrophenol formed g⁻¹ soil h⁻¹). $T_1$—50% recommended NPK in inorganic form + 50% recommended N from FYM + 10 kg·ha⁻¹ ZnSO₄, $T_2$—33% of recommended N each from FYM, vermicompost and neem cake, $T_3$—$T_2$ + Dhaincha as green manure, $T_4$—50% N from FYM + Azospirillum (4.5 kg culture ha⁻¹) and PSB (4.5 kg culture ha⁻¹), $T_5$—$T_2$ + use of biofertilizers same as $T_4$, $T_6$—100% recommended NPK in inorganic form + 10 kg·ha⁻¹ ZnSO₄, $T_7$—fallow; numbers followed by different letters are significantly different at $p\le 0.05$ according to Duncan’s multiple-range test (DMRT).

Abbreviations: EC, electrical conductivity (dS m⁻¹); SOC stock, soil organic carbon stock (Mg C ha⁻¹); TSB, total soil bacteria (× 10⁶ CFU).

Compared with all the organic and integrated treatments, the fully inorganic $T_6$ treatment resulted in lower N, P and K contents. The soil K content did not vary significantly across the different nutrient management treatments. Among all the treatments, inorganic treatment $T_6$ resulted in a significantly greater available S content, followed by integrated treatment $T_1$ and organic treatment $T_5$. The three contrasting treatments, $T_1$ (integrated), $T_2$ (full organic) and $T_6$ (full inorganic), presented significantly greater DTPA-extractable Fe contents, whereas all the organic treatments ($T_2$ to $T_5$) presented significantly greater DTPA-extractable Mn contents. The highest Zn content was observed in the treatments in which inorganic Zn was applied ($T_1$ and $T_6$) (Table 4). The mean values of the soil biological properties are also given in Table 4.

In general, only the organic plots ($T_2$ to $T_5$) presented significantly greater microbial populations and enzymatic activity than did the other treatments. Compared with the inorganic ($T_6$) or fallow ($T_7$) plots alone, the integrated treatment ($T_1$) resulted in better soil biological properties. The $T_5$ treatment (FYM + VC + NC + BF) resulted in the highest population of total soil bacteria, PSB and CDB. In contrast, the integrated $T_1$ treatment resulted in the greatest fungal population. Moreover, the $T_5$ treatment resulted in the highest acid phosphatase activity, whereas the $T_2$ treatment (33% of the recommended N from each FYM, VC and NC) resulted in the highest alkaline phosphatase activity (Table 4).

3.4. Soil Quality Indicators as Influenced by Different Sources of Organic and Inorganic Fertilizers

The SQI for the impact of the selected treatment variables on the system was established with 17 selected soil parameters, which were added to the PCA to identify the controlling factors for soil quality (Table 5).

Table 5

Results of the principal component analysis of the soil quality indicators.

Principal components	PC1	PC2	PC3	PC4	PC5
Eigen value	4.47	3.75	2.97	2.47	1.13
% Of total variance	0.26	0.22	0.17	0.15	0.07
Cumulative variance	0.26	0.48	0.66	0.8	0.87
Weightage factor	0.29	0.25	0.19	0.17	0.08
Factor loadings/Eigen vectors
Ph	0.67	0.44	−0.09	0.1	−0.36
EC	−0.03	0.14	0.01	0.03	0.95
Avl. N	0.34	0.79	−0.01	0.04	0.15
Avl. P	0.58	0.73	−0.03	0.18	0.1
Avl. K	0.09	0.73	−0.12	0.16	0
SOC	0.71	0.55	−0.21	0.29	−0.07
Avl. S	−0.23	−0.07	0.91	0.1	−0.05
Fe	0.31	0.02	0.84	−0.12	0.08
Mn	0.82	0.02	−0.05	0.23	0.14
Zn	−0.24	−0.01	0.91	−0.28	0
Soil TSB	0.31	0.31	−0.06	0.87	−0.01
Soil PSB	0.45	0.18	−0.25	0.79	0.05
Soil CBD	0.54	0.43	−0.45	0.45	0.08
Fungi	0.12	0.73	0.39	0.37	0.1
Acid Phtse	0.70	0.5	−0.13	0.47	−0.04
Alk.Phtse	0.78	0.37	0.25	0.36	−0.11
SOC stock	0.71	0.54	−0.21	0.28	−0.09

Note: N, P, K and S—available N, P, K and S; SOC—oxidizable organic C; Fe, Mn and Zn—DTPA-extractable Fe, Mn and Zn; PSB—total soil phosphate-solubilizing bacteria; CDB—total cellulose-decomposing bacteria; STF—total soil fungi; acid phtse and alk phtse—acid and alkaline phosphatase activities of soil.

Abbreviations: EC, electrical conductivity; SOC stock, soil organic carbon stock; TSB, total soil bacteria.

PC1 explained 26% of the total variability. The high-loading variables associated with PC1 included Mn, SOC, SOC stock, acid phosphatase and alkaline phosphatase. Importantly, all these factors exhibited significant correlations ($p<0.01$) with each other. Although available Mn presented the highest factor loading, SOC was chosen as a globally recognized soil quality indicator. PC2 accounted for 22% of the overall variation, highlighting crucial soil properties such as available N, P and K and the soil fungal count. Among these, available N was identified as the MDS because of its significance as a key fertility component in sustaining crop yield. Similarly, PC3 and PC4 accounted for 17% and 15% of the total variation, respectively, and available S and soil TBC were selected as the MDS with the highest factor loading. PC5 explained 7% of the total variability, and EC was retained as MDS, as it was the only highly weighted parameter (Table 5).

3.5. Changes in the SQI Under Different Long-Term Treatments

The quality of the soil was evaluated via selected soil indicators from PCA, namely, SOC, available N, available S, TSB and EC. Four distinct SQIs were employed: linear additive (SQI_LA), nonlinear additive (SQI_NLA), linear weightage (SQI_LW) and nonlinear weightage (SQI_NLW) (Figure 1). The outcomes indicated that across all the treatments, the SQI_LA values ranged from 2.51 to 4.018, the SQI_NLA values ranged from 1.71 to 3.51, the SQI_LW values ranged from 0.456 to 0.709, and the SQI_NLW values ranged from 0.287 to 0.485. In all methods, the organic treatment $T_5$ yielded the highest SQI value (SQI_LA—4.01; SQI_LW—0.71; SQI_NLA—3.04; SQI_NLW—0.51) ($p<0.001$), closely followed by the integrated treatment $T_1$ (SQI_LA—3.66; SQI_LW—0.66; SQI_NLA—2.82; SQI_NLW—0.47) irrespective of crop, cropping year and methodology, whereas the fallow treatment $T_7$ presented the lowest SQI value (SQI_LA—2.51; SQI_LW—0.45; SQI_NLA—1.71; SQI_NLW—0.28). The $T_5$ and $T_1$ treatments consistently outperformed the inorganic treatment $T_6$ alone across all methods. The exclusion of biofertilizers in the organic treatments ($T_2$ and $T_3$) significantly decreased the SQI values compared with those in the organic treatment supplemented with biofertilizers ($T_5$) (Figure 1).

[figure(s) omitted; refer to PDF]

The estimated SQI values for both LSF and NLSF were regressed with the SREY to evaluate the best representative SQI with better management goals (Figure 2). Linear regression analysis of the output of the whole cropping sequence of all 3 years revealed a stronger interrelationship between the SQI and SREY for weightage-based SQIs ($R^2$ values of 0.56 and 0.44 for SQI_LW and SQI_NLW, respectively; n = 54) than for additive-based SQIs ($R^2$ values of 0.302 and 0.231 for SQI_LA and SQI_NLA, respectively; n = 54) (Figure 2).

[figure(s) omitted; refer to PDF]

The contributions of the indicators to the weight-based SQIs (SQI_LW and SQI_NLW) are presented in Figure 3. The changes in the SQI throughout the cropping season were not significant ($p<0.001$). With respect to the soil indicators contributing to the SQI, SOC, available nitrogen (available N) and available sulphur (available S) collectively accounted for 83.3%–88.6% of the SQI_LW and 87.2%–90.5% of the SQI_NLW across all the treatments. For all the organic treatments ($T_2$ to $T_5$), integrated treatment ($T_1$) and fallow plot ($T_7$), SOC contributed the most to SQI_LW (35.0%–45.6%), followed by available N (28.8%–35.6%) and available S (9.4%–25.2%). In the inorganic treatment ($T_6$), the order of contribution to the SQI_LW was available S (31.7%) > SOC (30.3%) > available N (28.2%). For all the organic treatments ($T_2$ to $T_5$), SOC contributed the most to SQI_NLW (34.2%–43.1%), followed by available N (27.5%–37.0%) and available S (7.6%–23.2%). In the integrated treatment ($T_1$), the contributions of these three parameters to the SQI_NLW were roughly equal (SOC—30.3%, available N—29.2% and available S—29.1%). The sequence for contribution to the SQI_NLW in the inorganic treatment ($T_6$) was available S (36.7%) > available N (27.3%) > SOC (24.3%), whereas for the fallow plot ($T_7$), the sequence was available N (33.2%) > SOC (29.9%) > available S (20.2%). The SOC was identified as the limiting factor in treatments $T_6$ and $T_7$, while available S emerged as the primary limiting factor in $T_3$, followed by $T_2$ and $T_4$ (Figure 3). The limiting factors are much more prominent in the case of SQI-NLSF (Figure 1).

[figure(s) omitted; refer to PDF]

4. Discussion

4.1. Crop Yield as Influenced by Different Sources of Organic and Inorganic Fertilizers

The INM practice ($T_1$) consistently exhibited superior performance across all crops and study years (Table 2). This result is in line with previous research findings of several workers, viz. Mondal et al. [38]; Kumari et al. [39]; Gangmei and George [40]; and Bose et al. [41]. The combination of bulky organic manures such as FYM with chemical fertilizers ensures a continuous supply of nutrients, thereby fostering robust plant growth and development. In contrast, compared with $T_1$, the purely inorganic treatment ($T_6$) resulted in 20.2%, 4.3% and 16.7% lower yields of rice, potato tubers and groundnut pods, respectively. Among the organic treatments, $T_5$, which involved a combination of organic sources and microbial inoculants (FYM + VC + NC + BF), yielded promising results, particularly for rice and groundnuts. Soil microbial activity is a key factor of crop productivity in organic farming systems. Beneficial microorganisms perform important roles in nutrient cycling, improving soil structure, suppressing pathogenic fungi and promoting overall plant health. Together, these functions create optimal conditions for crop growth. Such microbial interactions are especially critical for nutrient-demanding crops like rice and groundnut, where increased soil biological activity can greatly enhance yield potential and promote long-term sustainability [42, 43]. This finding is supported by studies highlighting the positive impacts of diverse organic inputs and biofertilizers on crop productivity [44–47]. However, the performance of organic treatments varies across crops. While organic practices generally increase rice and groundnut yields, they have contrasting effects on potato productivity. This may be due to the high and timely nutrient requirement of potato for optimal growth. The heavy feeding nature of potato makes it highly responsive to nutrient inputs [48, 49]. Some organic treatments ($T_4$) were supplied with limited organic sources with low nutrient content, which may have been inadequate to support optimal crop growth. These restricted nutrient supplies cause long-term decreases in system productivity [50–52]. Similar findings were reported elsewhere [7], indicating that the absence of synthetic nitrogen fertilizers in purely organic systems contributes to a wider production gap between organic and conventional practices, particularly in nutrient-intensive crops like potato. The absence of Zn in organic treatments may have resulted in reduced photosynthetic activity, and it simultaneously impaired the uptake of essential macronutrients, including N, P and K. Zn deficiency weakens potato plant growth, which results in decreased total tuber production [53, 54]. The superior performance of pure inorganic treatment in terms of potato productivity underscores the high nutrient requirements of potato and its responsiveness to chemical fertilizers, especially zinc. On the other hand, the suboptimal performance of certain organic treatments ($T_2$, $T_3$ and $T_4$) emphasizes the importance of the use of diverse organic amendments and biofertilizers to ensure optimal nutrient sources for sustaining long-term soil fertility and crop productivity [48, 49, 52]. The observed increase in crop yields over time under organic treatments suggests the cumulative benefits of long-term organic soil management, as highlighted in other studies [55, 56].

4.2. Yield Economics as Influenced by Different Sources of Organic and Inorganic Fertilizers

The organic treatment $T_5$, which included diverse organic amendments and biofertilizers, generally resulted in relatively high SREYs and GRs (Table 3). However, it incurred greater cultivation costs because of expenses associated with organic inputs, transportation and availability constraints [57, 58]. Despite these higher initial costs, both organic and integrated treatments demonstrated economic superiority over purely inorganic approaches in the final year of the study. This shift towards profitability can be attributed to sustained yield improvements under organic practices and the rising prices of organic products over time. These findings underscore the importance of integrated approaches for achieving both agronomic and economic sustainability in agricultural systems.

4.3. Soil Quality Parameters as Influenced by Different Sources of Organic and Inorganic Fertilizers

The results (Table 4) clearly indicate that the organic treatments presented the highest SOC content, followed by the integrated, purely inorganic and fallow land treatments. This pattern is consistent with the findings of various researchers [50, 59, 60]. The observed trend was attributed to the continuous application of organic manure, which led to the accumulation of SOC. The increased organic matter stimulated soil microorganism activity, significantly influencing the decomposition of organic matter and maintaining a consistent mineralization rate. As a result, the available nitrogen (N) and phosphorus (P) contents of the soil improved in the plots treated with organic matter. Additionally, the introduction of Azospirillum and PSB increased N availability and facilitated P release from insoluble P minerals. This improved N and P availability contributed to increased crop growth, leading to greater amounts of above- and below-ground organic residues being incorporated into the soil [61–63]. Moreover, organic amendments can increase the cation exchange capacity, a crucial indicator for retaining and making nutrients available to plants [64, 65]. Among the amendments, FYM is a rich source of carbon and contains multiple nutrients that boosted microbial biomass, enzymatic activity and nutrient cycling. The fine-textured VC is a biologically active compound that contains multiple nutrients and humic substances. Beneficial microbes (nitrogen fixers and phosphate solubilizers) in the VC enhanced microbial diversity and activity in agricultural soils. The bulky organic manure NC contained antimicrobial substances that selectively killed detrimental microorganisms but maintained the beneficial organisms responsible for nutrient mineralization and pest management. Biofertilizers bring beneficial microbes to the soil or boost populations of Rhizobium, Azotobacter and PSB, which play essential roles in key nutrient transformation activities like nitrogen fixation and phosphorus solubilization [66–68]. Compared with the inorganic plots, the organic-treated plots presented greater potassium (K) contents. The increased availability of K with organic amendments may be attributed to the reduction in K fixation and the release of K due to the interaction of organic matter with clay [69, 70]. Compared with the other plots, inorganic plot $T_6$ and integrated plot $T_1$ presented greater amounts of available sulphur (S)- and DTPA-extractable Zn, as these plots received single superphosphate (SSP) and zinc sulphate (ZnSO₄) as inorganic fertilizers (Table 4).

Compared with those in both the purely inorganic treatment ($T_6$) and fallow land ($T_7$), the microbial populations in the organically amended treatments ($T_1$–$T_5$) presented significantly greater values (Table 4). This phenomenon can be attributed to the prolonged application of various organic manures, resulting in increased SOC. This carbon, which serves as a primary energy source for biological activity, contributes to increased microbial density [45, 71–74]. The least noticeable abundance of total bacteria was observed in the unfertilized ($T_7$) and typical chemical fertilizer treatments ($T_6$), which aligns with findings from previous studies [71, 75]. The soil phosphatase activity also showed the same trend as that of the soil microbial population in this study, where the application of organic manure ($T_1$-$T_5$) had a greater positive impact than did the application of unfertilized manure ($T_7$) and typically inorganic fertilized plots ($T_6$), which is supported by many earlier studies [76–78]. The addition of organic matter increases soil nutrients throughout the cropping sequence; improves soil (physical, chemical and biological) characteristics, such as nutrient content and water holding capacity; increases the ability of soil to support microbial activity as well as various bioprocesses; and ultimately stimulates soil phosphatase activity during long-term studies [76, 79]. Conversely, soil phosphatase activity was lower under typical inorganic nutrient management ($T_6$) in this study because the application of inorganic fertilizers alone suppressed the long-term activities of acid and alkaline phosphatases [80].

4.4. Changes in the SQI Under Different Long-Term Treatments

The SQI is a product of soil quality parameters, and the use of a TDS may best represent soil quality, although it may be complicated, labour-intensive and time-consuming. Following prior studies [23, 34, 37, 81–83], PCA was used for the selection of the MDS, which can be an easy and unbiased tool for predicting soil quality for better crop planning. The results (Table 5) revealed that PC1 was a dynamic soil component, where the major weighted variables were SOC, available Mn, acid and alkaline phosphatase and total SOC stocks. In particular, organic amendments have beneficial effects on soil quality and SOC dynamics in the short term [84, 85] and long term [86, 87], and SOC is a globally accepted critical soil quality indicator [88–90] that impacts soil nutrient availability [91, 92] and soil biological properties [93, 94]. Among the well-correlated variables under the dynamic soil component, the SOC is selected based on the practicability of the indicator over the weight of the variables [31, 95]. PC2 and PC3 (Table 5) can be regarded as nutrient components where available N and available S are selected as MDSs because of their direct influence on soil productivity and yield under organic and INM practices [8, 96, 97]. PC4 can be regarded as a soil biological component, where highly correlated soil TBC and PSB present the highest loading factor. The application of organics for a prolonged period of time and throughout the cropping sequences in treatments $T_1$ to $T_5$ certainly affected the soil microbial strata, particularly the soil TSB, in terms of soil health and nutrient dynamics (Figures 1 and 3). TSB was selected as a critical biological indicator in comparison with the SQI under various organic applications [98, 99]. Similarly, with comparatively high loading values, EC was selected as the MDS from PC5. These selected quality indicators can be further recommended for the estimation of overall soil quality and for monitoring or tracking soil fertility under similar soil types, crop management practices and climatic settings. In this study, both LSF and NLSF were used to estimate the SQI through a weighted index as well as an additive index, and these indices were compared with the overall long-term cropping sequence performances in terms of the holistic yield parameter SREY. All four SQIs have reasonably good relationships with the SREY values calculated for all three crops; however, the differences are significant. Figure 2 clearly indicates the superiority of weight indexing over the additive method, whereas LSF was found to be better representative of yield potential than NLSF was. The results of the current investigation were in line with those of earlier studies [30, 33, 100, 101]. The accuracy and simplicity of LSF’s mathematical calculations make it more suitable than NLSF for the transformation and normalization of soil quality indicators [100]. However, researchers have discovered that NLSF is more thorough in terms of how each indicator functions than the linear method is [101, 102], and [103]. In this experiment, the weighted-based scoring methods outperform the additive method in both LSF and NLSF because subjective weights are assigned to all the selected indicators. The trade-offs among soil ecosystem functions, the spatiotemporal scale of soil quality estimates and the heterogeneity and complexity of soil environments [101, 104, 105] largely influence the choice of scoring functions.

In practical terms, SQI offers significant utility for farmers. The measurement of SQI helps the farmers to find degraded areas in their field, and they can apply specific corrective measures to the area. Farmers can use the SQI data to determine appropriate fertilizer rates that reduce both farm expenses and environmental damage that occurs due to excessive fertilizer applications [106]. SQI also helps in assessing field-level variability, guiding precision management and long-term soil health strategies [107–109]. SQI data also enable policymakers to distribute subsidies that will support restoration of soil health. SQI maps help to identify the areas at risk, which enables targeted conservation work that matches the objectives of India’s Soil Health Card Scheme.

The SQI values observed in this study (SQI_LW = 0.59–0.78) reflect the unique interplay of tropical monsoonal climates, alluvial soils and rice-based cropping intensification in eastern India. These values are comparable to those reported under semi-humid environmental conditions in parts of the Black Sea region (SQI_LW = 0.44–0.75; [110]). However, they differ significantly from values recorded during various sugarcane monoculture periods (SQI_LW = 0.38–0.71) as reported by Kusumawati et al., [111]. Local weather patterns, soil composition and farming techniques significantly influence both SQI measurements and their responsiveness, affecting which MDSs and scoring techniques researchers employ during indexing processes [24, 112]. Several challenges persist, however. Variations in climate, especially monsoon patterns, can disrupt soil biology and nutrient processes, potentially destabilizing SQI measurements across different timeframes [112]. Furthermore, nonlinear scoring and weighting methodologies may place excessive emphasis on extreme soil measurements such as pH levels and EC values. Additive approaches, meanwhile, risk failing to properly represent crucial biological markers like enzyme functionality [31]. Ultimately, this work confirms that developing soil assessment systems specifically designed for particular agroclimate–cropping combinations remains fundamental for properly evaluating soil conditions and understanding agricultural ecosystem functions, thereby promoting sustainable farming intensification under fluctuating environmental circumstances.

5. Conclusions

Combining NPK fertilizers with FYM consistently increased crop productivity and achieved the highest yield for all three crops, viz. rice, potato and groundnut compared with organic application alone and/or chemical fertilizers alone. However, the highest SREY (14.28 t·ha⁻¹) and GR (4.30 $ ha⁻¹) due to higher prices for the organic products) as well as soil N, P, K, SOC content and SOC stock were achieved with the sole organic application (FYM + VC + NC  + biofertilizers) over the nutrient management options comprising integrated and/or sole inorganic fertilizers. The application of organic nutrient management through diverse sources as well as integrated nutrient sources has emerged as the best management method for improving the physical, chemical and biological properties of soil and thereby contributing to sustaining its quality. Among the 17 soil quality attributes in the present study, SOC, available N, available S and total soil bacteria count were the most sensitive MDS indicators influencing soil quality, in which SOC, available N and available S collectively accounted for 83.3%–90.5% across all the treatments and methods. From the calculation of four SQIs, including linear additive (SQI_LA), nonlinear additive (SQI_NLA), linear weightage (SQI_LW) and nonlinear weightage (SQI_NLW) were used. The application of sole organics through FYM, VC and NC along with biofertilizers yielded the highest SQI value, closely followed by the INM treatment. Overall, the weighted linear (SQI_LW) method was found to be the most sensitive and suitable method for modelling soil quality. The exclusion of biofertilizers from different organic nutrient management options in scented rice–potato–groundnut systems significantly decreased the SQI value (13.36%) compared with that in the same organic treatment, which included biofertilizers. These results indicate that the long-term use of biofertilizers can simultaneously improve yield sustainability and soil quality in intensive cropping systems. Additionally, soil quality and crop productivity in organic production systems can be further improved by periodic assessment of the selected indicators, viz. SOC, available N and available S in soil under a scented rice–potato–groundnut system in the Gangetic alluvial soils of Eastern India. This holistic approach for assessing soil quality can be useful for assessing related soil types and agroclimatic conditions throughout a region.

Ethics Statement

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The authors have nothing to report.

Author Contributions

Conceptualizations: Pinaki Bose, Manabendra Ray, Pulak Kumar Patra, Shubhadip Dasgupta, Kaushik Saha, Arup Sen, Sushanta Saha, Ratneswar Poddar, Soumitra Chatterjee and Swapan Kumar Mukhopadhyay; data collection, management, software and analysis: Pinaki Bose, Sulaiman Ali Alharbi, Mohammad Javed Ansari and Akbar Hossain; methodology: Pinaki Bose, Manabendra Ray, Pulak Kumar Patra, Shubhadip Dasgupta, Kaushik Saha, Arup Sen, Sushanta Saha, Ratneswar Poddar, Soumitra Chatterjee and Swapan Kumar Mukhopadhyay; investigation, visualization and resources: Pinaki Bose, Manabendra Ray, Pulak Kumar Patra, Shubhadip Dasgupta, Kaushik Saha, Arup Sen, Sushanta Saha and Ratneswar Poddar; writing the article: Pinaki Bose, Manabendra Ray, Pulak Kumar Patra, Shubhadip Dasgupta, Kaushik Saha, Arup Sen, Sushanta Saha, Ratneswar Poddar, Soumitra Chatterjee and Swapan Kumar Mukhopadhyay; review and editing: Pinaki Bose, Sulaiman Ali Alharbi, Mohammad Javed Ansari and Akbar Hossain; supervision: Pinaki Bose, Sulaiman Ali Alharbi, Mohammad Javed Ansari and Akbar Hossain; project administrators: Pinaki Bose, Sulaiman Ali Alharbi, Mohammad Javed Ansari and Akbar Hossain; funding: Pinaki Bose, Sulaiman Ali Alharbi, Mohammad Javed Ansari and Akbar Hossain; all the authors agreed to submit the article to the journal.

Funding

The study was funded by All India Co-ordinated Research Project on Integrated Farming System, Kalyani Centre, Bidhan Chandra Krishi Viswavidyalaya (PROJECT CODE-905), Nadia, West Bengal, India.

Acknowledgements

The authors acknowledge All India Co-ordinated Research Project on Integrated Farming System, Kalyani Centre, Bidhan Chandra Krishi Viswavidyalaya (PROJECT CODE-905), Nadia, West Bengal, India, for financially supported the current study.

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