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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 (
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 |
50% recommended NPK in inorganic form + 50% recommended N from FYM + 10 kg·ha−1 ZnSO4·7H2O | |
33% of recommended N each from FYM, vermicompost and neem cake | |
50% N from FYM + Azospirillum (4.5 kg culture ha−1) and PSB (4.5 kg culture ha−1) | |
100% recommended NPK in inorganic form + 10 kg·ha−1 ZnSO4·7H2O | |
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 (
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:
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]:
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.
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)).
For the NLSF, soil indicators are transformed via the following sigmoidal curve equation [23, 33, 34]:
Finally, additive SQI calculation through LSF and NLSF methods was calculated, which were denoted as SQILA and SQINLA, and weighted SQI through LSF and NLSF methods was finally calculated , which were denoted as SQILW and SQINLW.
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 | |
1.76a | 1.78a | 1.80a | 1.78a | 16.50a | 16.80a | 16.92a | 16.74a | 1.68a | 1.72a | 1.83a | 1.74a | |
1.42d | 1.62c | 1.63b | 1.56bc | 12.37c | 12.80e | 12.89d | 12.68c | 1.21cd | 1.35c | 1.40c | 1.32c | |
1.53c | 1.57d | 1.64b | 1.58bc | 13.12b | 13.43d | 13.89c | 13.48bc | 1.25cd | 1.36c | 1.41c | 1.34c | |
1.44cd | 1.53e | 1.58b | 1.52bc | 10.85d | 11.33f | 10.62e | 10.94d | 1.19d | 1.33c | 1.38c | 1.3c | |
1.65b | 1.65b | 1.66ab | 1.65ab | 13.52b | 14.30c | 15.00b | 14.27b | 1.31c | 1.58ab | 1.61b | 1.50b | |
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 ( | 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:
The INM approach (
Among the different organic nutrient management treatments (
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 $·ha−1) | ||||||
2016 | 2017 | 2018 | Pooled | 2016 | 2017 | 2018 | Pooled | |
12.78b | 13.13b | 13.67c | 13.20ab | 1.77c | 1.82c | 1.92c | 1.84c | |
12.00c | 12.81c | 13.49c | 12.76bc | 1.98b | 2.09b | 2.23b | 2.10b | |
12.68b | 13.16b | 14.19b | 13.34ab | 1.98b | 2.09b | 2.23b | 2.10b | |
11.04d | 11.76e | 11.91d | 11.57d | 1.59d | 1.64d | 1.72d | 1.65d | |
13.19a | 14.26a | 15.38a | 14.28a | 2.02a | 2.13a | 2.28a | 2.14a | |
11.93c | 12.22d | 11.87d | 12.00cd | 1.76c | 1.81c | 1.89c | 1.82c | |
SEm (±) | 0.06 | 0.07 | 0.10 | 0.07 | 0.01 | 0.01 | 0.02 | 0.01 |
CD ( | 0.18 | 0.21 | 0.31 | 0.22 | 0.03 | 0.03 | 0.04 | 0.03 |
Treatment | System GR ( | Benefit cost ratio (BCR) | ||||||
2016 | 2017 | 2018 | Pooled | 2016 | 2017 | 2018 | Pooled | |
3.74b | 3.98b | 4.21c | 3.98ab | 2.11a | 2.19a | 2.20a | 2.17a | |
3.51c | 3.88c | 4.15c | 3.85bc | 1.77f | 1.86e | 1.86d | 1.83e | |
3.71b | 3.98b | 4.36b | 4.02ab | 1.87e | 1.90 d | 1.95c | 1.91d | |
3.23d | 3.55e | 3.67d | 3.49c | 2.03b | 2.16b | 2.13b | 2.11b | |
3.86a | 4.32a | 4.73a | 4.30a | 1.91d | 2.03c | 2.08b | 2.00c | |
3.49c | 3.70d | 3.65d | 3.61c | 1.98c | 2.04c | 1.93c | 1.99c | |
SEm (±) | 0.02 | 0.02 | 0.03 | 0.01 | 0.01 | 0.01 | 0.02 | 0.01 |
CD ( | 0.05 | 0.06 | 0.09 | 0.04 | 0.03 | 0.03 | 0.05 | 0.02 |
Note:
The SREY ranged from 11.57 to 14.28 t ha−1 among the treatments, whereas the GR varied between 3.61 and 4.30$ ha−1 across the treatments. The organic treatment
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 (
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 |
7.50b | 0.12 | 266.9b | 50.3b | 146.6a | 0.81b | 21.4b | 18.6b | 22.8a | |
7.57ab | 0.12 | 265.9b | 52.6b | 152.0a | 0.94a | 24.7a | 8.5f | 22.5a | |
7.65a | 0.12 | 287.4ab | 61.3a | 164.4a | 0.96a | 24.9a | 7.4g | 13.2bc | |
7.64ab | 0.10 | 237.5c | 45.7c | 142.3a | 0.90a | 23.7a | 11.4d | 14.3b | |
7.54ab | 0.12 | 297.0a | 61.6a | 168.3a | 0.95a | 24.8a | 14.8c | 15.2b | |
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 ( | 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 | |
17.2c | 1.23a | 14.5d | 6.8d | 5.3d | 19.8a | 94.1e | 160.1e | ||
27.2a | 0.70bc | 16.4c | 13.0b | 8.5bc | 14.1b | 105.4c | 180.5a | ||
26.4a | 0.65cd | 13.3d | 8.3c | 9.0b | 12.8b | 106.8b | 161.9d | ||
25.7ab | 0.60d | 20.5b | 14.3b | 7.8c | 10.5c | 103.7d | 167.6c | ||
27.0a | 0.73b | 24.0a | 16.1a | 10.2a | 19.5a | 114.1a | 175.4b | ||
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).
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
In general, only the organic plots (
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 (
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 (SQILA), nonlinear additive (SQINLA), linear weightage (SQILW) and nonlinear weightage (SQINLW) (Figure 1). The outcomes indicated that across all the treatments, the SQILA values ranged from 2.51 to 4.018, the SQINLA values ranged from 1.71 to 3.51, the SQILW values ranged from 0.456 to 0.709, and the SQINLW values ranged from 0.287 to 0.485. In all methods, the organic treatment
[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 (
[figure(s) omitted; refer to PDF]
The contributions of the indicators to the weight-based SQIs (SQILW and SQINLW) are presented in Figure 3. The changes in the SQI throughout the cropping season were not significant (
[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 (
4.2. Yield Economics as Influenced by Different Sources of Organic and Inorganic Fertilizers
The organic treatment
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
Compared with those in both the purely inorganic treatment (
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
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 (SQILW = 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 (SQILW = 0.44–0.75; [110]). However, they differ significantly from values recorded during various sugarcane monoculture periods (SQILW = 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 (SQILA), nonlinear additive (SQINLA), linear weightage (SQILW) and nonlinear weightage (SQINLW) 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 (SQILW) 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
The authors have nothing to report.
Consent
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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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 (SQILA), nonlinear additive (SQINLA), linear weighting (SQILW) and nonlinear weighting (SQINLW). Among all the treatments, the INM approach (
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Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
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1 Department of Environmental Studies Visva-Bharati University Santiniketan West Bengal, 731235 India
2 Department of Agronomy Bidhan Chandra Krishi Viswavidyalaya Haringhata West Bengal, 741252 India
3 Department of Agricultural Chemistry and Soil Science Bidhan Chandra Krishi Viswavidyalaya Haringhata West Bengal, 741252 India
4 Department of Agricultural Economics Bidhan Chandra Krishi Viswavidyalaya Haringhata West Bengal, 741252 India
5 Department of Botany and Microbiology College of Science King Saud University P.O. Box-2455, Riyadh 11451 Saudi Arabia
6 Department of Botany Hindu College Moradabad (Mahatma Jyotiba Phule Rohilkhand University Bareilly) Bareilly Uttar Pradesh, 244001 India
7 Soil Science Division Bangladesh Wheat and Maize Research Institute Nashipur, Dinajpur 5200 Bangladesh