Assessment of fertilizer prescription equations, crop and soil performance with different nutrient management practices for green gram (Vigna radiata) in Southern India’s Alfisols

Abstract

The effect of fertilizers application based on “fertilizing the soil versus fertilizing the crop” which ensure real balance between the applied and available soil nutrient is urgently needed. Hence, the present study was conducted during two consecutive crop seasons (Kharif 2022 and Kharif 2023) to assess the effect of imbalanced and balanced fertilization based on initial soil test values and targeted yields, and to determine the effect of different approaches of nutrient recommendation on soil quality, nutrient acquisition, and yield of green gram. The eight fertilizer treatments were laid out in a randomized block design with three replications. The results revealed that STCR integrated approach showed seed yield increases of 41.07% and 55.24% over GFRD, and 54.90% and 64.65% over SFR in 2022 and 2023, respectively. Similarly, root nodules per plant increased by 17.41% and 20.78% over GFRD in 2022 and 2023, respectively. The STCR approach for different yield targets also showed significant improvements in nutrient uptake and value cost ratio (VCR). Utilizing the principal component analysis, the soil quality index effectively reflected the impact of nutrient management on soil properties, with the STCR—integrated application showing the highest correlation with improved soil physical and chemical properties.

Introduction

Soil is a crucial nutrient reservoir essential for crop growth and development. To sustain soil fertility and enhance yields while preserving ecosystem services, it is vital to replenish nutrients extracted by crops through balanced applications of fertilizers and manures. Maintaining soil health is critical for agricultural productivity and environmental quality, supporting beneficial soil organisms and creating a healthy rhizosphere¹. Soil tests and appropriate nutrient applications improve the biological, physical, and chemical properties of the soil, fostering a favorable environment for microbial communities. Fertilizers are key for producing vegetables, cereals, pulses, and other crops. To meet the increasing demand for food grains and vegetables, the application of chemical fertilizers must be balanced, as their nutrient use efficiencies vary: nitrogen (30–50%), phosphorus (15–20%), potassium (60–70%), sulfur (8–10%), and micronutrients (1–2%)¹. When chemical fertilizers are integrated with organic manures, the nutrient use efficiency improves significantly; this combination enhances nitrogen availability through mineralization and increases potassium availability by reducing K-fixation and interacting with soil clays². The Soil Test Crop Response (STCR) approach is a precise method for fertilizer recommendation that integrates soil test values with targeted yield goals. Unlike other methods such as General Recommended Dose (GRD) and soil fertility rating approach (STL), STCR accounts for nutrient contributions from both organic and inorganic sources as well as existing soil nutrients. This approach optimizes nutrient use efficiency, improves productivity, and ensures sustainability by preserving soil health while meeting growing food demands. Its tailored recommendations make STCR ideal for modern, efficient agriculture. This method, validated through extensive field trials, involves soil test-based nutrient management for targeted yields. The targeted yield approach, rooted in Liebig’s law of the minimum, is a fertilizer recommendation method designed to achieve a predetermined crop yield by considering the crop’s nutrient requirements, the soil’s nutrient-supplying capacity, and the contribution of applied fertilizers³. This approach demonstrates a linear relationship between crop yield and nutrient uptake, as nutrient absorption by the crop increases proportionally with nutrient availability in the soil and fertilizers, provided other growth factors such as water, light, and temperature are not limiting. The linearity persists up to a point where the crop’s physiological limits or diminishing returns set in, making this method effective for precise nutrient management across various crops and soil conditions..

India is the largest producer of pulses in the world, contributing around 25% of global production. Pulses account for approximately 23% of the area under food grains and contribute around 9–10% of the total food grains production in the country. The total area under pulses cultivation is about 30 million hectares, with an annual production of around 25 million tonnes, and an average productivity of 851 kg ha⁻¹ (average of 2018–19 to 2022–23). Karnataka contributes around 11% of the total area and 8% of the total production of pulses in India. Green gram, also known as mung bean, is a significant pulse crop in India. The country accounts for over 70% of the world’s green gram production⁴. In the 2022–23 period, the area under green gram cultivation was approximately 15.93 lakh hectares, yielding around 3.74 million tonnes with an average productivity of 570 kg ha⁻¹. Karnataka is one of the major producers of green gram in India. The state contributes around 9% of the total area and 6% of the total production of green gram in the country. The average productivity in Karnataka is 623 kg ha^{−1 4}. Intensive cultivation practices, coupled with environmental changes and the introduction of high-yielding varieties, have highlighted the need for precise nutrient management. Traditional fertilizer practices often involve imbalanced and outdated applications, neglecting soil nutrient variability and resulting in inefficiencies, wasteful expenditures, and risks to soil health⁵. The Soil Test Crop Response (STCR) approach addresses these issues by providing fertilizer recommendations tailored to soil test data, crop requirements, and targeted yield goals, incorporating both organic and inorganic nutrient sources⁶. This study evaluates the performance of green gram under STCR-based fertilizer recommendations, comparing its effectiveness and sustainability with other conventional methods to promote efficient and responsible nutrient management practices.

Material and methods

Experimental site

Field investigations were conducted during the Kharif seasons of 2022 and 2023 at a farmer’s field in Devanahalli village, Bangalore Rural district. This site is situated in the Eastern Dry Zone of Karnataka, geographically located at 13° 24′ 41.1″ N latitude and 78° 08′ 01.9″ E longitude, with an elevation of 880 m above mean sea level. The region’s climate is characterized as a dry tropical savanna, featuring hot summers and relatively mild winters. The experimental site received an annual rainfall of 1556.80 mm in 2022 and 829.20 mm in 2023. Temperature fluctuations across the 2 years ranged from 14.80 to 33.50 °C in 2022 and 13.50 to 33.30 °C in 2023 (as illustrated in Fig. 1). The soil at the research location is classified as well-drained red soil, specifically identified within the taxonomic framework as Typic Kandic Paleustalfs, belonging to the fine mixed Isohyperthermic family.

Fig. 1.

Variation in monthly rainfall and maximum and minimum temperatures during the field experiments during 2022 and 2023 (RF: rainfall; Tmax: maximum temperature; Tmin: minimum temperature).

Prior to the initiation of investigation, the EC and pH of the soil were 7.20 and 0.25 dS m⁻¹. The textural class of soil was sandy loam with 53.72% sand, 34.86% silt and 11.42% clay determined using International pipette method⁷. The potassium dichromate oxidizable organic carbon content in soil was 4.80 g kg⁻¹. Soil available nitrogen, phosphorus and potassium were 285.69 kg ha⁻¹, 97.21 kg ha⁻¹ and 150.35 kg ha⁻¹, respectively. The biological parameters viz., microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), dehydrogenase, alkaline phosphatase activity and acid phosphatase activity were found to be 105.06 mg kg⁻¹, 15.18 mg kg⁻¹, 32.47 µg TPF g⁻¹ 24 h⁻¹, 2.10 µg PNP g⁻¹ h⁻¹ and 7.02 µg PNP g⁻¹ h⁻¹, respectively.

Fertilizer prescription equations for green gram

The development of fertilizer prescription equations based on the STCR approach involves two consecutive field experiments viz., (a) fertility gradient experiment during 2020: This experiment establishes soil fertility levels by dividing the field into three strips: low fertility, medium fertility, and high fertility. A nutrient-exhaustive crop is grown, and post-harvest soil samples are analyzed for nutrient concentrations⁸. and (b) test crop experiment during 2021. After confirming the variation in soil fertility with respect to available NPK, test crop experiment was conducted to develop the targeted yield equation for green gram. This experiment evaluates crop nutrient uptake and fertilizer efficiency under varying nutrient regimes by subdividing fertility strips into blocks with different FYM levels and further into subplots receiving various NPK combinations. Pre-sowing soil samples provide baseline nutrient data, while harvest measurements of crop biomass and nutrient uptake determine nutrient requirement (NR) and contributions from soil (%CS), fertilizer (%CF), and manure (%C-OM). Using these parameters, precise fertilizer prescription equations are developed for NPK alone and NPK combined with FYM to achieve desired yield targets. The data on basic parameters for the development of targeted yield equations viz., (a) nutrient requirement, (b) contribution of nutrients from soil, and (c) fertilizer and farmyard manure were computed from the main experiment (Table 1). Fertilizer recommendations were given separately for the use of chemical fertilizer alone (NPK) as well as for the integrated use of chemical fertilizer and farmyard manure (NPK + FYM) (Table 1).

Table 1.

Basic data and Fertilizer prescription equation.

Sl.no	Particulars	Inorganic			Integrated
		N	P2O5	K2O	N	P2O5	K2O
Basic data for fertilizer prescription equations
1	NR (kg q−1)	6.324	1.056	10.032	6.127	0.973	10.031
2	CS (%)	18.282	8.179	31.415	18.282	8.179	31.415
3	CF (%)	59.994	17.729	117.267	55.420	14.015	141.856
4	C-FYM (%)	–	–	–	0.408	0.069	0.407

Fertilizer dose (kg ha−1)	Inorganic	Integrated
Fertilizer prescription equations
FN	11.06 T − 0.33 SN	10.54 T − 0.31 SN − 0.65 OM
FP	6.65 T − 0.58 SP	5.96 T − 0.46 SP − 0.09 OM
FK	7.07 T − 0.22 SK	8.55 T − 0.27 SK − 0.84 OM

NR nutrient requirement, CS contribution of nutrients from soil, CF contribution of nutrients from fertilizers, C-FYM contribution of nutrients from farmyard manure: FN, FP and FK are fertilizer N, P₂O₅ and K₂O in kg ha⁻¹ respectively; T is the yield target in q ha⁻¹; SN, SP and SK are available soil nutrients as KMnO₄-N, Bray’s-P₂O₅ and NH₄OAc-K₂O in kg ha⁻¹ respectively and OM is the amount of farm yard manure (organic manure) added in t ha⁻¹.

Experimental design and treatments

The field experiment consists of seven fertilization treatments along with one absolute treatment were arranged in randomized complete block design with three replications. The treatment comprised viz., T₁: STCR- inorganic -yield target 1.5 t ha⁻¹, T₂: STCR- integrated—yield target 1.5 t ha⁻¹, T₃: STCR—inorganic- yield target 1.2 t ha⁻¹, T₄: STCR—integrated- yield target 1.2 t ha⁻¹, T₅: General fertilizer recommended dose (100% GFRD), T₆: Soil fertility rating (SFR), T₇: Farmers Practice, and T₈: Absolute control. The experimental design was established, and soil samples were collected from the 0–20 cm soil layer to analyze the initial and post-harvest levels of available nitrogen, phosphorus, and potassium in each experimental plot. Farmyard manure (FYM) was incorporated 15 days prior to sowing, with a nutrient composition of 0.59% nitrogen, 0.30% phosphorus, and 0.55% potassium on a dry weight basis. A basal fertilizer application included half of the recommended nitrogen dose (applied as urea) along with the full recommended doses of phosphorus (applied as single super phosphate) and potassium (applied as muriate of potash). The specific fertilizer rates for each treatment are detailed in Table 2. The crop was grown using standard agricultural practices and harvested at physiological maturity. Protein content in the harvested grain was estimated by multiplying the total nitrogen content by a conversion factor of 6.25, and protein yield was calculated by multiplying the protein content by the seed yield. The response yard stick (RYS), percent deviation and value cost ratio (VCR) were computed via the standard formulae shown below^8,9.

Table 2.

Fertilizer dose under each treatment for green gram.

Treatment	FYM (t ha−1)	N (kg ha−1)		P2O5 (kg ha−1)		K2O (kg ha−1)
		2022	2023	2022	2023	2022	2023
T1	0	59.88	62.40	27.17	29.65	47.07	48.16
T2	7.50	48.16	52.93	24.16	26.84	43.55	51.50
T3	0	51.52	53.68	25.84	27.29	24.69	25.60
T4	7.50	42.44	43.83	19.27	17.76	26.74	25.85
T5	7.50	25.00	25.00	50.00	50.00	50.00	50.00
T6	7.50	31.25	31.25	37.50	37.50	50.00	50.00
T7	7.50	20.50	20.50	30	30	0	0
T8	0	0	0	0	0	0	0

T₁: STCR-inorganic- yield target 1.5 t ha⁻¹; T₂: STCR- integrated- yield target 1.5 t ha⁻¹; T₃: STCR- inorganic- yield target 1.2 t ha⁻¹; T₄: STCR- integrated- yield target 1.2 t ha⁻¹; T₅: General fertilizer recommended dose; T₆: Soil fertility rating; T₇: Farmers Practice; and T₈: Absolute control.

Analysis of soil and plant samples

Soil samples were air-dried and then passed through a 2 mm sieve. Bulk density was determined using metal rings of known volume. Soil cores were oven-dried at 105 °C for 48 h, and bulk density was calculated as the ratio of the oven-dried soil weight to the volume of the ring$^9$. Soil pH was measured using a glass electrode in a 1:2.5 soil-to-water suspension. Electrical conductivity was determined in the supernatant of a 1:2.5 soil-to-water suspension using a conductivity meter¹⁰. Soil organic carbon content was determined by oxidation with 1N K₂Cr₂O₇ followed by titration with 0.5 N ferrous ammonium sulfate¹¹. Available nitrogen was estimated using the alkaline potassium permanganate method¹². Available phosphorus was extracted using Bray’s reagent (0.025 M HCl and 0.03 M NH₄F) and its concentration was measured colorimetrically using the ascorbic acid method¹³. Available potassium was extracted with 1N ammonium acetate (at pH7.0) and quantified using a flame photometer¹⁴.

Plant samples were collected from each treatment, dried in shade and then in a hot air oven at 65 °C, and ground in willey mill. Nitrogen content in plant samples was determined by the micro Kjeldahl method¹⁵. Di-acid extract was prepared using a 9:4 mixture of HNO₃:HClO₄ to determine P and K content in the plant samples¹⁰. The pre-digestion of the sample was done by using 10 mL of HNO₃ g⁻¹ of sample. Phosphorus was determined spectrophotometrically by vanadomolybdate phosphoric acid yellow colour method¹⁰ and potassium was estimated using flame photometer¹⁰. From the chemical analytical data, the uptake of each nutrient was calculated as follows⁹:

The apparent nutrient recovery of fertilizer use expressed as kg kg⁻¹ was calculated by comparing nutrient uptake in above-ground biomass between treatment and control as follows⁹:

Microbial analysis of soil

To evaluate the impact of combined inorganic fertilizer (NPK) and farmyard manure (FYM) application on soil biological characteristics, soil microbial properties, specifically Microbial Biomass Carbon (MBC), Microbial Biomass Nitrogen (MBN), and soil enzymatic activities, were analyzed. Fresh soil samples were promptly transported to the laboratory and stored in a freezer pending the assessment of these biological parameters. The microbial biomass carbon in soil was estimated using chloroform fumigation extraction method using the formula Bc = Fc/Kc, where, Bc represents biomass carbon, Fc represents the difference in the amount of carbon that can be extracted from fumigated and non-fumigated soil, and Kc represents the efficiency factor, which is 0.45¹⁶. The soil extract obtained from the fumigation extraction procedure for microbial biomass carbon analysis was digested and subsequently analyzed for total nitrogen content to determine the microbial biomass nitrogen¹⁷. Dehydrogenase, acid phosphatase, and alkaline phosphatase activities were determined following established standard protocols^9,18,19.

Soil quality index

To construct the tool, a minimal dataset (MDS) was initially identified, followed by the calculation of a soil quality index based on indicator scores²⁰. The MDS was refined through univariate statistical analysis and a correlation matrix of the initial indicators. Key variables showing significant differences (P < 0.05) across various soil parameters were included in the MDS and further analyzed using Principal Component Analysis (PCA) with varimax rotation in SPSS. Indicators were grouped into Principal Components (PCs) for relational assessment, with only PCs having eigenvalues above 1 and explaining at least 5% of the variance being considered for indicator selection²¹. The indicator with the strongest factor loading (positive or negative) within each selected PC was assigned a score. Multivariate correlation was then applied to variables within the same PC to address data redundancy. Variables with a correlation greater than 0.60 were reduced to a single representative in the MDS²², while uncorrelated but highly influential variables were retained to ensure the MDS’s robustness.

The Soil Quality Index (SQI) was calculated using data from the top 20 cm of soil, which was analyzed for its physical, chemical, and biological characteristics. Indicators for the minimal dataset (MDS) were chosen if their weighted loading was within 10% of the highest loading for each principal component. A comprehensive principal component analysis was used to assess the influence of each variable, with those exhibiting a correlation coefficient below 0.60 being retained²². Each observation for the selected MDS indicators was standardized, yielding an “indicator score” (S). A linear scoring approach was applied to evaluate these indicators, categorizing them into three types: “more is better,” “less is better,” and “optimum is better.” For “more is better” indicators, each observation was divided by the maximum observed value, resulting in a score of 1 for the highest value and scores less than 1 for others. For “less is better” indicators, the minimum observed value was divided by each observation, assigning a score of 1 to the lowest value and decreasing scores for higher values, up to a defined threshold. Beyond this threshold, the scoring for “optimum is better” indicators transitioned from the “more is better” to the “less is better” method⁹.

1

2

where L(Y) is the linear score varying from 0 to 1, X is the soil indicator value, Xmax is the maximum value of each soil indicator, Xmin is the minimum value of each soil indicator.

The SQI is computed by integrating the score and weight factor of each indicator. This can be explained by the following equation⁹:

where Si = Score for subscripted variable, Wi = Weighing factor derived from the PCA.

Statistical analysis

The data were subjected to analysis of variance (ANOVA) technique of randomized block design²³. Online statistical program OPSTAT was used for the data analysis and least significance difference (LSD) values at α = 0.05 were used to compare treatment means. Pearson’s correlation coefficients were used as a measure of the strength of linear dependence between studied parameters at p ≤ 0.05 and p ≤ 0.01.

Results

Seed yield, haulm yield and root nodules per plant of green gram

The table presents yield data for different treatments over 2 years (Table 3). In 2022 and 2023, the STCR integrated approach for the yield target of 1.5 t ha⁻¹ (T₂) consistently yielded higher seed yield, haulm yield, and root nodules per plant compared to general fertilizer recommended dose (100% GFRD), soil fertility rating approach (SFR), and farmers practice. Specifically, STCR integrated approach with 1.5 t ha⁻¹ target showed a seed yield increase of 41.07% and 55.24% over GFRD, and 54.90% and 64.65% over SFR approach in 2022 and 2023, respectively. Similarly, haulm yield was 40.59% and 54.50% higher than GFRD, and 56.91% and 56.99% higher than SFR in the respective years. Similarly, the yield in treatment T₃ with STCR target 1.2 t ha⁻¹ through inorganics and T₄ with STCR target 1.2 t ha⁻¹ through integrated approach was significanrly higer compared to GFRD and STL approach. A similar trend of results was noticed with respect to root nodules per plant, where STCR integrated approach for the yield target of 1.5 t ha⁻¹ increased the root nodules per plant by 17.41% and 20.78% over GFRD, and 13.96% and 10.18% over SFR for 2022 and 2023. STCR integrated and inorganic approach for the yield target of 1.5 and 1.2 t ha⁻¹ exhibited similar trends with significant improvements over GFRD, SFR, and farmers practice across all parameters, affirming that STCR treatments are superior in optimizing crop performance and nutrient management. Similarly, the higher protein content (21.04 and 21.94 percent in 2022 and 2023, respectively), and protein yield (332.43 and 357.62 kg ha⁻¹) was recorded in STCR approach with NPK + FYM for the targeted yield of 1.5 t ha⁻¹. Whereas, lower protein content and protein yield was recorded in GRD and SFR during both the year.

Table 3.

Influence of different approaches of fertilizer prescription on seed yield, haulm yield and root nodules per plant of green gram.

Treatment	Seed yield (t ha−1)		Haulm yield (t ha−1)		Root nodules per plant		Protein content (%)		Protein yield (kg ha−1)
	2022	2023	2022	2023	2022	2023	2022	2023	2022	2023
T1	1.57	1.61	2.92	2.94	21.62	22.22	20.92	21.02	328.44	338.42
T2	1.58	1.63	2.84	2.92	23.00	23.19	21.04	21.94	332.43	357.62
T3	1.17	1.20	1.98	1.97	19.98	21.19	20.65	20.52	241.61	246.24
T4	1.23	1.30	2.29	2.43	20.89	21.78	20.96	20.99	257.81	272.87
T5	1.12	1.05	2.02	1.89	19.59	19.20	20.32	20.47	227.58	214.94
T6	1.02	0.99	1.81	1.86	20.18	21.05	21.00	20.99	214.20	207.80
T7	0.83	0.80	1.50	1.56	15.29	16.57	20.99	20.86	174.22	166.88
T8	0.38	0.36	0.70	0.72	13.30	13.19	20.51	20.53	77.94	73.91
SEm±	0.04	0.07	0.07	0.18	0.34	0.42	0.45	0.47	2.12	2.19
CD @ 5%	0.11	0.21	0.22	0.56	1.03	1.27	NS	NS	6.36	6.79

T₁: STCR-inorganic- yield target 1.5 t ha⁻¹; T₂: STCR- integrated- yield target 1.5 t ha⁻¹; T₃: STCR- inorganic- yield target 1.2 t ha⁻¹; T₄: STCR- integrated- yield target 1.2 t ha⁻¹; T₅: General fertilizer recommended dose; T₆: Soil fertility rating; T₇: Farmers Practice; and T₈: Absolute control.

Significant values are in (bold).

Response ratio, percent deviation and value cost ratio of green gram

During the study, the STCR-integrated approach for a target yield of 1.2 t ha⁻¹ demonstrated the highest response ratio, with 9.47 and 17.57 kg kg⁻¹ in 2022 and 2023, respectively. This was closely followed by the integrated approach aiming for 1.5 t ha⁻¹, which achieved response ratios of 8.52 and 15.91 kg kg⁻¹ in the same years. These results indicate that STCR-based treatments most effectively improved crop yield relative to the amount of fertilizer applied (as shown in Table 4). Similarly, the STCR-inorganic approach for target yields of 1.5 and 1.2 t ha⁻¹ also exhibited high response ratios in both years, highlighting its effectiveness. Regarding percent deviation, GFRD exhibited the highest deviation in 2022 (11.93%), indicating substantial variability in response compared to the expected yield. Whereas, STCR integrated approach with a yield target of 1.5 t ha⁻¹ (5.27%) and STCR inorganic approach with a yield target of 1.5 t ha⁻¹ (4.64%), showing moderate deviation. Notably, among treated plots farmers practice had the lowest percent deviation (− 17.33%), suggesting significant underperformance. In 2023, the STCR-integrated approach with a lower target showed the highest positive deviation (10.00%), implying better adaptability and yield performance under varying conditions. The value-cost ratio (VCR) reflects the economic efficiency of the treatments. STCR-inorganic approach with only NPK fertilizers for the lower yield target were consistently the most cost-effective treatments over the 2 years (20.50 and 21.08 during 2022 and 2023, respectively). Among FYM treatments, STCR integrated approach has recorded higher VCR during both years compared to GFRD, SFR approach and farmers practice.

Table 4.

Influence of different approaches of fertilizer prescription on response ratio, per cent deviation and value cost ratio of green gram.

Treatment	Response ratio		Per cent deviation		Value cost ratio (VCR)
	2022	2023	2022	2023	2022	2023
T1	8.10	15.79	4.64	7.20	18.94	19.02
T2	8.52	15.91	5.27	5.27	5.83	5.92
T3	8.19	13.73	0.78	0.03	20.50	21.08
T4	9.47	17.57	1.09	10.00	5.45	5.96
T5	5.91	10.54	11.93	5.27	4.08	3.91
T6	4.65	8.09	1.97	− 1.37	3.10	2.89
T7	6.87	12.34	− 17.33	− 19.67	2.21	2.07
T8			− 62.33	− 64.33	–	–

T₁: STCR-inorganic- yield target 1.5 t ha⁻¹; T₂: STCR- integrated- yield target 1.5 t ha⁻¹; T₃: STCR- inorganic- yield target 1.2 t ha⁻¹; T₄: STCR- integrated- yield target 1.2 t ha⁻¹; T₅: General fertilizer recommended dose; T₆: Soil fertility rating; T₇: Farmers Practice; and T₈: Absolute control.

Nutrient uptake and nutrient recovery

Congenial conditions provided for crop growth result in the proper supply of nutrients to the crop. The nutrient uptake study revealed that there was more nutrient uptake in the soil test crop response treatment (Table 5). In both 2022 and 2023, the treatment STCR-integrated approach for the yield target of 1.5 t ha⁻¹ consistently exhibited the highest uptake of nitrogen (98.46 and 99.06 kg ha⁻¹), phosphorus (10.51 and 11.41 kg ha⁻¹), and potassium (80.14 and 81.39 kg ha⁻¹) which is on par with other STCR treatments. Also, STCR—integrated approach for the yield target of 1.5 t ha⁻¹ showed a 20.02% increase over GFRD, a 27.77% increase over SFR, and a 42.91% increase over farmer’s practice for nitrogen, 35.11%, 30.79%, and 67.62% increase over GFRD, SFR and farmers practice, respectively for phosphorus and the potassium uptake was 41.59%, 16.66%, 82.43% higher compared to GFRD, SFR and farmers practice. The nutrient recovery efficiency by green gram is represented in Table 6. These results highlight superior performance STCR NPK + FYM approach for the yield target of 1.2 t ha⁻¹ which is comparable with other STCR treatments. For nitrogen, STCR NPK + FYM approach for the yield target of 1.2 t ha⁻¹ exhibited a 36.91% increase compared to GRD and a 37.07% increase compared to SFR. In phosphorus, the increases were particularly striking, with a 91.95% rise over GRD and 172.55% over SFR. Similarly, potassium levels were significantly higher, with an increase of 171.51% compared to GRD and 103.35% compared to SFR.

Table 5.

Influence of different approaches of fertilizer prescription on nutrient uptake of green gram.

Treatment	Nitrogen (kg ha−1)		Phosphorus (kg ha−1)		Potassium (kg ha−1)
	2023	2024	2023	2024	2023	2024
T1	90.21	91.79	9.72	10.15	74.21	79.77
T2	98.46	99.06	10.51	11.41	80.14	81.39
T3	89.14	89.57	9.13	10.07	65.56	67.18
T4	91.62	95.15	9.65	10.97	68.61	76.34
T5	81.84	82.70	7.89	8.32	53.82	60.25
T6	75.44	79.16	8.39	8.36	77.43	61.01
T7	67.64	70.59	6.09	6.98	42.41	46.14
T8	33.57	31.40	3.17	2.11	22.74	18.69
SEm±	1.45	1.68	0.21	0.56	3.52	4.06
CD @ 5%	4.39	5.09	0.64	1.70	10.67	12.31

T₁: STCR-inorganic- yield target 1.5 t ha⁻¹; T₂: STCR- integrated- yield target 1.5 t ha⁻¹; T₃: STCR- inorganic- yield target 1.2 t ha⁻¹; T₄: STCR- integrated- yield target 1.2 t ha⁻¹; T₅: General fertilizer recommended dose; T₆: Soil fertility rating; T₇: Farmers Practice; and T₈: Absolute control.

Significant values are in (bold).

Table 6.

Influence of different approaches of fertilizer prescription on nutrient recovery of green gram.

Treatment	Nitrogen (kg kg−1)		Phosphorus (kg kg−1)		Potassium (kg kg−1)
	2023	2024	2023	2024	2023	2024
T1	0.946	0.968	0.241	0.271	1.093	1.268
T2	1.347	1.278	0.304	0.346	1.318	1.217
T3	1.079	1.084	0.230	0.292	1.734	1.894
T4	1.368	1.455	0.336	0.499	1.715	2.230
T5	1.931	2.052	0.094	0.124	0.622	0.831
T6	1.340	1.528	0.139	0.167	1.094	0.846
T7	1.662	1.911	0.097	0.162	–	–
T8	–	–	–	–	–	–

T₁: STCR-inorganic- yield target 1.5 t ha⁻¹; T₂: STCR- integrated- yield target 1.5 t ha⁻¹; T₃: STCR- inorganic- yield target 1.2 t ha⁻¹; T₄: STCR- integrated- yield target 1.2 t ha⁻¹; T₅: General fertilizer recommended dose; T₆: Soil fertility rating; T₇: Farmers Practice; and T₈: Absolute control.

Soil properties

Figure 2a,b present radar charts comparing six crucial soil parameters—pH, electrical conductivity, organic carbon, available nitrogen, phosphorus, and potassium—across eight distinct treatments (T₁–T₈). The soil fertility rating treatment displayed the peak pH value of 6.20, a 4.91% rise from the absolute control’s baseline of 5.91. In contrast, the absolute control also registered the lowest electrical conductivity at 0.19 dS m⁻¹, significantly lower (by 61.20%) than the maximum EC observed in the STCR—inorganic approach targeting a yield of 1.5 t ha⁻¹. Notably, the STCR—integrated approach for a 1.5 t ha⁻¹ yield target showed the highest concentrations of organic carbon (0.52%), available nitrogen (296.52 kg ha⁻¹), and potassium (162.45 kg ha⁻¹). These values are 1.36 times higher for OC, 2.33 times greater for N, and 1.59 times more for K compared to the absolute control’s values of 0.38% OC, 126.82 kg N ha⁻¹, and 102.11 kg K ha⁻¹. However, the general recommended fertilizer dose exhibited the highest available phosphorus content at 152.17 kg ha⁻¹.

Fig. 2.

Effect of different treatments on (2a) soil pH, EC, OC and (2b) available N, P and K after harvest of green gram during Kharif 2023.

Soil quality index

The development of the Minimal Dataset (MDS) involved Principal Component Analysis (PCA) on a set of soil properties that exhibited significant correlations (p < 0.05), as outlined in Table 7. Only principal components with eigenvalues exceeding 1 were considered, a criterion visually represented in the scree plot in Fig. 3. This plot shows that PC1 explained the largest proportion of variance (around 76%), followed by PC2 (approximately 11%) and PC3 (around 6%), with subsequent components (PC4-PC7) each explaining less than 5%. This suggests that the primary variability in the data was captured by the first few components (Table 8; Fig. 3). Within PC1, dehydrogenase activity, MWHC, porosity, BD, pH, EC, K, Fe, Mn, Cu, MBC, MBN, acid phosphatase, and alkaline phosphatase showed the highest loadings and were highly intercorrelated, leading to the choice of dehydrogenase activity as a representative indicator for the MDS. Additionally, bulk density (with a higher loading in PC2), pH (with a higher loading in PC3), and alkaline phosphatase (with a higher loading in PC4, as shown in Table 9) were also included in the MDS. To formulate the Soil Quality Index (SQI) equation, these selected indicators were normalized to a 0–1 scale using a linear scoring function, weighted according to their respective principal components: 0.79 for PC1, 0.11 for PC2, 0.070 for PC3, and 0.040 for PC4. The relationships between soil variables and treatments (T₁–T₈) are displayed in a PCA biplot (Fig. 4), using the first two principal components (PC1 and PC2) as the x-axis and y-axis, respectively. Soil variables (BD, Mn, Zn, Cu, Fe, MBC, dehydrogenase activity, EC, alkaline phosphatase, acid phosphatase, porosity, N, and pH) are represented by blue arrows, showing their directions of maximum variance. Arrow length indicates the degree of contribution to the PCs (e.g., BD, Mn, Zn, Cu have strong contributions). Positive correlations are shown by arrows pointing in similar directions, while opposite directions indicate negative correlations. Smaller angles between arrows signify stronger positive correlations (e.g., Zn and Cu), and angles near 180 degrees denote negative correlations (e.g., BD and porosity). Perpendicular arrows indicate no correlation. Treatment positions (T₁–T₈) relative to the arrows reflect variable values; for example, T₁ has high values for variables on the positive sides of PC1 and PC2, and T₈ has low values for those on the negative sides.

Table 7.

Pearson correlation coefficient values (r) between various soil quality attributes of 0–20 cm soil layer.

	MWHC	Porosity	BD	pH	EC	OC	N	P	K	Fe	Mn	Cu	Zn	MBC	MBN	APA	ALPA	DHA
MWHC	1
Porosity	0.98	1.00
BD	− 0.54	− 0.61	1.00
pH	0.19	0.28	− 0.59	1.00
EC	0.88	0.93	− 0.48	0.17	1.00
OC	0.87	0.83	− 0.72	0.14	0.70	1.00
N	0.82	0.90	− 0.66	0.40	0.91	0.73	1.00
P	0.87	0.85	− 0.49	− 0.18	0.79	0.84	0.71	1.00
K	0.83	0.80	− 0.35	0.08	0.85	0.78	0.73	0.68	1.00
Fe	0.85	0.85	− 0.32	− 0.01	0.93	0.74	0.79	0.78	0.96	1.00
Mn	0.70	0.68	− 0.06	− 0.03	0.79	0.55	0.64	0.55	0.94	0.94	1.00
Cu	0.86	0.84	− 0.24	0.15	0.88	0.66	0.74	0.64	0.95	0.95	0.95	1.00
Zn	0.67	0.67	− 0.21	0.06	0.81	0.59	0.66	0.53	0.96	0.93	0.97	0.92	1.00
MBC	0.96	0.91	− 0.33	0.00	0.87	0.81	0.72	0.84	0.90	0.91	0.82	0.92	0.78	1.00
MBN	0.98	0.95	− 0.39	0.06	0.91	0.81	0.79	0.86	0.88	0.92	0.80	0.92	0.76	0.99	1.00
Acid phosphatase	0.94	0.93	− 0.47	0.39	0.85	0.76	0.81	0.67	0.86	0.84	0.78	0.93	0.76	0.91	0.92	1.00
Alkaline phosphatase	0.81	0.82	− 0.65	0.17	0.84	0.89	0.79	0.77	0.91	0.89	0.75	0.80	0.84	0.80	0.81	0.78	1.00
Dehydrogenase	0.98	0.96	− 0.43	0.13	0.91	0.81	0.80	0.84	0.89	0.91	0.80	0.93	0.77	0.99	1.00	0.95	0.82	1.00

MWHC maximum water holding capacity, BD bulk density, OC organic carbon, MBC microbial biomass carbon, MBN microbial biomass nitrogen, APA-acid phosphatase, AlPA alkaline phosphatase, DHA dehydrogenase.

Fig. 3.

Scree plot explaining the relationship of the eigenvalues and the principal components.

Table 8.

Eigenvalues from principal component analysis (PCA) of soil quality parameters.

Principal components	Eigenvalue	Percentage of variance	Cumulative percentage of variance	Weightage factor
PC1	13.784	76.579	76.579	0.79
PC2	1.936	10.755	87.334	0.11
PC3	1.157	6.427	93.761	0.07
PC4	0.629	3.494	97.255	0.04

PC principal component.

Significant values are in (bold).

Table 9.

Principal component analysis of soil quality parameters.

Variables	PC1	PC2	PC3	PC4
MWHC	− 0.258	− 0.083	− 0.142	0.254
Porosity	− 0.257	− 0.149	− 0.08	0.226
BD	0.136	0.58	0.107	0.349
pH	− 0.046	− 0.544	0.568	0.171
EC	− 0.253	0.011	0.091	0.017
OC	− 0.229	− 0.192	− 0.307	− 0.197
N	− 0.233	− 0.221	0.086	− 0.024
P	− 0.223	− 0.011	− 0.501	− 0.01
K	− 0.254	0.143	0.125	− 0.227
Fe	− 0.258	0.173	0.045	− 0.149
Mn	− 0.228	0.315	0.252	− 0.111
Cu	− 0.253	0.159	0.216	0.116
Zn	− 0.229	0.232	0.3	− 0.331
MBC	− 0.258	0.108	− 0.122	0.21
MBN	− 0.261	0.054	− 0.115	0.237
Acid phosphatase	− 0.253	− 0.074	0.17	0.288
Alkaline phosphatase	− 0.245	− 0.069	− 0.021	− 0.493
Dehydrogenase	− 0.263	0.022	− 0.06	0.243

PC principal component.

Fig. 4.

Principal Component Analysis (PCA) Biplot of Various Variables.

The STCR integrated approach with the higher yield target showed high dehydrogenase activity and low bulk density. Given the clear influence of nutrient management on these parameters, they are appropriate SQI indicators. The SQI was significantly higher for the STCR integrated approach targeting the higher yield (0.99) than for other treatments (Table 10). The fertilizer recommendation approaches ranked as follows, in terms of SQI: STCR- integrated- yield target 1.5 t ha⁻¹ > STCR- integrated- yield target 1.2 t ha⁻¹ > STCR- inorganic- yield target 1.5 t ha⁻¹ > STCR- inorganic- yield target 1.2 t ha⁻¹ > soil fertility rating > general fertilizer recommendation dose > Farmers practice > absolute control.

Table 10.

Soil quality index values of selected minimum dataset variables under STCR alongside various fertilization strategies in green gram crop.

	DHA	BD	pH	AL PA	SQI
T1	0.73	0.111	0.067	0.033	0.94
T2	0.77	0.112	0.069	0.040	0.99
T3	0.72	0.111	0.068	0.030	0.92
T4	0.75	0.112	0.069	0.035	0.97
T5	0.66	0.112	0.068	0.032	0.87
T6	0.69	0.112	0.070	0.028	0.90
T7	0.50	0.112	0.070	0.030	0.71
T8	0.40	0.110	0.067	0.022	0.60

1. T₁: STCR-inorganic- yield target 1.5 t ha⁻¹; T₂: STCR- integrated- yield target 1.5 t ha⁻¹; T₃: STCR- inorganic- yield target 1.2 t ha⁻¹; T₄: STCR- integrated- yield target 1.2 t ha⁻¹; T₅: General fertilizer recommended dose; T₆: Soil fertility rating; T₇: Farmers Practice; and T₈: Absolute control.

2. DHA dehydrogenase, BD bulk density, ALPA alkaline phosphatase, SQI soil quality index.

Discussion

The significant improvements observed in seed yield, haulm yield, and root nodules per plant under the STCR integrated approach with a yield target of 1.5 t ha⁻¹ can be attributed to optimum application of nutrients based on soil fertility status using soil test crop response approach which resulted in proper availability of nutrients thus help in enhancement in yield¹. Integrating of chemical fertilizer with organic fertilizer enhances the nutrient use efficiency which contribute for the yield increase of green gram. The nitrogen application during the early growth stages of plants stimulates vegetative growth, creating favorable conditions for high yields²⁴. Nitrogen, crucial for chlorophyll formation and protein synthesis, directly elevates plant protein levels and overall yield. Phosphorus plays a pivotal role in plant metabolism and energy production, fostering blooming and seed development, thereby boosting yield. In green gram, potassium application enhanced both the quantity and quality of the harvest⁹. Integrating chemical fertilizers with Farm Yard Manure (FYM) established a beneficial soil environment, supplying vital nutrients that improved yield parameters and maximized seed yield. Conversely, the untreated control group, lacking any fertilization, displayed poor yield characteristics and the lowest grain yield, highlighting the detrimental impact of nutrient deficiency on plant growth and productivity²⁵. The increased yield at the higher target with integrated approach, compared to inorganic, was mainly due to the addition of FYM. This might have enhanced the microbial population and provided enough carbon, leading to higher availability and contribution of nutrients to the crop, thereby increasing growth and yield²⁶. The higher protein yield STCR approach might be because better availability of all the nutrients to the green gram plants through both organic and inorganic sources resulting in higher seed yield, which is reflected in maximum protein yield. On the contrary, the lowest protein yield was noticed in control which might be due to lower seed yield on account of reduced growth and development due to poor nutrition of the crop²⁵.

The higher yield response observed in the STCR approach over GFRD and SFR methods is attributed to its precision in considering spatial variability in soil nutrient status, which the other approaches overlook²⁷. By tailoring fertilizer recommendations based on soil test values and specific nutrient requirements of the crop, the STCR method ensures balanced and efficient nutrient application, leading to superior yields and higher response ratios, particularly in integrated treatments⁹. In contrast, the SFR approach modifies GFRD without fully addressing the crop’s nutrient needs or accounting for soil fertility variations, resulting in a lower response ratio. The STCR approach’s consistent performance, with percentage deviations within ± 10% of the target yield, underscores its reliability²⁸, while the higher value-cost ratio (VCR) achieved in STCR inorganic treatments highlights its economic efficiency²⁹. Emphasizing STCR’s ability to adapt to spatial variability strengthens its position as a superior method for sustainable and precise nutrient management. The application of increased NPK levels, combined with farmyard manure (FYM) based on soil test values for targeted yields of green gram, significantly enhanced the uptake of nitrogen (N), phosphorus (P), and potassium (K) compared to other fertilizer recommendation methods. This improvement can be attributed to the higher yields and increased fertilizer doses, which enhanced nutrient availability in the root zone of green gram. The balanced application of NPK fertilizers promoted the development of a more extensive root system, enabling efficient absorption of nutrients and resulting in higher N, P, and K uptake²⁴. The use of FYM alongside inorganic fertilizers further improved nutrient availability, as organic manures enriched soil organic matter, improved soil structure, and increased its buffering capacity, while also releasing essential nutrients⁹. Organic acids from FYM solubilized insoluble forms of P and K in the soil, boosting nutrient availability near the roots and enhancing cellular metabolic activity²⁹. This facilitated greater nutrient accumulation in vegetative parts and effective translocation to reproductive organs, thereby increasing nutrient content in seeds and haulms. Moreover, FYM combined with chemical fertilizers fostered a favorable soil environment for microbial and chemical activities, leading to enhanced nutrient mineralization and recycling, which expanded the nutrient pool for plant uptake³⁰. The nutrient recovery was strongly correlated with crop yields, efficient nutrient utilization, and fertilizer doses, although it varied depending on the applied fertilizer rates across treatments. Higher N and P uptake promoted efficient photo-assimilate synthesis and translocation, contributing to increased grain yields³¹. The balanced fertilization strategy under STCR minimized nutrient losses by ensuring effective utilization during critical growth stages³². Additionally, the integration of FYM with balanced fertilizers stimulated microbial activities, improving nutrient acquisition through solubilization, fixation, and recycling, ultimately supporting higher nutrient uptake and yield sustainability²⁵. Incorporating FYM with STCR NPK significantly boosts the organic carbon content and enhances nitrogen and potassium availability³³. Applying STCR NPK + FYM for higher yield goals leads to increased DHA and decreased bulk density, making them key indicators of soil quality³⁴. These indicators, representing the soil’s physical and chemical properties, strongly correlate with other soil parameters³⁵. The data suggests that STCR integrated treatment significantly improves soil quality indicators, with higher yield targets showing a greater SQI than the general recommendation dose. STCR-based treatments demonstrate higher SQI compared to GFRD, owing to balanced and profitable fertilization combined with organic manure based on soil nutrient status and crop needs³⁶. Fertilizer and manure application following the STCR approach helps achieve target yields while maintaining overall soil health³⁴. Among STCR treatments, combining inorganic fertilizers with FYM results in better soil quality compared to using inorganic fertilizers alone³⁷. This synergistic approach not only enhances soil physical health but also maximizes nutrient efficiency, leading to increased biomass production³⁸.

Conclusion

This study validated soil test-based fertilizer prescription equations for green gram, integrating crop nutrient requirements, soil nutrient availability, applied fertilizers, and farmyard manure (FYM). The findings demonstrated that STCR-based nutrient management approaches, particularly those integrating FYM, significantly enhanced crop yields, improved soil biological properties, and facilitated higher nutrient uptake compared to general fertilizer recommendations, soil fertility ratings, and traditional farmer practices. The integration of chemical and organic nutrient sources under the STCR approach boosted enzyme activity in the rhizosphere, enhancing nutrient mineralization and availability. Consequently, this resulted in greater nutrient accumulation, improved soil health, and higher economic returns for farmers. Beyond these immediate benefits, the adoption of STCR-based practices holds broader implications for sustainable agriculture by addressing critical challenges such as resource efficiency, environmental sustainability, and climate resilience. These results provide a pathway for scaling up soil test-based recommendations across diverse agroecosystems, ensuring precise and balanced fertilizer use. This approach not only supports the economic stability of farmers but also aligns with global efforts toward sustainable intensification and long-term soil health maintenance. Local soil testing laboratories can leverage these findings to provide accurate fertilizer recommendations, promoting evidence-based nutrient management in Southern India and beyond.

Author contributions

K.M.R.— Design of the work, revision of the article Final approval of article; B.N.—Drafting the article, Data analysis, Revision of the article; G.K.—Drafting the article, revision of the article; S.M.N.- Drafting the article, revision of the article; U.S.N.- Data analysis and interpretation; S.S.—Design of the work, Fund acquisition; P.D.- Design of work.

Funding

This study was funded by the Indian Council of Agricultural Research and the University of Agricultural Sciences, Bangalore. (Grant number: CRP-18).

Data availability

The data that support this study will be shared upon reasonable request to the corresponding author.

Declarations

Competing interests

The authors declare no competing interests.