Enhancing Nutrient Use Efficiency in Cowpea through Soil Test Crop Response and Decision-Support Tools in Karnataka

Cowpea (Vigna unguiculata L.) is an important legume crop grown for its protein-rich seeds, tender pods, and fodder value Rukhsar et al., (2020). It plays a significant role in the diets of millions and serves as a crucial crop in sustainable agriculture due to its nitrogen-fixing ability and adaptability to marginal soils (Yururdurmaz 2022). India is one of the largest producers of cowpea, with cultivation spread across states such as Uttar Pradesh, Madhya Pradesh, Maharashtra, Karnataka, Tamil Nadu, and Andhra Pradesh. In India, cowpea is cultivated over an area of approximately 4.0 lakh hectares with an annual production of around 2.5 lakh tonnes. However, the national average productivity remains relatively low, at around 600–700 kg ha^–1, primarily due to suboptimal crop management and poor nutrient management practices (Agarwal and Devra, 2024). Karnataka, especially the northern dry zones, contributes significantly to the total area under cowpea cultivation in India. The state grows cowpea both as a pulse and as a vegetable crop, with considerable acreage in districts like Dharwad, Belagavi, and Raichur. Despite its regional importance, cowpea productivity in Karnataka also remains below its genetic potential, highlighting the need for improved agronomic and nutrient management strategies (Ashoka et al., 2025).

Among the various factors limiting cowpea productivity, improper and imbalanced fertiliser application stands out as a major concern. Blanket fertiliser recommendations often fail to address the specific nutrient needs of soils and crops, resulting in either nutrient deficiency or excess. Soil testing plays a crucial role in determining the existing nutrient status of the soil and serves as the basis for precise fertiliser recommendations (Krishna Murthy et al., 2021). Site-specific nutrient management through soil testing ensures better crop response, efficient fertiliser use, and environmental sustainability. Several modern approaches have emerged to improve the accuracy of fertiliser recommendations. One such method is the Soil Test Crop Response (STCR) approach, which combines soil test values, crop nutrient requirements, and fertiliser efficiency to provide balanced nutrient recommendations for targeted yields (Bhavya and Basavaraja, 2021). This approach has proven effective in enhancing productivity and maintaining soil health. In recent years, mobile and software-based platforms, such as ‘Dhartimitra’ developed by All India Coordinated Research Project on Soil Test Crop Response, have revolutionised nutrient management by providing real-time, location-specific fertiliser recommendations to farmers based on soil test data. These digital tools bridge the gap between scientific knowledge and on-farm decision-making, promoting efficient and need-based fertilizer use among farmers.

Another critical issue in cowpea cultivation is nutritional imbalance, which results from continuous cultivation without adequate nutrient replenishment or due to the omission of secondary and micronutrients (Omomowo and Babalola, 2021). Imbalanced fertilization leads to poor growth, low pod setting, and reduced grain quality, ultimately affecting the economic returns of the crop. Addressing these nutritional gaps through appropriate recommendation strategies is essential to harness the full yield potential of cowpea and ensure soil sustainability. Given this background, the present research aims to evaluate and compare different approaches to fertiliser recommendation — including conventional, STCR, and software-based methods—for their effectiveness in improving cowpea productivity, nutrient uptake, and nutrient use efficiency.

A field experiment evaluating different approaches to fertiliser recommendation was conducted during the Kharif season of 2024 at the farmers' fields in various districts of Karnataka, including Bengaluru Rural, Tumakuru, and Chikkaballapura, which belong to the Eastern dry zone of Karnataka. The area experiences a dry tropical savanna climate, marked by hot summers and mild winters, with an average annual rainfall of 943.9 mm. The initial soil properties of the experimental sites are provided in Table 1.

Table 1: Initial Properties of the experimental site

Fertilizer Prescription Equation Development for Cowpea

To derive fertiliser recommendations, the STCR methodology, involving two sequential field trials, was adopted. First, a fertility gradient experiment was conducted to generate spatial variability in nutrient status, followed by a test crop experiment using cowpea as the test crop (Ramamoorthy et al., 1967). The data collected from the test crop experiment were used to determine the basic parameters, including nutrient requirements, soil and fertiliser contributions, and manure efficiency.

Fertilizer requirements for cowpea were calculated using the derived STCR equations:

• FN = 11.02T – 0.43 SN
• FP₂O₅ = 8.48 – 0.466SP₂O₅
• FK₂O = 1.775T − 0.150SK₂O

Where:

FN, FP₂O₅, FK₂O = Fertilizer nitrogen, phosphorus, and potassium (kg ha⁻¹); T = Target yield (q ha⁻¹); SN, SP₂O₅, SK₂O = Soil-available phosphorus and potassium. FYM was added @ 7.5 t ha^-1

Experimental Design and Treatment Details

The experiment was structured using a Randomized Complete Block Design (RCBD) with three replications and comprised seven fertilization treatments and an absolute control: T₁: STCR TY 15 q ha⁻¹ through dhartimitra software, T₂: STCR TY 15 q ha⁻¹ through actual soil test values, T₃: STCR TY 12 q ha⁻¹ through dhartimitra software, T₄: STCR TY 12 q ha⁻¹ through actual soil test values, T₅: Soil test laboratory approach, T₆: General Recommended dose T₇: Farmers practice, T₈: Absolute control. Before sowing, soil samples were collected from a depth of 0–20 cm were collected for nutrient analysis. Farmyard manure (FYM) was added at the rate of 7.5 t ha^-1 from treatment 1 to 7 and incorporated 15 days before planting, with nutrient contents of 0.57% N, 0.34% P, and 0.51% K. Calculated quantity of fertilisers was applied as per treatments, and for treatments 1 & 3, fertiliser dose obtained with the software was used. Half of the nitrogen, full phosphorus, and potassium were applied as a basal dose using urea, single superphosphate, and muriate of potash, respectively. Crop management followed standard agronomic practices, and harvesting was done at physiological maturity. To assess treatment efficacy, the following indices were computed using standard methods (Ramamoorthy et al., 1970):

Soil and Plant Analysis

Soil samples were air-dried, sieved through a 2 mm mesh, and analysed for various parameters. Soil pH and electrical conductivity (EC) were determined in a 1:2.5 soil–water suspension (Jackson, 1973); organic carbon was estimated via the Walkley and Black method (1934); available nitrogen using the alkaline KMnO₄ method (Subbiah and Asija, 1956); available phosphorus through Bray's No. 1 extractant and colorimetry (Bray and Kurtz, 1945); and available potassium by 1N ammonium acetate extraction and flame photometry (Page et al., 1982). Plant samples from each plot were shade-dried, then oven-dried at 65°C, and ground for nutrient analysis. Nitrogen content was determined using the Micro-Kjeldahl method (Piper, 1966). Phosphorus and potassium were extracted through di-acid digestion (HNO₃:HClO₄ in a 9:4 ratio) and measured using the vanadomolybdate yellow colour method and flame photometry, respectively (Jackson, 1973). Nutrient uptake (kg ha⁻¹) was calculated as (nutrient concentration × dry matter yield in kg ha⁻¹) / 100.

Nutrient Use Efficiency Calculations

Nutrient (N/P/K) use efficiency parameters, viz., Agronomic nutrient use efficiency (AE), Recovery efficiency (RE), and Reciprocal internal utilisation efficacy (RIUE), were calculated using the following formulae, as per Krishna Murthy et al., (2023a):

Statistical Analysis

Statistics were used for the information gathered on yield, nutrient uptake, and nutrient availability. A P-value of 0.05 was selected as the level of significance for the "F" and "t" tests. When the 'F' test revealed a significant result, critical difference (CD) values were determined for P = 0.05 following the procedures outlined by Gomez and Gomez (1984).

Yield

The grain yield of the crop was significantly influenced by different nutrient management approaches (Table 2). Among the treatments, the STCR approach for a targeted yield of 15 q ha⁻¹ through the Dhartimitra software recorded the highest mean yield (15.28 q ha⁻¹) across the three locations, which was statistically superior to all other treatments. This was followed by the STCR approach, targeting a yield of 15 q ha⁻¹ based on actual soil test values, with an actual yield of 14.97 q ha⁻¹. Moderate yield levels were observed in T₃ (12.38 q ha⁻¹) and T₄ (12.35 q ha⁻¹), which corresponded to STCR-based fertiliser application with a lower yield target of 12 q ha⁻¹. The soil test laboratory approach and the general recommended dose recorded yields of 11.43 q ha⁻¹ and 11.07 q ha⁻¹, respectively, which were significantly lower than those of the STCR-based treatments. The farmers' practice resulted in a still lower yield of 9.67 q ha⁻¹, whereas the absolute control produced only 5.29 q ha⁻¹, highlighting the importance of nutrient application in realising higher productivity. The critical difference (CD) values indicated that the yield advantage of the STCR approach, as determined by Dhartimitra software and actual soil test values, over the conventional approaches was statistically significant. The consistency of performance across locations also confirmed the robustness of STCR-based nutrient management.

Table 2: Quantity of fertilisers added through different nutrient management approaches and yield of cowpea at Bangalore Rural, Tumakuru, and Chikkaballapura Districts of Karnataka

The higher yields obtained in the STCR approach at a higher target through the Dhartimitra software and actual soil test values may be attributed to the balanced and site-specific application of N, P₂O₅, and K₂O, coupled with the inclusion of FYM, which not only supplied essential nutrients but also improved soil organic matter and nutrient-use efficiency (Rangaiah et al., 2025). The use of Dhartimitra software for fertiliser prescription was found to be as effective as direct soil test-based calculations, indicating its practical utility for farmers as a digital decision-making tool. The slight but consistent yield advantage of T₁ over T₂ suggests that software-guided recommendations can serve as a reliable extension tool for broader adoption. The lower yield targets (T₃ and T₄) also responded positively, though their yields remained below those of T₁ and T₂, reflecting the importance of setting appropriate yield targets in STCR technology. In contrast, the soil test laboratory approach and general recommended dose did not perform as well, likely due to their inability to account for site-specific nutrient dynamics and target yields (Krishna Murthy et al., 2024a; Annappa et al., 2025). Farmers' practices resulted in suboptimal yields due to imbalanced nutrient application, particularly the low use of phosphorus and potassium. The absolute control confirmed the inherent low productivity of the soil when no external nutrients were applied, with yields less than half of the STCR-based approaches (Sing et al., 2021; Krishna Murthy et al., 2024b).

Response Yardstick (RYS), Per cent Deviation and Value Cost Ratio (VCR)

The response yardstick (RYS), per cent deviation, and value cost ratio (VCR) of cowpea varied significantly across different fertiliser recommendation approaches and locations (Table 3). The STCR-based 15 q ha⁻¹ targets recorded the highest efficiency with RYS values of 8.3–9.0, minimal deviation from the target (–2.33 to +5.20%), and consistently high VCR (>2.5), indicating both yield stability and economic viability. The STCR 12 q ha⁻¹ targets also showed favourable responses (RYS 6.1–8.4, VCR 1.6–2.2), though deviations were slightly larger (–7.17 to +8.25%). In contrast, the soil test laboratory approach and general recommended dose were less efficient (RYS <6.5, VCR ~1.6–1.7) with negative deviations (–5.83 to –9.75%), while the farmer’s practice was least effective (RYS ~6.3, VCR ~1.4, deviation –16.75 to –21.17%). The absolute control had the most significant yield gap (>–54% deviation), underscoring the importance of nutrient application. Overall, the results clearly establish the superiority of STCR-based recommendations, especially with FYM integration, in enhancing nutrient use efficiency, achieving targeted yields, and ensuring higher profitability compared to conventional or blanket recommendations.

The findings of the present study are in close agreement with earlier reports, where STCR-based nutrient management has proven superior to blanket or conventional recommendations. Studies have highlighted that STCR approaches, especially when integrated with FYM, not only improve yield realisation but also enhance nutrient use efficiency and profitability compared to farmers' practices or recommended doses. Similar results were obtained, where STCR-IPNS treatments recorded higher nutrient uptake and better benefit–cost ratios, confirming the consistency of this approach across legumes and agro-ecological zones (Krishna Murthy et al., 2024c). Evidence from aerobic rice also showed that STCR with FYM was economically superior and contributed to sustaining soil fertility (Bhavya et al., 2022). The present results, showing higher response yardstick, lower deviation from targets, and favourable VCR in STCR treatments (T1 and T2), reinforce these observations and establish that precision-based fertiliser prescription models, particularly STCR coupled with organics, are more effective in achieving targeted yields, improving efficiency, and ensuring sustainable cowpea production than conventional recommendations or farmers’ practice

Table 3: Effect of different nutrient management approaches and Response Yardstick (RYS), Per cent deviation and Value Cost Ratio (VCR) of cowpea at Bangalore Rural, Tumakuru, and Chikkaballapura Districts of Karnataka

Nutrient Uptake

The nutrient uptake of cowpea differed significantly among the nutrient management treatments across the three locations (Bangalore Rural, Tumakuru, and Chikkaballapura districts of Karnataka) (Table 4). Among the treatments, STCR approach at 15 q ha⁻¹ through Dhartimitra software recorded the highest N, P₂O₅ and K₂O uptake at all three locations, with values of 68.71, 11.54, and 35.48 kg ha⁻¹, respectively, in Bangalore Rural; 68.02, 10.84, and 35.13 kg ha⁻¹ in Tumakuru; and 71.80, 12.01, and 37.08 kg ha⁻¹ in Chikkaballapura. This was followed by the STCR approach at 15 q ha⁻¹, based on actual soil test values, which also showed consistently higher nutrient uptake, although slightly lower than T₁. Intermediate uptake values were observed under the STCR approach at 12 q ha⁻¹, as determined by Dhartimitra software, and at 12 q ha⁻¹ through actual soil test values. In contrast, the Soil Test Laboratory approach and the General Recommended Dose showed comparatively lower uptakes. The lowest nutrient uptake among fertilised treatments was recorded under the Farmers’ practice. In contrast, absolute control recorded the minimum uptake of N (23.8–24.8 kg ha⁻¹), P₂O₅ (3.75–4.25 kg ha⁻¹), and K₂O (12.15–12.83 kg ha⁻¹) across locations. The statistical analysis indicated that differences among treatments were statistically significant.

The superior performance of STCR-based nutrient management, integrated with FYM, can be attributed to a balanced and site-specific supply of nutrients that more efficiently matched the crop requirements (Krishna Murthy et al., 2023b; Tiwari et al., 2020[Ka1] [BN2] ). The use of the Dhartimitra software further optimised nutrient allocation based on soil fertility status, enhancing nutrient uptake efficiency. These findings align with earlier reports that STCR-based fertilisation, when integrated with organics, enhances nutrient availability, root growth, and microbial activity, resulting in higher uptake of N, P, and K (Mahajan et al., 2019; Krishna Murthy et al., 2023c).

The slightly higher uptake in Dhartimitra-guided treatments compared to soil test-based STCR suggests the advantage of decision-support tools in improving precision nutrient management at the field level. Treatments receiving lower target yields (12 q ha⁻¹) recorded lower uptake than their corresponding 15 q ha⁻¹ counterparts, reflecting the direct relationship between nutrient supply, crop demand, and nutrient absorption. In contrast, the soil test laboratory approach and general recommended dose did not consider site-specific variability or yield targets, resulting in sub-optimal uptake (Bhavya et al., 2021; Spoorthishankar et al., 2025).

Table 4: Effect of different nutrient management approaches on total uptake of major nutrients in cowpea at Bangalore Rural, Tumakuru, and Chikkaballapura Districts of Karnataka

Nutrient use efficiency

The nutrient use efficiency indices in cowpea varied considerably across the nutrient management treatments at Bangalore Rural, Tumakuru, and Chikkaballapura districts. For nitrogen, agronomic efficiency (AE) was highest in T₁ (39.6 kg kg⁻¹) and declined progressively to the lowest in T₇ (4.36 kg kg⁻¹). The apparent recovery efficiency (RE) of nitrogen ranged from 1.8 kg kg⁻¹ in T₁ to 0.19 kg kg⁻¹ in T₇, showing that higher fertiliser inputs did not proportionally increase nitrogen uptake. Relative internal utilisation efficiency (RIUE) of nitrogen remained relatively constant across treatments (4.48–4.60 kg kg⁻¹), indicating that the crop efficiently used the absorbed nitrogen regardless of application levels. Phosphorus use efficiency showed a different trend. AE ranged from 9.2 kg kg⁻¹ in T₄ to 31.5 kg kg⁻¹ in T₇, while RE remained low across all treatments (0.11–0.21 kg kg⁻¹), reflecting strong soil fixation and limited phosphorus uptake. Despite low recovery, RIUE values were relatively stable (0.69–0.75 kg kg⁻¹), indicating that phosphorus absorbed by the plant was effectively utilised. Potassium efficiency was comparatively stable. AE varied from 23.3 kg kg⁻¹ in T₇ to 42.15 kg kg⁻¹ in T₁, while RE remained low (0.73–0.99 kg kg⁻¹), suggesting that only a small fraction of applied potassium was recovered by the crop. RIUE values were consistent across treatments (2.12–2.38 kg kg⁻¹), indicating efficient internal utilisation of absorbed potassium.

These results highlight that nitrogen use efficiency declines with increasing fertiliser inputs (Moharana et al., 2017; Nagendrachari et al., 2025), phosphorus efficiency is limited by soil fixation despite effective internal utilisation, and potassium efficiency remains relatively stable (Banerjee et al., 2018). These patterns are consistent with previous studies on nutrient use efficiency as reported by Krishna Murthy et al., (2023d) and emphasise the need for optimised fertiliser management to maximise nutrient recovery while maintaining sustainable crop productivity

Table 5: Effect of different nutrient management approaches on nutrient use efficiency in cowpea at Bangalore Rural, Tumakuru, and Chikkaballapura Districts of Karnataka

Post-Harvest Soil Available NPK Status

Post-harvest soil test values of N, P₂O₅, and K₂O varied significantly across locations and treatments, reflecting the influence of nutrient management strategies (Figures 1, 2, and 3). In Bangalore Rural, residual N among fertilised treatments ranged from 187.06 kg ha⁻¹ in T₁ to 215.11 kg ha⁻¹ in T₇. At the same time, P₂O₅ was highest in T₃ (81.06 kg ha⁻¹) and lowest in T₇ (52.02 kg ha⁻¹), with K₂O values remaining comparatively stable but higher under balanced treatments (T₅ and T₆). In Tumakuru, residual N was lowest in T₁ (241.34 kg ha⁻¹) and highest in T₇ (269.11 kg ha⁻¹), P₂O₅ was better maintained in STCR treatments (T₁ and T₃: 105.04–102.64 kg ha⁻¹) compared to T₇ and T₈ (73.39–69.56 kg ha⁻¹), while residual K₂O was highest in T₅ and T₆ (149.52–151.86 kg ha⁻¹). Similarly, in Chikkaballapura, residual N varied from 243.08 kg ha⁻¹ in T₁ to 272.22 kg ha⁻¹ in T₇, P₂O₅ was higher in T₁ and T₃ (80.25 and 78.05 kg ha⁻¹) but lowest in T₈ (50.45 kg ha⁻¹), and K₂O was best sustained under T₅ and T₆ (195.81–198.20 kg ha⁻¹). Across all districts, STCR-based targeted yield approaches (T₁ and T₂) maintained relatively higher and balanced nutrient levels, notably P and K, compared to conventional recommendations, while farmers’ practice (T₇) and absolute control (T₈) consistently depleted soil P and K due to imbalanced or no fertiliser application (Sinchana et al., 2025). These results highlight that precision nutrient management through STCR and Dhartimitra software not only improves yields and nutrient uptake but also conserves post-harvest soil fertility, ensuring the sustainability of cowpea production systems. These results conform with the findings of (Rangaiah et al., 2025; Spoorthishankar et al., 2024)

Fig. 1 Influence of different approaches of nutrient recommendations on post-harvest soil available N, P₂O₅ and K₂O at Bangalore Rural district of Karnataka

Fig. 2 Influence of different approaches of nutrient recommendations on post-harvest soil available N, P₂O₅ and K₂O at Tumakuru district of Karnataka

Fig. 3 Influence of different approaches of nutrient recommendations on post-harvest soil available N, P₂O₅ and K₂O at Chikkaballapura district of Karnataka

The study clearly demonstrated that the Soil Test Crop Response (STCR) approach significantly improved cowpea productivity, nutrient uptake, and nutrient use efficiency compared to conventional fertiliser recommendations and farmers' practices. Among the treatments, STCR-based recommendations for a targeted yield of 15 q ha⁻¹, as determined by Dhartimitra software and actual soil test values, recorded the highest yields and nutrient uptake, highlighting the superiority of precision-based prescriptions over blanket applications. Nitrogen agronomic efficiency was maximised under STCR treatments, while phosphorus recovery was constrained by soil fixation, although its internal utilisation was efficient. Potassium efficiency was comparatively stable across treatments. The findings highlight the importance of soil testing and decision-support tools in correcting nutrient imbalances, optimising fertiliser use, and ensuring both economic viability and environmental sustainability. Thus, the integration of STCR technology with digital platforms, such as Dhartimitra, offers a practical, scalable, and farmer-friendly approach for enhancing cowpea production and promoting sustainable nutrient management in diverse agro-ecological zones.

Agarwal, D., and Devra, S.J. 2024. Pulses in India: Comprehensive analysis of production, challenges, and strategic vision for 2030. Journal of Experimental Agriculture International 46(11): 293–304. https://doi.org/10.9734/jeai/2024/v46i113053

Annappa, N.N., Krishna Murthy, R., Saralakumari, J., Thimmegowda, M.N., and Veeranagappa, P. 2025. A comprehensive analysis of the effects of balanced NPK fertilization on maize quality, nutrient uptake and soil sustainability using hierarchical cluster dendrogram techniques. Plant Science Today 12(2): 1–19. https://doi.org/10.14719/pst.7282

Ashoka, N., Ravi, Y., Vishwanatha, S., and Venkatesha, M.M. 2025. Optimizing cowpea (Vigna unguiculata (L.) Walp) for economic prosperity: A strategic approach to amplify agricultural revenue in Karnataka. Range Management and Agroforestry 46(1): 145–150. https://doi.org/10.59515/rma.2025.v46.i1.20

Banerjee, H., Ray, K., Dutta, S.K., Majumdar, K., Satyanarayana, T., and Timsina, J. 2018. Optimizing potassium application for hybrid rice (Oryza sativa L.) in coastal saline soils of West Bengal, India. Agronomy 8(12): 292. https://doi.org/10.3390/agronomy8120292

Bhavya, N., and Basavaraja, P.K. 2021. Influence of different approaches of nutrient recommendations on growth and yield of aerobic rice. Mysore Journal of Agricultural Sciences 55: 61–69.

Bhavya, N., Basavaraja, P.K., and Krishna Murthy, R. 2021. Assessment of soil test crop response based fertilization on nutrient requirement, nutrient uptake and nutrient use efficiency of aerobic rice. Chemical Science Review and Letters 9: 1–5. https://doi.org/10.37273/chesci.CS2820501002

Bhavya, N., Basavaraja, P.K., Krishna Murthy, R., Saqeebulla, M.H, and Gangamrutha, G.V. 2022. Impact of different approaches of nutrient recommendations for aerobic rice on soil fertility of Alfisols of Eastern Dry Zone of Karnataka. International Journal of Plant & Soil Science 34: 37–46. https://doi.org/10.9734/ijpss/2022/v34i1030921

Bray, R.H., and Kurtz, L.T. 1945. Determination of total, organic, and available phosphorus in soil. Soil Science 9: 39–46.

Gomez, K.A., and Gomez, A.A. 1984. Statistical Procedures for Agricultural Research. John Wiley & Sons, New York, p. 680.

Jackson, M.L. 1973. Soil Chemical Analysis. Prentice Hall of India Pvt. Ltd., New Delhi, p. 498.

Krishna Murthy, R., Basavaraja, P.K., Bhavya, N., Dey, P., Mohamed Saqueebulla, H., Gangamurtha, G.V., and Venkata Shiva Reddy, J.S. 2023. Development and validation of soil test based fertilizer prescription equations for enhancing yield, uptake and nutrient use efficiency of foxtail millet (Setaria italica) under dryland condition. Journal of Plant Nutrition 46(13): 3082–3100. https://doi.org/10.1080/01904167.2023.2188065

Krishna Murthy, R., Bhavya, N., Govinda, K., Uday Kumar, S.N., Basavaraja, P.K., Mohamed Saqueebulla, H.S., Srivastava, S., and Dey, P. 2023. Modelling soil, plant and fertilizer relationship to optimize nutrient management for chilli (Capsicum annuum L) under Alfisols of Southern India. Archives of Agronomy and Soil Science 69(15): 3570–3586. https://doi.org/10.1080/03650340.2023.2264210

Krishna Murthy, R., Bhavya, N., Govinda, K., Uday Kumar, S.N., Basavaraja, P.K., and Mohamed Saqueebulla, H. 2023. Influence of different approaches and forms of fertilizer application on yield and nutrient uptake of hybrid maize. Theoretical Biology Forum 12: 252–259. https://doi.org/10.61739/TBF.2023.12.2.252

Krishna Murthy, R., Nagaraju, B., Govinda, K., Uday Kumar, S.N., Basavaraja, P.K., Saqeebulla, H. M., Gangamrutha, G.V., Srivastava, S., and Dey, P. 2024. Soil test crop response nutrient prescription equations for improving soil health and yield sustainability—A long-term study under Alfisols of Southern India. Frontiers in Plant Science 15: 1439523. https://doi.org/10.3389/fpls.2024.1439523

Krishna Murthy, R., Nagaraju, B., Kasturappa, G., Kadappa, B.P., Narayanaswamy, U.K. S., Sab, M.S.H., Veerabadraiah, G.G., Srivastava, S., and Dey, P. 2024. Optimizing nutrient management for Kodo millet (Paspalum scrobiculatum L.) in the Alfisols of Southern India through modeling soil, plant, and fertilizer interactions. Scientific Reports 14(1): 31852. https://doi.org/10.1038/s41598-024-83265-y

Krishna Murthy, R., Nagaraju, B., Kasturappa, G., Nagendrachari, A.N., Kadappa, B.P., Narayanaswamy, U.K.S., Sab, M.S.H., Veerabadraiah, G.G., Srivastava, S., and Dey, P. 2024. Inductive cum targeted yield model-based integrated fertilizer prescription for sweet corn (Zea mays L. Saccharata) on Alfisols of Southern India. PLoS One 19(8): e0307168. https://doi.org/10.1371/journal.pone.0307168

Krishna Murthy, R., Nagaraju, B., Shankaraiah, S.K., Kasturappa, G., Kadappa, B.P., Sugatur Narayanaswamy, U.K., Saqeebulla, M., and Dey, P. 2024. Enhancing yield, uptake and nutrient use efficiency of brinjal through soil test crop response approach. Communications in Soil Science and Plant Analysis 55(7): 998–1014. https://doi.org/10.1080/00103624.2023.2289988

Mahajan, G.R., Pandey, R.N., Datta, S.C., Kumar, D., Sahoo, R.N., Patel, K.P., Murgaonkar, D., and Das, B. 2019. Predicting post-harvest soil test values in hybrid rice (Oryza sativa L.)–wheat (Triticum aestivum L.) cropping sequence using a multivariate analysis technique. Communications in Soil Science and Plant Analysis 50(13): 1624–1639. https://doi.org/10.1080/00103624.2019.1631328

Moharana, P.C., Sharma, B.M., and Biswas, D.R. 2017. Changes in the soil properties and availability of micronutrients after six-year application of organic and chemical fertilizers using STCR-based targeted yield equations under pearl millet-wheat cropping system. Journal of Plant Nutrition 40: 165–176. https://doi.org/10.1080/01904167.2016.1201504

Nagendrachari, A.N., Rangaiah, K.M., Jayaramreddy, S., Nanjundegowda, T.M., and Parameshwarappa, V. 2025. Hierarchical cluster analysis approach for evaluating variability in nutrient uptake and composition of maize. Communications in Soil Science and Plant Analysis 56(11): 1685–1708. https://doi.org/10.1080/00103624.2025.2472894

Omomowo, O.I. and Babalola, O.O. 2021. Constraints and prospects of improving cowpea productivity to ensure food, nutritional security and environmental sustainability. Frontiers in Plant Science 12: 751731. https://doi.org/10.3389/fpls.2021.751731

Page, A.L., Miller, R.H., and Keeny, D.R. 1982. Methods of Soil and Plant Analysis. American Society of Agronomy, Madison, WI, USA, p. 498.

Piper, C. 1966. Soil and Plant Analysis. Hans Publications, Bombay, pp. 137–153.

Ramamoorthy, B., Narasimham, R.L., and Dinesh, R. S. 1967. Fertilizer application for specific yield targets on Sonora 64 (wheat). Indian Farming 17: 43–45.

Rangaiah, K.M., Nagaraju, B., Kasturappa, G., Nanjundappa, M.S., Narayanaswamy, U. K.S., Srivastava, S., and Dey, P. 2025. Assessment of fertilizer prescription equations, crop and soil performance with different nutrient management practices for green gram (Vigna radiata) in Southern India’s Alfisols. Scientific Reports 15(1): 31954. https://doi.org/10.1038/s41598-025-04738-2

Rukhsar, C.B., Pithia, M.S., and Raval, L.J. 2020. Discriminate function analysis in cowpea (Vigna unguiculata (L.) Walp.). Electronic Journal of Plant Breeding 12(1): 54-57. https://doi.org/10.37992/2021.1201.008

Sinchana, M.A., Krishna Murthy, R., Bhavya, N., Govinda, K., and Shivakumara, M.N. 2025. Evaluation of different approaches of fertilizer recommendations on yield, nutrient uptake, and use efficiency of finger millet (Eleusine coracana L. Garten). Madras Agricultural Journal 112: 94–102. https://doi.org/10.29321/MAJ.10.701201

Singh, V.K., Gautam, P., Nanda, G., Dhaliwal, S.S., Pramanick, B., Meena, S.V., Alsanie, W.F., Ahmed, G., Sayed, S., and Hossain, A. 2021. Soil test-based fertilizer application improves productivity, profitability and nutrient use efficiency of rice (Oryza sativa L.) under direct seeded condition. Agronomy 11: 1756. https://doi.org/10.3390/agronomy11091756

Spoorthishankar, K.S., Krishna Murthy, R., Kadalli, G.G., Vasanthi, B.G., Pushpa, K., and Muthuraju, R. 2025. Comparative study on nutrient recommendation approaches for optimizing green gram productivity in Alfisols. Mysore Journal of Agricultural Sciences 59(1): 386–397.

Spoorthishankar, K.S., Krishna Murthy, R., Kadalli, G.G., Vasanthi, B.G., Pushpa, K., and Muthuraju, R. 2024. Comparison of STCR and conventional fertilizer approaches for improved yield, uptake and nutrient use efficiency in green gram. Journal of Advances in Biology and Biotechnology 27(11): 312–323. https://doi.org/10.9734/jabb/2024/v27i111614

Subbiah, B.V. and Asija, G.L. 1956. A rapid procedure for the determination of available nitrogen in soils. Current Science 37: 259–260.

Tiwari, R., Upadhyay, A.K., Rai, H.K., and Dey, P. 2020. Impact of fertilizers and manure on growth, yield, nutrient uptake by rice and soil properties in a Vertisol under STCR approach. International Archives of Applied Sciences and Technology 11: 57–65. https://doi.org/10.15515/iaast.0976-4828.11.2.5765

Walkley, A. and Black, I.A. 1934. An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science 37: 29–38.

Yururdurmaz, C. 2022. Impact of different fertilizer forms on yield components and macro–micronutrient contents of cowpea (Vigna unguiculata L.). Sustainability 14(19): 12753. https://doi.org/10.3390/su141912753