Legume Research

  • Chief EditorJ. S. Sandhu

  • Print ISSN 0250-5371

  • Online ISSN 0976-0571

  • NAAS Rating 6.67

  • SJR .391

  • Impact Factor .669 (2022)

Frequency :
Monthly (January, February, March, April, May, June, July, August, September, October, November and December)
Indexing Services :
BIOSIS Preview, ISI Citation Index, Biological Abstracts, Elsevier (Scopus and Embase), AGRICOLA, Google Scholar, CrossRef, CAB Abstracting Journals, Chemical Abstracts, Indian Science Abstracts, EBSCO Indexing Services, Index Copernicus

Effect of Integrated Nutrient Management on Nodulation, Yield, Quality, Energetics and Economics of Soybean [Glycine max (L.) Merrill.] Varieties in Eastern India

Saikat Biswas1,*, Lay Lay Nwe1, Rupa Das2, Dhananjoy Dutta1
1Department of Agronomy, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia-741 252, West Bengal, India.
2Department of Seed Science and Technology, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia-741 252, West Bengal, India.
  • Submitted03-09-2022|

  • Accepted02-02-2023|

  • First Online 25-03-2023|

  • doi 10.18805/LR-5036

Background: The oilseed crop soybean has the potential to bridge the demand-supply gap of edible oil. Varietal adaptation to environment and agronomic interventions like integrated nutrient management (INM) are hypothesized to elevate the yield, quality and save energy in soybean, assuring environmental safety.

Methods: The experiment was conducted in three times replicated factorial randomized block design (RBD) with 3 varieties and 5 INM options at Instructional Farm, Bidhan Chandra Krishi Viswavidyalaya, West Bengal during kharif season of 2019 and 2020.

Result: Comparatively greater nodules/plant (51.21) and dry weight of nodules (0.51 g), pods/plant (34.04), 100-seeds weight (15.24 g), yield (2601 kg/ha), protein (41.42%) and economic profitability (B:C- 2.32) were observed from PS 24 grown under 75% RDF + 1.5t/ha vermicompost + 25 kg/ha ZnSO4. Energy budgeting further indicated that soybean cultivation mostly consumed energy from indirect non-renewable sources and PS 24 grown under that INM option generated highest energy outcome (112679.3 MJ/ha) in Eastern Indian condition.
Soybean [Glycine max (L.) Merrill.], being first in the world as a supplier of vegetable protein (60%) and edible oil (30%), is a great source of micronutrients and minerals for human and animal diets (Yan et al., 2015). India contributes only 2.75% of the global production from 10% (12 mha) of the global area (122.68 mha) due to its low productivity (0.98 t/ha) (USDA, 2020). Since history, oilseeds have played pivotal role in human life through their multiple uses. However, now, India has a huge shortfall of edible oil production and meets nearly 50% of the requirement through imports, resulting in demand-supply gap as well as high market price. The projected demand of edible oil is likely to be increased from 25.26 mt in 2020 to 35.90 mt in 2050 (FICCI, 2015). In this scenario, high attention towards soybean cultivation may play a major role to meet the edible oil deficit and it could be achieved by augmenting the present crop yield level.

Improved varieties, in any crop, are the pre-requisite for increasing productivity. The yield of soybean is due to interaction of variety’s genetic potential and environment. Therefore, response to crop management and environment varies markedly with soybean varieties. However, variety specific standardization of agro-techniques across the agro-climatic zones is still lacking. In this regard, appropriate nutrient management can play a key role. Excessive, unscientific chemical fertilizer application leads to environmental foot-prints and stagnation in production through destroying soil health, while unavailability of bulky organic manures is also a constraint. So, integrated nutrient management (INM) incorporating balanced use of organic and inorganic nutrient sources can create a win-win situation for sustaining the soybean productivity by eliminating the constraints and utilizing their individual benefits (Maheshbabu et al., 2008). Therefore, the present experiment was conducted with the aim to uplift soybean productivity in eastern India by selecting improved variety through its response to ideal INM option.
The field experiment was carried out at Instructional Farm, Bidhan Chandra Krishi Viswavidyalaya, West Bengal (22° N latitude, 89°E longitude and 9.75 m above MSL) during kharif season of 2019 and 2020. The sandy-loam experimental soil had soil pH of 7.20, organic carbon of 0.61%, available nitrogen, phosphorus and potassium of 191.8, 27.1 and 171.5 kg/ha, respectively and zinc of 0.50 ppm. The crop received rainfalls of 1017.1 and 1077.1 mm during experimental period of 2019 and 2020, respectively. The fully rainfed experiment followed factorial RBD comprising 3 soybean varieties (V1: PS 1225; V2: YEZIN 15; V3: PS 24) and 5 nutrient management options (N1: 100% RDF i.e., N: P2O5: K2O-20:60:40kg/ha; N2: 75% RDF + 3t/ha FYM; N3: 75% RDF+ 1.5t/ha vermicompost; N4: N2 + 25 kg/ha ZnSO4; N5: N3 + 25 kg/ha ZnSO4), replicated thrice. As per the treatments, organic manures were applied during land preparation and fertilizers (urea, S.S.P. and M.O.P. and ZnSO4, 7H2O for N, P2O5, K2O and Zn, respectively) were applied at basal. Individual plot size was 4.5 m ´ 3 m. Soybean seeds @75 kg/ha were sown at a spacing of 45 cm × 10 cm on 28th and 25th June in 2019 and 2020, respectively. YEZIN 15 was harvested at around 110 DAS, while rests were harvested at around 115 DAS.

Observations included nodules/plant, dry weight of nodules/plant (g), pods/plant, seeds/pod, 100-seeds weight (g), seed yield (kg/ha), oil and protein contents (%), energetics and economics of soybean cultivation. Oil and protein were estimated using Soxhlet-apparatus (Ajayi et al., 2004) and method suggested by Gupta et al., (1972) (nitrogen content × 6.25), respectively. Energetics included source wise energy sharing (direct, indirect, renewable and non-renewable sources) (Fig 1) and energy indices. Energy involved in all the inputs and outputs were computed using their energy equivalent values as shown in Table 1.

Fig 1: Different sources of energy input.

Table 1: Equivalent energy of different inputs and outputs of soybean cultivation.

Energy indices were calculated using the following formulae:
Net energy gain (NEG) =
Energy output (EO)- Energy input (EI)


Economic analysis was done based on cost of cultivation, gross return, net return and benefit-cost ratio (B:C). Experimental data were statistically analysed by ‘analysis of variance’ method (Panse and Sukhatme, 1967). Treatment means were compared using critical difference values at 5% level of significance.
Nodules count and dry weight/plant
Results (Table 1) indicated that variety V3 registered maximum nodules count (46.55) and dry weight (0.47 g) (pooled). It might be due to higher proliferation and activities of Rhizobium as well as adequate root growth under favourable physico-chemical soil condition. Irrespective of years and pooled analysis, V2 produced lowest nodule numbers and dry weight/plant. Dependence of nodulation on soybean variety was earlier confirmed by Vyas and Kushwah (2015). These parameters also varied significantly under INM options. Greater nodulation was observed in INM options over N1. Application of N5 ensured maximum nodules count (43.19) and dry weight (0.45 g) (pooled). Specifically, V3N5 exhibited maximum nodule numbers and dry weight/plant, followed by V3N3. Application of vermicompost as well as zinc might play positive role in root growth and biological activities of soil and thereby, influenced nodulation of legume plant. Solanki et al., (2018) also stated that nodule number and dry weight were enhanced by INM options over N1.
Yield attributes and seed yield
Regarding yield attributes and yield of soybean (Table 2), varieties showed significant differences among themselves except seeds/pod. Variety V3 expressed the highest number of pods/plant (31.55), seeds/pod (2.41) and 100 seeds weight (14.94 g) (pooled), followed by V1.

Table 2: Effect of integrated nutrient management on nodulation, yield attributes and yield of soybean varieties.

Similarly, in seed yield, V3 outperformed others (2354 kg/ha) (pooled). Greater variations in yield were probably due to variable genetic makeup among varieties. PS 24 due to greater adaptation potential, better utilized the resources; exhibited higher photosynthetic efficiency and translocation of photo-assimilates to reproductive organs over others. Meena et al., (2016) also confirmed strong influence of genetic factors on soybean yield.

Application of N5 ensured the highest number of pods/plant (30.98), 100 seeds weight (14.95 g) and yield (2320kg/ha) (pooled), followed by application of N3 possibly due to positive influence of vermicompost on soil physical, chemical and biological health, greater availability, uptakes of nutrients, supply of various growth promoting substances. Further, ZnSO4, as constituent of essential enzymes, might significantly influence the biosynthesis of chlorophyll, nodulation, nitrogen fixation, pollen viability, seed setting, photosynthesis and translocation of assimilates towards sink. The result corroborated the finding of Verma et al., (2017). It was noticed that seeds/pod did not vary among INM options as that trait might be genetically governed. Altogether V3N5 produced highest yield attributes and yield (2601kg/ha) (pooled), while V2N1 produced lowest yield attributes and yield. YEZIN 15 was introduced from Myanmar and might therefore, show poor adaptation to Eastern Indian agro-climatic condition. Further, perhaps various losses of nutrients from fertilizer sources particularly during rainy season resulted in low soybean yield.
Seed oil and protein
Results in Table 3 revealed that both the quality parameters varied differently among the varieties.

Table 3: Effect of integrated nutrient management on oil and protein contents of soybean varieties.

The highest oil content was registered by the seeds of V2 (20.55%), while V3 seeds ensured the highest protein (40.26%) (pooled). The lowest oil and protein contents were exhibited by the seeds of PS 24 and YEZIN 15, respectively. It might be due to specific genetic makeup of each variety as well as agro-climatic conditions of the harvest (Kumar et al., 2015). Although INM options did not vary significantly regarding soybean oil, but significant variations in protein were observed among them. Highest oil and protein contents were recorded from applications of N1 and N5, respectively and vice-versa. Specifically, highest oil (21.18%) and protein (40.98%) (pooled) were exhibited by V2N1 and V3N5, respectively. Synergistic effect of vermicompost and Zn along with inorganic NPK due to prolonged nutrient release and higher uptake of N, might accelerate protein synthesis (Verma et al., 2017). However, lower protein under N1 might be due to antagonistic relation between fat and protein synthesis.
Relationship of seed yield with yield attributes and nodule numbers
Linear regression relationships existed between yield and (a) pods/plant, (b)100 seeds weight, (c) nodules/plant (Fig 2 a,b,c).

Fig 2 (a, b, c): Relationship between seed yield and (a) number of pods per plant, (b) 100 seeds weight, (c) number of nodules per plant.

Linear models were able to explain 99.06%, 91.24% and 96.94% variations between seed yield (a) pods/plant (R2 = 0.9906), (b)100 seeds weight (R= 0.9124), (c) nodules/plant (R2 = 0.9694), respectively.
Energy sharing
It was found that irrespective of treatments, energies from indirect and non-renewable sources were more involved in soybean production than those from direct and renewable sources (Fig 3).

Fig 3: Source wise partitioning of total energy use (%).

Maximum energy was from indirect non-renewable sources. Application of N4 consumed least energy from direct renewable (14.7%) and non-renewable (17.1%) sources as well as indirect renewable source (18.5%), but consumed maximum energy from indirect non-renewable source (49.7%), followed by application of N5. Application of N3 used least indirect non-renewable energy source (42.0%) and maximum energy from direct renewable (17.0%) and non-renewable (19.7%) sources as well as indirect renewable source (21.3%). It might be due to less uses of inorganic fertilizer (75% RDF) and organic manure (vermicompost) and no use of ZnSO4, which were indirect non-renewable energy sources.
Energy indices
Irrespective of varieties, soybean consumed lowest and highest energy under applications of N3 (6379.6 MJ) and N4 (7352.1 MJ), respectively, showing its variations due to INM options (Table 4).

Table 4: Energy indices of soybean cultivation under INM options (pooled).

Highest EO (112679.3 MJ), NEG (105777.2 MJ) and EP (1.14 kg/MJ) were recorded from V3N5. It might be due to direct and positive influence of yield on energy outcome (Ganajaxi et al., 2011). Highest ER and EPt as well as lowest SE (0.85 MJ/kg) were achieved from V3N3, followed by V3N5. It indicated less use of EI to generate higher yield or EO. Conversely, V2N1 recorded highest SE (1.34 MJ/kg) and lowest EO, NEG, ER, EP and EPt. It might be due to less productivity of that variety resulted from poor adaptability and loss of nutrients under 100% RDF in kharif season.
Economic analysis explored that V3N1 incurred lowest cost of cultivation (US$ 321.4/ha), while V1Nrequired maximum cost (US$ 405.3/ha). N1 required less cost due to less requirement of fertilizers as those were in concentrated form as compared to bulky organic manures used in other treatments. However, regarding gross (GR) and net returns (NR), V3N5 outperformed others (GR: US$ 911.4/ha and NR: US$ 519.4/ha), followed by V3N3 (GR: US$ 858.9/ha and NR: US$ 477.7/ha). Consequently, highest B:C (2.32) was ensured by V3N5, followed by V3N4 (2.31). Lowest GR (US$ 488.1/ha), NR (US$ 162.5/ha) and B:C (1.50) were recorded by V2N(Fig 4).

Fig 4: Effect of integrated nutrient management on production economics of soybean varieties.

The trend in economic return was possibly due to the yield variations exhibited by various treatment combinations.
The study confirmed the variable response of soybean varieties to different INM options. The result indicated that application of 75% RDF+1.5 t/ha vermicompost+25 kg/ha ZnSO4 was suitable for growing of soybean varieties, specially, ‘PS 24’ during kharif seasons of 2019 and 2020. Therefore, cultivation of soybean variety ‘PS 24’ under application of 75% RDF+1.5 t/ha vermicompost+25 kg/ha ZnSO4 can be recommended for achieving better nodulation, yield, quality, energy and economic outcomes in Eastern Indian condition.

  1. Ajayi, I.A., Adebowale, K.O., Dawodu, F.O., Oderinde, R.A. (2004). A study of the oil content of Nigerian grown Monodora myristica seeds for its nutritional and industrial applications. Pak. J. Sci. Indus Res. 47: 60-65.

  2. Devasenapathy, P., Senthilkumar, G., Shanmugam, P.M. (2009). Energy management in crop production. Indian J. Agron. 54: 80-90.

  3. FICCI (Federation of Indian Chamber of Commerce and Industry). (2015). Evaluation of the PPPIAD Project on Soybean. pp. 14.

  4. Ganajaxi, H.S.I., Hiremath S.M., Chittapur, B.M. (2011). Productivity, profitability and energy use efficiency of different cropping sequences in northern transition zone of Karnataka. Indian Journal of Agricultural Sciences. 81(10): 921-926.

  5. Gupta, B.R., Pathak, R.K., Bhan, S., Singh, A. (1972). Effect of NPK on yield, nutrient and quality of toria (Brassica campestris var. toria). Indian Journal of Agronomy. 17: 88-91.

  6. Kitani, O. (1999). Energy and Biomass Engineering. CIGR Handbook  of Agricultural Engineering. 5: St.  Joseph, MI, USA: ASAE Publication.

  7. Kumar, R., Singh, M.K., Kumar, V., Verma, R.K., Kushwah, J.K., Pal, M. (2015). Effect of nutrient supplementation through organic sources on growth, yield and quality of coriander (Coriandrum sativum L.). Indian Journal of Agricultural Research. 49: 278-281.

  8. Maheshbabu, H.M., Hunje, R., Biradar, N.K., Babalad, H.B. (2008). Effect of organic manures on plant growth, seed yield and quality of soybean. Karnataka Journal of Agricultural Sciences. 21(2): 219-221.

  9. Meena, D.S., Ram, B., Jadone C. (2016). Effect of integrated nutrient management on productivity, profitability, nutrient uptake and soil fertility in soybean [Glycine max (L.) Merrill]. Soybean Research. 14(1): 21-33. 

  10. Mittal, V.K., Mittal, J.P., Dhawan, K.C. (1985). Research Digest on Energy Requirements in Agricultural Sector. Co-Ordinating Cell, AICRP on Energy Requirements in Agricultural Sector. Punjab Agricultural University, Ludhiana, India. p. 42.

  11. Panse, V.G., Sukhatme, P.V. (1967). Statistical Methods for Agricultural  Workers, ICAR Pub. New Delhi. pp. 336.

  12. Sadorsky, P. (2006). Modeling and forecasting petroleum futures volatility. Energy Econ. 28: 467-488.

  13. Solanki, A.C., Solanki, M.K., Nagwanshi, A., Dwivedi, A.K., Dwivedi, B.S. (2018). To investigate the impact of integrated application of inorganic fertilizer (NPK) and farmyard manure (FYM) on soybean grain nutrient uptake and yield. International Journal of Current Microbiology and Applied Sciences. 7(9): 1093-1102.

  14. USDA (United States Agricultural Department). (2020). World Agricultural Production. Circular series WAP 7-20, July 2020, (https://ipad.fas.usda.gov.). pp. 30.   

  15. Verma, A.K., Pandagare, T., Kolhe, S.S., Shrivastava, G.K., Pandey, N. (2017). Assessment of customized fertilizer for soybean  [Glycine max (L.) Merrill] in Chhattisgarh plains under rainfed condition. Soybean Research. 13(2): 19-25.

  16. Vyas, M.D., Kushwah, S.S. (2015). Response of soybean [Glycine max (L.) Merrill] varieties to fertility levels in vertisols of Vindhyan plateau of Madhya Pradesh. Soybean Research. 13(2): 9-18.

  17. Yan, C.J., Song, S.H., Wang, W.B., Miao, S.J., Cao, Y.Q., Wang, C.L., Zhang, L.J. (2015).  Impacts of fertilization on photosynthesis, growth and yield of two soybean cultivars (Glycine max L. Merr) in Northeast China. Legume Research. 38(1): 77-84.

Editorial Board

View all (0)