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

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 28^th and 25^th 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.





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.



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.



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).



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 (R² = 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).



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).



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.
 
Economics
 
Economic analysis explored that V3N1 incurred lowest cost of cultivation (US$ 321.4/ha), while V₁N₅ required 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 V₂N₁ (Fig 4).



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.

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