Genotypic Response to Salt Stress Tolerance in Barley (Hordeum vulgare L.)

Introduction

Barley (Hordeum vulgare L.) is the fourth most important cereal crop worldwide, following maize, wheat and rice, with an annual global production of approximately 150 million Mg (USDA-FAS, 2026) [1] . It is a self-pollinating diploid species (2n=2x=14) that has been cultivated since the Stone Age and was among the first domesticated plants in human history (Salamini et al., 2002; Haas et al., 2019) [2,3] . Barley is valued for its wide adaptability to diverse agro-climatic conditions, including tolerance to marginal soils, drought and salinity, making it an important crop in arid and semi-arid regions where other cereals struggle to perform (Mwando et al., 2020) [4] . Salinity is recognised as one of the most critical abiotic stresses limiting crop growth and agricultural productivity worldwide. Nearly 1.4 billion hectares of global land (approximately 10.7% of total land area) are affected by salinity (FAO, 2024) [5] , with the problem particularly severe in arid and semi-arid regions. In India, approximately 6.73 million hectares of land are affected by salinity and sodicity and Rajasthan alone accounts for about 1.18-1.19 million hectares of salt-affected land (ICAR, 2025) [6] . Projections by the Central Soil Salinity Research Institute indicate that without intervention, this area could increase to 16.2 Mha by 2050 due to climate change and poor-quality irrigation water (Sharma et al., 2024) [7] .

Salinity stress imposes two major types of injury on plants:

Osmotic stress, which occurs immediately upon salt accumulation in the root zone and reduces water availability and ionic stress, which develops as toxic Na and Cl ions accumulate in plant tissues, disrupting metabolic⁺ ⁻ processes, reducing stomatal conductance, limiting photosynthetic activity and ultimately impairing grain yield (Munns and Tester, 2008) [8] . Despite barley's relatively better tolerance to salinity compared to other cereals, considerable genotypic variation exists within the species for salinity tolerance and identification of salttolerant genotypes remains a priority in barley improvement programmes (Mwando et al., 2020) [4] . During 2024-25, India had approximately 6.62 lakh hectares under barley cultivation with a total production of 19.19 lakh tonnes and an average productivity of 29.0 q/ha (Anonymous, 2025) [9] . Grain yield in barley is a complex polygenic trait influenced by several component traits as well as environmental factors, including plant height, number of tillers per plant, spike length, grains per spike and 1000-grain weight (Patial et al., 2024) [10] . Understanding genetic variability and the interrelationships among these traits under salt stress is essential for designing effective breeding strategies (Dewey and Lu, 1959; Wright, 1921) [11,12] . Genetic parameters such as genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV), broad-sense heritability and genetic advance as percent of mean (GAM) are widely used to assess the extent of variability and predict selection response (Johnson et al., 1955) [13] . Path coefficient analysis, introduced by Wright (1921) [12] , is a powerful statistical tool that partitions correlation coefficients into direct and indirect effects, thereby providing insights into the relative importance of each trait in determining grain yield under stress environments (Dewey and Lu, 1959) [11] . With this background, the present investigation was undertaken to: (i) evaluate genetic variability among 18 barley genotypes under salt stress conditions; (ii) estimate correlation between yield and yieldattributing traits; (iii) determine direct and indirect effects of component traits on grain yield through path analysis; and (iv) identify promising salt-tolerant barley genotypes for future breeding programmes.

Materials and Methods

Experimental Location and Design

The experiment was conducted during the Rabi season of 2025-26 at the Agricultural Research Farm, Suresh Gyan Vihar University (SGVU), Jaipur, Rajasthan. The experimental farm is located at approximately 26°54′N latitude and 75°49′E longitude, at an altitude of 431 m above mean sea level, representing typical arid to semi-arid climatic conditions of Rajasthan.

Experimental Material

The experimental material consisted of 18 barley genotypes, comprising 16

Traits Recorded

Data were recorded on fourteen quantitative traits:germination percentage (GP), root length (RL, cm), shoot length (SL, cm), days to 50% flowering (DFF), days to maturity (DM), plant height (PH, cm), total number of tillers per plant (NTPP), number of effective tillers per plant (NETPP), spike length (SPL, cm), number of grains per spike (NGPS), test weight/1000-grain weight (TW, g), grain yield per plant (GYPP, g), biological yield per plant (BYPP, g) and harvest index (HI, %).

Statistical Analysis

Analysis of variance (ANOVA) was performed according to the procedure for RBD (Panse and Sukhatme, 1985) [14] . Genotypic variance (σ ²g) and phenotypic variance (σ ²p) were estimated following standard biometrical procedures. Genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) were calculated as per Burton (1952) [15] . Broad-sense heritability (h²bs) was estimated following Allard (1960) [16] and genetic advance (GA) and genetic advance as percent of mean (GAM) were estimated following Johnson et al. (1955) [13] at 5% selection intensity. Genotypic and phenotypic correlation coefficients were calculated following Al-Jibouri et al. (1958) [17] . Path coefficient analysis was carried out following Wright (1921) [12] and Dewey and Lu (1959) [11] with grain yield per plant as the dependent variable.

Results

Analysis of Variance

The analysis of variance (Table 1) revealed highly significant differences (p <0.01) among the 18 barley genotypes for all 14 quantitative traits studied. The mean sum of squares due to genotypes was significantly higher than the error mean sum of squares for every trait, confirming the existence of substantial genetic diversity and inherent variation within the experimental germplasm under salt stress conditions. This provides ample scope for direct phenotypic selection and genetic improvement for salt tolerance.

Mean Performance of Barley Genotypes

The mean performance of the 18 barley genotypes for all 14 quantitative traits under salt stress is presented in Table 2. Among the evaluated genotypes, RD 2552 recorded the highest values for germination percentage (90.3%), root length (14.5 cm), shoot length (17.5 cm), plant height (88.4 cm), total tillers per plant (9.2), effective tillers per plant (7.67), spike length (9.9 cm), grains per spike (51.3), test weight (47.4 g), grain yield per plant (20.5 g), biological yield per plant (46.5 g) and harvest index (44.09%), while also achieving the earliest flowering (78.2 days) and maturity (120.1 days). DWRB 223 consistently ranked second across most traits. RD 2907, Karan 18 and Rajkiran also performed commendably. In contrast, RD 2052 recorded the lowest values for most yield traits, suggesting high sensitivity to salt stress. The overall trial means were: germination 84.8%, root length 12.9 cm, shoot length 15.9 cm, days to 50% flowering 80.6 days, days to maturity 122.3 days, plant height 80.4 cm, NTPP 7.99, NETPP 6.07, spike length 8.92 cm, grains per spike 46.34, test weighs 43.95 g, grain yield per plant 16.92 g, biological yield per plant 39.58 g and harvest index 42.14%.

Genetic Variability Parameters

Estimates of GCV, PCV, broad-sense heritability (h²bs), genetic advance (GA) and genetic advance as percent of mean (GAM) for all 14 quantitative traits are presented in Table 3. Across all traits, PCV estimates were slightly higher than corresponding GCV estimates, indicating environmental influence on phenotypic expression. However, the narrow gap between GCV and PCV for most traits demonstrates that the variability is predominantly governed by genetic factors.

The highest GCV and PCV were recorded for grain yield per plant (GCV: 11.74%; PCV: 12.31%), followed by number of tillers per plant (GCV:

Correlation Coefficient Analysis

Genotypic and phenotypic correlation coefficients between grain yield per plant and other quantitative traits are presented in Table 4. Genotypic correlations were generally higher in magnitude than phenotypic correlations across all trait pairs, indicating strong inherent genetic associations with comparatively lesser environmental influence. Grain yield per plant showed highly significant positive correlations (p <0.01) at both genotypic and phenotypic levels with biological yield per plant (rg = 0.9985, rp = 0.9829), test weight (rg = 0.9976, rp = 0.9799), spike length (rg = 0.9975, rp = 0.9827), plant height (rg = 0.9970, rp = 0.9813), number of grains per spike (rg = 0.9967, rp = 0.9755), number of tillers per plant (rg = 0.9964, rp = 0.9827), shoot length (rg = 0.9937, rp = 0.9696), root length (rg = 0.9930, rp = 0.9688), germination percentage (rg = 0.9868, rp = 0.9587), harvest index (rg = 0.9708, rp = 0.9434) and number of effective tillers per plant (rg = 0.8635, rp = 0.8512). Strong negative correlations were observed with days to 50% flowering (rg = -0.9868, rp = -0.9780) and days to maturity (rg = -0.9847, rp = -0.9602), indicating that early-flowering and earlymaturing genotypes avoid terminal salt stress and maintain higher grain production.

Path Coefficient Analysis

Path coefficient analysis was performed to partition the correlation coefficients into direct and indirect effects, providing an accurate assessment of the individual contribution of each trait towards grain yield per plant. The direct and indirect effect estimates at both genotypic and phenotypic levels are presented in Table 5. At the genotypic level, biological yield per plant exerted the maximum positive direct effect on grain yield per plant (0.9241), followed by spike length (0.2887), harvest index (0.1934), days to maturity (0.0728), root length (0.0341), effective tillers per plant (0.0121) and germination percentage (0.0050). Negative genotypic direct effects were registered for test weight (-0.1665), number of tillers per plant (-0.1469), plant height (-0.0656), days to 50% flowering (-0.0534), grains per spike (-0.0380) and shoot length (-0.0132). At the phenotypic level, the highest positive direct effects were exerted by days to 50% flowering (0.3037), biological yield per plant (0.2658), spike length (0.2203), grains per spike (0.1242), days to maturity (0.1217), number of tillers per plant (0.1127), test weight (0.0569) and plant height (0.0541). Biological yield per plant also served as the primary indirect pathway driving high positive correlations of component traits with grain yield per plant at the genotypic level, with indirect contributions ranging from 0.7972 (effective tillers per plant) to 0.9214 (plant height). Days to 50% flowering and days to maturity influenced grain yield indirectly through strong negative contributions via biological yield per plant (-0.9082 and -0.9058, respectively).

Discussion 4.1 Analysis of Variance and Mean Performance

Analysis of variance revealed highly significant differences (p<0.01) among the 18 barley genotypes for all 14 quantitative traits, confirming the existence of substantial genetic diversity within the evaluated germplasm under salt stress. These results are consistent with the findings of Kumar et al. (2018) [18] , Sisodiya et al. (2025) [19] , Meena et al. (2025) [20] and Hailu et al. (2024) [21] , who also reported highly significant genotypic variation for morpho-physiological and yieldcontributing traits in barley under similar conditions. Among the evaluated genotypes, RD 2552 exhibited the highest performance across all major yield and yield-contributing traits, including the highest grain yield per plant (20.5 g), biological yield per plant (46.5 g) and harvest index (44.09%), while also achieving the earliest flowering (78.2 days) and maturity (120.1 days). This superior performance of RD 2552 under saline conditions reflects better osmotic adjustment, physiological tolerance and efficient assimilate partitioning. DWRB 223, which ranked second across most traits, demonstrated commendable salt tolerance. The lowest performance across traits was consistently observed in RD 2052 and RD 2035, indicating high sensitivity to saline conditions. These results are in agreement with Almohisen (2021) [22] , who observed significant genotypic differences in barley under saline and normal conditions and with Juskiw and Helm (2003) [23] and Rathore et al. (2016) [24] , who documented significant genotypic variation in germination and early seedling establishment in barley under salinity.

Genetic Variability

The narrow gap between PCV and GCV across most traits in the present study demonstrates that the observed variability is predominantly governed by genetic factors, with minimal environmental influence. Similar findings were reported by Kumar et al. (2018) [18] , Verma et al. (2022) [25] and Saha et al. (2024) [26] in barley. High GCV and PCV for grain yield per plant, number of tillers per plant and biological yield per plant indicate substantial genetic variation and strong selection potential for these traits under saline conditions, in agreement with Kabir et al. (2023) [27] and Kaur and Vishwakarma (2020) [28] . High broad-sense heritability (>80%) was recorded for most traits, particularly for number of effective tillers per plant (91.8%), grain yield per plant (91.0%), number of tillers per plant (89.6%) and biological yield per plant (88.5%). Similar high heritability estimates in barley were reported by Yadav et al. (2015) [29] , Fekadu et al. (2022) [30] and Zewodu et al. (2025) [31] . High heritability coupled with high GAM-as observed for grain yield per plant (91.0%, 23.17%), number of effective tillers per plant (91.8%, 21.42%) and number of tillers per plant (89.6%, 21.40%)-indicates the predominance of additive gene action, making direct phenotypic selection effective for these traits. This is consistent with Johnson et al. (1955) [13] , who emphasized that high heritability alone without correspondingly high genetic advance does not necessarily indicate significant genetic gain, whereas high heritability coupled with high GAM is indicative of additive gene action.

Correlation Analysis

The stronger genotypic correlations relative to phenotypic correlations in the present study suggest that the observed trait associations have a strong genetic basis with comparatively lesser environmental masking. The highly significant positive associations of grain yield per plant with biological yield per plant, spike length, plant height, number of grains per spike and number of tillers per plant indicate that these traits are important indirect selection criteria for improving grain yield under saline conditions. Similar positive associations of grain yield with biological yield, harvest index and tillers per plant were reported by Verma et al. (2022) [25] , Saha et al. (2024) [26] and Aklilu et al. (2020) [32] . The significant negative correlations of grain yield per plant with days to 50% flowering and days to maturity confirm the advantage of early-maturing genotypes in escaping terminal salinity stress, consistent with Almohisen (2021) [22] and Zewodu et al. (2025) [31] . The positive association of harvest index with grain yield (rg = 0.9708) further emphasises the role of assimilate partitioning efficiency as a key determinant of yield under salt stress, in agreement with Baye et al. (2020) [33] and Adhikari et al. (2018) [34] .

Path Coefficient Analysis

Path coefficient analysis identified biological yield per plant as the most important direct contributor to grain yield per plant at the genotypic level (direct effect: 0.9241), followed by spike length (0.2887) and harvest index (0.1934). Biological yield per plant also served as the primary indirect pathway explaining the high positive correlations of other component traits with grain yield. These findings are consistent with Aklilu et al. (2020) [32] , Fadel et al. (2022) [35] and Dimitrova-Doneva (2016) [36] , who demonstrated the supreme importance of biological yield, harvest index and spike length as primary selection criteria for grain yield improvement in barley. The positive direct effect of spike length on grain yield at both genotypic (0.2887) and phenotypic (0.2203) levels underscores its importance as a selection criterion, corroborating Almohisen (2021) [22] , Kaur and Vishwakarma (2020) [28] and Raikwar et al. (2014 [37] , 2018). The positive direct effect of harvest index further highlights the importance of assimilate partitioning efficiency in yield improvement under stress, supporting the findings of Carpici and Celik (2012) [38] and Kumari et al. (2020) [39] . Traits exhibiting negative genotypic direct effects but positive correlations with grain yield-such as plant height, number of tillers per plant and test weightexerted their influence predominantly through indirect pathways via biological yield per plant, reaffirming the utility of path analysis over simple correlation analysis in delineating the true determinants of grain yield.

Conclusion

The present study revealed ample genetic variability among 18 barley genotypes under salt stress for all 14 quantitative traits studied. High heritability coupled with high genetic advance as percent of mean for grain yield per plant, number of effective tillers per plant, number of tillers per plant and biological yield per plant indicates the predominance of additive gene action, rendering direct phenotypic selection effective for these traits. Correlation and path coefficient analyses identified biological yield per plant, spike length, harvest index and number of effective tillers per plant as the most important selection criteria for improving grain yield under saline conditions. Genotype RD 2552 consistently exhibited superior performance for all major yield and yield-attributing traits under salt stress, followed by DWRB 223, RD 2907, Karan 18 and Rajkiran. These promising genotypes can be utilised in future breeding programmes aimed at developing high-yielding, salt-tolerant barley cultivars. The simultaneous selection for plant height, number of tillers per plant, biological yield, harvest index, spike length and grain yield per plant is recommended as an effective strategy for developing superior barley genotypes with enhanced productivity under salt stress conditions.

Acknowledgement

AcknowledgementThe authors are grateful to the Department of Genetics and Plant Breeding, School of Agriculture, Suresh Gyan Vihar University, Jaipur, for providing the experimental facilities and logistical support required to conduct this research. The authors also acknowledge the advisory committee members for their valuable guidance and scientific input throughout the investigation.

References

1. USDA-FAS. World Agricultural Production. United States Department of Agriculture, Foreign Agricultural Service, Washington DC; 2026.
2. Salamini F, Ozkan H, Brandolini A et al. Genetics and geography of wild cereal domestication in the near east. Nature Reviews Genetics. 2002;3(6):429-441.
3. Haas M, Schreiber M, Mascher M. Domestication and crop evolution of wheat and barley: Genes, genomics and future directions. Journal of Integrative Plant Biology. 2019;61(3):204-225.
4. Mwando E, Han Y, Angessa TT et al. Genome-wide association study of salinity tolerance during germination in barley (Hordeum vulgare L.). Frontiers in Plant Science. 2020;11:118.
5. Food and Agriculture Organization of the United Nations. The global status of salt-affected soils. FAO, Rome; 2024.
6. ICAR. Handbook of Agriculture. Indian Council of Agricultural Research, New Delhi; 2025.
7. Sharma P, Singh R, Kumar A. Salt-affected land dynamics in Rajasthan: Extent, causes and management. Indian Journal of Soil Conservation. 2024;52(1):1-12.
8. Munns R, Tester M. Mechanisms of salinity tolerance. Annual Review of Plant Biology. 2008;59:651-681.
9. Anonymous. Status of barley cultivation, production and productivity in India (2024-25). Department of Agriculture and Farmers Welfare, Government of India; 2025.
10. Patial M, Kumar M, Bishnoi SK et al. Genetic variability and trait association for grain yield in barley (Hordeum vulgare L.). Agricultural Research. 2024:1-22.
11. Dewey DR, Lu KH. A correlation and path coefficient analysis of components of crested wheatgrass seed production. Agronomy Journal. 1959;51(9):515-518.
12. Wright S. Correlation and causation. Journal of Agricultural Research. 1921;20(7):557-585.
13. Johnson HW, Robinson HF, Comstock RE. Estimates of genetic and environmental variability in soybeans. Agronomy Journal. 1955;47(7):314-318.
14. Panse VG, Sukhatme PV. Statistical Methods for Agricultural Workers. Edn 4, ICAR, New Delhi; 1985:1347.
15. Burton GW. Quantitative inheritance in grasses. Proceedings of the 6th International Grassland Congress. 1952;1:277-283.
16. Allard RW. Principles of Plant Breeding. Edn 1, John Wiley & Sons, New York; 1960. 1-485.
17. Al-Jibouri HA, Miller PA, Robinson HF. Genotypic and environmental variances and covariances in an upland cotton cross of interspecific origin. Agronomy Journal. 1958;50(10):633-636.
18. Kumar A, Shekhawat PS, Singh D, Kumar R. Genetic variability and association studies in barley genotypes across environments. Forage Research. 2018;44(3):212218.
19. Sisodiya AS, Chaudhary KK, Sharma V. Genetic variability and yield contributing traits in barley (Hordeum vulgare L.) under semi-arid conditions of Jaipur. Journal of Agrometerology. 2025;27(1):56-62.
20. Meena BL, Meena SS, Gupta AK. Assessment of genetic variability and character association in accessions under high salinity stress in Eastern Rajasthan. Rajasthan Journal of Agricultural Sciences. 2025;12(1):34-41.
21. Hailu T, Gebremedhin M, Desta A, Tesfaye B. Genetic variability, heritability and genetic advance in barley genotypes across multiple environments in Ethiopia. Ethiopian Journal of Agricultural Sciences. 2024;34(1):5567.
22. Almohisen IA. Correlation and path coefficient analysis in barley under saline and normal conditions. Saudi Journal of Biological Sciences. 2021;28(7):3941-3948.
23. Juskiw PE, Helm JH. Barley response to seeding date in central Alberta. Canadian Journal of Plant Science. 2003;83:275-281.
24. Rathore AK, Sharma RK, Lal P. Relative salt tolerance of different varieties of barley (Hordeum vulgare L.) at germination and seedling stage. Annals of Arid Zone. 2016;16(1).
25. Verma RS, Singh D, Kumar A, Sharma S. Genetic variability and character associations in barley (Hordeum vulgare L.) germplasm lines. Electronic Journal of Plant Breeding. 2022;13(2):521-528.
26. Saha S, Mondal T, Ghosh A. High heritability and genetic advance for seed yield components in barley. Bangladesh Journal of Agricultural Research. 2024;49(1):45-52.
27. Kabir MH, Rahman MM, Islam MS, Hasan MA. Genetic variability and association analysis in barley varieties. Bangladesh Journal of Agricultural Research. 2023;48(2):201-210.
28. Kaur S, Vishwakarma SR. Genetic variability and path coefficient analysis in barley under heat stress conditions. Journal of Pharmacognosy and Phytochemistry. 2020;9(5):1654-1659.
29. Yadav SK, Singh AK, Pandey P, Singh S. Genetic variability and direct selection criterion for seed yield in segregating generations of barley (Hordeum vulgare L.). American Journal of Plant Sciences. 2015;6(9):1543-1549.
30. Fekadu W, Mekbib F, Lakew B, Haussmann BI. Assessment of genetic variability and acid soil tolerance in Ethiopian barley landraces. Ethiopian Journal of Agricultural Sciences. 2022;32(4):1-29.
31. Zewodu A, Mohammed W, Shiferaw E. Genetic variability and association of morphoagronomic traits among Ethiopian barley (Hordeum vulgare L.) accessions. Scientifica. 2025;25(1):3957883.
32. Aklilu E, Dejene T, Worede F. Genotypic and phenotypic correlation and path coefficient analysis for yield and yield related traits in barley (Hordeum vulgare L.) landraces in North Gondar, Ethiopia. Indian Journal of Pure & Applied Biosciences. 2020;8:24-36.
33. Baye A, Berihun B, Bantayehu M, Derebe B. Genotypic and phenotypic correlation and path coefficient analysis for yield and yield-related traits in advanced bread wheat (Triticum aestivum L.) lines. Cogent Food & Agriculture. 2020;6(1):1752603.
34. Adhikari B, Joshi BK, Bhatta MR, Ghimire KH. Correlation and regression analysis for yield and yield components in barley. Nepal Agriculture Research Journal. 2018;9(1):45-53.
35. Fadel AA, Abdulhamed ZA, Yousif SA. Study correlation and path coefficient analysis for barley under seed rates. Anbar Journal of Agricultural Sciences. 2022;20(2):436449.
36. Dimitrova-Doneva M. Correlation and path-coefficient analysis of yield components in barley. Bulgarian Journal of Crop Science. 2016;53(5-6):3-8.
37. Raikwar RS, Tomar SMS, Singh D. Correlation and path coefficient analysis in barley (Hordeum vulgare L.). The Bioscan. 2014;9(3):1333-1337.
38. Carpici EB, Celik N. Correlation and path coefficient analyses of grain yield and yield components in two-rowed barley (Hordeum vulgare convar. distichon) varieties. Notulae Scientia Biologicae. 2012;4(2):128-131.
39. Kumari A, Vishwakarma SR, Singh Y. Evaluation of combining ability and gene action in barley (Hordeum vulgare L.) using line x tester analysis. Electronic Journal of Plant Breeding. 2020;11(1):97-102.
40. Abdelghany AM, Lamlom SF, Naser M. Dissecting the resilience of barley genotypes under multiple adverse environmental conditions. BMC Plant Biology. 2024;24(1):16.
41. Abebe K, Mekonnen F, Zewdu D. Genetic variability, heritability and genetic advance for yield and yield related traits of food barley (Hordeum vulgare L.) recombinant inbred lines at Woreilu district, South Wollo, Ethiopia. Annals of Biological Research. 2024;40(1):787-791.
42. Al-Tabbal JA, Al-Fraihat AH. Genetic variation, heritability, phenotypic and genotypic correlation studies for yield and yield components in promising barley genotypes. Journal of Agricultural and Biological Science. 2009;4(3):193-210.
43. Dinsa Y, Sentayehu A, Tesfaye K. Genetic variability, heritability and association of traits in barley (Hordeum vulgare L.) genotypes. International Journal of Plant Breeding and Genetics. 2018;12(2):45-53.
44. Kobir MA, Rahman MM, Islam MS, Hasan MA. Genetic variability and yield association studies in barley. Bangladesh Agronomy Journal. 2023;26(1):33-42.
45. Kumar K, Singh PN, Nageshwar et al. Elucidate gene actions, GCV, PCV and regression coefficient for grain yield in barley (Hordeum vulgare L.). International Journal of Research in Agronomy. 2025;8(1):398-402.
46. Kumar M, Vishwakarma SR, Bhushan B, Kumar A. Estimation of genetic parameters and character association in barley (Hordeum vulgare L.). Journal of Wheat Research. 2013;5:76-78.
47. Munns R, Schachtman DP, Condon AG. The significance of a two-phase growth response to salinity in wheat and barley. Australian Journal of Plant Physiology. 1995;22(4):561-569.
48. Singh PN, Mahajan MM, Singh NK, Kumar D, Kumar K. Physiological and molecular response under salinity stress in bread wheat (Triticum aestivum L.). Journal of Plant Biochemistry and Biotechnology. 2020;29(1):125-133.
49. Yadav K, Yadav A, Kumar S, Lal K, Yadav H. Exploring genetic divergence in quantitative traits of exotic and indigenous barley (Hordeum vulgare L.) genotypes. International Journal of Advanced Biochemistry Research. 2024;8(3):654-658.