Study on genetic variability, correlation and path analysis in barley (Hordeum vulgare L.)

Introduction

Barley (Hordeum vulgare L.) is one of the earliest domesticated crop plants and ranks fourth among global cereal crops in terms of production and cultivated area after wheat, rice and maize (Bothmer et al., 2003) [1] . Botanically, barley belongs to the family Poaceae and is a self-pollinated diploid species with a chromosome number of 2𝑛 = 2𝑥 = 14 and a haploid genome size of approximately 5.5 pg (Bennett et al., 1976) [2] . Originating in the fertile crescent of the Middle East, barley exhibits broad ecological adaptability, short duration and remarkable tolerance to environmental abiotic stresses such as salinity and drought (Salamini et al. 2002, El-Hashash and El-Absy 2019, Riehl 2019) [3] . In India, barley is primarily cultivated as a Rabi season crop in the north-western plains. The grain is widely utilized as livestock feed, poultry feed, raw material for malting and brewing industries and human dietary food items such as sattu, chapatis and pearl barley (Agricultural Statistics at a Glance 2024) [4] . In India, barley is cultivated over an area of approximately 0.67 million hectares with a total production of 1.72 million tonnes and an average productivity of 25.7 q ha -1 (Directorate of Economics and Statistics 2024) [5] . Rajasthan is the leading barley-producing state in the country, occupying about 0.33 million hectares with a production of 0.93 million tonnes and a productivity of 28.2 q ha -1 (DES, 2024) [5] . Major barley growing districts in Rajasthan include Jaipur, Alwar, Bharatpur, Jhunjhunu, Churu, Nagaur, Sikar, Hanumangarh and Ganganagar (Directorate of Agriculture Rajasthan, 2023) [6] .

Despite its economic and nutritional significance, barley productivity remains limited by changing climatic patterns and variable environmental conditions (Safhi et al., 2024) [7] . Genetic enhancement of grain yield requires systemic knowledge of the nature and magnitude of genetic variability available in germplasm collections (Allard, 1999) [8] . Selection efficiency depends on partitioning phenotypic variation into heritable (genotypic) and non-heritable (environmental) components (Burton, 1952) [9] . Estimation of GCV, PCV, broad-sense heritability (h 2bs

) and genetic advance as per cent of mean (GAM) provides clear insight into whether a trait is governed by additive or non-additive gene action (Johnson et al., 1955) [10] . Grain yield is a complex polygenic character influenced by several component traits and environmental factors. Direct selection for yield alone is often inefficient; therefore, understanding character associations through correlation analysis is essential (Al-Jibouri et al., 1958) [11] . Furthermore, correlation coefficients do not reveal the exact direct contribution of individual component traits to yield. Path coefficient analysis, developed by Wright (1921) [12] and elaborated for plant breeding applications by Dewey et al. (1959) [13] , partitions correlation coefficients into direct and indirect effects, enabling plant breeders to identify reliable selection criteria. The present study was undertaken to evaluate 22 diverse barley genotypes to quantify genetic variability, estimate heritability and genetic advance, examine phenotypic and genotypic correlations and partition trait relationships via path coefficient analysis to formulate effective selection strategies for barley crop improvement.

Materials and Methods

Experimental site and agro-climatic conditions

The field experiment was conducted during the Rabi season of 2025-26 at the Agricultural Research Farm, Department of Genetics and Plant Breeding, School of Agriculture, Suresh Gyan Vihar University, Jaipur (Rajasthan). The experimental site is geographically situated at 26°48'35" N latitude and 75°51'44" E longitude. The region experiences a semi-arid climate with temperature ranging from sub-zero levels (-1 °C) in winter to high temperatures (48 °C) in summer. Average annual rainfall varies between 400 and 500 mm, received mostly during the South-West monsoon season (July-August). The soil of the experimental field was loamy sand in texture (70.18% sand, 20.56% silt and 8.96% clay) with an alkaline pH (7.62), EC of 0.48 dSm-1, organic carbon of 0.34%, available N of 134.25 kg ha -1 , available P 2 O 5 of 13.25 kg ha -1 and available K 2 O of 178.15 kg ha -1 .

Experimental material and field layout

The experimental material comprised 22 diverse barley genotypes, including checks, listed in Table 1. The experiment was laid out in a Randomized Block Design (RBD) with three replications. Seeds were sown manually on 30th November 2024 at a row-to-row spacing of 22.5 cm and plant-to-plant spacing of 10 cm in net plot size of 3.6 m x 1.8 m (6.48 m 2 ). Recommended agronomic package of practices, including seed treatment with carbendazim @ 2 g kg -1 seed, weed management by hand weeding at 25-30 DAS and plant protection measures (Monocrotophos application), were uniformly adopted. Two flood irrigations were provided at tillering and grain filling stages.

Observations recorded

Observations were recorded on five randomly selected competitive plants per plot in each replication for nine characters, while days to 50% flowering and days to maturity were evaluated on a whole plot basis:

Statistical analysis

Analysis of variance (ANOVA) was executed according to Panse et al. (1967) [14] for Randomized Block Design. Genotypic variance (σ 2g

) and phenotypic variance (σ 2p ) were calculated as

and 𝜎 2𝑝 = 𝜎 2𝑔 + 𝑀𝑆 𝑒 . Phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were calculated as per Burton (1952) [9] . Broad-sense heritability (h 2bs ) was estimated as per Allard (1960) [15] . Expected genetic advance (GA) and genetic advance as per cent of mean (GAM) under 5% selection intensity (k = 2.06) were calculated following Johnson et al. (1955) [10] . Genotypic (r g ) and phenotypic (r p ) correlation coefficients were computed following Al-Jibouri et al. (1958) [11] . Path coefficient analysis was carried out according to Wright (1921) [12] and Dewey et al. (1959) [13] using grain yield per plant as the dependent variable.

Results

Analysis of variance

The mean sum of squares due to genotypes was highly significant (P ≤ 0.01) for all eleven quantitative characters studied, confirming the presence of substantial genetic variation among the evaluated barley genotypes (Table 2).

Mean performance of genotypes

Considerable variation was recorded across all genotypes for growth and yield traits (

Genetic variability parameters

Estimates of phenotypic coefficient of variation (PCV) were slightly higher than genotypic coefficient of variation (GCV) for all characters, indicating low environmental influence on trait expression (

Correlation coefficient analysis

Genotypic correlation coefficients were generally higher in magnitude than phenotypic correlation coefficients, indicating inherent genetic relationships among traits (Table 5 and Table 6). Grain yield per plant exhibited highly significant positive genotypic correlations (r g ) with number of grains per spike (r g = 0.8835**), number of effective tillers per plant (r g = 0.8519**), test weight (r g = 0.8456**), spike length (r g = 0.8231**), harvest index (r g = 0.7767**) and biological yield per plant (r g = 0.7767**). Significant positive genotypic associ-ations were also observed with number of tillers per plant (r g = 0.3090*) and plant height (r g = 0.2770*). At the phenotypic level (r p ), grain yield per plant showed highly significant positive correlations with number of effective tillers per plant (r p = 0.8253**), biological yield per plant (r p = 0.8198**), number of grains per spike (r p = 0.7946**), test weight (r p = 0.7772**), spike length (r p = 0.7414**) and harvest index (r p = 0.7413**). Significant positive phenotypic correlations were also observed with number of tillers per plant (r p = 0.3090*) and days to 50% flowering (r p = 0.2665*).

Path coefficient analysis

Path coefficient analysis partitioned genotypic and phenotypic correlation coefficients into direct and indirect effects on grain yield per plant (Table 7 and Table 8).

Genotypic path coefficient analysis revealed that harvest index exerted the highest positive direct effect on grain yield per plant (0.3888), followed by number of effective tillers per plant (0.2624), spike length (0.2015), biological yield per plant (0.1604) and test weight (0.0804). Negative direct effects were registered by number of tillers per plant (-0.5042), days to maturity (-0.4392), days to 50% flowering (-0.2867), plant height (-0.1368) and number of grains per spike (-0.1051). Phenotypic path coefficient analysis showed that harvest index (0.2589), biological yield per plant (0.2564), number of effective tillers per plant (0.1597), spike length (0.1464), test weight (0.1093), number of grains per spike (0.0494), days to maturity (0.0165), number of tillers per plant (0.0016) and plant height (0.0003) recorded positive direct contributions to grain yield per plant. Days to 50% flowering (-0.0920) exerted a negative direct effect.

Discussion

Genetic variability and selection scope

The presence of highly significant differences among genotypes for all eleven traits demonstrates the availability of substantial genetic variability in the studied barley germplasm. Similar results were documented by Amabile et al. (2017) [16] , Sunil et al. (2017) [17] ,

Adhikari et al. (2018) [18] , Abebe et al. (2024) [19] and Sisodiya et al. (2025). High PCV and GCV estimates for number of tillers per plant, grain yield per plant and harvest index indicates high potential for direct phenotypic selection. Small differences between GCV and PCV across traits further confirm that environmental factors had minimal masking influence on trait expression, in line with findings reported by Matin et al. (2019) [20] and Kobir et al. (2023) [21] .

Heritability and genetic advance

Heritability estimates combined with genetic advance provide a reliable basis for predicting selection gains. High broad-sense heritability coupled with high genetic advance as per cent of mean (GAM) was recorded for number of tillers per plant, harvest index, grain yield per plant, days to maturity and number of effective tillers per plant. This pattern indicates that these traits are predominantly controlled by additive gene action, making direct phenotypic selection effective. These conclusions align with earlier observations by Kaur et al.

(2016) [22] , Kumar et al. (2018) [23] , Abebe et al. (2024) [19] and Sisodiya et al. (2025). Conversely, low heritability coupled with low GAM for plant height and days to 50% flowering suggests significant non-additive or environmental influence, indicating limited scope for yield enhancement via direct selection for these two traits.

Trait associations and selection criteria

Correlation analysis showed that grain yield per plant was strongly and positively associated with number of effective tillers per plant, number of grains per spike, test weight, spike length, biological yield per plant and harvest index at both genotypic and phenotypic levels. Similar significant positive yield correlations were reported by Dyulgerova (2012), Pilania et al. (2014) [24] , Singh et al. (2014) [25] , Kumari et al. (2022) [26] and Zewodu et al. (2025) [27] . Path coefficient analysis demonstrated that harvest index, number of effective tillers per plant, biological yield per plant, spike length and test weight exerted strong positive direct effects on grain yield at both genotypic and phenotypic levels. Traits such as number of grains per spike also contributed substantially to yield through positive indirect pathways via effective tillers, test weight and biological yield. These findings are supported by Zaefizadeh et al. (2011) [28] , Madakemohekar et al. (2015) [29] , Solemani et al. (2017) [30] , Shrimali et al. (2017) [31] , Nagesh et al. (2019) [32] and Robir et al. (2023) [21] .

Superior genotype identification

Conclusion and suggestions for further work

The study concludes that substantial genetic variability exists among the evaluated barley genotypes. Direct phenotypic selection based on harvest index, number of effective tillers per plant, spike length, test weight and biological yield per plant will be effective in improving grain yield in barley. Genotypes RD 3109, K 499, K 603, RD 2907 and K 125 are recommended as elite parental lines for future breeding initiatives.

Suggestions for future research

Multi-location and multi-season trials should be conducted to test the stability and environmental adaptability of promising genotypes (RD 3109, K 499, K 603, RD 2907 and K 125).

2.

Hybridization programs involving these diverse elite lines should be initiated to combine high harvest index, tillering efficiency and grain bold weight.

3.

Identified genotypes should be evaluated under abiotic stress conditions (salinity, drought and heat) to isolate climate-resilient lines.

4.

Molecular marker-assisted selection (MAS) and QTL mapping should be utilized to target key genomic regions controlling harvest index and productive tillering.

Declarations

Acknowledgement

AcknowledgementThe authors express sincere gratitude to the Department of Genetics and Plant Breeding, School of Agriculture, Suresh Gyan Vihar University, Jaipur (Rajasthan), India for providing necessary research facilities and technical support.

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