Characterization of physiological, biochemical, and agronomical traits of elite induced chickpea mutants from the northwestern Himalayan region of the UT of J & K, India

1 Introduction

Chickpea is an economically and nutritionally important grain legume globally; however, cold stress has adverse effects on its growth. Cold stress is one of the primary environmental stresses that greatly limits plant growth, survival, and productivity (Yadav, 2010; Zhu et al., 2016). Chickpea is the second dominant cool-season food legume in the world after dry beans (Mallikarjuna et al., 2017). India is the largest producer, consumer, and importer of chickpeas in the world, reflecting its importance as a protein source in the diet of people in India (Mallikarjuna et al., 2017; Gaur et al., 2019). However, chickpea production is not fully realized owing to several biotic and abiotic stresses, restricting chickpea productivity to 1 t ha^-1 (Chaturvedi et al., 2018; Roorkiwal et al., 2018). Being a winter-season crop in northern South Asia and parts of Australia, chickpea faces low-temperature stress (0-15 °C) during the reproductive stage, causing substantial loss of flowers and pods, and reducing yield potential by 30-40%. The optimum temperature for chickpea growth and reproduction has been suggested to be 21-26°C (day) and 18-21 °C (night), with annual rainfall of 600–1000 mm (Duke, 1981; Berger et al., 2012).

Cold stress adversely affects plant growth and development (Janska et al., 2010), membrane structure (Ruelland et al., 2009), and photosynthetic activity (Kalaji et al., 2016; Arslan et al., 2018). Cold stress represents a primary environmental factor, especially in northern India, where chickpea crop is predominantly sown in winter and low temperatures restrict vegetative and reproductive growth, development, and crop yield, leading to significant crop losses (Thakur et al., 2010; Hedhly, 2011; Awasthi et al., 2015). Low-temperature stress is an important issue for winter-sown chickpea in countries surrounding the Mediterranean Sea, tropical highlands, and temperate growing areas. The most affected regions are northern South Asia and parts of Australia, where chickpea faces low-temperature stress (15 °C), limiting chickpea growth and vigor at all phenological stages, particularly during vegetative and reproductive stages. This leads to chlorosis, necrosis of leaf tips, substantial loss of flowers, pod abortion, reduced pollen viability and pollen tube growth, and thus, reduced seed quality and yield potential by 30-40% (Rani et al., 2020). Over time, sensitivity to low temperature has increased, possibly due to repeated use of limited germplasm donor parents, which has led to more adverse effects on growth and yield (Muehlbauer and Sarker, 2017). Significant economic losses due to drought/heat (1.3 billion USD), cold (186 million USD), and salinity (354 million USD) have raised concerns among chickpea-growing countries (Jha et al., 2014). The situation is exacerbated by climate change, which may increase the intensity and frequency of abiotic stresses, such as cold spells in temperate regions of the world, necessitating the identification and development of climate-resilient chickpea cultivars with region-specific traits that can perform well under stress. Chickpea, being a predominantly winter crop in the Indian subcontinent, is exposed to chilling/freezing temperatures and decreasing photoperiod during germination, vegetative, and reproductive phases, especially in the northern parts of India (Srinivasan et al., 1999; Berger et al., 2006). Some chickpea accessions have been shown to survive at temperatures as low as - 8 °C and -12°C during early growth stages (Wery, 1990; Croser et al., 2003), suggesting their potential for selection as cold-tolerant cultivars at germination and during seedling growth from existing chickpea germplasm. A rating scale of 1–9 has been used to measure cold stress injury during the early vegetative stage or seedling stage in previous studies (Singh et al., 1989). Cold stress also affects membrane integrity, leading to ice formation in plant tissues and solute leakage. Exposing plants to temperatures below the optimum temperature can induce stress in plants (Stoddard et al., 2006).

Plants have various survival strategies by which they respond to and tolerate cold stress. These can include changes in the structure, composition, and function of the plasma membrane (Ding et al., 2019). After sensing cold stress, cells undergo an increase in membrane rigidity due to a reduction in plasma membrane fluidity (Los and Murata, 2004). This increase in rigidity also increases electrolytic leakage from the cell and triggers the expression of cold-responsive genes, which act as the primary signal for initiating a cold response (Dasgupta et al., 2020). Electrolyte leakage is widely used to assess stress-induced injury in plant tissues (Demidchik et al., 2014). Nayyar et al. (2005) found that an increase in electrolyte leakage and triphenyl tetrazolium (TTC) content was decreased at 4 °C. Physiological traits, including electrolyte leakage index, carotenoid content, total chlorophyll content, cold tolerance rating (CTR) scale, and frost resistance ratio (FRR), are considered crucial for the selection of cold-tolerant mutants.

The narrow genetic base of cultivars is a bottleneck for crop improvement; therefore, mutation breeding is a valuable tool for enhancing genetic variability in chickpea, particularly because of its self-pollinating nature and relatively narrow genetic base. By inducing mutations, breeders can create new traits or variations that are not naturally present, leading to the development of improved chickpea varieties. Mutation breeding is an established tool used to develop new varieties with higher yield and resistance to major diseases and abiotic stresses, along with wider adaptability (Kharkwal et al., 1988). The first examples of micro-mutant varieties in chickpea in the world, developed at IARI, New Delhi, were released for the first time in India through mutation breeding during 1985 following gamma irradiation (Kharkwal et al., 2004). Induced mutations have great potential for enhancing genetic variability and thus improving yield in chickpea through effective handling of mutagenized populations (Wani, 2011). Radiation may affect the expression of genes that are responsible for freezing tolerance (Akhar et al., 2011). Mutation breeding with gamma rays played a great role in the induction of cold tolerance in chickpea mutants (Haq et al., 1993).

Keeping this in view, we developed and evaluated a set of 257 induced mutants (100 of ≤ LD₅₀ and 157 of LD₅₀ doses) in the M₂ generation during the Rabi season of 2023–24 under field conditions to screen for damage symptoms caused by cold stress and used electrolyte leakage index, carotenoid content, total chlorophyll content, FRR and CTR values as indicators to assess cold injury in stressed plant tissues. During the second year (Rabi 2024-25), 217 induced mutants (122 of LD₅₀ and 95 of ≤ LD₅₀) out of 257 were shortlisted and screened for cold tolerance and seed yield (g plant^-1) traits. However, 15 top putative mutants were assessed for proline, sugar, and protein content (M₃), along with seed yield (g plant^-1) and physiological traits related to cold tolerance in chickpea. Identifying such traits will not only enhance our understanding of cold tolerance mechanisms but also support future breeding strategies aimed at developing more resilient chickpea cultivars with broader environmental adaptability and improved food security potential. The comparison of different induced mutants could be useful in identifying the relative potential of each mutant and their ultimate use in chickpea breeding programs aimed at enhancing cold tolerance in already released chickpea varieties.

2 Materials and methods

In the present study, a well-known high-yielding chickpea variety (GNG1958), which is susceptible to cold stress and released for the northwestern plain zone (NWPZ) of India, was used. Seeds were irradiated with gamma rays at different doses (Gy) at the Bhabha Atomic Research Centre (B.A.R.C.), Mumbai, India. The doses used were 0, 250 Gy, 300 Gy, 350 Gy, 400 Gy, and 450Gy, and the lethal dose (LD₅₀) and slightly less than LD₅₀ (≤ LD₅₀) were determined using pivotal analysis. Two doses were selected, i.e., 254.90 Gy (≤ LD₅₀) and 335.74 Gy (LD₅₀), for treating 500 seeds at each dose level. A total of 257 mutants from both dose levels (100 of ≤ LD₅₀ and 157 of LD₅₀) in the M₂ generation were screened at the vegetative stage during January and February 2024 for cold tolerance (electrolyte leakage index, carotenoid content, total chlorophyll content, cold tolerance rating scale, frost resistance ratio, and seed yield (g plant^-1) during the Rabi season of 2023-24. This period is ideal for testing cold tolerance in northern India, as temperatures are typically lowest (Figure 1). Data from 25 plants per mutant were recorded in the field, while laboratory analyses for electrolyte leakage index (ELI %), carotenoid content, and total chlorophyll content were conducted on samples from 10 plants per mutant. Of the 257 mutants, 217 (122 of LD₅₀ and 95 ≤ LD₅₀) in the M₃ generation were again screened at the vegetative stage for cold tolerance during January and February 2025, when winter temperatures are at their lowest in northern India (Figure 1). This screening was conducted at the experimental farm of Shere Kashmir University of Agricultural Sciences Technology (SKUAST), Jammu, during the Rabi season of 2024-25. The farm is located at 32.68°N latitude and 74.82°E longitude, at an altitude of 281 meters above mean sea level. A complete detailed programme exhibited for the development of cold tolerance mutants was shown in the flow diagram (Figure 5).

2.1 Experimental design and layout

A total of 257 induced mutants (157 of LD₅₀ and 100 of ≤ LD₅₀) in the M₂ generation were sown on November, 11 during the season 2023-24. In addition, 217 mutants from both dose levels (LD₅₀ and ≤ LD₅₀) in the M₃ generation were sown during Rabi 2024-25 in an augmented block design (ABD) at the research field of the Division of Genetics and Plant Breeding, Faculty of Agriculture (FoA), (SKUAST-J), Chatha Campus, Jammu and Kashmir, India. The experimental site has a cold climate and is located at 32.68°N latitude and 74.82°E longitude, at an altitude of 281 meters above mean sea level. The soil of the experimental field was a typical sandy-loam. Prior to planting, the experimental field was ploughed and manually leveled using spades and forked jembes. Seeds were hand drilled and sown at a depth of 5 cm, which proved beneficial for chickpea growth and yield. The induced mutants were sown in 4 m rows with inter-row spacing of 30 cm and intra-row spacing of 10 cm. For comparison, a known cold-sensitive check (ICC 12968), a cold-tolerant check (ICC92944) at the national level, and the untreated control (GNG1958) were included and repeated after every 10 rows (Malhotra and Saxena, 1993). Pre-sowing irrigation, pre-emergence application of herbicides, and other standard agronomic practices were followed to maintain a healthy crop.

2.2 Weather conditions at the experimental site

The cold weather conditions in Jammu during the winter season were found to be ideal for screening 257 induced chickpea mutants in the M₂ generation and 217 mutants in the M₃ generation for cold stress tolerance. During 2023-24 (November-March), average high/daytime temperatures ranged from 10.46 °C to 31.18 °C, while average low/nighttime temperatures ranged from 3.29 °C to 14.99 °C. However, during 2024-2025 (November-March), average high/daytime temperatures ranged from 17.83 °C to 30.34°C, while average low/nighttime temperatures ranged from 5.76 °C to 16.67 C (Figures 1, 2).

2.3 Methodology

Cold tolerance parameters, viz., electrolyte leakage index (%), cold tolerance rating (CTR) scale, total chlorophyll content (TCC), carotenoid content (CC), frost resistant ratio (FRR), sugar content, and proline content were estimated under in vitro conditions, while seed yield was recorded under in vivo conditions, during the Rabi seasons of 2023–24 and 2024-25. The methods are described below.

2.4 Electrolyte leakage index (%)

Data were recorded for cold stress tolerance during January and February of Rabi 2023–24 and 2024–25 after the death of the susceptible check and exposure of induced mutant plants to low winter temperatures. Plants were evaluated for cold tolerance only after the susceptible check (ICC12968) had died. Induced mutants were further assessed for cold injury under laboratory conditions using electrolyte leakage index (%) test from leaf tissues damaged by cold stress. Electrolyte leakage from cold-stressed plants grown under natural conditions (field) was measured using young leaf tissues of chickpea seedlings (Lutts et al., 1996). To remove surface-adhered electrolytes, 1 g samples were washed with deionized water. Samples were then placed in closed glass vials containing 10 mL of deionized water and incubated at room temperature for 30 min with continuous shaking on a rotary shaker at 150 rpm. Electrical conductivity of the solution (L₁) was measured in microsiemens/centimeter (µS/cm). Each sample was then autoclaved at 120 °C for 10 min, and the electrical conductivity (L₂) was measured again after shaking for 30 min at room temperature. The electrolyte leakage index (ELI) was calculated as:

ELI (%) = (L₁/L₂) × 100.

The electrolyte leakage index represents the percentage of electrolytes leaked from damaged plant tissues relative to total electrolytes released after complete tissue destruction after autoclaving (100%).

2.5 Cold tolerance screening (CTR scale)

The set of 257 induced chickpea mutants was evaluated for cold tolerance screening under field conditions using a cold tolerance rating (CTR) scale of 1-9. The scale was used to measure how well plants withstood low temperatures, where 1 indicates high tolerance and 9 indicates high susceptibility or plantdeath. The rating categories were defined as follows: 1: highly tolerant—no damage, normal leaf color, no growth retardation; 2-3: tolerant—slight leaf tip drying, folded leaves, light green color; 4-5: moderately tolerant—moderate wilting, pale green to yellowish leaves, some yellowing; 6-7: sensitive—severe leaf rolling, dried reddish-brown leaves; and 8-9: highly sensitive/susceptible—most seedlings dead or dying, nearly entirely dead. The cold tolerance scale was used as per Singh et al., 1989.

2.6 Total chlorophyll content (mg g^-1FW)

Procedure: Chlorophyll was extracted using the non-destructive method described by Gunes et al. (2007) using dimethyl sulphoxide (DMSO). For estimation of photosynthetic pigments, 30 mg of leaf sample was placed in a test tube, and 3 ml of DMSO was added. The tubes were then placed at room temperature in the dark until the tissue became chlorophyll pigment-free (12–14 h). After incubation for 12–14 h, the extract was transferred to another test tube, and absorbance was read at 665 nm, 645 nm, and 454 nm using a spectrophotometer (Systronics India, Model 117), as described by Kaloyereas (1958). DMSO was used as a blank. Mean values from 10 plants per mutant, primarily affected by cold stress, were used for estimation of total chlorophyll content. Plant samples were taken after the death of the susceptible check (ICC12968).

According to the formulae of Lichtenthaler (1987), the concentrations of different pigments were calculated using the following equations:

Chlorophyll a (mg g^-¹ FW) = (11.75 × A665 − 2.35 × A645) × 3/30.

Chlorophyll b (mg g^-¹ FW) = (18.61 × A645 − 3.96 × A665) × 3/30.

Quantities of these pigments were expressed as mg g^-¹FW of fresh tissue weight.

Total chlorophyll = Chl.a + Chl.b.

Carotenoids (mg g^-¹ FW) = [(1000 × A454) − (2.27 × Chl. a) − (81.4 × Chl. b)/227] × 3/30.

2.7 Frost resistance ratio

Frost resistance ratio (FRR) was estimated for each induced mutant at both doses, i.e., 335.74 Gy and 254.90 Gy, during both seasons (Rabi 2023–24 and 2024-25) under field conditions during the frost period. It was calculated by the ratio of (PL_H/PL_E), where PL_H- indicates the number of plants per line at harvest and PL_E indicates the number of plants per line after emergence and before the first frost (ICARDA, 1986). An FRR value approaching 1 was considered as Resistant, values between 0.75 to 0.5 were considered Tolerant, and values between 0.5 and 0 were considered Susceptible.

2.8 Cryoprotective molecules (proline, sugar, and protein)

2.8.1 Proline content

Proline content, a biochemical parameter, was calculated following the Bates et al. (1973) technique. Fresh leaf samples (200 mg) were homogenized in 5 mL of 3% sulphosalicylic acid and centrifuged at 272 g for 15 min. The supernatant was transferred to a different test tube; 2 mL of the supernatant were mixed with 2 mL of orthophosphoric acid and 2 mL of reagent acid ninhydrin. The mixture was boiled in a water bath at 100 °C for 1 h, and this process was then stopped by placing the tubes in an ice bath. Subsequently, 4 mL of toluene was added, followed by vigorous shaking. The top-colored organic phase was removed once it had reached room temperature, and absorbance was measured at 520 nm. The blank remained toluene. A standard curve was prepared using graduated proline concentrations in 3% sulphosalicylic acid. The formula used for estimation is given below:

µmol per gram tissue = [(µg proline/ml) x ml toluene)/115.5 µg/µmole]/[(g sample)/5].

2.8.2 Sugar content

Sugar content was determined by homogenizing harvested tissues in ethanol (80%), followed by centrifugation at 5000 rpm for 10 min. The derived upper layer was collected, and the process was carried out multiple times with the residue to ensure complete sugar extraction. The entire supernatant was used to estimate total sugars, while the residue was preserved in ethanol (20%) to determine starch content by conducting acid hydrolysis. Total sugar content was estimated using the method described by Yemm and Willis (1954). A 0.1 mL aliquot of ethanol extract was combined with 4 mL of chilled anthrone reagent. The solution was carefully mixed, then heated in boiling water for 10 min, followed by cooling in an ice bath. Using a spectrophotometer, the resulting blue-green liquid’s absorbance (optical density) was quantified at 625 nm. Sugar concentration (mg g^-1 FW) was then determined using a glucose standard curve.

Soluble sugar = absorbance of sample x k value x dilution factor (mg/g FW).

weight of fresh tissue.

2.8.3 Protein content (%)

Near-infrared (NIR) was used for the estimation of protein percentage in 15 putative chickpea mutants. In total, 10 samples from each mutant were analyzed, and the mean value was used for to estimate protein content. The NIR method for protein estimation is a rapid, non-destructive, and low-cost technique that uses spectroscopy to analyze the unique spectral signatures of molecules containing specific chemical bonds, particularly N-H bonds found in proteins. Samples are exposed to near-infrared light, and the instrument measures how the light is absorbed and reflected, creating a spectrum that is then analyzed using multivariate calibration models to predict protein content (%).

2.9 Methodology for selection of mutants

2.9.1 Selection of elite mutants

In the M₂ generation, 869 progenies from both doses, comprising 16,024 plants were sown at the experimental field during rabi 2023-24. This included, 517 progenies of LD₅₀ (8,347 plants) and 352 progenies of ≤ LD₅₀ (7,677 plants). From these, 157 mutants were selected from the LD₅₀ dose (335.74Gy) and 100 mutants from the ≤ LD₅₀ dose (254.90 Gy) were selected based on desired mean values for cold tolerance traits, viz., higher total chlorophyll content, carotenoid content, lower electrolyte leakage index (ELI), frost resistance ratio (FRR), cold tolerant rating (CTR), and higher seed yield (g plant^-1).

In the M₃ generation, seeds of 217 promising induced mutants (335.74Gy and 254.90 Gy) were sown, comprising 8,439 plants from the 335.74Gy dose and 4,480 plants from the 254.90 Gy dose, at the experimental field of the Plant Breeding & Genetics Division of Shere Kashmir University of Agricultural Sciences & Technology (SKUAST J), during rabi 2024-25. Out of the original 257 mutants, 217 were re-evaluated for targeted traits in the M₃ generation during rabi 2024-25. Additionally, 71 mutants from LD₅₀ and 95 mutants from ≤ LD₅₀ were also analyzed for sugar content and proline content in the M₃ generation. Of 217 mutants, the 50 top putative mutants were again shortlisted based on pooled mean value of targeted traits. From these, the best 15 elite mutants were shortlisted based on targeted traits.

In the M₂ generation, during rabi 2023-24, 157 induced mutants from the lethal dose, i.e., LD₅₀ (335.74 Gy), were selected based on lower ELI (%), resistance against the frost (ratio of PL_H/PL_E, where PL_H is the number of plants per line at harvest and PL_E is the number of plants per line after emergence and before the first frost), cold tolerant rating scale (1-9), total chlorophyll content (higher value compared to control), carotenoid content, and higher seed yield (g plant^-1). Similarly, 100 mutants from the ≤ LD₅₀ (254.90 Gy) dose were also selected based on lowerELI (%), higher values of total chlorophyll content, carotenoid content, seed yield (g plant^-1), frost resistant ratio approaching unity, cold tolerance rating (1-3). Of 157 mutants, 122 elite induced chickpea mutants from the LD₅₀ dose and 95 from the 100 mutants from the ≤ LD₅₀ dose of gamma rays were further screened for cold tolerance parameters (electrolyte leakage index, total chlorophyll content, and carotenoid content) under laboratory conditions, along with cold tolerance rating(1-3), frost resistance ratio approaching unity, and seed yield under in vivo conditions in the M₃ generation during rabi 2024-25.

In addition, sugar content and proline content of 71 and 95 elite induced mutants from LD₅₀ and ≤ LD₅₀ doses, respectively, were also estimated in the laboratory in the M₃ generation. Based on the desired mean value of the targeted traits, namely, lower value of electrolyte leakage index (%), higher values of total chlorophyll content, carotenoid content, seed yield (g plant^-1), and cold tolerance rating (1-3), and frost resistance ratio approaching unity, top 50 putative mutants were selected from the 217 mutants. These putative mutants were again characterized in the field for seed yield in (g plant^-1). From these 50, 15 elite mutants were further shortlisted based on the desired pooled mean value of targeted traits across M₂ and M₃ generations of respective mutants of field (agronomical) and laboratory (in vitro) traits during rabi 2024-25.

2.9.2 Statistical analysis

The phenotypic data were analyzed separately for cold tolerance rating (CTR), frost resistant ratio (FRR), electrolyte leakage index, carotenoid content, total chlorophyll content, and seed yield. Diverse statistical parameters, including mean, range, and standard deviation (SD), were calculated using SPSSv17.0 (http://www.spss.com/statistics). Genetic parameters (coefficient of variation, Phenotypic Coefficient of Variation (PCV), Genotypic Coefficient of Variation (GCV), Environmental Coefficient of Variation (ECV), heritability in broad sense, genetic advance, and genetic advance as a percentage of the mean) were estimated by using R package (version 4.5.3) and an augmented Randomized Complete Block Design (RCBD). The R package “corrplot” was used for the depiction of correlation plots. Linear regression analysis was performed using the LINEST function in Microsoft Excel 2021 (Microsoft Corporation, Redmond, WA, USA), following the ordinary least squares (OLS) procedure as described by Draper and Smith (1998).

Additionally, 15 top-performing promising mutants were analyzed for sugar, proline, and protein contents in the M₃ generation of chickpea only. Ten samples per mutant for each biochemical parameter (proline, sugar, and protein contents) were analyzed, and the mean values were used for analysis.

3 Results and discussion

In recent decades, the production of cool-season legumes, including chickpea, has been adversely affected in subtropical regions of the world. However, yield stability in chickpea lags far behind due to several biotic and abiotic stresses, particularly cold stress in subtropical areas). The limited genetic diversity available to breeders requires the creation of genetic variation through mutation and extensive field screening over multiple years to identify cold-tolerant induced mutants.

To this end, in the present study, 257 induced mutants in the M₂ generation (Supplementary Tables S1, S2), along with the control (GNG1958), a national susceptible check (ICC12968), and a cold-tolerant check (ICC92944), were evaluated under field conditions for cold stress at the vegetative stage, particularly during January and February (the coldest period of the crop) (Figure 2) during 2023-24. Additionally, 217 mutants in the M₃ generation (Supplementary Tables S3, S4) were evaluated (Figure 1) during rabi 2024–25 in the Jammu region (northwestern Himalayas). Screening was conducted using visual field assessment on a scale of 1-9 (CTR), along with measurements of carotenoid content, frost resistance ratio, total chlorophyll content, electrolyte leakage index for measuring cell membrane stability, and seed yield (g plant^-1). Of 257 mutants, 50 promising mutants for seed yield (g plant^-1) and cold tolerance traits are discussed here. Physiological traits, including total chlorophyll content, electrolyte leakage index, carotenoid content, cold tolerance rating, and frost resistance ratio, are considered crucial for selecting cold-tolerant genotypes (Rani et al., 2021).

Climate change necessitates the identification of new sources of cold tolerance. However, the narrow genetic base of cultivated chickpea (C. arietinum) and the limited number of cold-tolerant accessions in chickpea (Berger et al., 2012) mandate the search for additional sources of cold tolerance in cultivated chickpea. The present study addresses this gap by identifying 15 top-performing putative mutants exhibiting both cold tolerance and higher seed yield (g plant^-1), namely: 254.90 Gy-Ch-M-01, 254.90 Gy-Ch-M-05, 254.9–0 Gy-Ch-M-09, 335.74 Gy-Ch-M-32, 335.74 Gy-Ch-M-45, 300 Gy-Ch-M-52, 335.74 Gy-Ch-M-58, 335.74 Gy-Ch-M-65, 335.74 Gy-Ch-M-66, 335.74 Gy-Ch-M-68, 335.74 Gy-Ch-M-115, 335.74 Gy-Ch-M-135, 335.74 Gy-Ch-M-140, 335.74 Gy-Ch-M-153, and 335.74Gy-Ch-M-156. These mutants can be exploited by breeders to develop cold-tolerant varieties (Table 1).

Table 1. Pooled mean performance for agro-morphological, physiological, and biochemical traits of 15 elite induced chickpea mutants.

*****ELI, Electrolyte leakage index (%); Total Chl. Cont., Total chlorophyll content (mg g^-1FW); Cart. Cont., Carotenoid content (mg g^-1FW); FRR, Frost resistance ratio; CTR, Cold tolerance rating (scale 1-9); Proline (µmol g^-1FW), Sugar (mg g^-1FW), Protein (%) SY, Seed yield (g plant^-1); NSC-National Susceptible check, NTC-National tolerant check.

3.1 Field evaluation

Mean electrolyte leakage index values varied from 5.3 to 41.31% and 5.45 to 41.31% in the M₂ and M₃ generations, respectively, with overall pooled mean ranging from 6.79 to 41.31%, with a standard deviation of 5.92 (Table 2). The pooled mean total chlorophyll content varied from 0.95 to 2.55 mg g^-1FW, with a mean 1.89 mg g^-1FW and a standard deviation of 0.40. Carotenoid content also varied across both generations, with a pooled mean range of 0.42-0.88 mg g^-1FW, a standard deviation of 0.12, a general mean of 0.67 mg g^-1FW, and a standard error of 0.02. The cold tolerance rating also varied with pooled mean value and ranged from 1.0 to 6.0, with a general mean of 1.91 and a standard deviation of 0.72. Frost resistance ratio ranged from 0.39 to 0.97, with a standard deviation of 0.11. Seed yield exhibited a pooled mean of 42.89 g plant^-1, with a range of 20.48 to 74.20 g plant^-1 and a standard deviation of 13.66. Proline content, estimated only in elite mutants of the M₃ generation, exhibited a mean value of 1.89 µmol g^-1FW, with a standard deviation of 14.38 and a range of 0.90-3.44 µmol g^-1FW. Protein content varied from 21.26 to 23.08%, with a general mean of 22.48% and a standard deviation of 0.48. Similarly, sugar content exhibited a general mean of 22.63 mg g^-1FW, with a standard deviation of 11.96 and a range of 6.64-46.16 mg g^-1FW (Table 2). All cold tolerance parameters showed substantial variability for the targeted traits under study. Similar results for CTR and ELI were reported by Mir et al. (2021). Karimzadeh Soureshjani et al. (2022) also showed a wide range of electrolyte leakage index and proline content under cold stress, with increased levels of ELI and proline in chickpea genotypes. Such variations in these traits can be used to enhance cold tolerance in commercially cultivated varieties of chickpea.

Table 2. Mean ± standard error, range, and standard deviation of cold tolerance component traits of induced chickpea mutants across environments (2023–24 and 2024-25).

ELI, Electrolyte leakage index (%); Total Chl., Total chlorophyll content (mg g^-1FW); Cart., Carotenoid content (mg g^-1FW); CTR, Cold tolerance rating; FRR, Frost resistance ratio; (scale 1-9); SY, Seed yield (g plant^-1); Proline (μmol g^-1FW); Protein (%); Sugar (mg g^-1FW).

3.2 Electrolyte leakage index (ELI %)

Membrane stability refers to the integrity of the cell membrane and is defined as the percentage of electrolyte leakage from cells, calculated as the ratio of conductivity after immersion (of leaves) in water to conductivity after boiling. It reflects the extent of damage to cellular membranes under stress; thus, membrane stability is directly related to electrolyte leakage. Wery et al. (1993) considered membrane stability as an important mechanism of cold tolerance to adjust the number of seeds according to the level of stress and osmotic adjustment, which is involved in cold tolerance. During the present study, ELI was used as a physiological index to identify the magnitude of injury between different mutants after plants were exposed to cold stress conditions in the field. A leakage score of more than 50% is considered lethal to plants. Higher ELI indicates lower cold tolerance. The results of the ELI test under natural conditions indicated significant differences between mutants and checks. Among the 50 selected chickpea mutants, based on pooled mean performance (2023–24 and 2024-25), the lowest electrolyte leakage was observed in induced mutant 335.74 Gy-Ch-M-45 (6.79%), followed by 335.74 Gy-Ch-M₂-155 (7.24%), 335.74 Gy-Ch-M-66 (7.30%), and 335.74 Gy-Ch-M-68 and 335.74 Gy-Ch-M-140 (7.47% each). These values were lower than those recorded for the control GNG1958 (31.23%), the susceptible check ICC12968 (42.77%), and the cold-tolerant check ICC 92944 (25.94%). The overall ELI range among mutants was 6.79-41.31%. The mean ELI under cold conditions was found to be 11.28 with a standard deviation of 1.81 (Table 3). Mutants may have genetic adaptations that maintain the integrity of the plasma membrane, preventing it from becoming “leaky” under stress. This is often due to improved composition of lipids or stronger cell walls. The rate of electrolytes leakage is related to alterations in membrane properties, such as membrane fluidity and deactivation of membrane proteins (Kazemi-Shahandashti et al., 2014; Nejadsadeghi et al., 2014). Due to the lower ELI observed in tolerant mutants under cold stress conditions, it is suggested that ELI may be used as a physiological marker for assessing stress tolerance in chickpea, even under normal conditions. The results of electrolyte leakage index (%) obtained in the current study are consistent with earlier published reports. For example, Kumar et al. (2011) observed lower electrolyte leakage in ICC 92944, which was able to maintain flower and set pods when subjected to cold stress conditions. Similarly, Chohan and Raina (2011) observed significantly higher electrolyte leakage in the leaves of chilling-sensitive genotypes compared to chilling-tolerant genotypes. All induced mutants exhibited lower ELI values compared to the susceptible check by twofold. The electrolyte leakage analysis was in agreement with the observed data for cold tolerance rating, suggesting higher leakage of electrolytes in the identified cold-susceptible check, i.e., ICC12968 (42.77%). The mean ELI under cold stress conditions was estimated to be 11.28 ± 0.81, with a range of 6.79-41.31% and standard deviation of 1.81 (Table 3). Mutants showing lower ELI also showed minimum CTR values. For instance, 254.9 Gy-Ch-M-52 (CTR = 1; ELI = 7.79%) and 335.74 Gy-Ch-M-58 (CTR = 1; ELI = 8.28%) indicated positive correlation compared to checks and may be recommended for utilization in developing cold-tolerant genotypes of chickpea.

Table 3. Pooled mean performance of elite induced chickpea mutants for ELI %, CTR, and seed yield traits over the years.

*****ELI, Electrolyte leakage index (%); Total Chl., Total chlorophyll (mg g^-1 FW); Cart., Carotenoids (mg g^-1FW); FRR, Frost resistance ratio; CTR, Cold tolerance rating (scale 1-9); SY, Seed yield (g plant^-1).

3.3 Cold tolerance rating scale

Evaluation of the 50 most promising mutants in the M₂ and M₃ generations, based on pooled mean performance during rabi 2023–24 and 2024–25 under field conditions for cold stress, revealed substantial and highly significant differences in the levels of cold tolerance among the mutants (Tables 3, 4). On a cold tolerance rating (CTR) scale of 1-9, mutants varied significantly, with CTR scores ranging from 1 to 6. The mean CTR was 1.91 and the standard deviation was 0.95 (Table 3). The cold-sensitive check ICC 12968 showed a CTR of 6 (Table 3) and succumbed to cold injury, whereas the cold-tolerant check exhibited a CTR of 2.50. The control (GNG1958) recorded a CTR of 4. The two most promising cold-tolerant induced mutants, 254.90 Gy-Ch-M-52 and 335.74 Gy-Ch-M-58, showed cold-tolerant scores of 1.00 each on the scale of 1-9, indicating a strong tolerance compared to the control GNG1958 (CTR = 4) and the cold-tolerant check ICC92944 (CTR = 6). Out of 50 induced mutants, 20 mutants showed a CTR of 1.50 each, indicating resistance against cold stress. However, 26 induced mutants showed a CTR of 2.00 and the remaining two showed a CTR of 2.50 each. All induced mutants showed tolerance to cold stress based on the CTR scale (Table 3).

Table 4. Mean performance for total chlorophyll content, carotenoid content, and FRR traits of elite induced chickpea mutants over the years.

FRR, Frost resistance ratio (scale 1-9).

Of 50 mutants, the two most promising cold-tolerant induced mutants, 254.90 Gy-Ch-M-52 and 335.74Gy-Ch-M-58, consistently showed a CTR score of 1.00 each on the scale of 1–9 during rabi 2023–24 and 2024-25, indicating stable cold tolerance in comparison to the control GNG1958 (CTR-4) and the cold-tolerant check ICC 92944 (CTR-6). These mutants can be used in chickpea breeding programs to enhance cold tolerance in cultivated chickpea genotypes. The use of these cold-tolerant chickpea mutants may help in the development of next-generation chickpea cultivars with enhanced cold tolerance. The mutants can also be used as parental genotypes in the development of mapping populations for gene mapping for cold tolerance and in differential gene expression for the identification of differentially expressed genes for cold tolerance. A significantly positive correlation was found between electrolyte leakage index and cold tolerance rating (r=0.874), implying increased leakage of electrolytes in mutants with higher cold tolerance rating (Figure 3). Similar results were also reported by Kanouni et al. (2009), where 16 entries showed a desirable reaction (≤ 3 on a 1-9 scale, where 1= free, 9 = killed due to frost). The highly cold-tolerant entries (FLIP 95-255C, FLIP 93-260C, and Sel95TH1716) were derived from hybrids of cultivated varieties with ILWC 182 (C. reticulatum), a wild relative of cultigen (C. arietinum L.). Mir et al. (2021) also reported that the wild genotype ‘‘Ortan-066’’ was a strong source of cold tolerance, with a mean cold tolerance rating (CTR) of 2.

3.4 Total chlorophyll (mg g^-1FW)

Chlorophyll content: Significant genotypic variations were observed for total chlorophyll content among the studied mutants. The leaf pigment content was assessed in order to study the plant physiological status during the activity of defensive processes under cold stress conditions. The total chlorophyll content varied between 0.95-2.55 mg g^-1FW, which is higher than the control and the susceptible check but lower than the cold-tolerant check. The mean value was 1.89 and the standard deviation was 0.38 (Table 4). 335.74 Gy-Ch-M2-60 (2.55 mg g^-1FW) showed the highest total chlorophyll content, followed by 335.74 Gy-Ch-M2-66 (2.49 mg g^-1FW) and 335.74 Gy-Ch-M2-45 (2.48 mg g^-1 FW), along with very low leakage (ELI 6.79%, CTR-1.5), (ELI-9.4%, CTR-2.00), and (ELI-7.30%, CTR-2.0), respectively. Out of the 50 induced mutants, 24 mutants showed higher total chlorophyll content than the control (GNG1958), and 39 mutants showed higher values of total chlorophyll content than the susceptible check (ICC12968), indicating that the majority of the induced mutants exhibited reduced chlorophyll damage when compared to the control and the susceptible check (Table 4). However, all mutants exhibited lower total chlorophyll content than the cold-tolerant check (ICC92944). Variation in total chlorophyll content may be associated with a malfunctioning in electron flow of the photosystem, causing increased photooxidation of pigments during the reproductive stage. Earlier studies in chickpea have also reported a loss in leaf chlorophyll, decline in photosystem II activity, and increased chlorophyll fluorescence under cold stress conditions (Tatar et al., 2013; Turan and Ekmekc, 2014; Thakur et al., 2020).

3.5 Carotenoid content in induced mutants of chickpea

Carotenoid content (mg g^-1FW): Out of 50 induced mutants, 27 showed higher carotenoid content than the cold-tolerant check (ICC92944). The range of carotenoid content in mutants varied between 0.46 and 0.88 mg g^-1FW, with a pooled mean 0.67 mg g^-1FW and a standard deviation of 0.02. A significant increase in levels of pooled mean value of carotenoid content was observed in 28 mutants compared to the cold-tolerant line (ICC92944), whereas 32 mutants showed higher amount of carotenoid content than the susceptible check and 41 mutants showed higher values than the control (GNG1958) (Table 4). The increase in carotenoids under cold stress may help prevent cold-induced damage of chlorophyll due to its role in non-photochemical quenching and degradation of oxidative species. In the present study, cold-tolerant chickpea mutants also exhibited higher carotenoid content than sensitive accessions, suggesting that these pigments may support better chlorophyll retention and membrane stability under cold stress, thereby playing a possible auxiliary role in tolerance mechanisms (Frank and Brudvig, 2004). Furthermore, the antioxidant capacity of carotenoids may enhance stress tolerance in chickpea plants (Kaur et al., 2008). In our investigation, induced chickpea mutants that demonstrated superior yield traits under cold stress conditions also exhibited superiority in physiological traits under cold stress. These characteristics identified those induced mutants as more cold-tolerant, consistent with previous findings in cultivated chickpea (Mir et al., 2021; Kiran et al., 2021; Rani et al., 2021). Cold stress damaged chlorophyll, resulting in greater loss in cold-sensitive mutants, thereby causing a greater reduction in photosynthetic efficiency in sensitive mutants (Rani et al., 2021). Previous studies have indicated that cool-season grasses that maintain high chlorophyll levels under cold stress exhibit enhanced growth performance (Li et al., 2024), whereas enhanced chlorophyll retention in mung bean is associated with reduced oxidative damage and improved cold tolerance (Manasa et al., 2023). Similarly, higher chlorophyll levels were observed in cold-tolerant chickpea mutants under cold stress (Thakur et al., 2020).

In the present study, cold-tolerant chickpea mutants also exhibited higher carotenoid content than sensitive, cold-tolerant checks, and control, thereby suggesting that these pigments may support better chlorophyll retention and membrane stability under cold stress, suggesting a possible auxiliary role in tolerance mechanisms (Frank and Brudvig, 2004). Furthermore, the antioxidant capacity of carotenoids may enhance stress tolerance in chickpea plants (Kaur et al., 2008).

3.6 Frost resistant ratio

Mutant 335.74 Gy-Ch-M-32 showed a frost resistant ratio of 0.99, i.e., approaching 1.00, indicating resistance. Mutants 335.74 Gy-Ch-M-115 and 335.74 Gy-Ch-M-147 also exhibited frost resistant ratios over 0.90 (0.95 and 0.93, respectively), indicating resistance to frost. The remaining mutants showed frost resistance ratios between 0.65 and 0.89, indicating tolerance. All these induced mutants showed reaction of resistance. A frost-resistant score was used as an index to describe genotypes’ tolerance to low temperature (Heidarvand et al., 2011). Mutants 335.74 Gy-Ch-M-32, 335.74 Gy-Ch-M-115, and 335.74 Gy-Ch-M-147 may be of great value as sources of cold tolerance and for studying the mechanisms of cold tolerance. These results indicate substantial genetic variability for cold tolerance induced through mutation breeding in chickpea. Similar results were also reported by Wery et al. (1990) and Admas et al. (2025).

3.7 Seed yield (g plant^-1)

Seed yield (g plant^-1) of induced chickpea mutants varied between 20.48 and 74.20 g pl^-1, with a mean value of 42.89 g plant^-1 and a standard deviation of 13.78. Out of the 50 mutants, 14 mutants (28%) showed higher seed yield (g plant^-1) than the control (GNG1958), the susceptible check (ICC12968), and the cold-tolerant check (ICC92944) (Tables 3, 4). The mutants 254.9 Gy-Ch-M-01 showed minimum electrolyte leakage (ELI = 9.42%), higher carotenoid content (0.72 mg g^-1FW), lower CTR value (1.50), and a high frost resistance ratio (FRR = 0.85), along with a significant higher seed yield of 73.93 g plant^-1 compared to the control, the susceptible check, and the cold-tolerant check (Table 3). However, mutant 254.9 Gy-Ch-M-09 showed maximum seed yield 74.20 g plant^-1, along with a low CTR score (1.50) and a higher value of FRR (0.78) compared to the cold-tolerant check, indicating its potential for developing cold-tolerant varieties.

Other mutants, namely, 254.9 Gy-Ch-M-09, 335.74Gy-Ch-M-140, 335.74 Gy-Ch-M-153, 335.74 Gy-Ch-M-156, and 335.74 Gy-Ch-M-32, showed lower ELI, higher content of carotenoids, low CTR values, and FRR values approaching unity, along with higher seed yield (g plant^-1); except mutant 335.74 Gy-Ch-140, which showed lower total chlorophyll content than the control. These mutants may be used for appropriate breeding programs aimed at developing chickpea varieties with enhanced cold tolerance and higher seed yield. Other mutants, viz., 335.74 Gy-Ch-M-45, 254.9 Gy-Ch-M-05, 254.9 Gy-Ch-M-52, 335.74 Gy-Ch-M-58, 335.74 Gy-Ch-M- 65, 335.74 Gy-Ch-M- 66, 335.74 Gy-Ch-M- 68, 335.74 Gy-Ch-M- 115, and 335.74 Gy-Ch-M- 135, showed desired values for cold tolerance traits, such as ELI (%), carotenoid content, CTR, and FRR, along with a par seed yield comparable to the control. Hence, such mutants are recommended as donor parents for introgressions of targeted traits into well adapted variety for the development of cold tolerant varieties of chickpea. Figure 4 presents photographs of 13 putative mutants exhibiting desired cold-tolerant traits along with higher seed yield (g plant^-1). These characteristics identified those mutants as more cold-tolerant, consistent with previous findings in cultivated chickpea (Mir et al., 2021 and Rani et al., 2021). These trends support the physiological results, where induced mutants maintained better membrane integrity, higher total chlorophyll and carotenoid contents, lower CTR values, and FRR between 0.50 and 0.97 under cold stress conditions. The enhanced accumulation of sugar, proline, and protein in 15 putative mutants likely acted synergistically with physiological adjustments to minimize oxidative and structural damage during chilling episodes/cold stress.

3.8 Biochemical traits

3.8.1 Proline content (µmol g^-1FW)

Proline content was estimated in 71 mutants from the LD₅₀ dose and 95 mutants from the ≤ LD₅₀ dose in the M₃ generation only. Among the 15 selected mutants, proline content ranged from 0.9 to 3.44 µmol g^-1FW, with a mean value of 1.84 µmol g^-1FW, a standard deviation of 0.70, and a standard error 0.16 (Tables 1, 2). The maximum proline content was exhibited by mutant 335.74 Gy-Ch-M-153 (3.44 µmol g^-1FW), which is higher than that of the checks (control, susceptible check, and tolerant check), indicating higher tolerance to cold. However, the lowest valued was exhibited by 254.9 Gy-Ch-M-52 (0.90µmol g^-1FW). Proline content increased significantly among putative mutants (254.90 Gy-Ch-M-01, 254.90 Gy-Ch-M-09, 254.90 Gy-Ch-M-45, 335.74 Gy-Ch-M-135, and 335.74 Gy-Ch-M-153) under cold stress at the vegetative stage compared to the control (Table 4). Kumar et al. (2011) also confirmed the role of proline in providing cold tolerance in chickpea. Despite its role in osmoregulation, proline acts as a chemical chaperone via preventing protein thermal denaturation, assisting refolding of unfolded proteins, and enhancing stabilization of unfolded proteins (Chattopadhyay et al., 2004).

Another category of biomolecules associated with cold tolerance in induced mutants of chickpeas is cryoprotectants, such as proteins. Induced chickpea mutants accumulated significantly higher amounts of proline and proteins than low-yielding/cold-sensitive mutants, suggesting that these molecules are part of the key adaptive mechanisms in wild chickpea to mitigate cold stress. Enhanced levels of proline have been associated with cold stress resilience in chickpea, along with the upregulation of antioxidant enzymes, which mitigate Reactive Oxygen Species (ROS) damage (Kumar et al., 2010, Kumar et al., 2011; Shahandashti et al., 2013). Proline, a crucial osmoprotectant and cryoprotectant, potentially enhances cold tolerance in chickpea mutants by maintaining cell turgor and osmotic balance and stabilizing proteins and cellular membranes under cold stress conditions, which are essential for cellular integrity (Mir et al., 2021; Thakur et al., 2020). Studies on other crops, such as sorghum (Vera Hernandez et al., 2023) and olive (Dadras et al., 2024), have demonstrated that genotypes with higher proline content exhibit greater resilience to cold stress, supporting the hypothesis that proline accumulation enhances cold tolerance and seed yield potential. Dogra et al. (2025) also reported that genotypes resistant to cold stress exhibited elevated levels of proline and sugar content parameters compared to susceptible ones. Kaur et al. (2025) also reported that cold tolerant accessions accumulated more proline content compared to their sensitive counterparts.

3.8.2 Sugar content (mg g^-1FW):

Sugar content was evaluated only in the M3 generation as compared to M2 due to non-segregating nature, selecting for desirable agro- nomic and cold tolerance traits are more effective and leads directly to advancing potential varieties. Mutant 335.74 Gy-Ch-M-45 exhibited the maximum sugar content (46.16 mg g^-1FW), followed by 335.74 Gy-Ch-M-65 (44.56 mg g^-1FW), both exceeding the cold- tolerant check and indicating desired cold tolerance (Table 1). Ten mutants showed higher sugar content than the control, with three also exhibiting higher sugar content than the cold-tolerant check. Higher sugar content in mutants compared to the control typically results from genetic modifications that disrupt the normal metabolic fate of carbohydrates, causing them to accumulate rather than be transported or stored as starch. The underlying causes include inhibited sugar transport, decreased conversion to starch, altered signaling, or increased vacuolar storage. Cold-tolerant mutants (e.g. 335.74Gy-Ch-M-45) showed superior cold acclimation abilities, maintaining higher sugar content as compared to the control, which help protect reproductive structures, such as anthers and ovules, from damage. Sugar accumulation in induced chickpea plants under cold stress, on the other hand, functions as a buffer to provide energy, sustain growth, and stabilize cellular components and membranes (Tarkowski and Van den Ende, 2015). Increased sugar accumulation has already been demonstrated to contribute to cold tolerance success in rice (Kawakami et al., 2008) and chickpea (Thakur et al., 2020; Kiran et al., 2021; Dogra et al., 2025). The present study focused on sugar accumulation at vegetative stage in leaves, while previous studies have also identified sugar metabolism genes, such as UGPase, CWINs, GBSS2, and BAM3, as important components to the cold tolerance machinery in chickpea leaves (Kiran et al., 2021; Sharma et al., 2021), thereby supporting the physiological patterns observed in our study. Sugar and starch accumulation are common plant responses to low temperatures and, by stabilizing cell membranes, they can contribute to cold tolerance in plants (Ruelland et al., 2009).

3.8.3 Protein content (%)

Six mutants showed higher protein content in induced mutants than the control (GNG1958), with the maximum recorded value exhibited by mutant 335.74 Gy-Ch-M-140 (23.08%) and the minimum value by 335.74 Gy-Ch-M-115 (21.65%). The range varied between 21.26 and 23.08%, with a standard deviation of 0.48. Ten mutants revealed higher protein content than the cold-tolerant check (ICC92944) (Table 1). The increased protein content in the M₃ generation may be a direct result of the targeted mutation and subsequent genetic segregation. The plants produced in the M₃ generation may have a new gene combination that expresses the mutation. These new genes may alter the proportions of different protein fractions, such as albumin and globulin, which can result in an overall higher protein percentage. This is because when a chickpea plant experiences cold stress, it initiates a cellular process known as cold acclimation. As part of this process, the plant synthesizes and accumulates specific proteins that act as cryoprotectants, protecting cells and membranes from freezing damage.

3.9 Regression analysis between CTR and ELI

A high ELI % corresponds to greater electrolyte leakage, indicating lower membrane stability and higher sensitivity to stress. Studies indicate that lower ELI values are indicative of better cold tolerance (CTR), as the membrane remains intact. All the mutants (Table 1, Figure 4) showed lower CTR values, along with low electrolyte leakage index, as compared to the control (GNG1958), indicating resistance to cold stress and a positive correlation between them. However, CTR values ranged from 1.5 to 2.00 and ELI from 6.79% to 11.04%. A significantly positive correlation was found between electrolyte leakage index (ELI%) and cold tolerance rating (CTR) (r = 0.87, p < 0.01), implying greater leakage of electrolytes in mutants with increased cold tolerance rating (Figure 3). The minimum pooled electrolyte leakage index over theM₂ and M₃ generations was exhibited by 254.90 Gy-Ch-M-45 (6.79%), which had a mean CTR value of 2.00 (Table 2). The 15 promising putative mutants identified in our study were found to be stable across generations and were declared highly cold-tolerant mutants with adequate seed yield (g plant^-1) in comparison to the control, and may prove useful worldwide in breeding chickpea for cold tolerance through a variety of approaches. .

3.9.1 Correlation between seed yield and cold-tolerant traits

The phenotypic association of seed yield (gplant^-1) and cold tolerance traits (chlorophyll content, carotenoid content, frost resistance ratio, electrolyte leakage index, cold tolerance rating scale) was analyzed based on pooled mean values (M₂ and M₃ generations) of the recorded traits of all induced mutants, and the results are given in Table 5. A strong positive correlation between two traits indicates that an increase in one trait is accompanied by an increase in the other trait also. In contrast, if the correlation is negative, an increase in one trait results in a reduction in the other. Such types of traits are governed by pleiotropic effects of genes or linkage of genes controlling the inheritance of two or more characters (Dabholkor, 1992).

Table 5. Phenotypic Pearson’s correlation matrix for six traits in induced chickpea mutants.

Total Chl.cont., Total chlorophyll content; Cart. Cont., Carotenoid content; FRR, Frost resistance ratio; ELI, Electrolyte leakage index (%); CTR, Cold tolerance rating; SY, Seed yield. “*”: Significant at 5% level (P ≤ 0.05);” **”: Significant at 1% level (P ≤ 0.01); NS: Non-significant.

The variable carotenoid content showed a significant positive correlation with chlorophyll content (0.91). Frost resistance ratio showed positive correlation with chlorophyll content (0.20) and carotenoid content (0.22). ELI exhibited a significant negative correlation with chlorophyll content, carotenoid content, and frost resistance ratio (-0.433), indicating that a decrease in any one of these traits led to a reduction in FRR. CTR showed a positive correlation with ELI but a negative and significant correlation with FRR and a non-significant negative correlation with chlorophyll and carotenoid content. Seed yield (g plant^-1) exhibited a significant positive correlation with chlorophyll content, carotenoid content, and frost resistance ratio but showed a significant negative correlation with ELI and CTR. Mir et al. (2021) also reported a significant positive correlation between CTR and ELI%, suggesting their usefulness as reliable indicators of cold tolerance in chickpea.

3.9.2 Genetic variability for seed yield and cold tolerance traits

Genetic variability is a prerequisite for the selection of superior genotypes in any crop. Thousands of years of human selection have led to the loss of potentially important allelic variation. Strategies that aim to increase genetic variation within a crop species can significantly improve the efficiency of the breeding process. Unlike wide crosses, mutagenesis can be applied directly to elite cultivars without unwanted linkage drag. In this study, we used gamma irradiation on the chickpea variety GNG1958. The success of this approach relies on the application of an irradiation dose that induces sufficient variation while still maintaining fertility. Variation can be measured phenotypically and genotypically. While genotypic analysis allows precise estimation of DNA variation, phenotypic analysis provides important data on the expression of useful traits and their heritability.

Estimates of genetic parameters, such as phenotypic and genotypic coefficients of variability (PCV, GCV), heritability (H²), and genetic advance (GA) for various cold tolerance traits, are useful for designing effective breeding programs, according to Kozgar (2014). Wani and Khan (2006) reported that the genotypic coefficient of variation measures the extent of genetic variability shown by the plant trait; however, GCV alone cannot determine the amount of variation that is heritable. Knowledge of heritability is essential for selection-based improvement, as it indicates the extent of transmissibility of a trait to future generations (Kozgar (2014)). Wani and Khan (2006) further emphasized that estimates of heritability alone do not provide an idea about the expected gain in the next generation; it must be considered in conjunction with estimates of genetic advance, the change in mean value across generations.

The analysis of data showed that the maximum coefficient of variation (CV%) was observed for seed yield/plant (5.47%), followed by cold tolerance rating (5.45%), carotenoid content (3.39%), and total chlorophyll content (2.19%) in the M₂ generation. For seed yield in the M₃ generation and pooled mean value values, the coefficient of variation for all traits was less than 6.53% in the M₃ generation. The lowest coefficient of variation was observed for total chlorophyll content (0.69%) in pooled mean values and for frost resistance ratio in the M₂ generation (Table 6). We observed that the estimated values of corresponding genetic parameters for cold tolerance traits and seed yield in M_2, M₃, and pooled mean data differed from trait to trait by very slight values, except for seed yield, indicating that these traits are less affected by the environment and have similar values.

Table 6. Estimates of genetic parameters for yield and cold tolerance traits in M₂ and M₃ generations and pooled mean values of chickpea.

ELI, Electrolyte leakage index; T. Chl. Cont., Total chlorophyll content; Car. Cont., Carotenoid content; CTR, Cold tolerant rating; FRR, Frost resistant ratio; SY, Seed yield; PMV, Pooled mean value.

However, higher phenotypic coefficient of variation was observed for seed yield (37.29%) across M₃, M₂, and pooled mean values (Table 6). The estimates of phenotypic coefficient of variations (PCV) were higher for seed yield/plant, followed by cold tolerance rating(CTR), electrolyte leakage index (ELI %), total chlorophyll content, and carotenoid content, and moderate for frost resistance ratio (FRR) in the M₂ generation. However, the same trend was found in the M₃ generation, except for ELI %. Low phenotypic coefficient of variation (PCV) was observed for frost resistance ration (FRR) in the pooled mean value, while moderate values were recorded for carotenoid content, ELI %, and CTR. In general, the estimates of PCV were higher than the estimates of GCV for all traits under study in both M₂ and M₃ generations, indicating that the apparent variation was not only due to genotypes but also due to the influence of the environment on the expression of the traits. Similar results were reported by Tabasum et al. (2010) in greengram and by Konda et al. (2009) and Reddy et al. (2011) in black gram. Moderate PCV and GCV were observed for FRR in both M₂, and M₃ generations. Kumar et al. (2016) also reported similar findings for seed yield (g plant^-1). Selection will be effective when based on the heritable nature of traits. Unche et al. (2008) reported wide variability in heritability and genetic advance (as percent of mean) across all generations. The estimate of heritability acts as a predictive instrument in expressing the reliability of phenotypic values. Therefore, it helps plant breeders in selecting particular traits when heritability is high in magnitude. Tikka et al. (1977) exhibited that high value of heritability together with high genetic advance for any trait indicates additive gene action, where selection can be effective for improvement of such traits. In contrast, high heritability associated with low genetic advance might suggest the presence of non-additive gene action, which indicates epistasis, dominance, and genotypic and environmental interactions, resulting in limited response to selection r. The maximum variation in polygenic traits (seed yield) may show stable gene mutations in subsequent generation.

High heritability along with low genetic advance was observed for all traits in M₂, M₃, and pooled mean data, except seed yield (g plant^-1), indicating non-additive gene action, which indicates epistasis, dominance, and genotypic and environmental interactions. The increased genetic variability for these traits provides great possibility for further selection. The mutant population showed significant differences among mutants for all traits in M₂, M₃, and pooled mean values. The highest variability was observed for seed yield, for which the coefficient of variation was low. This variation was mainly due to genetics and was less influenced by the growing environment. High heritability coupled with high genetic gain observed in the mutant for seed yield/plant indicates that the trait was governed by additive gene action, suggesting that selection for this trait may be effective.

4 Conclusion

In the present study, we screened 257 induced mutants in the M₂ generation and 217 in the M₃ generation of chickpea and identified 50 of the most promising cold-tolerant mutants based on physiological, biochemical, and seed yield traits. Out of 50, 15 top elite mutants were identified on the basis of physiological, biochemical, and seed yield traits. These putative mutants were then subjected to an in-depth analysis to evaluate their responses to cold stress, involving a comprehensive assessment of physiological and biochemical traits at the vegetative stage during the peak period of cold stress. These findings highlight the importance of various physiological traits in conferring cold tolerance. Cold-tolerant mutants exhibited reduced cellular damage, coupled with a strong antioxidant defense, compared to the control, susceptible, and tolerant checks. Additionally, the study highlighted the role of carotenoids in mitigating oxidative damage and the significance of cryoprotectants, such as proline and sugars, in maintaining cellular stability. These traits collectively contributed to the ability of mutants to sustain growth under cold stress, as evidenced by their superior seed yield (g plant^-1). The insights obtained from this research provide a valuable foundation for breeding programs aimed at enhancing cold tolerance in cultivated chickpea varieties by utilizing the induced resilience of the chickpea mutants to improve crop performance under cold stress.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphgy.2026.1766618/full#supplementary-material

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Footnotes

Edited by: El-Hadji Malick Cisse, The Matuszyńska Lab Computational Biology RWTH Aachen, Germany

Reviewed by: Nidhi Sharma, Aligarh Muslim University, India

Roja Veeraghattapu, Acharya N G Ranga Agricultural University, India