Evaluation of genetic variability, heritability and trait interrelationships in Potato (Solanum tuberosum L.) genotypes for yield and nutritional quality through planting at two environmental conditions in Northern India

Introduction

Potato (Solanum tuberosum L.) is one of the most important food crops globally and ranks as a major staple in India which account for a substantial share of national production (Pandey et al., 2020) [1] ; (Singh et al., 2024) [2] . It is the food and vegetable crop that is considered one of the most promising crops for alleviating inadequate and unbalanced nutrition crisis because of its high yield potential and large production area (Aksoy et al., 202) [3] ; (Zhou et al., 2023) [4] . Beyond its contribution to food security, potato is recognized for its high nutritional value, serving as an excellent source of carbohydrates, high-quality protein, dietary fiber, vitamin C, vitamin B6, potassium and several essential minerals (Devaux et al., 2021) [5] . As malnutrition and micronutrient deficiencies remain serious public health concerns, particularly in developing countries, strengthening the nutritional profile of widely consumed crops like potato is considered a practical and sustainable strategy to combat hidden hunger (WHO, 2024) [6] . Consequently, modern potato improvement programmes increasingly emphasize the simultaneous enhancement of yield and nutritional quality to ensure both food and nutrition security (Devaux et al., 2021) [5] . In Northern India, potato cultivation forms a major component of the cropping system; however, productivity is often influenced by varietal performance, environmental fluctuations and planting time (Pandey et al., 2020) [1] Yield gaps persist due to limited availability of high-yielding, stress-tolerant and nutritionally enhanced cultivars adapted to variable planting dates (Singh et al., 2024) [2] . Although breeding programmes in India have traditionally focused on improving yield potential and disease resistance, comparatively less emphasis has been placed on enhancing nutritional and biochemical quality traits alongside productivity (Sood et al., 2025) [7] . The growing demand for nutritionally enriched food products necessitates the development of potato genotypes combining superior yield performance with enhanced levels of dry matter, vitamins, minerals and antioxidant compounds (Aishwarya et al., 2024) [8] . Understanding the extent of genetic variability among genotypes, estimating heritability of key traits and studying correlations among yield and nutritional attributes are essential prerequisites for designing effective selection strategies in crop improvement programmes (Johnson et al., 1955) [9] ; (Panse & Sukhatme, 1985) [10] . Simultaneous improvement of tuber yield and nutritional quality can be challenging due to the complex inheritance patterns and environmental sensitivity of quantitative traits (Falconer and Mackay, 1996) [11] . In Northern India, variation in planting dates leads to differences in temperature regimes and crop phenology, which may alter the expression of yield and quality traits and influence genetic parameter estimates. Heritability and genetic advance are critical parameters for understanding the inheritance pattern of quantitative traits and predicting the efficiency of selection (Johnson et al., 1955) [9] ; Panse and Sukhatme, 1985) [10] . Moreover, correlation and path coefficient analyses help elucidate the interrelationships among yield and its component traits, enabling breeders to identify key attributes influencing overall productivity (Dewey and Lu, 1959) [12] . Nevertheless, comprehensive information on genetic variability, heritability and the association between tuber yield and nutritional traits under multiple planting dates is still limited. Therefore, the present investigation was undertaken to (i) assess the extent of genetic variability and heritability among potato genotypes evaluated under two planting dates in Northern India (ii) analyze the interrelationships between tuber yield and nutritional quality traits to facilitate the development of high-yielding and nutritionally enriched cultivars adapted to North Indian conditions.

Materials and Methods

Planting materials, experimental site and design

Five potato genotypes were used in this study namely Kufri Lima, Kufri Kiran, Kufri Bahar, Kufri Pukhraj and Kufri Surya procured from the Department of Vegetable Science, CCS Haryana Agricultural University, Hisar (Haryana). The present investigation was conducted during the Rabi season of 2024-25 at the Research Farm of the Department of Vegetable Science, CCS Haryana Agricultural University, Hisar, Haryana, India during the main cropping season of 2024 at 29°10′ N, 75°46′ E; 215.2 m amsl altitude. The experiment was laid out in a factorial randomized block design (RBD) with three replications. The treatments consisted of five potato genotypes and two planting dates, viz. 25 September 2024 and 10 October 2024, resulting in ten treatment combinations. Each plot measured 3.0 × 2.4 m², with a planting geometry of 60 cm × 20 cm.

Data Collection Yield and Yield Components Total number of tubers /plot

This metric was calculated by counting the number of tubers from each of the randomly selected plants at the time of harvest from each plot and taking their average.

Total yield of potato was obtained from each plot and was expressed in kg.

Marketable tuber yield for each plot was calculated by subtracting the yield of tubers less than 25g in weight from total tuber yield obtained in kilogram per plot and then, the values were converted to tonnes per hectare.

Total tuber yield (kg/plot and t/ha) The total tuber yield obtained from each plot was noted in kilogram per plot, and then, converted into tons per hectare.

Nutritional Traits

Carotenoid content, total phenolic content, total flavonoid content, anthocyanin content, total soluble sugar, total reducing sugar and anti-oxidant acitivity were determined using the following methods:

To measure carotenoid content, the protocol by (Hiscox and Israelstam 1979) [13] was followed. 50mg of finely chopped fresh leaves were ground with 5ml of 80% acetone and kept in the dark overnight. The supernatant was collected, and the process was repeated until the residue becomes colorless. The absorbance of the resulting solution was measured at 480nm, 645nm, and 663nm against an 80% acetone blank. Carotenoid content was calculated using the following equation:

Where; V-Volume of 80% acetone solution. W-Weight of leaf tissue in milligrams.

Total phenolic content

Total phenolic content was estimated using the method of Bray and Thorpe (1954) [14] . Sample was homogenized with 10ml of 80% methanol and centrifuged at 2000 rpm for 20 minutes. The supernatant was collected, and the residue was re-extracted with 80% methanol. This process was repeated 2 to 3 times collecting the supernatant each time. In a test tube, 1 ml of supernatant was mixed with 1 ml of Folin Ciocalteu reagent and 2 ml of 20% sodium carbonate. This solution was then placed in a water bath for 5 minutes, and absorbance was measured at 650 nm, using catechol as the standard.

Total flavonoid content

Fresh leaf samples (200 mg) were extracted in 5 ml of 80% acetone and then the extracts were centrifuged. To 1.5 ml of methanolic extract, add 1.5 ml of 2% AlCl 3 solution. The samples were incubated for an hour at room temperature. The absorbance was determined using spectrophotometer at 415 nm using blank without methanolic extract. The same procedure was repeated for the standard solution of Quercetin and the calibration line was construed. The content of flavonoids in extracts was expressed in terms of Quercetin Equivalent (QE).

Anthocyanin content

Total anthocyanin content was recorded with the method as described by Hodges and Nozzolillo (1996) [15] . Fresh leaves (0.1 g) were crushed in 2 mL of acidified methanol (methanol+1% HCI) by using the mortar and pestle. The homogenized material shifted to the test tube and boiled at 100 o C for half an hour in the water bath. Centrifuge the material at 12000 rpm for 15 minutes. The absorbance was recorded at 530 nm and 600 nm with the help of the spectrophotometer (GENESYS 180UV-Visible Spectrophotometer). The anthocyanin content of the sample was expressed by cyaniding-3-glucoside (CGE) and calculated by using the following equation:

Where, A 530nm and A 600nm are the absorbance at 530 and 600 nm, respectively; V is the total volume of extract; Mw is the relative molecular weight of CGE (449.4); ε is the molar extinction coefficient (29,600 M-1 cm-1); m is the mass of the sample.

Total soluble sugar (mg/g) For estimation, the method given by (Dubois et al.,1956) [16] was used. To extract total sugars from tubers, 100mg of crushed sample was taken and 10ml of 80% (v/v) methanol was added. The mixture was kept in water bath for one hour at 800C with intermittent shaking. After cooling the homogenate was centrifuged at 4000 rpm for 20 minutes. The supernatant obtained after centrifugation was collected in separate tube and the residue left was heated again with 10ml of 80% (v/v) methanol at 800C for one hour. Homogenate was centrifuged at 4000 rpm for 20 minutes. Supernatant thus obtained as pooled into the supernatant obtained earlier. This procedure is repeated thrice. The pooled was then concentrated by drying at 400 •C under water bath and 50% methanol was added to make the volume to 5ml. An aliquot of 0.5ml of above solution was taken in a test tube and 2ml of 2% (w/v) phenol and 5ml of concentrated H 2 SO 4 added. The resultant mixture was cooled to room temperature and shaked using a vortex. The resultant colour was read by using spectrophotometer at 490 mm against blank reagent. The standard curve prepared using dextrose (50-300 μg/ml) was used to determine the amount of sugar in sample.

Total reducing sugar(mg/g) It was estimated through method suggested by (Hulme and Narain, 1996) [17] . The measurement was performed according to the procedure of Krivorotova and Sereikaite with slight modification. DNSA reagent was prepared by dissolving 1g of DNSA and 30g of sodium-potassium tartaric acid in 80mL of 0.5N NaOH at 45°C. After dissolution, the solution was cooled down to room temperature and diluted to 100mL with the help of distilled water. For the measurement, 2mL of DNSA reagent was pipetted into a test tube containing 1mL of plant extract (1mg/mL) and kept at 95°C for 5 minutes. After cooling, 7mL of distilled water was added to the solution and the absorbance of the resulting solution was measured at 510nm using a spectrophotometer. The reducing sugar content was calculated from the calibration curve of standard D-glucose (200-1000mg/L), and the results were expressed as mg D-glucose equivalent (GE) per gram dry extract weight.

The antioxidant activity was calculated using DPPH method by the procedure reported by Apak et al., (2016) [18] . By dissolving 0.025g of DPPH in 1L of 70% methanol the standard DPPH solution was prepared. This solution is stored in dark conditions until use (absorbance value of 0.8+ 0.02 at 517 nm). The working solution was made by mixing 3.9ml of DPPH solution followed by addition of 0.1ml of potato leaf sample extract and left for 30 min to react. The absorbance values were read at 517nm, by using a UV-visible spectrophotometer. High percent inhibition values indicate the high antioxidant activity. DPPH activity can be measured using the following formulae:

Data analysis

• The data obtained were subjected to ANOVA using OPSTAT software. The traits that exhibited significant mean squares in general ANOVA was further subjected to genetic analyses using the software R version 4.3.2 (R Core Team, 2024) [19] .

Phenotypic and genotypic variance

The phenotypic and genotypic variance and coefficients of variance of each trait were calculated using the formula suggested by (Singh and Chaudhary,1996) [20] as follows.

Genotypic variance (σ 2 g) = MSg-MSe/ r, where σ 2 g = genotypic variance, MSg = mean square of genotypes, MSe = mean square of experimental error, r = number of replications, and environmental variance (σ 2 e) = mean square of error. Phenotypic variance (σ2p) = σ 2 g + σ 2 e, where σ 2 p, σ 2 g and σ 2 e = phenotypic, genotypic, and environmental variance, respectively.

PCV = (√σ 2 p / X ̄) * 100 and GCV = (√σ 2 g / X ̄) * 100

where PCV and GCV = phenotypic and genotypic coefficient of variation, respectively, and X ̄ = grand mean of the character evaluated.

Heritability

Broad-sense heritability (H 2 ) is the proportion of phenotypic variance (σ 2 p) and genotypic variance (σ 2 g) using the formula suggested by (Falconer and Mackay, 1996) [11] . as follows: H 2 = (σ 2 g/ σ 2 p) * 100

Expected genetic advance under selection (GA) and as percent of mean (GAM)

Genetic advance in absolute unit (GA) and percent of the mean (GAM), assuming selection of the superior 5% of the genotypes, were calculated using the methods illustrated by (Johnson et al., 1955) [9] ; as follows: GA = K *SDp × H 2 where, GA = genetic advance, SDp = phenotypic standard deviation on mean basis, H 2 = broad-sense heritability, and K = standardized selection differential at 5% selection intensity (K = 2.063). GAM = GA/ X ̄ * 100, where GAM = genetic advance as percent of mean, GA = genetic advance, and X ̄ = grand mean of the character evaluated.

Pearson correlation and principal component analysis

Pearson correlation and principal component analysis were performed by the variability package of the software R version 4.3.2 (R Core Team, 2024) [19] .

Results and Discussion

Analysis of variance, mean performance of tuber yield and nutritional quality traits

The analysis of variance revealed highly significant (p ≤ 0.01) differences among the ten treatment combinations comprising five potato genotypes evaluated under two planting dates for yield and nutritional quality traits (Table 1). [2] . Quality traits also exhibited marked variability. Carotenoid content ranged from 0.11 to 0.43 mg/g (D₁) and 0.16 to 0.48 mg/g (D₂), with Kufri Kiran showing the highest values. Total phenolic content (TPC) ranged from 1.40 to 3.38 mg/g under D₁ and 0.95 to 2.30 mg/g under D₂, while total flavonoid content (TFC) varied from 1.16 to 3.32 mg/g (D₁) and 0.81 to 2.15 mg/g (D₂). Anthocyanin content ranged from 0.03 to 0.12 mg/g under D₁ and 0.04 to 0.15 mg/g under D₂. Total soluble sugars (TSS) ranged from 17.2 to 25.5 mg/g (D₁) and 16.8 to 24.1 mg/g (D₂), whereas total reducing sugars (TRS) varied from 1.8 to 2.9 mg/g (D₁) and 1.4 to 2.6 mg/g (D₂). Antioxidant activity (AA) ranged from 30.7 to 53.5% under D₁ and 22.7 to 41.2% under D₂, with Kufri Kiran and Kufri Surya exhibiting comparatively higher antioxidant potential. The observed genotypic differences suggest the presence of exploitable genetic variability for simultaneous improvement of yield and nutritional quality traits (Devaux et al., 2021) [5] .; Sood et al., 2025) [7] .

Genetic Parameters

The estimates of genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV), broad-sense heritability (H²), genetic advance (GA), and genetic advance as percent of mean (GAM) are presented in (table 3) For all the traits, PCV values were slightly higher than their corresponding GCV values, indicating limited environmental influence and a predominant genetic control over trait expression. The narrow PCV-GCV differences suggest that phenotypic performance reliably reflects genotypic potential, thereby improving the efficiency of selection. Similar observations have been reported in recent potato studies under controlled field conditions (Alam et al., 2023) [21] ; (Reddy et al., 2024) [22] . High GCV and PCV were observed for anthocyanin content (49.73% and 51.34%), total flavonoid content (42.20% and 42.30%), carotenoid content (40.12% and 40.45%), and total phenolic content (37.05% and 37.07%), indicating substantial genetic variability for antioxidant-related traits. Moderate variability was recorded for total number of tubers, total reducing sugars, and antioxidant activity, while comparatively lower values were noted for total tuber weight, total tuber yield, and total soluble sugars. Traits exhibiting moderate to high GCV are considered amenable to improvement through direct selection. Comparable results for yield and biochemical traits have been documented in recent germplasm evaluations of potato (Verma et al., 2024) [23] ; (Khan et al., 2025) [24] . Heritability estimates were uniformly high, ranging from 93.83% (anthocyanin content) to 99.87% (total phenolic content). High heritability coupled with high GAM was particularly evident for anthocyanin (99.23%), total flavonoid content (86.72%), carotenoid content (81.95%), total phenolic content (76.26%), and total reducing sugars (43.89%), indicating predominance of additive gene effects and good response to selection. Yield traits exhibited high heritability with moderate GAM, suggesting involvement of both additive and non-additive gene action. Similar trends have been reported in recent potato breeding studies (Sharma et al., 2023) [25] ; Gupta et al., 2025) [29] . Overall, the results demonstrate substantial exploitable genetic variability for simultaneous yield and nutritional improvement.

Correlation

Pearson correlation analysis among the studied traits revealed that total tuber yield (TTY) showed a strong and positive correlation with marketable tuber yield (MTY), indicating the importance of these traits for improving overall tuber productivity in potato. (Fig. 1). Positive correlations were also observed among yield-related traits such as total number of tubers (TNT) and tuber weight (TWT) with MTY and TTY, suggesting that these traits play a significant role in determining tuber yield. Similar positive associations among yield components in potato were reported by Luthra et al. (2018) [26] , who observed a strong relationship between marketable tuber yield and total tuber yield. Furthermore, biochemical traits including total phenolic content (TPC), total flavonoid content (TFC), and anthocyanin (Anth) showed positive correlations with each other, indicating a close association among antioxidant compounds. Comparable relationships among antioxidant compounds in potato tubers were also reported by Navarre et al., (2011) [27] and Tsao et al., (2006) [28] . However, sugar-related traits such as total soluble sugars (TSS) and total reducing sugars (TRS) exhibited negative correlations with several yield and biochemical traits, suggesting a possible tradeoff between sugar accumulation and yield performance.

Principal Component Analysis

Principal component analysis (PCA) revealed that the first two principal components explained a large proportion of the total variation among the studied traits. The first principal component (Dim1) accounted for 78.7% of the total variability, while the second component (Dim2) explained 17.6%, together contributing 96.3% of the cumulative variation. Yield-related traits including total number of tubers (TNT), tuber weight (TWT), marketable tuber yield (MTY), and total tuber yield (TTY) across both planting dates were closely grouped and oriented in a similar direction, indicating strong positive associations among these traits. Similarly, biochemical traits such as total phenolic content (TPC), total flavonoid content (TFC), and anthocyanin (Anth) formed a distinct cluster, suggesting a strong relationship among antioxidant compounds.

In contrast, sugar-related traits including total soluble sugars (TSS) and total reducing sugars (TRS) were positioned in a different direction, indicating weaker associations with several yield traits. Similar patterns of trait contribution and variability using PCA in potato genotypes have been reported by (Sood et al., 2025) [7] .

Conclusion

The present investigation highlights the significant impact of both genotype and planting date on the productivity and nutritional profile of potato in Northern India. The second planting date proved more conducive for maximizing tuber yield, with Kufri Kiran and Kufri Surya emerging as the most high-yielding and nutritionally sound genotypes. The study revealed high broad-sense heritability coupled with high genetic advance for most antioxidant-related traits, suggesting that direct selection for these characters would be highly effective for crop improvement. Furthermore, the strong positive correlation among yield components and the distinct clustering of biochemical traits in PCA provide a clear roadmap for breeders to select for multiple traits simultaneously. However, the negative relationship observed between sugar accumulation and yield suggests that a balanced selection strategy is necessary to maintain productivity while enhancing quality. Overall, these results provide a robust genetic basis for developing superior potato cultivars that cater to both food security and the growing demand for nutritionally enriched produce.

Acknowledgement

AcknowledgmentsThis work was carried out with the support of Department of Vegetable Science, College of Agriculture CCS Haryana Agricultural University, Hisar, Haryana, India.

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