Research Article

Phenotypic Stability for Fruit yield and its Components of Brinjal

V. Chaitanya and   R. V. S. K. Reddy

  • Page No:  438 - 447
  • Published online: 27 May 2022
  • DOI : HTTPS://DOI.ORG/10.23910/1.2022.2667

  • Abstract
  •  chaitanya.hortico@gmail.com

The present study was carried out at Vegetable Research Station, ARI, Rajendranagar, Hyderabad, Telangana State, India during kharif (June–October, 2014), rabi (October–February, 2014–15) and Summer (February–May 2015) to assess the performance of genotypes in terms of yield as well as quality across seasons under wide range of environments through phenotypic stability studies. Thirty brinjal genotypes were evaluated for yield and quality parameters under three environments comprising of three different seasons. The portioning of environments + (genotypes × environments) mean squares showed that environments (linear) differed significantly and were quite diverse with regards to their effect on the performance of the genotypes for fruit yield and quality traits. A perusal of stability parameters indicated two genotypes C3 and P6 showed stable performance for earliness, C3 for days to first fruit harvest and C3, C10, C16, C21, P1 fordays to last fruit harvest hence these genotypes were adapted to all types of environments. Among the stable hybrids, five hybrids C3, C11, C13, C16 and C21 were significantly more yield plant-1 and more number of marketable fruits  plant -1 over the best check Chhaya. Whereas, for ascorbic acid content 5 hybrids i.e C2, C4, C11, C14 and C18 to be stable over the check Utkarsha. C3 was showed stable performance over best check Chhaya for the trait fruit and shoot borer infestation on shoots and four hybrids C3, C11, C13 and C21 were found to be stable for the trait fruit and shoot borer infetstation on fruits over the best check Chhaya.

Keywords :   Brinjal, environment, genotypes, hybrids, quality parameters, stability, yield

  • INTRODUCTION

    Brinjal is one of the popular vegetable crops grown in the subtropics and tropics, therefore, can play a vital role in achieving the nutritional security. India has more diversity of the crop, being the origin place of the crop.

    The area under the brinjal cultivation in India is about 757.57 thousand hectares with production of 13153.52 thousand metric tonnes. The productivity was 17.36 metric tonnes per hectare. (Anonymous, 2020).  Being an important source of plant-derived nutrients, the identification crop of brinjal genotypes with higher nutrients and better consumer preference could be beneficial for society, particularly for poor consumers (Gogoi et al., 2018, Shankar et al., 2022). Brinjal is a stable vegetable high in nutritive value. It is low in fat and high in dietary fiber. It contains mostly water, some protein and carbohydrates besides it is a good source of nutrients such as ascorbic acid, vitamin K, niacin, vitamin B6, pantothenic acid and rich in minerals like Ca, Mg, P, K and Fe. Nutritive value of brinjal is well compared with tomato (Suneetha et al., 2006, Tiwari and Lal, 2014, Dhaka et al., 2017, Akhtar et al., 2019 and Djidonou et al., 2020). They are also known to have alkaloid solanine in roots and leaves. Some medicinal uses of brinjal include treatment of diabetes, asthma, cholera and bronchitis. Fruits and leaves are administered to lower blood cholesterol levels. It is rich in total water soluble sugars, free reducing sugars, amide proteins among other nutrients. Amino acid content is higher in purple varieties. It also has Ayurvedic properties. The fruits are excellent remedies for those suffering from lever troubles (Kumari et al., 2020). Extracts of brinjal are known to have significant effect in reducing blood and liver cholesterol rates (Karak et al., 2012, Dhakre and Bhattacharya, 2013, Taher et al., 2017, Kumar et al., 2018).

    Phenotypically stable genotypes (varieties/hybrids) are of high importance, because environmental condition differs from season to season. Wide adaptation to a particular environment and constant performance of suggested genotypes is one of the main objectives in breeding programme. Brinjal is grown round the year and is highly influenced by diverse agro-climatic conditions (Mehta et al., 2011, Dia et al., 2016, Taher et al., 2017, Akhtar et al., 2019). Therefore, it is necessary to improve varieties or hybrids having stable performance through environments. Precise knowledge on the nature and magnitude of genotype x environment interactions is important in indulgent the stability in yield of a particular variety or hybrid before it is being recommended for a given situation. Testing of genotype under different environments differing in unpredictable variation is a known approach for selecting stable genotypes. In order to identify stable genotypes, the genotype by environment interactions must be partitioned into stability statistics that are assignable to each genotype evaluated across a range of environments. Stability indices have allowed researchers to identify widely adapted genotypes for use in breeding programmes (Chaurasia et al., 2005, Dhakre and Bhattacharya, 2013, Dia et al., 2016, Raghavendra et al., 2017a, Kumar et al., 2017, Koundinya et al., 2019, Kumari et al., 2020, Khankahdani et al., 2021).

    In Telangana, brinjal is grown as a vegetable crop under varied climatic conditions, it is necessary to develop varieties or hybrids having stable performance over varied environments. However, a very scanty work is being reported regarding the stability analysis of quality and yield traits in brinjal in and outside the country. Therefore, the present investigation was carried out to determine the stable genotypes both in terms of yield as well as qualitative traits which are suitable to Telangana sate.


  • MATERIALS AND METHODS

    Thirty genotypes of Brinjal (Solanum melongena L.) were assessed over three environments i.e., kharif (June–October) of 2014, rabi (October–February) of 2014 and Summer (February–May) of 2015 at Vegetable Research station, ARI, Rajendranagar, Hyderabad, Telengana state, India. The farm is located at an altitude of 542.6 m above mean sea level. Geographically it lies at latitude of 17.19° N and a longitude of 79.23° E. The study materials were developed by a randomized complete block design (RCBD) with three replications. The seeds were sown in the nursery during the second week of June, 2014, first week of October, 2014 and February, 2015 the seedlings were transplanted 35 days after sowing in a randomized block design at 50×50 cm2 spacing with three replications. Standard cultural practices were followed to raise the normal crop. The data were recorded on five randomly selected plants in each treatment over replications for eight characters viz., days to first flowering, days to first fruit harvest, days to last fruit harvest, number of marketable fruits plant-1 (g), marketable yield plant-1 (g), Ascorbic acid content (mg 100 g-1), fruit and shoot borer infestation on shoots (%) and fruit and shoot borer infestation on fruits (%).

    The genotype (G) ×environment (E) interaction was calculated by the pooled analysis of variance. The mean value of genotypes for unlike traits under different environments was castoff for this analysis. The analysis of stability parameters was assessed by the model suggested by Eberhart and Russel (1966).

    Yij=m+Bi Ij+∂ij

    Where: Yij is mean of ith variety in jth environment, m is mean of entire varieties over all environments, Bi is regression coefficient of ith variety on environmental index; which measures the response of this variety to varying environments, Ij is environmental index i.e. the deviation of the mean of all the varieties at a given environment from the overall mean, and ∂ij is the deviation from regression of ith variety at jth environment.


  • RESULTS AND DISCUSSION

    Pooled analysis of variance over environments as presented in Table 1 indicates that variances due to brinjal genotypes were highly significant for both the traits which revealed the presence of genetic variability among the genotypes.


    The mean sum of square due to environments was significant for all the characters which indicated genotypes interacted with environments significantly. The presence of genotypes ×environment interaction were also significant for all the traits which provides an opportunity for selecting suitable genotypes with high mean for the trait of interest except non-significant mean square value for  days to first flowering and days to last fruit harvest which means less variation and least scope of selection for this trait. The presence of both significant and non-significant interactions indicated the differential response of genotype to various environment conditions. Significant mean squares due to environment+ (genotype×environment) were observed for six characters which emphasizing the existence of G×E interactions for these traits. Similar reports were earlier made by Vaddoria et al.,2009a, Tiwari and Lal, 2014 and Koundinya et al., 2019.

    The linear contribution of the environmental effects on the performance of genotypes was significant for all the traits under study. Significant differences due to G×E (linear) indicated that different genotypes differ genetically in their response to different environments except for days to first flowering and days to last fruit harvest which is in accordance with the observations of Kumar et al., 2017 and Kumari et al., 2020 in brinjal. The mean sum of squares for pooled deviation was significant for five characters except days to first flowering, number of marketable fruits per plant and fruit and shoot borer infestation on fruits indicating that the deviation from linear regression contributed substantially towards the difference in stability of genotypes. Similar findings were also reported by Gogoi et al.,2018 and Pacheco et al., 2020.

    To assess the stability of genotype regression coefficient (bi) is considered as a parameter of response of a particular genotype and deviation from regression (S2di) as a parameter of stability. Hence, the mean performance of genotypes, along with both parameters i.e., regression coefficient (bi) and deviation from regression (S2di) were estimated and are presented in Table 2. The genotypes with regression coefficient (bi) near to unity (1) and non-significant deviation from regression (S2di) were considered as stable genotypes as their performance can be predicated over the environment.


    Days to first flowering among the genotypes ranged between 41.85 (C3) to 51.32 (P1) days with a general mean of 45.11 days (Table 2). The stability parameters high mean, bi=1 and S2di=0) for days to first flowering, showed that out of 30 genotypes, one hybrid C3 (41.85 days) and parent P6 (41.99 days) recorded significantly earlier flowering compared with best check Chhaya (45.68 days) with unit regression values (bi=1). Hence, these genotypes are considered to possess the average stability whose performance does not change with the change in environments. The parent P4 (44.15 days) and C9 (46.62 days) recorded more than unity (bi>1) and thus possess less than the average stability and is adaptable to favourable environments.

    For days to first fruit harvest, all the genotypes recorded non-significant deviation from regression (S2di=0) values (Table 2). The days to first fruit harvest among the genotypes was ranged from 67.63 (P1) to 59.32 days (C3) with an overall mean of 62.33 days. Among the stable hybrids, one hybrid C3 (59.32 days) with significantly low mean value for days to first fruit harvest compared to best check Chhaya (63.30 days), recorded unit regression coefficient (bi) value and hence possess the average stability and is widely adaptable. The cross C10 (61.05 days) and parent P5 (62.01 days) had low mean value for days to first fruit harvest and recorded regression coefficient of more than one (bi>1) and hence are adapted to favourable environments. These results were consonance with the findings of Sabolu et al., 2014, Shalini, 2016 and Kanakahdani et al., 2021.

    Number of days to last fruit harvest among the genotypes varied from 136.14 (P3) to 151.01 (C21) days with a general mean of 143.20 days (Table 3). Out of 30 genotypes 28 genotypes recorded non-significant deviation from regression (S2di) values i.e., the genotypes are statistically within the range of minimum deviation from regression and whose performance can be predicted. Among the stable genotypes, four hybrids C3 (148.59), C10 (148.19), C16 (149.94) and C21 (151.01) and one parent P1 (149.94 days) with significantly superior to the best check Utkarsha (141.99 days) recorded unit regression coefficient values (bi=1) and hence are adaptable to different environments. Similar results were reported by Rodriguez- Burruezo et al., 2012 and Bhushan and Samnothra, 2017.


    Number of marketable fruits per plant ranged from 21.80 (P7) to 37.06 (C3) with an overall mean of 30.47. Five hybrids viz., C3 (37.06), C5 (34.64), C13 (34.23), C16 (34.40) and C21 (33.94) possessed significantly more number of marketable fruits per plant than the best check Chhaya (31.93) and these are also recorded regression values equal to one. Hence, they are considered to be stable, which can be recommended for wider environments.

    The marketable fruit yield per plant of the entries ranged from 1311.00 g (P3) to 2207.10 g (C11) with an overall mean of 1611.80 g (Table 4). The nonlinear component was significant for two hybrids (C8 and C15) denoting unpredictable performance of the genotypes over environments. The rest of genotypes registered the non-significant deviation from regression. Among the stable hybrids, five hybrids C3 (1879.00 g), C11 (2207.10 g), C13 (1952.70 g), C16 (1805.90 g) and C21 (2003.40 g) were significantly more yield per plant over the best check Chhaya (1715.00 g). These hybrids were recorded stable performance in wider environments. Parent P1 (1548.30 g) registered more than one of bi value and hence, is adaptable to favourable environments. The hybrid C19 (1652.80 g) exhibited less than one of regression coefficient value and is considered to be adaptable to poor environments. These results were in agreement with the previous observations in brinjal of Vaddoaria et al. (2009 b), Mehta et al. (2011) and Kumar et al. (2017).

    The ascorbic acid content among the genotypes ranged from 4.74 mg 100 g-1 (P2) to 10.68 mg 100 g-1 (C4) with an overall mean of 7.21 mg 100 g-1 (Table 4). Out of 30 genotypes, 24 genotypes registered the non-significant deviation from regression hence these genotypes performance was predictable. Among the stable hybrids, five hybrids viz., C2 (8.39), C4 (10.68), C11 (10.35), C14 (10.15) and C18 (10.41) recorded significantly more ascorbic acid content than the best standard check Utkarsha (7.77 mg/100g) and these were adaptable to wider environments. Similar observation was also made by Mehta et al.(2011) and Stommel et al. (2015) and Tembhurne and Rao, 2013.


    The fruit and shoot borer infestationon shootsranged from 11.67% (C3) to 16.86% (C9) with an overall mean of 15.05% (Table 5). Twelve genotypes exhibited significant deviation from regression indicating the preponderance of unpredictable component of G×E interaction. One hybrid C3 (11.67%) was significantly superior to the best check Chhaya (14.39%) which one also had the unit regression value hence it was suitable for wider environments.


    Among the stable genotypes, one parent P1 (13.29%) and three hybrids viz., C1 (14.35%), C10 (13.86%) and C21 (14.16%) showed significantly on par resistance to fruit and shoot borer infestation on shoots along with the best check Chhaya (14.39%) with unit regression values (bi). Hence, these genotypes are considered to possess the average stability whose performance does not change with change in environments. The hybrid C4 (15.72%) recorded less than unit of bi value and thus possessed more than average stability and is adaptable to poor environments, whereas hybrid C11 (15.16%) exhibited more than unit bi value and considered to have less than the average stability and is adaptable to favourable environments. These results were in agreement with the previous observations in brinjal of   Kumar et al. (2017) and Koundinya et al. (2019).

    Among the genotypes the fruit and shoot borer infestation on fruits varied from 18.56% (C3) to 27.23% (P7) with a general mean of 24.13% (Table 5). Out of 30 genotypes 28 genotypes registered the non-significant deviation from regression hence these genotypes performance was predictable. Among the stable hybrids, four hybrids C3 (18.56%), C11 (20.62%), C13 (21.05%) and C21 (21.17%)  with  significantly less fruit and shoot borer infestation on fruits than the best check Chhaya (23.07%) recorded unit regression coefficient values and hence possess the average stability and are widely adaptable. The hybrid C20 (22.95%) and parent P1 (22.22%) displayed significantly on par performance with best check Chhaya (23.07%) and average stability with unit regression values. The hybrids C16 (21.46%) and C19 (22.62%) with less than one of bi values, possess more than average stability and are adaptable to poor environments.

    Considering the stability for yield and quality concurrently, C3, C11, C13 and C21  were found most promising to an extent under specific environments and can be recommended for general cultivation under Telangana state (Table 6). The results are in consonance with the findings of Bhushan and Samnotra, 2017, Kumari et al. (2020) who also reported higher fruit yield during kharif-rabi seasons as compared to summer season.


  • CONCLUSION

    Based on the performance of  yield and  yield attributing traits in brinjal, C3, C11, C13 and C21 could be identified as the most promising and stable genotypes that could be grown in three seasons. These genotypes may be further utilized in breeding programme for developing stable varieties. Present study outstandingly brought out the fact that advantages of F1’s may not only in the area of increased yield and quality, but also for greater stability in production over three seasons.


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Cite

1.
Chaitanya V, Reddy &&VSK. Phenotypic Stability for Fruit yield and its Components of Brinjal IJBSM [Internet]. 27May.2022[cited 8Feb.2022];13(1):438-447. Available from: http://www.pphouse.org/ijbsm-article-details.php?article=1608

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