Genotype × Environment Interaction Studies in Colored Capsicum (Capsicum annum L. var. grossum Sendt.)

Genotype × Environment Interaction Studies in Colored Capsicum (Capsicum annum L. var. grossum Sendt.)

Amreena Sultan¹ , Baseerat Afroza¹ , Ambreen Nabi¹ , Afroza Akhter¹ , Insha Javeed¹ , Sayed Azrah Indrabi , Najmu Sakib² , Bilal Ahmad Lone^3* , Aijaz Nazir³ , Ishfaq Aabidi⁴ , Asma Fayaz⁵

¹Division of Vegetable Science, SKUAST-Kashmir, Shalimar Campus-190025, Srinagar, J&K, India

²Division of Plant Pathology, SKUAST-Kashmir, Shalimar Campus-190025, Srinagar, J&K, India

³AMFU, SKUAST-Kashmir, Shalimar Campus-190025, Srinagar, J&K, India

⁴Division of Plant Breeding and Genetics, SKUAST-Kashmir, Shalimar Campus 190025, Srinagar, J&K, India

⁵Division of Agronomy, Chandigarh University, India

Corresponding Author Email: alonebilal127@gmail.com

DOI : http://dx.doi.org/10.53709/CHE.2021.v02i04.025

Abstract

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Introduction

Bell pepper (Capsicum annum L. var. grossum Sendt.) also known as Sweet pepper, green pepper and Shimla Mirch is grown worldwide for its delicate taste, pleasant flavour and color. It is also the most leading crop under protected structures. Some cultivars of the Sweet pepper plant produce immature fruits in different colours, including red, yellow, orange, green, chocolate/brown, vanilla/white, and purple.

The area and production of capsicum in India are 46 thousand hectares and 327 thousand metric tonnes respectively [1]. In J&K, sweet pepper is grown as a summer crop over about 1.05 thousand hectares with an annual production of 23.16 thousand metric tonnes [2]. The consumption of sweet pepper is on the increase all over the world. It has become a multibillion-dollar industry, as well as a part-time hobby for home gardeners. Moreover, the colored bells command a higher market price and provide an alternate channel for this crop.

Due to changing food habits of people, increasing health concerns, and knowledge about the benefits of consuming colored vegetables, colored sweet pepper is gaining popularity day by day. The farmers are shifting towards the cultivation of colored capsicums. The climatic conditions of the Kashmir valley are ideal for the production of fruits as well as for quality seed production, which offers excellent scope for production and export of quality seed to the rest of the country. It is necessary to identify the varieties showing stable performance over varied environmental conditions of Kashmir.

 Precise knowledge of the nature and magnitude of genotype × environment interactions is essential in understanding the stability in yield of a particular variety before it is recommended for cultivation. Stability indices have allowed researchers to identify widely-adapted genotypes for use in breeding programmes. The stable genotypes, thus, identified may subsequently be used for cultivation in a wide range of environments and also serve as base materials for future breeding programmes.

The genotype × environmental (G × E) interactions are a significant concern to plant breeders for developing improved cultivars. For a cultivar to be commercially successful, it must perform well across a range of environments in which the cultivar has to be grown. Since genotype-environment interaction has a masking effect on the performance of genotypes, the relative ranking of the genotypes does not remain the same when tested over the number of environments [3]. Though, conventional statistical procedures are available to scale-out such interaction effects, but these do not pin-point genotypes that have minimum interaction with the environment and thus, provide information on stability [4]. Precise knowledge of the nature and magnitude of genotype × environment interactions is essential in understanding the strength in yield of a particular variety before it is recommended for cultivation. Stability indices have allowed researchers to identify widely-adapted genotypes for use in breeding programmes. The stable genotypes, thus, identified may subsequently be used for cultivation in a wide range of environments and also serve as base materials for future breeding programmes.

The study aimed to evaluate the colored capsicum genotypes under open field conditions in Kashmir valley and to work out the stability parameters for yield and its components over a set of random environments.

Materials and Methods

The present investigation was carried out to determine adaptive potential, genotype × environment interaction, and phenotypic stability of fifteen colored capsicum genotypes, however, the basic materials consisted of fifteen diverse genotypes of colored capsicum (Capsicum annuum L. var. grossum) viz., SH-SP-1, SH-SP-2, SH-SP-3, SH-SP-4, SH-SP-5, SH-SP-7, SH-SP-8, SH-SP-9, SH-SP-10, SH-SP-11, SH-SP-12, SH-SP-14, SH-SP-15, SH-SP-16 and Nishat-1 (Check). These lines have been maintained by the Division of Vegetable Science, SKUAST-K, Shalimar.

 The experimental materials were evaluated in three diverse environments viz., Experimental Farm of Division of Vegetable Science, SKUAST-K, Shalimar; Krishi Vigyan Kendra, Budgam and Faculty of Agriculture, Wadura during Kharif 2018. Since the climate of Kashmir is continental type, characterized by mild summers. The hottest month in Kashmir in July, and maximum rainfall is received in spring, ie. from mid-March to mid-May. The Vegetable Experimental Farm, Shalimar is situated at 34° 1´ North latitude and 74° 89´ East longitude, Krishi Vigyan Kendra, Budgam is located at 34° 06´ North latitude and 74°.79´ East longitude and Regional Research Station & Faculty of Agriculture (RRS & FOA), Wadura is located at 34° 28´ N latitude and 74° 55´ E longitude. The experiments were laid out in a completely randomized block design with three replications at each location. The observations were recorded on plant height (cm), plant spread (cm), number of secondary branches plant^-1, number of fruits plant^-1, average fruit weight (g), fruit length (cm), fruit diameter (cm), pedicel length (cm), flesh thickness (mm), average fruit yield plant^-1 (kg), average fruit yield  plot^-1 (kg), number of seeds fruit^-1, average seed weight fruit^-1 (g), seed yield plant^-1 (g) and seed yield  plot^-1 (g). The observations thus recorded were subjected to standard statistical and biometrical analysis and the results thus obtained are described as under.

Results and discussion

Analysis of variance for the individual as well as over environments (Table 1a and b) revealed highly significant differences among genotypes for all the traits under study, indicating the presence of a sufficient amount of variability in the genotypes. This provides an ample opportunity for selecting suitable genotypes with a high mean for all the traits of interest. These results are following those of [5,6,7]. The mean squares due to environments were significant for all the traits indicating the presence of variable environments in the expression of the characteristics. The mean sum of squares due to Genotype × Environment interaction was significant for all traits indicating that the genotypes responded differently to environmental changes. These results conform with the findings of [8,9,6,5,7]. Since there was significant genotype by environment interaction, it will lessen the usefulness of genotype means as a single parameter to measure stability [10,11].

Table 2 depicts that the Environment E₁ i.e. (Experimental Farm of Division of Vegetable Science, SKUAST-Kashmir, Shalimar) was the most favorable environment for the expression of all traits except flesh thickness as indicated by the highest environmental index for plant height (0.901), plant spread (0.566), number of secondary branches plant^-1 (0.156), number of fruits plant^-1 (0.731), average fruit weight (3.325), fruit length (0.112), fruit diameter (0.085), pedicel length (0.061), fruit yield plant^-1 (0.090), fruit yield plot^-1 (0.913), number of seeds fruit^-1 (1.790), average seed weight fruit^-1 (0.046), seed yield  plant^-1 (3.303), seed yield plot^-1 (33.034). The Environment E₂, i.e., Vegetable Farm of Krishi Vigyan Kendra, Haran, Budgam, was most favorable for expression of flesh thickness (0.045). E₂ was also favorable for expression of various other traits like plant height (0.090), plant spread (0.366), number of secondary branches plant^-1 (0.102), number of fruits plant^-1 (0.018), average fruit weight (0.030), fruit length (0.020), fruit diameter (0.008), average fruit yield plant^-1 (0.009), average fruit yield plot^-1 (0.002), number of seeds fruit^-1 (1.141), average seed yield fruit^-1 (0.013), seed yield plant^-1 (0.226) and seed yield plot^-1 (2.239). The Environment E₃, i.e., Regional Research Station and Faculty of Agriculture, Wadura, was found to be unfavorable for expression of all traits. The influence of various environments, as depicted by estimates of environmental indices, was also reported by [5].

The pooled analysis of variance for stability (Table 3a and b) revealed significant variation among genotypes for all traits indicating the presence of a large amount of variability in the material chosen for study. The mean sum of squares due to environments were significant for all traits except flesh thickness, indicating that environments selected to conduct the study were variable and influenced the expression of traits. Similar results have been reported by [5,12,7], etc. Environmental (linear) component of variance was significant for all traits except flesh thickness indicating that environmental effects were predictable. These results agree with the findings of [9,5,12,7]. The linear component of genotype × environment was significant only for plant spread, number of fruits plant^-1, average fruit length, fruit diameter, pedicel length, number of seeds fruit^-1, average fruit yield plant¹, average fruit yield plot^-1, average seed yield plant^-1 and average seed yield plot^-1indicating the significant linear response of genotype to environmental changes for these traits. The non-significant effect of genotype × environment (linear) for rest of the traits indicated that the different genotypes did not differ genetically in their response to different environments. The linear component was found to be greater in magnitude than the corresponding non-linear component for almost all the traits suggesting that the performance of genotypes across environments could be predicted with greater precision for these traits. The pooled deviation was significant for average fruit weight and flesh thickness, indicating the important contribution of the non-predictable component in respect of these traits. Similar results have been reported by [13,5,12,7].

According to the definition of a stable genotype given by [14], the non-significant linear (bi) and non-linear (S²di) estimates indicate average stability of a genotype across different environments, whereas significant ‘bi’ and non-significant ‘S²di’ estimates indicate stability to specific environments. And the significant ‘S²di’ estimate, irrespective of whether the corresponding ‘bi’ estimate is significant or non-significant, suggest that the behaviour of a genotype is unpredictable. Further, variation among regression coefficients for a given character indicates that the genotypes also differ for the degree of response to changing environments, suggesting that alleles which confer broader adaptation may be necessary for stability across environments [15].

In the present study, the estimates of regression coefficients (Table 4a, b and c) for fifteen genotypes exhibited a wide range indicating that the genotypes possess a different set of alleles for adaptation across environments. The present investigation also revealed that the degree of response to changing environments did not coincide for all the traits under evaluation for a given genotype, indicating the role of a different set of alleles for conferring adaptation across environments for various traits.

As indicated by the stability parameters the genotypes that were well adapted to all the environments (Table 5 and 6) were SH-SP-1, SH-SP-3, SH-SP-4, SH-SP-5, SH-SP-7, SH-SP-14, SH-SP-16 and Nishat-1 for plant height;  SH-SP-1, SH-SP-3, SH-SP-5, SH-SP-7, SH-SP-8, SH-SP-10, SH-SP-12 and SH-SP-14 for plant spread; SH-SP-1, SH-SP-7, SH-SP-14, SH-SP-15 and Nishat-1 for number of secondary branches plant^-1; SH-SP-4, SH-SP-7, SH-SP-8, SH-SP-10, SH-SP-14, SH-SP-16 and Nishat-1 for number of fruits plant^-1; SH-SP-2 and SH-SP-12 for average fruit weight; SH-SP-1, SH-SP-2, SH-SP-3, SH-SP-7, SH-SP-8, SH-SP-9, SH-SP-12 and Nishat-1 for fruit length; all genotypes except SH-SP-3 for fruit diameter; SH-SP-2, SH-SP-3, SH-SP-7, SH-SP-8, SH-SP-9 and SH-SP-16 for flesh thickness; SH-SP-1, SH-SP-2, SH-SP-7,  SH-SP-12 and Nishat-1 for pedicel length; SH-SP-1, SH-SP-2, SH-SP-5, SH-SP-7, SH-SP-8, SH-SP-9, SH-SP-11, SH-SP-12 and SH-SP-15 for average seed weight; SH-SP-1, SH-SP-2, SH-SP-5, SH-SP-9, SH-SP-11, SH-SP-12, SH-SP-15 and SH-SP-16 for number of seeds fruit^-1; SH-SP-1, SH-SP-7, SH-SP-14 and SH-SP-16 for average fruit yield plant^-1; SH-SP-1, SH-P-7, SH-SP-8, SH-SP-14 and SH-SP-16 for fruit yield plot^-1 and SH-SP-1, SH-SP-4, SH-SP-8, SH-SP-11, SH-SP-14, SH-SP-15, SH-SP-16 and Nishat-1 for seed yield plant^-1 as well as seed yield plot^-1.Similar results with respect to various traits have been reported by [13,12,7,16,17].

The genotypes showing above-average stability as shown by significant but less than unity value of regression coefficient were SH-SP-1 for the higher number of fruits plant^-1, SH-SP-3 for larger fruit diameter; SH-SP-4, SH-SP-5 and SH-SP-14 for longer pedicel length; Nishat-1 for high average fruit yield plant^-1 and fruit yield plot^-1.

The genotypes exhibiting below average stability as depicted by bi values significantly greater than unity and non-significant deviation from regression, SH-SP-11 for late flowering; SH-SP-8 and SH-SP-11 for lesser plant height; SH-SP-4 for higher plant spread; SH-SP-5 for lower number of secondary branches plant^-1 and SH-SP-7 for higher seed yield plant^-1 and seed yield plot^-1. These genotypes were adapted explicitly to favourable environments in respect of these traits. Similar results with respect to various traits have been reported by [13,12,18,7].

The genotypes which show unpredictable behaviour (Table 7) as depicted by significant deviation from regression irrespective of regression coefficient whether it is significant or not were SH-SP-10 for the number of secondary branches plant^-1; SH-SP-5, SH-SP-7, SH-SP-8, SH-SP-9 and SH-SP-15 for average fruit weight; SH-SP-12 for flesh thickness; SH-SP-8 for fruit yield plant^-1 and fruit yield plot^-1.

The genotypes that were found to be   stable in all environments for most of the traits were SH-SP-1, SH-SP-7, SH-SP-8, SH-SP-14, and SH-SP-16, among which SH-SP-16 was found to be most durable.

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Conclusion

It could be concluded from the present investigation that a sufficient amount of genetic variation existed in the set of materials used for current studies, which could be used in future breeding programmes for bringing about improvement in the crop. The genotypes viz. SH-SP-1, SH-SP-2, SH-SP-4, SH-SP-7, SH-SP-14, SH-SP-16 and Nishat-1, showing stable performance over environments for most of the traits under study, could be recommended to farmers for cultivation after further testing and evaluation.

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