Stable Genotypes Research Articles

Assessment of genetic diversity and stability performance of 38 genotypes of onion (Allium cepa L.)

ABSTRACT The current study was conducted at four different locations in India in order to evaluate the genetic diversity and stability of 38 genotypes of onion (Allium cepa L.) under various climatic circumstances. The genetic diversity was evaluated through cluster analysis which divided thirty-eight genotypes into two major clusters (cluster I and cluster II) each of which had 20 and 18 genotypes. As per Additive Main-effects and Multiplicative Interaction (AMMI) model, five genotypes from cluster I, namely Accession (Acc.) 1217, 1209, Bhima Shweta, 1639, and 1640 were shown to have greater adaptation, with regressive values of 0.14, 0.74, 1.51, 2.11, and 2.17, respectively and marketable yields ranging from 176.23–227.31 q/ha. The mean performance of genotypes in individual clusters revealed that the cluster I and cluster II genotypes showed maximum marketable yield, total yield and average bulb weight. Based on the findings of the assessment study, these stable genotypes could be utilised as parent plants in future breeding programmes to develop high-yielding onion varieties that not only showcase stability but also possess widespread adaptability, thereby enhancing overall onion crop performance.

Parametric and Non-Parametric Measures to Identify Stable and Adaptable Cotton (Gossypium Hirsutum L.) Genotypes

ABSTRACT The study, comprised seventeen genotypes, was conducted during 2016 to 2019 growing seasons. The design was Randomized Complete Block Design (RCBD) with three replications to identify adaptable cotton genotypes. Parametric and non-parametric measures, principal component analysis (PCA) and correlation among the ranks of the parameters computed using R software. The environment, genotype and genotype-environment interaction contributed 79.58, 3.2 and 15.46% of the variation, respectively, and were significant (p < 0.001). The highest (1510.7 kg/ha) and lowest (1080.1 kg/ha) seed cotton yield was recorded from G12 and G8 genotypes respectively. Coefficients of determination (R2) and superiority index (Pi) recognized G13, G11, G10 and G11, G12, G7 as the top three stable genotypes correspondingly. G10 was selected three times as superior by stability variance (σ2), wricke’s ecovalence (Wi) and deviation from regression (S2di). Seed cotton yield positively correlated with GAI, Kang’s rank-sum (KRS), Huhn and Nassar S(1), S(6) S(3) while negatively correlated with Thennarasu stability measures NP(2), NP(3) and NP(4 . The first, second and third clusters of the PCA biplot comprised, 4, 4 and 10 parameters respectively. G12, with highest seed cotton yield and good stability, was the best genotype and recommended for variety verification.

Analysis of Yield Stability in Diverse Rice Genotypes

Aims/ objectives: This study aims to improve stable and sustainable rice varieties adaptable to changing climatic conditions. It involves assessing genetic variability and Genotype X Environment interaction (G x E) among 186 diverse rice genotypes. The goal is to select genotypes with high breeding value, contributing to the development of rice varieties well-suited to varying climatic conditions. Study Design: The study employed an augmented design in a Randomized Complete Block Design (RCBD). Swarna, Madhuraj-55, Safri-17, Improved Samba Mahsuri, Thavalkannan, and IR64 checks were replicated across environments. Place and Duration of Study: The study took place in College of Agriculture, Raipur, IGKV over two years (wet season-2020 and 2021). Methodology: Analysis of genetic variability and G X E interaction among 186 rice genotypes. Execution of the experiment in an augmented design with a randomized complete block design. Replication of standard rice checks across different environments. Assessment of yield-attributing traits such as plant height, number of effective tillers, panicle length, number of grains per panicle, biological yield per plot, and grain yield. Evaluation of stability was done using univariate (Shukla stability variance, Wricke’s ecovalence, Kang stability statistic) and multivariate (AMMI yield stability index and GGE biplot) stability parameters. Selection of stable genotypes with high yield based on stability analyses. Results: Significant phenotypic variation was observed in yield-attributing traits across seasons. Genetic variability and G x E interaction effect demonstrated by variable genotype performance across environments. Univariate and multivariate stability parameters (S2i, W2i, KSi, AMMI stability value, GGE biplot) were used for stability analyses. Identification of stable genotypes with high yield across environments, including IR13f167, ARC13156, IR93354, F50, Ngalongyi, Giza 178, Arc 10159, Sadajira 19-317, Arith, IR 57920-Ac 25-2-B, Pesagro 102, Mekenzie small, Nasaenge, Kula Karuppan, Vary Gony, MR 69, Kanu Dam, IRRI 123, Sativa IRGC17083-1, Kalia, and Swarna. Conclusion: The study concludes that stability in genotype performance across diverse environments is crucial for the development of sustainable rice varieties. Genotypes with high stability and yield, as identified through stability analyses, hold potential breeding value for developing rice varieties adaptive to climate change. The stable genotypes listed, including IR13f167, ARC13156, IR93354, and others, are recommended for further breeding and development efforts to enhance rice productivity and adaptability.

Genotype by environment interaction, AMMI, GGE biplot, and mega environment analysis of elite Sorghum bicolor (L.) Moench genotypes in humid lowland areas of Ethiopia

This study aimed to evaluate high-yielding, stable sorghum genotypes and determine the ideal (representative and discriminating) testing environments for genotypes in the humid lowlands of Ethiopia. A total of forty-two sorghum genotypes were used for a field trial conducted in six different environments using a randomized complete block design. Yield stability, Additive main effect, multiplicative interaction (AMMI), and genotype and genotype by environment interaction (GGE) were computed. The AMMI analysis explained 62.85% of the G×E variance. The AMMI1 biplot revealed that (G4; Mok079 and (G16; Ba066) genotypes had higher grain yields. AMMI2 biplot suggested that genotypes (G18; Y0470),(G23;100620), (G29; PML981475), and (G11; ETSC300373-4) show higher sensitivity to environmental changes because of their strong genotype-by-environment interactions. The GGE captured 79.46% of the GGE variance, and the GGE biplot identified genotypes (G4; Mok079), (G10; Sl081) and (G16; Ba066) were the most stable genotypes whereas(G39; ETSC120051-3) was the least stable genotypes. The GGE biplot identified Assosa (AS20) as a suitable environment, whereas PW20 and JM20 were the most discriminating and non-representative environments. The GGE biplot was found to identify three main mega-environments for sorghum growing in the humid lowlands of Ethiopia., both the AMMI and GGE biplots revealed (G4; Mok079) had the highest level of adaptability to all tested environments and was approved by the National Variety Release Committee for release in 2022.

Tropomyosin1 isoforms underlie epithelial to mesenchymal plasticity, metastatic dissemination, and resistance to chemotherapy in high-grade serous ovarian cancer

Phenotypic plasticity, defined as the ability of individual cells with stable genotypes to exert different phenotypes upon exposure to specific environmental cues, represent the quintessential hallmark of the cancer cell en route from the primary lesion to distant organ sites where metastatic colonization will occur. Phenotypic plasticity is driven by a broad spectrum of epigenetic mechanisms that allow for the reversibility of epithelial-to-mesenchymal and mesenchymal-to-epithelial transitions (EMT/MET). By taking advantage of the co-existence of epithelial and quasi-mesenchymal cells within immortalized cancer cell lines, we have analyzed the role of EMT-related gene isoforms in the regulation of epithelial mesenchymal plasticity (EMP) in high grade serous ovarian cancer. When compared with colon cancer, a distinct spectrum of downstream targets characterizes quasi-mesenchymal ovarian cancer cells, likely to reflect the different modalities of metastasis formation between these two types of malignancy, i.e. hematogenous in colon and transcoelomic in ovarian cancer. Moreover, upstream RNA-binding proteins differentially expressed between epithelial and quasi-mesenchymal subpopulations of ovarian cancer cells were identified that underlie differential regulation of EMT-related isoforms. In particular, the up- and down-regulation of RBM24 and ESRP1, respectively, represent a main regulator of EMT in ovarian cancer cells. To validate the functional and clinical relevance of our approach, we selected and functionally analyzed the Tropomyosin 1 gene (TPM1), encoding for a protein that specifies the functional characteristics of individual actin filaments in contractile cells, among the ovarian-specific downstream AS targets. The low-molecular weight Tpm1.8/9 isoforms are specifically expressed in patient-derived ascites and promote invasion through activation of EMT and Wnt signaling, together with a broad spectrum of inflammation-related pathways. Moreover, Tpm1.8/9 expression confers resistance to taxane- and platinum-based chemotherapy. Small molecule inhibitors that target the Tpm1 isoforms support targeting Tpm1.8/9 as therapeutic targets for the development of future tailor-made clinical interventions.

Stability analysis and genotype X environment interaction of cotton seed and fiber yield of some cotton (Gossypium hirsutum L.) genotypes in multi-environment in Mali

Cotton is a strategically important crop for Mali. Mainly used for export, it represents 50–60 % of the value of the country's exports. Genotype by environment interaction study was carried out to identify the most stable cotton genotype(s) and the most desirable for seed cotton yield and lint yield in 35 villages across six different environments(High Valley of the Niger, New Cotton Growing Zone, North Sudan-Sahel, Old Basin, Southern Extension Zone and Zone Sikasso-Bougouni) in a dispersed block design. Where each village represented a block, giving a total of 35 blocks or replications. A combined analysis of variance showed that yields of seed cotton and fiber were significantly affected by environments (p&lt;0.05), but did not reveal any significant difference between genotypes or genotype-environment interactions. The result of GGE biplot analysis method showed that the polygonal view identified three mega-environments (ME1, ME2 and ME3) with three winning genotypes: BRS 293, NTA B149 and NTA L66, respectively, for seed cotton yield. For fiber yield, the winning genotypes were CG1, NTA L65 and BRS 293, respectively. Given that all the varieties tested have a mean fiber yield within the recommended norms, the two promising varieties NTA B149 and NTA L66 with good seed cotton yields and the standard check BRS 293 can be recommended for extension in the environments to which they have been assigned.

Exploring the genotype-environment interaction of bread wheat in ambient and high-temperature planting conditions: a rigorous investigation

The current study is carried out to find out the stable wheat genotype in ambient and high temperature planting conditions. The objective was to estimate the genotype x environment interactions through various univariates and multivariate techniques. Twenty wheat genotypes were evaluated at Jabalpur, Narmadapuram, and Sagar districts of Madhya Pradesh, India, across cropping years 2019–20 and 2021–21, considering both timely and late planting conditions. The univariate and multivariate stability analysis were performed based on per-plant grain yield and grain filling rate. Our result revealed that environment, genotype, and GEI effects were significant (P < 0.001) across all the environments. The wheat genotypes JW3288, L8, and L13 have been discerned as top performers, exhibiting remarkable stability in grain yield per plant. Similarly, for grain filling rate, genotypes L11 and L13 have emerged as superior and consistently stable performers. Notably, the AMMI and GGE models demonstrated superior effectiveness and accuracy compared to the linear regression model. In conclusion, based on thorough univariate and multivariate stability analyses, L13 emerges as the most stable genotype across all environments under both planting conditions. Consequently, L13 holds promise for inclusion in future breeding programs. It's noteworthy that Jabalpur stands out as the most discriminating and representative environment among all the conditions assessed.

Envirotypes applied to evaluate the adaptability and stability of wheat genotypes in the tropical region in Brazil

Envirotypes applied to evaluate the adaptability and stability of wheat genotypes in the tropical region in Brazil

Analysis of stability for nut yield and ancillary traits in cashew (Anacardium occidentale L.)

Cashew is cultivated in varied agro-ecological regions of India and yield levels vary with regions. Therefore, to identify stable genotype for yield, 18 genotypes were tested in four environments for nut yield and ancillary traits during 2008 to 2018 in randomized block design with two replications. The data of 6th annual harvest and cumulative nut yield of six years was analyzed employing additive main effect and multiplicative interaction (AMMI) and genotype and genotype by environment (GGE) methods. Analysis of variance for 6th annual harvest indicated significant differences (p < 0.01) for eight traits. Environments varied significantly (p < 0.01) for seven traits. Genotype by environment (G × E) interactions were significant (p < 0.01) for all traits. Analysis of variance for cumulative yield revealed significant variations between genotypes, environments, G x E interactions. Interaction principal component analysis (IPCA) 1 (84.39%) and IPCA 2 (10.27%) together captured 95% of variability. Genotypes, environments and G × E interaction were accounted for 16.18%, 4.50% and 77.22% respectively of total variation. The environment Pilicode discriminated better while Vridhachalam was representative. BPP-8 and Vengulra-7 were the winning genotypes in Bhubaneswar while Kanaka and Priyanka in Pilicode, Vengurla-4 in Jhargram and UN-50 in Vridhachalam. Therefore, promoting cultivation of these winning genotypes in the corresponding environments is highly recommended to enhance cashew nut production. As per ASV (AMMI stability value,) K-22-1 was stable genotype followed by Bhubaneswar-1. As per YSI (yield stability index), Bhubaneswar-1 was stable and high yielding followed by K-22-1 and BPP-8. Thus stable genotypes identified in this study viz., K-22-1 and Bhuvaneswar-1 are recommended for cultivation in west and east regions of India which have most cashew growing areas for increasing the cashew nut production.

Assessing the Impact of Genotype-by-Environment Interactions on Agronomic Traits in Elite Cowpea Lines across Agro-Ecologies in Nigeria

The yield of cowpea varieties is affected by environmental variability. Hence, candidate varieties must be tested for yield stability before release. This study assessed the impacts of genotypes, environments, and their interaction on the performance of elite cowpea lines for key adaptive, grain yield, and associated traits across different locations. A total of 42 elite genotypes were evaluated in five Nigerian environments, representing various savanna ecologies, during the 2021 growing season. The experimental design employed was an alpha lattice arrangement, with each genotype replicated three times. The results revealed significant differences among genotypes, environments, and genotype-by-environment interaction (G × E) for most traits, including days to maturity, 100-seed weight, and grain yield. The genotype and genotype-by-environment interaction (GGE) biplot showed G21 (IT14K-2111-2) and G25 (IT15K-2386-1) as the most stable genotypes across the five environments, G41 (IT11K-61-82) was best adapted to Ibadan and Shika, G5 (245-1) was best adapted to Bagauda and Gumel, and G30 (IT16K-2365-1) was best adapted to Bauchi. G21 (IT14K-2111-2) and G25 (IT15K-2386-1) could be recommended across the five test environments, whereas G41 (IT11K-61-82), G30 (IT16K-2365-1), and G5 (245-1) were specific to the adapted environments.

Climate change enhances stability of wheat-flowering-date

The stability of winter wheat-flowering-date is crucial for ensuring consistent and robust crop performance across diverse climatic conditions. However, the impact of climate change on wheat-flowering-dates remains uncertain. This study aims to elucidate the influence of climate change on wheat-flowering-dates, predict how projected future climate conditions will affect flowering date stability, and identify the most stable wheat genotypes in the study region. We applied a multi-locus genotype-based (MLG-based) model for simulating wheat-flowering-dates, which we calibrated and evaluated using observed data from the Northern China winter wheat region (NCWWR). This MLG-based model was employed to project flowering dates under different climate scenarios. The simulated flowering dates were then used to assess the stability of flowering dates under varying allelic combinations in projected climatic conditions. Our MLG-based model effectively simulated flowering dates, with a root mean square error (RMSE) of 2.3 days, explaining approximately 88.5 % of the genotypic variation in flowering dates among 100 wheat genotypes. We found that, in comparison to the baseline climate, wheat-flowering-dates are expected to shift earlier within the target sowing window by approximately 11 and 14 days by 2050 under the Representative Concentration Pathways 4.5 (RCP4.5) and RCP8.5 climate scenarios, respectively. Furthermore, our analysis revealed that wheat-flowering-date stability is likely to be further strengthened under projected climate scenarios due to early flowering trends. Ultimately, we demonstrate that the combination of Vrn and Ppd genes, rather than individual Vrn or Ppd genes, plays a critical role in wheat-flowering-date stability. Our results suggest that the combination of Ppd-D1a with winter genotypes carrying the vrn-D1 allele significantly contributes to flowering date stability under current and projected climate scenarios. These findings provide valuable insights for wheat breeders and producers under future climatic conditions.

Adaptability and stability of black bean genotypes with normal and early cycle

Adaptability and stability of black bean genotypes with normal and early cycle

Deciphering G × E Interaction of Photo-insensitive Horsegram [Macrotyloma uniflorum (Lam.) Verdc.] Mutants using AMMI and GGE Biplot Models

Background: Photosensitivity in horsegram restricts horizontal expansion in its cultivable area. Using induced mutagenesis, photo-insensitive mutants of a popular variety PAIYUR 2 were evolved. Methods: The mutants were experimented with across seasons and locations. All the experiments were conducted in the randomized block design with five replications. The AMMI and GGE biplot models were employed to tag the best-yielding and stable genotype(s). Result: The ANOVA indicated significant effects of genotypes (G), environments (E) and their combined genotype × environment interaction (G × E) for all the experimental traits implying a large range of variation. The interaction effect in AMMI has been partitioned into several principal components. Of the six Principal Components (PC), the first PC explained the major variation. It is 60.20% for the number of clusters per plant, 99.60% for days to fifty percent flowering, 92.57% for the number of pods per cluster, 96.63% for the number of pods per plant, 99.66% for days to maturity and 96.34% for yield per hectare. AMMI and GGE biplot analyses helped to identify the best performing and stable photo-insensitive mutants TNAU-HG-DM-001 and TNAU-HG-DM-004 for further exploitation.

Cultivar variability and stability of vegetable-type soybean for seed yield and pod shattering

Vegetable-type soybean, also known as “edamame”, was introduced to South Africa in 2009 to serve as an alternative high-protein cash crop for production by small-scale growers. Since their introduction, cultivars have been evaluated and morphologically characterised to identify suitable cultivars for production in specific environments and also to gather data for future breeding programmes. The objective of this study was to investigate genotype by environment interaction and cultivar stability through additive main effects and multiplicative interaction (AMMI) as well as genotype plus genotype by environment biplot analysis across seven environments and two growing seasons in 15 vegetable-type soybean cultivars for seed yield and pod shattering. Based on AMMI stability values and genotype stability index scores, cultivars AGS418, AGS457 and AGS458 were the most desirable in terms of stability and performance for both seed yield and pod shattering resistance and could be considered for commercial release. Of all vegetable-type soybean cultivars studied, cultivar AGS354 had the best pod shattering resistance and it showed specific adaptation to the Winterton, and Mariannhill October and Mariannhill November planting dates mega-environment in terms of seed yield. This cultivar should be considered for production in these environments. Ladysmith was identified as the most suitable environment for selecting and breeding cultivars with wider adaptability.

The Stability of Sugar Yield in Promising Sugarcane Genotypes (Saccharum officinarum L.)

Stability analysis is one of the most important steps that a breeder should use to release a new variety for a region. To identify and introduce the best sugarcane genotypes, an experiment with 26 promising Sugarcane genotypes along with four commercial varieties was planned and implemented during 2017–2019. This study aimed to determine a promising stable sugarcane genotype for the sugarcane-growing areas of Khuzestan Province in Iran. The effects of genotype × environment interactions on the sugar yield of 26 promising sugarcane genotypes and four standard varieties as controls were investigated for the new plant (P), first ratoon (R1), and second ratoon (R2) at three locations (Amir Kabir, Imam Khomeini, and Mianab) for three cropping seasons. For the final analysis every year, quantitative and qualitative characteristics were measured by sampling 10 stalks of each genotype in each replication and each experiment. A combined analysis was conducted with regard to the fixed effects of the genotype and environment and the random effect of the year. The sugar yield stability of the genotypes was evaluated based on nonparametric, ecovalence, Shukla, simultaneous selection, and AMMI statistical methods. According to the results, g2,g4,g5,g7,g11,g12,g14,g16,g19,g20,g21,g23,g24,g26,g27,g28,g29, and g30 are the most stable genotypes based on statistical analyses. As these genotypes have general stability and can be cultivated in three crops, they are introduced for commercial cultivation. According to an AMMI biplot, genotype g6 was considered to have special adaptation to the first location (Imam Khomeini), genotypes g8 and g15 were specifically adapted to the second location (Amir Kabir), and genotypes g10,g17, and g22 showed special adaptation to the third location (Mianab). Therefore, these genotypes are introduced for commercial cultivation in the mentioned locations.

Assessment of Genotype X Environment Interaction and Seed Yield Stability in Sesame (Sesamum indicum L.) Using AMMI Analysis

Sesame is considered as an important oilseed crop in terms of nutritional and medicinal properties. Seed yield of any crop is highly labile to environmental factors, which may cause significant reduction in the yield performance. To get substantial stable yield production in crops like sesame, there is urgent need to identify stable genotypes that perform well under different environmental conditions. Thus, the present study has been planned with the aim to assess genotype x environment interaction (GEI) and stability analysis for seed yield per plant using additive main effect and multiplicative interaction (AMMI) model. The experiment involved evaluation of 20 sesame genotypes in randomized complete block design (RCBD) with three replications under three artificially created environments by providing different levels of fertilizers. The environment wise analysis of variance (ANOVA) revealed significant genotypic variance across all the environments. Additionally, AMMI ANOVA revealed significant environment, genotypes and GEI components. GEI component was further classified to interaction principal components (IPC), IPC1 and IPC2, which explain 66.18% and 33.82% of variance due to GEI, respectively. Further, through different stability indices (ASV and GSI) and AMMI biplots, it was revealed that genotype RT-346 and RT-351 are considered as stable genotypes and these can be further recommended to farmer under varying fertility conditions.

Unraveling stability in rice genotypes for resistance against leaffolder (Cnaphalocrocis medinalis) under varied environmental conditions

Unraveling stability in rice genotypes for resistance against leaffolder (Cnaphalocrocis medinalis) under varied environmental conditions

Variety × Environment interactions for essential oil yield and cultivar recommendations of lemongrass in drought-prone areas

Lemongrass (Cymbopogon spp.), is a well-known aromatic grass distinguished by its unique lemon scent. The importance of lemongrass in gastronomic, pharmacological, and cosmetic uses is examined in this research paper. The chemical makeup of lemongrass is discussed, emphasizing the presence of citral and other bioactive chemicals that give the herb its flavor, aroma, and medicinal qualities. The study highlights the possible advantages of growing lemongrass in semi-arid areas like Lalitpur (U.P.) and Datia (M.P.) in the Indian state of Bundelkhand, since it may flourish there with little water and improve soil fertility. Five lemongrass varieties—Nima, Praman, CIMAP-Suwarna, Krishna, and CIM-Shikhar—are compared in the study according to their morphological traits, oil yield, herb yield, and citral content. Results demonstrate that the total citral content and essential oil output of the two kinds, LG1 and LG4, are constant between the two genotypes, LG3 and LG5, respectively, across the locations. Regarding the stable types, the location-specific recommendations for essential oil yield were LG1 and LG3, while LG2 and LG4 were suggested for the total citral content. With a few exceptions in the first and second locations, where some traits showed low to medium heritability, most traits in the different sites had broad-sense heritability that was generally high. In all three locations, there were substantial percentages of genetic advance over mean (GAM) for specific features. The retention of genotype stability depends on these characteristics. These cultivars possess advantageous characteristics for growing in the Bundelkhand area, potentially providing farmers with financial gains and improving land in semi-arid regions. The findings highlight the importance of lemongrass as a useful plant with a wide range of applications and aid in the selection of cultivars that are appropriate for a certain area.

Stability Analysis for Biological Nitrogen Fixation and Seed Yield in Mungbean [Vigna radiata (L.) Wilczek] Genotypes

Background: Mungbean is a versatile legume crop grown in various cropping systems. Despite its adaptability, the yield is hindered by the low yielding capacity of undifferentiated varieties and their limited ability to fix nitrogen. The current study aims to identify stable genotypes through continuous cultivation in eight different environments over two years. Methods: To assess the stability for BNF and seed yield, twenty-five mungbean genotypes were measured at eight diverse agro-ecological conditions, represented as two different seasons during spring and summer in two consecutive years (2016 and 2017) at two locations - TCA, Dholi, Muzaffarpur and Pusa Farm, RPCAU, Pusa, Samastipur, Bihar. To evaluate the most stable and superior genotypes, three parameters, viz., , bi and S2di were calculated. Result: There is a substantial amount of variability across genotypes and environments, as shown by the pooled analysis of variance. The genotype, Pusa 11-31 had shown regression near to unity, comprised with non-significant standard deviation along with higher mean seed yield and biological nitrogen fixation indicates that this is the most stable genotypes for the above traits.

Mean performances, character associations and multi-environmental evaluation of chilli landraces in north western Himalayas

Even though many varieties have been recommended across agro-climate zones of Himachal Pradesh, yet the information on stability is lacking in this State. Hence, the present investigation was carried out to identify high yielding stable genotypes among various pre-adapted landraces. The material consists of 20 chilli landraces including check i.e. DKC-8. The experiment was laid out in a RCBD. The data were recorded and analyzed to work out mean performances and the inferences were drawn for parameters of variability, correlation coefficients, path coefficients and stability analysis. As per mean performances, CS7 and CS9 were earliest in flowering, CS13 is earliest in days to ripe maturity, CS10 had highest plant height and CS9 had highest average fruit weight and ripe fruit yield plant−1. High PCV and GCV were recorded for ripe fruit yield plant−1. Heritability and genetic advance were recorded maximum for plant height in summer seasons and were recorded maximum for number of ripe fruits plant−1 in winter season. Correlation coefficients showed that number of ripe fruits plant−1 and average ripe fruit weight were positively and significantly correlated with ripe fruit yield plant−1. Path coefficient analysis in summer and winter seasons showed that average ripe fruit weight had the highest positive direct effect on ripe fruit yield plant−1. The pooled data over environments were analyzed to estimate the interaction effects between genotypes × environment. The mean sum of squares due to genotypes, environments and genotypes × environment interaction were significant for all the characteristics. CS1, CS3, CS6, CS10, CS13, CS15 were adapted to all environments, CS7 and CS9 were specifically adapted to favourable environment and CS2 was specifically adapted to unfavorable environment for 50% flowering, landraces CS1, CS2 and CS3were well adapted to all environments for ripe maturity whereas landraces CS6, CS10 and CS19 were well adapted to all environment for number of ripe fruit and ripe fruit yield.