Assessment of genotype by environment interaction for yield stability and sucking pest resistance in cotton (Gossypium hirsutum L.) using AMMI, GGE biplot and multi-trait stability index
Cotton (Gossypium hirsutum L.) is a major fibre crop underpinning the global textile industry; however, its productivity is increasingly threatened by climatic variability and the resurgence of sap-sucking insect pests. The interaction between genotype and environment (G × E) further complicates the identification of stable and high-yielding genotypes, particularly under rainfed conditions. The present study evaluated 7 cotton genotypes across 9 environments (three locations over 3 consecutive years: 2021–24) in Odisha, India, to assess G × E interaction for 14 quantitative traits related to yield and sucking pest resistance using advanced statistical approaches. Combined analysis of variance revealedhighly significant (p < 0.01) effects of genotypes, environments and G × E interaction for all traits studied. The interaction component was particularly significant for key traits, including seed cotton yield (SCY), lint yield (LY) and populations of major sucking pests, necessitating a comprehensive stability analysis. High broad-sense heritability coupled with moderate to high genetic advance for yield and pest resistance traits indicated the predominance of additive gene action. Stability analyses using the Eberhart and Russell model, additive main effects and multiplicative interaction (AMMI) and genotype plus genotype by environment (GGE) biplot consistently identified genotype BS 3-17 as superior, exhibiting the highest mean SCY (1978 kg ha-1) and LY (675 kg ha-1), along with the lowest mean populations of aphids (APH) (5.28 per 3 leaves) and jassids (JAS) (1.82 per 3 leaves) across environments. The multi-trait stability index (MTSI) further ranked BS 3-17 as the most desirable genotype (MTSI score = 5.51), indicating its closest proximity to the ideotype. Agronomic validation trials demonstrated that high-density planting (90 × 30 cm) combined with 125 % of the recommended dose of fertilisers significantly enhanced the yield potential of BS 3-17, achieving up to 3114 kg ha-1. These findings establish BS 3-17 as a climate-resilient, high-yielding genotype suitable for commercial cultivation and a promising donor parent for breeding programs targeting yield stability and sucking pest resistance.
Cotton (Gossypium hirsutum L.) is a major commercial crop and an important source of natural fibre, making the understanding of its genetic architecture essential for crop improvement. The present study employed generation mean analysis using six generations (P₁, P₂, F₁, F₂, BC₁ and BC₂) to investigate the nature of gene action governing yield-contributing and fibre quality traits. Significant scaling tests and chi-square (X2) values for several traits indicated the presence of epistatic gene interactions. The analysis revealed that both additive and non-additive gene effects contributed to trait inheritance, with duplicate epistasis predominating for several economically important traits. These findings suggest that the inheritance of yield and fibre quality traits is complex, involving multiple gene interactions. Therefore, breeding strategies that exploit both additive and non-additive genetic variation, including selection in advanced generations and population improvement approaches, are likely to be more effective for developing superior cotton genotypes.
P. Ashita, K. Chaudhari, Chintan v Kapadia et al.· Plant Science Today· 0 citations
CONTEXT
Sunflower, as a significant source of edible oil worldwide, requires the development of stable and high-yield genotypes, particularly under salinity and adverse environmental conditions.
OBJECTIVES
This research was conducted with the aim of assessing the stability of seed yield and yield-related traits in 61 sunflower genotypes under normal and salinity stress conditions across two cropping years (2015-2016).
METHODS
For a more accurate assessment of genotype × environment interactions (GEI) in sunflower, robust statistical methods including mixed model analysis of variance, AMMI and GGE models, weighted average of absolute scores (WAASB), Multi-Trait Stability Index (MTSI), and parametric and non-parametric stability indices were used and addressed.
RESULTS
Mixed modeling ANOVA revealed the significant effect of environment, genotype, as well as GEI for seed yield and associated traits, demonstrating different responses of genotypes across environments. The outcomes of various statistical methods showed a high level of consistency. Genotypes 4, 6, 44, 22, and 59 demonstrated high stability and yield. The MTSI identified genotypes 4, 61, 25, 50, 55, 26, 32, 40, and 34 as the most desirable with maximum stability across various traits.
CONCLUSION
Genotype 4 (270-ENSAT) is introduced as a promising candidate for creating salt-resistant hybrid varieties.
SIGNIFICANCE
Different stability assessment methods helped in the discovery of superior genotypes that can be used for developing salt-resistant and stable hybrids, thereby establish opportunity for exploiting salt-effected soils for crop production.
Sanaz Khalifani, R. Darvishzadeh, N. Azad et al.· BMC Plant Biology· 0 citations
Understanding genotype-environment interaction is crucial for optimizing cotton hybrid performance, facilitating consistent yield and stability in semi-arid regions. To address this challenge, a total of 45 hybrids were evaluated across three kharif seasons (2021–2023) at Punjab Agricultural University Regional Research Station, Abohar. The analysis integrated combined analysis of variance (ANOVA), additive main effects and multiplicative interaction (AMMI), and genotype + genotype × environment (GGE) biplot approaches, supplemented with stability indices such as AMMI stability value (ASV), genotype stability index (GSI), and the weighted average of absolute scores (WAASBY) index, to identify high-performing and stable hybrids. Seed-cotton yield ranged from 2 122 to 3 168 kg·ha−1, with environmental effects explaining the largest proportion of phenotypic variation. The significant genotype × environment interaction (GEI) indicated differential hybrid performance across seasons. AMMI and GGE analyses identified hybrids exhibiting both broad and specific adaptation. Stability indices (ASV, GSI, and WAASBY) consistently identified G7, G8, G17, G22, and G45 as both stable and high-yielding, with mean seed cotton yield (SCY) ranging from 2 625 to 2 996 kg·ha−1. For yield component traits, G35 and G21 showed the highest sympodia per plant, G20 and G41 exhibited superior boll weight, and G38 had the highest bolls per plant. GGE biplot analysis indicated that E1 was both discriminative and representative for SCY, whereas E2 and E3 were most informative for BW and BPP, respectively, reflecting pronounced seasonal variations. Overall, the integrated analytic pipeline (ANOVA → AMMI → GGE → ASV/GSI/WAASBY) effectively partitioned and interpreted complex GEI into robust selection decisions. This approach facilitated the identification of a refined set of hybrids exhibiting temporal stability, high yield potential, and suitability for both wide and season-specific adaptation.
Sunayana Punia, Manpreet Singh, S. Yadav· Journal of Cotton Research· 0 citations
Peanut (Arachis hypogaea L.) yield stability and aflatoxin B1 (AFB1) contamination are critical determinants of food and nutritional security in sub-Saharan Africa, where the crop is widely grown under marginal, drought-prone conditions. This study evaluated genotype × environment (G×E) interactions for yield-related traits and AFB1 accumulation in eight peanut genotypes (55–437, 57–313, 73 − 33, Manipintar, Fleur 11, Grimari, Peau lisse, Siksa) across four environments in Cameroon, combining two locations (Bafia and Dschang) with two moisture regimes (normal rainfall and terminal drought stress) in a randomized complete block design. Total biomass, pod number, hundred-seed weight, dry matter, dry yield, and AFB1 concentration were analyzed using combined ANOVA, the AMMI and GGE models, Best Linear Unbiased Prediction (BLUP), and the Multi-Trait Stability Index (MTSI). Combined ANOVA revealed significant (p < 0.05) genotype, environment, and G×E effects for all traits, with environment explaining the largest share of variation for AFB1 content (43.6% of the sum of squares) and genotype explaining the largest share for hundred-seed weight (52.4%). Based on weighted average of absolute BLUP scores (WAASB), genotypes Grimari, 55–437, Fleur 11, and Peau-lisse combined the lowest and most stable AFB1 contamination, while Grimari, 57–313, and Siksa combined high and stable dry yield. The MTSI identified Grimari as the ideal multi-trait genotype, combining acceptable yield potential (2.5 t.ha− 1) with the lowest, most stable AFB1 levels, and is proposed as a promising candidate for further multi-year, multi-location validation toward climate-resilient, safer peanut varieties for Cameroonian smallholders. However, all genotypes exceeded the EU regulatory limit for aflatoxin B1 in groundnuts intended for direct human consumption (2 µg.kg− 1; Regulation (EC) No 1881/2006, as amended by Regulation (EU) No 165/2010), indicating that genetic selection alone is insufficient and must be combined with integrated pre- and post-harvest aflatoxin management in smallholder systems across sub-Saharan Africa.
Pierre Germain Ntsoli, Marie Amperes Bedine, Grace Arielle Mpoam Miague et al.· Discover Agriculture· 0 citations
Fine-grain rice is highly preferred by consumers due to its superior cooking quality and higher market price. Consequently, the Bangladesh Rice Research Institute (BRRI) has developed several fine-grain rice genotypes through its breeding programs. This study evaluated the yield performance and stability of nine advanced fine-grain rice lines along with two local cultivars and five released varieties across ten agro-ecological zones during the 2023–24 Boro season. Genotype–environment interaction (GEI) for grain yield (GY) and yield-related traits was analyzed using the Additive Main Effects and Multiplicative Interaction (AMMI) model, the Weighted Average of Absolute Scores and Yield (WAASBY) index, and the Multi-Trait Stability Index (MTSI). Combined analysis of variance revealed that genotype, environment, and GEI significantly influenced grain yield and its components. Most traits exhibited moderate to high heritability (0.74–0.97) with selection accuracy above 80%, indicating strong genetic control. Among the tested genotypes, BR17-23-8-2-7B (G6) recorded the highest mean yield, while Bhanga, Faridpur (E3) was identified as the most favorable environment. Stability analyses indicated that G6 ranked highest based on WAASBY (91.45), whereas BRH11-7-17-10B (G7) and BRH9-3-2B (G8) were identified as the most stable genotypes according to MTSI. Overall, G6, G7, and G8 showed superior performance compared with the local and released varieties, suggesting their potential for cultivation and use in breeding programs targeting diverse agro-ecological environments.
Bangladesh Rice J.29(1): 1-17, 2025
M. M. Hasan, M. H. R. Mukul, Shamsunnaher et al.· Bangladesh Rice Journal· 0 citations
Evaluation across contrasting crop seasons is important for identifying rice (Oryza sativa L.) genotypes with stable performance under variable field conditions. In the present study, 16 rice genotypes were evaluated across three seasonal environments at Rudrur to assess grain yield stability, adaptability and genotype × environment interaction. The experiment was conducted under recommended agronomic practices and grain yield data were subjected to stability analysis using the additive main effects and multiplicative interaction (AMMI) model and the best linear unbiased prediction (BLUP) approach. Stability was further assessed using the weighted average of absolute scores from the BLUP(WAASB) index and the AMMI stability value (ASV). Significant differences were observed among genotypes, environments and genotype × environment interactions, indicating that the performance of genotypes varied considerably across seasonal conditions. The AMMI analysis effectively partitioned the interaction effects, with the first two interaction principal component axes explaining most of the genotype × environment interaction variation. Based on WAASB and ASV, the genotypes RDR5289, KNM1638 and JGL24423 exhibited high stability and consistent grain yield across the tested environments. Multi-trait stability analysis further identified KNM1638 as the most balanced genotype, combining stable grain yield with desirable expression of important agronomic traits. Among the test environments, kharif 2022 provided the most stable conditions for evaluating genotype performance. The identified stable genotypes can be utilised formultilocation testing, varietal recommendation and as valuable parental lines in rice breeding programmes aimed at developing high-yielding cultivars with broad adaptation and stable performance under diverse seasonal environments.
R. Ramya, K. Parimala, B. Soundharya et al.· Plant Science Today· 0 citations