Aug 2026· Discover Agriculture· Vol 4· 0 citations· 83 references
Abstract
Vetch, a highly valued herbaceous legume for livestock, was studied in Northern Ethiopia from 2021 to 2023 to analyze genotype by environment interaction (GEI) in multi-environment trials (MET) and identify adaptable vetch genotypes. Combined analysis of variance (ANOVA), AMMI ANOVA and Eberhart and Russell regression for forage yield were analyzed. GGE, AMMI1 and AMMI2 bi-plots for forage yield of vetch genotypes were created. Principal Component Analysis (PCA) and correlation network plots were created, along with the analysis of multi-trait stability index (MTSI) for agronomic traits. AMMI stability measures, Best Linear Unbiased Prediction (BLUP) based indexes; parametric and non-parametric statistics were computed using R-statistical software. AMMI ANOVA revealed significant effects of genotype (G) (28.35% SS), environment (E) (30.29% SS), and GEI (17.48% SS) for the dry matter yield. The likelihood ratio test (LRT) revealed significant genotypic effects for all agronomic traits except days to emergence and survival rate, while the LRT for GEI was significant for all traits except the number of branches. AMMI and GGE bi-plots selected G3 and G4 as the most adaptable genotypes. Stability parameters: ASTAB, ASI, ASV, AVAMGE, DA, FA, Shukla, Ecovalence, Wi_u, Pi_u, S2 N3, and MASI identified G4 and G1 as the most stability genotypes. SIPC, DZ, EV, MASV, Wi_g, HMGV, S3, S6, R2 and ZA ranked G4 and G3 as most stable genotypes. HMRPGV, HPGV, WAAS, WAASB, Wi_f, Pi_a, Pi_f and GAI also ranked G3 and G4 most stable genotypes. The average rank sum (ARS) of the stability statistics groups was considered. AMMI-based stability statistics identified G4 and G1 as the top two stable. The WAAS index, BLUP-based, and non-parametric statistics identified G3 and G4 as the most stable genotypes. In contrast, parametric statistics ranked G4 first and both G1 and G3 second in stability. Overall, G4 and G3 were identified as the most stable genotypes. The MTSI also selected G4 (MTSI = 1.65) and G3 (MTSI = 1.79), are recommended for further verification and demonstration before being recommended for wider forage production in Tigray.
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
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.
D. Subhashree, S. N. Bhabani, R. Jyoti et al.· Plant Science Today· 0 citations
Sorghum (Sorghum bicolor (L.) Moench) is a critical staple cereal in semi-arid tropics, yet its productivity is highly constrained by genotype × environment interaction (GEI), which complicates variety selection and recommendation. This study aimed to estimate the magnitude of GEI, evaluate grain yield performance, and identify stable, high-yielding and early-maturing sorghum genotypes for potential release in East Hararghe, Ethiopia. Fourteen sorghum genotypes alongside two standard checks (Fadis 01 and Melkam) were tested across six environments, combining two locations (Fadis and Erer) over three consecutive main cropping seasons (2022–2024) using a randomized complete block design with three replications. Data on grain yield and agronomic traits were subjected to combined analysis of variance, Additive Main Effects and Multiplicative Interaction (AMMI) analysis, and Genotype Main Effect plus GEI (GGE) biplot analysis. Combined ANOVA revealed highly significant (P < 0.001) effects for genotype, environment, and GEI, confirming differential genotypic responses across testing environments. AMMI analysis partitioned the total grain yield variation, attributing 18.54% to genotype, 25.15% to environment, and 28.86% to GEI, indicating that environmental factors and their interaction with genotypes were the dominant sources of variation. The first two interaction principal component axes (IPCA1 and IPCA2) jointly explained 75.56% of the GEI variation, with IPCA1 contributing 52.6% and IPCA2 contributing 22.96%. Genotype G6 (ETSC14576-5-1) recorded the highest mean grain yield (4265 kg ha⁻¹) and demonstrated exceptional stability across environments, as evidenced by its proximity to the IPCA zero line in the AMMI1 biplot, favorable AMMI stability value, and low genotype selection index. GGE biplot analysis further ranked G6 closest to the ideal genotype, confirming its superior mean performance and stability. Polygon view identified three mega-environments, with G6 emerging as the winning genotype in one of them. Based on the integrated assessment using mean yield, AMMI parameters, and GGE biplot outputs, genotype ETSC14576-5-1 (G6) is identified as the most stable and high-yielding genotype across the tested environments. Therefore, this genotype is recommended for variety verification and subsequent release for cultivation in East Hararghe and similar agro-ecologies.
Zeleke Legesse, Fikadu Tadesse, Berhanu Diribsa et al.· American Journal of Bioscien...· 0 citations
A field investigation involving 40 bread wheat genotypes was conducted across three sowing environments: normal (E1, 15 November 2023), late (E2, 15 December 2023), and very late (E3, 15 January 2024). The experiment used a Randomised Complete Block Design (RCBD) with three replications to characterise genetic variability, trait correlations, and genotype stability. Pooled ANOVA, genetic parameters (GCV, PCV, h², and GAM), genotypic and phenotypic correlation coefficients, and Eberhart–Russell stability analysis were used to evaluate 13 traits across environments. Highly significant (P < 0.01) differences were detected among environments, genotypes, and their interactions for all traits. Seed yield showed a GCV of 4.03%, a PCV of 5.06%, broad-sense heritability (h²) of 39.49%, and a GAM of 6.60%, indicating relatively limited scope for improvement through direct phenotypic selection. Seed yield showed strong positive genotypic correlations with biological yield (rᵍ = 0.748), grains per spike (rᵍ = 0.756), and 1000-seed weight (rᵍ = 0.628). Stability analysis using the Eberhart–Russell model identified Raj 3077, Raj 4027, and GW 387 as stable, high-yielding genotypes across environments. The relatively narrow difference between the PCV and GCV estimates indicated limited environmental influence on the expression of several traits. These stable genotypes, with favourable biofortification traits (Fe, Zn, and protein), may provide useful breeding material for developing climate-resilient, nutrient-rich wheat varieties suited to variable sowing conditions in India.
C. Singh, Shahil Kumar, M. Singh et al.· International Journal of Env...· 0 citations
Background: To increase area and production of greengram, breeders must develop high yielding, stable and adaptable varieties. This study aims to identify such superior greengram genotypes that perform consistently well across different locations. Methods: The experiment was carried out during Kharif, 2023 in six locations at different Agricultural Research Stations of Telangana state. A total of 15 genotypes were evaluated in RBD with three replications. The data on seed yield was subjected to statistical analysis. The G×E interaction was studied as per Eberhart and Russel model, AMMI, GGE biplot and WAAS model analysis. Result: Regression analysis revealed that genotypes MGG-385 and MGG-573 considered as stable. AMMI analysis of variance indicated that genotypes contributed 13.1% followed by environment (55.4%) and G×E interaction (20.48%). With further examination of GGE using AMMI analysis, two significant principal components were separated explaining 69.7% of variance interaction (PCI-42% and PC2-27.7%). The results of AMMI 1 and 2 biplot analysis, the genotypes MGG-385 and MGG-295 were considered as stable with high mean seed yield and recorded nearly zero IPCA1 score. The environments of Madhira and Tandur had recorded the lowest IPCA1 scores had less interaction effects. AMMI stability value and stability indexes were indicated MGG-385 was more stable. Y×WAAS graph revealed the genotypes VBN-4, MGG-570, MGG-571 were unstable had high seed yield, while the environments Palem, Tornala and Warangal provided high seed yield and presented good discrimination ability as had recorded high WAAS values. The genotypes MGG-573, MGG-556, MGG-385 and MGG-564 were broadly adopted. Based on all the stability models, the genotype MGG-385 was considered as stable with high mean seed yield across locations.
K. Rukminidevi, A. Saritha, K. Parimala et al.· Agricultural Science Digest...· 0 citations