Aug 2026· Discover Agriculture· Vol 4· 0 citations· 58 references
Abstract
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.
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
Soybean (
Glycine max
) seed yield and quality are strongly affected by drought, particularly during reproductive stages. We evaluated 22 genotypes under irrigated and rainfed conditions across three Missouri environments (2023–2024) for seed yield, 100 seed weight, protein, oil, and five fatty acids. Mixed models were used to partition genotype, environment, and genotype × environment effects and to obtain best linear unbiased predictions (BLUPs) for each trait. Seed yield showed strong environmental sensitivity, whereas protein, oil, and most fatty acids exhibited moderate to high broad‐sense heritability (
H
2
= 0.65–0.95), indicating good prospects for genetic gain. Performance and stability were jointly assessed using the weighted average of absolute scores from BLUPs (WAASB) and the multi‐trait stability index (MTSI). WAASB biplots highlighted genotypes G9, G13, and G17 as combining below‐average WAASB with favorable mean performance for seed yield and key quality traits. MTSI rankings confirmed these three lines as the most desirable multi‐trait ideotypes. Selection based on WAASB and MTSI increased mean yield from 2603 to 2775 kg ha
−1
(+5.17%) and oil from 17.7% to 17.9% (+1.05%), while slightly reducing protein (−0.49%) and oleic acid (−1.60%) and substantially decreasing linolenic acid (−5.00%), improving oil stability. These results demonstrate that BLUP‐based stability metrics integrated with MTSI provide an effective decision‐support framework for identifying drought‐resilient, high‐value soybean cultivars for variable water regimes.
F. Ravelombola, Harmeet Singh-Bakala, C. Adeva et al.· Agrosystems, Geosciences &am...· 0 citations
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.
S. Markos, K. Gebremeskel, S. Gebreyowhans et al.· Discover Agriculture· 0 citations
Climate change and environmental variability pose major challenges to sustainable wheat production, highlighting the need for stable and high-yielding cultivars. This study evaluated fifteen bread wheat (Triticum aestivum L.) genotypes across twelve environments during two consecutive growing seasons using a randomized complete block design with three replications. Stability and performance were assessed using Additive Main Effects and Multiplicative Interaction (AMMI), Genotype main plus Genotype × Environment (GGE) biplot, and multi-trait stability index (MTSI). AMMI combined ANOVA indicated that both grain yield per plot and falling number were significantly affected by genotype, environment and their interaction (GEI). The GGE biplot revealed that the first two principal components together explained 86.92% of the total variation in grain yield per plot and 88.27% for falling number, demonstrating the reliability of the model in interpreting GEI patterns. AMMI and GGE biplot analyses consistently identified Sakha 95 (G1), Misr 4 (G3), Sakha Line#1 (G6), Sakha Line#2 (G7), Gemmeiza Line#2 (G14), and Gemmeiza Line#3 (G15) as high-yielding and stable genotypes across environments. In contrast, Sakha Line#4 (G9), Sakha Line#6 (G11), Sakha Line#7 (G12), and Gemmeiza Line#1 (G13) showed poor adaptation and low stability. MTSI further refined selection by integrating yield and quality traits, identifying Gemmeiza Line#3, Sakha Line#3, Sakha Line#2, and Giza 171 as superior genotypes at 25% selection intensity, characterized by low MTSI values. The identification of stable, high-performing genotypes with desirable grain quality can contribute to sustainable wheat production by improving yield reliability under diverse environmental conditions and supporting more efficient cultivar selection for climate-resilient wheat production. Overall, integrating AMMI, GGE biplot, and MTSI provided a robust framework for identifying stable and high-performing wheat genotypes, supporting selection decisions in multi-environment breeding programs.
M. Genedy, Mahmoud A. Hussein, A. R. Ibrahim et al.· Sustainability· 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
Winter wheat (Triticum aestivum L.) is a staple cereal crops that provides income and support the livelihoods of local farmers of Afghanistan, while also playing a vital role in food security. It is a major source of carbohydrates and proteins for human consumption, while its straw is commonly used as livestock feed in Afghanistan. yet productivity remains below potential due to limited availability of locally adapted improved varieties. This study evaluated the agronomic performance, yield, and yield-related traits of four winter wheat varieties (Sohla 02, Sohla 018, Chont, and local variety Riza Gul) under irrigated conditions in Char Asiab District, Kabul, during the 2024–2025 growing season. Using a Randomized Complete Block Design with three replications, the data was analyzed with Statistical Tools for Agricultural Research (STAR 2.0.1) software. significant varietal differences were observed for most traits. Result showed that Sohla 018 produced the highest grain yield (4,866.67 kg ha⁻¹), 1000-grain weight (44.36 g), and grains per spike (51.11). While Sohla 02 showed superior vegetative growth, this did not translate to higher yield. The findings identify Sohla 018 as the most suitable variety for cultivation in Char Asiab District, with potential to improve wheat productivity and contribute to food security in Afghanistan.
Mohammad Zekrullah Matin, A. Afghani, Amira Saidi· International Journal of Cur...· 0 citations
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