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#software testing Open access Sep 2026

Evaluation of hybrid maize for grain yield and associated traits during the winter in Terai region of Nepal

In Nepal, hybrid maize is increasingly popular in the Terai region due to rising demand for maize grains, higher yield potential, and superior agronomic performance. The purpose of this study was to assess the agronomic performance, yield stability, and adaptability of maize hybrids. A total of 23 single cross maize hybrids was evaluated at four locations in the Terai region of Nepal: Tarahara, Parwanipur, Rampur, and Khajura. The trial was laid-out in randomized complete block design with three replicates per location during the winter season of 2023-24. Flowering traits, growth, and yield-related traits were recorded following the protocols developed by CIMMYT. Analysis of variance was performed using ADEL-R, correlation analysis with PBTools, and stability analysis with GEA-R software. Results indicated significant genotype, environment, and genotype × environment interactions for all traits, with heritability estimates ranging from 0.78 to 0.96. The grain yield varied across locations, with Rampur recorded the highest average of 8.05 t ha-1. RML108/RL2118, CML161/RML96, and RML36/RML2244 were the top performing hybrids with grain yield of 8.13, 7.93, and 7.85 t ha-1 respectively. RML108/RL2118 identified as high yielding and stable (Pi=1.10) hybrid, RL143/RML96 as moderate in yield but excellent stability (bi=-0.10), while Sultan (bi=1.98) and RML62/RML2 (bi=1.69) showed poor stability despite high mean yield. The positive genetic correlations observed between days to anthesis with grain yield (r=0.31), ear position with grain yield (r=0.57), and kernel weight with grain yield (r=0.92). This study highlighted the value of multi-location testing to identify region-specific hybrids that are resilient to local environmental conditions. The results indicate that the identified hybrids possess high yield potential and can be promoted as candidate hybrids for winter season in the Terai region.

Mahendra Prasad Tripathi, B. Adhikari, Jiban Shrestha et al. · 0 citations
Open access Aug 2026

Integrating qualitative and quantitative traits with multivariate analysis to decipher the diversity structure of barley landraces for sustainable agriculture

Understanding genetic diversity in barley (Hordeum vulgare L.) landraces is crucial for identifying potential parental lines and conserving valuable genetic resources. This study evaluated the agro-morphological and genetic diversity of seventy-five barley landraces under the field of College of Natural Resource Management (CNRM) Bardibas of Madhesh Province, Nepal, to identify valuable germplasm for breeding and conservation for Terai condition. The experiment was conducted in Alpha Lattice design with two replications. Qualitative and quantitative data were collected at different stage of barley. Multivariate test and Analysis of variance (ANOVA) test were conducted to test the diversity of barley. The ANOVA revealed highly significant genotypic differences (p < 0.001) for key agro-morphological traits, including days to flowering (78.5–123 days), days to maturity (115–148 days), spike length (4.4–19.4 cm), filled grains per panicle (7.4–59.8), sterility (5.6–84.6%), plot yield (3.0–181.2 g plot− 1), and plot biomass (25–475 g) confirming a broad phenotypic basis for selection. Substantial phenotypic diversity was observed with Shannon-wiener diversity indices reaching up to 0.99 for grain-related traits such as grain surface (0.994), rachilla hair (0.997), grain crease width (0.976), and spike density (0.899). Principal Component Analysis (PCA) revealed that the first two principal components (PC1 and PC2) explained 54.89% of the total variance (PC1 = 42.63%, PC2 = 12.26%). Traits with the highest loadings on PC1 included days to maturity (0.931), sterility (0.865), spike length (0.865), and days to flowering (0.889), while PC2 was strongly influenced by thousand-grain weight (0.739). Cluster analysis using both K-means (K = 2) and hierarchical Ward’s methods consistently grouped the landraces into four distinct clusters, with a high degree of concordance between the two methods. The PCA bi-plot and heat-map visualizations clearly differentiated these clusters and highlighted specific landraces (NGRC7732 and NGRCO7726) occupying extreme positions, indicating unique genetic backgrounds. The results highlight the presence of valuable genetic variation and adaptive traits for suitable for barley improvement ad breeding programs aimed at enhancing yield stability and resilience.

Abhisek Shrestha, K. Dhakal, D. Gauchan et al. · 0 citations

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