Search Results (5)

A pivotal food crop in arid and semi-arid zones, proso millet boasts remarkable economic value, making the breeding of stable high-yield varieties critical for industrial sustainability. This study employed a randomized complete block design to conduct a two-year multi-environment trial on nine new varieties across six representative spring-sown test regions in China. Analytical tools, including additive main effects and multiplicative interaction (AMMI) biplots, AMMI stability values (ASV), genotype and genotype × environment (GGE) models, and genotype by yield–trait (GYT) biplots were utilized to assess genotype–environment (G × E) interactions and screen superior genotypes. AMMI variance analysis showed extremely significant effects of genotype, environment, and G × E on yield traits (p < 0.01). G × E principal component analysis identified JS8, PS3, PS6, and PM4 as dominant genotypes. Based on ASV indices, varietal stability rankings were PS5 > YS13 > JS8 > PS3 > PS6 > PM4 > others. GGE analysis indicated PM4 had the broadest adaptability across tested environments, while JS15 exhibited specific adaptability in Datong. Huairen and Shuozhou were validated as ideal testing environments via an ideal environment plot. GYT biplots further confirmed that YS13, JS15, PS3, and PM4 excelled in comprehensive yield–trait combinations. These findings offer a scientific foundation for ecological adaptability evaluation, breeding material selection, and commercial variety promotion. Full article

Stable, drought-tolerant, and high-yielding dual-purpose hybrids are needed for cultivation in the drought-prone areas of India. Working towards this, this study was conducted to assess the associations between grain yield and its component traits and the relationships among genotypes to select the most promising hybrids based on multiple traits. In the present investigation, thirty newly developed three-way hybrids (TWHs), along with four popular commercial single-cross hybrids and two open pollinated varieties (OPVs) were evaluated at three sites in the drought-prone ecology of India during the rainy season of 2021–2022. A principal component analysis (PCA) revealed that the first three component axes (PC) were significant, with eigenvalues more than one, and together contributed to 74.10% of the total variance. A hierarchical cluster analysis based on the Euclidean distance between hybrids suggested the existence of three clusters. Cluster III (C-III) had hybrids with maximum grain yield, dry fodder yield, and important component traits such as panicle harvest index and grain harvest index that are required for adaptation to drought-prone environments. A genotype by yield × trait (GYT) biplot and a superiority index (SI) were generated to identify the best hybrids with high grain yield and other component traits. These results were used to identify TWHs, namely TH-114, TH-138, TH-49, TH-67, and TH-79, with more than 30% standard heterosis and stable performance coupled with better drought-adaptive traits. Full article

Pea (Pisum sativum L.) is a vital leguminous crop farmed worldwide. Pea plays an essential role in China’s crop rotation system, but the major restrictions to its cultivation are stability and low yield. Breeding for promising cultivars with a significantly high yield will impact the sustainability of pea production. Additionally, diverse environment trials are crucial in determining the best genotype. The new cultivar “Yunwan 52” was developed by hybridization and subsequently evaluated through yield trials among six pea genotypes across 14 environments during the 2016–2018 growing seasons. The results showed that the average yield of “Yunwan 52” for all tested environments was 2.64 t ha⁻¹ compared to the control cultivar (Yunwan 18, 1.83 t ha⁻¹). Analysis of AMMI variance showed significant differences (p < 0.01) between genotypes, environments, and their interaction. Based on the GGE biplot, some genotypes possessed wide and narrow adaptability to environments, such as Yunwan 52 was considered the most stable and ideal gen-otype across all tested environments. GYT biplot analysis also revealed that this realized cultivar was a superior and stable genotype that can be identified visually by combining all characters in breeding programs. Yunwan 52 distinguishes with purple blossoms and seed coat peas. It is possible to infer that the newly released cultivar “Yunwan 52” has outstanding yield performance and wide adaptability to multiple environmental conditions (resilience to abiotic stress). It will contribute to developing nutritional pea genotypes and increase pea production in irrigated areas. Full article

Drought is an environmental abiotic stress that diminishes wheat production worldwide. In the present study, we evaluated fifty bread wheat genotypes (arranged in alpha lattice design) under two main water regimes, water-deficit (two surface irrigations) and well-watered (four irrigations), at different sites in two consecutive cropping seasons, 2019/20 and 2020/21. To identify the drought-tolerant genotypes, utilized several selection/phenotyping criteria, including agronomic traits, e.g., grain yield (GY) and yield components (SM); physiological parameters such as canopy temperature (CT), leaf transpiration rate (TRN), intercellular CO₂ concentration (INCO); spectral reflectance indices, e.g., Leaf Chlorophyll Index (LCI), curvature index (CI), and normalized difference vegetation index (NDVI); and stress tolerance indices (STI) were determined concurrently with the grain yield. The results revealed significant differences (p ≤ 0.01) among the environments, genotypes, and their interaction for grain yield (GY), days to heading (DH), days to maturity (DM), grain filling period (GFP), grain filling rate (GFR), Normalized difference vegetation index (NDVI), plant height (PH), and spikes per square meter (SM). The genotype plus genotype by environment (GGE) and genotype by yield*trait (GYT) biplot techniques indicated that Genotype 37 (Sakha 95) and Genotype 45 performed best under well-watered and water-deficit environments. Furthermore, the same genotypes were the best from the genotype by stress tolerance indices (GSTI) approach view. Genotype 37 (Sakha 95) was superior to the GYT selection method, with physiological parameters and spectral reflectance indices. Likewise, we can identify this genotype as low-water-tolerant based on GSTI, GYT, and SRI results and recommend involving it in the drought breeding program. Full article

Salinity stress harms crop yield and productivity worldwide. This study aimed to identify genotypes with higher grain yield and/or salinity tolerance from forty bread wheat genotypes evaluated under seawater diluted at 4.0, 8.0, or 12.0 dS/m or control (0.4 dS/m) in the 2019/20 and 2020/21 seasons. Six elite genotypes, namely 6, 16, 31, 33, 34, and 36, were chosen and tested in a lysimeter under diluted seawater stress in 2020/21. The results showed significant differences (p ≤ 0.01) among the genotypes for the traits grain yield (GY), harvest index (HI), chlorophyll content index (CCI), chlorophyll fluorescence parameter Fv/Fm, and their interaction with salinity treatments. Additionally, significant differences (p ≤ 0.01) were detected among ten genotypes for all agronomic traits along with spectral reflectance indices (SRI), e.g., curvature index (CI), normalized difference vegetation index (NDVI), triangular vegetation index (TVI), modified chlorophyll absorption reflectance index (MCARI), and their interaction with salinity treatments. Genotype by traits (GT) and genotype by yield*trait (GYT) biplots are useful for genotypes screening and selection based on grain yield and other associated traits (agronomic, physiological traits, and spectral reflectance indices combinations) as well as genotypes by stress tolerance indices (GSTI). In conclusion, this study identified that genotypes 6, 16, 31, 33, 34, and 36 in the 2019/20 season and genotypes 2 and 1 performed better than Kharchia 65 and Sakha 8 in the 2020/21 season, which detected as superior genotypes and might be recommended for sowing and/or inclusion in the breeding program in salt-affected soils. It was possible to draw the conclusion that spectral reflectance indices were efficient at identifying genotypic variance. Full article