Biplot analysis of phenotypic stability in upland cotton genotypes in Mato Grosso

Assessment of yield performances for grain sorghum varieties by AMMI and GGE biplot analyses

Grain sorghum is an exceptional source of dietary nutrition with outstanding economic values. Breeding of grain sorghum can be slowed down by the occurrence of genotype × environment interactions (GEI) causing biased estimation of yield performance in multi-environments and therefore complicates direct phenotypic selection of superior genotypes. Multi-environment trials by randomized complete block design with three replications were performed on 13 newly developed grain sorghum varieties at seven test locations across China for two years. Additive main effects and multiplicative interaction (AMMI) and genotype + genotype × environment (GGE) biplot models were adopted to uncover GEI patterns and effectively identify high-yielding genotypes with stable performance across environments. Yield (YLD), plant height (PH), days to maturity (DTM), thousand seed weight (TSW), and panicle length (PL) were measured. Statistical analysis showed that target traits were influenced by significant GEI effects (p < 0.001), that broad-sense heritability estimates for these traits varied from 0.40 to 0.94 within the medium to high range, that AMMI and GGE biplot models captured more than 66.3% of total variance suggesting sufficient applicability of both analytic models, and that two genotypes, G3 (Liaoza No.52) and G10 (Jinza 110), were identified as the superior varieties while one genotype, G11 (Jinza 111), was the locally adapted variety. G3 was the most stable variety with highest yielding potential and G10 was second to G3 in average yield and stability whereas G11 had best adaptation only in one test location. We recommend G3 and G10 for the production in Shenyang, Chaoyang, Jinzhou, Jinzhong, Yulin, and Pingliang, while G11 for Yili.

Sensitivity of different flax (Linum usitatissimum L.) genotypes to salinity determined by GE biplot

Plants respond differently to salt stress depending on their genetic structure and the severity of the stress. Salinity reduces seed germination, delays plant emergence, and inhibits seedling growth. The selection of the tolerant genotypes, however, plays a vital role in increasing agricultural output since various genotypes greatly vary for their tolerance to salinity. Therefore, this study determined the impact of five different NaCl levels (i.e., 0, 50, 100, 150 and 200 mM) on seed germination and growth attributes of 10 flax (Linum usitatissimum L.) genotypes. The germination and growth characteristics of the genotypes under study were examined using the biplot approach at varied salt levels. The results indicated that individual and interactive effects of genotypes and salinity levels significantly (p ≤ 0.01 or p ≤ 0.05) affected several seed germination traits. The relations of genotype × germination traits indicated that 'G4' and 'G6' were the most stable genotypes with the highest performance regarding seed germination characteristics. The genotype 'G2' was associated with shoot length, while 'G7' was linked with salinity tolerance index. The biplot divided the germination characteristics into five different groups according to sector analysis. Most of the germination parameters had higher values under 100 mM, while some of the parameters had better values under 0, 50 and 200 mM NaCl levels. The tested genotypes varied for their seed germination and growth response depending on the NaCl levels. The genotypes 'G4', 'G5' and 'G6' proved more tolerant to high NaCl levels. Therefore, these genotypes can be used to improve flax productivity under saline soils.

Multi-environment Evaluations Across Ecological Regions Reveal That the Kernel Oil Content of Cottonseed Is Equally Determined by Genotype and Environment

Cotton is the fifth-largest oil crop in the world. A high kernel oil content (KOC) and high stability are important cottonseed attributes for food security. In this study, the phenotype of KOC and the genotype-by-environment interaction factors were collectively dissected using 250 recombinant inbred lines, their parental cultivars sGK156 and 901-001, and CCRI70 across multi-environments. ANOVA and correlation analysis showed that both genotype and environment contributed significantly to KOC accumulation. Analyses of additive main effect multiplicative interaction and genotype-by-environment interaction biplot models presented the effects of genotype, environment, and genotype by environment on KOC performance and the stability of the experimental materials. Interaction network analysis revealed that meteorological and geographical factors explained 38% of the total KOC variance, with average daily rainfall contributing the largest positive impact and cumulative rainfall having the largest negative impact on KOC accumulation. This study provides insight into KOC accumulation and could direct selection strategies for improved KOC and field management of cottonseed in the future.