Dissecting the genetic basis of yield stability in faba bean by multi-environment analysis

Multi-trait model-based assessment of selection efficiency and stability in soybean germplasm.

Integration of graphical and diallel analyses for identifying elite bread wheat genotypes for grain yield and its yield-related traits.

Physiological determinants of heterosis in hybrid rice (Oryza sativa L.): identifying the optimal phenotyping stage for yield prediction

Introduction Hybrid rice productivity depends on heterosis, yet the developmental stage at which physiological traits become sufficiently integrated to reliably predict grain yield across diverse environments remains poorly understood. Although physiological phenotyping is increasingly used in crop improvement, the biological basis for selecting the most informative developmental stage for early yield prediction has received limited attention. Methods A panel of 39 rice genotypes, including hybrids, parentals, and check varieties, was evaluated across four environments. Fourteen physiological and agronomic traits were measured at the early (30 days after sowing, DAS) and mid-vegetative (60 DAS) stages. Best linear unbiased predictors (BLUPs) derived from mixed models were used to estimate genotype performance from an unbalanced multi-environment dataset, followed by multivariate analysis, leave-one-season-out cross-environment validation, and development of a physiology-based selection index. Results BLUP-based multivariate analyses revealed a distinct productivity axis separating hybrids from parental lines, indicating that heterosis was associated with coordinated physiological and structural development rather than enhancement of individual traits alone. High-performing two-line and three-line hybrids exhibited broadly similar physiological patterns, suggesting shared physiological mechanisms underlying superior yield performance. Traits measured at 60 DAS consistently outperformed those measured at 30 DAS for predicting grain yield across environments. Chlorophyll content (SPAD60) and plant height (PH60) emerged as the most robust predictors, while stomatal conductance (FLGS60) provided complementary physiological information. Leave-one-season-out validation demonstrated that mid-vegetative traits achieved the highest predictive performance (r ≈ 0.38), whereas early-stage traits showed weak and inconsistent predictive ability. Combining early and mid-stage measurements did not improve prediction. A selection index based on key 60 DAS traits achieved a 39.08% yield advantage over the population mean. Conclusion This study demonstrates that the mid-vegetative stage represents a critical developmental transition during which physiological stabilization, canopy development, biomass consolidation, and coordinated source-sink function converge to support reliable yield prediction. Rather than identifying individual predictor traits alone, our findings establish a developmentally informed physiological framework explaining why 60 DAS provides the most biologically informative stage for hybrid evaluation across environments. This framework offers a practical basis for physiology-guided phenotyping, cost-efficient early hybrid selection, and improved decision-making in hybrid rice breeding programs.

Dissecting genetic architecture of growth and yield traits in horsegram using GWAS

Genome-wide association study of image-based traits reveals genetic architecture of salt tolerance in rice

Transgenic evaluations confirmed that OsSLT1 acts as a positive regulator of salt tolerance at the seedling stage, and no significant changes in Na+ or K+ accumulation were observed in flag leaves under the tested salt-stress condition, suggesting that OsSLT1 may regulate salt tolerance through mechanisms beyond classical shoot ion accumulation.

Agronomic Evaluation and MGDI-Based Multi-Trait Selection of S4 Sweet Corn Inbred Lines

Sweet corn is a high-value commodity whose productivity can be improved through hybrid variety development. The development of superior hybrids depends on establishing inbred lines with superior agronomic performance and broad phenotypic variability as the basis for parental selection. This study aimed to evaluate agronomic traits and phenotypic variability, analyze correlations among traits, perform principal component analysis (PCA), and apply the Multi-Trait Genotype–Ideotype Distance Index (MGIDI) for multi-trait selection in S4 sweet corn inbred lines. The experiment was conducted at the Experimental Farm of Universitas Andalas from January to April 2025. An Augmented II design consisting of two blocks was used with 36 S4 inbred lines, comprising 33 test lines and 3 check lines. Results revealed wide phenotypic variability for most agronomic traits, except ear diameter and total soluble solids. Yield exhibited positive correlations with plant height, ear length, and ear diameter, but negative correlations with flowering and harvest traits. In contrast, total soluble solids were not significantly correlated with the other agronomic traits. Principal component analysis (PCA) showed that most agronomic traits contributed to the variation among inbred lines, whereas total soluble solids had a relatively small contribution. Based on multi-trait selection using MGIDI, seven inbred lines, i.e. CD3, MM1, AG2, PN1, CD2, IN2, and SL1, were identified as the closest to the ideotype. These lines have the potential to be prioritized as parental lines and should be further evaluated in the S5 generation and subjected to combining-ability evaluations in hybrid sweet corn breeding.