Water deficit affects rice growth, development, and yield. Knowledge of genetic diversity for water deficit tolerance, as well as the genetic architecture that is responsible for this trait, can accelerate rice cultivars’ improvement. In this study, different tools were applied to assess genetic diversity and identify genome regions associated with shoot and root traits in rice germplasm grown under water deficit at an early vegetative stage. A panel of 177 rice genotypes grown under water deficit was evaluated for root length (RL), root dry weight (RDW), shoot length (SL), and shoot dry weight (SDW). Genetic diversity was investigated using means grouping and principal component analysis. For the genome-wide association study, a general linear model was applied, using RL, RDW, SL, and SDW phenotypic data converted into Best Linear Unbiased Prediction (BLUPs); genotypic data (1185 single nucleotide polymorphism—SNPs-loci); and population structure. Overall, little genetic diversity was observed, but genotypes with a higher water deficit tolerance were identified. Several significant SNPs were mapped, 81, 5, 53, and 41 for RL, RDW, SL, and SDW, respectively. Among the identified genes, there are those encoding kinases, proteins involved in phytohormone and cell wall metabolism, and Cytochrome P450. The obtained results provide insight into genetic diversity and the genetic architecture of water deficit tolerance, which will be useful in improving this trait in rice grown in Brazil.

The quality of rice grain impacts milling yield, market acceptance, and product value. Physical quality is determined by many traits, such as chalkiness, whiteness, vitreous whiteness, caryopsis length, and width. Breeding for these traits is challenging due to their quantitative nature, environmental effects, and time and labor requirements to evaluate these traits. The digital grain analyzer (S21) equipment determines rice grain physical quality by image-based analysis; however, its use remains restricted. Thus, here we aimed to evaluate S21 efficiency to determine the physical quality of rice grains and estimate the genetic diversity of the trait using a Brazilian panel of 152 irrigated rice genotypes as a working model. We accessed total whiteness, vitreous whiteness, chalkiness degree, chalky grain rate, white belly, grain length, width, and length/width ratio. Our results demonstrated that S21 allowed the characterization of the genotypes according to physical traits, facilitating grouping and separation of accessions and correlation analyses between quality traits. It was also possible to estimate the heritability of quality traits. S21 was efficient in characterizing the physical quality of rice grains and determining their genetic diversity. The equipment is an effective tool exhibiting potential application by breeder programs.

Background: Wheat (Triticum aestivum L.) seedling performance under heat and drought stress is critical for crop establishment. Identifying cultivars with superior seedling vigor under stress conditions helps breeding programs and seed production systems. Methods: We aimed to evaluate the early growth performance of wheat seedlings from 28 Brazilian commercial cultivars under heat and osmotic stress. Seedlings were assessed through shoot and root lengths and dry weight and analyzed using Scott–Knott grouping, heritability estimates, PCA and stress tolerance index. Results: Osmotic stress affected a wider range of traits than heat stress and revealed phenotypic and genetic variability among cultivars. Root length and root dry weight exhibited high broad-sense heritability, indicating a substantial genotypic contribution under the tested conditions and supporting their use for early-stage screening. Multivariate analyses revealed contrasting adaptation strategies, including root elongation and preferential allocation of biomass to roots under stress conditions. Through uni- and multivariate analyses, we suggested cultivars are potentially tolerant to heat and osmotic stresses. Conclusions: Root-related traits proved to be the most informative indicators of adaptation to stress, and the integration of stress tolerance indices and multivariate analyses enabled the identification of promising cultivars for further validation under greenhouse and field conditions.