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 genus Cymbopogon comprises neocosmopolitan grasses widely used as medicinal plants and in the perfume, pharmaceutical and herbal product industries. Despite their economic relevance, these species are still considered orphan crops, with limited phytotechnical, genomic and evolutionary studies within the Poaceae family. In this study, we investigated the evolutionary relationships of Cymbopogon flexuosus and Cymbopogon winterianus, with a focus on differences in gene expression associated with the biosynthesis of secondary metabolites. De novo transcriptome assembly yielded 25,576 transcripts in C. flexuosus and 42,250 in C. winterianus. A total of 5318 and 8631 more informative differentially expressed transcripts (DETs) were identified in each mapping, among which 76 and 94 were associated with secondary metabolism pathways. When mapping the libraries against related species, the highest percentages of mapped reads per transcriptome and per gene (depth) were observed in Andropogon gerardi, Sorghum bicolor, Saccharum officinarum, Miscanthus sinensis, Miscanthus lutarioriparius and Zea mays. These results indicate A. gerardi, S. bicolor and Z. mays as the most promising genomic models for future studies within the genus Cymbopogon. Comparison of the expression of transcripts that are homologous to the precursor enzymes of terpenoids, phenylpropanoids, flavonoids and other secondary metabolites revealed a complex and non-linear interaction between the metabolic pathways in each species and it was not possible to predict the predominance of greater expression of a class of metabolites on a given species.

A rice grain’s proximate composition determines its nutritional potential. Macronutrient quantification is essential to identify superior genotypes and direct breeding efforts to reach more people who are vulnerable. Conventional methods to determine proximate composition are highly accurate; however, they remain time-consuming, costly, and destructive. Near-infrared (NIR) spectroscopy enables proximate composition analysis in a non-destructive, rapid, inexpensive, and practical manner, providing results similar to well-established conventional methods. This study aimed to evaluate the feasibility of NIRs-based selection to identify more nutritious rice genotypes. A collection of 155 rice genotypes grown in Southern Brazil was used. After harvest, grains were hulled, polished, and milled. NIRs was used to determine moisture, starch, protein, fat, ash, and fiber contents in rice flour. It was possible to differentiate genotypes with higher and lower levels of the investigated components. Similar and distinct values were observed in comparison to other studies, indicating the accuracy of NIRs and the effect of genotype and environment, respectively. Starch is correlated negatively with protein and fat, preventing the identification of genotypes with high levels of these three components. PCA enabled the separation of the genotypes but highlighted the complexity of sample distribution. NIRs is an effective and accurate method to determine the proximate composition of rice, enabling the selection of more nutritious genotypes.

Rice (Oryza sativa L.) is a staple food for more than half of the world's population, but its production is threatened by salinity, which affects its development in both early and reproductive stages. Gamma radiation-induced mutation has been used to generate genetic variability and develop cultivars better adapted to saline conditions. However, selecting tolerant mutants is challenging due to the genetic complexity of salinity response and the need for large populations. In this study, 100 rice mutants (M5 and M6) and two control cultivars (sensitive and tolerant) were evaluated under salt stress (NaCl 120 mM) in a greenhouse, assessing shoot and root growth and dry weight. Despite the variability generated, no mutant outperformed the tolerant cultivar in all traits analyzed, highlighting the difficulty of selecting promising individuals from small populations. Furthermore, environmental factors may have contributed to inconsistencies between generations, reinforcing the need for large-scale screening. The most effective strategy involves initial field selection, validation under controlled conditions, and further agronomic reassessment. Technologies such as remote sensing-based phenotyping could improve efficiency, but they remain costly. Future studies should integrate new methodologies and keep the selection of salt-tolerant mutants in early generations (M2 and M3) from large populations, alongside yield evaluation to confirm their agronomic applicability under salinity conditions.