Nitrogen fertilization and planting density are two key factors that can interactively affect the grain yield of rice. Three different types of rice cultivars—inbred Shendao 47, inbred Shendao 505, and hybrid Jingyou 586—were applied to investigate the effects of the nitrogen (N) rate and planting density (D) on the aboveground biomass, harvest index, leaf photosynthetic features, grain yield, and yield components using a split-split-plot design at two sites over two continuous years. The main plots were assigned to four nitrogen fertilizer rates: 0 (N0), 140 (N1), 180 (N2), and 220 (N3) kg ha⁻¹ N; the subplots were assigned to three planting densities: 25 × 10⁴ (D1), 16.7 × 10⁴ (D2), and 12.5 × 10⁴ (D3) hills ha^-1, and the sub-subplots were assigned to three rice cultivars. The results showed that the grain yield had a significantly positive correlation with the stomatal conductance (Gs), net photosynthesis rate (Pn), transpiration rate (Tr), chlorophyll content (SPAD value), leaf area index (LAI), panicles per unit area, and spikelets per panicle. The N rate and planting density had significant interaction effects on grain yield, and the maximum values of Shendao 47, Shendao 505, and Jingyou 586 appeared in N3D2, N2D1, and N3D3, respectively. The higher grain yield of midsized panicle Shendao 47 was mostly ascribed to both panicles per unit area and spikelets per panicle. More panicles per unit area and spikelets per panicle primarily contributed to a larger sink capacity of small-sized panicle rice Shendao 505 and large-sized panicle rice Jingyou 586. We found that the treatments N3D2, N2D1, and N3D3 could optimize the contradiction between yield formation factors for Shendao 47, Shendao 505, and Jingyou 586, respectively. Across years and sites, the regression analysis indicated that the combinations of nitrogen fertilization of 195.6 kg ha⁻¹ with a planting density of 22 × 10⁴ hills ha⁻¹, 182.5 kg ha⁻¹ with 25 × 10⁴ hills ha⁻¹, and 220 kg ha⁻¹ with 13.1 × 10⁴ hills ha⁻¹ are recommended for medium-, small-, and large-sized panicle rice cultivars, respectively.

Basmati rice is a premium aromatic rice that consumers choose primarily because of its distinct aroma and excellent grain quality. The grain quality of Basmati rice (GQBR) reflects the perspectives of producers, processors, sellers, and consumers related to the production, processing, marketing, and consumption of Basmati rice. Consumers, an invaluable part of the production demand and value chain of the Basmati rice industry, have the freedom to choose from different types of aromatic rice. Consumers expect their preferred Basmati rice to possess all superior rice grain qualities, including the physical, biochemical, and physiological properties. Gene functional analysis explained that a 10-base pair deletion in the promoter region of the OsSPL16 gene causes the slender grains in Basmati rice, whereas an 8-base-pair deletion in exon 7 of the OsBadh2 gene (located in the fgr region on rice chromosome 8) results in the distinct aroma. Furthermore, a combination of the genetic characteristics of the gw8 and gs3 genes has led to the creation of a long-grain Basmati-type rice cultivar. It has also been demonstrated that agricultural, genetic, and environmental conditions significantly influence GQBR. Hence, research on improving GQBR requires a multidimensional approach and sophisticated elements due to the complexity of its nature and preference diversity. This review covers the basic definitions of grain quality traits, consumer preference criteria, influencing factors, and strategies for producing superior-quality Basmati rice in the United States. This knowledge will be useful in improving the grain quality of Basmati and Basmati-type rice, as well as developing appropriate breeding programs that will meet the preferences of different countries and cultures.

Apparent amylose and protein concentrations (AAC and PC) affect the milling, cooking, and eating quality of rice. This study was conducted to assess the phenotypic and genotypic variation and to identify genomic regions and putative candidate genes associated with AAC and PC in milled rice grain. Two hundred and seventeen and 207 diverse rice accessions were grown at the Texas A&M AgriLife Research Center in 2018 and 2019, respectively. Milled rice samples were analyzed for AAC and PC using the iodine colorimetry and Dumas method, respectively. Genome-wide association studies (GWAS) for AAC and PC were conducted using 872,556 single nucleotide polymorphism (SNP) markers following the mixed linear model. Significant variation among the accessions was found for both variables each year. Associations between 32 SNPs with PC and seven SNPs with AAC were detected. Gene models linked to these SNPs have a wide range of biological functions, including protein and carbohydrate metabolism, DNA methylation, and response to abiotic and biotic stresses. Seven of the identified SNPs colocalized with previously reported quantitative trait loci (QTL) for protein concentration. Fine-mapping of significant genomic regions and gene validation are necessary for this information to aid in marker-assisted breeding for improved grain quality.

The development of stress-resistant crops without compromising their yield and nutritional quality is one of the major challenges in plant biology. Plant breeders have made considerable effort in developing cultivars with high yield and tolerance to biotic and abiotic stresses. This research aimed to evaluate pre-breeding resources for favorable genes conferring good yield and resistance to biotic and abiotic stresses for rice varietal improvement. Two hundred forty diverse rice cultivars from top countries with which the United States imports and exports rice were selected from the Germplasm Resource Information Network (GRIN) collection, and these were characterized using molecular markers for various desirable traits. Among the cultivars, 16 were used as checks. The genes of various traits evaluated were grain number (Gn1a), panicle weight (WFP), cold stress tolerance (qSCT-11 and qCTS-12), anaerobic germination (AG1 and AG2), rice blast disease resistance (PiB, Pi-ta, Ptr, and Piz), wide compatibility (S5-n), and restoring ability (Rf4). Out of these 240 accessions, 192 have the Gn1a gene, 168 possess the qSCT-11 cold tolerance gene, 139 carry the Rf4 gene, 152 have the qCTS12 cold tolerance gene, 87 have blast resistance genes, and 44 possess the WC gene. None of the 240 accessions possess the WFP (Wealthy Farmer’s Panicle) gene. The selections with favorable genes can be used by breeders as potential donor parents for varietal improvement programs to produce new and promising cultivars with desired specific traits that may contribute towards alleviating global issues related to food security.

The quantity and quality of light captured by a plant’s canopy control many of its growth and development processes. However, light quality-related processes are not very well represented in most traditional and functional–structural crop models, which has been a major barrier to furthering crop model improvement and to better capturing the genetic control and environment modification of plant growth and development. A main challenge is the difficulty in obtaining dynamic data on plant canopy architectural characteristics. Current approaches on the measurement of 3D traits often relies on technologies that are either costly, excessively complicated, or impractical for field use. This study presents a methodology to estimate plant 3D traits using smart mobile app and data modeling. Leaf architecture data on 16 genotypes of rice were collected during two crop seasons using the smart-app PocketPlant3D. Quadratic Bézier curves were fitted to leaf lamina for estimation of insertion angle, elevation angle, and curve height. Leaf azimuth angle distribution, leaf phyllotaxis, canopy leaf angle distribution, and light extinction coefficients were also analyzed. The results could be used for breeding line selection or for parameterizing or evaluating rice 3D architectural models. The methodology opens new opportunities for strengthening the integration of plant 3D architectural traits in crop modeling, better capturing the genetic control and environment modification of plant growth and development, and for improving ideotype-based plant breeding.

High head rice and low chalky grain percentages are key grain quality traits selected in developing rice cultivars. The objectives of this research were to characterize the phenotypic variation of head rice and chalky grain percentages in a diverse collection of rice accessions, identify single nucleotide polymorphism (SNP) markers associated with each of these traits using genome-wide association studies (GWAS), and identify putative candidate genes linked to the SNPs identified by GWAS. Diverse rice varieties, landraces, and breeding lines were grown at the Texas A&M AgriLife Research Center in Beaumont. Head rice percentages (HRP) and chalky grain percentages (CGP) of 195 and 199 non-waxy accessions were estimated in 2018 and 2019, respectively. Phenotypic data were analyzed along with 854,832 SNPs using three statistical models: mixed linear model (MLM), multi-locus mixed model (MLMM), and fixed and random model circulating probability unification (FarmCPU). Significant variations in HRP and CGP were observed between rice accessions. Two significant marker-trait associations (MTAs) were detected on chromosomes 1 and 2, respectively, based on best linear unbiased prediction (BLUP) values in 2018, while in 2019, one SNP was significantly associated with HRP in each of chromosomes 6, 8, 9, and 11, and two in chromosome 7. CGP was significantly associated with five SNPs located in chromosomes 2, 4, 6, and 8 in the 2018 study and ten SNPs in chromosomes 1, 2, 3, 4, 7, 8, 11, and 12 in the 2019 study. The SNPs are located within or linked to putative candidate genes involved in HRP and CGP. This study reports five and ten novel MTAs for HRP and CGP, respectively, while three and five MTAs co-located with previously reported quantitative trait loci for HRP and CGP, respectively. The validation of candidate genes for their roles in determining HRP and CGP is necessary to design functional molecular markers that can be used to effectively develop rice cultivars with desirable grain quality.