Background: Aroma is an important grain quality trait in rice, controlled by mutations within the BADH2 gene. The trait is simply inherited, and its importance in variety development makes it a practical target for marker-assisted selection in applied breeding programs. The predominant functional mutation within BADH2, an 8-bp indel, can be reliably detected using a PCR-based assay, but the available assays and associated genotyping platforms are insufficient for large-scale applied molecular breeding applications and are not compatible with outsourcing genotyping services. Results: We first characterized SNP diversity across the BADH2 gene in a collection of 2932 rice varieties to determine the number of gene haplotypes in O. sativa. Using 297 gene-based SNPs, 11 haplotype groups were detected, and subsequently identified a minimal set of nine informative SNPs that uniquely identified the BADH2 haplotypes. These nine SNPs were developed into KASP assays and used to examine a panel of 369 U.S. rice accessions. The panel represented modern breeding germplasm and included all known aroma pedigree sources in U.S. rice. Six haplotypes were detected within the U.S. panel, of which two were found in majority (85%) of varieties. A representative set of 39 varieties from all haplotype groups was evaluated phenotypically to distinguish aromatic from non-aromatic lines. Conclusion: One haplotype (Hap 6) was found to be perfectly associated with the aromatic phenotype. A single KASP SNP unique to Hap 6 was demonstrated to reliably differentiate aromatic from non-aromatic rice varieties across U.S. germplasm.

The potential of genomic selection (GS) to increase the efficiency of breeding programs has been clearly demonstrated; however, the implementation of GS in rice ( Oryza sativa L.) breeding programs has been limited. In recent years, efforts have begun to work toward implementing GS into the Louisiana State University (LSU) Agricultural Center rice breeding program. One of the first steps for successful GS implementation is to establish a suitable marker set for the target germplasm and a reliable, cost‐effective genotyping platform capable of providing informative marker data with an adequate turnaround time. The objective of this study was to develop a marker set for routine GS and demonstrate its effectiveness in southern U.S. rice germplasm. The utility of the resulting marker set, the LSU500, for GS applications was demonstrated using four years of breeding data across 7,607 experimental lines and four elite biparental populations. The predictive ability of GS ranged from 0.13 to 0.78 for key traits across different market classes and yield trials. Comparisons between phenotypic selection and GS within biparental populations demonstrates similar performance of GS compared with phenotypic selection in predicting future performance. The prediction accuracies obtained with the LSU500 marker set demonstrates the utility of this marker set for cost‐effective GS applications in southern U.S. rice breeding programs. The LSU500 marker set has been established through the genotyping service provider Agriplex Genomics, and in the future, it will undergo improvements to reduce the cost and increase the accuracy of GS.

Improving drought resistance in crops is imperative under the prevailing erratic rainfall patterns. Drought affects the growth and yield of most modern rice varieties. Recent breeding efforts aim to incorporate drought resistance traits in rice varieties that can be suitable under alternative irrigation schemes, such as in a (semi)aerobic system, as row (furrow-irrigated) rice. The identification of quantitative trait loci (QTLs) controlling grain yield, the most important trait with high selection efficiency, can lead to the identification of markers to facilitate marker-assisted breeding of drought-resistant rice. Here, we report grain yield QTLs under greenhouse drought using an F_2:3 population derived from Cocodrie (drought sensitive) × Nagina 22 (N22) (drought tolerant). Eight QTLs were identified for yield traits under drought. Grain yield QTL under drought on chromosome 1 (phenotypic variance explained (PVE) = 11.15%) co-localized with the only QTL for panicle number (PVE = 37.7%). The drought-tolerant parent N22 contributed the favorable alleles for all QTLs except qGN3.2 and qGN5.1 for grain number per panicle. Stress-responsive transcription factors, such as ethylene response factor, WD40 domain protein, zinc finger protein, and genes involved in lipid/sugar metabolism were linked to the QTLs, suggesting their possible role in drought tolerance mechanism of N22 in the background of Cocodrie, contributing to higher yield under drought.

Core Ideas: Genomic data from diverse germplasm used for application in targeted breeding germplasm. Six SNPs identified that can characterize all haplotypes present at SD1 locus in diverse rice. Three alleles of the SD1 gene identified in US rice germplasm including two semidwarf alleles. Two SNPs identified and validated that differentiate the SD1 allele present in US germplasm. KASP assays designed for both SNPs for use in high‐throughput breeding applications. Plant height is an important target in US rice ( Oryza sativa L.) breeding programs and the large effect of the sd1 semidwarf gene makes it a suitable target for marker‐assisted selection. Although the deletion underlying the semidwarf allele is known and a gel‐based DNA marker is available, this marker is not ideal for applied breeding because of throughput and cost constraints. The objectives of this study were to characterize the haplotype diversity at the SD1 locus within US rice germplasm and develop a single nucleotide polymorphism (SNP) assay for breeding applications. The International Rice Research Institute (IRRI) SNP‐Seek database was used to characterize the haplotype diversity present at the SD1 locus across a set of rice accessions and seven haplotypes were identified. The US rice germplasm was not well represented in the IRRI database, so a set of six SNPs was identified that could differentiate all detected haplotypes. These SNPs were designed into Kompetitive allele specific polymerase chain reaction (KASP) assays and screened across 359 elite US genotypes. Of the seven haplotypes, two were present within the US germplasm, one of which was the semidwarf deletion allele. A third haplotype was observed within the US medium‐grain germplasm and demonstrated to be a semidwarf allele derived from the induced mutation in the ‘Calrose76’. Two SNPs were identified that distinguish the three SD1 haplotypes present in the US germplasm. These SNPs were validated across the US germplasm and two biparental populations.

Background and objectives: To maintain the competitiveness of U.S. long‐grain rice in U.S. and foreign markets, having translucent whole milled grain is critical. An objective technique to detect grain chalk, opaque areas in the grain, will provide breeders and industry with an effective tool for developing low‐chalk varieties or agronomic practices that reduce chalk occurrence. Two instruments developed at the Center for Grain and Animal Health Research, U.S. Department of Agriculture‐Agricultural Research Service (USDA‐ARS), a single‐kernel near‐infrared (SKNIR) tube instrument and a silicon‐based light‐emitting diode (SiLED) high‐speed sorter, were compared with two commercially available imaging instruments, WinSEEDLE and SeedCount used for chalk quantification. Three 2‐way chalk classifications were defined for single kernels based on visual inspection: (a) <50% or ≥50% opacity or chalk (modified Grain Inspection, Packers & Stockyards Administration [GIPSA]), (b) <10% or ≥10% opacity (10% cutoff), and (c) 100% opacity or 100% translucent (MaxLevel).Findings: The SKNIR method provided the best classification for the modified GIPSA definition with an 82.4% average correct classification (CC), that is, 89% and 76% for nonchalky and chalky kernels, respectively. The WinSEEDLE had the best classification for the 10% cutoff definition, with an 84% CC for nonchalky kernels and a 96% CC for chalky kernels. For the MaxLevel definition, average CCs of both the SKNIR and SiLED methods were similar, at 93% and 95%, respectively. The average CCs were lower for both the WinSEEDLE method and the SeedCount method at 14% and 58%, respectively. These low CC values are a result of using a threshold of 100% for chalky or nonchalky kernels, where a single misclassified pixel within the image will cause misclassification. Calibration models developed for both the SKNIR and SiLED methods indicate that their classifications were based mainly on spectral differences near the adsorption bands for starch, protein, and water content.Conclusions: All of the instruments can be used to classify chalk, but their level of accuracy depends on how chalk is defined.Significance and novelty: The SiLED has the capability to process seeds at a high rate, and the SKNIR has the potential to measure compositional traits in addition to chalk measurements.

Rice (Oryza sativa L.) yield and grain quality are highly influenced by the planting date; thus, it is crucial to determine the optimal planting window for an area of cultivation. In this study, date of planting experiments conducted at the Louisiana State University (LSU) AgCenter Rice Research Station from 2011 to 2018 were analyzed to quantify the effect of planting date, genotype, and year on key agronomic traits. Planting date was strongly associated with yield across every year, explaining 55.6% of the overall phenotypic variation. A clear relationship was observed between later plantings and reduced yields; and the highest yields were observed during plantings between 11 and 31 March. Rice planted after the optimal planting window displays a yield reduction following a cubic regression trend. Planting date was associated with milling yield, explaining 28.6% of the phenotypic variation, however the effect size varied year to year. Milling yields were highest with the early and late planting dates, while reduced milling yields were observed in plantings between mid‐April and early June. Days to heading and days to seedling emergence were strongly associated with planting date; with planting date explaining, on average, 86 and 96% of the variation, respectively. Plant height was less influenced by planting date, with genotype being the largest source of variation. The results from this study highlight the importance of choosing the optimal planting date and identifies an optimal planting window to maximize grain and milling yield for Southwest Louisiana.