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Improving plant performance in salinity-prone conditions is a significant challenge in breeding programs. Genomic selection is currently integrated into many plant breeding programs as a tool for increasing selection intensity and precision for complex traits and for reducing breeding cycle length. A rice reference panel (RP) of 241 Oryza sativa L. japonica accessions genotyped with 20,255 SNPs grown in control and mild salinity stress conditions was evaluated at the vegetative stage for eight morphological traits and ion mass fractions (Na and K). Weak to strong genotype-by-condition interactions were found for the traits considered. Cross-validation showed that the predictive ability of genomic prediction methods ranged from 0.25 to 0.64 for multi-environment models with morphological traits and from 0.05 to 0.40 for indices of stress response and ion mass fractions. The performances of a breeding population (BP) comprising 393 japonica accessions were predicted with models trained on the RP. For validation of the predictive performances of the models, a subset of 41 accessions was selected from the BP and phenotyped under the same experimental conditions as the RP. The predictive abilities estimated on this subset ranged from 0.00 to 0.66 for the multi-environment models, depending on the traits, and were strongly correlated with the predictive abilities on cross-validation in the RP in salt condition (r = 0.69). We show here that genomic selection is efficient for predicting the salt stress tolerance of breeding lines. Genomic selection could improve the efficiency of rice breeding strategies for salinity-prone environments. [ABSTRACT FROM AUTHOR]

Direct seeding has been widely adopted as an economical and labor-saving technique in rice production, though problems such as low seedling emergence rate, emergence irregularity and poor lodging resistance are existing. These problems are currently partially overcome by increasing seeding rate, however it is not acceptable for hybrid rice due to the high seed cost. Improving direct seeding by breeding is seen as the ultimate solution to these problems. For hybrid breeding, identifying superior hybrids among a massive number of hybrids from crossings between male and female parental populations by phenotypic evaluation is tedious and costly. Contrastingly, genomic selection/prediction (GS/GP) could efficiently detect the superior hybrids capitalizing on genomic data, which holds a great potential in plant hybrids breeding. In this study, we utilized 402 rice inbred varieties and 401 hybrids to investigate the effectiveness of GS on rice mesocotyl length, a representative indicative trait of direct seeding suitability. Several GP methods and training set designs were studied to seek the optimal scenario of hybrid prediction. It was shown that using half-sib hybrids as training set with the phenotypes of all parental lines being fitted as a covariate could optimally predict mesocotyl length. Partitioning the molecular markers into trait-associated and -unassociated groups based on genome-wide association study using all parental lines and hybrids could further improve the prediction accuracy. This study indicates that GS could be an effective and efficient method for hybrid breeding for rice direct seeding. [ABSTRACT FROM AUTHOR]

Genomic prediction (GP) is expected to become a powerful technology for accelerating the genetic improvement of complex crop traits. Several GP models have been proposed to enhance their applications in plant breeding, including environmental effects and genotype-by-environment interactions (G×E). In this study, we proposed a two-step model for plant biomass prediction wherein environmental information and growth-related traits were considered. First, the growth-related traits were predicted by GP. Second, the biomass was predicted from the GP-predicted values and environmental data using machine learning or crop growth modeling. We applied the model to a 2-year-old field trial dataset of recombinant inbred lines of japonica rice and evaluated the prediction accuracy with training and testing data by cross-validation performed over two years. Therefore, the proposed model achieved an equivalent or a higher correlation between the observed and predicted values (0.53 and 0.65 for each year, respectively) than the model in which biomass was directly predicted by GP (0.40 and 0.65 for each year, respectively). This result indicated that including growth-related traits enhanced accuracy of biomass prediction. Our findings are expected to contribute to the spread of the use of GP in crop breeding by enabling more precise prediction of environmental effects on crop traits. [ABSTRACT FROM AUTHOR]

The development of new improved varieties is one of the major goals of plant breeding. Concomitantly, the demand for stable, eco-friendly, and high-quality rice production is constantly increasing. However, most farmers prefer to cultivate familiar rice varieties developed more than 10 years ago to minimize economic risk. A strategy is needed to develop rice varieties without the limitations of the preferred old varieties. Here, we tested the rapid development of near isogenic lines (NILs) using a rapid generation advance system together with marker-assisted backcrossing to overcome the shortcomings of parental materials. For this purpose, we chose rice stripe virus (RSV) susceptible variety Unkwang and RSV resistant variety Haedamssal as experimental materials. First, we backcrossed and screened BC1F1 and BC2F1 plants having similar agronomic traits as Unkwang and the heterozygous genotype for RSV resistant specific marker InDel7 from Haedamssal. Secondly, the genetic background of 11 BC2F1 plants was identified with 73 KASP markers; plants of line YR32548-8 showed 84.5% of recovery of the recurrent parent genome. Among 28 BC2F2 plants, YR32548-8-16 was the line that showed maximum recovery of the recurrent parent genome (96.2%) while effectively introgressed with RSV-resistance loci on chromosome 11. Finally, we selected line YR32548-8-16 as an NIL showing an RSV resistant phenotype and similar agronomic traits to Unkwang. This fast breeding approach will be useful in rice breeding programs for the improvement of varieties preferred by farmers for their stress tolerance, yield, or quality. [ABSTRACT FROM AUTHOR]

Rice breeders in Asia and elsewhere in the world have long overlooked trying to shorten the time it takes to develop new varieties. Plant breeders have proposed a technique called Rapid Generation Advance (RGA) as a way to accelerate the results of public rice breeding programs. However, little is known about RGA’s potential impact. Here, we present the first results of a global impact study of RGA. More specifically, we calculated the multiplicator effects of RGA on the research benefits generated by conventional rice breeding programs and applied them to a meta-analysis of selected impact studies in the literature. These insights are a first crucial step in developing a targeted approach for disseminating RGA technology among rice breeders to accelerate the impact of their public rice breeding programs around the world. We show that the additional benefits due to time savings are considerable and offer some insights into the economics of breeding. Our results confirm that the adoption of accelerated breeding would lead to substantial advantages to rice breeding programs and the earlier variety release leads to significant economic benefits to society. This can be important to policy makers when reshaping their public breeding methods and optimising their return on research investments in breeding. [ABSTRACT FROM AUTHOR]

While the functionality and healthy food value of red rice have increased its popularity, such that market demand for it is expected to rise, most strains suffer from low grain yield. To perform diversity and population structure analyses of red rice germplasm, therefore, becomes essential for improving yields for commercial production. In this study, fifty red rice germplasm from the Bangladesh Rice Research Institute (BRRI) genebank were characterized both morphologically and genetically using fifty simple sequence repeat (SSR) markers. Overall, 162 alleles were detected by the markers with the detected allele number varying from two to seven. Additionally, 22 unique alleles were identified for use as a germplasm diagnostic tool. The highest and lowest polymorphic information content (PIC) indices were 0.75 and 0.04 found in markers RM282 and RM304, respectively, and genetic diversity was moderate, varying from 0.05 to 0.78 (average: 0.35). While phylogenetic cluster analysis of the fifteen distance-based agro-morphological traits divided the germplasm into five clusters (I, II, III, IV and V), a similar SSR analysis yielded only three major groups (I, II, and III), and a model-based population structure analysis yielded four (A, B, C and D). Both principal component and neighbors joining tree analysis from the population structure method showed the tested germplasm as highly diverse in structure. Moreover, an analysis of molecular variance (AMOVA), as well as a pairwise FST analysis, both indicated significant differentiation (ranging from 0.108 to 0.207) among all pairs of populations, suggesting that all four population structure groups differed significantly. Populations A and D were the most differentiated from each other by FST. Findings from this study suggest that the diverse germplasm and polymorphic trait-linked SSR markers of red rice are suitable for the detection of economically desirable trait loci/genes for use in future molecular breeding programs. [ABSTRACT FROM AUTHOR]

Plant architecture is an important agronomic trait, and improving plant architecture has attracted the attention of scientists for decades, particularly studies to create desirable plant architecture for high grain yields through breeding and culture practices. However, many important structural phenotypic traits still lack quantitative description and modeling on structural-functional relativity. This study defined new architecture indices (AIs) derived from the digitalized plant architecture using the virtual blade method. The influences of varieties and crop management on these indices and the influences of these indices on biomass accumulation were analyzed using field experiment data at two crop growth stages: early and late panicle initiation. The results indicated that the vertical architecture indices (LAI, PH, 90%-DRI, MDI, 90%-LI) were significantly influenced by variety, water, nitrogen management and the interaction of water and nitrogen, and compact architecture indices (H-CI, Q-CI, 90%-LI, 50%-LI) were significantly influenced by nitrogen management and the interaction of variety and water. Furthermore, there were certain trends in the influence of variety, water, and nitrogen management on AIs. Biomass accumulation has a positive linear correlation with vertical architecture indices and has a quadratic correlation with compact architecture indices, respectively. Furthermore, the combination of vertical and compact architecture indices is the indicator for evaluating the effects of plant architecture on biomass accumulation. [ABSTRACT FROM AUTHOR]

A total of 787 non-waxy rice lines– 116 hybrids and 671 inbreds–were used to study the apparent amylose content (AAC), gelatinization temperature (GT), and rapid visco analyzer (RVA) pasting viscosity properties of rice starch to understand their importance in breeding better rice varieties. The investigated traits showed a wide range of diversity for both hybrid (HG) and inbred (IG) groups. The combinations of the different categories of AAC and GT were random in HG but were non-random in IG. For inbred lines, the high level of AAC tended to combine with the low level of GT, the intermediate level of AAC tended to have high or intermediate GT, and the low level of AAC tended to have high or low GT. Some stable correlations of the AAC, GT, and RVA properties may be the results derived from the physicochemical relationships among these traits, which rice breeders could utilize for selection in advanced breeding generations. Through cluster analysis, IG and HG were divided into 52 and 31 sub-clusters, respectively. Identifying the cultivars having AAC, GT, and RVA properties similar to that of popular high-quality rice varieties seems to be an interesting strategy and could be directly used for adaptation trials to breed high-quality rice varieties in targeted areas in a more customized manner. [ABSTRACT FROM AUTHOR]

Rice blast (Magnaporthe oryzae) is a key concern in combating global food insecurity given the disease is responsible for approximately 30% of rice production losses globally—the equivalent of feeding 60 million people. These losses increase the global rice price and reduce consumer welfare and food security. Rice is the staple crop for more than half the world’s population so any reduction in rice blast would have substantial beneficial effects on consumer livelihoods. In 2012, researchers in the US began analyzing the feasibility of creating blast-resistant rice through cisgenic breeding. Correspondingly, our study evaluates the changes in producer, consumer, and environmental welfare, if all the rice produced in the Mid-South of the US were blast resistant through a process like cisgenics, using both international trade and environmental assessment modeling. Our results show that US rice producers would gain 69.34 million dollars annually and increase the rice supply to feed an additional one million consumers globally by eliminating blast from production in the Mid-South. These results suggest that blast alleviation could be even more significant in increasing global food security given that the US is a small rice producer by global standards and likely experiences lower losses from blast than other rice-producing countries because of its ongoing investment in production technology and management. Furthermore, results from our detailed life cycle assessment (LCA) show that producing blast-resistant rice has lower environmental (fossil fuel depletion, ecotoxicity, carcinogenics, eutrophication, acidification, global warming potential, and ozone depletion) impacts per unit of rice than non-blast resistant rice production. Our findings suggest that any reduction in blast via breeding will have significantly positive impacts on reducing global food insecurity through increased supply, as well as decreased price and environmental impacts in production. [ABSTRACT FROM AUTHOR]

Quantitative trait loci conferring high grain yield under drought in rice are important genomic resources for climate resilient breeding. Major and consistent drought grain yield QTLs usually co-locate with flowering and/or plant height QTLs, which could be due to either linkage or pleiotropy. Five mapping populations used for the identification of major and consistent drought grain yield QTLs underwent multiple-trait, multiple-interval mapping test (MT-MIM) to estimate the significance of pleiotropy effects. Results indicated towards possible linkages between the drought grain yield QTLs with co-locating flowering and/or plant height QTLs. Linkages of days to flowering and plant height were eliminated through a marker-assisted breeding approach. Drought grain yield QTLs also showed interaction effects with flowering QTLs. Drought responsiveness of the flowering locus on chromosome 3 (qDTY3 . 2) has been revealed through allelic analysis. Considering linkage and interaction effects associated with drought QTLs, a comprehensive marker-assisted breeding strategy was followed to develop rice genotypes with improved grain yield under drought stress. [ABSTRACT FROM AUTHOR]