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Four hundred and thirty-seven different cultivars (59 Aromatics, 53 Hybrids, 172 Indica, 92 New Planting Types and 61 Varieties) were analyzed for grain yield (GY), straw nitrogen (N) content, neutral (NDF) and acid (ADF) detergent fiber, acid detergent lignin (ADL), silica, in vitro organic matter digestibility (IVOMD) and metabolizable energy (ME) and for relationships between GY and these straw fodder quality traits. Highly significant (P

Rice yield potential has been stagnant since the Green Revolution in the late 1960s, especially in tropical rice cultivars. We evaluated the effect of two major genes that regulate grain number, Gn1a/OsCKX2 and IPA1/WFP/OsSPL14, in elite indica cultivar backgrounds. The yield-positive Gn1a-type 3 and OsSPL14WFP alleles were introgressed respectively through marker-assisted selection (MAS). The grain numbers per panicle (GNPP) were compared between the recipient allele and the donor allele groups using segregating plants in BC3F2 and BC3F3 generations. There was no significant difference in GNPP between the two Gn1a alleles, suggesting that the Gn1a-type 3 allele was not effective in indica cultivars. However, the OsSPL14WFP allele dramatically increased GNPP by 10.6–59.3% in all four different backgrounds across cropping seasons and generations, indicating that this allele provides strong genetic gain to elite indica cultivars. Eventually, five high-yielding breeding lines were bred using the OsSPL14WFP allele by MAS with a conventional breeding approach that showed increased grain yield by 28.4–83.5% (7.87–12.89 t/ha) vis-à-vis the recipient cultivars and exhibited higher yield (~64.7%) than the top-yielding check cultivar, IRRI 156 (7.82 t/ha). We demonstrated a strong possibility to increase the genetic yield potential of indica rice varieties through allele mining and its application.

Over the past 15 years, biofortification, the process of breeding nutrients into food crops, has gained ample recognition as a cost-effective, complementary, feasible means of delivering micronutrients to populations that may have limited access to diverse diets, supplements, or commercially fortified foods. In 2008, a panel of noted economists that included five Nobel Laureates ranked biofortification fifth among the most cost-effective solutions to address global challenges such as reducing hidden hunger. The 2016 World Food Prize was awarded to biofortification. Biofortification involves breeding staple food crops to increase their micronutrient content, targeting foods widely consumed by low-income families in Africa, Asia, and Latin America. The focus is on providing sufficient levels of vitamin A, iron, and/or zinc through these crops, based on existing consumption patterns. HarvestPlus is part of the CGIAR Research Program on Agriculture for Nutrition and Health (A4NH). HarvestPlus works in partnership with more than 200 scientific and implementation organizations around the world to improve nutrition and public health by developing and promoting biofortified food crops that are rich in vitamins and minerals, and providing global leadership on biofortification evidence and technology. Crops bred for higher levels of micronutrients using conventional breeding methods have been released in 26 countries in Africa, Asia and Latin America, and are now being grown and eaten by millions of farmers and consumers. This paper reviews crop development progress and varietal release of primary (major) and secondary (regionally important) staple crops, with a focus on progress in Africa. Keywords: Biofortification, Micronutrients, Micronutrient Deficiency, Staple Crops, Breeding, Provitamin A, Iron, Zinc

Background Nutrient deficiency in humans, especially in children and lactating women, is a major concern. Increasing the micronutrient concentration in staple crops like rice is one way to overcome this. The micronutrient content in rice, especially the iron (Fe) and zinc (Zn) content, is highly variable. The identification of rice genotypes in which there are naturally high Fe and Zn concentrations across environments is an important target towards the production of biofortified rice. Results Phenotypic correlations between grain Fe and Zn content were positive and significant in all environments but a significant negative association was observed between grain yield and grain Fe and Zn. Promising breeding lines with higher Zn or Fe content, or both, were: IR 82475-110-2-2-1-2 (Zn: 20.24-37.33 mg kg-1 ; Fe: 7.47-14.65 mg kg-1 ); IR 83294-66-2-2-3-2 (Zn: 22-37-41.97 mg kg-1 ; Fe: 9.43-17.16); IR 83668-35-2-2-2 (Zn: 27.15-42.73 mg kg-1 ; Fe: 6.01-14.71); IR 68144-2B-2-2-3-1-166 (Zn: 23.53-40.30 mg kg-1 ; Fe: 10.53-17.80 mg kg-1 ) and RP Bio 5478-185M7 (Zn: 22.60-40.07 mg kg-1 ; Fe: 7.64-14.73 mg kg-1 ). Among these, IR82475-110-2-2-1-2 (Zn: 20.24-37.33 mg kg-1 ; Fe: 7.47-14.65 mg kg-1 ) is also high yielding with 3.75 t ha-1 . Kelhrie Cha (Zn: 17.76-36.45 mg kg-1 ; Fe: 7.17-14.77 mg kg-1 ), Dzuluorhe (Zn: 17.48-39.68 mg kg-1 ; Fe: 7.89-19.90 mg kg-1 ), Nedu (Zn: 18.97-43.55 mg kg-1 Fe: 8.01-19.51 mg kg-1 ), Kuhusoi-Ri-Sareku (Zn: 17.37-44.14 mg kg-1 ; Fe: 8.99-14.30 mg kg-1 ) and Mima (Zn: 17.10-45.64 mg kg-1 ; Fe: 9.97-17.40 mg kg-1 ) were traditional donor genotypes that possessed both high grain Fe and high Zn content. Conclusion Significant genotype × location (G × L) effects were observed in all traits except Fe. Genetic variance was significant and was considerably larger than the variance of G × L for grain Zn and Fe content traits, except grain yield. The G × L × year variance component was significant in all cases. © 2020 Society of Chemical Industry.

Highlight Experimental design and phenotyping methods were explored to enable efficient evaluation of rice stem non-structural carbohydrates for genetic studies. Evidence was found for phenology-dependent yield and stem NSC relationships., Rice plants (Oryza sativa) accumulate excess photoassimilates in the form of non-structural carbohydrates (NSCs) in their stems prior to heading that can later be mobilized to supplement photosynthate production during grain-filling. Despite longstanding interest in stem NSC for rice improvement, the dynamics of NSC accumulation, remobilization, and re-accumulation that have genetic potential for optimization have not been systematically investigated. Here we conducted three pilot experiments to lay the groundwork for large-scale diversity studies on rice stem NSC. We assessed the relationship of stem NSC components with 21 agronomic traits in large-scale, tropical yield trials using 33 breeder-nominated lines, established an appropriate experimental design for future genetic studies using a Bayesian framework to sample sub-datasets from highly replicated greenhouse data using 36 genetically diverse genotypes, and used 434 phenotypically divergent rice stem samples to develop two partial least-squares (PLS) models using near-infrared (NIR) spectra for accurate, rapid prediction of rice stem starch, sucrose, and total non-structural carbohydrates. We find evidence that stem reserves are most critical for short-duration varieties and suggest that pre-heading stem NSC is worthy of further experimentation for breeding early maturing rice.

Micronutrient especially iron and zinc-enriched rice hold immense promise for sustainable and cost-effective solutions to overcome malnutrition. In this context, BC2F5 population derived from cross between RP-Bio226 and Sampada was used to localize genomic region(s)/QTL(s) for grain Fe (iron) and Zn (zinc) content together with yield and yield-related traits. Genotyping of mapping population with 108 SSR markers resulted in a genetic map of 2317.5 cM with an average marker distance of 21.5 cM. Mean grain mineral content in the mapping population across the two seasons ranged from 10.5–17.5 ppm for Fe and 11.3–22.1 ppm for Zn. Based on the multi-season phenotypic data together with genotypic data, a total of two major QTLs for Fe (PVE upto 17.1%) and three for Zn (PVE upto 34.2%) were identified. Comparative analysis across the two seasons has revealed four consistent QTLs for Fe (qFe1.1, qFe1.2, qFe6.1 and qFe6.2) and two QTL for Zn content (qZn1.1 and qZn6.2). Additionally, based on the previous and current studies three meta-QTLs for grain Fe and two for grain Zn have been identified. In-silico analysis of the identified QTL regions revealed the presence of potential candidate gene(s) such as, OsPOT, OsZIP4, OsFDR3, OsIAA5 etc., that were previously reported to influence grain Fe and Zn content. The identified QTLs could be utilized in developing high yielding, Fe and Zn denser varieties by marker assisted selection (MAS).

Background Rice is one of the major staple foods in the world, especially in the developing countries of Asia. Its consumption as a dietary source is also increasing in Africa. To meet the demand for rice to feed the increasing human population, increasing rice yield is essential. Improving the genetic yield potential of rice is one ideal solution. It is imperative to introduce the identified yield-enhancing gene(s) into modern rice cultivars for the rapid improvement of yield potential through marker-assisted breeding. Results We report the development of PCR-gel-based markers for eight yield-related functional genes (Gn1a, OsSPL14, SCM2, Ghd7, DEP1, SPIKE, GS5, and TGW6) to introduce yield-positive alleles from the donor lines. Six rice cultivars, including three each of donor and recipient lines, respectively, were sequenced by next-generation whole-genome sequencing to detect DNA polymorphisms between the genotypes. Additionally, PCR products containing functional nucleotide polymorphism (FNP) or putative FNPs for yield-related genes were sequenced. DNA polymorphisms discriminating yield-positive alleles and non-target alleles for each gene were selected through sequence analysis and the allele-specific PCR-gel-based markers were developed. The markers were validated with our intermediate breeding lines produced from crosses between the donors and 12 elite indica rice cultivars as recipients. Automated capillary electrophoresis was tested and fluorescence-labeled SNP genotyping markers (Fluidigm SNP genotyping platform) for Gn1a, OsSPL14, Ghd7, GS5, and GS3 genes were developed for high-throughput genotyping. Conclusions The SNP/indel markers linked to yield related genes functioned properly in our marker-assisted breeding program with identified high yield potential lines. These markers can be utilized in local favorite rice cultivars for yield enhancement. The marker designing strategy using both next generation sequencing and Sanger sequencing methods can be used for suitable marker development of other genes associated with useful agronomic traits. Electronic supplementary material The online version of this article (doi:10.1186/s12284-016-0084-7) contains supplementary material, which is available to authorized users.

Genome-wide association mapping studies (GWAS) are frequently used to detect QTL in diverse collections of crop germplasm, based on historic recombination events and linkage disequilibrium across the genome. Generally, diversity panels genotyped with high density SNP panels are utilized in order to assay a wide range of alleles and haplotypes and to monitor recombination breakpoints across the genome. By contrast, GWAS have not generally been performed in breeding populations. In this study we performed association mapping for 19 agronomic traits including yield and yield components in a breeding population of elite irrigated tropical rice breeding lines so that the results would be more directly applicable to breeding than those from a diversity panel. The population was genotyped with 71,710 SNPs using genotyping-by-sequencing (GBS), and GWAS performed with the explicit goal of expediting selection in the breeding program. Using this breeding panel we identified 52 QTL for 11 agronomic traits, including large effect QTLs for flowering time and grain length/grain width/grain-length-breadth ratio. We also identified haplotypes that can be used to select plants in our population for short stature (plant height), early flowering time, and high yield, and thus demonstrate the utility of association mapping in breeding populations for informing breeding decisions. We conclude by exploring how the newly identified significant SNPs and insights into the genetic architecture of these quantitative traits can be leveraged to build genomic-assisted selection models.

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline., Author Summary Genomic selection is a promising breeding technique that aims to improve the efficiency and speed of the breeding process. While it has been shown to be effective in crops such as wheat and corn, it has not yet been applied to rice breeding. Genome-wide association studies (GWAS), by contrast, are used to identify genes or QTLs that underlie traits of importance to breeding such as yield, flowering time, or plant height, and has been performed successfully in rice. Here, we experiment with applying genomic selection in conjunction with GWAS to a rice breeding program at the International Rice Research Institute in the Philippines and show that genomic selection can result in more accurate predictions of breeding line performance than pedigree data alone and that GWAS results can inform the results of GS. Our results suggest that GS could be an effective tool for increasing the efficiency of rice breeding.