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To develop superior rice varieties with improved yield in most rainfed areas of Asia/Africa, we started an introgression-breeding program for simultaneously improving yield and tolerances of multiple abiotic stresses. Using eight BC1 populations derived from a widely adaptable recipient and eight donors plus three rounds of phenotypic selection, we developed 496 introgression lines (ILs) with significantly higher yield under drought, salt and/or non-stress conditions in 5 years. Six new varieties were released in the Philippines and Pakistan and many more are being evaluated in multi-location yield trials for releasing in several countries. Marker-facilitated genetic characterization revealed three interesting aspects of the breeding procedure: (1) the donor introgression pattern in specific BC populations was characteristic; (2) introgression frequency in different genomic regions varied considerably, resulting primarily from strong selection for the target traits; and (3) significantly lower heterozygosity was observed in BC progenies selected for drought and salinity tolerance. Applying strong phenotypic selection under abiotic stresses in early segregating generations has major advantages for not only improving multiple abiotic stress tolerance but also achieving quicker homozygosity in early generations. This breeding procedure can be easily adopted by small breeding programs in developing countries to develop high-yielding varieties tolerant of abiotic stresses. The large set of trait-specific ILs can be used for genetic mapping of genes/QTL that affect target and non-target traits and for efficient varietal development by designed QTL pyramiding and genomics-based recurrent selection in our Green Super Rice breeding technology.

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.

Multi-Environment Trials (MET) are conventionally used to evaluate varietal performance prior to national yield trials, but the accuracy of MET is constrained by the number of test environments. A modeling approach was innovated to evaluate varietal performance in a large number of environments using the rice model ORYZA (v3). Modeled yields representing genotype by environment interactions were used to classify the target population of environments (TPE) and analyze varietal yield and yield stability. Eight Green Super Rice (GSR) and three check varieties were evaluated across 3796 environments and 14 seasons in Southern Asia. Based on drought stress imposed on rainfed rice, environments were classified into nine TPEs. Relative to the check varieties, all GSR varieties performed well except GSR-IR1-5-S14-S2-Y2, with GSR-IR1-1-Y4-Y1, and GSR-IR1-8-S6-S3-Y2 consistently performing better in all TPEs. Varietal evaluation using ORYZA (v3) significantly corresponded to the evaluation based on actual MET data within specific sites, but not with considerably larger environments. ORYZA-based evaluation demonstrated the advantage of GSR varieties in diverse environments. This study substantiated that the modeling approach could be an effective, reliable, and advanced approach to complement MET in the assessment of varietal performance on spatial and temporal scales whenever quality soil and weather information are accessible. With available local weather and soil information, this approach can also be adopted to other rice producing domains or other crops using appropriate crop models.