Biomass yield of rice (Oryza sativa L.) is an important breeding target, yet it is not easy to improve because the trait is complex and phenotyping is laborious. Using progeny derived from a cross between two high-yielding Japanese cultivars, we evaluated whether quantitative trait locus (QTL)-based selection can improve biomass yield. As a measure of biomass yield, we used plant weight (aboveground parts only), which included grain weight and stem and leaf weight. We measured these and related traits in recombinant inbred lines. Phenotypic values for these traits showed a continuous distribution with transgressive segregation, suggesting that selection can affect plant weight in the progeny. Four significant QTLs were mapped for plant weight, three for grain weight, and five for stem and leaf weight (at α = 0.05); some of them overlapped. Multiple regression analysis showed that about 43% of the phenotypic variance of plant weight was significantly explained (P < 0.0001) by six of the QTLs. From F2 plants derived from the same parental cross as the recombinant inbred lines, we divergently selected lines that carried alleles with positive or negative additive effects at these QTLs, and performed successive selfing. In the resulting F6 lines and parents, plant weight significantly differed among the genotypes (at α = 0.05). These results demonstrate that QTL-based selection is effective in improving rice biomass yield.
Minimizing the deterioration of grain quality caused by the high temperature stress in the ripening stage is an important agronomical issue in rice cultivation. Field trial was conducted to investigate the effects of deep-fl ood irrigation on the growth and quality of rice under high and normal ripening temperatures. The experiment was carried out from 2004 to 2007 using three rice cultivars (Hatsuboshi, Sasanishiki and Koshihikari). Two water management regimes were prepared : DFI (deepflood irrigation; water level was kept in 18 cm from active tillering to maximum tiller stage) and CWI (conventional water irrigation). DFI decreased inferior tillers, resulting in a higher percentage of tillers that produce mature grains. Although DFI decreased the number of panicles, it increased the number of grains per panicle and thousand grain weight of brown rice; hence, the yield equal to that in the CWI plot. In addition, DFI markedly the decreased the occurrence of milky white grains. This effect was observed both in high and normal temperature conditions, suggesting that DFI is an effective method to overcome the deterioration effect of a high ripening temperature. The higher sensitivity of the cultivar to a high temperature, the higher the DFI effect. However, DFI should be started after production of enough tillers. Otherwise, the yield will be decreased due to a shortage of tillers. Three hundred and thirty tillers/m 2 before the start of DFI may be needed to have the same level of yield as that by the conventional cultivation in addition to reducing deteriorated grains.
The increased occurrence of chalky rice grains as a result of global warming is becoming a serious problem. The application of deep-flood irrigation (DFI) to a water depth of 18 cm from the active tillering stage to the maximum tillering stage suppresses the occurrence of chalky grains under both high and normal temperature conditions without decreasing yields. The mechanism by which DFI reduces chalky grains was analyzed relative to carbohydrate supply as carbohydrate deficiencies have been proposed as a cause of chalky grains. DFI suppresses the occurrence of chalky grains due to the increased supply of carbohydrates to the panicles. In paddy fields where a water depth of 18 cm is not possible, such as in paddy fields with low levees or low water supplies, the combined use of moderate DFI at a water depth of 10 cm with deep planting can be substituted to produce high-quality rice. DFI did not damage root activity at the ripening stage, even in soils with a low redox potential. Although the DFI treatment slightly decreased the penetration resistance of the soil, the bearing capacity was not affected at the time of combine harvest.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.