Most plants do poorly when flooded. Certain rice varieties, known as deepwater rice, survive periodic flooding and consequent oxygen deficiency by activating internode growth of stems to keep above the water. Here, we identify the gibberellin biosynthesis gene, (), whose loss-of-function allele catapulted the rice Green Revolution, as being responsible for submergence-induced internode elongation. When submerged, plants carrying the deepwater rice-specific haplotype amplify a signaling relay in which the gene is transcriptionally activated by an ethylene-responsive transcription factor, OsEIL1a. The SD1 protein directs increased synthesis of gibberellins, largely GA, which promote internode elongation. Evolutionary analysis shows that the deepwater rice-specific haplotype was derived from standing variation in wild rice and selected for deepwater rice cultivation in Bangladesh.

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Rice (Oryza sativa L.) is one of the most important staple foods in the world, however most improved rice varieties are susceptible to drought stress. A two-year study was conducted to explore the effects of various drought stresses and subsequent recovery on the accumulation and degradation of proline, total soluble sugar and starch in different rice varieties at vegetative stage. The results showed that relative water content in the leaves and sheaths of rice varieties significantly decreased under drought stresses, but not at the same rate. Under control and drought conditions, the water content in sheaths was higher than that in leaves. Interestingly, under severe drought stress in 2015, the leaf water content was higher than the sheath water content. The water distribution between leaves and sheaths might be a response of plants to protect leaf system from devastation by drought. Proline was highly accumulated under drought stress but rapidly decreased after re-watering. The drought tolerant variety DA8 expressed higher ability in accumulation of proline than susceptible varieties. In general, total soluble sugar and starch contents in leaves and sheaths of varieties decreased under drought stress conditions. Total soluble sugar and starch content of DA8 were less affected than other varieties under drought conditions. Our study indicated that metabolisms of total soluble sugar and starch in rice were affected by both environmental conditions and characteristics of varieties. Proline accumulation ability of varieties can be used as a useful indicator for drought tolerant potential in rice breeding for water-limited environments.

It is suggested that accuracy in predicting plant phenotypes can be improved by integrating genomic prediction with crop modelling in a single hierarchical model. Accurate prediction of phenotypes is important for plant breeding and management. Although genomic prediction/selection aims to predict phenotypes on the basis of whole-genome marker information, it is often difficult to predict phenotypes of complex traits in diverse environments, because plant phenotypes are often influenced by genotype-environment interaction. A possible remedy is to integrate genomic prediction with crop/ecophysiological modelling, which enables us to predict plant phenotypes using environmental and management information. To this end, in the present study, we developed a novel method for integrating genomic prediction with phenological modelling of Asian rice (Oryza sativa, L.), allowing the heading date of untested genotypes in untested environments to be predicted. The method simultaneously infers the phenological model parameters and whole-genome marker effects on the parameters in a Bayesian framework. By cultivating backcross inbred lines of Koshihikari × Kasalath in nine environments, we evaluated the potential of the proposed method in comparison with conventional genomic prediction, phenological modelling, and two-step methods that applied genomic prediction to phenological model parameters inferred from Nelder-Mead or Markov chain Monte Carlo algorithms. In predicting heading dates of untested lines in untested environments, the proposed and two-step methods tended to provide more accurate predictions than the conventional genomic prediction methods, particularly in environments where phenotypes from environments similar to the target environment were unavailable for training genomic prediction. The proposed method showed greater accuracy in prediction than the two-step methods in all cross-validation schemes tested, suggesting the potential of the integrated approach in the prediction of phenotypes of plants.

To determine water uptake by rice in watersaving culture, we examined root hydraulic conductance (L 0 ), plant growth, and root anatomy of three rice genotypes (Oryza sativa L. ssp. indica cv. Beodien, traditional upland; ssp. japonica cv. Sensho, traditional upland; ssp. japonica cv. Koshihikari, improved lowland) under three water regimes: water-saturated (hydroponic), well-irrigated aerobic (control), and water-saving aerobic in soil. In hydroponic culture, although shoot dry weight (SDW) and root number were the largest in Sensho, root L 0 was the highest in Koshihikari. There was no significant relationship between root L 0 and SDW in hydroponics, so root L 0 might not limit shoot growth under flooding. Root L 0 was much less in soil than in hydroponics, and that of Koshihikari was the lowest, especially in water-saving conditions. Root L 0 was highly correlated with SDW under water-saving conditions but not in the control, so root L 0 limits shoot growth under repeated water stress. Root anatomy was less affected by water regime than root L 0 and is genetically controlled. Thus, root L 0 may be more affected by water channels than by root anatomy.