A semi-dwarf and late-flowering Koshihikari d60Hd16: development, productivity, and regional suitability revealed by correlation-based network analysis.

BACKGROUND

Breeding rice varieties that are resilient to climate change and optimizing the cultivation methods for each developed variety are challenging issues in global food demands.

METHODS

In this study, the late-flowering gene of Koganebare was introduced into Koshihikari through backcrossing to create 'Koshihikari Hd16'. It was then crossed with 'Koshihikari d60' to develop an isogenic line of Koshihikari containing both and . Productivity tests were conducted in nine prefectures in Japan for two homogeneous rice genotypes: (late flowering) and (short culm and late flowering). By analyzing the relationship among genotypes, traits, and accumulation temperatures, we reexamined the characteristics of each genotype and inferred the optimal growing areas.

RESULTS

Correlation-based network analysis between yield, grain quality, and taste value, as well as other traits, showed that quality was negatively correlated with panicle length ( = 0.36) and 1,000-grain weight ( = 0.43), and yield was strongly positively correlated with 1,000-grain weight ( = 0.66). The genotype was negatively correlated with culm length ( = -0.82) and lodging degree ( = -0.58). These correlations were supported by partial correlation analysis, and significant differences compared with the wild-type were identified. Principal component analysis revealed that Yamanashi and Ehime, which provided long panicle and culm lengths to 'Koshihikari d60Hd16', were suitable in terms of yield; on the other hand, Shimane, which is warmer and produced shorter panicle and culm lengths, was suitable in terms of eating quality. Moreover, Koshihikari d60Hd16, the late-flowering and semi-dwarf strain, could express traits that are less prone to lodging while maintaining the same quality and yield as the wild type.

CONCLUSION

Thus, the and genotypes express stable traits adapted to a wide range of Japanese climatic conditions and growing environments. This study provides fundamental information for the promotion of new smart agriculture, in which improved varieties are deployed in different regions with different climatic conditions.