Motonori Tomita ^1* Hiroshi Honda ²

• ¹ Shizuoka University, Shizuoka, Japan
• ² Honda Biotech. Laboratory, Utsunomiya-Shi, Tochigi 329–1104, Japan

Backgrounds: Breeding rice varieties that are resilience to climate change and optimizing cultivation methods for each developed variety is challenging issues for global food problems.Methods: In this study, the late-flowering gene Hd16 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 Hd16 and d60. Productivity tests were conducted in nine prefectures in Japan for two homogeneous rice genotypes, Hd16 (late flowering) and d60Hd16 (short culm and late flowering). By analyzing the relationship among genotype, traits, and accumulation temperature, we reexamined the characteristics of each genotype and inferred the optimal growing areas.Results: Correlation-based network analysis among yield, grain quality, and value of taste and other traits showed that quality was positively correlated with panicle length (r=0.36) and 1,000grain weight (r=0.43), and yield was strongly positively correlated with 1,000-grain weight (r=0.66). d60 genotype was negatively correlated with culm length (r=-0.82) and lodging degree (r=-0.58). These correlations were supported by partial correlation analysis and the significant differences compared to the wildtype was identified. Principal component analysis revealed that Yamanashi and Ehime, which gave long panicle length and culm length for 'Koshihikari d60Hd16', were suitable in terms of yield, while Shimane, which is warmer and produced shorter panicle length and culm length, was suitable in terms of eating quality. Moreover, Koshihikari d60Hd16, the late-flowering and semidwarf strain, could express traits that are less prone to lodging while maintaining the same quality and yield as the wild type.Conclution: Thus, the d60 and H16 genotypes express stable traits adapted to a wide range of Japanese climatic conditions and growing environments. This study provides fundamental information for promoting new smart agriculture, in which improved varieties are deployed in different regions with different climatic conditions.