국내 육성 벼 품종의 출수일수 및 간장 변이 분석

Days to Heading and Culm Length Variation of Korean Rice Varieties in Different Environments

Recent advances in high-throughput sequencing technologies have enabled large-scale cost-effective genotypic analyses, andconsequently, obtaining reliable phenotypic data has now become a major bottleneck in data-driven plant breeding. In order to construct aphenotype database for commercial rice varieties released by the National Institute of Crop Science, Rural Development Administration, weinitiated a systematic phenotype evaluation project, with the aim of investigating the major agronomic traits of Korean rice varieties releasedduring the period between 1979 and 2017. Despite the narrow genetic background, we found that the days to heading (DTH) and culm length(CL) of the 297 Korean rice varieties assessed exhibited wide phenotypic variation under different environments. Under normal planting cultivationin 2018, the DTH ranged from 48 to 104 days in Suwon, 46 to 111 days in Wanju, and 39 to 97 days in Miryang, with CL values rangingfrom 59 to 134 cm, 55 to 122 cm, and 57 to 106 cm, respectively. During early planting cultivation in 2019, the DTH ranged from 56to 113 days (Suwon), 58 to 109 days (Wanju), and 58 to 100 days (Miryang), with corresponding CL values ranging from 63 to 119 cm,55 to 93 cm, and 51 to 115 cm. Despite the difference of one month in planting dates in 2018 and 2019, DTH in the different years andregions showed highly significant positive correlations (r=0.90-0.98), whereas CL showed positive but weaker correlations (r=0.45-0.82). Furthermore, we detected a weak, although significant, correlation between DTH and CL in each environment (r=-0.18-0.35). Analyses ofadditive main effects and multiplicative interaction (AMMI) were conducted for DTH and CL to identify rice varieties with stable phenotypesunder different environments. We anticipate that the findings of this study will provide a useful rice phenotype database to facilitategenotype-phenotype association studies and data-driven rice breeding.