Matsushita Kei, Onogi Akio, Yonemaru Jun-Ichi
Research Center for Agricultural Information Technology (RCAIT), National Agriculture and Food Research Organization (NARO), 3-1-1 Kannondai, Tsukuba, Ibaraki 305-8517, Japan.
Institute of Crop Science (NICS), NARO, 2-1-2 Kannondai, Tsukuba, Ibaraki 305-8602, Japan.
Breed Sci. 2024 Apr;74(2):114-123. doi: 10.1270/jsbbs.23040. Epub 2024 Mar 8.
Data from breeding, including phenotypic information, may improve the efficiency of breeding. Historical data from breeding trials accumulated over a long time are also useful. Here, by organizing data accumulated in the National Agriculture and Food Research Organization (NARO) rice breeding program, we developed a historical phenotype dataset, which includes 6052 records obtained for 667 varieties in yield trials in 1991-2018 at six NARO research stations. The best linear unbiased predictions (BLUPs) and principal component analysis (PCA) were used to determine the relationships with various factors, including the year of cultivar release, for 15 traits, including yield. Yield-related traits such as the number of grains per panicle, plant weight, grain yield, and thousand-grain weight increased significantly with time, whereas the number of panicles decreased significantly. Ripening time significantly increased, whereas the lodging degree and protein content of brown rice significantly decreased. These results suggest that panicle-weight-type high-yielding varieties with excellent lodging resistance have been selected. These trends differed slightly among breeding locations, indicating that the main breeding objectives may differ among them. PCA revealed a higher diversity of traits in newer varieties.
育种数据,包括表型信息,可能会提高育种效率。长期积累的育种试验历史数据也很有用。在这里,通过整理日本农业和食品研究组织(NARO)水稻育种项目中积累的数据,我们开发了一个历史表型数据集,其中包括1991年至2018年在NARO的六个研究站对667个品种进行产量试验所获得的6052条记录。利用最佳线性无偏预测(BLUP)和主成分分析(PCA)来确定包括产量在内的15个性状与各种因素(包括品种发布年份)之间的关系。每穗粒数、植株重量、籽粒产量和千粒重等与产量相关的性状随时间显著增加,而穗数则显著减少。成熟时间显著增加,而糙米的倒伏程度和蛋白质含量显著降低。这些结果表明,已选育出具有优异抗倒伏性的穗重型高产品种。这些趋势在不同育种地点略有不同,表明它们的主要育种目标可能有所不同。PCA显示新品种的性状多样性更高。