Evaluation of Grain Moisture Content at Maturity and Screening for Identification Indexes of Maize Inbred Lines
Yuqian Gao,
Jianping Li,
Ruiyao Ning,
Yunxiao Zheng,
Weibin Song,
Peng Hou,
Liying Zhu,
Xiaoyan Jia,
Yongfeng Zhao,
Wei Song,
Rui Guo,
Jinjie Guo
Affiliations
Yuqian Gao
State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Sub-Center of National Maize Improvement Center of China, College of Agronomy, Hebei Agricultural University, Baoding 071051, China
Jianping Li
State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Sub-Center of National Maize Improvement Center of China, College of Agronomy, Hebei Agricultural University, Baoding 071051, China
Ruiyao Ning
State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Sub-Center of National Maize Improvement Center of China, College of Agronomy, Hebei Agricultural University, Baoding 071051, China
Yunxiao Zheng
State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Sub-Center of National Maize Improvement Center of China, College of Agronomy, Hebei Agricultural University, Baoding 071051, China
Weibin Song
State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing 100193, China
Peng Hou
Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture and Rural Affairs, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
Liying Zhu
State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Sub-Center of National Maize Improvement Center of China, College of Agronomy, Hebei Agricultural University, Baoding 071051, China
Xiaoyan Jia
State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Sub-Center of National Maize Improvement Center of China, College of Agronomy, Hebei Agricultural University, Baoding 071051, China
Yongfeng Zhao
State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Sub-Center of National Maize Improvement Center of China, College of Agronomy, Hebei Agricultural University, Baoding 071051, China
Wei Song
Key Laboratory of Crop Genetics and Breeding of Hebei Province, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050051, China
Rui Guo
Key Laboratory of Crop Genetics and Breeding of Hebei Province, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050051, China
Jinjie Guo
State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Sub-Center of National Maize Improvement Center of China, College of Agronomy, Hebei Agricultural University, Baoding 071051, China
The grain moisture content of maize inbred lines at maturity is one of the most important indicators for mechanical harvesting of kernels. In this study, 116 maize inbred lines from a wide range of sources were used as research materials and 30 traits of grain moisture content were analyzed using multivariate statistical analysis. The results showed that all 30 traits had some correlations. Principal component analysis downscaled the 30 traits into 10 principal component factors, reflecting 77.674% of the information in the original traits. Cluster analysis categorized the 116 inbred lines into 5 major groups containing 26, 29, 31, 16 and 14 inbred lines. Based on the D value of the overall evaluation, discriminant analysis reclassified the maize inbred lines by principal component scores and 98 maize inbred lines were correctly discriminated with a probability of 84.48%, which can be regarded as a relatively reliable clustering result. The stepwise regression method was further used to screen seven traits: GMC2, GDR1, HMC3, NH, GDR2, CD and EL and to establish a comprehensive evaluation model for the grain moisture content of maize inbred lines. Among 116 maize inbred lines, 14, represented by H21 and MS71, had the lowest grain moisture content at maturity.
