关键词: 小麦, 耐低氮性, 模糊隶属函数法, 主成分分析, 聚类分析

Abstract: Nitrogen is an important nutrient element and restricts wheat growth and economic yield,  so screening and breeding low nitrogen tolerant wheat varieties is an effective way to improve nitrogen utilization rate and reduce production cost. In this study, 118 wheat varieties were grown in low nitrogen (0.1 mmol·L^-1) and normal nitrogen (5 mmol·L^-1)  conditions using hydroponic methods. Nitrogen efficiency of plants was measured based on 7 indicators about root dry weight, straw dry weight,  root/shoot ratio, plant dry weight, maximum root length, primary root number and secondary primary root. The low nitrogen tolerance of wheat varieties was evaluated by using fuzzy membership function method, principal component analysis and cluster analysis. The results showed that the root dry weight, root/shoot ratio and primary root number of wheat seedlings significantly increased, the stem and leaf dry weight, plant dry weight and maximum root length decreased in different degrees under low nitrogen stress, and the 7 seedling traits were significantly different at the two nitrogen levels. Three principal components were extracted by principal component analysis, and the contribution rate was 43.575%, 22.904% and 17.873%, respectively. The cumulative contribution rate reached to 84.351%. 118 wheat varieties were divided into 5 categories (strong resistance to low nitrogen, resistance to low nitrogen, medium resistance to low nitrogen, nitrogen sensitive, and nitrogen highly sensitive) according to the D value of comprehensive evaluation of low nitrogen tolerance. Qida 195, Jinfeng 7183 and Tianmin 198 were identified as low nitrogen tolerant wheat varieties. Two varieties (Shannong 0917 and Lumai 8) with relatively strong low nitrogen tolerance were screened. There were some obviously differences in the low nitrogen tolerance mechanisms of different wheat varieties. The research results provided theoretical and material basis for breeding of wheat varieties with low nitrogen resistance.

Key words: wheat, low nitrogen resistance, fuzzy membership function method, principal component analysis, cluster analysis