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草业学报 ›› 2021, Vol. 30 ›› Issue (5): 186-199.DOI: 10.11686/cyxb2020453

• 研究论文 • 上一篇    下一篇

耐亚磷酸盐紫花苜蓿品种筛选及评价指标的鉴定

王吉祥(), 宫焕宇(), 屠祥建, 郭侲洐, 赵嘉楠, 沈健, 栗振义(), 孙娟()   

  1. 青岛农业大学草业学院,山东 青岛 266109
  • 收稿日期:2020-10-13 修回日期:2020-12-21 出版日期:2021-05-20 发布日期:2021-04-16
  • 通讯作者: 栗振义,孙娟
  • 作者简介:sunjuan@qau.edu.cn
    E-mail: lizhenyily@163.com
    王吉祥(1995-),男,山东淄博人,在读硕士。 E-mail: wjx861739834@outlook.com
    宫焕宇(1999-),男,山东潍坊人,在读本科。E-mail: ghy17806245037@163.com第一联系人:共同第一作者These authors contributed equally to this work.
  • 基金资助:
    国家牧草产业技术体系(CARS-34);山东省一流学科-草学(1619002);山东省自然科学基金青年项目(ZR2020QC185);青岛农业大学高层次人才启动基金(6631119038);青岛农业大学大学生创新创业训练计划项目(201910435027)

Screening of phosphite-tolerant alfalfa varieties and identification of phosphite tolerance indicators

Ji-xiang WANG(), Huan-yu GONG(), Xiang-jian TU, Zhen-xing GUO, Jia-nan ZHAO, Jian SHEN, Zhen-yi LI(), Juan SUN()   

  1. College of Grassland Science,Qingdao Agricultural University,Qingdao 266109,China
  • Received:2020-10-13 Revised:2020-12-21 Online:2021-05-20 Published:2021-04-16
  • Contact: Zhen-yi LI,Juan SUN

摘要:

亚磷酸盐是正磷酸盐的一种还原形态,具有溶解度高、运输效率高、与土壤反应活性低等优势。通过探讨不同紫花苜蓿品种苗期对亚磷酸盐的耐逆能力,旨在为紫花苜蓿耐亚磷酸盐品种筛选和亚磷酸盐新型磷肥的开发提供科学依据。以37个紫花苜蓿品种为试验材料,设置正磷酸盐(0.5 mmol·L-1 KH2PO4)和亚磷酸盐(0.5 mmol·L-1 KH2PO3)两个处理,测定不同品种幼苗的株高(PH)、茎粗(SD)、茎叶磷含量(SPC)、茎叶干重(DWS)、根干重(DWR)、根冠比(RSR)、总根长(RL)、根表面积(RSA)、根磷含量(RPC)、叶绿素含量(Chl)、叶面积(LA)、净光合速率(Pn)12个形态和生理指标变化情况。以各单项指标的耐亚磷酸盐系数(PTC)作为衡量依据,运用主成分分析、隶属函数分析、聚类分析和逐步回归等方法对其耐亚磷酸盐的特性进行综合评价并建立数学模型。在亚磷酸盐胁迫下,不同紫花苜蓿品种根磷含量和叶绿素含量增加,其余指标均显示下降。通过主成分分析,可将12个单项指标转换成5个相互独立的综合指标,根据耐亚磷酸盐综合评价值和聚类分析,可将37个品种划分为4个类别,其中,高度耐亚磷酸盐包括WL903和可汗2个,中度耐亚磷酸盐12个,低耐亚磷酸盐17个,亚磷酸盐敏感型6个。进一步利用逐步回归方法建立了耐亚磷酸盐特性的评价回归模型Y=-0.174+0.102PH+0.189DWR+0.168 RL+0.187RSA-0.061PnR2=0.9908),且各品种对回归方程的估计精度均大于93.22%。综合表明:高度耐亚磷酸盐的品种有可汗和WL903;在亚磷酸盐胁迫下,株高、根干重、总根长、根表面积和净光合速率等可作为紫花苜蓿品种耐亚磷酸盐特性强弱的快速鉴定和预测指标。

关键词: 紫花苜蓿, 亚磷酸盐, 主成分分析, 聚类分析, 逐步回归

Abstract:

Phosphite, a reduced form of orthophosphate, is characterized by its high solubility, high transport efficiency, and low reactivity in soil. The aim of this study was to screen for phosphite-tolerant alfalfa varieties and provide information for the development of phosphite fertilizers by exploring differences in phosphite tolerance among different varieties of alfalfa at the seedling stage. Thirty-seven alfalfa varieties were cultured in Hoagland’s nutrient solution with orthophosphate (0.5 mmol·L-1 KH2PO4) or phosphate (0.5 mmol·L-1 KH2PO3) as the sole phosphorus resource. We analyzed 12 morphological and physiological characters: Plant height (PH), stem diameter(SD), shoot phosphorus content (SPC), shoot dry weight (DWS), root dry weight (DWR), root∶shoot, total root length (RL), root surface area (RSA), root phosphorus content (RPC), chlorophyll content (Chl), leaf area (LA), and net photosynthetic rate (Pn). Based on the phosphite-tolerance coefficient (PTC) of each individual index, we used principal component analysis, membership function analysis, cluster analysis, and stepwise regression to comprehensively evaluate phosphite tolerance characteristics and establish a mathematical model. The results showed that phosphite stress resulted in increased root total phosphorus content, increased chlorophyll content, and decreased values of other indexes in different varieties. The 12 indicators were divided into five composite indicator groups in a principal component analysis. The 37 alfalfa varieties were classified into four categories in terms of phosphite resistance: highly resistant (two varieties; WL903 and Kehan); Moderately resistant (12 varieties); Low resistance (17 varieties), and sensitive (six varieties). The following regression model was developed for evaluating phosphite tolerance: Y=-0.174+0.102PH+0.189DWR+0.168RL+0.187RSA-0.061PnR2=0.9908). The precision of the regression equation was higher than 93.22% for each alfalfa variety. The results show that the highly phosphite-resistant alfalfa varieties were Kehan and WL903. Under phosphite stress, the tolerance of alfalfa varieties to phosphite can be quickly identified and predicted by measuring PH, DWR, RL, RSA, and Pn.

Key words: alfalfa, phosphite, principal component analysis, cluster analysis, stepwise regression analysis