北方农业学报 ›› 2024, Vol. 52 ›› Issue (2): 15-25.doi: 10.12190/j.issn.2096-1197.2024.02.02

• 作物栽培·种质资源 • 上一篇    下一篇

苏北地区适宜玉米-大豆间作的耐荫大豆品种(系)评价及鉴定指标筛选

刘书华1, 张黎杰1, 周玲玲1, 余翔1, 田福发1, 孟佳丽1, 吴绍军1, 沈虹1, 杨年福1, 章燕柳2   

  1. 1.江苏省农业科学院 宿迁农科所,江苏 宿迁 223800;
    2.江苏瑞华农业科技有限公司,江苏 宿迁 223800
  • 收稿日期:2024-01-22 出版日期:2024-04-20 发布日期:2024-07-24
  • 通讯作者: 张黎杰(1982—),女,副研究员,硕士,主要从事大豆育种与栽培方面的研究工作。
  • 作者简介:刘书华(1994—),男,研究实习员,硕士,主要从事大豆育种与栽培方面的研究工作。
  • 基金资助:
    2022年宿迁市农业科技自主创新资金项目(SQCX202205)

Evaluation and identification index screening of shade-tolerant soybean varieties(lines)suitable for maize-soybean intercropping in northern Jiangsu

LIU Shuhua1, ZHANG Lijie1, ZHOU Lingling1, YU Xiang1, TIAN Fufa1, MENG Jiali1, WU Shaojun1, SHEN Hong1, YANG Nianfu1, ZHANG Yanliu2   

  1. 1. Institute of Suqian,Jiangsu Academy of Agricultural Sciences,Suqian 223800,China;
    2. Jiangsu Ruihua Agricultural Sciences and Technology Co.,Ltd.,Suqian 223800,China
  • Received:2024-01-22 Online:2024-04-20 Published:2024-07-24

摘要: 【目的】 筛选适宜苏北地区玉米-大豆间作种植模式的耐荫大豆品种(系),构建准确合理的大豆耐荫评价体系。【方法】 以苏北地区玉米-大豆间作所创造的田间自然荫蔽环境为处理,清种为对照,于成熟期测定24个大豆品种(系)的株高、结荚高度、分枝数等12个农艺性状,运用主成分分析、隶属函数法、多元线性逐步回归分析法,构建耐荫系数,评价各大豆品种(系)在间作模式下的综合耐荫能力。【结果】 12个农艺性状耐荫系数指标转化为4个彼此独立的综合指标,代表74.771%的原始数据信息量。综合耐荫性评价值(D)和聚类分析结果表明,24个大豆品种(系)根据荫蔽胁迫下的适应能力可以划分为三大类,其中,强耐荫品种(系)6个、中度耐荫品种(系)9个、弱耐荫品种(系)9个。利用多元线性逐步回归分析法构建耐荫回归方程:D=-1.158+0.062X2+0.249X5+0.201X7+1.002X10+0.665X11R2=0.972 2),检验其拟合精度在89.14%以上,筛选出结荚高度、单株荚数、单株粒数、百粒重、产量5个耐荫鉴定指标。【结论】 参试的24个大豆品种(系)按照耐荫能力划分为强耐荫、中度耐荫、弱耐荫3类;苏北地区玉米-大豆间作模式下,可选择结荚高度、单株荚数、单株粒数、百粒重、产量5个性状进行大豆耐荫能力综合评价。

关键词: 玉米-大豆间作, 大豆品种(系), 耐荫性, 主成分分析, 隶属函数法, 逐步回归

Abstract: 【Objective】Screening shade-tolerant soybean varieties(lines) suitable for maize-soybean intercropping planting mode in northern Jiangsu,and constructing an accurate and reasonable soybean shade tolerance evaluation system.【Methods】The natural shade environment created by maize-soybean intercropping in northern Jiangsu was used as the treatment,and the clear seed was used as the control. 12 agronomic traits such as plant height,pod height,and branch number of 24 soybean varieties(lines) were measured at maturity stage. Principal component analysis,membership function method,and multiple linear stepwise regression analysis were used to construct shade tolerance coefficients and evaluate the comprehensive shade tolerance of each soybean variety(lines) under intercropping mode.【Results】Index of shade tolerance coefficient of 12 agronomic traits were transformed into 4 independent comprehensive indexes,representing 74.771% of the original data information. The results of comprehensive shade tolerance evaluation value(D) and cluster analysis showed that 24 soybean varieties(lines) could be divided into 3 categories according to their adaptability under shade stress,including strong shade-tolerant(six varieties/lines),moderate shade-tolerant(nine varieties/lines)and weak shade-tolerant(nine varieties/lines). The regression equation of shade tolerance was constructed by multiple linear stepwise regression analysis:D=-1.158+0.062X2+0.249X5+0.201X7+1.002X10+0.665X11R2=0.972 2),and the fitting accuracy was more than 89.14%. Five shade tolerance identification indexes of pod height,pod number per plant,seed number per plant,100-seed weight and yield were selected.【Conclusion】According to the shade tolerance ability,the 24 soybean varieties(lines) were divided into three categories:strong shade-tolerant,moderate shade-tolerant and weak shade tolerant. Under the maize-soybean intercropping model in northern Jiangsu,five traits,including pod height,number of pods per plant,number of grains per plant,100 grain weight,and yield,can be selected for comprehensive evaluation of soybean shade tolerance. Under the maize-soybean intercropping mode in northern Jiangsu,five traits,including pod height,pod number per plant,seed number per plant,100 seed weight,and yield,could be selected to comprehensively evaluate the shade tolerance ability of soybean.

Key words: Maize-soybean intercropping, Soybean varieties(lines), Shade tolerance, Principal component analysis, Membership function method, Stepwise regression

中图分类号: 

  • S565.1