不同基因型大豆耐荫性分析及其鉴定指标的筛选

doi:10.3864/j.issn.0578-1752.2014.15.003

摘要/Abstract

摘要： 【目的】探讨作物耐荫性评价的方法，分析不同大豆基因型耐荫特性，筛选鉴定指标，建立可靠的耐荫性评价模型。【方法】以30个大豆品种（系）为试材，采用田间试验的方法，设遮光和自然光照2个处理。遮光处理采用玉米-大豆带状复合种植在田间创设荫蔽条件，玉米品种为郑单958，在玉米植株长成以后，遮光率为70%；大豆清种作为对照。在成熟期调查主茎高（X1）、茎粗（X2）、主茎节数（X3）、分枝数（X4）、底荚高度（X5）、节间长度（X6）、植株重量平衡点（X7）、倒伏性（X8）、单株荚数（X9）、单株粒数（X10）、每荚粒数（X11）、百粒重（X12）、粒茎比（X13）和单株产量（X14）。根据遮光处理和自然光照条件下的各项指标的耐荫系数,采用主成分分析、隶属函数法、聚类分析和逐步回归分析方法，对大豆的耐荫性进行综合评价。【结果】通过主成分分析将14个单项指标转换为5个相互独立的综合指标，代表了全部数据87.685%的信息量。通过隶属函数法计算综合耐荫评价值（D），并对其进行聚类分析，将30个大豆品种（系）按照耐荫性的强弱划分为3类。第Ⅰ类属于强耐荫类型，第Ⅱ类属于中度耐荫类型，第Ⅲ类属于不耐荫类型。通过逐步回归建立大豆耐荫性评价数学模型，D=-0.5495+0.2932X3+0.0592X4+0.1628X6-0.0244X8+0.5943X9+0.1345X12+0.8210X14（R2=0.9908），估计精度在93.67%以上，并筛选出7个鉴定指标，分别是主茎节数、分枝数、节间长度、抗倒性、单株荚数、百粒重和单株粒重。【结论】30个大豆品种（系）被分成强耐荫型、中等耐荫型和不耐荫型3类，且主茎节数、分枝数、节间长度、抗倒性、单株荚数、百粒重和单株粒重可以作为大豆耐荫性的鉴定指标。

关键词: 大豆 , 耐荫性 , 主成分分析 , 隶属函数法 , 聚类分析 , 逐步回归

Abstract: 【Objective】 The main purpose of the present study was to explore the methods of evaluating shade-tolerance, analyze the shade-tolerance of different soybean genotypes, screen suitable evaluation indicators of shade-tolerance, and establish a mathematical evaluation model of shade-tolerance. 【Method】 Thirty soybean cultivars(lines) were treated under shading and natural light conditions in a field experiment. Corn-soybean strip cropping system created the shaded conditions in the field. The corn variety Zhengdan 958 was used in the experiment. At the maize tasseling stage, shading rate was 70%. Soybean single planting was used as control. Main stem height (X1), stem diameter (X2), main stem node number (X3), branch number (X4), lowest pod height (X5), internode length (X6), plant weight balance (X7), lodging (X8), pods per plant (X9), seeds per plant (X10), seeds per pod (X11), 100-seed weight (X12), seed-stem ratio (X13) and yield per plant (X14) were investigated at the mature period. Shade-tolerance coefficient (STC) of all indexes from both treatments was used to comprehensively evaluate the shade-tolerance by principal component analysis, membership function method, hierarchical cluster analysis and stepwise regression analysis. 【Result】 Firstly, the 14 single indexes were transformed into 5 independent comprehensive components through principal component analysis, which represent 87.685% information of the raw data of the traits related to shade-tolerance. The membership function method was employed to calculate comprehensive shade-tolerance value (D) and carry on cluster analysis, and 30 soybean cultivars (lines) were divided into three shade-intolerance types. Class I was the high shade-tolerance type. Class II was the medium shade-tolerance type. Class III was the shade-intolerance type. At last, a mathematical evaluation model for soybean shade-tolerance was established by stepwise regression analysis, D=-0.5495+0.2932X3+0.0592X4+0.1628X6-0.0244X8+0.5943X9+0.1345X12+0.8210X14 (R2 =0.9908), whose accuracy was higher than 93.67%. Based on the model, seven indexes closely related to the shade- tolerance, including main stem nodes, branch number, internode length, lodging resistance, pods per plant, 100-seed weight and yield per plant were screened out. 【Conclusion】 Thirty soybean cultivars (lines) were divided into three shade-tolerance types, which were the high shade-tolerance type, the medium shade-tolerance type and the shade-intolerance type. Main stem nodes, branch number, internode length, lodging resistance, pods per plant, 100-seed weight and yield per plant can be used for identification of soybean varieties to shade-tolerance.

Key words: soybean , shade-tolerance , principal components analysis , membership function method , hierarchical cluster analysis , stepwise regression analysis method

李春红1, 2, 姚兴东1, 鞠宝韬1, 朱明月1, 王海英1, 张惠君1, 敖雪1, 于翠梅1, 谢甫绨1, 宋书宏2. 不同基因型大豆耐荫性分析及其鉴定指标的筛选[J]. 中国农业科学, 2014, 47(15): 2927-2939.

LI Chun-Hong-1, 2 , YAO Xing-Dong-1, JU Bao-Tao-1, ZHU Ming-Yue-1, WANG Hai-Ying-1, ZHANG Hui-Jun-1, AO Xue-1, YU Cui-Mei-1, XIE Fu-ti1, SONG Shu-Hong-2. Analysis of Shade-Tolerance and Determination of Shade-Tolerance Evaluation Indicators in Different Soybean Genotypes[J]. Scientia Agricultura Sinica, 2014, 47(15): 2927-2939.

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