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应用生态学报 ›› 2021, Vol. 32 ›› Issue (8): 2809-2817.doi: 10.13287/j.1001-9332.202108.025

• 研究论文 • 上一篇    下一篇

草莓种苗壮苗指数模型的构建与质量评价

宫彬彬, 吴晓蕾, 张斌, 陈一卓, 边鑫宇, 纪日翟, 高洪波*   

  1. 河北农业大学园艺学院, 河北保定 071001
  • 收稿日期:2020-12-15 接受日期:2021-05-23 出版日期:2021-08-15 发布日期:2022-02-15
  • 通讯作者: *E-mail: hongbogao@hebau.edu.cn
  • 作者简介:宫彬彬, 男, 1981年生, 硕士, 讲师。主要从事农业生物环境与无土栽培配套技术研究。E-mail: yygbb@hebau.edu.cn
  • 基金资助:
    河北省重点研发计划项目(19227214D,20326902D)资助

Construction and quality evaluation of strawberry seedling index model

GONG Bin-bin, WU Xiao-lei, ZHANG Bin, CHEN Yi-zhuo, BIAN Xin-yu, JI Ri-zhai, GAO Hong-bo*   

  1. College of Horticulture, Hebei Agricultural University, Baoding 071001, Hebei, China
  • Received:2020-12-15 Accepted:2021-05-23 Online:2021-08-15 Published:2022-02-15
  • Contact: *E-mail: hongbogao@hebau.edu.cn
  • Supported by:
    Key Research and Development Program of Hebei Province (19227214D, 20326902D).

摘要: 随着我国草莓栽培面积逐年增加,草莓种苗需求量越来越大,为了确保种苗育苗质量,亟需开展壮苗评价的研究。本研究以生长40 d的‘红颜’穴盘苗为对象,在测定地上部和地下部生长、鲜重、干重等16项指标的基础上,分别构建单项指标隶属函数,使用加权模糊评判法计算种苗综合评价指数;利用主成分分析筛选的关键指标组成多个壮苗指数模型,与种苗综合评价指数进行相关性分析后,确定最佳草莓壮苗指数模型并进行验证。结果表明: 随机选取的320株草莓种苗16项指标存在显著差异,综合评价指数为0.165~0.817,可作为壮苗指数模型构建和种苗质量评价的依据。主成分分析将16项指标划分为地上部相关指标、地下部相关指标和色素指标3个主成分,累计贡献率达到79.7%;从每个主成分中选择贡献值最大的3个指标随机组成27种壮苗指数模型,通过相关性分析筛选出与综合评价指数相关性最大的5个壮苗指数模型,其中“地上干重×根系表面积×叶绿素a”的相关性最高,用‘红颜’、‘香野’和‘甜查理’种苗验证相关系数均最大,分别为0.879、0.924和0.975,确定可作为草莓壮苗指数计算模型。以综合评价指数为种苗质量分级依据,可将种苗健壮程度分为3个等级:等级Ⅰ(综合评价指数≥0.5,壮苗指数≥4.0)为优质苗,等级Ⅱ(综合评价指数0.3~0.5,壮苗指数0.5~4.0)为合格苗,等级Ⅲ(综合评价指数≤0.3,壮苗指数≤0.5)为弱苗。研究结果可为草莓或其他种苗壮苗指数计算和种苗健壮程度评价提供理论依据与科学方法。

关键词: 草莓种苗, 壮苗指数, 模糊综合评判, 主成分分析, 相关性分析

Abstract: With the development of strawberry cultivated area in China, the demands for high-quality strawberry seedlings are increasing year by year. The research on the evaluation index of strawberry seedlings is needed to ensure the quality of the seedlings. This study aimed to establish an optimal index model of strawberry seedlings to improve the accuracy of seedlings evaluation. In this study, 320 seedlings of ‘Benihoppe’ strawberry seedlings growing for 40 days were taken as the materials. Based on the determination of 16 individual indicators including the growth of aboveground and underground parts, fresh weight, and dry weight, we firstly conducted the membership function corresponding to single indicator. Then the comprehensive evaluation index of strawberry seedlings was calculated using weighted fuzzy evaluation method. Furthermore, the key indicators out of the 16 indicators which were filtered out by means of the principal component analysis method were combined into different index models of strawberry seedlings. The correlation analysis between the comprehensive evaluation index and seedling index models was done and finally the optimal seedling index model was selected and verified. The results showed that there were significant differences in 16 indices of 320 randomly selected strawberry seedlings. The comprehensive evaluation index of strawberry seedlings was in the range of 0.165-0.817, indicating that the comprehensive evaluation index could totally reflect the quality of seedlings and could be used as the evaluation basis. The 16 individual indices of strawberry seedlings were classified into three principal components, including aboveground related indicators, underground related indicators, and the pigment indicators. The cumulative contribution rate of three principal components was 79.7%. Twenty-seven seedlings index models were combined by randomly selecting three indices with a large contribution value from each principal component. Five strawberry seedlings index mo-dels were selected from 27 models due to the highest correlation with the comprehensive evaluation index. Among them, the model “aboveground dry weight×root surface area×chlorophyll a” was identified as the optimal one to evaluate the quality of strawberry seedlings, due to the highest correlation with the comprehensive evaluation index. The correlation coefficient of between strong seedling index and comprehensive evaluation index in three strawberry variety ‘Benihoppe’, ‘Kantoseika’ and ‘Sweet Charlie’ were 0.879, 0.924, and 0.975, respectively. According to the comprehensive evaluation index, the quality of strawberry seedlings were classified into three grades: grade Ⅰ (comprehensive evaluation index ≥0.5, seedling index ≥4.0) with high-quality seedlings; grade Ⅱ (comprehensive evaluation index 0.3-0.5, seedling index 0.5-4.0) with qualified seedlings; grade Ⅲ (comprehensive evaluation index ≤0.3, seedling index ≤0.5) with weak seedlings. The results provided a theoretical basis and scientific method for the evaluation of the health status of strawberry seedlings or other fruits and vegetable seedlings.

Key words: strawberry seedling, seedling index, fuzzy comprehensive evaluation, principal component analysis, correlation analysis