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基于灰色关联度分析和DTOPSIS法的芦笋种质资源综合评价 被引量:7

Overall Evaluation of Asparagus Germplasm Resources Based on Grey Relational Analysis and DTOPSIS Method
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摘要 利用灰色关联度分析和DTOPSIS法综合评价芦笋新品种(系)在江西省的适应性,为芦笋种质综合评价及其在江西省示范推广提供理论依据。以2018—2019年吉安市良种场内芦笋资源圃的43份芦笋新品种(系)为材料,采用灰色关联度分析和DTOPSIS法综合评价43份芦笋新品种(系)的田间性状表现,比较芦笋种质不同评价方法的准确性,筛选出综合表现优异的芦笋新品种(系)。结果表明,基于灰色关联度分析的芦笋参评种质各指标在其综合性状评价中关联度顺序依次为优质笋率>优质笋产量>商品笋率>见笋时间>第一分枝高度>第二主茎株高>商品笋产量>株高>抽笋数>抗病性>笋外观品质>笋粗细均匀度>茎粗。采用灰色关联度和DTOPSIS法对芦笋种质评价结果显示,Gijnlim、Extremely male imperial crown、Florida、Jersey knight、California early、巨丰和UC115排在前10位,与参考种质最接近。DTOPSIS法C_(i)最大差异率为68.40%,C_(i)排序结果与产量排序结果相关系数为0.918^(**);灰色关联度分析中等权关联度γ_(i)最大差异率为41.03%,加权关联度△γ_(i)最大差异率为40.33%,排序结果与产量排序结果相关系数分别为0.652^(**)和0.710^(**)。可见,DTOPSIS法C_(i)差异率较大,种质间差异大,与产量的相关性更高。因此,芦笋新品种选育时应首先注重提升优质笋的质量比重,其次是选育春季出土早的优良材料作亲本,同时也要兼顾芦笋株型等其他性状;Gijnlim、Extremely male imperial crown、Florida、Jersey knight、California early、巨丰和UC115的适应性和丰产性均较好,可进一步考察、示范与推广。基于灰色关联度分析的DTOPSIS法更适于综合评价芦笋种质资源的适应性。 In order to provide theoretical basis for comprehensive evaluation of asparagus varieties(lines)and their demonstration and popularization in Jiangxi,the adaptability of new asparagus varieties(lines)in Jiangxi was comprehensively evaluated by grey relational analysis and DTOPSIS method.With 43 new asparagus varieties(lines)from the asparagus resource nursery in Ji'an from 2018 to 2019 as materials,comprehensive evaluation of field traits was performed by using grey relational analysis and DTOPSIS method.The accuracy of different methods was compared,and asparagus varieties(lines)with better comprehensive performance were selected.The result showed that the order of correlation grade value of each character in the overall evaluation of asparagus germplasms based on grey relational analysis was as follows:high-quality asparagus rate>high-quality asparagus yield>commodity asparagus rate>seeing asparagus time>first branch height>second main stem height>commercial asparagus production>plant height>number of asparagus shoots>disease resistance>appearance quality of asparagus>asparagus thickness uniformity>stem thickness.The result also showed that Gijnlim,Extremely male imperial crown,Florida,Jersey knight,California early,Jufeng and UC115 ranked in the top 10,which had good adaptability and high yield,and were the closest to the ideal germplasm.The C_(i) maximum difference rate of DTOPSIS was 68.40%and the correlation coefficient of its ranking result with the yield ranking was 0.918^(**).The maximum difference rate of y_(i) in grey correlation analysis was 41.03%,the maximum difference rate of△was 40.33%,the correlation coefficients between the ranking result and the yield ranking were 0.652^(**) and 0.710^(**)respectively.It can be seen that the DTOPSIS method had a greater rate of C_(i) variation,a greater variation among germplasms,and a higher correlation with yield.Therefore,when breeding new asparagus varieties,the first priority should be to increase the weight proportion of high-quality bamboo shoots,and secondly,high-quality materials unearthed early in spring are selected as parents.At the same time,other traits such as asparagus plant type should also be taken into consideration.Gijnlim,Extremely male imperial crown,Florida,Jersey knight,California early,Jufeng and UC115,which have good adaptability and high yield,can be further investigated,demonstrated and promoted.The DTOPSIS method based on the grey relational analysis is more suitable for comprehensive evaluation of adaptability of new asparagus varieties(lines).
作者 张冰冰 叶艳英 金水凤 罗稳根 周劲松 尹玉玲 罗绍春 汤泳萍 ZHANG Bingbing;YE Yanying;JIN Shuifeng;LUO Wengen;ZHOU Jinsong;YIN Yuling;LUO Shaochun;TANG Yongping(Vegetable and Flower Institute,Jiangxi Academy of Agricultural Sciences,Nanchang 330220,China;Ji’an Improved Crop Variety Fields,Ji’an 343016,China)
出处 《河南农业科学》 北大核心 2022年第5期96-107,共12页 Journal of Henan Agricultural Sciences
基金 江西现代农业科研协同创新专项(JXXTCX201904-03) 江西省科技厅重点研发一般项目(20202BBFL63008) 江西省科技厅重点项目(20202BBF62006)。
关键词 芦笋 灰色关联度分析 DTOPSIS法 适应性 综合评价 Asparagus Grey relational analysis DTOPSIS method Adaptability Comprehensive evaluation
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