李亚杰,白江平,张俊莲,王蒂.甘肃省马铃薯区试产量数据的AMMI模型分析[J].干旱地区农业研究,2013,31(1):61~66
甘肃省马铃薯区试产量数据的AMMI模型分析
Application of AMMI model in regional testing of potato in Gansu Province
  
DOI:10.7606/j.issn.1000-7601.2013.01.12
中文关键词:  马铃薯  AMMI模型  品种稳定性  试点鉴别力
英文关键词:potato  AMMI model  yield stability  site discrimination
基金项目:国家科技支撑计划(2012BAD06B03);甘肃省重大专 项项目(1102NKDA025)
作者单位
李亚杰 甘肃省干旱生境作物学重点实验室 甘肃省作物遗传改良与种质创新重点实验室 甘 肃 兰州 730070 甘肃农业大学农学院 甘肃 兰州 730070 
白江平 甘肃省干旱生境作物学重点实验室 甘肃省作物遗传改良与种质创新重点实验室 甘 肃 兰州 730070 甘肃农业大学农学院 甘肃 兰州 730070 
张俊莲 甘肃省干旱生境作物学重点实验室 甘肃省作物遗传改良与种质创新重点实验室 甘 肃 兰州 730070 甘肃农业大学农学院 甘肃 兰州 730070 
王蒂 甘肃省干旱生境作物学重点实验室 甘肃省作物遗传改良与种质创新重点实验室 甘 肃 兰州 730070 甘肃农业大学农学院 甘肃 兰州 730070 
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中文摘要:
      研究利用AMMI模型对2007—2008年甘肃省马铃薯品种区域试验中的10个马铃薯品种、7个试点进行综合评价,分析10个马铃薯品种的稳定性及丰产性,以及7个试点的鉴别力。结果表明:各品种的稳定性大小为L0206-6>L0227-17>L022 7-18>天2008-8-2>富薯3号>CK>34-126>98-6-2>35-62>陇薯6号,其中L0206-6 ,98-6-2品种的稳定性最强,陇薯6号的稳定性较差;L0227-17,L0206-6,L0227-18以及天 2008-8-2的产量最高,98-6-2品种的产量最低。各个试点的鉴别力也具有差异性,各试点的 鉴别力顺序为天水>会川>安定>静宁>临夏>秦王川>宕昌,其中天水地区对品种的选择 性最高,宕昌地区对品种的鉴别力低。AMMI模型中的主成分值,共解释总互作平方和的93% ,比线性回归模型能更有效地分析基因与环境互作效应。
英文摘要:
      AMMI(Additive Main Effects and Multiplicative Int eraction Model) was adopted to conduct comprehensive evaluation on the yield of ten cultivars at seven sites involved in the regional testing of potato in Gansu Province during 2007—2008. The results indicated that the stability of tested cultivars was as follows: L0206-6>L0227-17>L0227-18>Tian 2008-8-2>Fushu 3> CK>34-126>98-6-2>35-62>Longshu 6; the production stability of L0206-6 and 98 -6-2 was higher than others, while that of Longshu 6 was the lowest; the yield of L0227-17, L0206-6, L0227-18 and Tian 2 008-8-2 was relative high, while that of 98-6-2 was the lowest. The results also suggested that the discriminative parameter varied among different testing site s, which could be ranked as: Tianshui>Huichuan>Anding>Jingning>Linxia>Qinwa ngchuan>Tanchang. Therefore, Tianshui was the site with a highest discrimination, while Tanchang was the site with a lowest discrim ination in selecting cultivars. The principle component of AMMI model could expl ain 93% of total sum of squares of interaction, being more effective in analyzin g the interaction between gene and environment than the traditional regression m odel.
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