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马铃薯产量组分的基因型与环境互作及稳定性

叶夕苗1 程鑫1 安聪聪1 袁剑龙1 余斌1 文国宏2 李高峰2 程李香1 王玉萍1 张峰1

(1.甘肃农业大学/甘肃省干旱生境作物学国家重点实验室培育基地/甘肃省遗传改良与种质创新重点实验室, 甘肃兰州 730070)
(2.甘肃省农业科学院/马铃薯研究所, 甘肃兰州 730070)

【摘要】本研究主要探究基因型和基因型与环境互作(genotype+genotypes and environment interactions, GGE)双标图在马铃薯育种中的应用。综合评价马铃薯品系产量性状在不同环境中的丰产性、稳定性和适应性,筛选出适应不同生态环境的产量性状优良品系。同时评价各试点的区分力和代表性,为试点的选择提供依据。2015年和2016年在甘肃安定区鲁家沟镇、安定区内官镇、渭源县五竹镇3个试点种植国际马铃薯中心引进的101份高代品系和对照青薯9号。收获后记录小区产量、小区大薯产量、小区小薯产量、单株产量、单株大薯产量、单株小薯产量、单株结薯数、单株大薯数、单株小薯数;采用联合方差和GGE双标图对产量性状进行基因型与环境互作分析。方差分析表明,除小区小薯产量在基因型与环境互作效应中无显著差异外,其他产量组分在基因型效应、环境效应和互作效应中均呈现极显著差异(P<0.01)。小区产量、小区大薯产量、小区小薯产量、单株产量、单株大薯产量、单株结薯数环境效应平方和占总方差平方和最大;单株小薯产量、单株大薯数和单株小薯数的基因型与环境互作效应平方和占总方差平方和最大。GGE分析结果表明,适应性最强的品系在鲁家沟试点是G86;在五竹镇试点是G65;在内官镇试点是G86。参试品系中丰产品系有G86、G116、G124;稳产品系有G124、G125、G10;高产稳产品系有G86、G116、G124、青薯9号。单株大薯数高的品系有G45、G86、G67,稳定性好的品系有G67、G116、G51,对照青薯9号的单株大薯产量不稳定。综合鉴别力和代表性的强弱,依次为鲁家沟镇2016年、鲁家沟镇2015年、五竹镇2015年、五竹镇2016年、内官镇2015年、内官镇2016年。GGE模型能够直观地展现多年多点品系试验结果,并客观评价参试品系的丰产性、稳定性和适应性,同时可以对试点的代表性和区分力进行评价。以GGE模型综合评价,高产稳产品系有G116、G124、G125、G122、青薯9号;高产不稳定的品系有G86、G10、G121、G106、G107、G72。最理想的生态区试点是鲁家沟镇,对品种的鉴别力最强的试点是五竹镇。

【关键词】 产量组分;GGE双标图;多年多点;试点评价;

【DOI】

【基金资助】 国家重点研发计划项目(2017YFD0101905); 国家自然科学基金项目(31471433); 甘肃省高等学校协同创新团队项目(2018C-17); 甘肃省科技重大专项计划项目(17ZD2NA016);

Genotype × environment interaction and stability of yield components for potato lines

YE Xi-Miao1 CHENG Xin1 AN Cong-Cong1 YUAN Jian-Long1 YU Bin1 WEN Guo-Hong2 LI Gao-Feng2 CHENG Li-Xiang1 WANG Yu-Ping1 ZHANG Feng1

(1.Gansu Agricultural University/Gansu Provincial Key Laboratory of Aridland Crop Science/Gansu Key Laboratory of Crop Improvement & Germplasm Enhancement, Lanzhou, Gansu 730070)
(2.Gansu Academy of Agricultural Sciences/Potato Institute, Lanzhou, Gansu 730070)

【Abstract】This study mainly focused on the application of GGE (genotype plus genotype-by-environment interaction) biplot in potato breeding, to evaluate the productivity, stability and adaptability of yield components of potato lines in different environments comprehensively, and screen out the excellent lines adapted to different mega-environments. The representativeness and discriminating ability of each test-environment were also evaluated, providing a basis for the selection of test-environment. A total of 101 advanced lines from International Potato Center (CIP) and the potato variety Qingshu 9 were planted in Neiguan Town, Lujiagou Town and Wuzhu Town of Gansu province in 2015 and 2016. The plot yield, plot yield of large-sized tubers, plot yield of small-sized tubers, yield per plant, large-sized tuber yield per plant, small-sized tuber yield per plant, tuber number per plant, large-sized tuber number per plant and small-sized tuber number per plant were measured. The genotype and environment interactions were analyzed by the combined analysis of variance and GGE biplot. Except that plot yield of small-sized tubers was insignificantly affected by genotype × environment interaction, all the other yield components were significantly (P < 0.01) affected by genotype effect, environmental effect and genotype × environment interaction. The square sum of environmental effect on the plot yield, plot yield of large-sized tubers, plot yield of small-sized tubers, yield per plant, large-sized tuber yield per plant, and tuber number per plant, as well as the square sum of genotype × environment interaction effect on the plot yield of small-sized tubers, the large-sized tuber number per plant, and the small-sized tuber number per plant had the largest proportion in the square sum of total variance. The most adaptable line was G86 in Lujiagou Town, G65 in Wuzhu Town, and G86 in Neiguan Town. The high-yield lines were G86, G116, and G124; the stable-yield lines were G124, G125, and G10; the high- and stable-yield lines were G86, G116, G124, and Qingshu 9. The lines with more large-sized tuber number per plant were G45, G86, and G67, and the lines with good stability were G67, G116, and G51. Qingshu 9 did not have stable large-sized tuber yield per plant. According to the comprehensive discrimination and representativeness, the order of test-environments were Lujiagou Town in 2016, Lujiagou Town in 2015, Wuzhu Town in 2015, Wuzhu Town in 2016, Neiguan Town in 2015, and Neiguan Town in 2016. GGE model can intuitively display the results in the genotype-location-year framework, and objectively evaluate the productivity, stability and adaptability of tested lines, as well as the representativeness and discriminating ability of test-environment. According to the comprehensive evaluation of GGE model, the high-yield and stable lines were G116, G124, G125, G122, and Qingshu 9, and the high-yield and unstable lines were G86, G10, G121, G106, G107, and G72. The most ideal mega-environment is Lujiagou Town, and Wuzhu Town is the test-environment with the strongest discriminating ability for potato lines.

【Keywords】 yield component; GGE biplot; multi-years and sites; pilot evaluation;

【DOI】

【Funds】 National Key R&D Program of China (2017YFD0101905); National Natural Science Foundation of China (31471433); Gansu High Educational Scientific Special Project (2018C-17); Gansu Province Science and Technology Major Special Projects (17ZD2NA016);

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    References

    [1] Zaheer K, Akhta M H. Potato production, usage, and nutrition—a review. Crit Rev Food Sci, 2014, 56: 711–721.

    [2] Wang Y P, Sui J H, Liang Y C, Lu X. Screening for potato processing lines with tuber qulity index from two ecoregions. J Gansu Agric Univ, 2016, 51 (5): 39–45 (in Chinese with English abstract).

    [3] Mulema J M K, Adipala E, Olanya O M. Yield stability analysis of late blight resistant potato selections. Crop Sci, 2016, 56: 1645–1661.

    [4] Padi F K. Relationship between stress tolerance and grain yield stability in cowpea. J Agric Sci, 2004, 142: 431–443.

    [5] Bednarz C W, Bridges D C, Brown S M. Analysis of cotton yield stability across population densities. Semigroup Forum, 2000, 92: 128–135.

    [6] Fan X M, Kang M S, Chen H M. Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agron J, 2007, 99: 220–228.

    [7] Mekbib F. Yield stability in common bean (Phaseolus vulgaris L.) genotypes. Euphytica, 2003, 130: 147–153.

    [8] Aastveit A H, Martens H. ANOVA interactions interpreted by partial least squares regression. Biometrics, 1986, 42: 829–844.

    [9] EberhartSA, RusselWA. Stability parameters for comparing varieties. Crop Sci, 1966, 6: 36–40.

    [10] Blouin D C, Webster E P, Bond J A. On the analysis of combined experiments. Weed Technol, 2015, 25: 165–169.

    [11] Kang M S. Simultaneous selection for yield and stability in crop performance trials: consequences for growers. Agron J, 1993, 85: 754–757.

    [12] Gauch H G. Statistical analysis of yield trials by AMMI and GGE. Crop Sci, 2006, 46: 1488–1500.

    [13] Yan W K, Fetch J M, Fregeau-reid J. Genotype × location interaction patterns and testing strategies for oat in the Canadian. Prairies, 2011, 51: 1903–1914.

    [14] Yan W K, Kang M S, Ma B L. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci, 2007, 47: 641–653.

    [15] Nzuve F, Githiri S, Mulunya D M. Analysis of genotype × environment interaction for grain yield in maize hybrids. J Agric Sci, 2013, 5: 75–85.

    [16] SotoBJ, DuquidS, BookerH. Genomic regions underlying agronomic traits in linseed (Linum usitatissimum L.) as revealed by association mapping. J Integr Plant Biol, 2014, 56: 75–87.

    [17] Murphy S E, Lee E A, Woodrow L. Genotype × environment interaction and stability for isoflavone content in soybean. Crop Sci, 2009, 49: 1313–1321.

    [18] Wachira F, Ngetich W, Omolo J. Genotype × environment interactions for tea yields. Euphytica, 2002, 127: 289–297.

    [19] Burgueño J, Campos G D L, Weigel K. Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci, 2012, 52: 707–712.

    [20] Phuke R M, Anuradha K, Radhika K, Jabeen F, Anuradha G, Ramesh T, Hariprasanna K, Mehtre S P, Deshpande S P, Anil G, Das R R, Rathore A, Hash T, Reddy B V S, Kumar A A. Genotype × environment interaction, correlation, and GGE Biplot analysis for grain iron and zinc concentration and other agronomic traits in RIL population of sorghum (Sorghum bicolor L. Moench). Front Plant Sci, 2017, 5: 712–716.

    [21] Zolfaghar S, Bahram H, Dadkhodaie A. Dissection of genotype × environment interactions for mucilage and seed yield in Plantago species: application of AMMI and GGE biplot analyses. PLoS One, 2018, 13: e0196095.

    [22] Zhang Y C, Tian F. Potato Experiment Test Research Method. Beijing: China Agriculture Science and Technology Press, 2007. pp 90–93 (in Chinese).

    [23] Yan W K. Crop Variety Trials Data Management and Analysis. Beijing: China Agriculture Science and Technology Press, 2015. pp 133–146 (in Chinese).

    [24] Dia M, Weherr T C, Hasssel R, Price D S. Genotype × environment interaction and stability analysis for watermelon fruit yield in the United States. Crop Sci, 2016, 56: 1645–1661.

    [25] Wang R H, Hu D H, Zheng H Q, Yun S. Genotype × environmental interaction by AMMI and GGE biplot analysis for the provenances of Michelia chapensis in South China. J For Res, 2016, 27: 659–664.

    [26] Muthoni J, Shimelis H, Melis R. Genotype × environment interaction and stability of potato tuber yield and bacterial wilt resistance in Kenya. Am J Potato Res, 2015, 92: 367–378.

    [27] Yan W K. Optimal use of biplots in analysis of multi-location variety test data. Acta Agron Sin, 2010, 36: 1805–1819 (in Chinese with English abstract).

    [28] Yan W K, Sheng Q L, Hu Y G, Hunt L A. GGE Biplot: an ideal tool for studying genotype by environment interaction of regional yield trial data. Acta Agron Sin, 2001, 27: 21–28 (in Chinese with English abstract).

This Article

ISSN:0496-3490

CN:11-1809/S

Vol 46, No. 03, Pages 354-364

March 2020

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Article Outline

Abstract

  • 1 Materials and methods
  • 2 Results
  • 3 Discussion
  • 4 Conclusions
  • References