基于AMMI模型和GGE双标图分析荞麦品种的稳产性及适应性

doi:10.6048/j.issn.1001-4330.2024.09.009

新疆农业科学 ›› 2024, Vol. 61 ›› Issue (9): 2152-2159.DOI: 10.6048/j.issn.1001-4330.2024.09.009

• 作物遗传育种·种质资源·分子遗传学·耕作栽培·生理生化 • 上一篇下一篇

基于AMMI模型和GGE双标图分析荞麦品种的稳产性及适应性

常克勤(), 杜燕萍, 穆兰海, 杨崇庆, 陈一鑫

宁夏农林科学院固原分院/宁夏旱作农业工程技术研究中心,宁夏固原 756000

收稿日期:2024-02-17 出版日期:2024-09-20 发布日期:2024-10-09
作者简介:常克勤(1965-),男,宁夏固原人,研究员,研究方向为荞麦和燕麦作物新品种选育及栽培,(E-mail)nxgychkq@163.com
基金资助:
农业农村部国家燕麦荞麦产业技术体系-固原综合试验站(CARS-07-G-15);宁夏农林科学院院地合作-盐池特色产业关键技术集成及轻简栽培技术示范(NNKZZCGZH-2021-04);宁夏科技重点-宁夏小杂粮创新团队(2020CXTDLX04)

Analysis of stable yield and adaptability of buckwheat varieties based on AMMI model and GGE double label graph

CHANG Keqin(), DU Yanping, MU Lanhai, YANG Chongqing, CHEN Yixin

Guyuan Branch of Ningxia Academy of Agriculture and Forestry Sciences / Dry Farming Agricultural Engineering Technology Research Center of Ningxia,Guyuan Ningxia 756000, China

Received:2024-02-17 Published:2024-09-20 Online:2024-10-09
Correspondence author: CHANG Keqin (1965-), male, from Guyuan,Ningxia,researcher, research direction:breeding new varieties of buckwheat and oat crops and water-saving cultivation techniques,(E-mail)nxgychkq@163.com
Supported by:
Avena sativa and Buckwheat Industry Technology System of the Ministry of Agriculture and Rural Affairs-Guyuan Comprehensive Experimental Station(CARS-07-G-15);Local Cooperation of Ningxia Academy of Agriculture and Forestry Sciences-Key Technology Integration and Light and Simple Cultivation Technology Demonstration of Yanchi Characteristic Industry(NNKZZCGZH-2021-04);Ningxia Science and Technology Key Project - Ningxia Small Coarse Grain Innovation Team(2020CXTDLX04)

摘要/Abstract

摘要：

【目的】评价荞麦新品种的丰产性、稳产性和生态适应性。【方法】选择5个荞麦品种(系)在6县(区)点试验数据,基于AMMI模型和R语言的GGE-Biplot软件包相结合的方法,对宁夏南部山区不同气候区荞麦品种多年多点试验进行主成分分析,综合评价荞麦品种的丰产性、稳定性、适应性、代表性等。【结果】荞麦试点以半干旱区的西吉县或原州区、半干旱区易旱区的海原县、中部干旱带的盐池县代表性较好。固荞1号和固荞3号新品种的丰产性、稳定性和生态适应性最佳。【结论】以AMMI模型分析与大田生产验证相结合综合评价荞麦品种的稳定性方法可行,结果可靠,验证效果良好。

关键词: 荞麦; AMMI模型; GGE-Biplot; 稳定性与适应性

Abstract:

【Objective】 Evaluate the high yield, stable yield, and ecological adaptability of new varieties of tartary buckwheat. 【Methods】 Through the analysis of experimental data from 5 varieties (lines) in 6 counties (districts), By combining the AMMI model with the GGE-Blilot software package of R language, principal component analysis was conducted on sweet buckwheat varieties from different climate zones in the southern mountainous areas of Ningxia for multiple years and the stability, adaptability, pilot representativeness were comprehensively evaluated. 【Results】 The buckwheat pilot area represented by Xiji County or Yuanzhou District in semi-arid areas and Haiyuan County in semi-arid areas prone to drought, and Yanchi County in the central arid zone was better. The new varieties of Guqiao 1 and Guqiao 3 had the best yield, stability, and ecological adaptability. 【Conclusion】 By combining the AMMI model analysis with production practice verification, the method is feasible, the results are reliable, and the validation is good.

Key words: sweet buckwheat crop varieties; AMMI model; GGE Biplot; stability and adaptability

中图分类号:

S517

常克勤, 杜燕萍, 穆兰海, 杨崇庆, 陈一鑫. 基于AMMI模型和GGE双标图分析荞麦品种的稳产性及适应性[J]. 新疆农业科学, 2024, 61(9): 2152-2159.

CHANG Keqin, DU Yanping, MU Lanhai, YANG Chongqing, CHEN Yixin. Analysis of stable yield and adaptability of buckwheat varieties based on AMMI model and GGE double label graph[J]. Xinjiang Agricultural Sciences, 2024, 61(9): 2152-2159.

图/表 8

参考文献 27

[1]	谢锐, 路战远, 金晓蕾, 等. 基于AMMI模型的内蒙古中西部地区马铃薯品种(系)稳定性和适应性分析[J]. 北方农业学报, 2022, 50(1): 18-24.
	XIE Rui, LU Zhanyuan, JIN Xiaolei, et al. Analysis of potato varieties(lines) stability and adaptability in Midwestern Inner Mongolia base onAMMI model[J]. Journal of Northern Agriculture, 2022, 50(1): 18-24.
[2]	黄大明, 谢雄泽, 白桂萍, 等. AMMI模型和GGE双标图在湖北省油菜区域试验中的应用[J]. 湖北农业科学, 2018, 57(12): 24-29.
	HUANG Daming, XIE Xiongze, BAI Guiping, et al. Application of AMMI model and GGE biplot in rape regional trial of Hubei[J]. Hubei Agricultural Sciences, 2018, 57(12): 24-29.
[3]	步清, 鲁月, 郝德荣, 等. AMMI模型和GGE双标图在江苏省糯玉米品种区域试验中的应用[J]. 分子植物育种, 2022, 20(22):7628-7636.
	BU Qing, LU Yue, HAO Derong, et al. Application of AMMI model and GGE double label plot in regional trials of glutinous corn varieties in Jiangsu Province[J]. Molecular Plant Breeding, 2022, 20(20):7628-7636.
[4]	吴曹阳, 梁诗涵, 邱军, 等. 基于连续12年国家苦荞区域试验的中国苦荞品种选育现状分析[J]. 中国农业科学, 2020, 53(19): 3878-3894.
	WU Caoyang, LIANG Shihan, QIU Jun, et al. An examination on breeding status quo of Chinese Tartary buckwheat varieties based on the national cross-country tests of Tartary buckwheat varieties in China over 12 consecutive years[J]. Scientia Agricultura Sinica, 2020, 53(19): 3878-3894.
[5]	魏常敏, 许卫猛, 邢永锋, 等. AMMI模型和GGE双标图在黄淮海甜玉米中的应用[J]. 分子植物育种, 2021, 19(17): 5909-5916.
	WEI Changmin, XU Weimeng, XING Yongfeng, et al. Application ofAMMI model and GGE biplot of sweet maize varieties in Huang-Huai-Hai regional[J]. Molecular Plant Breeding, 2021, 19(17): 5909-5916.
[6]	王迪. GGE双标图分析黑龙江省甜瓜区域试验品种适应性和稳定性[J]. 黑龙江农业科学, 2021, (10): 64-68.
	WANG Di. Analysis of adaptability and stability of melon varieties in Heilongjiang regional test by GGE biplot[J]. Heilongjiang Agricultural Sciences, 2021, (10): 64-68.
[7]	宋慧, 刘金荣, 王素英, 等. GGE双标图评价谷子'豫谷18'的丰产稳产性和适应性[J]. 中国农业大学学报, 2020, 25(1): 29-38.
	SONG Hui, LIU Jinrong, WANG Suying, et al. Evaluation of foxtail millet yield stability and adaptability using GGE-biplot analysis: a case study of 'Yugu18' foxtail millet cultivar[J]. Journal of China Agricultural University, 2020, 25(1): 29-38.
[8]	昝凯, 陈亚光, 徐淑霞, 等. 基于R语言的GGE双标图在大豆区试中的应用[J]. 大豆科技, 2019, (4): 16-20.
	ZAN Kai, CHEN Yaguang, XU Shuxia, et al. Application of GGE double plot in soybean regional tests based on R language[J]. Soybean Science & Technology, 2019, (4): 16-20.
[9]	崔顺立, 何美敬, 侯名语, 等. 利用GGE双标图分析花生品质性状的基因型-环境互作[J]. 中国油料作物学报, 2021, 43(4): 617-626.
	CUI Shunli, HE Meijing, HOU Mingyu, et al. Genotype × environment interactions for the quality traits of peanut varieties based on GGE biplot analysis[J]. Chinese Journal of Oil Crop Sciences, 2021, 43(4): 617-626.
[10]	王育才, 王化俊, 李葆春, 等. 基于GGE双标图对青稞区试点代表性及品系稳定性的分析[J]. 草原与草坪, 2018, 38(5): 22-27.
	WANG Yucai, WANG Huajun, LI Baochun, et al. GGE-biplot based study on cultivar stability and representativeness of test sites for highland barley[J]. Grassland and Turf, 2018, 38(5): 22-27.
[11]	张大爱, 赵绪明, 钱一萍, 等. 用GGE双标图分析苦荞品种的产量稳定性及试验地点相似性[J]. 西北农林科技大学学报(自然科学版), 2015, 43(7): 101-108.
	ZHANG Daai, ZHAO Xuming, QIAN Yiping, et al. GGE biplot based yield stability and test site similarity of Tartary buckwheat varieties[J]. Journal of Northwest A & F University (Natural Science Edition), 2015, 43(7): 101-108.
[12]	唐启义, 冯明光. DPS数据处理系统: 实验设计、统计分析及数据挖掘[M]. 北京: 科学出版社, 2007.
	TANG Qiyi, FENG Mingguang. DPS data processing system[M]. Beijing: Science Press, 2007.
[13]	袁金海, 贾多英, 刘续汉, 等. GGE双标图在马铃薯产量及试验点评价分析中的应用[J]. 中国种业, 2018, (8): 56-59.
	YUAN Jinhai, JIA Duoying, LIU Xuhan, et al. Application of GGE double plot in evaluation and analysis of potato yield and test site[J]. China Seed Industry, 2018, (8): 56-59.
[14]	李亚杰, 李德明, 范士杰, 等. GGE双标图在马铃薯品种适应性及产量稳定性分析中的应用评价[J]. 兰州大学学报(自然科学版), 2016, 52(5): 617-622.
	LI Yajie, LI Deming, FAN Shijie, et al. Analysis of variety adaptability and yield stability of potato for evaluating GGE-biplot application[J]. Journal of Lanzhou University (Natural Sciences), 2016, 52(5): 617-622.
[15]	张志芬, 任长忠, 杨海顺, 等. 基于SD-GGE双标图进行饲草燕麦品种归类和区域划分[J]. 中国农业大学学报, 2021, 26(5): 10-21.
	ZHANG Zhifen, REN Changzhong, YANG Haishun, et al. Variety classification and region division of forage oat varieties based on SD-GGE Biplot[J]. Journal of China Agricultural University, 2021, 26(5): 10-21.
[16]	李琴琴, 高乙萍, 张志芬, 等. 甜荞品种稳定性和试验地点相似性的GGE双标图分析[J]. 干旱地区农业研究, 2013, 31(1): 67-70, 121.
	LI Qinqin, GAO Yiping, ZHANG Zhifen, et al. Stability and testing-site similarity for common buckwheat lines based on GGE-biplot analysis[J]. Agricultural Research in the Arid Areas, 2013, 31(1): 67-70, 121.
[17]	柳娜, 曹东, 王世红, 等. 基于GGE双标图的甘肃春小麦区试品系稳产性和试点代表性分析[J]. 西北农林科技大学学报(自然科学版), 2018, 46(4): 39-48.
	LIU Na, CAO Dong, WANG Shihong, et al. Yield stability and testing-site representativeness of regional trials for spring wheat lines in Gansu based on GGE-biplot[J]. Journal of Northwest A & F University (Natural Science Edition), 2018, 46(4): 39-48.
[18]	丁逸帆, 许明, 许如根, 等. 基于GGE双标图对江苏省大麦鉴定试验品种的高产稳产和适应性分析[J]. 扬州大学学报(农业与生命科学版), 2019, 40(3): 22-26.
	DING Yifan, XU Ming, XU Rugen, et al. Analyses for high-yield, stability and adaptability of barley varieties in identification test of Jiangsu Province using GGE biplot[J]. Journal of Yangzhou University (Agricultural and Life Science Edition), 2019, 40(3): 22-26.
[19]	陈新, 吴斌, 张宗文. 燕麦种质资源重要农艺性状适应性和稳定性评价[J]. 植物遗传资源学报, 2016, 17(4): 577-585.
	CHEN Xin, WU Bin, ZHANG Zongwen. Evaluation of adaptability and stability for important agronomic traits of oat(Avena spp.) germplasm resources[J]. Journal of Plant Genetic Resources, 2016, 17(4): 577-585.
[20]	陈四龙, 李玉荣, 程增书, 等. 用GGE双标图分析种植密度对高油花生生长和产量的影响[J]. 作物学报, 2009, 35(7): 1328-1335.
	CHEN Silong, LI Yurong, CHENG Zengshu, et al. GGE biplot analysis of effects of planting density on growth and yield components of high oil peanut[J]. Acta Agronomica Sinica, 2009, 35(7): 1328-1335.
[21]	王星宇, 程静, 高生, 等. 应用AMMI模型和GGE双标图评价裸燕麦品种在华北高寒区的适应性[J]. 草业学报, 2022, 31(12): 76-84.
	WANG Xingyu, CHENG Jing, GAO Sheng, et al. Evaluation of adaptability of naked oat varieties in the alpine region of North China based on the AMMI model and GGE Biplot[J]. Acta Prataculturae Sinica, 2022, 31(12): 76-84.
[22]	朱艳彬, 孙九超, 王显, 等. 基于Genstat的AMMI模型和GGE双标图在玉米区域试验分析中的应用[J]. 安徽农业科学, 2021, 49(6): 49-53.
	ZHU Yanbin, SUN Jiuchao, WANG Xian, et al. Application of AMMI model and GGE-biplot based on genstat in the analysis of maize regional experiment[J]. Journal of Anhui Agricultural Sciences, 2021, 49(6): 49-53.
[23]	黄大明, 谢雄泽, 白桂萍, 等. AMMI模型和GGE双标图在湖北省油菜区域试验中的应用[J]. 湖北农业科学, 2018, 57(12): 24-29.
	HUANG Daming, XIE Xiongze, BAI Guiping, et al. Application of AMMI model and GGE biplot in rape regional trial of Hubei[J]. Hubei Agricultural Sciences, 2018, 57(12): 24-29.
[24]	周启龙, 多吉顿珠, 刘云飞, 等. 应用AMMI模型分析饲用燕麦干草产量稳定性和适应性[J]. 种子, 2020, 39(6): 79-82.
	ZHOU Qilong, DUO Jidunzhu, LIU Yunfei, et al. Analysis of yield stability and adaptability of forage oat hay based onAMMI model[J]. Seed, 2020, 39(6): 79-82.
[25]	柴继宽, 慕平, 赵桂琴. 8个燕麦品种在甘肃的产量稳定性及试点代表性研究[J]. 草地学报, 2016, 24(5): 1100-1107.
	CHAI Jikuan, MU Ping, ZHAO Guiqin. Study on yield stability and test site representativeness of eight oat varieties in Gansu Province[J]. Acta Agrestia Sinica, 2016, 24(5): 1100-1107.
[26]	方彦杰, 张绪成, 于显枫, 等, 施肥对半干旱区旱地全膜覆土穴播苦荞产量及水肥利用率的影响[J]. 中国农业科技导报. 2020, 22(9):143-152
	FANG Yanjie, ZHANG Xucheng, YU Xianfeng, et aI. Impacts of Fertilization on Yield and Water-fertilizer Use Efficiency of Tartary Buckwheat with Whole Field Soil-plastic Mulching in Semiarid Area[J]. Journal of Agricultural Science and Technology, 2020, 22(9):143-152
[27]	周花, 戴丽君, 李永平, 等. 不同覆膜穴播种植方式对谷子和糜子生长发育及水分利用率的影响[J]. 甘肃农业科技, 2021, 52(4): 29-36.
	ZHOU Hua, DAI Lijun, LI Yongping, et al. Effects of different mulching hole-planting methods on growing development and water use efficiency of foxtail millet andPanicum miliaceum L[J]. Gansu Agricultural Science and Technology, 2021, 52(4): 29-36.

试验点 Test points	代号 Code	降水量 Annual rainfall (mm)		平均气温 Annual temperature (℃)		气候类型区 Climate
试验点 Test points	代号 Code	年均值	生育期	年均值	生育期	气候类型区 Climate
原州区Yuanzhou District	E₁	450.0	353.0	6.2	18.5	半干旱区
西吉县Xiji County	E₂	416.6	279.2	5.9	16.7
彭阳县Pengyang County	E₃	418.3	297.5	7.2	19.4
海原县Haiyuan County	E₄	350.0	331.8	7.1	18.7	半干旱易旱区
同心县Tongxin County	E₅	215.1	189.8	8.6	22.0	干旱带
盐池县Yanchi County	E₆	255.6	222.9	8.6	21.3	干旱带

试验点 Test points	代号 Code	降水量 Annual rainfall (mm)		平均气温 Annual temperature (℃)		气候类型区 Climate
试验点 Test points	代号 Code	年均值	生育期	年均值	生育期	气候类型区 Climate
原州区Yuanzhou District	E₁	450.0	353.0	6.2	18.5	半干旱区
西吉县Xiji County	E₂	416.6	279.2	5.9	16.7
彭阳县Pengyang County	E₃	418.3	297.5	7.2	19.4
海原县Haiyuan County	E₄	350.0	331.8	7.1	18.7	半干旱易旱区
同心县Tongxin County	E₅	215.1	189.8	8.6	22.0	干旱带
盐池县Yanchi County	E₆	255.6	222.9	8.6	21.3	干旱带

变异来源 The source of the mutation	自由度 Degrees of freedom (Df)	平方和 Sum of square (SS)	均方 Mean square (MS)	F值 F-value	占总变异比例 Proportion in the total (SS)
总变异Total variance	269	146 247 216.30	543 669.95
处理Treatment	29	17 397 882.96	599 927.00	1.12
基因型Genotype	4	6 620 101.48	1 655 025.37	3.08	38.1
环境Environment	5	8 015 794.07	1 603 158.81	2.99	46.1
基因型×环境Genotype × Environment	20	2 761 987.41	138 099.37	0.26	15.9
PCA₁	8	2 260 898.53	282 612.32	19.06^**	81.9
PCA₂	6	3 30 476.19	55 079.36	3.71^**	12.0
PCA₃	4	140 955.99	35 239.00	2.38^*	5.1
残差Residual	2	29 656.69	14 828.35

变异来源 The source of the mutation	自由度 Degrees of freedom (Df)	平方和 Sum of square (SS)	均方 Mean square (MS)	F值 F-value	占总变异比例 Proportion in the total (SS)
总变异Total variance	269	146 247 216.30	543 669.95
处理Treatment	29	17 397 882.96	599 927.00	1.12
基因型Genotype	4	6 620 101.48	1 655 025.37	3.08	38.1
环境Environment	5	8 015 794.07	1 603 158.81	2.99	46.1
基因型×环境Genotype × Environment	20	2 761 987.41	138 099.37	0.26	15.9
PCA₁	8	2 260 898.53	282 612.32	19.06^**	81.9
PCA₂	6	3 30 476.19	55 079.36	3.71^**	12.0
PCA₃	4	140 955.99	35 239.00	2.38^*	5.1
残差Residual	2	29 656.69	14 828.35

品种 Varieties	平均产量 Average production (kg/hm²)	位次 Order of seats	互作主成分 Reciprocal principal component			稳定性参数 Stability parameters Dg	位次 Order of seats
品种 Varieties	平均产量 Average production (kg/hm²)	位次 Order of seats	PCA₁	PCA₂	PCA₃	稳定性参数 Stability parameters Dg	位次 Order of seats
固荞1号Guqiao 1	1 656.3	2	-0.32	0.53	7.95	7.97	1
固原红花荞Guyuanhonghuaqiao	1 276.3	5	-13.49	-4.02	-3.06	14.41	5
定荞3号Dingqiao 3	1 620.9	3	11.29	-6.53	-2.26	13.23	4
固荞3号Guqiao 3	1 719.6	1	3.80	9.43	-3.03	10.61	2
信农1号Xinnong 1	1 498.3	4	-12.27	-1.42	-4.60	13.19	3

基于AMMI模型和GGE双标图分析荞麦品种的稳产性及适应性

Analysis of stable yield and adaptability of buckwheat varieties based on AMMI model and GGE double label graph

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参考文献 27

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Metrics

品种 Varieties		半干旱区 Semiarid region		半干旱区易旱区 Semiarid and drought prone areas		中部干旱带 Central arid zone		宁南山区6县(区) 6 counties in the southern mountainous areas of Ningxia
品种 Varieties		平均产量 (kg/hm²)	增产 (%)	平均产量 (kg/hm²)	增产 (%)	平均产量 (kg/hm²)	增产 (%)	平均产量 (kg/hm²)	增产 (%)	排名	稳定性排名
AMMI模型 AMMI model	固荞1号 Guqiao 1	1 623	9.3	1 707	13.9	1 681	10.8	1 656	10.5	2	1
	固荞3号 Guqiao 3	1 681	13.2	1 873	25.0	1 700	12.0	1 720	14.8	1	2
	平均值 Average value	1 652	11.2	1 790	19.4	1 691	11.4	1 688	12.7
	信农1号 Xinnong 1	1 485		1 499		1518		1498
生产示范 Demonstration	固荞1号、固荞3号 Guqiao 1、3	1 687	13.7	1 484	10.0	1 038	8.5
生产示范 Demonstration	信农1号 Xinnong 1	1 484		1 349		957

[1]	王仙, 罗正乾, 徐琳黎, 周志林, 唐君, 高海峰, 刘恩良, 金平. 基于甘薯区试产量数据的AMMI 模型分析[J]. 新疆农业科学, 2022, 59(5): 1093-1098.
[2]	王祥军;齐军仓;王仙;曹连莆. 基于AMMI模型分析大麦籽粒酚酸含量的基因型与环境效应[J]. , 2013, 50(3): 422-427.
[3]	聂迎彬;穆培源;桑伟;徐红军;庄丽;相吉山;邹波;田笑明. AMMI模型和GGE双标图法在新疆冬小麦区域试验产量分析上的应用[J]. , 2012, 49(9): 1569-1575.
[4]	李鸿文;林存亮;李诚;孔广超;王有武;吴庆红;王爱梅;李召锋;胡勇;曹连莆. 应用AMMI模型分析冬性饲草型小黑麦品种稳定性[J]. , 2012, 49(5): 802-807.
[5]	穆培源;庄丽;张吉贞;陆文辉;李建疆. 应用AMMI模型分析春小麦区试数据的研究[J]. , 2003, 40(1): 1-5.