GGE双标图在中国农作物品种试验中应用的必要性探讨

探讨了中国区域试验中品种特殊适应性以及品种生态区划分的现状、GGE双标图的统计原理及其在品种生态区划分、品种评价和试验环境评价中的应用方法与优势,指出GGE双标图在我国区域试验中应用的必要性和可行性,正确地使用GGE双标图可以直观地把目标种植区域的试验环境分为若干可能的品种生态区,同时可以依据试验环境对品种的鉴别力和对目标环境的代表性筛选出理想的区域试验环境,提高区域试验效率。     

长江流域棉花纤维比强度选择的理想试验环境筛选

采用GGE双标图方法对2000-2010年期间27组独立的长江流域棉花品种区域试验中的15个试点在纤维比强度选择上的鉴别力、代表性、理想指数和离优度指数进行了全面分析和综合评价.结果表明:南京、黄冈、常德、岳阳和南阳是以长江流域为目标环境的广适性纤维比强度选育和作为区域试验点鉴别理想品种的最理想试验环境,而江浙沿海棉区的试验环境(南通、盐城和慈溪)和四川盆地棉区试验环境(简阳和射洪)不适宜作为针对长江流域的纤维比强度选择与推荐环境.本研究充分展示了GGE双标图在区域试验环境评价方面的应用效果,也为长江流域棉花品种生态区划分和国家棉花区试方案的决策提供理论依据.     

不同玉米品种（系）在云南省不同生态区丰产、稳产及适应性分析

为筛选云南省不同生态区、不同栽培水平条件下的优良新品种，为该区域玉米新品种精准推广和培育提供参考依据。本研究以9个玉米品种（系）在云南省15个试点的区域试验籽粒产量数据为研究对象，通过AMMI模型和GGE双标图分析方法分析不同玉米品种（系）在云南省不同试点的丰产性、稳定性和适应性，同时综合评价参试地点的鉴别力和代表性。结果表明：基因型效应、环境效应以及基因型与环境的互作效应均对参试品种产量产生极显著影响；综合产量、AMMI模型分析及GGE双标图结果，G3（文17-115）、G6（文15-5851）和G5（文17-5313）属较理想品种；E15（普文镇试验点）和E2（石林县试验点）是综合性较好的试点，均具有较强的区分力和代表性。AMMI模型和GGE双标图分析的侧重点不同，但品种评价结果基本一致，两种方法优势互补，可以用来作为全面有效地评估品种和试点的理想工具。     

GGE双标图在河南省夏播花生品种区域试验中的应用

笔者研究了河南省夏播花生品种区域试验中参试品种与环境的互作关系,科学评价参试品种与试点,为品种审定及试点遴选提供理论依据。采用可直观分析农作物两向数据的GGE双标图对参试品种的高产稳产性、品种与试验点环境间的关系、各试点的代表性进行了分析。结果表明：2015年河南省夏播花生品种区域试验中理想品种为‘豫花55号’,能有效地选择高产稳产品种的试点是河南省农业科学院经济作物研究所、濮阳市农业科学院、洛阳市农林科学院。     

种植密度和施氮量对玉米‘伊单52’和‘伊单81’产量的影响

为蒙西地区玉米栽培及试验数据分析评价提供参考,以玉米品种‘伊单52’和‘伊单81’为试验材料,进行大田试验。应用GGE双标图法分析研究3 个氮肥水平和5 个密度水平下玉米产量、叶面积指数、干物质变化。结果表明在A2 氮肥水平下,‘伊单52’叶面积指数高于A1 和A3 氮肥水平,‘伊单52’和‘伊单81’群体干物质积累量最高。‘伊单52’和‘伊单81’在5 个密度、3 个肥料水平下的产量、叶面积指数、群体干物质重GGE双标图中,分别展示了各处理真实信息的82.3%和70.7%。     

基于HA-GGE双标图的长江流域棉花区域试验环境评价

采用遗传力校正的GGE (HA-GGE)双标图方法对2000-2010年间27个独立的长江流域棉花品种区域试验的15个试验环境(试验点)在皮棉产量选择上的鉴别力、代表性、理想指数和离优度指数进行分析和综合评价。结果表明,湖北黄冈、江苏南京和湖北荆州是最理想的试验环境,对以长江流域为目标环境的广适性新品种选育和作为区域试验点鉴别理想品种的效率最高,而四川射洪、四川简阳、湖北襄阳和河南南阳不适宜作为针对长江流域的新品种选择与推荐环境。理想试验环境都位于长江流域除南襄盆地以外的中下游棉区,而不理想试验环境中的四川射洪和四川简阳位于长江流域棉区最西边的品种熟期较早且种植密度较高的四川盆地棉区,河南南阳和湖北襄阳位于长江流域棉区最北边, 与黄河流域棉区接壤, 霜期较早且晚秋降温快的南襄盆地棉区。本研究充分展示了HA-GGE双标图在区域试验环境评价方面的应用效果,也为长江流域棉花品种生态区划分和国家棉花区试方案的决策提供了理论依据。     

基于R语言的GGE双标图法在甜菜品种区域试验中的应用

为了明确糖用甜菜品种在全国不同区域试验中参试品种的丰产性、稳产性、适应性及各试验点的区分力和代表力。2020年在全国不同生态区域的9个试验基地，以16个引种的KWS系列糖用甜菜品种为试材，采用基于R语言的GGE双标图法对糖用甜菜品种的产糖量指标进行分析。结果表明，在2020年品种区域试验中，KWS 0015(G₁₆)丰产性最佳；KWS 7748(G₆)、KWS 9921(G₁₅)具有较好的稳产性；KWS 0015(G₁₆)适宜种植的区试点最多，具有较强区域适应性，较其他品种高产稳产，为本试验理想品种。此外，呼和浩特(E₉)具有较高区分力和较好代表性，是本研究的理想区试点。GGE双标图法对综合分析糖用甜菜品种基因型、环境与基因型交互效应具有科学合理性。     

大果花生品种产量及其构成的可视化分析

对花生品种进行可视化的丰产稳产性及产量构成分析,为高产品种筛选和生产应用提供指导。本研究以2014年河南省花生区域试验为基础,利用GGE (Genotype+genotype-by-environment interaction)双标图评价参试品种的丰产性、稳产性和适应性,同时,以参试品种‘开农70’为例进行可视化的相关和通径分析。结果表明,‘豫花43号’、‘开农70’、‘开农0316’、‘豫花44号’、‘豫花42号’是理想的高产高稳品种。其中‘开农70’的丰产性、稳产性、适应性综合排名第二;单株生产力和百仁重与荚果产量分别呈极显著、显著正相关,直接通径系数分别居第四位和第二位。综上所述,GGE双标图可以全面有效的评估参试品种,其中‘开农70’丰产性好、稳定性强,适合在河南省大面积推广利用,在生产中可侧重做好单株生产力的选择。     

棉花区试中品种多性状选择的理想试验环境鉴别

农作物品种选育通常需要对多目标性状综合选择,依据育种目标性状和权重建立品种选择指数,选择遗传差异鉴别力强和目标环境代表性好的试验点,有助于提高品种选育的效率并降低实验成本。本研究依据国家棉花品种审定标准构建针对性和实用性强的品种选择指数,即SI=0.40?皮棉产量+0.13?纤维比强度+0.09?(纤维长度+马克隆值)+0.11?抗枯萎病+0.09?抗黄萎病+0.10?霜前花率,采用GGE双标图方法,对2000—2013年间39组(含585个单点试验)长江流域国家棉花区域试验中的15个试验点,综合评价品种选择指数的鉴别力、代表性和理想指数。结果表明,湖北黄冈和江苏南京试验环境被评为最理想的试验环境,湖北荆州、湖北武汉和江苏盐城为理想的试验环境,而河南南阳、湖北襄阳、湖南常德、四川简阳和四川射洪试验环境为不理想试验环境。可以看出,理想的试验环境均位于长江流域的中下游棉区,而不理想的试验环境中四川简阳和四川射洪位于上游的四川盆地、河南南阳和湖北襄阳位于长江流域北缘的南襄盆地、湖南常德虽然位于长江流域中游但栽培密度偏低。本研究构建的选择指数采用与国家棉花品种审定中品种评价准则相统一的目标性状和权重分配策略,理想试验环境对我国长江流域棉区的棉花生态育种试验点的选择提供了切实可行的决策方案。     

基于GGE双标图和AMMI模型对江苏省水稻区试品种的丰产性和稳定性分析

本研究综合利用GGE双标图方法和AMMI模型对2017年江苏省杂交中粳稻区域试验的12个参试品种的丰产性和稳产性进行了分析。结果表明,基因型、环境及基因型与环境互作效应对杂交中粳稻产量有极显著的影响。在所有参试品种中,杂中区03和杂中区12是丰产稳产的广适性品种,可在粳稻适区进行推广。江苏徐淮地区淮阴农业科学研究所试点代表性最强,而江苏欢腾农业有限公司试点的鉴别力最强。AMMI和GGE双标图的综合运用,可准确直观地评价各品种的丰产性、稳定性和适应性以及各试点的鉴别力和代表性。     

Adaptability and stability analysis of groundnut genotypes using AMMI model and GGE-biplot

Unpredictable rainfall, variations in farm inputs, crop-diseases, and the inherent potential of genotypes are among the major factors for low and variable crop yield. Fourteen elite groundnut genotypes were examined in 14 environments to analyze adaptability and stability of genotypes, and identify mega-environments if they exist. Additive main effect and multiplicative interaction (AMMI) model, cultivar-superiority measure, and genotype plus genotype-by-environment (GGE) biplot analysis were used for data analysis. The environment (69.8%) and genotype-by-environment interaction (GEI) effects (21.4%) were dominating the genotypic effect (8.8%). The GEI was significant (P < 0.01), and two distinct environments (mega-environments) were identified, suggesting separate national groundnut breeding strategies for Babile and Pawe. ICGV-94100 and ICGV-97156 were stable and had the highest-yield at Babile and Pawe, respectively. The higher heritability value was recorded in more homogeneous and favorable environments, indicating the genetic potential of groundnut genotypes were better attained in more homogeneous and favorable environments. AMMI model, cultivar-superiority measure, and GGE biplots were helpful methodologies and complemented each other to evaluate the adaptability and stability of groundnut genotypes in diverse environments.     

GGE叠图法─分析品种×环境互作模式的理想方法

本文介绍一种分析作物区域试验结果的方法－ＧＧＥ叠图法。首先，将原始产量数据减去各地 点的平均产量，由此形成的数据集只含品种主效应Ｇ和品种－环境互作效应ＧＥ，合称为ＧＧＥ。对ＧＧＥ 作单值分解，并以第一和第二主成分近似之。按照第一和第二主成分值将各品种和各地点放到一个平 面图上即形成ＧＧＥ叠图。借助于辅助线，可以直观回答以下问题：（１）什么是某一特定环境下最好的 品种；（２）什么是某一特定品种最适合的环境；（３）任意两品种在各环境下的表现如何；（４）试验中品 种×环境互作的总体模式是怎样的；（５）什么是高产、稳产品种；（６）什么是有利于筛选高产、稳产品 种的环境。     

Performance Evaluation of Malt Barley: from Malting Quality and Breeding Perspective

Lack of suitable malt barley varieties that exhibit high yielding, stable performance, and good malting quality is the major factor among several production constraints contributing to low productivity of malt barley in the North Gondar Zone. The present study was done to evaluate and recommend the best performing varieties in the major potential areas of North Gondar. The experiment was conducted at three locations for two consecutive years (2015 and 2016) during the main cropping season using twelve improved varieties. The design was randomized complete block design with three replications. Analysis of variance and GGE [genotype main effect (G) and genotype-by-environment interaction (GE)] biplot analysis were conducted following their respective procedures. Combined analysis of variance revealed a highly significant difference (P < 0.01) among genotypes, environments, and genotype-by-environment interaction for grain yield, most agronomic and malt quality traits. All the varieties had acceptable malt quality traits. The variety IBON-174/03 was found to be the highest yielding and the most stable variety across environments. According to the polygon view of biplot analysis, the varieties were spread across four sections and the test environments spread across two sections. Among the six test environments, D and C were more discriminating and F and B were less discriminating. Test environments F, E, and A were found to be more representative of the mega-environment than D. Considering early maturity, malt quality, grain yield, and stability performance; it is recommended to use the variety IBON-174/03 for production in the study areas and in similar areas.     

GGE Biplot Analysis of Genotype by Environment Interaction in Field Pea (<Emphasis Type="Italic">Pisum sativum</Emphasis> L.) Genotypes in Northwestern Ethiopia

Ten field pea genotypes were evaluated in randomized complete block design with four replications for three consecutive years (2010-2012) main cropping seasons at four locations in each year. The objectives were to determine magnitude of genotype by environment interaction and to identify stable field pea genotype with high grain yield to be released as a cultivar to producer for Northwestern Ethiopia. The GGE [genotype main effect (G) and genotype by environment interaction (GE)] biplot graphical tool was used to analyze yield data. The combined analysis of variance revealed a significant difference (P<0.01) among genotypes, environments and genotype-by-environment interaction for grain yield. The average environment coordinate biplot revealed that EH99005-7 (G2) was the most stable and the highest yielding genotype. Polygon view of GGE-biplot showed the presence of three mega-environments. The first section includes the test environments E1 (Adet 2010), E3 (Debretabor 2010), E5 (Adet 2011), E6 (Motta 2011), E7 (Debretabor 2011), E8 (Dabat 2011), E9 (Adet 2012) and E12 (Dabat 2012) which had the variety G1 (EH99009-1) as the winner; the second section contains the environments E4 (Dabat 2010), E10 (Motta 2012) and E11 (Debretabor 2012) with G2 as the best grain yielder and the third section contains the E2 (Motta 2010) with G4 (Tegegnech X EH90026-1-3-1) as the best grain yielder. The comparison GGE- biplot of field pea genotypes with the ideal genotype showed that G2 was the closest genotype for the ideal cultivar. Among the twelve environments, E2, E6 and E10 were more discriminating and E3, E9 and E12 were less discriminating. Genotype EH99005-7 was the most stable and the highest yielding genotype. As a result it is released officially for Northwestern Ethiopia. Therefore, it is recommended to use genotype EH99005-7 for wider cultivation in Northwestern Ethiopia and similar areas.     

A heritability-adjusted GGE biplot for test environment evaluation

Test environment evaluation has become an increasingly important issue in plant breeding. In the context of indirect selection, a test environment can be characterized by two parameters: the heritability in the test environment and its genetic correlation with the target environment. In the context of GGE biplot analysis, a test environment is similarly characterized by two parameters: its discrimination power and its similarity with other environments. This paper investigates the relationships between GGE biplots based on different data scaling methods and the theory of indirect selection, and introduces a heritability-adjusted (HA) GGE biplot. We demonstrate that the vector length of an environment in the HA-GGE biplot approximates the square root heritability (\( \sqrt H \)) within the environment and that the cosine of the angle between the vectors of two environments approximates the genetic correlation (r) between them. Moreover, projections of vectors of test environments onto that of a target environment approximate values of \( r\sqrt H \), which are proportional to the predicted genetic gain expected in the target environment from indirect selection in the test environments at a constant selection intensity. Thus, the HA-GGE biplot graphically displays the relative utility of environments in terms of selection response. Therefore, the HA-GGE biplot is the preferred GGE biplot for test environment evaluation. It is also the appropriate GGE biplot for genotype evaluation because it weights information from the different environments proportional to their within-environment square root heritability. Approximation of the HA-GGE biplot by other types of GGE biplots was discussed.     

应用GGE双标图分析我国春小麦的淀粉峰值粘度

将原始数据减去各试点均值后形成的数据集中只含基因型主效G和基因型与环境互作效应GE, 合称GGE. 对GGE做单值分解, 以第一和第二主成分近似, 按第一和第二主成分值将所有品种和试点绘于同一平面图即形成GGE双标图. 以其分析我国春麦区10个试点20个品种淀粉糊化特性的峰值粘度, 结果表明铁春1号在大部分试点峰值粘度表现较好,     

基于HA-GGE双标图的甘蔗试验环境评价及品种生态区划分

采用遗传力校正的GGE双标图(heritability adjusted GGE,HA-GGE),分析基因型(G)、环境(E)、基因型与环境互作效应(GE)对产量变异的影响,对14个试验点的分辨力、代表性和理想指数进行分析,并对这些试验点的生态区进行划分。结果表明,甘蔗试验环境对产量变异的影响大于基因型和基因型与环境互作;互作因素中以环境×基因型的互作效应最大,基因型×年份的互作效应最小。广东遂溪(E3)和广西崇左(E6)为最理想试验环境,对筛选广适性新品种和鉴别理想品种的效率最高;福建福州(E1)、福建漳州(E2)、广东湛江(E4)、云南保山(E11)、云南临沧(E13)、云南瑞丽(E14)为理想试验环境;广西百色(E5)、广西河池(E7)、海南临高(E10)、云南开远(E12)为较理想试验环境;广西来宾(E8)、广西柳州(E9)为不太理想的试验环境。根据HA-GGE双标图分析结果,可将我国甘蔗生态区划分为3个,即以广西百色、河池、来宾和柳州为代表的华南内陆甘蔗品种生态区,以云南保山、开远、临沧、瑞丽为代表的西南高原甘蔗品种生态区,涵盖福建福州、漳州、广东湛江、遂溪、广西崇左等试点的华南沿海甘蔗品种生态区。     

GGE叠图法—分析品种×环境互作模式的理想方法

本文介绍一种分析作物区域试验结果的方法—GGE叠图法。首先,将原始产量数据减去各地点的平均产量,由此形成的数据集只含品种主效应G和品种-环境互作效应GE,合称为GGE。对GGE作单值分解,并以第一和第二主成分近似之。按照第一和第二主成分值将各品种和各地点放到一个平面图上即形成GGE叠图。借助于辅助线,可以直观回答以     

Comparison of shifted multiplicative model,rank correlation,and biplot analysis for clustering winter wheat production environments

Categorization of locations with similar environments helps breeders to efficiently utilize resources and effectively target germplasm. This study was conducted to determine the relationship among winter wheat (Triticum aestivum L.) yield testing locations in South Dakota. Yield trial data containing 14 locations and 38 genotypes from 8 year were analyzed for crossover genotype (G) × environment (E) interactions according to the Azzalini-Cox test. G × E was significant (P < 0.05) and contributed a small proportion of variation over the total phenotypic variation. This suggested that for efficient resource utilization, locations should be clustered. The data were further analyzed using the Shifted Multiplicative Model (SHMM), Spearman’s rank correlation and GGE biplot to group testing locations based on yield. SHMM analysis revealed four major cluster groups in which the first and third had three locations, with four locations in each of the second and fourth groups. Spearman rank correlations between locations within groups were significant and positive. GGE biplot analysis revealed two major mega-environments of winter wheat testing locations in South Dakota. Oelrichs was the best testing location and XH1888 was the highest yielding genotype. SHMM, rank correlation and GGE biplot analyses showed that the locations of Martin and Winner in the second group and Highmore, Oelrichs and Wall in the third group were similar. This indicated that the number of testing locations could be reduced without much loss of grain yield information. GGE biplot provided additional information on the performance of entries and locations. SHMM clustered locations with reduced cross-over interaction of genotype × location. The combined methods used in this study provided valuable information on categorization of locations with similar environments for efficient resource allocation. This information should facilitate efficient targeting of breeding and testing efforts, especially in large breeding programs.