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     

利用AMMI模型分析红花花瓣产量性状的基因型与环境互作

基因型与环境的互作效应(G·E)决定作物在多生态环境中产量性状的稳定性,研究红花G·E互作效应对花瓣产量稳定性的影响有重要意义。采用随机区组试验设计,选用7个红花品种,4个试验点,3次重复,测定各品种花瓣产量,运用AMMI模型对红花品种的基因型、环境及G·E互作效应进行了分析。基因型、环境及G·E互作效应均达到极显著水平,基因型占总变异的15.94%、环境效应占16.29%,G·E互作效应占47.64%,表明G·E互作效应对产量变化的影响远大于基因型和环境。互作效应主成分计算出基因型稳定性参数(Dg),顺序为‘YN1805’‘YN2057’‘YN512’‘YN495’‘YN1959’‘YN2527’‘弥渡红花’(CK)。运用AMMI模型有效地解释了红花品种产量性状的基因型、环境和G·E互作效应。根据产量、稳定性参数及AMMI模型分析结果,高产而又稳定的品种有‘YN1805’和‘YN2057’,高产而又不稳定的品种有‘YN512’和‘YN1959’,不高产也不稳产的品种有‘YN495’、‘YN2527’和‘弥渡红花’(CK)。在红花生产中,应选用产量高、适应性强的品种。     

GGE双标图在湖南省棉花品种区域试验中的应用

为研究2013年湖南省棉花品种区域试验B组中参试品种与环境的互作关系,科学评价参试品种与试点,从而为品种审定、品种在生产中的有效利用及试点遴选提供理论依据。采用具有直观分析农作物两向数据的GGE双标图软件对参试品种的丰产性与稳定性、理想品种选择、品种适宜种植区域划分、各试点的代表性和鉴别力及理想试点选择等方面进行了分析。结果表明：2013年湖南省棉花品种区域试验B组各品种（系）皮棉产量的基因型、环境（试点）、基因型与环境互作效应均达极显著水平,其中环境主效（试点）、基因型主效及基因型与环境互作效应分别占处理变异平方和的57.99%、13.54%、28.48%;丰产性最好的品种是B3,稳产性最好的品种是B5,但最理想的品种是B3;大通湖管理区、君山和湖南省棉花科学研究所试点为最理想试点。     

大粒花生品种区域试验的AMMI模型分析

运用AMMI模型分析了2015-2016年国家北方片花生区域试验荚果产量数据,结果表明,品种、试验地点、品种与试验地点互作、交互效应主成分值（IPCA）均达极显著水平。不同花生品种在各试验地点的稳定性和不同试验地点对品种的分辨力差异较大,开农705的稳定性参数最大（D_g=32.9718）,对照品种花育33号的稳定性参数最小（D_g=11.3287）;8个花生品种中高产稳产的品种是商花11号和郑农花15号,产量高而稳定性一般的品种是开农705和龙花二号,稳产但不高产的品种是花育33号;19个试验地点中山东日照和河南洛阳的分辨力强,河南漯河和安徽固镇的分辨力较差。     

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,     

山西中部地区旱地小麦新品种丰产性稳产性研究

采用Ｅｂｅｒｈａｒｔ模式和方法分析了１９８８－１９８９年山西省中部地区旱地组区试中６个小麦，品种的丰产性和稳产性。结果表明，地点效应是影响小麦丰产性和稳产性的主要因素。各参试品种的丰产性存在相当大的差异，高产品种也是适应性强的稳产品种。丰产性和稳产性没有相关性。晋麦３４号（８４－１６ｒ）是个丰产性好、适应广的稳产品种，可以在较大的范围内推广种植。     

盐胁迫条件下旱作面包小麦品种的基因型×环境互作

本研究的目的是鉴定１５个印度旱作小麦品种在不同盐胁迫环境下，即两个盐浓度水平、两个碱度水平和一个正常水平下基因型与环境互作效应和稳定性。结果表明籽粒产量、每穗粒数、千粒重和单株成穗数存在高度的线性互作。品种Ｈｙ－ｂｒｉｄ６５和ＰＢＷ６５证明在参试的各个环境中具有高的产量和稳定性。Ｈｙｂｒｉｄ６５也表现出较高的和稳定的每穗粒数和粒重的特性。     

早熟优质糯玉米杂交种西山糯的选育与特征特性

西山糯是贵州大学农学院玉米研究所以一个利用热带种质改良的温带 材料选育的优良自交系（南糯白）作母本，与一个带有独山白糯地方血缘和自交系（糯－1）作父本组配的糯玉米杂交种，根据品比、区试、生产试验和品质分析结果，该品种生育期早、抗逆性好、品质优良、丰产稳产、适应性广、制种产量较高，于2002年通过贵州省农作物品种审定委员会审定，予以推广。     

海岛棉不同果枝品种间杂交产量性状的遗传及F1和F2群体优势分析

采用MINQUE（1）统计方法及AD模型对9个海岛棉品种（系）及其20个F1组合9个产量性状的两年资料进行了遗传分析。结果表明，海岛棉长果枝与零式果枝品种间杂交，各产量性状均存在极显著的加性效应方差比例；衣分、铃重、霜后铃数、总铃数和皮棉产量存在极显著的显性效应；均存在显性与环境的互作。品种3836具有霜前铃数和霜前     

陆地棉重组自交系产量及产量构成因子性状的上位性QTL分析

棉花的产量及产量构成因子性状是以复杂的方式遗传,遗传力较低并易受环境条件影响。经典数量遗传学指出,上位性是复杂性状的遗传基础。本研究以湘杂棉2号F8和F9世代重组自交系为材料,调查了3个环境下的产量及产量构成因子性状,并构建了遗传连锁图。旨在定位产量及产量构成因子性状的上位性QTL并分析QTL与环境的互作效应。所有产量及产量构成因子性状均检测到上位性QTL,共检测到16对加性互作QTL（AA）,涉及的位点中仅4个有单位点效应,这反映了上位性的复杂性及其对产量和产量构成因子性状的重要贡献。共检测到17对QTL加性和环境互作（AE）,以及14对上位性QTL与环境的互作,表明环境因素对产量和产量构成因子性状起重要影响作用。研究结果还表明上位性效应作为湘杂棉2号的遗传基础起着重要作用。对各性状在不同环境的优良基因型进行了预测。综合优良家系（GSL）和特定环境下的优良家系（SL）的性状表现高于两亲本,表明湘杂棉2号重组自交系各性状都有提高的潜力。由于QTL加性和环境互作以及上位性QTL与环境互作的影响,预测的优良家系基因型会随着环境的改变而不同,表明应针对特定环境开展棉花育种。     

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 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.     

The Effectiveness of HA-GGE Biplot Application in Analyzing the Data from the National Cotton Cultivar Regional Trial in the Yangtze River Valley

A large number of regional crop trials have demonstrated the ubiquitous existence of genotype × environment interactions (G×E), which make it complicated to select superior cultivars and identify the ideal testing sites. The GGE (genotype main effect plus genotype × environment interaction) biplot is the most powerful statistical and graphical displaying tool available for regional crop trial dataset analysis. The objective of the present study was to demonstrate the effectiveness of the biplot in evaluating the high and stable yields of candidate cultivars simultaneously, and in delineating the most adaptive planting region, analyzing trial location discrimination ability and representativeness, and identifying the ideal cultivar and trial locations. The lint cotton yield dataset with nine experimental genotypes and 17 test locations (three replicates in each) was collected from the national cotton regional trial in the Yangtze River Valley (YaRV) in 2012. The results showed that: (1) the effects of genotype (G), environment (E), and genotype × environment interaction (G×E) were significant (P < 0.01) for lint cotton yield. Differences among environments accounted for 78.7% of the treatment total variation in the sum of squares, whereas the genotype main effect accounted for 8.7%, and the genotype × environment interaction accounted for 12.6%. (2) The “ideal cultivar” and “ideal location” view of the HA-GGE biplot identified Zhongcj408 (G2) and Nannon12 (G9) as the best ideal genotypes; Cixi in Zhejiang Province and Jiangling in Hubei Province were the most ideal locations.(3) The “which-won-where” view of the biplot outlined the adaptive planting region for each experimental cultivar. (4) The “similarity among locations” view clustered the trial locations into four groups, among of which the two outlier locations, Shehong (SH) and Chengdu (QBJ), located in Shichuan Basin in the upper reaches of YaRV, were clustered in one group, whereas the Nanyang (NY) of Henan Province at the northern edge of YaRV was singled out as a sole group. Such location clustering results implied an apparent association with the geographical environment.     

双标图分析在农作物品种多点试验中的应用

双标图分析越来越多地被用于直观分析农作物品种多点试验数据和其他类型的两向数据。这种方法深受植物育种家和农业研究人员的推崇, 认为它可以提高研究者理解和驾驭试验数据的能力;但也受到一些学者的批评, 认为它是统计分析方面的旁门左道。事实上,学术界对什么是双标图的认识尚存混乱。一些双标图的使用者并不总能正确地选择和解释双标图。一些双标图的批评者对双标图分析及其研究对象也缺乏深入了解。为使研究者对双标图分析有一个客观全面的认识, 本文就用双标图分析农作物品种多点试验中的几个问题进行阐述：(1) 如何针对特定的研究目的选择适当的双标图; (2) 如何选择适当的GGE双标图来分析多点试验数据; (3) 如何使用GGE双标图的不同功能形态进行品种评价、试验点评价和品种生态区划分; (4) 如何判断双标图是否充分表现试验数据中的规律; (5) 如何检验双标图显示的结果是否显著。     

Genotype by environment interaction components underlying variations in root,sugar and white sugar yield in sugar beet (<Emphasis Type="Italic">Beta vulgaris</Emphasis> L.)

The success of plant breeding programs depends on the ability to provide farmers with genotypes with guaranteed superior performance in terms of yield across a range of environmental conditions. We evaluated 49 sugar beet genotypes in four different geographical locations in 2 years aiming to identify stable genotypes with respect to root, sugar and white sugar yields, and to determine discriminating ability of environments for genotype selection and introduce representative environments for yield comparison trials. Combinations of year and location were considered as environment. Statistical analyses including additive main effects and multiplicative interactions (AMMI), genotype main effects and genotype?×?environment interaction effects (GGE) models and AMMI stability value (ASV) were used to dissect genotype by environment interactions (GEI). Based on raw data, root, sugar and white sugar yields varied from 0.95 to 104.86, 0.15 to 20.81, and 0.09 to 18.45 t/ha across environments, respectively. Based on F-Gollob validation test, three interaction principal components (IPC) were significant for each trait in the AMMI model whereas according to F ratio (F_R) test two significant IPCs were identified for root yield and sugar yield and three for white sugar yield. For model diagnosis, the actual root mean square predictive differences (RMS PD) were estimated based upon 1000 validations and the AMMI-1 model with the smallest RMS PD was identified as the most accurate model with highest predictive accuracy for the three traits. In the GGE biplot model, the first two IPCs accounted for 60.52, 62.9 and 64.69% of the GEI variation for root yield, sugar yield and white sugar yield, respectively. According to the AMMI-1 model, two mega-environments were delineated for root yield and three for sugar yield and white sugar yield. The mega-environments identified had an evident ecological gradient from long growing season to intermediate or short growing season. Environment-focused scaling GGE biplots indicated that two locations (Ekbatan and Zarghan) were the most representative testing environments with discriminating ability for the three traits tested. Environmentally stable genotypes (i.e. G21, G28 and G29) shared common parental lines in their pedigree having resistance to some sugar beet diseases (i.e. rhizomania and cyst nematodes). The results of the AMMI model were partly in accord with the results of GGE biplot analysis with respect to mega-environment delineation and winner genotypes. The outcome of this study may assist breeders to save time and costs to identify representative and discriminating environments for root and sugar yield test trials and creates a corner stone for an accelerated genotype selection to be used in sweet-based programs.     

Genotype × environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran

The genotype × environment (GE) interaction influences genotype selection and recommendations. Consequently, the objectives of genetic improvement should include obtaining genotypes with high potential yield and stability in unpredictable conditions. The GE interaction and genetic improvement for grain yield and yield stability was studied for 11 durum breeding lines, selected from Iran/ICARDA joint program, and compared to current checks (i.e., one durum modern cultivar and two durum and bread wheat landraces). The genotypes were grown in three rainfed research stations, representative of major rainfed durum wheat-growing areas, during 2005–09 cropping seasons in Iran. The additive main effect and multiplicative interaction (AMMI) analysis, genotype plus GE (GGE) biplot analysis, joint regression analysis (JRA) (b and S²di), six stability parameters derived from AMMI model, two Kang’s parameters [i.e., yield-stability (YSi) statistic and rank-sum], GGE distance (mean performance + stability evaluation), and two adaptability parameters [i.e., TOP (proportion of environments in which a genotype ranked in the top third) and percentage of adaptability (Ad)] were used to analyze GE interaction in rainfed durum multi-environment trials data. The main objectives were to (i) evaluate changes in adaptation and yield stability of the durum breeding lines compared to modern cultivar and landraces (ii) document genetic improvement in grain yield and analyze associated changes in yield stability of breeding lines compared to checks and (iii) to analyze rank correlation among GGE biplot, AMMI analysis and JRA in ranking of genotypes for yield, stability and yield-stability. The results showed that the effects due to environments, genotypes and GE interaction were significant (P < 0.01), suggesting differential responses of the genotypes and the need for stability analysis. The overall yield was 2,270 kg ha^?1 for breeding lines and modern cultivar versus 2,041 kg ha^?1 for landraces representing 11.2 % increase in yield. Positive genetic gains for grain yield in warm and moderate locations compared to cold location suggests continuing the evaluation of the breeding material in warm and moderate conditions. According to Spearman’s rank correlation analysis, two types of associations were found between the stability parameters: the first type included the AMMI stability parameters and joint regression parameters which were related to static stability and ranked the genotypes in similar fashion, whereas the second type consisted of the rank-sum, YSi, TOP, Ad and GGED which are related to dynamic concept of stability. Rank correlations among statistical methods for: (i) stability ranged between 0.27 and 0.97 (P < 0.01), was the least between AMMI and GGE biplot, and highest for AMMI and JRA and (ii) yield-stability varied from 0.22 (between GGE and JRA) to 0.44 (between JRA and AMMI). Breeding lines G8 (Stj3//Bcr/Lks4), G10 (Ossl-1/Stj-5) and G12 (modern cultivar) were the best genotypes in terms of both nominal yield and stability, indicating that selecting for improved yield potential may increase yield in a wide range of environments. The increase in adaptation, yield potential and stability of breeding lines has been reached due to gradual accumulation of favorable genes through targeted crosses, robust shuttle breeding and multi-locational testing.