The Application of GGE Biplot Analysis for Evaluating Test Locations and Mega-Environment Investigation of Cotton Regional Trials

In the process to the marketing of cultivars, identification of superior test locations within multi-environment variety trial schemes is of critical relevance. It is relevant to breeding organizations as well as to governmental organizations in charge of cultivar registration. Where competition among breeding companies exists, effective and fair multi-environment variety trials are of utmost importance to motivate investment in breeding. The objective of this study was to use genotype main effect plus genotype by environment interaction（GGE） biplot analysis to evaluate test locations in terms of discrimination ability, representativeness and desirability, and to investigate the presence of multiple mega-environments in cotton production in the Yangtze River Valley（YaRV）, China. Four traits（cotton lint yield, fiber length, lint breaking tenacity, micronaire） and two composite selection indices were considered. It was found that the assumption of a single mega-environment in the YaRV for cotton production does not hold. The YaRV consists of three cotton mega-environments： a main one represented by 11 locations and two minor ones represented by two test locations each. This demands that the strategy of cotton variety registration or recommendation must be adjusted. GGE biplot analysis has also led to the identification of test location superior for cotton variety evaluation. Although test location desirable for selecting different traits varied greatly, Jinzhou, Hubei Province, China, was found to be desirable for selecting for all traits considered while Jianyang, Sichuan Province, China, was found to be desirable for none.

GGE biplot analysis of yield stability and test location representativeness in proso millet (Panicum miliaceum L.) genotypes

The experiments were conducted for three consecutive years across 14 locations using 9 non-waxy proso millet genotypes and 16 locations using 7 waxy proso millet genotypes in China. The objectives of this study were to analyze yield stability and adaptability of proso millets and to evaluate the discrimination and representativeness of locations by analysis of vari- ance （ANOVA） and genotype and genotype by environment interaction （GGE） biplot methods. Grain yields of proso millet genotypes were significantly influenced by environment （E）, genotype （G） and their interaction （GxE） （P〈0.1%）. GxE inter- action effect was six times higher than G effect in non-waxy group and seven times in waxy group. N04-339 in non-waxy and Neimi 6 （NM6） in waxy showed higher grain yields and stability compared with other genotypes. Also, Neimi 9 （NM9, a non-waxy cultivar） and 90322-2-33 （a waxy cultivar） showed higher adaptability in 7 and in 11 locations, respectively. For non-waxy, Dalat, Inner Mongolia （E2） and Wuzhai, Shanxi （E5） were the best sites among all the locations for maximizing the variance among candidate cultivars, and Yanchi, Ningxia （El0） had the best representativeness. Wuzhai, Shanxi （e9） and Yanchi, Ningxia （e14） were the best representative locations, and Baicheng, Jilin （e2） was better discriminating location than others for waxy genotypes. Based on our results, El0 and e14 have enhanced efficiency and accuracy for non-waxy genotypes and waxy genotypes selection, respectively in national regional test of proso millet varieties.

One compound approach combining factor-analytic model with AMMI and GGE biplot to improve multi-environment trials analysis

To improve multi-environmental trial(MET)analysis,a compound method—which combines factor analytic(FA)model with additive main effect and multiplicative interaction(AMMI)and genotype main effect plus genotype-by-environment interaction(GGE)biplot—was conducted in this study.The diameter at breast height of 36 open-pollinated(OP)families of Pinus taeda at six sites in South China was used as a raw dataset.The best linear unbiased prediction(BLUP)data of all individual trees in each site was obtained by fitting the spatial effects with the FA method from raw data.The raw data and BLUP data were analyzed and compared by using the AMMI and GGE biplot.BLUP results showed that the six sites were heterogeneous and spatial variation could be effectively fitted by spatial analysis with the FA method.AMMI analysis identified that two datasets had highly significant effects on the site,family,and their interactions,while BLUP data had a smaller residual error,but higher variation explaining ability and more credible stability than raw data.GGE biplot results revealed that raw data and BLUP data had different results in mega-environment delineation,test-environment evaluation,and genotype evaluation.In addition,BLUP data results were more reasonable due to the stronger analytical ability of the first two principal components.Our study suggests that the compound method combing the FA method with the AMMI and GGE biplot could improve the analysis result of MET data in Pinus teada as it was more reliable than direct AMMI and GGE biplot analysis on raw data.

Cultivar Selection and Test Site Evaluation of Cotton Regional Trials in Jiangsu Province Based on GGE Biplot

This study was to evaluate the high yielding and stability of candidate cultivars, depict the adaptive planting region, analyze trial location discrim-ination ability and representativeness, as wel as identify the ideal cultivar and trial location, with the aim to provide theory background for cultivar selection and rea-sonable scheme of test location in Jiangsu Province. [Method] The GGE biplot method was used to analyze the lint cotton yield of 12 experimental genotypes in the 6 test locations （three replicates in each） of the cotton regional trial in Jiangsu Province in 2013. [Result] The effects of genotype （G）, environment （E）, and geno-type by environment interaction （G＆#215;E） on lint cotton yield were al highly significant （P〈0.01）, which made it necessary to further explore the specific pattern of geno-type by environment interaction. Jinmian118 （G4） and SF3303 （G5） were the best ideal genotypes screened by the ＂ideal cultivar＂ and ＂ideal location＂ view of GGE biplot, and the ordination of test sites based on the ideal index were in the order of Dafeng （DF）, Yanliang （YL）, Liuhe （LH）, Dongtai （DT）, Yancheng （YC）, and Nantong （NT）, among which NT was relatively weak in representing of the whole target cot-ton planting region in Jiangsu Province. The ＂similarity among locations＂ view of GGE biplot clustered al trial locations into one group, showing that the test sites in the cotton planting region in Jiangsu Province were in the same mega-environment. The ＂which-won-where＂ view of GGE biplot indicated that cotton cultivar Jinmian118 （G4） was the most appropriate cultivar in the homogeneous cotton planting region in Jiangsu Province. [Conclusion] Among the candidate cultivars, Jinmian118 and SF3303 were identified as the most ideal cultivars in this set of conventional cotton regional trial in Jiangsu Province; the test site of Dafeng ranked the first out of al locations in terms of discrimination and representativeness, and al test locations were clustered into the same mega-environmet, which indicated the high efficiency of cultivar selection in the cotton regional trial in Jiangsu Province.

Multi-environmental Evaluation of Triticale, Wheat and Barley Genotypes by GGE Biplot Analysis

The research was carried out with 9 triticale, 3 bread wheat, 3 durum wheat and 3 barley varieties and advanced lines in Tekirdag, Edime and Silivri locations during three years. In the study, the data obtained from combined variance analysis were performed and the significance of the differences between the averages was determined by LSD multiple comparison test. GGE biplot analysis and graphics were made by using the statistical package program. The genotypes G2 and G3 for thousand kernel weight, genotype G1 for the heading time and test weight, genotypes G14 and G15 for the maturation time, number of spikelets per spike and grain weight per spike and G13 for the plant height, spike length and grain yield per hectare decare revealed the highest values. The genotypes G6, GS, G4, G14, G9, G8 and G7 gave lower values than the average in terms of grain yield, whereas the other genotypes gave higher values than the general average. According to biplot graphical results, while locations 1 and 8 were closely related, locations 9, 2 and 7 were positively related to these environments. Although the location 7 is slightly different from the other 4 locations, these 5 locations can be seen as a mega environment. Genotypes G12, G2, G3 and G10 for this mega-environment showed the best performances. According to the results of grain yields obtained from 9 different locations, the location 5 was the most discriminating area while the location 1 was the least discriminating. Location 2 was the best representative location, while locations 4 and 7 were with the lowest representation capability. The locations that are both descriptive and representative are good test locations for the selection of adapted genotypes. Test environments, such as location 8, with low ability to represent are useful for selecting genotypes that perform well in specific regions if the target environments can be subdivided into sub-environments.

Evaluating Varieties and Test Sites in the 2017 Rice Regional Trials of Hubei Province by GGE Biplot Based on Genstat

为分析水稻区试参试品种的丰产性、稳产性、适应性以及区试地点的代表力和鉴别力,采用Gen Stat软件中的GGE双标图对湖北省2017年水稻区试A组12个参试品种和10个区试地点进行了分析。结果表明,深两优10号、亮两优1212、隆晶优4393、襄优5327产量较高,亮两优1212、隆晶优4393、聚两优639、深两优10号具有较好的稳产性,襄优5327稳产性较弱,但在生产上仍有推广利用的价值。区试地点沙洋县、黄冈市、孝南区的代表力和鉴别力较强。

Multienvironmental evaluation of wheat landraces by GGE Biplot Analysis for organic breeding

This study was conducted to determine the performance of wheat landraces cultivated under organic conditions and to analyze their stability across diverse environments. Six wheat landraces with specific characteristics (high protein content, drought tolerance, stay green) were tested under organic growing environment. The experiments were applied in three locations (Larisa (LAR), Thessaloniki (THES), Kilkis (KIL)) for three growing seasons. The role of specific agronomic traits (stay green, lodging) and their correlation with yield components were analyzed. Stability and genotypic superiority for grain yield were determined using ANOVA and genotype × environment (GGE) biplot analysis. Furthermore, the interrelationships among wheat traits and genotype-by-trait using regression analysis, coefficient of variation and (GT)-biplot technique were studied. Significant differences were found in yield among wheat landraces tested, and also in yield components, as related to specific traits expressed into organic environment. Best varieties in terms of yield were the medium statured landraces Skliropetra and M. Argolidas, characterized by lowest weight of 1000 grains, large number of spikes per m2 meter and the highest number of grains per spike as compared to the other landraces. The statistical model GGE biplot provides useful information for experimentation of wheat landraces when grown under organic environment. It identifies clearly the ideal and representative environment for experimentation and underlines the effect of specific traits for each wheat cultivar on yield performance and stability across environments.

基于GGE双标图的绿豆品种郑绿20号的丰产稳定性分析

郑绿20号在2019年河南省绿豆[Vigna radiate(L.)R. Wilczek]新品种鉴定试验中,田间性状表现优良、抗性好、产量高,4个参试点均比对照郑绿8号增产。为了进一步考察郑绿20号的丰产稳产性,采用GGE双标图分析2019年河南省绿豆新品种鉴定试验参试品种的农艺及产量性状,并对绿豆品种9个性状进行变异性和相关性分析。结果表明,郑绿20号的主茎分枝数、单株荚数、株高、主茎节数变异系数在试验点中变异程度较大;主茎节数与生育期呈显著正相关,与产量呈显著负相关,与株高呈正相关;产量与主茎分枝数、单株荚数、荚长、荚粒数呈正相关。丰产稳定性分析结果表明,郑绿20号为适宜推广的兼具丰产性和稳定性的绿豆新品种。

郑绿20在GGE双标图分析区试参试品种中的综合评价研究

对连续两年河南省绿豆多点试验的10个新品种农艺及产量性状进行变异性和相关性分析,探讨其相关关系;通过采用GGE双标图分析2019~2020年度河南省绿豆新品种鉴定试验参试品种,综合评价参试品种的丰产性、稳产性、适应性及各试点的试验代表性、鉴别力和区分能力。结果表明,株高、主茎分枝数、单株荚数的变异系数较大;单株荚数与主茎分枝、产量呈显著正相关,主茎节数与株高呈显著正相关。四个试点可以分为两个生态区,参试品种产量表现在双标图中能直观体现。安阳试点对品种有较强的鉴别能力,郑绿20具有较好的丰产性和稳产性,综合表现良好,具有推广价值。

基于BLUP-GGE双标图的白桦子代多地点速生性及稳定性分析

【目的】通过白桦子代多地点试验,分析其速生性和稳定性,筛选出优良家系,为种子园的改建和重建提供科学依据。【方法】以3个试验点的8年生白桦半同胞家系子代试验林为研究对象,调查其树高、胸径、材积、通直度、保存率性状,采用R语言中的ASReml-R4.0软件包,拟合具有异质方差的混合线性模型,通过最佳线性无偏预测法(BLUP)获得不同试验点各家系的综合育种值,并结合GGE双标图对各参试点和家系进行综合评价及选择。【结果】(1)以地点为固定效应的混合线性模型分析中,白桦半同胞家系子代的5个性状在地点间、家系间、以及地点×家系的互作间的差异均达到显著水平(P<0.05,Z ratio>1.5)。(2)基于各家系综合育种值的GGE双标图显示,尚志试验点的区分度和代表性均最优,庆安和尚志试验点的相关性最强,永吉与尚志试验点几乎不相关、与庆安试验点负相关。(3)16号和15号白桦家系的速生性最优,4号和32号白桦家系的稳定性最强。基于各家系速生性和稳定性的综合性状排序,按20%的入选率共选出16号、40号、15号和38号4个优良家系。【结论】白桦半同胞家系在不同试验地点的生长表现存在显著差异,同一试验地点内不同家系之间的生长表现也存在差异。基因型(家系)与环境(地点)的交互作用对白桦的生长有显著影响。依据各家系速生性及稳定性综合性状,选出16号、40号、15号和38号为白桦半同胞优良家系。

基于GGE双标图分析籼粳杂交稻新品种浙粳优27的丰产稳产性和适应性

水稻是我国重要粮食作物,充分利用籼粳亚种间杂种优势培育丰产稳产广适性新品种对保障我国粮食安全具有重要作用。为全面了解籼粳杂交稻浙粳优27的生产特性并有效挖掘其生产潜力,实现大面积推广应用,采用GGE双标图对其区试数据进行综合评价。结果表明,浙粳优27的丰产稳产性及适应性均较好,品质较优(部标优质2级),是一个理想的籼粳杂交稻新品种。

基于R语言的AMMI模型和GGE双标图在大豆区试中的应用评价

为提高甘肃省大豆品种的选育和应用效率,利用大豆区域试验数据,从基因型与环境的互作分析出发,对甘肃省大豆新品种的稳定性、适应性以及各试点的鉴别力进行全面评估。本研究采用AMMI模型与GGE双标图相结合的方法对甘肃省9个大豆品种在5个试验点的产量进行分析,结果表明,AMMI模型中主成分值(IPCA1、IPCA2)占总变异平方和的95%;其中‘中黄318’属于高产稳产性品种,而‘陇黄3号’和‘铁丰31’虽然产量较高,但其稳定性中等,适合在特定区域栽培。在5个试验点中,凉州分辨力最强,镇原分辨力较弱。综合运用AMMI模型和GGE双标图法,能够更准确直观地反映各品种生产力、稳定性和适应能力,以及在不同试验区域的分辨能力和代表性。

基于GGE模型的甘薯品系产量性状和试验环境评价

为了综合评价甘薯区域试验品系基因型及基因型与环境互作关系,采用GGE双标图法对2020-2021年福建省甘薯区试2组优质淀粉新品系产量数据进行分析。结果表明:2020年‘泉薯26’丰产性最好,在漳浦、福州、泉州、莆田、三明和南平具有较强区域适应性,‘龙薯39号’丰产性和稳产性较好,是试验理想品系;2021年‘金薯43’丰产和稳产性最好,是试验理想品系,‘红金薯2号’具有较好丰产性,在宁德、龙岩、三明和福州具有较强区域适应性。此外,三明试验点在2年试验中具有较高鉴别力和代表性,是理想的试验点。GGE双标图法能够直观评价参试品系的产量特征和试验点的代表性,为客观评价甘薯新品系的高产性和稳产性提供简便、有效的分析手段。

基于AMMI模型和GGE双标图分析荞麦品种的稳产性及适应性

【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。

基于AMMI模型和GGE双标图对芝麻新品种周芝11号的综合评价

以周芝11号参加的2018—2019年黄淮区域内芝麻新品种区域试验为研究基础,利用AMMI模型和GGE双标图的方法,综合评价芝麻新品种周芝11号的丰产性、稳产性以及适应性。结果表明,周芝11号具有丰产稳产广适特性,可在黄淮等适宜地区广泛推广种植。

基于GGE双标图及TOPSIS法对高油酸花生豫花155号主要性状综合分析

阐述了高油酸花生品种豫花155号的产量、品质和抗病性表现,利用GGE双标图法和TOPSIS综合分析法对该品种在河南省花生联合体区域试验中的主要性状进行分析。结果表明,豫花155号油酸含量稳定,丰产性稳产性好,综合性状优良,适宜大面积推广应用。

基于GGE双标图对新疆地区冬小麦品种丰产性和稳定性综合评价

为测试新疆不同地区冬小麦品种的适应性、丰产性和稳定性以及各试验地区的区分力和相似性,利用GGE双标图分析法对新疆小麦产区6个试验点8个品种的产量、株高、穗粒数和千粒重进行综合分析和评价。结果表明,基因型、环境及基因型与环境互作均对产量产生显著影响。品种与环境互作和环境的平方和分别占总平方和的37.62%和35.82%;金冬麦029的丰产性排名第二且稳产性排名第一,综合表现最好,为产量性状的理想品种;产量最高的试验点是拜城县,其中新冬18的产量最高,比产量最低品种金冬麦028增产13.91%。地域差异会引起产量变化,以此为依据可以将参试地点划分为2个生态区:昌吉市、额敏县、喀什市、拜城县为一个生态区,其中新冬18在该生态区适应性最好;奇台和伊宁市为另一个生态区,以金冬麦008在该生态区适应性最好。通过对冬小麦多点区域试验结果可以得出,对产量有显著影响的因素有品种、环境以及品种与环境的互作效应,其中引起产量变异的重要原因是品种与环境互作效应。使用GGE双标图是明确不同生态区的品种产量及其他性状的差异变化,结果表明,新冬18作为综合表现最好的品种,在昌吉市、额敏县、喀什市、拜城县等地区推广价值高且能够获得高产。

基于BLUP和GGE双标图的花生产量基因型与环境互作效应分析

本研究采用最佳线性无偏预测值(BLUP)和基因型主效加基因型-环境互作效应(GGE)双标图的评价方法,对黄淮海中南片多点联合试验的小粒花生品种的丰产稳产适应性及试验点的区分力和代表性进行分析,探究基因型和环境对花生生长的影响,明确各品种的丰产稳产性及适应性。对BLUP值和原始数据进行方差分析和热图对比,结果表明,两组数据之间有较多差异,对于产量变异的解释,BLUP值比原始数据更加可靠。BLUP-GGE双标图联合分析结果表明,开农308的丰产稳产适宜性排在首位,其次为汾花8号、远杂9102;泰安、开封、濮阳的区分力和代表性较强。

基于R语言的GGE双标图在水稻品种区域试验中的应用

为准确评价水稻区试品种稳产性和适应性,利用GGE双标图对2019年辽宁省中早熟区试6个试验点的14个参试品种的产量数据进行分析。结果表明,基因型(G)、环境及基因型与环境互作(GE)均对水稻产量存在极显著影响。6个区试点可分为3个生态类型区,源粳2号在所属的生态区表现最优。美锋稻245、源粳2号和富禾稻258属丰产稳产性较好的品种,为理想品种。区试点中开原市示范繁殖农场区分力最好,桓仁满族自治县种子管理站代表性最强,抚顺市种子管理站具有很好的区分力及较强的代表性,是较理想的区域试验点。

基于GGE双标图的玉米丰产稳产及适应性研究

本研究旨在通过GGE双标图法(Genotype Main Effects and Genotype x Environment Interaction)探讨其对玉米新品种在高海拔地区黔西北的丰产性、稳产性和适应性的综合评价能力。研究选取了5个新的玉米组合,在7个不同试点进行了产量数据收集与分析。结果显示,这些试验点可以划分为2个主要的生态区。具体来说,盘州被识别为一个独立的生态区,而六盘水、赫章、大方、纳雍、水城和威宁则构成了另一个生态区。在所测试的玉米新组合中,‘惠农单5号’在高产和稳产方面的表现尤为突出。进一步的试验地点分辨力和代表性分析表明,盘州和大方这2个试验点的鉴别力强且具有较好的代表性。因此,本研究不仅为玉米新品种的综合评价提供了科学依据,还为未来试验地点的选择提供了重要的理论支持。