HA-GGE双标图在长江流域棉花品种区域试验中的应用

本研究采用HA-GGE双标图对2012年长江流域国家棉花品种区域试验品种丰产性与稳定性、品种适宜种植区域划分、试点的代表性和鉴别力以及理想品种与环境筛选等进行全面评价,以展示HA-GGE双标图在棉花区域试验中的应用效果。结果表明:(1)皮棉产量的基因型、环境、基因型与环境互作效应均达极显著水平(P<0.01),其中环境主效占处理变异平方和的78.7%,而基因型主效占8.7%,基因型与环境互作效应占12.6%。(2)借助双标图的"理想品种"和"理想试点"功能图筛选出最理想的品种中CJ408(G2)和南农12号(G9),筛选出最理想的试点为慈溪和江陵。(3)用"适宜品种与环境组合"功能图为各品种划分了适宜的种植区域。(4)用"试点间关系"功能图将试点划分为4类,其中位于四川盆地的射洪和成都试点聚为1类,位于长江流域棉区北缘的河南南阳单独聚为1类,说明试点聚类与地理环境密切相关。

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。

应用AMMI和HA-GGE双标图分析甘蔗品种产量稳定性和试点代表性

对甘蔗区域试验数据进行基因型与环境互作分析,有利于全面了解参试品种的丰产性和各试点的代表性,对优良新品种的推广和品种的区域分布也有着重要意义。本文综合利用AMMI模型和HA-GGE双标图对2014年国家甘蔗第10轮区域试验11个品种和13个试点的蔗茎产量和蔗糖产量数据进行产量稳定性和丰产性分析,评价试点的代表性和分辨力。结果表明:蔗茎产量和蔗糖产量在不同品种和试点间存在极显著差异,品种和试点存在极显著互作效应。‘福农40号’综合表现最佳,是产量高、丰产性好且蔗茎产量和蔗糖产量的稳定性均较强的品种;‘云蔗08-2060’的产量略低于‘福农40号’,但蔗茎产量和蔗糖产量的稳定性强于‘福农40号’;与对照品种‘ROC22’相比,‘粤甘43号’、‘粤甘46号’和‘闽糖02-205’的蔗茎产量和蔗糖产量较高,稳定性中等,‘福农40号’、‘粤甘43号’、‘粤甘46号’和‘云蔗08-2060’均具有较强的适应性,可在适宜蔗区推广应用。综合AMMI和HA-GGE双标图分析结果表明,广东遂溪、云南开远和福建福州具有较高的地点分辨力和试点代表性。因此,AMMI和HA-GGE双标图的综合运用,可更准确直观地评价出各品种的丰产性、稳定性和适应性以及各试点的分辨力和代表性。本研究可为甘蔗新品种的鉴定与推广提供有价值的理论参考。

基于GYT双标图综合评价黄河流域中熟杂交棉花区域试验品种

对黄河流域中熟杂交棉花区域试验参试品种进行分析和评价,可以为品种合理布局和品种性状改良提供科学依据。本研究利用GYT双标图对2022—2023年黄河流域30个参试品种的产量、品质、农艺性状、抗病性进行综合评价,深入分析皮棉产量与铃重、单株铃数、霜前花率、衣分、第一果枝节位、果枝数、生育期、株高、子指、纤维长度、断裂比强度、马克隆值、伸长率、整齐度、枯萎病指数、黄萎病指数等性状的组合水平,联合方差分析结果表明, 2年试验中所有性状的基因型效应和环境效应均达到了极显著水平,大部分基因型与环境互作效应达到了显著或极显著水平,同时大部分互作效应平方和占比总变异平方和比例大于基因型效应。筛选出中棉所9B07等产量-性状组合优良的品种,比对照品种中棉所9711适应性更广、丰产性更好,具有较高的应用推广价值。GYT双标图比GT双标图具有解释的变异比例更高、拟合度更好、分析结果可信度更高等优点,可以更加直观展示参试品种特点,为我国作物品种多性状综合评价提供参考。

基于GGE-biplot的大豆根瘤菌抗逆性资源筛选

为了准确评价大豆根瘤菌在干旱及酸碱环境中的稳定性和适应性,采用GGE双标图对黑龙江省不同生态区分离、鉴定、纯化的7个大豆根瘤菌菌株分别进行耐旱性、耐酸碱性能力分析评价。结果表明:各供试菌株随着PEG6000浓度的增加,菌株生长量均呈现逐渐下降的趋势。GGE双标图分析表明,耐旱性强且稳定性较好的菌株为111-1;供试菌株在耐酸碱性上均有较大优势,菌株在pH3.0和pH12.0的环境条件下均能缓慢生长,并且均在pH9.0的环境条件下生长量最大。GGE双标图分析得出,耐酸性强且稳定性较好的菌株为112-2,耐碱性强且稳定性较好的菌株为111-3。该结果对适于黑龙江地区不同环境条件下大豆根瘤菌的应用具有重要的指导意义。

基于GGE-Biplot的甘肃省不同生态区燕麦生产性能及适应性分析

为研究不同燕麦品种在甘肃省不同生态地区的生产性能和适应性,筛选适宜不同产区推广种植的品种,本文从2011—2013年采用7个燕麦品种在甘肃省天祝县、通渭县、夏河县、岷县、安定区、榆中县、合作市等7个不同生态区进行了为期3年的田间试验,分析参试材料干草和种子产量、生育期、株高、有效分蘖、穗长、穗粒数、穗粒重等指标的变化情况,利用GGE-Biplot双标图法对供试品种的生产性能及适应性进行了分析。结果表明,种植区生态环境对燕麦的生产性能有显著影响,7个试验点中通渭县的平均种子产量最高,为5 671.3 kg·hm-2,安定区种子产量和干草均最低,分别为1 709.7 kg·hm-2和3 301.2 kg·hm-2。不同品种在不同地区的适应性、丰产性和稳产性差异很大。‘陇燕2号’和‘陇燕3号’在天祝县、岷县、通渭县和榆中县种植可收获较高的青干草产量;‘陇燕1号’、‘陇燕3号’、‘青引2号’在合作市、通渭县、岷县种植可获得较高的种子产量;‘白燕7号’适宜在通渭县生产种子。7个试验点中最具代表性的是通渭县和岷县,通渭县适合干草生产,岷县适合种子生产。GGE-Biplot双标图法可以简便而直观地分析不同燕麦品种在不同利用目的下、不同生态区域的生产性能及其稳定性和试验点的代表性,提高试验效率和试验结果的准确性。

基于GGE-biplot的大豆耐低磷资源筛选

为了准确评价大豆耐低磷基因型在不同环境中的稳定性和适应性,采用GGE双标图,通过4种评价指标数据计算耐性因子GGE双标图数学模型对前期鉴定、评价获得的7个大豆耐低磷种质资源分别进行不同环境下耐低磷能力分析评价。结果表明:耐低磷性强且多环境下稳定性较好的品种为丰收24。以地下部干重计算耐性因子双标图显示垦鉴27表现出多环境下稳定的耐低磷性,而以地上部干重为评价指标则显示其耐低磷性较好但并不稳定;同样,以单株磷含量为评价指标显示克交05-1397同样表现出多环境下较稳定的耐低磷性,而以根系活跃吸收表面积评价指标显示其耐低磷性较好但不稳定。因此在利用GGE-biplot筛选耐低磷大豆资源时应结合具体的环境条件。研究结果对适于黑龙江地区不同环境条件下耐低磷大豆的应用具有重要的指导意义。

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。

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.

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.

Perform Stability of Isoflavones of Soybean Cultivar Evaluated by Genotype-genotype×environment(GGE) Biplot

As one of the secondary metabolites,the isoflavones formed during the development of soybean[Glycine max(L.)Merr.]seeds.The total and individual isoflavone contents,a typical quantitative trait,were affected by significant genotypes of environments(GE)interaction and controlled by many genes with main or minor effects.In the present study,99 soybean cultivars,collected from northeastern China,were used to analyze the isoflavone performances.Genotype-genotype×environment(GGE)biplot software demonstrated an ability to provide information on genetic main effects than solely on phenotypic perform.Highperformance liquid chromatography(HPLC)system was used to extract and determine the isoflavone contents.The results indicated that most genotypes significantly varied among six tested environments.P40(Xiaolimoshidou)was the best-performed genotype with mean performance and stability for glycitein content across six different environments.P88(L-59Peking)was the super genotype with mean performance and stability on each tested environment for daidzein,genistein and the total isoflavone.E5(Gongzhuling in 2016)was the best environment for optimal environmental factor mining.P70(Charleston),P67(Baichengmoshidou)and P50(Jiunong 20)were the optimal genotypes with the highest field among 99 cultivars on each tested environment for genistein.P70(Charleston),P67(Baichengmoshidou)and P14(Hefeng 25)were the optimal genotypes with the highest field among 99 cultivars on each tested environment for daidzein.P40(Xiaolimoshidou),P45(Jinshanchamodou),P33(Dongnong 48)and P56(L-5)were the optimal genotypes with the highest field among 99 cultivars on each tested environment for glycitein.P70(Charleston)and P67(Baichengmoshidou)were the optimal genotypes with the highest field among 99 cultivars on each tested environment for the total isoflavone.GGE biplot was a rational method for stability and adaptation evaluation of soybean isoflavones,and could assist soybean breeder to select a good culture and a suitable tested site.It provided a scientific basis for the establishment of a breeding site and a selection site of soybean isoflavones.This study was valuable to identify genotypes with stable performances of isoflavones of these 99 cultivars for developing new cultivars.

Biplot Analysis of Genotype by Environment for Cooking Quality in Hybrid Rice: A Tool for Line × Tester Data

A study of combining ability for improving rice cooking quality was carried out via genotype plus genotype x environment （GGE） biplot. Four restorer lines and three male sterile lines were used to obtain F1 in a line x tester trial at the Rice Research Institute, Amol, Iran in 2009. GGE biplot analysis showed that Neda and IR56 were the best general combiners for amylose content （AC）, whereas Nemat and IR28 had the highest general combining ability （GCA） effects for gelatinization temperature （GT）, and IR58 and IR59 showed the highest GCA effects in terms of gel consistency （GC）. Meanwhile IR58 and IR59 showed large specific combining ability （SCA） effects for AC, while Neda and SA13 had high SCA effects for GT. Nemat and IR28 had large SCA effects for GC. Because intermediate levels ofAC, GT and GC are ideal, Nemat × IR59 was considered as the best possible cross. Based on these results, the GGE biplot showed good potential for identifying suitable parents, heterotic crosses and the best hybrids in line x tester 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.

基于GYT双标图分析对黄淮海生态区玉米品种综合评价

针对不同环境、多性状条件下优良品种选择效率低下的问题,探讨整合环境型鉴定技术(envirotyping techniques,ET)和多性状选择对黄淮海夏玉米区试参试品种进行综合评价,以期为品种合理布局提供理论依据。本研究以2016—2017年黄淮海夏玉米组区域试验数据为材料,基于当年19个环境协变量信息采用ET将40个试点划分为不同生态区(mega-environments,ME)。采用品种-产量×性状(genotype by yield×trait,GYT)双标图技术对不同生态区(mega-environments,ME)籽粒产量与生育期、株高、穗位高、倒伏率、空秆率、穗长、秃尖、穗行数、穗粒重、百粒重、茎腐病和黑粉病等农艺性状的组合表现进行综合评价,研究GYT双标图技术在玉米区域试验多性状评价中的作用。AMMI方差分析表明,2016年被测农艺性状基因型、环境和互作效应均达到了极显著水平(P<0.01),2017年被测农艺性状除穗位高互作效应不显著外,其余性状基因型、环境和互作效应均达到了极显著水平。根据当年气象因子信息将位于8个省份的40个试点划分为4个ME,降水亏缺(dbp)、饱和水汽压差(vpd)、相对湿度(rh)和最高温度(Tmax)在5个物候期中呈现出较大的变化趋势。GYT双标图与ME结合,可以筛选出不同ME的优势品种。2016年参试品种中,衡玉321和冀丰118在划定的4个ME中均表现出丰产性突出、稳定性较好的特征,属于丰产稳产型品种。而潞玉36和潞研1502则属于参试品种中丰产性、稳定性均较差的品种。2017年参试品种中,DK56在ME2和ME4试点中产量-性状组合表现较为协调,DK205和衡玉6105分别在ME1和ME3生态区中有较好的表现。对照品种郑单958两年区域试验表现出较好的稳定性但丰产性一般。基于环境型鉴定技术划分生态区与GYT双标图相结合对参试品种的丰产性、适应性和稳定性进行评价,实现品种推广的精细定位,为黄淮海夏玉米区品种多性状综合评价提供理论基础。

基于环境型鉴定技术划分生态区综合评价黄淮海青贮玉米品种

气候因子对农作物区域试验丰产性和适应性的影响较大。为准确评价青贮玉米品种在黄淮海夏播区的适应性、丰产性和稳定性,采用2002-2021年20 a的气象数据资料,依据环境型鉴定技术(ET)对2022年青贮玉米区域试验中12个试点进行生态区(ME)划分,依据品种-性状(GT)双标图和品种-产量×性状(GYT)双标图对15个参试品种的生物干重、干物质含量、倒伏率、倒折率、空秆率、小斑病、弯孢叶斑病、南方锈病、茎腐病、瘤黑粉病、生育期、株高和穗位高13个农艺性状以及全株淀粉含量、中性洗涤纤维含量、酸性洗涤纤维含量和粗蛋白质含量4个品质指标进行综合评价。结果表明,加性主效应和积性互作效应(AMMI)方差分析被测的13个农艺性状中基因型效应和环境效应均达到了极显著水平(P<0.01),除穗位高外其余性状基因型与环境互作效应也达到了极显著水平。6个省份的12个试点被划分为4个生态区,不同生态区间气象因子呈较大的变化趋势。生物干重与株高、穗位高呈极显著正相关,而与倒伏率、倒折率呈极显著负相关。GYT双标图与生态区结合,可以鉴别出不同生态区的优势品种。参试品种中渝单805在划定的4个生态区中均表现出丰产性突出、稳定性较好的特征,属于丰产稳产型品种。皖农科青贮8号、成单3601、正大511和衡玉1996等品种在ME2、ME3和ME4中丰产性和稳定性较好。安科青2号和KNX2202等品种在ME1和ME4中丰产性较差,金诚6在ME2和ME3中丰产性和稳定性均较差。基于环境型鉴定技术划分生态区和GYT双标图相结合评价青贮玉米品种的丰产性、稳定性和适应性,可以实现品种推广的精细定位。

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.

黄淮海11个夏大豆品种(系)产量稳定性和适应性分析

为比较不同大豆品种(系)产量的稳定性和适应性,筛选优良大豆品种(系),对2020—2021年国家黄淮海夏大豆南组区域试验数据进行多因素方差分析和GGE双标图分析。结果表明:除年份效应外,各因素及互作效应对大豆产量影响都达到极显著水平(P<0.01),其中地点(55.31%)的贡献率最大,品种(5.97%)和年份(0.02%)贡献率较小。12个试点中平均产量最高的是山东济宁,比产量最低的安徽阜阳高33.63%,差异显著(P<0.05);11个参试品种中平均产量最高的是邯豆13,比对照中黄13(CK A)和中黄13(CK B)分别增产10.93%和9.91%,差异显著。GGE双标图分析结果显示,江苏灌云和徐州,山东临沂和济宁相似度较高,试点有重复设置的可能。河南周口对参试品种的鉴别力和代表性最强,是理想试点。12个试点被分为两组,徐9416-8在第一组试点产量最高,柳豆108在第二组试点产量最高。丰产性和稳产性分析结果表明,邯豆13、圣育6号和南农60的丰产性和稳产性较好。本研究筛选得到稳定性和适应性较强的大豆品种(系),并为优异种质资源的推广应用提供参考。