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.

Genotype-by-environment interaction for grain yield among novel cowpea(Vigna unguiculata L.) selections derived by gamma irradiation

This study determined the effects of genotype-by-environment(G × E) interaction and stability of yield among elite cowpea(Vigna unguiculata L.) selections derived by gamma irradiation. The study was conducted in Namibia at three selected sites: Bagani, Mannheim,and Omahenene, during 2014/2015 and 2015/2016. Thirty-four newly developed mutant genotypes and three local checks were evaluated using a randomized complete block design with three replications. Grain yield data were analyzed using the additive main effects and multiplicative interaction(AMMI) and the genotype main effect plus genotype-by-environment interaction(GGE) biplot methods. The AMMI and GGE biplot models explained 77.49% and 75.57% of total observed genotypic variation, respectively.Bagani and Omahenene were the environments best discriminating the test genotypes during 2014/2015 and 2015/2016, respectively. Four promising mutant genotypes: G9(Sh L3 P74), G10(Sh R3 P4), G12(Sh R9 P5), and G4(Sh L2 P4), showed wide adaptation and grain yields of 2.83, 2.06, 1.99, and 1.95 t ha^(-1), respectively. The novel mutant lines are useful genetic resources for production or future cowpea breeding programs in Namibia or similar environments.

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.

Agronomic Performance, Stability Analysis and Evaluation of Anthracnose Disease Resistance of Common Bean Lines Derived by Marker-Assisted Backcrossing in Uganda

The present study focused on evaluating the agronomic performance, stability, and anthracnose resistance of common bean lines derived through Marker-Assisted Backcrossing in Uganda. Eight marker-assisted selection (MAS) backcross-derived bush bean lines with red seed types, alongside two checks, were evaluated in a randomized complete block design replicated two times in five locations for three consecutive crop-growing seasons in 2021 and 2022. The study aimed to identify lines with both high stable yields and enhanced resistance to anthracnose disease for potential release and utilization in future bean varietal development in Uganda. Agronomic traits, including days to 50% flowering, days to 90% physiological maturity, seed yield, seed yield components, and anthracnose disease reaction under natural infestation were assessed. The response to anthracnose disease was further assessed using six isolates of Colletotrichum lindemuthianum representing six different races. Results indicated that the agronomic performances of the MAS backcross-derived bush bean lines were statistically comparable to the recurrent parent NABE14. Specifically, six lines exhibited statistically equal to or higher performance than NABE14 in terms of seed yield, total number of seeds and number of pods per plant. The combined analysis of variance for seed yield showed significant (p Co-42 and Co-5 anthracnose resistance genes in the derived line. In conclusion, UGKT-B157-4, identified as the best-performing and stable genotype, demonstrates promise for release and use in future bean varietal development in Uganda, offering a combination of high yields and enhanced anthracnose disease resistance. The study provides valuable insights into the potential of Marker-Assisted Backcrossing in improving common bean varieties in the region.

Multi-Environment Evaluation and Genotype ×Environment Interaction Analysis of Sorghum [<i>Sorghum bicolor</i>(L.) Moench] Genotypes in Highland Areas of Ethiopia

Sorghum [Sorghum bicolor (L.) Moench] is a high-yielding, nutrient-use efficient, and drought tolerant crop that can be cultivated on over 80 per cent of the world’s agricultural land. However, a number of biotic and abiotic factors are limiting grain yield increase. Diseases (leaf and grain) are considered as one of the major biotic factors hindering sorghum productivity in the highland and intermediate altitude sorghum growing areas of Ethiopia. In addition, the yield performance of crop varieties is highly influenced by genotype × environment (G × E) interaction which is the major focus of researchers while generating improved varieties. In Ethiopia, high yielding and stable varieties that withstand biotic stress in the highland areas are limited. In line with this, the yield performance of 21 sorghum genotypes and one standard check were evaluated across 14 environments with the objectives of estimating magnitude G × E interaction for grain yield and to identify high yielder and stable genotypes across environments. The experiment was laid out using Randomized Complete Block Design with three replications in all environments. The combined analysis of variance across environments revealed highly significant differences among environments, genotypes and G × E interactions of grain yield suggesting further analysis of the G × E interaction. The results of the combined AMMI analysis of variance indicated that the total variation in grain yield was attributed to environments effects 71.21%, genotypes effects 4.52% and G × E interactions effects 24.27% indicating the major sources of variation. Genotypes 2006AN7010 and 2006AN7011 were high yielder and they were stable across environments and one variety has been released for commercial production and can be used as parental lines for genetic improvement in the sorghum improvement program. In general, this research study revealed the importance of evaluating sorghum genotypes for their yield and stability across diverse highland areas of Ethiopia before releasing for commercial production.

Genotype × Environment Interaction for Iron Concentration of Rice in Central Java of Indonesia

To explore the effect of genotype and genotype x environment interaction on Fe concentration in rice grains, Fe concentrations of 10 genotypes were analyzed across eight paddy field environments during 2007-2008 using the AMMI-biplot method. Experiments were conducted using a randomized completely block design with three replications in eight environments. Results indicated that environment （E）, genotype （G） and genotype x environment interaction （GE） significantly affected Fe concentration in rice grains. Environment explained 74.43 % of total （G＋E＋GE） variation, whereas G and GE captured 5.60% and 19.67%, respectively. Rice genotype Barumun was desirable in terms of the highest ability and stability for Fe concentration in rice grains. Environment in genotype Cilongok was the best representative of the overall environments and the most powerful to discriminate rice genotypes.

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。

基于GGE双标图分析新麦草种质部分性状的基因型-环境互作

评价新麦草(Psathyrostachys juncea(Fisch.)Nevski)种质资源在不同地区生长特性的差异,可为不同环境新麦草种质材料的选育提供参考依据。本研究以21份新麦草种质为材料,利用方差分析、GGE双标图模型等方法对6个性状在2个地点3年间的数据进行分析。结果表明:21份种质材料在不同环境下性状存在显著差异,变异性最大的性状为分蘖数,变异性最小的是叶长;方差分析表明基因型与年份效应对新麦草的表型影响更大;GGE双标图模型分析结果显示,叶层高最高且稳定的材料是598614,株高较高且相对稳定的材料是619565,基丛径最大的材料是502573,但基丛径稳定性最好的是502572,冠幅最大且稳定的材料是502572,分蘖数最多且稳定的材料是619565,叶长最长且稳定的材料是598610;综合区分力和适应性,较为理想的试点是包头。研究初步确定了不同种植环境下适宜的种质材料,将为新麦草种质资源利用提供参考。

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。

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 2 0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 ,其次为晋春 9号 ,而宁作 17表现最差 ,且不稳定。哈尔滨试点具有较高的淀粉峰值粘度 ,可用于筛选品种的淀粉品质及其稳定性。

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。

基于GGE双标图的水稻区试品种丰产性、稳产性和适应性评价

区域试验是作物新品种从选育到审定、推广的重要环节。而对试验数据的分析汇总,尤其是参试品种的丰产性、稳产性和适应性分析,比较流行和有效的方法是AMMI模型和SREG模型的GGE双标图,但基于SREG模型的GGE双标图的方法应用在近年来更为广泛。本文简要介绍GGE双标图,并以2012年南方稻区晚籼早熟B组品种区域试验数据为例,利用GGE双标图对参试品种进行丰产性、稳产性和适应性评价,并对评价中的注意事项作了讨论。

安徽麦区软质小麦面粉溶剂保持力的基因型与环境互作

为了深入了解安徽麦区软质小麦溶剂保持力(SRC)性状的基因型效应与不同种植区域环境效应的互作关系及稳定性,利用GGE双标图对种植于7个生态试点的12个大田推广软质小麦品种的蔗糖SRC、乳酸SRC、碳酸钠SRC和水SRC 4个性状进行了综合评价。GGE结果表明,供试材料的4个SRC性状的GGE变异值均大于50%,环境-基因型互作效应复杂。能较好区分蔗糖SRC和乳酸SRC的试点是濉溪,能较好区分碳酸钠SRC且具有强代表性的生态点是新马桥,能区分水SRC的生态点是阜阳。生态适应性和稳定性分析表明:蔗糖SRC值表现较稳定的品种包括华成863、龙科1109、荃麦725和涡麦99;乳酸SRC值稳定的品种有徽研912和瑞华麦516,碳酸钠SRC值稳定的品种包括徽研912和瑞华麦516,水SRC值表现较稳定的品种是华成863。

基于GGE双标图对南方冬大麦区域试验的分析

用GGE双标图对2009—2010年度南方冬大麦品种区域试验进行分析,以图谱的形式阐述了参试品种在各试验点的表现、各品种的平均表现和稳定性、各品种的适应地区、各参试品种的相似性和环境的相似性及两品种之间的比较等问题,很好的解答了品种与环境互作的问题。双标图显示‘苏啤3号’是一个适应性很广的品种。产量相关性状的图谱分析表明:本年度试验中有效穗与产量相关性最高,是影响产量的制约因素。

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双标图在我国旱地春小麦稳产性分析中的应用

GGE双标图法是研究基因型与环境互作以及不同环境下作物品种产量稳定性的新型有力工具。对2005年由17个试点、9个新品种(系)组成的国家旱地春小麦区域试验的产量资料进行方差分析和GGE分析表明:在我国旱地春小麦产区的大环境尺度下,品种与环境互作效应对产量变异的影响约为品种效应的5.37倍,品种间的稳产性差异很大,丰产性、稳产性均较理想的品种只占供试品种的11%,但有些品种对某些环境具有特殊适应性。17个试点可划分为3个类型区,其中在黄土高原中部旱作区表现最好的品种是"8821-1-1"和"陇春9143",在青藏高原寒旱区和华北旱作区表现最好的品种分别是"青春193"和"乌麦7号"。从环境代表性和对品种鉴别能力两方面分析,较理想的试点为甘肃省榆中县和青海省互助县。在7个环境因子中,生育期的降雨量与产量关系最为密切。

基于GGE双标图分析筛选生态适应性好的优质稻品种

为筛选具有生态适应性好的优质水稻,同时评价试点的代表性和区分力,采用GGE(基因型主效应及其与环境互作)双标图法综合分析12个杂交稻品种在6个试验点的产量及品质指标,并讨论试验点环境间的关系。结果表明:深两优5814、德优4727和T优6135表现出丰产、优质的特征。其中深两优5814多点平均产量10 096.25kg/hm^2,垩白粒率和整精米率分别为14%和56.5%且稳定性好。同时GGE分析表明,试验点中万州与梁平,秀山与南川紧密正相关。

基于BLUP和GGE双标图的黑杨派无性系生长性状基因型与环境互作效应

【目的】基于BLUP和GGE双标图分析法综合评价多试验点区域化试验黑杨派无性系(品种)的速生性、稳定性和各试验地点的区分力、代表性,可为黑杨派引种无性系的选择和应用提供理论依据。【方法】对位于北京、河北和山东的9个试验地点15个黑杨派引种无性系(品种)进行生长性状测定,使用ASReml-R程序包拟合误差异质的线性混合效应模型,通过最佳线性无偏预测(BLUP)法获得各无性系在各试验点6年生胸径和树高BLUP数据,进行GGE双标图分析,对无性系和试验地点进行评价。【结果】基于胸径和树高BLUP数据GGE双标图分析的前2个主成分(PC1和PC2)的方差解释百分比为84.69%和69.83%。基于胸径BLUP数据GGE双标图显示,9个试验点之间均存在正相关关系,被分为2组:河北永清和山东金乡为一组,胸径最大的无性系为50;其余7个试验点为另一组,胸径最大的无性系为Por。区分力最好的地点为北京昌平和河北永清,代表性最好的地点为山东宁阳(高桥)和河北魏县。对于无性系高产性和稳产性,Por胸径最大,Pa、36、108、50、111和107的胸径均大于均值,而Og的胸径接近总体均值,La胸径最小;107、Me、108、Br、Og和36稳定性较好。【结论】胸径与树高BLUP-GGE双标图存在差异,胸径BLUP-GGE双标图较树高的可靠;无性系Por速生性突出,其次是Pa、36、108、50、111和107,稳定性较好的无性系为107、Me、108、Br、Og和36,速生性和稳定性均较强的无性系为Por、Pa、36、108和107。