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。

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.

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.

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.

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。

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

[Objective] This study was to evaluate the high yielding and stability of candidate cultivars, depict the adaptive planting region, analyze trial location discrimination ability and representativeness, as well as identify the ideal cultivar and trial location, with the aim to provide theory background for cultivar selection and reasonable 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 genotype by environment interaction(G×E) on lint cotton yield were all highly significant(P<0.01), which made it necessary to further explore the specific pattern of genotype 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 cotton planting region in Jiangsu Province. The "similarity among locations" view of GGE biplot clustered all 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 all locations in terms of discrimination and representativeness, and all 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SNP标记的小麦品种遗传相似度及其检测准确度分析

遗传相似度检测的准确度估计是对SNP标记法在农作物品种检测体系中应用的必要补充和完善。本研究基于2021年小麦品种SNP标记法跨实验室协同验证实验数据,分析了该方法的检测准确度及在品种间的遗传相似度。分析结果表明:(1)10个实验室对55组小麦品种组合的标记位点相似度检测的总体准确度约为98%。(2)GGE双标图的品种遗传关系功能图显示,7组小麦品种的组内遗传相似度在95%以上,其余组合的遗传相似度较低。(3)依据GGE双标图的“正确度-精确度”功能图和“准确度排序”功能图,发现洛旱7号/洛旱11等品种组合的相似度检测准确度较高,晋麦47/临抗11的检测准确度一般,而济麦22/婴泊700的检测准确度较差。(4)10个实验室的检测准确度存在显著差异,其中2个实验室检测的正确度、精确度和准确度表现显著差于其余实验室。(5)各实验室检测正确度的容许误差分布于1.3%~1.9%之间,平均为1.5%;准确度的容许误差分布于1.5%~2.0%之间,平均为1.7%。其中,Lab2和Lab3的检测正确度和准确度的容许误差显著差于其余实验室。本研究构建了SNP标记法对品种相似性检测的准确度统计模型,分析了品种组合和实验室的检测准确度及其容许误差,采用GGE双标图方法对检测正确度、精确度和准确度进行可视化分析,验证了各实验室对品种位点相似性检测的准确度和可靠性,为SNP标记法在农作物品种遗传相似性检测中的准确度评价提供了理论支持和应用范例。

基于GT双标图对小麦新品系的分类评价

为了从产量、品质方面准确评价旱肥地试验小麦新品系,以2020—2021年连续2年参加国家黄淮冬麦区旱地组多点比较试验的26份新品系为材料,应用多元统计方法分析了大田条件下2年间产量和品质性状的变化。结果表明:26份品系的产量和品质性状2年变异系数分别为2.0%~74.2%和2.1%~95.1%,变异较大,变异系数大小顺序依次为稳定时间>湿面筋含量>蛋白质含量>吸水率=单位面积产量>容重;相关分析表明,2年产量与品质性状存在负相关关系,稳定时间均与蛋白质含量、吸水量正相关,稳定时间与产量负相关,其中稳定时间与蛋白质含量相关系数均较高。在相关分析的基础上,采用聚类分析方法将2年中26份小麦参试品系聚为4类,并在主成分品种、性状(genotype by trait,GT)双标图(biplot)和聚类图中进行展示,聚类结果与新品系的实际表现一致,其中‘泰科麦4835’‘洛旱35’‘农大162’‘山农611436’连续2年划为同一类型,表现为产量较高、品质优良。该研究结果可为参试新品系的合理评价和推广应用提供理论依据。

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。

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。