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.

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.

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.

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-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。

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。

基于AMMI模型和GGE双标图的玉米区域试验分析

为了明确甘肃省2013年玉米区域试验参试品种的丰产性和稳定性,为玉米品种的推广提供参考,选用2013年甘肃省中晚熟组玉米品种区试参试14个品种(系)、5个试点的试验数据,利用基于R语言的AMMI和GGE双标图分析参试品种的丰产性和稳产性以及试点环境的区分力和代表性。结果表明,高产品种为泰玉1028、五谷568、龙博士7号、SN318,理想区试点是酒泉和武威。综合分析2种模型,高产又稳定的理想品种为龙博士7号。SN318适宜在酒泉种植,泰玉1028、五谷568、龙博士7号适宜在武威、张掖地区种植。AMMI模型与GGE双标图可以合理有效地评估品种(系)和区域。

基于R语言GGE双标图的辽宁地区薄皮甜瓜品种2年区域试验产量分析

为了综合评价2年甜瓜区域试验品种基因型及基因与环境的互作关系,为甜瓜品种区域试验和育种工作提供理论依据,应用GGE双标图对2年甜瓜区域试验品种多点产量数据进行分析。结果表明,堪甜98和金凤凰9号是兼具丰产性和稳产性,并具有广泛适应性的优良甜瓜品种。试点分析表明,5个试点存在或强或弱的正向相关性,2020年和2021年5个试点分为2个和3个生态亚区。2020年盘锦、2021年沈阳试点是兼备区分力和代表性的较为理想的试点环境,可以用于筛选高产稳产甜瓜品种;丹东试点是具备强区分力的种植环境,可以用来淘汰不稳定甜瓜品种,但是不能筛选稳产的优良品种。GGE双标图能直观展示甜瓜品种的丰产性和稳产性,确认参试品种的生态适应区域,鉴别试点环境的区分能力和目标生态区的代表性,在甜瓜的种植推广工作中具备一定的参考价值。

采用GGE双标图评价甘薯郑红22的丰产稳产性和适应性

采用R语言GGE-biplot双标软件对2008—2009年国家甘薯品种北方薯区区域试验鲜薯产量数据进行作图,分析参试品种郑红22的丰产稳产和适应性。结果表明,郑红22在平均环境轴上垂足较靠近正方向,丰产性能较好,并高于对照;与平均环境轴垂直的轴距离较近,稳产性能较好,并高于对照;适应性功能图扇形区中所处位置靠近坐标轴原点,适应性仅次于对照徐薯18,说明郑红22具有较高的推广价值。

基于GGE双标图的北方旱寒区冬油菜适应性分析

为评判白菜型冬油菜品种(系)的抗寒性以及在北方地区越冬的稳定性和适应性,对北方11个省(区、市)两年25个试点43点次试验的12个白菜型冬油菜品种的越冬率进行了GGE双标图法分析。结果表明,品种效应为55.12%,远远大于环境效应和品种与环境互作效应,为环境效应、品种与环境互作效应的2.5倍,因此,北方旱寒区冬油菜生产关键是选择超强抗寒品种。陇油6号、7号和12号品种抗寒性优良、越冬率稳定、广适性好,以‘陇油6号’和‘陇油7号’为最佳。GGE双标图分析还表明,本试验25个试点除祁县和天水两个试点外,其余试点对白菜型冬油菜品种抗寒性的鉴别力均较好,可作为北方冬油菜抗寒性鉴定的试点,以泾源、临河和靖边为最佳试点。同时将所有试点划分成不同的生态区,并筛选出了适宜不同生态区的特异适应性品种。‘陇油6号’和‘陇油7号’适于在参试的所有试点栽培;‘07皋兰DQW-1-3’和‘07兰州MXW-1-3’适于在拉萨、酒泉、定州、顺义、延边等地栽培;‘06468’适于在天水栽培。本研究表明,品种效应对冬油菜越冬率的影响占主导地位,但环境效应和品种与环境互作效应对冬油菜越冬率的影响也占较大比重,故在北方旱寒区冬油菜生产中,要综合考虑品种、环境及品种与环境互作效应,因地制宜地选择适宜地区以及不同地区的种植品种,才能保证其安全越冬。

基于R语言的GGE双标图法在甜菜品种区域试验中的应用

为了明确糖用甜菜品种在全国不同区域试验中参试品种的丰产性、稳产性、适应性及各试验点的区分力和代表力。2020年在全国不同生态区域的9个试验基地,以16个引种的KWS系列糖用甜菜品种为试材,采用基于R语言的GGE双标图法对糖用甜菜品种的产糖量指标进行分析。结果表明,在2020年品种区域试验中,KWS 0015(G16)丰产性最佳;KWS 7748(G6)、KWS 9921(G15)具有较好的稳产性;KWS 0015(G16)适宜种植的区试点最多,具有较强区域适应性,较其他品种高产稳产,为本试验理想品种。此外,呼和浩特(E9)具有较高区分力和较好代表性,是本研究的理想区试点。GGE双标图法对综合分析糖用甜菜品种基因型、环境与基因型交互效应具有科学合理性。

基于GGE双标图的甘肃胡麻区试品系稳定性与试点代表性研究

【目的】评价胡麻高代品系在不同试点的适应性、高产性及稳定性,筛选出适应性强的优良高代胡麻品系,为胡麻新品种鉴定及推广工作的完善提供可靠的科学依据。【方法】2017-2018年在甘肃兰州、景泰、榆中、清水、定西、张掖、白银、平凉、镇原9个试点种植甘肃省区域试验的12个胡麻高代品系,收获后测产,采用联合方差分析和Genstat GGE双标图模型对产量性状适应性、高产性和稳定性及试点鉴别力和代表性进行分析。【结果】参试的12个胡麻品系产量在基因型、环境及基因型与环境互作效应均达到极显著水平(P<0.01)。产量性状适应性结果显示,对兰州和景泰适应性最强的品系是99012-3(G10);对榆中、清水、定西、平凉和镇原适应性最强的品系是10287(G4);对张掖、白银适应性最强的品系是988-1(G11)。试点间相关性结果显示,榆中、平凉、镇原之间存在着密切正相关关系,兰州、景泰、定西、张掖、白银之间存在密切正相关关系。高产性和稳定性结果显示,参试胡麻品系中高产不稳产的品种有200617-8(G7)、09162(G8)、99012-3(G10)和0523-2-1-3(G12),低产稳产的品种有0354(G1)、1S×980369(G2)、9894-4(G3)、陇亚13号(G5,对照)、0818-6-13(G6)和1S×QS3(G9),不稳定且低产品种是988-1(G11),高产稳产品种有10287(G4)。试点鉴别力和代表性结果显示,景泰和兰州试点对12个胡麻高代品系最具鉴别力和代表性。【结论】GGE能够直观显示胡麻多点多年高代品系试验结果,客观评价品种的适应性、丰产和稳产性及试点的代表性和鉴别力。

基于GGE双标图分析山西大豆区试品种的稳定性及适应性

为研究山西大豆区域试验中参试品种的高产稳定性及适应性,2019-2020年在山西高平、介休、汾阳、文水、清徐、长治6个试点种植中部复播区的7个大豆品种,收获后测产。采用联合方差分析和GGE双标图模型对产量性状适应性、高产性、稳定性及试点鉴别力和代表性等进行分析。结果表明,参试大豆品种的产量性状在基因型、环境及基因型与环境互作效应均达到显著水平,基因型、基因型与环境互作效应、环境对大豆产量的影响逐渐增强。GGE双标图分析表明:高平、介休、文水和长治4个试点适应性最强的品种是品豆25;汾阳适应性最强的品种是汾豆98;清徐适应性最强的品种是同豆8181,因此要根据品种的特性选择适宜的种植环境,最大限度地发挥地域优势与品种生产潜力。同豆8181既高产又稳产,同时更有区域适应性优势,适宜在山西省中部复播区种植,是一个比较理想的品种。试点间相关性结果显示:高平、介休、文水、长治之间存在着密切正相关关系,说明在试点的选择存在着重复设置问题。试点鉴别力和代表性结果显示,介休试点对7个大豆品种最具鉴别力和代表性,是较为理想的环境。GGE双标图能够直观清晰地显示大豆多点多年品种试验结果和品种的代表性。研究结果为山西大豆新品种选育及推广提供了理论指导。