GGE双标图在玉米区域试验中的应用

采用GGE-biplot软件对2012年吉林省中早熟组玉米区域试验进行了分析。结果表明:A01品种丰产性、稳产性综合表现最佳,白山河口点鉴别力及代表性综合表现最佳。利用GGE双标图可以直观、简单、便捷地展示出品种及试验点的优劣,为今后的品种筛选、推广及试验点分布提供了参考依据。     

不同育苗基质对翠菊幼苗质量的影响

用蛭石、珍珠岩、草炭、腐熟鸡粪、腐熟猪粪配制育苗基质,在温室条件下研究其时翠菊[Calliste-phus chinensis(L.)Nees]幼苗质量的影响.综合分析结果表明,翠菊苗在处理3(草炭:蛭石:珍珠岩:腐熟鸡粪为3:3:2:2)基质上生长最好.苗期指标显示.生长在处理3基质配方上的苗全株干重为1440.21mg、地下干重为406.56mg、茎粗为0.37cm、株高为7.22 cm、壮苗指数为1367.43、叶绿素含量为2.82mg·dm~(-2)、光合速率达到5.39 μmolCO_2·m~(-2).s~(-1),差异均达到显著水平.     

基于玉米产量及籽粒相关性状的GGE双标图分析

通过GGE双标图法分析了7个玉米新品种在5个试点的产量及籽粒相关性状,为玉米新品种鉴定、各优良品种的区域布局,及玉米育种实践提供参考。通过GGE双标图法分析了7个玉米新品种在5个试点的产量及籽粒相关性状,分析各品种各个性状的适应性、表现及稳定性;同时分析相应试点的分辨力及代表性。ZP122为百粒重高且较稳定品种;盛农4号为产量最高且稳定性相对较好的品种;盛农4号和水玉108为穗长表现优异品种,前者穗长最长,稳定性稍差,后者穗长稍差,稳定性最好;盛农4号为穗粗最为理想品种;水玉108为行粒数最多且稳定性较好的品种。威宁县卯家村、盘县沙坡村和赫章县桃园村3试点百粒重代表性和分辨力较强;大方县关井村为产量分辨力最高试点,赫章县桃园村为代表性最好试点,但分辨力稍差;盘县沙坡村和水城县小河村2试点穗长代表性和分辨力均最为理想;大方县关井村和水城县小河村2试点穗粗代表性和分辨力都很高;盘县沙坡村和赫章县桃园村2试点穗行数分辨力和代表性均最好,但二者可能存在试点重复设置的问题。各品种不同性状间适应性及稳定性都存在一定的差异,显示出了较高水平的基因多样性。不同试点之间代表性和分辨力存在一定的差异,GGE双标图在玉米产量及籽粒相关性状分析方面比较直观而且有效。     

GGE双标图分析黑龙江省联合体玉米试验品种适应性和稳定性

为促进黑龙江省玉米生产发展,本研究利用GGE双标图法综合评价黑龙江省玉米品系在不同试验环境中的丰产性、稳定性和适应性,筛选适应不同生态环境的优良参试品系,同时评价参试地点的鉴别力和代表性。结果表明:在参试品种中AF1、PY9属于高产稳产品系,益农玉10号稳产性强,适宜为对照品种;在9个参试地点分析中,品种AF1在试点明水和依兰,XN1567在试点巴彦和牡丹江,品种ZC99在林甸和富裕适应性较好;试点明水代表性和鉴别力最强;牡丹江和林甸鉴别力较强,代表性稍差;依兰代表性较强但鉴别力一般;富裕鉴别力与代表性都一般。     

基于R语言的GGE双标图在江苏省淮南玉米区域试验中的应用

通过基于R语言的GGE双标图法分析了15个玉米品种的丰产性、稳产性同时分析了8个试验点对参试品种的区分力和代表性。结果表明,先玉1980(G10)高产且稳产;而DJ1803(G09)较高产,但稳产性稍差。综合各试验点的区分力和代表性,如皋(E6)试点的区分力和代表性最好,是理想的试验点。江苏中江种业股份有限公司(E2)、江苏省农业科学院(E3)之间以及大华育种研究院盐城研究所(E5)、江苏神农大丰种业科技有限公司盐城基地(E7)之间存在紧密正相关,表明有些试验点也许是重复设置的,应该剔除冗余的试点,以减少试验成本。     

腐熟玉米秸秆基质在翠菊育苗上的应用

[目的]探索新型的花卉育苗基质。[方法]用腐熟玉米秸秆配制育苗基质,设5种基质配比,3次重复,随机区组,研究其对翠菊（Callistephus chinensis（L.）Nees）幼苗质量的影响。[结果]翠菊幼苗在处理③（腐熟玉米秸秆∶含10%腐熟鸡粪的园土=1∶3）基质上生长最好,苗期指标与其他处理比均达到显著差异（P〈0.05）,即叶片数为19.1片,株高为6.32cm,茎粗为3.48mm,地上干重为0.81mg,地下干重为0.41mg。[结论]腐熟玉米秸秆完全可以替代草炭作为育苗基质的组份。     

基于R语言的GGE双标图在江苏省玉米联合体区域试验中的应用

通过基于R语言的GGE双标图法分析了5个玉米新组合的丰产性、稳产性和适应性,同时分析了7个试验点对参试组合的区分力和代表性.结果表明:神龙美玉28高产且稳产;LY1825较高产,但稳产性稍差.综合各试验点的区分力和代表性,东辛试点的区分力和代表性最好,是较理想的试验点.     

西瓜新品种稳产性和适应性GGE双标图分析

通过对西瓜新品种区试试验中的产量结果进行GGE双标图分析,从而获得了比简单联合统计分析更为详尽的品种与试验点的相关信息,更有利于对试验品种进行客观的综合评价,对于品种的审定具有一定的借鉴意义。该文对4个供试品种产量性状进行了GGE双标图分析,发现了各自品种特性与环境的适应性关系,对于这些品种的推广具有较强的指导作用。     

应用GGE双标图对不同基因型谷子农艺性状及产量稳定性分析

通过GGE双标图分析了7种不同基因型的谷子品种在晋中生态条件下的产量与性状差异以及各品种的突出优势表现,为筛选该地区适宜品种以及根据所需培育适宜品种提供依据.结果表明,张杂谷系列表现出较大的杂种优势,尤其以张杂3号的籽粒产量最高,常规谷种以tg的分蘖力较强,产量高于晋谷.谷粒籽粒的产量与有效穗数、单穗粒重和千粒重呈显著的正相关关系,与穗数、有效穗数、分蘖数和单穗粒重呈负相关关系.     

一年两季鲜食糯玉米区域试验的GGE双标图分析

[目的]综合评价广西一年两季鲜食糯玉米区域试验品种基因型及基因型与环境互作的关系,为玉米区域试验和育种工作提供参考.[方法]应用GGE-biplot对广西2016年春季和秋季鲜食糯玉米区域试验的鲜穗产量进行GGE双标图分析,评价参试玉米品种的丰产性、稳产性和适应区域,以及各承试点的代表性和鉴别力.[结果]参试玉米品种与环境的综合评价分析结果表明,多数试点间存在正相关,其中春季试验北海和玉林及秋季试验北海和柳州试点间存在紧密正相关,对品种产量排序的影响相似.参试玉米品种适宜环境的分析结果表明,春季7个试点可分为4个生态区,秋季7个试点可分为3个生态区,直观展示了品种的适应性.参试品种丰产性和稳定性的分析结果显示,春季品种桂W1518和秋季品种百香糯1601在当季具有较好的丰产性和稳定性.环境鉴别力与代表性分析结果表明,春季柳州和南宁试点为较理想的试验环境,秋季玉林和河池试点为较理想的试验环境.[结论]GGE双标图直观展示了参试玉米品种的丰产性、稳产性和适应性,以及承试环境的鉴别力、代表性和生态类型,对玉米区域试验和育种工作具有重要参考价值.     

多效唑对翠菊矮化的效应

以高型翠菊为试材,喷施不同浓度多效唑后发现,多效唑对翠菊具有显著的矮化效果,增加茎粗,对花朵数量、花茎大小影响不大,最佳处理浓度为200×10-6～300×10-6.     

应用GGE双标图进行向日葵杂交种产量稳定性分析

采用GGE模型对2009年度全国向日葵杂交种区域试验数据资料进行统计分析,对参试品种的丰产性、稳产性做出了评价,结果表明:参试品种S18、S11和NX34250表现为高产、稳产,建议继续参加生产试验;S26和S086在白城、松原表现减产,其它地区增产,可继续参试;而UK301、B09-1、S02、NX44166表现不好,建设停止参试。     

不同激素组合和附加物对翠菊下胚轴愈伤组织诱导的影响

用翠菊下胚轴作外植体,研究不同激素组合和附加物对翠菊下胚轴愈伤组织诱导的影响。结果表明,在初代培养过程中,以MS+6-BA 0.5mg/L+IAA 2.0mg/L对翠菊愈伤组织诱导具有较好的促进作用,愈伤诱导率最高,为86.67%。培养基中同时加入500mg/L水解酪蛋白和1 000mg/L的L-脯氨酸可显著提高胚性愈伤组织的诱导率,诱导率达86.67%;甘露醇和AgNO3对胚性愈伤组织诱导率提高无明显作用,但愈伤组织生长更加旺盛,较致密,更有利于以后继代和分化。     

不同栽培基质对海巴戟幼苗生长的影响

[目的]筛选比较适合海巴戟幼苗生长的基质配方。[方法]试验采用随机区组设计,以生土为对照,设6个处理：①园土＋草炭土＋农家肥、②椰糠＋草炭土＋农家肥、③椰糠＋园土＋农家肥、④椰糠＋珍珠岩＋草炭土＋农家肥、⑤椰糠＋珍珠岩＋园土＋草炭土＋农家肥、⑥园土,研究6种不同栽培基质对海巴戟幼苗生长的影响。[结果]处理④的植株生长最好,与处理⑥和对照差异极显著。处理③、④的平均株高分别为22.90和21.64 cm;处理④、⑤的平均茎基分别为0.69和0.66 cm;处理③、④的平均叶片数分别为24.53和24.13;处理④的单株地上、地下部分鲜重分别达86.3和25.9 g。[结论]椰糠＋珍珠岩＋草炭土＋农家肥（1∶1∶1∶1）为海巴戟幼苗生长的最佳配方。     

基于Genstat GGE双标图评价甘肃省马铃薯区域试验的参试品种和试点

以2007-2008年甘肃省马铃薯区域试验的9个品种在7个试点的块茎产量为材料,利用基于Genstat的GGE双标图分析评价参试品种(系)的丰产性、稳定性、适应性以及各试点的代表性和区分力。结果表明,参试品种(系)‘陇薯9号’‘L0227-18’‘天薯10号’‘陇薯8号’的丰产性和稳产性较好;7个试点被划分为2个类型区域,在2个类型区域中表现最好的品种分别为‘陇薯9号’和‘陇薯6号’,安定、会川和临夏是比较理想的试点,具有较强的区分力和代表性。     

翠菊离体培养及再生体系的建立

[目的]建立翠菊离体快速繁殖及再生体系。[方法]以无菌播种获得的翠菊试管苗为外植体,以MS为基本培养基,添加不同浓度的6-BA、KT、NAA,对翠菊试管苗进行扩繁及生根培养试验,研究不同生长调节剂对翠菊继代培养和生根培养的影响,筛选翠菊快繁及再生的最佳培养基。[结果]6-BA浓度对愈伤组织的诱导有显著影响,随着6-BA浓度增高,诱导率显著增高;KT对愈伤组织的诱导率最低。MS＋6-BA 0.5mg/L＋NAA 0.1mg／L是翠菊离体快繁的适宜培养基,分生数为5.67±0.82,愈伤诱导率为（53.33±0.09）％;MS＋6-BA 2.0mg／L＋NAA 0.2mg,／L为最佳的再生体系建立培养基,分生数为6.33±1.03,愈伤诱导率为（83.70±0.04）％。在MS＋NAA0.2mg／L培养基中,试管苗生根率可达98.0％以上,炼苗移栽成活率可达90.0％以上。[结论]该试验建立了翠菊离体快速繁殖及再生体系。     

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(P0.01), 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.     

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.     

The Application of GGE Biplot Analysis for Evaluat ng 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.