利用GGE双标图分析花生品质性状的基因型-环境互作

花生品质性状的稳定性和基因型-环境互作研究，可为花生品质育种及不同生态区域花生品种的选择提供参考依据。本研究利用GGE-biplot工具，对我国黄淮海花生主产区16个花生品种两年间的品质性状进行了综合分析，包括含油量、油酸、亚油酸、棕榈酸和蛋白质含量等。结果显示，5个品质性状均有较高的GGE总变异值（主成分因素PC1和PC2变异的总和），变幅在61.5%-79.9%之间，以含油量的GGE变异值最低，为61.5%，油酸含量的GGE变异值最高，为79.9%。16个花生品种2年间部分品质性状表现较为一致，其中濮花9519的含油量表型值波动最小，山花9号的亚油酸含量表型值波动最小，开农49是油酸含量表型值最为稳定的品种，天府23号是棕榈酸含量和蛋白质含量表型值最为稳定的品种。初步明确了徐州和濮阳分别适合高油花生和高油酸花生种植，确定了不同生态类型试点下较适合种植的品种，为花生品种推广应用提供了参考。     

低氮胁迫对谷子苗期性状的影响和耐低氮品种的筛选

筛选和培育耐低氮能力强的作物品种,是提高作物氮素利用效率,减少氮肥施用量,降低环境污染的有效措施。本研究以45份谷子品种为试材,采用水培的方法,在低氮(0.1mmol·L~(-1))和正常氮(5mmol·L~(-1))条件下,测定苗高、根长和根数等22个氮效率相关指标,采用综合耐低氮系数法以及基于主成分分析的隶属函数法评价参试谷子品种的耐低氮性。结果表明,与正常氮条件相比,低氮胁迫下,谷子苗期根长、根冠比、地上部氮素生理效率、地下部氮素生理效率、单株氮素生理效率有不同程度提高,其余17个指标都有不同程度降低。两种评价方法均根据45个谷子品种的耐低氮能力将其划分为强耐低氮型、耐低氮型、中间型、较敏感型和敏感型5类。筛选出耐低氮性较强的品种5份,编号分别为11、14、17、35和39。利用GGE双标图对品种-耐低氮相关指标的分析表明,编号39和14的耐低氮品种主要耐低氮性状为地下部干重、地下部鲜重、根长;编号为11、35和17的耐低氮品种主要耐低氮性状为地上部鲜重、叶片数、叶宽、叶长、单株氮累积量、地上部氮累积量、单株干质量、地上部干重、地下部氮累积量、根数、苗高和SPAD。可见不同谷子品种的耐低氮机制存在一定差异,研究结果可为谷子耐低氮品种的选育提供材料基础。     

高油酸花生品种开农1760产量及其构成的可视化分析

分析高油酸小粒花生品种开农1760的产量及其与农艺性状的关系,为高产优质育种提供有效指导。本研究以开农1760参加的2014-2015年河南省小粒花生区试数据为基础,运用GGE (genotype+genotype-by-environment interaction)双标图评价其丰产性和稳产性,利用R语言进行可视化的相关分析和通径分析。结果显示,开农1760的丰产稳产性综合排名第一,出米率的变异系数最小,饱果率与产量呈极显著正相关,百果重、单株生产力、百仁重、主茎高、侧枝长和结果枝数与产量呈显著正相关,结果枝数的直接通径系数最大,是影响开农1760荚果产量的关键性状。综上所述,开农1760丰产性好、稳定性强,适合在河南省大面积推广利用;在选育小粒花生新品种时可侧重做好对结果枝数的选择。     

基于GGE双标图对苜蓿品种丰产性和稳定性综合评价

为了解不同苜蓿（Medicago sativaL.）品种在宁夏南部山区的丰产性、稳产性，以及试点的代表性和区分力，本研究利用方差分析、GGE双标图模型对区域3个不同试点的6个苜蓿品种的产量进行分析。结果表明：苜蓿产量最高的试验区是原州区彭堡镇彭堡三队，最低的是隆德县关庄乡前庄村，前者比后者高43.7%；产量最高的品种是‘甘农7号’，最低的是‘阿尔冈金’，前者比后者高44.9%；联合方差分析显示，基因型与环境互作（G×E）、环境（E）、基因型（G）对品种产量影响显著（P<0.05），其中G×E是引起苜蓿产量差异的最主要原因，它引起的变异是E的1.19倍，是G的1.58倍。利用GGE双标图模型分析可知，高产稳产的品种是‘三得利’、‘苜蓿王’；3个不同区域可划分为2个生态区，原州区头营镇徐河村和原州区彭堡镇彭堡三队为1个，隆德县关庄乡前庄村为1个；综合代表性和区分力，较理想的试点为头营镇徐河村。     

长江流域国家棉花区域试验审定品种的主要特征特性演变分析(英文)

[目的]采用GGE双标图方法分析历年长江流域棉花区域试验中通过国审的棉花品种的特征特性,探索棉花品种类型和特征的演化趋势。[方法]依据国家棉花品种审定标准对品种进行分类和综合评价指数构建。采用GGE双标图分析1981~2012年期间长江流域53个国审棉花品种主要目标性状的相关性。根据区域试验历年采用的对照品种,将区域试验数据分为5个时期,分析各时期内审定品种类型的比例和总体评价指数的变化动态。[结果]棉花育种目标性状间存在着复杂的相关关系,有必要采用评价指数方法对品种进行综合评价。品种类型的变化动态研究表明,Ⅱ型品种开始于泗棉3号时期,其后呈下降趋势;Ⅲ型品种在各个时期均有一定比例,总体呈上升趋势;Ⅳ型品种为1993年前审定品种的主要类型,在泗棉3号时期以后占比例较小,且呈下降趋势。评价指数的动态变化表明,2003年前品种都在目前合格线以下;2004~2008年期间审定品种总体评分较高;2009年后审定品种的总体评分仅略高于合格线。[结论]长江流域的棉花育种和品种审定中应更注重对马克隆值的改良和评价,以引导棉花丰产性和纤维品质的协调发展。     

Biplot analysis of trait relations of white lupin in Spain

A 2-year study of autumn-sown white lupin (Lupinus albus L.) was conducted under rainfed Mediterranean conditions in southern Spain. 108 landraces were evaluated for yield and major phonological and plant structural characteristics. Path coefficient and Genotype-Trait (GT) biplot analysis were used. Applying both types of analyses to the multiple trait data revealed that GT biplot graphically displayed the interrelationships among traits and facilitated visual comparison of accessions and selection. Wide variation was observed for trait phenology (dates of flowering and maturity), plant structure (plant and first order inflorescence height) and yield characters (first order branches, first order inflorescence and total yield). It was found that the accessions with the highest yields were the tallest and the latest to flower, both on the mainstem and on the lowest first order branch, and also were the latest to mature. GT biplots also had suggested as the accessions have been displayed according to their origins. The results showed that foreign germplasm may be an important material for improving some yield components such as number of pods and seeds per plant of locally adapted lupins in Spain. This revised version was published online in July 2006 with corrections to the Cover Date.     

The Effectiveness of HA-GGE Biplot Application in Analyzing the Data from the National Cotton Cultivar Regional Trial in the Yangtze River Valley

A large number of regional crop trials have demonstrated the ubiquitous existence of genotype × environment interactions (G×E), which make it complicated to select superior cultivars and identify the ideal testing sites. The GGE (genotype main effect plus genotype × environment interaction) biplot is the most powerful statistical and graphical displaying tool available for regional crop trial dataset analysis. The objective of the present study was to demonstrate the effectiveness of the biplot in evaluating the high and stable yields of candidate cultivars simultaneously, and in delineating the most adaptive planting region, analyzing trial location discrimination ability and representativeness, and identifying the ideal cultivar and trial locations. The lint cotton yield dataset with nine experimental genotypes and 17 test locations (three replicates in each) was collected from the national cotton regional trial in the Yangtze River Valley (YaRV) in 2012. The results showed that: (1) the effects of genotype (G), environment (E), and genotype × environment interaction (G×E) were significant (P < 0.01) for lint cotton yield. Differences among environments accounted for 78.7% of the treatment total variation in the sum of squares, whereas the genotype main effect accounted for 8.7%, and the genotype × environment interaction accounted for 12.6%. (2) The “ideal cultivar” and “ideal location” view of the HA-GGE biplot identified Zhongcj408 (G2) and Nannon12 (G9) as the best ideal genotypes; Cixi in Zhejiang Province and Jiangling in Hubei Province were the most ideal locations.(3) The “which-won-where” view of the biplot outlined the adaptive planting region for each experimental cultivar. (4) The “similarity among locations” view clustered the trial locations into four groups, among of which the two outlier locations, Shehong (SH) and Chengdu (QBJ), located in Shichuan Basin in the upper reaches of YaRV, were clustered in one group, whereas the Nanyang (NY) of Henan Province at the northern edge of YaRV was singled out as a sole group. Such location clustering results implied an apparent association with the geographical environment.     

Parental Selection in Rice Cultivar Improvement

The evaluation of rice (Oryza sativa L.) cultivars assists breeders in identifying useful trait relationships and in selecting parents as donors of specific traits. This study was conducted to compare long-grain rice cultivars using genotype × trait (GT) biplot analysis and determine potential donors of traits related to grain yield and quality. Seventeen cultivars in the 2005 and 2006 Uniform Regional Rice Nursery in Beaumont, Texas, USA were analyzed for 20 traits using GT biplot analysis. The GT biplots showed the diversity among cultivars with regards to yield-related traits. Cultivars recommended as donor parents were: Trenasse, Spring, Presidio, and Cocodrie for high grain yield and head rice rate; Trenasse and Presidio for semi-dwarfness; Banks for tall plant height; Wells for high flag leaf area, panicle weight, and number and mass of filled grains per panicle; Hidalgo for high tiller density; Francis for high number of spikelets per panicle; Spring and Trenasse for short flowering duration; Cheniere for more days to heading and maturity and Spring for less days; and Spring and Hidalgo for high grain weight. Breeders can use these cultivars with specific traits to increase grain yield and quality.     

基于BLUP和GGE双标图的华北落叶松家系区域试验分析

【目的】为综合评价华北落叶松参试家系的速生丰产性、稳定性及各试验地点的区分力和代表性,利用基因型主效加基因型-环境互作效应(GGE)双标图对2017年度华北落叶松区域试验中参试家系的生长数据进行分析。【方法】基于冀北地区4个试验地点26个华北落叶松家系的胸径数据,首先拟合3个线性混合效应模型,各个模型均具有相同的固定效应(“地点”和“地点中的区组”)和残差方差结构(行、列自回归AR1×AR1,以进行空间分析),其中,模型1为随机效应中包含2个公因子的因子分析模型(FA模型),模型2、3分别为随机效应里不含测量误差以及包含测量误差的非结构化矩阵模型(US模型);基于AIC信息准则选出一个最优模型,之后利用最佳线性无偏预测(BLUP)法得到各家系在各地点的胸径BLUP数据;基于胸径BLUP数据做GGE双标图分析,对华北落叶松家系和试验地点进行评价。【结果】基于AIC信息准则,模型3(空间变异结合包含测量误差的非结构化矩阵模型(US模型))被选为最优模型;基于胸径BLUP数据的GGE双标图的前2个主成分的方差解释百分比之和为92.4%,表明结果可靠;4个试验地点被分成2组,地点L1(张家口赤城马营沟)、地点L3(张家口沽源柳条沟)和地点L4(承德围场查字)为一组(以111号家系的胸径最大),地点L2(承德围场御道口)为一组(以78号家系的胸径最大),相对而言,地点L3(张家口沽源柳条沟)能更有效地选择速生丰产且稳定的家系;各家系在不同试验地点上的表现有所不同,总体而言,26个华北落叶松家系中,111号的胸径(产量)最大,接着是78、72、82、76、59、100、77、56、86、96等系号,胸径(产量)最小的是1号,97、116、53、35、46、66和49号等家系的胸径(产量)也较低,68和42号家系的胸径(产量)接近总体均值;96、86、100和76号是速生丰产且稳定的家系,速生丰产家系111、72、56号的稳定性居中,速生丰产家系78、82和77号的稳定性中等偏下,而速生丰产家系59号则不稳定。【结论】模型3(空间变异结合包含测量误差的非结构化矩阵模型(US模型))较另外2个模型而言,结果更为可靠。地点L3(张家口沽源柳条沟)既具高区分力,又具高代表性,能更有效地评价家系。家系96、86、100和76号兼具速生性、丰产性和稳定性,可被广泛推广。基于BLUP的GGE双标图能有效应用于华北落叶松家系及试验地点的评价,本研究可为冀北地区华北落叶松的家系选择和应用提供决策支持。     

基于BLUP和GGE双标图的林木多地点试验分析

【目的】建立基于无偏预测值(BLUP)与基因型主效加基因型-环境互作效应(GGE)双标图的分析模型,以提高林木多点试验数据分析的准确性。【方法】以火炬松36个基因型在6个试验地(S1~S6)的种子产量为基础数据,利用ASReml软件对实测数据进行空间变异结合因子分析法的模型拟合,以获取各地点下每个基因型的BLUP值;从试验地划分、试验地评估和林木基因型评估3个方面,对原始数据、BLUP数据进行GGE双标图分析与比较。【结果】BLUP数据具有明显的空间变异,比原始数据具有更高的产量变异解释能力;原始数据和BLUP数据的试验地分组结果一致,均分为2组,但BLUP数据的试验地点间的相关关系变弱;原始数据的理想试验地为地点S5,而BLUP数据为地点S1;原始数据和BLUP数据的最理想基因型均为21,但2种数据高产和稳产基因型的一致性比较低。【结论】基于BLUP与GGE双标图相结合的模型,可用于林木多点试验分析,其比原始数据的GGE双标图分析结果更为可靠。