棉花区试中品种多性状选择的理想试验环境鉴别

农作物品种选育通常需要对多目标性状综合选择,依据育种目标性状和权重建立品种选择指数,选择遗传差异鉴别力强和目标环境代表性好的试验点,有助于提高品种选育的效率并降低实验成本。本研究依据国家棉花品种审定标准构建针对性和实用性强的品种选择指数,即SI=0.40?皮棉产量+0.13?纤维比强度+0.09?(纤维长度+马克隆值)+0.11?抗枯萎病+0.09?抗黄萎病+0.10?霜前花率,采用GGE双标图方法,对2000—2013年间39组(含585个单点试验)长江流域国家棉花区域试验中的15个试验点,综合评价品种选择指数的鉴别力、代表性和理想指数。结果表明,湖北黄冈和江苏南京试验环境被评为最理想的试验环境,湖北荆州、湖北武汉和江苏盐城为理想的试验环境,而河南南阳、湖北襄阳、湖南常德、四川简阳和四川射洪试验环境为不理想试验环境。可以看出,理想的试验环境均位于长江流域的中下游棉区,而不理想的试验环境中四川简阳和四川射洪位于上游的四川盆地、河南南阳和湖北襄阳位于长江流域北缘的南襄盆地、湖南常德虽然位于长江流域中游但栽培密度偏低。本研究构建的选择指数采用与国家棉花品种审定中品种评价准则相统一的目标性状和权重分配策略,理想试验环境对我国长江流域棉区的棉花生态育种试验点的选择提供了切实可行的决策方案。     

基于HA-GGE双标图的长江流域棉花区域试验环境评价

采用遗传力校正的GGE (HA-GGE)双标图方法对2000-2010年间27个独立的长江流域棉花品种区域试验的15个试验环境(试验点)在皮棉产量选择上的鉴别力、代表性、理想指数和离优度指数进行分析和综合评价。结果表明,湖北黄冈、江苏南京和湖北荆州是最理想的试验环境,对以长江流域为目标环境的广适性新品种选育和作为区域试验点鉴别理想品种的效率最高,而四川射洪、四川简阳、湖北襄阳和河南南阳不适宜作为针对长江流域的新品种选择与推荐环境。理想试验环境都位于长江流域除南襄盆地以外的中下游棉区,而不理想试验环境中的四川射洪和四川简阳位于长江流域棉区最西边的品种熟期较早且种植密度较高的四川盆地棉区,河南南阳和湖北襄阳位于长江流域棉区最北边, 与黄河流域棉区接壤, 霜期较早且晚秋降温快的南襄盆地棉区。本研究充分展示了HA-GGE双标图在区域试验环境评价方面的应用效果,也为长江流域棉花品种生态区划分和国家棉花区试方案的决策提供了理论依据。     

GGE双标图在湖南省棉花品种区域试验中的应用

为研究2013年湖南省棉花品种区域试验B组中参试品种与环境的互作关系,科学评价参试品种与试点,从而为品种审定、品种在生产中的有效利用及试点遴选提供理论依据。采用具有直观分析农作物两向数据的GGE双标图软件对参试品种的丰产性与稳定性、理想品种选择、品种适宜种植区域划分、各试点的代表性和鉴别力及理想试点选择等方面进行了分析。结果表明：2013年湖南省棉花品种区域试验B组各品种（系）皮棉产量的基因型、环境（试点）、基因型与环境互作效应均达极显著水平,其中环境主效（试点）、基因型主效及基因型与环境互作效应分别占处理变异平方和的57.99%、13.54%、28.48%;丰产性最好的品种是B3,稳产性最好的品种是B5,但最理想的品种是B3;大通湖管理区、君山和湖南省棉花科学研究所试点为最理想试点。     

长江流域棉花纤维比强度选择的理想试验环境筛选

采用GGE双标图方法对2000-2010年期间27组独立的长江流域棉花品种区域试验中的15个试点在纤维比强度选择上的鉴别力、代表性、理想指数和离优度指数进行了全面分析和综合评价.结果表明:南京、黄冈、常德、岳阳和南阳是以长江流域为目标环境的广适性纤维比强度选育和作为区域试验点鉴别理想品种的最理想试验环境,而江浙沿海棉区的试验环境(南通、盐城和慈溪)和四川盆地棉区试验环境(简阳和射洪)不适宜作为针对长江流域的纤维比强度选择与推荐环境.本研究充分展示了GGE双标图在区域试验环境评价方面的应用效果,也为长江流域棉花品种生态区划分和国家棉花区试方案的决策提供理论依据.     

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。     

利用GGE双标图划分长江流域棉花纤维品质生态区

长江流域棉区棉纤维品质区域特征明显,合理划分纤维品质生态区有助于提高原棉品质和配棉效率。本研究采用GGE双标图的“环境-性状”功能图分析2000-2012年期间长江流域国家棉花品种区域试验中环境与纤维品质性状的互作模式,提出长江流域棉纤维品质生态区划分方案。结果表明,长江流域棉区可划分为“中等品质生态区”、“高长度与比强度生态区”和“低马克隆值生态区”。其中,长江流域中等纤维品质生态区涵盖湖北省江汉平原和鄂东南岗地棉区、河南省与湖北省交界的南襄盆地棉区、湖南省环洞庭湖东部和西部棉区、江西省环鄱阳湖棉区、安徽省沿江与江淮棉区、江苏省宁镇丘陵与沿江棉区和浙江省沿海棉区,纤维品质较好,代表了长江流域的总体水平;高长度与比强度生态区位于湖南省环洞庭湖北部滨湖沃土棉区,纤维长度和比强度优良,而马克隆值偏高;低马克隆值生态区涵盖长江流域最西边海拔较高的棉花熟期早长势较弱的四川丘陵棉区和最东边土壤含盐度较高且棉花长势较弱的江苏沿海棉区,纤维马克隆值达到B级水平,为长江流域马克隆值最好的区域,但纤维比强度水平一般。本研究充分展示了GGE双标图的“环境-性状”功能图在纤维品质生态区划分方面的应用效果,可为长江流域棉花区域化种植和纺织企业合理用棉提供决策支持,也为其他棉区和作物生态区划分研究提供参考。     

Assessment of genotype × environment interactions for yield and fiber quality in cotton performance trials

Plant breeding programs involving a wide range of crop plants routinely practice selection (directly or indirectly) for genotypes that display stability for a given trait or set of traits across testing environments through the genotype evaluation process. Genotype stability for trait performance is a direct measure of the presence and effect of genotype × environment interactions, which result from the differential performance of a genotype or cultivar across environments. The genotype evaluation process also requires selection of the proper field trial locations that best represent the target environments the breeding program is directed toward. In this study, we assessed the extent to which genotype × environment interactions affected agronomic performance (lint yield, gin turnout) and fiber quality (fiber length, fiber strength, uniformity index, micronaire, fiber elongation) in a series of cotton (Gossypium hirsutum) performance trials in 12 location–year environments in South Carolina. Genotype × environment interactions affecting lint yield were larger in higher yielding environments, while interactions for fiber strength were greater for genotypes with lower mean fiber strength values. Two regions within the South Carolina cotton production areas were identified as proper testing locations for lint yield performance, while testing for fiber strength can be accomplished in any location within the statewide cotton production areas. The U.S. Government's right to retain a non-exclusive, royalty-free license in and to any copyright is acknowledged.     

GGE Biplot Analysis of Genotype by Environment Interaction in Field Pea (<Emphasis Type="Italic">Pisum sativum</Emphasis> L.) Genotypes in Northwestern Ethiopia

Ten field pea genotypes were evaluated in randomized complete block design with four replications for three consecutive years (2010-2012) main cropping seasons at four locations in each year. The objectives were to determine magnitude of genotype by environment interaction and to identify stable field pea genotype with high grain yield to be released as a cultivar to producer for Northwestern Ethiopia. The GGE [genotype main effect (G) and genotype by environment interaction (GE)] biplot graphical tool was used to analyze yield data. The combined analysis of variance revealed a significant difference (P<0.01) among genotypes, environments and genotype-by-environment interaction for grain yield. The average environment coordinate biplot revealed that EH99005-7 (G2) was the most stable and the highest yielding genotype. Polygon view of GGE-biplot showed the presence of three mega-environments. The first section includes the test environments E1 (Adet 2010), E3 (Debretabor 2010), E5 (Adet 2011), E6 (Motta 2011), E7 (Debretabor 2011), E8 (Dabat 2011), E9 (Adet 2012) and E12 (Dabat 2012) which had the variety G1 (EH99009-1) as the winner; the second section contains the environments E4 (Dabat 2010), E10 (Motta 2012) and E11 (Debretabor 2012) with G2 as the best grain yielder and the third section contains the E2 (Motta 2010) with G4 (Tegegnech X EH90026-1-3-1) as the best grain yielder. The comparison GGE- biplot of field pea genotypes with the ideal genotype showed that G2 was the closest genotype for the ideal cultivar. Among the twelve environments, E2, E6 and E10 were more discriminating and E3, E9 and E12 were less discriminating. Genotype EH99005-7 was the most stable and the highest yielding genotype. As a result it is released officially for Northwestern Ethiopia. Therefore, it is recommended to use genotype EH99005-7 for wider cultivation in Northwestern Ethiopia and similar areas.     

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20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,     

Performance of cowpea varieties under <Emphasis Type="Italic">Striga gesnerioides</Emphasis> (Willd.) Vatke infestation using biplot analysis

Striga gesnerioides (Willd) Vatke, is a major destructive parasitic weed of cowpea (Vigna unguiculata (L.) Walp.) which causes substantial yield reduction in West and Central Africa. The presence of different virulent races within the parasite population contributes to significant genotype × environment interaction, and complicates breeding for durable resistance to Striga. A 3-year study was conducted at three locations in the dry savanna agro-ecology of Nigeria, where Striga gesnerioides is endemic. The primary objective of the study was to identify cowpea genotypes with high yield under Striga infestation and yield stability across test environments and to access suitability of the test environment. Data collected on grain yield and yield components were subjected to analysis of variance (ANOVA). Means from ANOVA were subjected to the genotype main effect plus genotype × environment (GGE) biplot analysis to examine the multi-environment trial data and rank genotypes according to the environments. Genotypes, environment, and genotypes × environment interaction mean squares were significant for grain yield and yield components, and number of emerged Striga plants. The environment accounted for 35.01%, whereas the genotype × environment interaction accounted for 9.10% of the variation in grain yield. The GGE biplot identified UAM09 1046-6-1 (V7), and UAM09 1046-6-2 (V8), as ideal genotypes suggesting that these genotypes performed relatively well in all study environments and could be regarded as adapted to a wide range of locations. Tilla was the most repeatable and ideal location for selecting widely adapted genotypes for resistance to S. gesnerioides.     

棉花品种区域试验适宜试验点数量的抽样估计

 基于长江流域国家棉花区域试验的对照品种泗棉3号、湘杂棉2号、湘杂棉8号和鄂杂棉10号在2000－2011年期间84～270次试验中早熟性、农艺性状、产量性状和纤维品质性状表现,构建了对照品种抽样总体,采用随机抽样方法估计了在不同精确度水平下鉴别棉花品种主要性状表现所需要的试验点数量,旨在为长江流域棉区国家棉花区域试验的试验点数量设置提供理论依据,并为其它棉区乃至其它作物区域试验中适宜试验点数量的估计提供参考方法。研究结果表明：试验点数量的需求与目的性状选择及变异度密切相关,皮棉产量的表型标准差最高,在同等精确度水平下需要的试点数量也最多。目前,长江流域棉花区域试验设置18个试点,对皮棉产量的估计精确度为90%;增加16个试点,估计精确度将提高到93%;而随后再增加试点数量对提高估计精确度收效甚微。     

北部冬麦区冬小麦区试品种（系）的稳定性和适应性分析

为客观准确地评价区域试验中北部冬麦区冬小麦新品种（系）的丰产性和稳产性,以2014-2015年度国家北部冬麦区水地组冬小麦品种（系）区域试验产量数据为资料,应用AMMI模型对小区产量的基因型、环境和基因型与环境（G×E）互作进行了研究。结果表明,稳定性最好的为农大3486、京农12-79,较好的有科遗4174、长6794、中麦93,较差的为航麦109、晋太102、众信7198;试点以天津武清、山西榆次、新疆阿拉尔、河北遵化、山西屯玉分辨力较高,河北固安、河北滦县、北京顺义、北京昌平分辨力较低。在对区域试验中品种（系）稳定性和试点的分辨力进行判定时,综合使用双标图和稳定性参数两种方法,既直观又准确。     

Comparison of shifted multiplicative model,rank correlation,and biplot analysis for clustering winter wheat production environments

Categorization of locations with similar environments helps breeders to efficiently utilize resources and effectively target germplasm. This study was conducted to determine the relationship among winter wheat (Triticum aestivum L.) yield testing locations in South Dakota. Yield trial data containing 14 locations and 38 genotypes from 8 year were analyzed for crossover genotype (G) × environment (E) interactions according to the Azzalini-Cox test. G × E was significant (P < 0.05) and contributed a small proportion of variation over the total phenotypic variation. This suggested that for efficient resource utilization, locations should be clustered. The data were further analyzed using the Shifted Multiplicative Model (SHMM), Spearman’s rank correlation and GGE biplot to group testing locations based on yield. SHMM analysis revealed four major cluster groups in which the first and third had three locations, with four locations in each of the second and fourth groups. Spearman rank correlations between locations within groups were significant and positive. GGE biplot analysis revealed two major mega-environments of winter wheat testing locations in South Dakota. Oelrichs was the best testing location and XH1888 was the highest yielding genotype. SHMM, rank correlation and GGE biplot analyses showed that the locations of Martin and Winner in the second group and Highmore, Oelrichs and Wall in the third group were similar. This indicated that the number of testing locations could be reduced without much loss of grain yield information. GGE biplot provided additional information on the performance of entries and locations. SHMM clustered locations with reduced cross-over interaction of genotype × location. The combined methods used in this study provided valuable information on categorization of locations with similar environments for efficient resource allocation. This information should facilitate efficient targeting of breeding and testing efforts, especially in large breeding programs.     

建国以来我国长江流域棉区棉花品种的遗传改良Ⅰ.产量及产量组分性状的改良

本文是我国长江流域棉区棉花品种遗传改良研究的系列报道之一, 目的在于探讨建国以来我国长江棉区棉花品种在产量和产量组分性状(株铃数、铃重和衣分)上遗传改良的成效. 对不同历史时期11个代表性品种两年7点的试验资料和30多年区域试验历史资料的研究表明, 建国以来, 我国长江棉区棉花品种的产量性状改良成效显著, 品种的产     

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     

远缘杂交棉花新品种石远321综合分析

利用棉花远缘杂交技术,用海岛棉（G.barbadense）、野生瑟伯氏棉(G.thurberi)、陆地棉(G.hirsutum)进行杂交,对杂交铃喷(滴)GA3(50ppm)、NAA（40ppm）、杂种胚离体培养,试管内染色体加倍,对获得的种间杂种进行回交较育,南繁北育,异地鉴定选育而成棉花新品种“石远321”。1993~1994年国家黄河流域棉花品种区域试验,亩产子棉、皮棉、霜前皮棉均居八个参试品种第一位,其中霜前皮棉产量885 kg/hm2,比对照增产19.7%,是1982~2000年19年间国家黄河流域棉花品种区域试验中霜前皮棉增产幅度最大的一个品种。     

2005—2012年新疆南疆棉区棉花杂交种主要经济性状优势分析

研究杂交棉在高密度植棉模式下主要经济性状优势,为新疆南疆棉区棉花杂交种利用提供理论依据。利用2005—2012年南疆中早熟杂交棉和常规陆地棉区域试验品系和审定品种的皮棉产量、纤维品质性状的多年多点数据,进行整理和对比分析。在高密度膜下滴灌种植模式下,杂交棉参试组合经历了起步-快速发展-急速下降的过程。杂交棉参加区试组合在单株铃数、单铃重、衣分变幅较大。陆陆杂交棉组合平均皮棉产量略高于常规陆地棉,纤维长度、比强度等品质指标略低于常规陆地棉,但均不显著。陆陆杂交棉审定品种在单株结铃、单铃重方面显著高于常规陆地棉,但每公顷铃数较常规陆地棉低3.0万个,平均皮棉产量差异不显著。陆海杂交种在单株铃数、纤维品质、抗病性等总体优于陆陆杂交种和常规棉,但在单铃重、衣分、皮棉产量平均分别低于常规陆地棉27.0%、11.3%、7.0%。在目前高密度植棉模式下,参试杂交棉组合较常规陆地棉生产优势并不明显,继续开展高密度强优势杂交棉育种及种植模式研究非常必要。     

远缘杂交棉花新品种石远321的综合分析

利用棉花远缘杂交技术,用海岛棉(G.barbadense)、野生瑟伯氏棉(G.thurberi)、陆地棉(G.hir-sutum)进行杂交,对杂交铃喷(滴)GA3(50ppm)、NAA(40ppm)、杂种胚离体培养,试管内染色体加倍,对获得的种间杂种进行回交较育,南繁北育,异地鉴定选育而成棉花新品种“石远321”。1993—1994年国家黄河流域棉花品种区域试验,亩产子棉、皮棉、霜前皮棉均居八个参试品种第一位,其中霜前皮棉产量885kg/hm2,比对照增产19.7%,是1982—2000年19年间国家黄河流域棉花品种区域试验中霜前皮棉增产幅度最大的一个品种。     

Genotype × environment interactions for seed yield in rainfed winter safflower (Carthamus tinctorius L.) multi-environment trials in Iran

Evaluation of genotype × environment interaction (GEI) is an important component of the variety selection process in multi-environment trials. The objectives of this study were first to analyze GEI on seed yield of 18 spine safflower genotypes grown for three consecutive seasons (2008–2011) at three locations, representative of rainfed winter safflower growing areas of Iran, by the additive main effects and multiplicative interaction (AMMI) model, and second to compare AMMI-derived stability statistics with several stability different methods, and two stability analysis approaches the yield-stability (Ysi) and the GGE (genotype + genotype × environment) biplot that are widely used to identify high-yielding and stable genotypes. The results of the AMMI analysis showed that main effects due to genotype, environment, and GEI as well as first six interaction principle component axes (IPCA1 to 6) were significant (P < 0.01). According to most stability statistics of AMMI analyses, genotypes G5 and G14 were the most stable genotypes across environments. According to the adjusted stability variance (s²), the high-yielding genotype, G2, was unstable due to the heterogeneity caused by environmental index. Based on the definition of stable genotypes by regression method (b = 1, S _d ²  = 0), genotypes G11, G9, G14, G3, G12 and G13 had average stability for seed yield. Stability parameters of Tai indicated that genotype G5 had specific adaptability to unfavorable environments. The GGE biplot and the Ysi statistic gave similar results in identifying genotype G2 (PI-209295) as the best one to release for rainfed conditions of Iran. The factor analysis was used for grouping all stability parameters. The first factor separated static and dynamic concepts of stability, in which the Ysi and GGED (i.e., the distance from the markers of individual genotypes to the ideal genotype) parameters had a dynamic concept of stability, and the other remaining parameters had static concept of stability.     

基于GGE双标图与纤维长度选择的棉花品种生态区探索与划分

采用GGE双标图分析方法对2000~2010年期间27组长江流域国家棉花品种区域试验以安庆、南阳、黄冈、荆州、武汉、襄阳、常德、岳阳、南京、南通、盐城、九江、简阳、射洪和慈溪等15个试验点为代表的长江流域棉区目标环境中可能存在的基于棉花纤维长度选择的品种生态区进行探索与划分,并对品种生态区划分结果进行信息比（IR）校正,以提高品种生态区划分的可靠性,为长江流域棉区棉花品种纤维长度的选择和推荐策略提供科学的决策依据。结果表明：我国长江流域棉区大致可划分为基于纤维长度选择的3个品种生态区,第1个品种生态区包括安庆、九江、武汉、南通、黄冈、常德和岳阳,第2个品种生态区包括荆州、盐城、南京和射洪,第3个品种生态区包括慈溪、简阳、襄阳和南阳。