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，可以直观回答以下问题：（１）什么是某一特定环境下最好的 品种；（２）什么是某一特定品种最适合的环境；（３）任意两品种在各环境下的表现如何；（４）试验中品 种×环境互作的总体模式是怎样的；（５）什么是高产、稳产品种；（６）什么是有利于筛选高产、稳产品 种的环境。     

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,     

黄淮区试试验点油菜主要农艺性状分析

解析黄淮区试各试验点油菜主要农艺性状的基因与环境互作关系,并对各试验点进行评价。本研究用GGE双标图方法对2010—2015 年油菜黄淮区试农艺性状数据进行分析,用油菜产量三因素的基因与环境互作结果对试验点进行分类,并按照区分能力和代表性对各试验点进行评价。结果显示,富平试点的产量、单株角果数、株高和分枝部位表现最好。淮安试点的千粒重、单株产量、分枝数和主花序角果数表现最好,成县试点的每果粒数和主花序结角密度表现最好。按单株角果数和千粒重分析,宿州试点都单独聚为1 类;杨凌试点的区分能力和代表性最高。宿州试点被划分为另外一个品种生态区,杨凌试点是最理想的试验点     

黄麻种质资源数量分类研究

估算了我国和东南亚等地区１００份黄麻品种１１个产量和纤维品质性状的主成分，以主成分欧氏距离为基础作系统聚类分析；以第一和第二主成分一作二维排序分类。     

不同玉米品种（系）在云南省不同生态区丰产、稳产及适应性分析

为筛选云南省不同生态区、不同栽培水平条件下的优良新品种，为该区域玉米新品种精准推广和培育提供参考依据。本研究以9个玉米品种（系）在云南省15个试点的区域试验籽粒产量数据为研究对象，通过AMMI模型和GGE双标图分析方法分析不同玉米品种（系）在云南省不同试点的丰产性、稳定性和适应性，同时综合评价参试地点的鉴别力和代表性。结果表明：基因型效应、环境效应以及基因型与环境的互作效应均对参试品种产量产生极显著影响；综合产量、AMMI模型分析及GGE双标图结果，G3（文17-115）、G6（文15-5851）和G5（文17-5313）属较理想品种；E15（普文镇试验点）和E2（石林县试验点）是综合性较好的试点，均具有较强的区分力和代表性。AMMI模型和GGE双标图分析的侧重点不同，但品种评价结果基本一致，两种方法优势互补，可以用来作为全面有效地评估品种和试点的理想工具。     

不同类型小麦品种(系)农艺性状、生理指标与产量关系的多元统计分析

本文对不同类型小麦品种（系）的11个与产量密切相关的农艺性状,生理指标进行了主成分分析。11个原始指标综合成为三个独立的新指标,分别表示“光合同化因子”、“库与碳氮代谢因子”、“库与氮素营养因子”。主成分值与产量相关分析表明,小麦产量与第一、第二主成分值有极显著正相关关系。通过回归分析,建立了小麦产量与主成分值之间的回归方程。它能较好地反映不同类型品种的产量水平。     

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。     

基于灰色关联度分析和主成分分析法评估糖用甜菜品种的适应性

为了筛选出最适宜黑龙江哈尔滨地区种植的产质量高并抗根腐病的糖用甜菜品种。2020年在黑龙江省哈尔滨市黑龙江大学呼兰校区试验基地,以21个引种的KWS系列及1个BTS2730糖用甜菜品种（KWS1197为对照）为试材,采用主成分分析和灰色关联度分析法对根产量、含糖率、产糖量和根腐病4个指标进行综合评价。两种方法得出的甜菜品种的排序大致一致;第一主成分根产量的贡献率为69.704%,第二主成分含糖率的贡献率为26.283%,累计贡献率为95.987%,因此能够全面地反映甜菜的产质量性状;最适合本地种植的综合评价值高于对照的6个品种为：KWS0023(0.8231)>KWS0015(0.7685)> KWS6661(0.7511) >KWS9921(0.7103)>KWS0860(0.7097)>BTS2730(0.7065)>CK(0.6823);其他品种的综合评价值低于对照。主成分分析法和灰色关联度分析能够较为全面得分析甜菜品种,得出的结果具有可靠性。     

小麦定向育种中的亲本选配法

本文结合中国部分冬小麦品种的性状资料,提出定向育种中亲本选配的一种方法。从考察的品种数量性状主成分分析结果来看,第一主成分所对应的特征向量决定性状是与产量有关的单株粒重、千粒重、每穗粒数等性状,并进行了聚类分析,用以着重探讨以产量为主要目标的育种优势分析;第二、三主成分所对应的特征向量决定性状与株高和穗     

二棱啤酒大麦品种资源农艺性状的聚类分析和主成分分析

对37个二棱啤酒大麦选取8个农艺性状进行主成分分析，结果表明亲本选配时，以第二主成分较高，而第一，三，四主成分中等为宜。聚类分析结果表明供试品种分为六类。许多品种来源不同，但遗传差异较小，选配亲本时应加以注意。     

Genotype × environment interaction and yield stability in a cowpea-based cropping system

Twenty-four cowpea genotypes were evaluated under sole cropping or additive series intercropping with sorghum from 2004 to 2005 at four sites representative of the Guinea and Sudan savannah ecologies in Ghana. The aim was to determine whether cowpea breeding programs that emphasize selection under sole-crop conditions have the potential to produce cultivars that are effective under additive series intercropping. Genotype × cropping systems interaction was significant for days to 50% flowering but not for grain yield, biomass and other studied traits. Genotypic yield reaction to cropping systems indicated that bridging the yield gap between sole cropping and intercropping systems is best addressed by agronomic interventions that reduce stress on intercrop cowpea rather than by selecting for specifically adapted genotypes for intercropping. Significant genotype × environment interactions were observed for all traits when data was pooled over cropping systems. Partitioning of the genotype × environment interaction variance indicated that days to 50% flowering was dominated by heterogeneity of genotypic variance, whereas genotype × environment interactions for grain yield and biomass was mainly due to imperfect correlations. Large differences in genotypic yield stability were observed as estimated by the among-environment variance, regression of yield on the environmental index, Kataoka’s index, and by partitioning of genotype × environment interaction sum of squares into components attributable to each genotype. The results suggest that in regions where genotype × environment interaction for yield frequently causes re-ranking across environments, genotypes with the least contribution to the interaction sum of squares are likely to be most productive. On the whole, the results support the contention that breeding under sole-crop conditions has the potential to produce cultivars effective under intercropping conditions.     

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。     

马铃薯产量组分的基因型与环境互作及稳定性

本研究主要探究基因型和基因型与环境互作(genotype+genotypes and environment interactions, GGE)双标图在马铃薯育种中的应用。综合评价马铃薯品系产量性状在不同环境中的丰产性、稳定性和适应性,筛选出适应不同生态环境的产量性状优良品系。同时评价各试点的区分力和代表性,为试点的选择提供依据。2015年和2016年在甘肃安定区鲁家沟镇、安定区内官镇、渭源县五竹镇3个试点种植国际马铃薯中心引进的101份高代品系和对照青薯9号。收获后记录小区产量、小区大薯产量、小区小薯产量、单株产量、单株大薯产量、单株小薯产量、单株结薯数、单株大薯数、单株小薯数;采用联合方差和GGE双标图对产量性状进行基因型与环境互作分析。方差分析表明,除小区小薯产量在基因型与环境互作效应中无显著差异外,其他产量组分在基因型效应、环境效应和互作效应中均呈现极显著差异(P0.01)。小区产量、小区大薯产量、小区小薯产量、单株产量、单株大薯产量、单株结薯数环境效应平方和占总方差平方和最大;单株小薯产量、单株大薯数和单株小薯数的基因型与环境互作效应平方和占总方差平方和最大。GGE分析结果表明,适应性最强的品系在鲁家沟试点是G86;在五竹镇试点是G65;在内官镇试点是G86。参试品系中丰产品系有G86、G116、G124;稳产品系有G124、G125、G10;高产稳产品系有G86、G116、G124、青薯9号。单株大薯数高的品系有G45、G86、G67,稳定性好的品系有G67、G116、G51,对照青薯9号的单株大薯产量不稳定。综合鉴别力和代表性的强弱,依次为鲁家沟镇2016年、鲁家沟镇2015年、五竹镇2015年、五竹镇2016年、内官镇2015年、内官镇2016年。GGE模型能够直观地展现多年多点品系试验结果,并客观评价参试品系的丰产性、稳定性和适应性,同时可以对试点的代表性和区分力进行评价。以GGE模型综合评价,高产稳产品系有G116、G124、G125、G122、青薯9号;高产不稳定的品系有G86、G10、G121、G106、G107、G72。最理想的生态区试点是鲁家沟镇,对品种的鉴别力最强的试点是五竹镇。     

不同基因型小麦新品系主要性状稳定性分析

利用AMMI模型对5个绵阳小麦新品系的生育期、株高、单位面积有效穗数、穗粒数、成穗率、千粒重和产量等7个主要性状在四川不同试点的表现及其品种稳定性与适应性进行了研究,以期评选出综合性状优良、丰产性和适应性好的优良小麦新品系。结果表明,小麦生育期、株高、穗粒数、千粒重等性状的基因型效应＞环境效应＞基因型×环境互作效应;对于产量性状则表现为环境＞基因型×环境＞基因型,所占百分比分别为81.9%、12.3%和5.8%;各基因型在7个试点中产量最高的是MY1227-185（6093 kg/hm2）和ML2652（6082 kg/hm2）,极显著高于对照川麦107（5202 kg/hm2）;品种稳定性最高（Di≤0.42）的是ML2652、ML1131-95和MY1227-185,其次是ML1403-84（Di＝0.45）和MY68942（Di＝0.75）,稳定性均优于对照（Di＝0.85）。综合考虑各性状表现、稳定性和产量等因素,MY1227-185和ML2652丰产性和品种稳定性最优,可大面积推广以提高四川冬麦区产量及其稳定性。     

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.     

Field experiments and simulation to evaluate rice cultivar adaptation to elevated carbon dioxide and temperature in sub-tropical India

Location specific adaptation option is required to minimize adverse impact of climate change on rice production. In the present investigation, we calibrated genotype coefficients of four cultivars in the CERES-Rice model for simulation of rice yield under elevated CO₂ environment and evaluation of the cultivar adaptation in subtropical India. The four cultivars (IR 36, Swarna, Swarn sub1, and Badshabhog) were grown in open field and in Open Top Chamber (OTC) of ambient CO₂ (≈390 ppm) and elevated CO₂ environment (25% higher than the ambient) during wet season (June–November) of the years 2011 and 2012 at Kharagpur, India. The genotype coefficients; P1 (basic vegetative phase), P2R (photoperiod sensitivity) and P5 (grain filling phase) were higher, but G1 (potential spikelet number) was lower under the elevated CO₂ environment as compared to their open field value in all the four cultivars. Use of the calibrated model of elevated CO₂ environment simulated the changes in grain yield of −13%, −17%, −4%, and +7% for the cultivars IR 36, Swarna, Swarna sub1, and Badshabhog, respectively, with increasing CO₂ level of 100 ppm and rising temperature of 1 °C as compared to the ambient CO₂ level and temperature and they were comparable with observed yield changes from the OTC experiment. Potential impacts of climate change were simulated for climate change scenarios developed from HadCM3 global climate model under the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios (A2 and B2) for the years 2020, 2050, and 2080. Use of the future climate data simulated a continuous decline in rice grain yield from present years to the years 2020, 2050 and 2080 for the cultivars IR 36 and Swarna in A2 as well as B2 scenario with rising temperature of ≥0.8 °C. Whereas, the cultivar Swarna sub1 was least affected and Badshabhog was favoured under elevated CO₂ with rising temperature up to 2 °C in the sub-tropical climate of India.     

A technique for minimising confounding of genotype x year and genotype x crop type effects in sugarcane

Summary Genotype x year (G x Y) and genotype x crop (G x C) interactions may be confounded in sugar cane when data is obtained from the plant crop and/or from ratoon crops in successive years. A technique to minimise the effects of confounding of the G x C interaction with the G x Y interaction is presented. The mean yield of cane from neighbouring farms was used as an indicator of the year (environment) effect. It was used to re-analyse yields from three experiments comparing six genotypes grown over a plant crop and three ratoons. Two experiments were grown under rainfed conditions and one experiment was irrigated. The confounded interactions were partitioned into a linear contrast on the farm yields (a year effect) and a crop effect that was the remainder. When the yields adjusted for the farm yield effects were compared with original yields of cane, yields of plant and first ratoon were reduced and those of third ratoon were increased. There were only minor changes in the ranking of genotypes on yield. It was concluded that the re-analysis using farm yield showed that the confounding effect of years on the interpretation of genotype x crop effects was samll.     

薏苡品种(系)的产量稳定性及地点鉴别力分析

旨在准确、合理的评价基因型与基因型?环境互作效应(G?E)对薏苡产量稳定性及地点鉴别力的影响,为优良品种的鉴定、推荐和登记提供科学依据。采用AMMI模型结合双标图及稳定性参数Dg(e)对第二轮（2012-2014年）国家薏苡区域试验的产量数据进行了分析。结果表明,G1~G6在不同试点的产量变异范围分别：2380.0~6061.0 kg/hm2、1933.3~5790.0 kg/hm2、2192.0~5632.3 kg/hm2、905.3~5485.7 kg/hm2、978.3~4680.0 kg/hm2、991.0~5340.0 kg/hm2;基因型效应、环境效应和基因型×环境交互效应(G×E)均达到极显著或显著水平,环境效应占总变异的55.10%,G×E 交互效应占14.03%,基因型效应占8.41%,IPCA1、IPCA2 和IPCA3 分别解释了交互作用(G?E)的60.97%、18.97%和3.07%,三者加起来解释了全部交互作用的83.01%,而且第一主成分(IPCA1)达到差异极显著水平。AMMI双标图及稳定性参数显示,‘黔薏鉴2 号’、‘安紫薏苡’和‘文薏2 号’属于高产稳定型品种（系）,可作为推荐品种,‘莆薏6 号’产量中等、稳定性最差,‘文薏3 号’、‘金沙1 号’的丰产性较差,综合稳定性一般;此外,云南昆明、福建莆田、贵州兴义和贵州安顺的试点代表性较强;云南文山、贵州凯里、广西百色和福建福州的地点鉴别力相对较弱。