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.     

Heterotic patterns in hybrids involving cultivar-groups of summer squash, Cucurbita pepo L.

Heterotic response for total yield among five cultivar-groups of summer squash, Cocozelle, Vegetable Marrow, Zucchini, Crookneck and Straightneck, was studied in two sets of diallel crosses evaluated in two harvest seasons. Each diallel cross included two representatives of each the five cultivar-groups. Griffing and diallel GGE biplot (genotype main effect and interaction) analyses were carried out. The relative importance of the specific combining ability variance on total variance was greater than that reported earlier for intragroup crosses. Highly significant heterosis was manifested only in combinations of Crooknecks or Straightnecks crossed to Cocozelles, Vegetable Marrows or Zucchinis. GGE biplots showed a consistent arrangement where testers of ssp. texana interacted with cultivars of ssp. pepo and vice versa. We propose that the heterotic pattern of texana-pepo crosses could be potentially used to achieve increased yields in hybrids by introgressing one group into another in a manner that would maintain fruit attributes of the cultivar-group. This revised version was published online in July 2006 with corrections to the Cover Date.     

双标图在大豆区域试验产量分析中的应用

运用主效可加互作可乘模型(Additive Main Effectsand Multiplicative Interaction Model)简 称 AMMI 模型，结合双标图(biplot)对2003年黑龙江省大豆品种区域试验，第一积温带西部 风沙干旱区(2区)各承试点的平均产量结果进行分析。从中可以看出：AMMI 模型将方差分析和 主成分分析相结合，使品种在各个试验点的适应性和稳定性更加形象化；而双标图(biplot)是解释 AMMI 分析结果的一种直观有效的图形工具。     

Phenotypic and molecular genetic characterization indicate no major race‐specific interactions between Xanthomonas translucens pv. graminis and Lolium multiflorum

Bacterial wilt of forage grasses, caused by the pathogen Xanthomonas translucens pv. graminis (Xtg), is a major disease of forage grasses such as Italian ryegrass (Lolium multiflorum). The plant genotype‐bacterial isolate interaction was analysed to elucidate the existence of race‐specific responses and to assist the identification of plant disease resistance genes. In a greenhouse experiment, 62 selected plant genotypes were artificially inoculated with six different bacterial isolates. Significant differences in resistance were observed among L. multiflorum genotypes (P < 0·001) and in virulence (intensity of disease symptoms) among Xtg isolates (P < 0·001) using the area under the disease progress curve (AUDPC). No significant genotype‐isolate interaction (P > 0·05) could be observed using linear regression modelling. However, additive main effects and multiplicative interaction effects (ammi ) analysis revealed five genotypes which did not cluster close to the origin of the biplot, indicating specific interactions between these genotypes and some bacterial isolates. Simple sequence repeat (SSR) markers were used to identify marker‐resistance associations using the same plant genotypes and bacterial isolates. The SSR marker NFA027 located on linkage group (LG) 5 was significantly associated with bacterial wilt resistance across all six bacterial isolates and explained up to 37·4% of the total variance of AUDPC values. Neither the inoculation experiment nor the SSR analyses revealed major host genotype‐pathogen isolate interactions, thus suggesting that Xtg resistance, observed so far, is effective across a broad range of different bacterial isolates and plant genotypes.     

小黑麦基因型与环境互作效应及产量稳定性分析

选用26个小黑麦品种(系),在西北高寒农牧交错区的不同试点和供水条件下,利用GGE双标图法研究了小黑麦基因型与环境互作效应以及稳产性.结果表明:在自然干旱条件下,小黑麦平均籽粒产量为1805.5 ks/hm2,较普通小麦对照增产54.6%;在灌水条件下,小黑麦平均产量为7196.1 kg/hm2,较对照增产67.2%....     

优质油菜新品种(系)的稳定性和适应性分析

应用AMMI模型对8个甘蓝型优质油菜新品种（系）区试产量进行了分析。结果表明：AMMI模型解释基因型与环境互作明显优于线性回归模型。通过AMMI1模型双标图可以看出产量在品种和地点间的变异程度,找出稳定性较好的品种。从AMMI2双标图上可以找出与每一个地点产量互作最大的品种。     

基于SD-GGE双标图进行饲草燕麦品种归类和区域划分

为明确饲草燕麦品种在不同区域的丰产性及适应性,2017—2019年进行全国饲草燕麦鉴定试验,包括国内外饲草品种20个,分布在全国22个燕麦主产区的试点,采用标准差校正的(standard deviation of means,SD)GGE双标图(SD-GGE)分析。结果显示:参试品种可分为4类,分别为籽粒高产、饲草高产和生育日数较长且株高较高的品种,其余与上述性状相关性差的品种聚为一类。穗铃数和穗粒数与饲草产量均极显著正相关,千粒重与籽粒产量极显著正相关。G1(‘坝莜3号’)和G3 (‘坝燕6号’)为鲜草高产稳产品种,G11(‘陇燕3号’)和G16(‘草莜1号’)为干草高产稳产品种。试点E13(四川省凉山彝族自治州盐源县)和E21(新疆维吾尔自治区昌吉州奇台县)代表性较好。以生产鲜草为目的,试点E1(吉林省白城市)和E20(黑龙江省哈尔滨市)最适宜种植品种为G2(‘坝莜18号’);E17(内蒙古自治区赤峰市)和E18(河北省承德市)最适宜种植品种为G14(‘青引2号’);E3(河北省张家口市张北县)、E6(内蒙古自治区乌兰察布市察右前旗)、E9(山西省忻州市岢岚县)和E10(青海省海东市)最适宜品种为G11;其余试点最适宜品种均为G3。以生产干草为目的,E4(内蒙古自治区呼和浩特市武川县)最适宜品种为G10(‘GL381’);E3、E6、E9、E10和E20最适宜种植品种为G8 (‘远杂1号’);E18、E14(江苏省泰州市)和E15(西藏自治区山南市)最适宜种植品种为G9(‘张燕4号’);E1、E2(河北省张家口市崇礼区)、E13、E21和E22(青海省海北藏族自治州海晏县)最适宜品种为G16;E5(内蒙古自治区兴安盟乌兰浩特市)、E8(山西省怀仁市)和E19(内蒙古自治区通辽市)没有最适宜品种,相对表现较好的品种为G8、G12(‘定燕2号’)和G16。明确20个品种在各个区域的丰产性和适应性,为品种的推广提供依据。     

利用加性主效应和乘积交互作用模型对国际杂交水稻圃数据的分析

 国际水稻遗传评价网 （INGER）的1994年杂交水稻圃（IRHON）采用增广设计,包括了6个国家的17个地点和89个品种。为充分了解这类多点试验中关于品种和地点的交互作用，用加性主效应和乘积交互作用(AMMI)模型，以若干项乘积之和估算品种和地点的交互作用。借助双标图，可鉴别对地点有特殊适应性的品种。如果有参试地点的环境因子的数据，AMMI 模型还可帮助我们用环境因子解释品种和地点的交互作用。通过本文对1994年杂交水稻圃数据的分析,我们看到AMMI模型是分析多点试验数据的非常有效的工具，能为育种项目和决策部门提供非常有价值的品种适应信息。