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 sativa L.)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...     

用GGE双标图分析燕麦品种（系）农艺与品质性状

通过GGE双标图法分析了17个燕麦新品种（系）在阴山北麓半干旱农牧交错区生态条件下的丰产性、适应性和品质,为筛选高产优质燕麦新品种提供依据。结果表明：H44号的秸秆产量最高,科燕一号的籽粒产量最高,2004R-17的蛋白质和脂肪含量最高,坝莜十号β-葡聚糖含量最高,坝莜八号赖氨酸含量最高。籽粒产量与千粒重和有效穂数呈显著正相关关系,秸秆产量与株高、分蘖数以及有效穂数间也呈正相关关系,小穗数和穗粒数与籽粒产量或者秸秆产量间均呈负相关关系,赖氨酸和β-葡聚糖、β-葡聚糖和脂肪、脂肪和蛋白质呈显著正相关关系,赖氨酸和脂肪、赖氨酸和蛋白质、β-葡聚糖和蛋白质均呈负相关关系。     

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。     

Analysis of Genotype-by-Environment Interaction for Agronomic Traits of Durum Wheat in Iran

Abstract

Grain yield of durum wheat (Triticum turgidum L. var. durum) under Mediterranean conditions is frequently limited by both high temperature and drought during grain filling. Genotype-by-environment (GE) interaction and genotype-by-trait (GT) data were analyzed for agronomic performance of durum wheat breeding lines. Data were obtained from 18 durum wheat breeding lines and two cultivar checks (Zardak and Sardari) for their agronomic performance under three different climate locations (moderate, warm and cold winters) and two moisture regimes (rainfed and two supplemental irrigations conditions) in two cropping seasons (2006 and 2007) in Iran. Analysis of GE interaction data based on multiple traits showed that the environment (combination of year-location- moisture regimes) effect was always the most important source of trait variation, accounting for 58.6 to 98.4% of the total variation. Biplot analysis of the studied traits revealed that (i) the locations tended to discriminate genotypes in dissimilar fashions, and (ii) the relationships among traits were not consistent over the locations, where they facilitated visual genotype comparisons and selection at each location.     

Interpretation of genotype × environment interactions of sugarcane: Identifying significant environmental factors

An understanding of the causes of genotype × environment (G × E) interactions is essential for the implementation of efficient selection and evaluation networks. Currently, studies involving the interpretation of sugarcane (Saccharum spp.) G × E interactions are limited. The objective of this study was to investigate the relative influence of environmental factors on the G × E interactions of sugarcane under rainfed conditions in South Africa through a comprehensive analysis of a multi-environment trial (MET) dataset. Fifteen commercial cultivars were evaluated over 147 environments (trial × ratoon combinations) across the coastal (C), hinterland (H) and midlands (M) regions of the sugar industry. Environments were characterized according to five site covariates (soil depth, clay percentage, organic matter percentage, nitrogen mineralization category, and total available moisture) and nine seasonal covariates (time of harvest, age at harvest, average daily heat units, solar radiation, rainfall, evaporation, and three derived water stress indices).Additive main effects and multiplicative interaction (AMMI) biplots for cane yield (TCANE), estimated recoverable crystal percent (ERC) and tons ERC (TERC) revealed overlapping of C and H environments, while M environments formed unique clusters characterized by specific cultivar adaptabilities. Principal components analysis (PCA) allowed visualization of the covariates determining the regional separation patterns. AMMI interaction principal components axes (IPCA) 1 and 2 scores were correlated to the covariates and showed that harvest age, temperature, and water stress were mainly responsible for separation of M environments from C and H environments on the TCANE and TERC biplots. Time of harvest was identified as an important covariate influencing ERC G × E patterns in the C and H regions. The third water stress index (based on a ratio of observed yields to simulated irrigated yields) was a dominant factor influencing G × E patterns within the C and H regions and was identified as a superior indicator of water deficient environments for future studies. The M trials were characterized by shallower soils with lower total available moisture and greater variability in this regard compared with the C and H trials. Nitrogen mineralization category, organic matter percent, and clay percent were not significantly correlated to IPCA scores, while soil depth was identified as a major site selection criterion in the M region. The M region should be treated as a single mega-environment, while the C and H regions could be combined for future interpretive studies, where covariates should be summarized within growth phases. The results of this study will assist in restructuring the MET network through exploitation and targeting of the relevant environmental factors within the different regions.     

Evaluation for heat stress tolerance in durum wheat genotypes using stress tolerance indices

Heat stress is a major environmental stress limiting wheat productivity in most cereal growing areas of the world. The objective of this study was to evaluate heat stress tolerance in durum wheat (Triticum turgidum var. durum) genotypes. For this purpose, 45 genotypes were grown during two growing seasons (2012–2013 and 2013–2014) under non-stress (normal sowing) and heat-stress (late sowing) conditions. The heat tolerance indices were calculated based on grain yield under normal sowing (Yp) and late sowing (Ys) conditions. Results of combined analysis of variance showed the significant influences of heat stress on grain yield as well as significant differences among genotypes for grain yield and the indices. Results of correlation coefficients and multivariate analyses revealed that the stress tolerance index (STI), geometric mean productivity and mean productivity (MP) indices were the most profitable criteria for selection of heat tolerant and high yielding genotypes. Using STI, GMP and MP, the genotypes G29, G41 and G10 were found to be the best genotypes with relatively high yield and suitable for both normal and heat stressed conditions. Based on biplot analysis using Yp, Ys and the indices, it was possible to identify superior genotypes across the conditions.     

基于GGE双标图和马克隆值选择的棉花区域试验环境评价

棉纤维马克隆值是与纤维成纱品质密切相关的重要品质指标,也是长江流域棉花品质改良的主要制约因子。选择利用对目标环境代表性强的试验点进行区域试验布局有助于提高马克隆值育种效率和节省试验成本。GGE双标图是进行试验点代表性评价和选择最有效的统计和图形展示工具,已经在多种作物区域试验中用于品种稳定性和试验环境相似性分析,但在基于马克隆值选择的棉花区域试验环境评价中应用报道较少。本文采用GGE双标图方法对2000—2010年期间27组长江流域国家棉花区域试验中的15个试验环境基于马克隆值选择的鉴别力、代表性和理想指数进行综合评价与分析。结果表明:各试验环境依理想度指数优劣排序为荆州市>黄冈市>南通市>九江市>岳阳市>射洪县>常德市>安庆市>武汉市>南阳市>南京市>慈溪市>襄阳市>简阳市>盐城市;从中筛选出最理想试验环境为湖北省荆州市,较理想试验环境为湖北省黄冈市、江苏省南通市和江西省九江市,这些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;而位于江浙沿海棉区的江苏省盐城市和浙江省慈溪市不适宜作为针对长江流域的马克隆值选择与推荐环境。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,也为以长江流域棉区为目标环境的广泛适应性和特殊适应性品种的马克隆值选择和应用提供理论依据。     

用AMMI双标图分析糜子品种的产量稳定性及试点代表性

为准确评价基因型和环境互作效应对糜子品种产量稳定性及试点对品种分辨力的影响,采用AMMI模型结合双标图和稳定性参数Dg(e)对第8轮(2006—2008年)国家糜子(粳性)品种区域试验的6个品种和9个试点的试验数据进行了分析。结果表明:基因型效应、环境效应和基因型×环境交互效应(G×E)均达到极显著水平,环境效应占总变异的52.85%,G×E交互效应占6.26%,基因型效应占2.76%。交互效应中IPCA1、IPCA2、IPCA3解释了92.58%基因与环境互作信息。试验也表明不同糜子品种在各试点的稳定性及不同试点对糜子品种的分辨力差异较大。6个参试品种中,"榆糜3号"(CK)、"伊8414-1-2-1"属于高产稳产型品种;"甘9109-6-1-1-2"、"固01-391"产量较高,但稳定性较差;"甘9133-1-3-4-1"稳产性好,但产量较低;"固02-25"产量低且稳定性差。在9个试点中,陕西府谷、宁夏同心、陕西榆林、内蒙赤峰4个试点对品种的分辨力较强,宁夏固原、宁夏盐池、甘肃会宁、山西五寨、内蒙鄂尔多斯5个试点对品种的分辨力较弱。     

A comparison of dry matter yield stability assessment methods for small numbers of genotypes of bermudagrass

No information is available on the efficacy of various nonparametric stability parameters when compared with GGE biplot methodology in assessing the stability of dry matter yield in bermudagrass (Cynodon dactylon L. Pers.) when a small number of genotypes is assayed. This study was conducted to compare the results of four nonparametric stability parameters developed by Huehn and Nassar ( , , , ), Kang’s rank-sum method and the GGE biplot method for five genotypes over 11 location–year environments at Oklahoma State University experiment stations. Results from analysis of variance procedures indicated highly significant levels of genotype-by-environment interaction (P < 0.01), which further indicated the need for stability analysis measures to be conducted. Results of the stability analysis indicated agreement among , , Kang’s rank-sum method, and the biplot method for the stability rankings of the genotypes and between these methods and the overall yield rankings of the genotypes. The and statistics were not in agreement with each other or any of the previously mentioned methods concerning the stability rankings of the genotypes. From examination of the formulae for the nonparametric statistics it was concluded that, when a small number of genotypes is assayed, the , , and statistics have the potential to be extremely sensitive and to produce misleading results. It was further concluded that for assessment of small numbers of genotypes the GGE biplot stability analysis method, augmented with Kang’s rank-sum method, would produce the most reliable estimates of genotype stability.     

GGE Biplots and Traditional Stability Measures for Interpreting Genotype by Environment Interactions

Abstract

Cotton (Gossypium hirsutum L.) breeders conduct multienvironment trials to determine the performance of genotypes in relation to environmental changes and to determine their area of adaptation. The objective of this study was to compare within-model and within-scaling GGE Biplot stability values (GE distance) with those generated by some of the “traditional” stability analytical methods. Correlation coefficients of GE distance of GGE Biplot (stability evaluation) with Cultivar Superiority Measure, Shukla's Stability Variance, Eberhart-Russell regression model, Kang's yield stability statistic, and AMMI were 0.54, 0.91, 0.86, 0.63, and 0.55, respectively. Correlation coefficients between GGE distance of GGE Biplot (mean performance + stability evaluation) and the Cultivar Superiority Measure, the Eberhart-Russell regression model, Kang's yield stability statistic, and AMMI were 0.95, 0.60, 0.85, and ?33, respectively. Some of the “traditional” methods focus heavily on yield, while others focus on stability; GGE Biplot allows for a more versatile and easily comprehensible presentation of the data and variety selection based on both yield and stability. Based on the results of this study and our experience using GGE Biplot, Model 3 (uses replicated and standard error-standardized data) with an entry-focused scaling is the most valuable analysis for breeders to select widely adapted genotypes.