首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到17条相似文献,搜索用时 127 毫秒
1.
GGE叠图法—分析品种×环境互作模式的理想方法   总被引:19,自引:1,他引:18  
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  相似文献   

2.
基因型和环境对面团流变学特性的影响研究   总被引:2,自引:0,他引:2  
用揉混仪和质构仪研究了3个不同品质类型的冬小麦品种在8个不同的生长环境条件下的面团流变学特性,分析了基因型和环境对性状的影响及各性状间的相关性。结果表明:面团流变学特性受基因型(G)和环境(E)及其互作的共同作用,其中基因型方差均值>环境方差均值>G×E互作均值;从性状的表现看,基因型以济麦20表现最佳,环境以烟台点综合表现最好;相关分析表明,面团粘度性状与最大拉伸阻力、拉伸比、峰值时间、峰值面积和8min带宽呈显著负相关,而与延伸度呈显著正相关。最大拉伸阻力与峰值时间、峰值面积和8min带宽呈显著正相关。因此,进行品质评价时,可用质构仪测定的面团粘度特性对面团品质进行初步快速的评定。  相似文献   

3.
小麦品种面粉粘度性状的品质分析   总被引:2,自引:0,他引:2  
黄婷  汪鑫 《种子》2008,27(1):73-76
研究了2个地区21个小麦品种面粉的快速粘度仪参数。结果表明,不同地区不同品种对淀粉快速粘度仪参数均有一定的影响。峰值粘度主要受基因型与环境互作的影响,衰减度相对受基因型的影响较大,其他性状受环境影响比较大。而弱筋小麦品种的淀粉糊化特性指标的环境变异明显高于强筋、中筋品种,说明弱筋小麦淀粉糊化特性受环境影响的程度较大。  相似文献   

4.
基于HA-GGE双标图的甘蔗试验环境评价及品种生态区划分   总被引:3,自引:0,他引:3  
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。  相似文献   

5.
GGE双标图在湖南省棉花品种区域试验中的应用   总被引:1,自引:1,他引:0  
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。  相似文献   

6.
《种子》2021,(6)
本研究综合利用GGE双标图方法和AMMI模型对2017年江苏省杂交中粳稻区域试验的12个参试品种的丰产性和稳产性进行了分析。结果表明,基因型、环境及基因型与环境互作效应对杂交中粳稻产量有极显著的影响。在所有参试品种中,杂中区03和杂中区12是丰产稳产的广适性品种,可在粳稻适区进行推广。江苏徐淮地区淮阴农业科学研究所试点代表性最强,而江苏欢腾农业有限公司试点的鉴别力最强。AMMI和GGE双标图的综合运用,可准确直观地评价各品种的丰产性、稳定性和适应性以及各试点的鉴别力和代表性。  相似文献   

7.
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。  相似文献   

8.
GGE叠图法─分析品种×环境互作模式的理想方法   总被引:7,自引:1,他引:6  
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。  相似文献   

9.
稻米理化特性与食味品质的相关性研究   总被引:1,自引:0,他引:1  
选用16个水稻品种(系)进行了随机区纽的品种比较试验,分析了供试品种(系)的9项品质指标.结果表明,所测定的9项品质指标在供试品种间均存在极显著差异;在蒸煮食味品质特性中,最高粘度、崩解值、消减值的品种间变异系数较大;最高粘度和崩解值与食味值呈显著或极显著正相关,消减值与食味值呈极显著负相关,直链淀粉与食味值呈极显著负相关;在主成分分析中,被入选的3个主成分的贡献率达89.28%,其中第一主成分大的品种直链淀粉含量低,最高粘度、最低粘度小,崩解值大,消减值小.  相似文献   

10.
薏苡品种(系)的产量稳定性及地点鉴别力分析   总被引:1,自引:1,他引:0  
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。  相似文献   

11.
播期对小麦面粉粉质参数及糊化特性的影响   总被引:4,自引:0,他引:4  
于2002—2003年度采用4个不同基因型小麦品种豫麦18号、豫麦49号、百泉41和郑引1号,研究了播期对面粉粉质参数及淀粉糊化特性的影响。结果表明,小麦基因型和播期对品质性状均有一定的影响,但基因型效应明显大于播期效应。面粉弱化度、糊化温度及糊化时间随播期推迟呈现递增趋势,而湿面筋含量、峰值粘度和反弹值随播期的推迟呈递减趋势;播期对某些品质性状的影响因基因型不同而存在差异,这与其适宜播期不同有关。在试验条件下品质性状间的相关分析结果表明,淀粉峰值粘度、低谷粘度、最终粘度和膨胀势间的相关性达到显著或极显著水平。  相似文献   

12.
双标图分析在农作物品种多点试验中的应用   总被引:40,自引:1,他引:39  
严威凯 《作物学报》2010,36(11):1805-1819
双标图分析越来越多地被用于直观分析农作物品种多点试验数据和其他类型的两向数据。这种方法深受植物育种家和农业研究人员的推崇,认为它可以提高研究者理解和驾驭试验数据的能力;但也受到一些学者的批评,认为它是统计分析方面的旁门左道。事实上,学术界对什么是双标图的认识尚存混乱,一些双标图的使用者并不总能正确地选择和解释双标图,一些双标图的批评者对双标图分析及其研究对象也缺乏深入了解。为使研究者对双标图分析有一个客观全面的认识,本文就用双标图分析农作物品种多点试验中的几个问题进行阐述:(1)如何针对特定的研究目的选择适当的双标图;(2)如何选择适当的GGE双标图来分析多点试验数据;(3)如何使用GGE双标图的不同功能形态进行品种评价、试验点评价和品种生态区划分;(4)如何判断双标图是否充分表现试验数据中的规律;(5)如何检验双标图显示的结果是否显著。  相似文献   

13.
A heritability-adjusted GGE biplot for test environment evaluation   总被引:2,自引:0,他引:2  
Test environment evaluation has become an increasingly important issue in plant breeding. In the context of indirect selection, a test environment can be characterized by two parameters: the heritability in the test environment and its genetic correlation with the target environment. In the context of GGE biplot analysis, a test environment is similarly characterized by two parameters: its discrimination power and its similarity with other environments. This paper investigates the relationships between GGE biplots based on different data scaling methods and the theory of indirect selection, and introduces a heritability-adjusted (HA) GGE biplot. We demonstrate that the vector length of an environment in the HA-GGE biplot approximates the square root heritability (\( \sqrt H \)) within the environment and that the cosine of the angle between the vectors of two environments approximates the genetic correlation (r) between them. Moreover, projections of vectors of test environments onto that of a target environment approximate values of \( r\sqrt H \), which are proportional to the predicted genetic gain expected in the target environment from indirect selection in the test environments at a constant selection intensity. Thus, the HA-GGE biplot graphically displays the relative utility of environments in terms of selection response. Therefore, the HA-GGE biplot is the preferred GGE biplot for test environment evaluation. It is also the appropriate GGE biplot for genotype evaluation because it weights information from the different environments proportional to their within-environment square root heritability. Approximation of the HA-GGE biplot by other types of GGE biplots was discussed.  相似文献   

14.
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.  相似文献   

15.
The success of plant breeding programs depends on the ability to provide farmers with genotypes with guaranteed superior performance in terms of yield across a range of environmental conditions. We evaluated 49 sugar beet genotypes in four different geographical locations in 2 years aiming to identify stable genotypes with respect to root, sugar and white sugar yields, and to determine discriminating ability of environments for genotype selection and introduce representative environments for yield comparison trials. Combinations of year and location were considered as environment. Statistical analyses including additive main effects and multiplicative interactions (AMMI), genotype main effects and genotype?×?environment interaction effects (GGE) models and AMMI stability value (ASV) were used to dissect genotype by environment interactions (GEI). Based on raw data, root, sugar and white sugar yields varied from 0.95 to 104.86, 0.15 to 20.81, and 0.09 to 18.45 t/ha across environments, respectively. Based on F-Gollob validation test, three interaction principal components (IPC) were significant for each trait in the AMMI model whereas according to F ratio (FR) test two significant IPCs were identified for root yield and sugar yield and three for white sugar yield. For model diagnosis, the actual root mean square predictive differences (RMS PD) were estimated based upon 1000 validations and the AMMI-1 model with the smallest RMS PD was identified as the most accurate model with highest predictive accuracy for the three traits. In the GGE biplot model, the first two IPCs accounted for 60.52, 62.9 and 64.69% of the GEI variation for root yield, sugar yield and white sugar yield, respectively. According to the AMMI-1 model, two mega-environments were delineated for root yield and three for sugar yield and white sugar yield. The mega-environments identified had an evident ecological gradient from long growing season to intermediate or short growing season. Environment-focused scaling GGE biplots indicated that two locations (Ekbatan and Zarghan) were the most representative testing environments with discriminating ability for the three traits tested. Environmentally stable genotypes (i.e. G21, G28 and G29) shared common parental lines in their pedigree having resistance to some sugar beet diseases (i.e. rhizomania and cyst nematodes). The results of the AMMI model were partly in accord with the results of GGE biplot analysis with respect to mega-environment delineation and winner genotypes. The outcome of this study may assist breeders to save time and costs to identify representative and discriminating environments for root and sugar yield test trials and creates a corner stone for an accelerated genotype selection to be used in sweet-based programs.  相似文献   

16.
基因型、地点及其互作对内蒙古小麦主要品质性状的影响   总被引:3,自引:0,他引:3  
选用来自我国春播麦区高、中、低3种筋力类型的9个品种, 于2003和2004年分别种植在内蒙古6个代表性地点, 研究了不同品种在年份和地点间籽粒硬度、蛋白质含量、和面仪参数和淀粉糊化特性等主要品质性状的变化规律。结果表明, 所测品质性状受基因型和地点效应的影响均达极显著水平, 除籽粒蛋白质含量外, 其他品质性状受基因型和地点互作效应的影响达显著或极显著水平。强筋类品种的蛋白质含量、灰分含量、沉降值、和面时间、耐揉性和峰值黏度均较高, 出粉率和稀澥值中等。中筋类品种出粉率、和面时间和耐揉性较高, 灰分含量、峰值黏度和稀澥值较低。弱筋类品种的灰分含量、峰值黏度和稀澥值较高, 籽粒硬度、蛋白质含量、出粉率、沉降值、和面时间、耐揉性低。所有品种品质性状在地点间存在较大差异, 乌海市灰分含量、和面时间和耐揉性高, 籽粒硬度、沉降值、峰值黏度和稀澥值较低。杭锦后旗出粉率高, 蛋白含量和沉降值较低, 其他性状表现中等。呼和浩特市籽粒硬度、蛋白含量、面粉灰分、沉降值、和面时间和耐揉性高, 出粉率、峰值黏度和稀澥值低。赤峰市多数性状表现中等。通辽市籽粒硬度、蛋白质含量、峰值黏度、稀澥值和耐揉性较高, 其他性状表现中等。额尔古纳市蛋白含量和沉降值较高, 和面时间和耐揉性低。初步认为强筋和中筋类品种较适于种植在呼和浩特市与乌海市, 不适于种植在额尔古纳市; 2个弱筋类品种在6个地点均不太适宜种植。  相似文献   

17.
探索和划分长江流域棉区基于棉花纤维长度选择的品种生态区,为棉花品种纤维长度的选择和推荐提供决策依据。采用GGE双标图方法分析了2000—2010年期间长江流域国家棉花品种区域试验目标环境基于纤维长度选择的品种生态区划分方案。以GGE双标图的"品种与环境最佳组合"功能图展示的试验环境组合模式为基础,并采用信息比进行较正,结果表明中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。长江流域棉区可划分为3个品种生态区,可针对不同的品种生态区进行区域内品种选择和推广应用,以提高纤维长度选择和应用效率。  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号