甜玉米区域试验点对品种鉴别能力估计方法的研究

摘要：为了比较不同试验点在区域试验中鉴别力的大小,利用变异系数法、回归系数法和AMMI模型Dj值法对福建甜玉米区试点的鉴别力进行了比较研究。结果表明,变异系数法与AMMI模型Dj值法结论大致相同,回归系数法差异较大,而AMMI模型Dj值法相对最准确。试验还表明,Dj值小的试点,其与基因型互作效应弱,包含较少的互作信息。     

福建省夏大豆区试品种稳定性与适应性分析

应用AMMI模型对2020年福建省夏大豆品种区域试验中的11个品种、7个试验地点进行综合评价,分析参试品种的丰产性、稳定性及试点的鉴别力。结果表明,华夏10号、苏闽夏2号和闽诚豆8号属高产稳产品种。福清、尤溪2个试点对品种鉴别力较强,较适合开展品种区域试验。基因型效应、环境效应和基因型×环境交互效应均达到极显著水平,AMMI模型中的主成分值,共解释总互作平方和的91.65%,有效地分析了基因与环境互作效应。     

用AMMI模型分析作物区域试验中的地点鉴别力

为了比较不同试验点在区域试验中鉴别力的大小,利用AMMI模型中地点的得分向量长度衡量地点鉴别力,并与传统的联合线性回归模型中的斜率进行了比较。以1995~96和1997~2000年度黄淮南片春水组小麦区域试验4年的产量数据为例进行了地点鉴别力分析。结果表明, AMMI模型比联合线性回归模型能更好地解释基因型与环境互作效应;多     

用AMMI模型分析杂交水稻基本性状的稳定性

以近年来生产上的5个不育系和9个恢复系配组成45个F1, 进行3地点试验; 运用AMMI模型对产量、穗粒性状等的稳定性进行分析, 结果表明: (1) 各性状G×E互作均达到显著水平; 产量及大部分性状的变异以环境效应为主, 其次是基因型效应, G×E互作效应最小; 千粒重的环境效应和基因型效应相当. (2) 互作效应的构成, 即"线性作用/非     

利用AMMI模型分析红花花瓣产量性状的基因型与环境互作

基因型与环境的互作效应(G·E)决定作物在多生态环境中产量性状的稳定性,研究红花G·E互作效应对花瓣产量稳定性的影响有重要意义。采用随机区组试验设计,选用7个红花品种,4个试验点,3次重复,测定各品种花瓣产量,运用AMMI模型对红花品种的基因型、环境及G·E互作效应进行了分析。基因型、环境及G·E互作效应均达到极显著水平,基因型占总变异的15.94%、环境效应占16.29%,G·E互作效应占47.64%,表明G·E互作效应对产量变化的影响远大于基因型和环境。互作效应主成分计算出基因型稳定性参数(Dg),顺序为‘YN1805’‘YN2057’‘YN512’‘YN495’‘YN1959’‘YN2527’‘弥渡红花’(CK)。运用AMMI模型有效地解释了红花品种产量性状的基因型、环境和G·E互作效应。根据产量、稳定性参数及AMMI模型分析结果,高产而又稳定的品种有‘YN1805’和‘YN2057’,高产而又不稳定的品种有‘YN512’和‘YN1959’,不高产也不稳产的品种有‘YN495’、‘YN2527’和‘弥渡红花’(CK)。在红花生产中,应选用产量高、适应性强的品种。     

花期玉米(Zea mays)穗三叶叶形结构的遗传效应及配合力分析

为了剖析玉米叶形结构的遗传规律,进而拓宽优良株型自交系的遗传基础。本研究以12份不同叶形结构玉米自交系组配的32份F1杂交种为试材,在2个生态环境下,花期采用加性-显性-母体遗传模型(ADM)对其穗三叶叶长、叶宽、叶夹角、叶向值及叶面积进行遗传效应和配合力分析。结果表明:(1)玉米穗三叶叶长、叶宽及叶向值主要受基因的加性效应调控,其次是显性效应,同时还兼受加性×环境互作效应或母体×环境互作效应等遗传体系的调控,育种改良中宜在早代对其进行选择;叶夹角主要受基因的母体效应调控,其次是加性效应,另外还受加性×环境互作效应及母体×环境互作效应影响,育种中宜选择叶夹角较小的材料作为母本进行改良;叶面积只受基因的显性效应及显性×环境互作效应的调控,其应从较晚世代中进行遗传选择。(2)除父本叶长的一般配合力差异不显著外,父本其余性状的一般配合力和母本全部性状的一般配合力间差异均显著或极显著,且这5个叶形相关性状的全部特殊配合力间差异极显著。(3)从相应自交系各性状的一般配合力相对效应值分析发现,‘锋1999马’和‘锋1913硬’的综合性状表现优良,有利于组配出叶片大小适中及株型紧凑的优良耐密高产F1杂交组合。研究结果表明,玉米穗三叶5个叶形结构的遗传效应不尽相同,相应亲本5个叶形结构的一般配合和特殊配合力间存在明显差异,因此在玉米叶形结构遗传改良上应按照相应性状的遗传特征选择对应改良策略进行改良,并根据综合性状表现优良的亲本有目的地组配杂交组合,提高玉米理想株型育种效率。本研究为进一步剖析玉米叶形结构的遗传机理及玉米理想株型育种提供参考。     

小麦磷效率相关性状配合力与环境互作分析

为了研究小麦磷效率相关性状的配合力与环境互作,随机选用8个亲本,组配成28个双列杂 交组合在两种环境条件下进行试验。结果表明,所有4个性状的一般配合力与环境互作和特殊配合 力与环境互作均达极显著水平;子粒全磷含量以环境效应的作用最大,非加性效应与环境互作次之; 叶片和茎秆磷含量以及磷素利用效率以非加性效应与环境互作为主,非加性效应次之;除子粒全磷 含量外,其它3个性状的一般配合力和特殊配合力稳定性都较低,在50％以下。最后讨论了配合力的 测度和不同器官磷含量与磷素利用效率的关系。     

贵州山区粳稻基因型与环境效应分析及其在育种上的应用研究

应用AMMI模型对2002～2009年贵州省粳稻区域试验品种（组合）产量相关性状基因型与环境效应分析结果表明：基因型、环境、基因型与环境互作对考察的7个性状影响程度各不相同,生育期、有效穗主要受环境影响,株高主要以基因型效应和环境效应为主,穗粒数、结实率和千粒重同时受基因型、环境、基因型与环境互作影响,但环境效应相对较大,对产量影响的大小顺序为基因型与环境互作＞基因型＞环境。在贵州山区粳稻育种中,需针对基因型、环境、基因型与环境互作效应进行产量相关性状选择,以提高育种效率。     

贵州山区粳稻基因型与环境效应分析及其在育种上的应用

应用AMMI模型对2002—2009年贵州省粳稻区域试验品种(组合)产量相关性状基因型与环境效应分析结果表明:基因型、环境、基因型与环境互作对考察的7个性状影响程度各不相同,生育期、有效穗主要受环境影响,株高主要以基因型效应和环境效应为主,穗粒数、结实率和千粒重同时受基因型、环境、基因型与环境互作影响,但环境效应相对较大,对产量影响的大小顺序为基因型与环境互作基因型环境。在贵州山区粳稻育种中,需针对基因型、环境、基因型与环境互作效应进行产量相关性状选择,以提高育种效率。     

利用AMMI模型分析湖北省区试品种稳定性和适应性

利用AMMI模型对2017年湖北省马铃薯品种区域试验中的8个马铃薯品种,7个试验地点进行综合评价,分析参试品种的稳定性及丰产性,评价试点的鉴别力。结果表明,HB 2010 XS 6和HB 2010 XS 3属于高产稳产品种,适合在湖北省二高山以上地区推广;HB 201079-1属于产量高,但稳定性差,适合在特定区域种植的品种。各试点的鉴别力顺序为:建始巴东五峰恩施兴山竹山利川,其中对品种选择性最高的地点是建始,代表性较强、选择性最低的是利川。AMMI模型中的主成分值,共解释总互作平方和的90.15%,更有效地分析基因与环境互作效应。     

AMMI模型在蓖麻杂交育种中的应用

在蓖麻品种的育成与推广过程中,品种产量性状的稳定性是一个重要影响因子。由于品种的基因型和环境存在着交互作用,用一般的线性回归方程只能解释小部分交互作用。主效可加互作可乘模型（AMMI模型）,不仅最大程度地反应互作变异,而且能准确地分析品种的稳定性。本文应用AMMI模型及双标图对4个蓖麻杂交组合2008-2009年多点试验的产量性状进行稳定性分析,进而评价各参试组合的稳定性和适应性。     

基于AMMI模型的品种稳定性分析

ＡＭＭＩ模型发展了分析基因型与环境互作的统计方法,但缺乏度量品种稳定性的定量指标。本文提出一个度量品种稳定性的新参数Ｄｉ。实例分析表明,Ｄｉ与生态价方法和稳定性方差法的分析结果有很好的一致性。讨论了ＡＭＭＩ模型在分析品种稳定性方面的意义以及应用新参数时应注意的问题。     

AMMI analysis of rainfed lowland rice (Oryza sativa) trials in Nigeria

The additive main effects and multiplicative interaction (AMMI) model is used to analyse the grain yield data of 13 rice genotypes grown in 12 rainfed lowland rice environments. The trials were organized by the International Network for Genetic Evaluation of Rice in Africa (INGER-Africa) and conducted in Nigeria. Main effects due to environments (E), genotypes (G) and G × E interaction were found to be significant (P = 0.001). Cross validation analysis suggested that an AMMI model with one interaction principal component axis (IPCA) was most useful predictively, whereas Gollobs’ test declared two components, IPCA1 and IPCA2, statistically significant (P = 0.01). The IPCAl, however, accounted for most (47.8%) of the G × E sum of squares. Correlation and regression analysis, and relative scatter of genotype and environment points on the AMMI biplot suggest that the interaction partitioned in IPCA1 resulted from differences in the days to flowering among the genotypes. The paper discusses these in relation to the occurrence of Fe toxicity at the test sites and varietal tolerance to the stress.     

水稻品种胚乳淀粉RVA谱的稳定性分析

利用AMMI模型对18个水稻品种的淀粉RVA谱特征值——最高黏度、热浆黏性、冷胶黏度、崩解值、回复值和消减值进行稳定性分析,并以6个性状的表型值及相应的稳定性参数（Di）为指标,对供试品种进行聚类分析和评价。结果表明,6项RVA谱特征值在不同品种和环境间的差异以及G×E互作效应都达到极显著水平（P<0.01）;6项特征值的     

Differential Adaptation of Complete and Substituted Triticale

Adaptation of seven complete and twelve substituted triticales to specific soil types has been studied, based on a series of twenty trials carried out in 1989 and 1990 across Spain. The nature of the GE interaction for grain yield was revealed by means of the additive main effects multiplicative interaction (AMMI) model and using the soil pH at the different sites as linear covariate. The percentage of the variability explained by the first principal component axis of the AMMI model was 72 and 65 % for the two years, suggesting a specific pattern of adaptation. Soil pH was the single most important environmental factor to explain the adaptation of complete and substituted types. Complete triticales outyielded substituted genotypes in the majority of sites. Triticale adaptation to acid and alkaline soils seems to be largely controlled by the single wheat/rye chromosome 2D(2R) substitution, for which both types differ. Complete triticales seem better adapted to the acid soils, whereas substituted types are, in general, more suited to alkaline soils.     

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。     

品种区域试验中算术平均值、 BLUP和AMMI估值的精度比较

利用1982年以来我国棉花、小麦、水稻和玉米的60套区域试验数据,采用交叉验证方法,对区域试验中算术平均值、最佳线性无偏预测值(best linear unbiased predictor,BLUP)和AMMI(additive main effects and multiplicative interaction)模型估值的预测精度进行比较,结果表明,与算术平均值相比,AMMI估值精度的增益倍数(gain     

Adaptability and stability analysis of groundnut genotypes using AMMI model and GGE-biplot

Unpredictable rainfall, variations in farm inputs, crop-diseases, and the inherent potential of genotypes are among the major factors for low and variable crop yield. Fourteen elite groundnut genotypes were examined in 14 environments to analyze adaptability and stability of genotypes, and identify mega-environments if they exist. Additive main effect and multiplicative interaction (AMMI) model, cultivar-superiority measure, and genotype plus genotype-by-environment (GGE) biplot analysis were used for data analysis. The environment (69.8%) and genotype-by-environment interaction (GEI) effects (21.4%) were dominating the genotypic effect (8.8%). The GEI was significant (P < 0.01), and two distinct environments (mega-environments) were identified, suggesting separate national groundnut breeding strategies for Babile and Pawe. ICGV-94100 and ICGV-97156 were stable and had the highest-yield at Babile and Pawe, respectively. The higher heritability value was recorded in more homogeneous and favorable environments, indicating the genetic potential of groundnut genotypes were better attained in more homogeneous and favorable environments. AMMI model, cultivar-superiority measure, and GGE biplots were helpful methodologies and complemented each other to evaluate the adaptability and stability of groundnut genotypes in diverse environments.     

Genotype × environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran

The genotype × environment (GE) interaction influences genotype selection and recommendations. Consequently, the objectives of genetic improvement should include obtaining genotypes with high potential yield and stability in unpredictable conditions. The GE interaction and genetic improvement for grain yield and yield stability was studied for 11 durum breeding lines, selected from Iran/ICARDA joint program, and compared to current checks (i.e., one durum modern cultivar and two durum and bread wheat landraces). The genotypes were grown in three rainfed research stations, representative of major rainfed durum wheat-growing areas, during 2005–09 cropping seasons in Iran. The additive main effect and multiplicative interaction (AMMI) analysis, genotype plus GE (GGE) biplot analysis, joint regression analysis (JRA) (b and S²di), six stability parameters derived from AMMI model, two Kang’s parameters [i.e., yield-stability (YSi) statistic and rank-sum], GGE distance (mean performance + stability evaluation), and two adaptability parameters [i.e., TOP (proportion of environments in which a genotype ranked in the top third) and percentage of adaptability (Ad)] were used to analyze GE interaction in rainfed durum multi-environment trials data. The main objectives were to (i) evaluate changes in adaptation and yield stability of the durum breeding lines compared to modern cultivar and landraces (ii) document genetic improvement in grain yield and analyze associated changes in yield stability of breeding lines compared to checks and (iii) to analyze rank correlation among GGE biplot, AMMI analysis and JRA in ranking of genotypes for yield, stability and yield-stability. The results showed that the effects due to environments, genotypes and GE interaction were significant (P < 0.01), suggesting differential responses of the genotypes and the need for stability analysis. The overall yield was 2,270 kg ha^?1 for breeding lines and modern cultivar versus 2,041 kg ha^?1 for landraces representing 11.2 % increase in yield. Positive genetic gains for grain yield in warm and moderate locations compared to cold location suggests continuing the evaluation of the breeding material in warm and moderate conditions. According to Spearman’s rank correlation analysis, two types of associations were found between the stability parameters: the first type included the AMMI stability parameters and joint regression parameters which were related to static stability and ranked the genotypes in similar fashion, whereas the second type consisted of the rank-sum, YSi, TOP, Ad and GGED which are related to dynamic concept of stability. Rank correlations among statistical methods for: (i) stability ranged between 0.27 and 0.97 (P < 0.01), was the least between AMMI and GGE biplot, and highest for AMMI and JRA and (ii) yield-stability varied from 0.22 (between GGE and JRA) to 0.44 (between JRA and AMMI). Breeding lines G8 (Stj3//Bcr/Lks4), G10 (Ossl-1/Stj-5) and G12 (modern cultivar) were the best genotypes in terms of both nominal yield and stability, indicating that selecting for improved yield potential may increase yield in a wide range of environments. The increase in adaptation, yield potential and stability of breeding lines has been reached due to gradual accumulation of favorable genes through targeted crosses, robust shuttle breeding and multi-locational testing.