长江流域早籼新品种丰产稳产性和适应性分析

根据１９９８年南方稻区长江流域早籼品种区域试验的资料，运用ＡＭＭＩ模型分析了新育成的早籼新品种的丰产性，稳产性及其适应性。结果表明，早中熟组有６个品种产量超过了对照浙８５２，增产幅度达１．８％￣１７．２％；中迟熟组品种产量均低于对照威优４０２，但有５个品种产量超过对照浙７３３，增产幅度达２．１％￣９．７％，品种Ｆ１３１Ｓ／Ｒ４０２和ＣＫ１浙８５２的稳定性相对较好。中组１号，Ｋ优４０４，嘉早９３５和     

小麦发育期动态模拟模型的研究

研究孙同类型小麦品种的发育与温、光等主要环境因子的数量关系，发展小麦最短累计春化日（ＡＶＤ）的概念，指出不同类型品种的最短累计春化日不同，给 同类型有代表性品种ＡＶＤ的数量指标。在借鉴吸收“水稻种”模型和ＣＥＲＥＳ－Ｗｈｅａｔ模型的思想方法基础上应的以叶片构建了析因指数形式的小麦发育期动态模拟模型（ＷＤＳＭ）。ＷＤＳＭ具有精度高，机理怀强，适应性好等特点，在全国范围内，模拟误差在一周之内，绝大多数     

作物区试中品种稳定性评价的秩次分析模型

本文提出了一种用于作物区域试验中度量非参数资料品种稳定性的模型。 介绍了 品种稳定性评价的统计数估算、 统计检验及品种稳定性分类的具体方法。     

小麦新品种豫麦39号灌浆期生理特性的研究

研究结果表明，与对照品种豫麦２号比较，豫麦３９号灌浆期叶片的超氧物歧化酶（ＳＯＤ）、过氧化氢酶（ＣＡＴ）活性强，丙二醛（ＭＤＡ）积累速度慢，功能叶寿命长。硝酸还原酶（ＮＲ）活性、叶绿素及蛋白质含量均明显高于对照，且叶面积系数（ＬＡＩ）适当，光合性能好，灌浆速度快，持续时间长。为增加粒重、获得高产奠定了良好的生理基础。     

江淮丘陵棉区棉花生产计算机管理决策系统(AHCCE)的研究 Ⅰ 基本思路与总体设计

ＡＨＣＣＥ（ＡＮＨＵＩＣＯＴＴＯＮＣＵＬＴＩＶＡＴＩＯＮＥＸＰＥＲＴＳＹＳＴＥＭ：ＣｏｔｏｎＰｒｏｄｕｃ－ｔｉｏｎＭａｎａｇｅｍｅｎｔａｎｄＤｅｃｉｓｉｏｎＳｙｓｔｅｍｏｆＡｎｈｕｉＪｉａｎｇｈｕａｉｈｉｌｃｏｔｏｎｒｉｇｉｏｎ）是一个基于作物模拟模型的专家系统，它由数据库、知识库、模拟模型、推理机和数据及知识获取系统等部分组成。本文评述了建立ＡＨＣＣＥ的基本思路和步骤。     

油菜素内酯在棉花组织培养中的应用研究Ⅰ．油菜素内酯对陆地棉体细胞胚胎发生和根器官发生的影响

本文研究了ＢＲ及其与ＩＡＡ、２，４－Ｄ、ＫＴ和６－ＢＡ等配合使用对陆地棉愈伤组织诱导、继代、分化、体细胞胚胎和根器官发生的影响。ＢＲ０．０１ｍｇ／Ｌ能使陆地棉Ｃｏｋｅｒ２０１、３１２两品种分化产生胚性愈伤组织，并有效地保持该种愈伤组织的生活力和胚胎发生能力。ＢＲ０．０１ｍｇ／Ｌ＋ＩＡＡ０．５ｍｇ／Ｌ促进Ｃｏｋｅｒ２０１、３１２两品种体细胞胚胎发生，ＢＲ０．０１ｍｇ／Ｌ＋２，４－Ｄ０．０５ｍｇ／Ｌ能诱导所有供试品种产生疏松黄绿色愈伤组织，２，４－Ｄ用量逐步降低或除去后，一些品种便分化产生胚性愈伤组织或体细胞胚状体。另在ＢＲ与ＩＡＡ的某些组合中还观察到根器官的发生。     

光周期对大豆叶片内源激素含量及其平衡的影响

选用生育期不同的３个品种，采用酶联免疫检测技术（ＥＬＩＳＡ），研究了开花前和开花后光周期处理对大豆叶片内源激素含量及其平衡状况的影响。结果表明，短日照与长日处理相比，脱落酸（ＡＢＡ）含量显著上升，赤霉素（ＧＡ１＋３）含量及ＧＡ／ＡＢＡ比例明显下降，细胞分裂素（ＤＨＺＲ，二氢玉米素核苷）有下降趋势，生长素（ＩＡＡ）的变化不大。短日下ＧＡ和ＩＡＡ含量负相关，ＧＡ与ＡＢＡ正相关，长日下ＩＡＡ与ＤＨＺＲ负     

冬春杂交小麦新品种川麦30

川麦 ３０组合为 ７７／ＹＡＡ／／ＡＬＤ‘Ｓ’／３／ＹＳＺ／／ＳＴ２０２２／９８３，是我所与 ＣＩＭＭＹＴ合作采用国际最新冬春杂交模式和穿梭育种等技术方法育成。由于引进了国际高产、抗病、白皮优质资源，扩大了遗传背景，弥补了四川主推品种的部分缺点。１９９８年通过四川省农作物品种审定委员会审定。１９９９年被国家科技部列为“九五”国家科技成果重点推广计划。 植物学特征春性，早熟，幼苗半直立，色深绿，分蘖力强，成穗率高，株型紧凑，直立，株高８０～８５ｃｍ，长方形穗，白壳长芒。白粒，长卵圆形、角质，千粒重４５…     

陆地棉原生质体培养与植株再生

以陆地棉品种“珂字２０１”为材料，比较了ＩＡＡ＋ＫＴ和２，４－Ｄ＋ＫＴ在愈伤诱导和悬浮培养中的效应，结果表明：愈伤组织诱导中，ＩＡＡ和２，４－Ｄ表现为正效应，且２，４－Ｄ的效应强于ＩＡＡ；ＫＴ表现为负效应；胚性愈人务悬浮培养中；３种激素都表现出负效应。以胚性细胞悬浮系了原生质体的分离和培养试验，分离原生质体的最佳酶组合为纤维素酶３％＋果胶酶１．５％，原生质体培养的最佳激素为ＩＡＡ０．５ｍｇ／Ｌ＋Ｋ     

不同的抑制剂处理对水分胁迫诱导的冬小麦幼苗ABA累积的影响

研究了不同放线菌素Ｄ（ＡＭＤ）及环己亚胺（ＣＨＭ）处理方法对水分胁迫诱导的冬小麦幼苗内源脱落酸（ＡＢＡ）累积的影响。结果表明，在水分胁迫下，植物体根及叶片中ＡＢＡ随着水分胁迫时间的延长而增加，根ＡＢＡ增加早于叶片ＡＢＡ增加。在ＡＭＤ和ＣＨＭ与ＰＥＧ的３种处理后，ＡＭＤ和ＣＨＭ与ＰＥＧ同时处理根对于由胁迫诱导的根及叶片中ＡＢＡ增加具有明显的抑制作用。     

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。     

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.     

应用AMMI模型分析烤烟区试品种稳定性

AMMI模型在分析基因型与环境互作效应方面有明显的优势，可定量地描述各品种稳定性差异以及各地点对品种鉴别力的大小。为筛选适应河南省特定生态条件下的优良烤烟新品种，应用AMMI模型对2010年河南省烤烟区域试验4个试点7个参试品种的丰产性及稳产性进行分析。结果表明，所有参试新品种产量均值均高于对照‘NC89’，其中，‘Y106’和‘H8190’具有丰产稳产的特性，‘Y102’产量最高，但稳定性较差；‘8182’丰产性差，稳定性好；‘8122’丰产性较差，稳定性较差；‘优选一号’丰产性差，稳定性较好；对照‘NC89’丰产性差，稳定性一般。     

几种模型在玉米区域试验的适用性与品种评价中的差异研究

为了综合评价玉米区域试验的结果,采用Shukla模型、Finlay-Wilkinson模型、Eberhart-Russell模型和加性主效乘式互作模型(AMMI)等4种常用模型对中国5个类型的玉米品种区域试验资料进行了拟合与分析。结果表明,没有一个模型对所有试验资料的拟合效果都最佳,Finlay-Wilkinson模型对5个试验拟合效果均最差,AMMI模型对3个试验的数据拟合效果最佳,Shukla模型和Eberhart-Russell模型则分别对一个试验拟合效果最佳。各个模型在玉米品种产量差异显著性检验和稳定性排序等方面存在较大的差异。因此,在玉米区域试验的实际分析中应采用AMMI模型或者利用模型拟合信息量准则选用最佳的模型进行分析,以提高玉米区域试验对品种高产与稳产评价的准确性。     

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     

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     

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。     

Responses of different arabica coffee (Coffea arabica L.) clones to varied environmental conditions

Different improved coffee arabica crosseshaving resistance to coffee berry diseaseand leaf rust, plus a standard cultivar,were grown under a range of environmentalconditions in a series of field trials inKenya. The effects of location and year ofproduction and their interactions with theclones were determined for berry yield.These data were used to estimate andcompare methods of analysis of GEI. An AMMImodel, joint regression model and ANOVAwith repeated measurements were used toinvestigate these interactions. The resultsindicated the relative effects of theclones and the environmental conditions,with Meru being the location where therewas greatest discrimination between theclones, probably because it was bettersuited for coffee. Clones,environments and joint regressions combinedaccounted for 31.20% of the interaction,leaving a residual of 68.80%. Significantresidual mean squares showed therelationship between clones andenvironments not to be strictly linear,thus indicating that regression analysisdid not always provide good estimates ofindividual clonal performance acrossenvironments. High correlations wereobtained between mean yield and regressioncoefficients and between ecovalence andmean square deviations as well as thecoefficient of determination. Resultsshowed that the improved clones surpassedthe standard cultivar both in terms ofyield and stability.Different analyses of the various aspectsof genotype response enabled the 21 clonesto be classified into different similaritygroups based on their performance andphenotypic stability as measured by variousstability parameters and the use of biplotsfrom the AMMI analysis.     

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 interactions for seed yield in rainfed winter safflower (Carthamus tinctorius L.) multi-environment trials in Iran

Evaluation of genotype × environment interaction (GEI) is an important component of the variety selection process in multi-environment trials. The objectives of this study were first to analyze GEI on seed yield of 18 spine safflower genotypes grown for three consecutive seasons (2008–2011) at three locations, representative of rainfed winter safflower growing areas of Iran, by the additive main effects and multiplicative interaction (AMMI) model, and second to compare AMMI-derived stability statistics with several stability different methods, and two stability analysis approaches the yield-stability (Ysi) and the GGE (genotype + genotype × environment) biplot that are widely used to identify high-yielding and stable genotypes. The results of the AMMI analysis showed that main effects due to genotype, environment, and GEI as well as first six interaction principle component axes (IPCA1 to 6) were significant (P < 0.01). According to most stability statistics of AMMI analyses, genotypes G5 and G14 were the most stable genotypes across environments. According to the adjusted stability variance (s²), the high-yielding genotype, G2, was unstable due to the heterogeneity caused by environmental index. Based on the definition of stable genotypes by regression method (b = 1, S _d ²  = 0), genotypes G11, G9, G14, G3, G12 and G13 had average stability for seed yield. Stability parameters of Tai indicated that genotype G5 had specific adaptability to unfavorable environments. The GGE biplot and the Ysi statistic gave similar results in identifying genotype G2 (PI-209295) as the best one to release for rainfed conditions of Iran. The factor analysis was used for grouping all stability parameters. The first factor separated static and dynamic concepts of stability, in which the Ysi and GGED (i.e., the distance from the markers of individual genotypes to the ideal genotype) parameters had a dynamic concept of stability, and the other remaining parameters had static concept of stability.