小麦产量的基因型×环境互作效应分析

品种稳定性取决于品种的基因型×环境互作,互作越大品种越不稳定。自Mooers(1921)用线性回归分析GE(品种×环境)互作,30年代Immer(1934)等,又首次用联合方差分析法,分析作物品种区域试验品种适应性以来,作物GE互作问题,是评价品种在不同环境条件下适应性的中心,1936年Finlay等引用回归系数(bi)     

作物品种区域试验中品种均值估计的模型和方法——算术平均值、加权最小二乘估值和BLUP的比较

针对作物区域试验中的品种均值估计问题,根据混合线性模型的一般原理,总结和提出多种加权最小二乘估计 (WLSE)和最佳线性无偏预测(BLUP)的方法,推导了这些方法的平衡数据计算简式;同时,利用14套2年多点的棉花区试资料和一套4年多点的棉花品种试验对这些方法的预测效果进行验证比较.结果表明,与算术平均值相比,以环境内误差方     

玉米区域试验中误差方差的异质性及其对品种评价的影响

为了研究我国玉米区域试验中误差方差的异质性存在状况及其对品种评价的作用, 以2003-2006年东北和华北16组玉米区域试验资料为依据,对玉米区域试验各环境试验误差方差差异状况及误差方差同质模型和异质模型的拟合效果进行了验证,并对品种效应差异显著性测验在误差同质模型和误差异质模型分析结果的差异状况进行了比较。结果表明,在分析的所有试验中,试验误差方差在环境间具有较大差异;误差方差异质模型比误差方差同质模型对试验数据拟合效果普遍较好;模型是否考虑误差方差的异质性对品种-环境交互效应测验结果有较大影响,而对品种主效应测验结果影响极小;误差方差异质模型比误差方差同质模型测验效率高。     

经验性最佳线性无偏预测(eBLUP)在油菜区域试验品种评价的效果

线性混合模型最佳线性无偏预测(BLUP)不仅适用于数据不平衡和误差方差异质试验的分析,而且对随机效应的排序会更准确。在实际试验分析中由于真实方差参数值未知而采用估计值时,BLUP转变为所谓经验性BLUP(eBLUP)。为了探讨eBLUP在作物区域试验品种评价的效果,本文以我国2012—2014年长江流域油菜区域试验12套产量资料为例,对eBLUP在品种主效应和特定环境中效应的估计、排序及差异比较t测验等方面与方差分析综合比较。结果表明,对品种主效应,eBLUP与方差分析算术平均值仅有较小差异,品种排序在eBLUP与算术平均值法相同;对特定环境中品种效应,eBLUP与算术平均值法有较大差异,品种排序在eBLUP较算术平均值法更准确;用Kenward-Roger法估算基于eBLUP的效应差异t测验的自由度,无论对品种主效应还是对特定环境中品种效应,eBLUP和方差分析有着相近的显著性(α=0.05)测验效果。     

基于HA-GGE双标图的甘蔗试验环境评价及品种生态区划分

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。     

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     

应用GGE双标图分析我国春小麦的淀粉峰值粘度

将原始数据减去各试点均值后形成的数据集中只含基因型主效G和基因型与环境互作效应GE, 合称GGE. 对GGE做单值分解, 以第一和第二主成分近似, 按第一和第二主成分值将所有品种和试点绘于同一平面图即形成GGE双标图. 以其分析我国春麦区10个试点20个品种淀粉糊化特性的峰值粘度, 结果表明铁春1号在大部分试点峰值粘度表现较好,     

秩次分析法对安徽花生产量性状评价研究

为筛选适合安徽省种植的花生品种,利用秩次分析法对9个环境点、22个参试品种的产量表现进行数据分析,在方差分析基础上,对各品种的秩次值(H2)、环境区分指数(YM)、秩次均方(S2)等统计数进行计算,从品种的高产性和稳产性2个方面进行比较。结果表明,在参试的22个品种(系)中4个品种(系)具有较好的高产性和稳产性,秩次分析法能够较为客观准确地评价参试品种的产量表现,具有较高的应用价值。     

1993年黄河流域棉花品种区域试验结果分析

本文给出的处理区试资料数学模型，对１９９３年黄河流域区试１７个试点９个参试品种资料进行分析。结果表明：品种间产量差异达显著统计学水平；各品种间均能抗枯萎病，但对黄萎病的抗性不及对照中棉所１２，属感黄萎；品种对环境的稳定和适应性存在差异；品种与环境正、负互作效应显著；多性状模糊综合评判以石远３２１、苏杂１６和邯４１０４较好；以品种的丰产、早熟性和纤维品质等方面确定了地点对品种及品种对地点的优化决策。     

烟草品系试验稳定性研究

以双列杂交选育的8个烤烟品系和两个对照品种K326、红花大金元为试验材料,选取了株高、总糖等10个农艺和化学品质性状为研究对象.采用多试点的联合方差分析和AMMI模型分析,对烤烟各个品种(系)的各个性状进行了稳定性、丰产性的估计和评价.分析结果表明除了叶数外,其它各个性状在不同的试点都存在着基因型与环境互作效应;各品种的产量、总氮、烟碱等随着环境的不同而存在较大的变异.在地点间、品种间、品种x地点间变异均达到1%极显著水平.通过稳定性分析明确了各参试材料的稳定性及适应范围.     

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.     

Implications of the number of years assessment on recommendation of common bean cultivars

In the common bean crop in Brazil, the requirement of the value for cultivation and use trials is that these experiments must be conducted over two years in three locations per region. Information in regard to the necessary number of years to ensure precision in cultivar recommendation and the influence of evaluated years in the estimation of the GE interaction are still scarce. Using grain yield of five check varieties assessed over 11 years in three sowing seasons per year, the aims of this study are as follows: to measure the relative contribution of the GE interaction, evaluating the implication of the number of years in the estimates of the GE interaction, and infer how many years are needed to ensure precision in cultivar recommendation. For instances, analysis of variance was carried out involving all environments and also combinations of years. The results showed that the GE interaction was greater than all other cross‐effects involving lines. The use of at least two years allows good coincidence in cultivar recommendation compared to the whole period. Increasing the evaluation time is a good strategy, especially when it is difficult to grow three different sowing seasons.     

Specific and General Adaptation and Yield Stability of Wheat (Triticum aestivum L.) Genotypes

Mean grain yield performance of 12 wheat and one triticale genotypes were measured at four locations over four consecutive years, using a randomized complete block design with four replications. The genotypes used were commercial cultivars and advanced lines from different wheat breeding projects located in different areas in Iran. Two locations were in semiarid regions and the other two locations in the temperate zones. The combined analysis of variance indicated highly significant genotype-environment (GE) interactions. From combinations of locations and years three sets of environments were generated. Set I and set II, each, consisted of eight environments (two locations and four years) representing semiarid and temperature environments, respectively. Set III consisted of 16 environments including both semiarid and temperate conditions. Set I and set II were used to measure specific adaptation of the genotypes while set III was employed for measuring general adaptation. The methods of Eberhart and Russell (1966) were used for partitioning GE interactions. The mean square associated with the heterogeneity of regression was highly significant under all sets of environments. These observations indicated that a major part of GE interaction could be accounted for by differences in the regression of the individual genotypes. All the genotypes had significant regression mean square under set I, set II, and set III environments, with the exception of two genotypes under set II. However, mean yields, regression coefficients, and the mean squares associated with deviation from regression greatly varied over the sets of environments. Only three genotypes, a commercial cultivar and two new advanced line, were identified as having specific adaptation and yield stability to semiarid environments. Among all the genotypes, only a commercial cultivar was identified as adapted and stable to temperate conditions. Two of the three genotypes which were adapted to semiarid environments also showed general adaptation to set III environments. However, the mean yield of these two genotypes under semiarid conditions (set I) were significantly greater than their respective mean yields under set III environments. Thus, wider adaptability was compensated by lower mean yield. The present study indicates that, while a wide range of environments is necessary and recommended for measuring general adaptation reactions and yield stability of various genotypes, one should not ignore the possibility of finding some genotypes with specific adaptation to specific environments and thus maximizing yield production. Stable genotypes with general of specific adaptation should be utilized in breeding projects in order to develop even more desirable lines.     

Response of Sericea Lespedeza Genotypes Low in Tannins to Different Environments

Transfer of the low-tannin trait to otherwise desirable sericea lespedeza high-tannin lines produced genotypes with considerably lowered forage yield. It is not known if low-tannin sericeas are proportionally less productive than high-tannin sericeas in high-yielding environments. If so, low-tannin sericeas would be less desirable to grow in more productive environments. Regression analysis was used to partition GE interactions between regressions and the residuals after regression, and to measure the response to changing environments, Shukla 's stability-variance parameters were used to measure genotype stability. Variance and coefficient of variation were calculated across environments for each genotype and rank correlated to the aforementioned parameters of stability to determine their usefulness in the early stages of cultivar testing. Stability analysis was carried out on the dry forage yield of 10 genotypes grown in 10 environments in Alabama. The only low-tannin genotype that consistently responded to environmental fluctuations like ‘Serala’ was 74-100-5. Low-tannin sericeas were found to be proportionally less productive than high-tannin sericeas in high-yielding environments. However, it is possible to select low-tannin lines with an environmental response similar to high-tannin sericeas. Rank correlations among stability parameters indicated that EV is a stability parameter which is easy to calculate and could be used in the early stages of cultivar testing.     

超干燥保存的烟草种子活力变化规律研究

本文是在烟草种子超干燥保存技术研究取得初步成功的基础上，对1993－1994年采收并经过超干燥处理的4份烟草种子分别在原干燥器内继续保存2年和4年后进行的种子活力跟踪试验。并对种子发芽势、发芽率和发芽指数等指标作方差分析和多重比较。结果为：2年后不同干燥剂间种子的发芽势、发芽率和发芽指数差异显著，其中，各处理的种子活力排列为：硅胶＞硅胶＞生石灰＞生石灰；2：1＞1：1＞3：1＞4：1；牛津3号＞其它品种。4年后，烟草种子的发芽指数硅胶＞硅胶＋生石灰＞生石灰。万良烟＞其它品种，比例间无显著差异。对种子的含水量、发芽势、发芽率、发芽指数和活力指数相关矩阵进行分析，结果为含水量与发芽指数和活力指数之间，发芽势与发芽指数之间，发芽指数与活力指数之间差异显著，种子含水量对种子活力贡献最大，其次为发芽率，发芽势、发芽指数。几项指标间的相互关系可用多元方程表示：Y＝0．496507＋0．136777X1＋0．000567X2－0．007197X3－0．000201X4。方程可信度达93．12％。该试验为长期，安全保存烟草种子和延长超干燥烟草种子保存寿命提供理论依据及配套措施。     

Development of an automated monitoring device to quantify changes in firmness of apples during storage

An automated firmness monitoring system for apples was developed to estimate loss of firmness during storage and determine the time when cool stores should be opened. The non-destructive acoustic impulse response technique was chosen to measure firmness. This technique was very reproducible and its sensitivity to firmness changes was greater than the sensitivity of penetrometer measurements with a materials testing device. The correlation between the acoustic impulse response technique and penetrometer varied according to the apple cultivar and freshness. The developed automated fruit firmness monitoring system is composed of a rotating disc on which a representative fruit sample is located, an electromagnetic excitation mechanism, and an optical sensor to detect the position of the apples. A microphone records the apple vibrations at impact and is linked to a computer with a data acquisition and analysis programme, which is placed outside the cool store. A first-order degradation model, fitted to the measured firmness data, is used to estimate the time when the cool stores should be opened to guarantee an average firmness after storage.     

Isoyield Analysis of Barley Cultivar Trials in the Canadian Prairies

Classification of test sites used for cultivar trials into groups with similar within‐group site performance and response (isoyield groups) is an important step towards identification of appropriate cultivars that are best suited for different productivity levels in farm fields. The objective of this study was to determine isoyield environments in the Canadian prairies based on the analysis of cultivar trials consolidated from individual provinces for barley (Hordeum vulgare L.). Yield data for the analysis were taken from 324 replicated trials at 84 sites across the prairies during 1995–2003. The combined use of regression and cluster analyses of the data normalized for averaging the multi‐year unbalanced data led to a stratification of the 84 sites into 13 isoyield groups. A comparison was made of the distributions of the variability among and within groups according to three modes of grouping: isoyield groups, soil zones and agroecoregions. There was more variability among isoyield groups and correspondingly less within the groups than that among and within soil zones or agroecoreions. Similar contrasting pattern existed for the variance components involving genotype–environment interaction (GEI), although the GEI variability was generally small under all three modes of grouping. Relationships of site sensitivity (regression coefficient) and stability (coefficient of determination) with site productivity were shown to be a useful aid for selecting a subset of test sites in an effort to improve efficiency and quality of future cultivar testing. Thus, isoyield analysis should be a valuable tool for subsetting heterogeneous environments and for reducing GEI impact in cultivar testing and recommendation.     

A method of choosing locations based on genotype × environment interaction for regional trials of rice

An ideal test location with particular environmental conditions should generate a relatively large genotype × environment interaction (GE) value in regional trials. A method of selecting test locations based on GE was proposed. Using the statistic D_j, the ranking of ability to discriminate the best genotypes for locations could be obtained. The locations among the group with the smallest D_j value were deleted from the whole set step by step, and the number of locations was reduced to a rational level at which most (over 85%) of the GE sum of squares of trials were maintained. A comparative evaluation based on data collected from Zhejiang Province (China) rice regional trials showed that the new method was more feasible than two other methods without loss of accuracy and reliability.     

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。