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     

基于协方差阵结构优选的作物品种区域试验分析

论述了线性混合模型方差协方差结构与作物品种区域试验分析模型的对应关系,以我国2005-2006年东北华北玉米8组区域试验资料为例,按照线性混合模型分析原理及模型拟合信息量准则与似然比测验,对区域试验品种方差协方差的结构特性及不同方差协方差结构模型在品种效应估计与评价的差异状况进行了探讨。结果表明,在分析的所有试验中,环境间品种效应方差协方差均不符合方差分析模型假设的同质性结构,而是呈现为各种异质性结构;产量效应测验差异显著的品种对数目在方差分析模型与最佳方差协方差结构线性混合模型间的一致率平均为86%,品种产量效应排序在两种模型间也存在明显不同,品种产量效应估计的平均误差在最佳方差协方差结构线性混合模型小于在方差分析模型。     

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     

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。     

GGE双标图法对天津市夏玉米品种区域试验的分析

<正>玉米是天津市第一大粮食作物,其中夏玉米属黄淮海夏播玉米区和京津唐夏播玉米区交汇点,生育期在100d左右,主要病害是玉米小斑病、茎腐病。区域试验主要通过算术平均值法评价品种的高产、稳产和适应性,通常采用多年多点试验材料进行联合方差分析,估计试验合并误差,并进行品种间差异显著性比较。由于环境差异大,试点数据相差悬殊或试点报废等原因,影响了对品种做出客观、公正的评定。另外,试点代表性和鉴别力也是产量稳定性和品种适应性分析中必不可少的     

夏玉米不同品种各阶段氮素效应综合评价方法

了解作物养分效应机制是合理施肥的重要理论基础。本文以浚单20、农华101和中农大4号3个玉米栽培品种为材料,综合品种遗传特征、光温及群体大小3个主要影响植株氮素效应的因素,以BLUP方法为基础,建立玉米品种间、不同器官在各生育阶段的氮素效应综合评价方法。结果表明,氮素效应评价模型中,各阶段日平均干物质积累量的理论值与实测值的相关性r值分别为0.988、0.881、0.973、0.929;用独立试验样本对氮素效应评价模型检验,其配对t检验的P值均大于0.05,差异不显著;不同生育阶段、不同玉米品种氮素效应参数值有较大差异;氮素固定效应值在不同生育阶段、同一阶段的不同氮素构成中均表现出较大差异。综上,利用本文改进后的BLUP方法进行氮素效应评价及特征分析可以深入阐释不同玉米品种在各生长阶段积累的氮素对干物质生产的作用及影响,并进一步明确玉米氮素效应的共性特征、品种间的遗传差异、光温及群体影响机制等生物学规律。     

AMMI模型在玉米品种区域试验中的应用

在农作物品种区域试验中,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一小部分交互作用。而AMMI模型把方差分析和主成分分析综合于一个模型中,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。本文利用AMMI模型及双标图对2007年贵州省玉米区域试验的产量数据进行分析,进而评价各参试品种的稳定性和适应性。     

基于R语言的AMMI模型和GGE双标图在大豆区试中的应用评价

为提高甘肃省大豆品种的选育和应用效率,利用大豆区域试验数据,从基因型与环境的互作分析出发,对甘肃省大豆新品种的稳定性、适应性以及各试点的鉴别力进行全面评估。本研究采用AMMI模型与GGE双标图相结合的方法对甘肃省9个大豆品种在5个试验点的产量进行分析,结果表明,AMMI模型中主成分值(IPCA1、IPCA2)占总变异平方和的95%;其中‘中黄318’属于高产稳产性品种,而‘陇黄3号’和‘铁丰31’虽然产量较高,但其稳定性中等,适合在特定区域栽培。在5个试验点中,凉州分辨力最强,镇原分辨力较弱。综合运用AMMI模型和GGE双标图法,能够更准确直观地反映各品种生产力、稳定性和适应能力,以及在不同试验区域的分辨能力和代表性。     

西藏春青稞β-葡聚糖和食用纤维含量的基因型与环境及互作效应分析

为了解基因型、环境及互作效应对西藏春青稞β-葡聚糖和食用纤维含量的影响,选择西藏不同地区春青稞8个品种（品系）,分别在四个地区种植,对其β-葡聚糖和食用纤维含量进行了分析。通过方差分析表明：不同春青稞品种（品系）的β-葡聚糖含量在四个地区间存在差异,而食用纤维含量没有差异;地区间的青稞品种食用纤维含量存在差异,β-葡聚糖含量则不存在差异;从基因型、环境以及基因型与环境互作对二者含量的影响分析得出：在本试验条件下青稞β-葡聚糖含量的变异主要来自供试品种和环境的互作效应作用,其次是品种间差异,环境作用相对较小;而对食用纤维含量的变异主要来自环境效应作用,其次是品种和环境的互作效应,而品种间的差异相对较小。用AMMI模型对β-葡聚糖与食用纤维基因型和环境互作的影响进行分析可以看出：990852在各区域的β-葡聚糖平均含量相对较高,PCA1值较小,对环境的反应比较稳定,是β-葡聚糖含量较理想的品种;品种990625、山青24和喜马拉雅19的食用纤维含量相对较低,但其PCA1值很小,即与环境的互作效应很小,是食用纤维含量较理想的品种。     

空间统计分析在作物育种品系选择中的效果

为了研究空间统计分析法在作物育种田间试验品系选择中的效果,采用剩余误差空间相关线性混合模型对一个具有56个品系的小麦育种随机区组设计田间试验产量资料进行了空间统计分析。运用地理统计学中的半变异函数法确定剩余误差空间协方差的函数。结果表明,试验的剩余误差存在着典型的空间相关性,利用剩余误差空间协方差结构的信息可降低品系效应估计的误差和提高品系效应差异F检验与t检验的效率。此外,空间分析法对品系效应估计受试验条件不均匀的影响小,可导致较经典方差分析法不同的品系排序和优系选择结果。     

Modified full-sib selection and best linear unbiased prediction of progeny performance in a European F2 maize population

Four cycles of modified recurrent full‐sib (FS) selection were conducted in an intermated F₂ population of European flint maize. The objectives of our study were to monitor trends across selection cycles in the estimates of population mean, inbreeding coefficients and variance components, and to investigate the usefulness of best linear unbiased prediction (BLUP) of progeny performance under the recurrent FS selection scheme applied. We used a selection rate of 25% for a selection index, based on grain yield and dry matter content. A pseudo‐factorial mating scheme was used for recombination. In this scheme, the selected FS families were divided into an upper‐ranking group of parents mated to the lower‐ranking group. Variance components were estimated with restricted maximum likelihood (REML). Average grain yield increased 1.2 t/ha per cycle, average grain moisture decreased 20.1 g/kg per cycle, and the selection index relative to the F₂ check entries decreased 0.3% per cycle. For a more precise calculation of selection response, the four cycles should be tested together in multi‐environmental trials. We observed a significant decrease in additive variance in the selection index, suggesting smaller future selection response. Predictions of FS family performance in Cn + 1 based on mean performance of parental FS families in Cn were of equal precision as those based on the mean additive genetic BLUP of their parents, and corresponding correlations were of moderate size for grain moisture and selection index.     

Genotype × Environment Interacion in multi-environment Trials using shrinkage factors for ammi models

Shrinkage factors applied to the additive main effects and multiplicative interaction (AMMI) models improve prediction of cultivar responses in multi-environment trials (MET). Estimates of shrinkage factors based on the eigenvalue partition (EVP) method may get a further improvement in the predictions of cell means. Objectives of this work were: (1) to compare the EVP-based shrinkage method with unshrunken AMMI, best linear unbiased predictor (BLUP) and other shrunken method (herein named CCC), when they were applied to five maize MET and simulation data; (2) to assess by cross validation the equation which estimates the standard error of predicted means (SEPM) based on the EVP theory; (3) to estimate the genotype × environment interaction (GEI) variance components after applying the EVP shrinkage method to the five maize MET. Empirical data of five maize MET and simulation data were used for cross validation of the methods using the root mean square predictive difference (RMSPD) criterion. The RMSPD of the shrunken EVP predicted cell means was generally smaller than those of the other methods, suggesting that the EVP method was generally better predictor than the other methods. The truncated AMMI was the worst among the four methods studied. The EVP-based equation, which predicts the SEPM, was a good predictor as determined by the RMSPD cross validation criterion, with the advantage that it does not need one replication for validation. Estimates of mean squares, and GEI and error variances associated with the GEI effects were smaller for the shrunken EVP predicted effects than for the original data. This revised version was published online in July 2006 with corrections to the Cover Date.     

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。     

A comparison between single seed descent and early cross selection in wheat breeding

Summary Two selection procedures in wheat breeding were compared on the basis of their ability to supply high yielding inbred lines. The first procedure consists of an early selection between crosses in the F₃ generation, based on predictions of the cross mean and the between line variance. In the second procedure selection is postponed until the F₆, which is derived by single seed descent. The two procedures are evaluated in a two year test, using pseudo-lines of spring wheat. These pseudo-lines consist of mixtures of varieties and enable an estimation of the exact genetic parameters. In this way the accuracy of the predictions can be examined.In case of early selection, it appears that the predictions of the cross mean and especially the between line variance are very inaccurate. This is caused by the effects of plot size, intergenotypic competition and, to a lesser extent, dominance and/or epistasis. It results in an erroneous ranking of the crosses and the discarding of the potentially best cross. The F₆-SSD line estimates are much more accurate and thus the better lines are indeed selected. A first comparison between the two selection procedures therefore indicates a preference to the SSD method.     

BLUP for phenotypic selection in plant breeding and variety testing

Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method was originally developed in animal breeding for estimation of breeding values and is now widely used in many areas of research. It does not, however, seem to have gained the same popularity in plant breeding and variety testing as it has in animal breeding. In plants, application of mixed models with random genetic effects has up until recently been mainly restricted to the estimation of genetic and non-genetic components of variance, whereas estimation of genotypic values is mostly based on a model with fixed effects. This paper reviews recent developments in the application of BLUP in plant breeding and variety testing. These include the use of pedigree information to model and exploit genetic correlation among relatives and the use of flexible variance–covariance structures for genotype-by-environment interaction. We demonstrate that BLUP has good predictive accuracy compared to other procedures. While pedigree information is often included via the so-called numerator relationship matrix $({\user2{A}})Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method was originally developed in animal breeding for estimation of breeding values and is now widely used in many areas of research. It does not, however, seem to have gained the same popularity in plant breeding and variety testing as it has in animal breeding. In plants, application of mixed models with random genetic effects has up until recently been mainly restricted to the estimation of genetic and non-genetic components of variance, whereas estimation of genotypic values is mostly based on a model with fixed effects. This paper reviews recent developments in the application of BLUP in plant breeding and variety testing. These include the use of pedigree information to model and exploit genetic correlation among relatives and the use of flexible variance–covariance structures for genotype-by-environment interaction. We demonstrate that BLUP has good predictive accuracy compared to other procedures. While pedigree information is often included via the so-called numerator relationship matrix , we stress that it is frequently straightforward to exploit the same information by a simple mixed model without explicit reference to the -matrix. This paper is dedicated to Prof. Dr. Wolfgang K?hler (University of Giessen, Germany) on the occasion of his 65th birthday.     

Study of heritable variation and genetics of earliness in mungbean (<Emphasis Type="Italic">Vigna radiata</Emphasis> L. Wilczek)

Fifty-five mungbean lines were evaluated for days to maturity and grain yield per plant. This material showed considerable range of variability for the target traits. Eight genetically diverse parents were selected and used for a full diallel set of crosses to study the mode of inheritance for earliness related parameters (days to flowering, days to maturity and length of reproductive phase) during summer 2005. The F₁ generation of these crosses was sown during the spring of 2006 and the selfed seeds were used to raise the F₂ generation during kharif season. The data recorded from two generations were subjected to genetic analysis. The formal ANOVA showed the significance of both additive and dominance effects for all the traits in both generations. Significance of D, H₁ and H₂ components also confirmed the contribution of both additive and dominance effects in controlling the inheritance of these traits. The estimates of narrow sense heritability were low to moderate except higher estimates for days to maturity in F₂ generation, while the broad sense heritability estimates were relatively higher. Seasonal and environmental effects were also found to be significant. In view of the complex nature of gene action for earliness, it is suggested that breeders should look for transgressive recombinants of earliness and other desirable attributes in later segregating generations to gain higher genetic advance. The variety NM92 was found to be the best source of earliness in mungbean breeding.     

Investigation of long-term field experiments on response of breeding lines to common scab in a potato breeding program

Common scab, caused by Streptomyces scabies, is a disease that produces scab-like surface lesions on potato tubers. Testing for susceptibility/resistance of breeding lines at the Potato Research Centre of Agriculture & Agri-Food Canada (AFC) is carried out in a scab nursery maintained at AFC for the annual scab evaluation field trials. A replicated field trial routinely consists of breeding lines from previous testing season(s) plus newly selected lines. Data of scab scores generated from long-term experiments thus formulate an incomplete 2-way table over combinations of breeding lines (genotypes) and trials (years). This requires an advanced statistical method to estimate genetic parameters for evaluation purposes. A data base with 1,435 scab index scores from 344 breeding lines were extracted from 5 years (1995–1999) of field experiments in the scab nursery maintained at AFC. The statistical method Residual Maximum Likelihood (REML) was employed to estimate variance components of the breeding population and Best Linear Unbiased Predictor (BLUP) was used to predict genetic merit of breeding lines. High heritability was obtained from variance components estimated by REML. The BLUP scores of breeding lines provided reliable evaluation of their responses to common scab. Two data base sub-sets were separately formulated from the original data base for those parents and grandparents of the breeding lines having pedigree records available. They were again subjected to REML and BLUP analyses to compare the responses to common scab and identify sources of resistance at the parental and grandparental levels. Two random data sets with equal sized samples of breeding lines were also generated from the over-all data base. The two sets of BLUP scores between corresponding breeding lines and parents showed high association which provides an assessment of the validity of the evaluation process.