欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2009, Vol. 35 ›› Issue (11): 1981-1989.doi: 10.3724/SP.J.1006.2009.01981

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

基于协方差阵结构优选的作物品种区域试验分析

胡希远,尤海磊,任长宏,吴冬,李建平   

  1. 西北农林科技大学农学院，陕西杨凌712100
  • 收稿日期:2009-02-23 修回日期:2009-06-25 出版日期:2009-11-12 网络出版日期:2009-09-07
  • 通讯作者: E-mail:xiyuanhu@yahoo.com.cn;Tel:029-87081390
  • 基金资助:

    本研究由国家自然科学基金项目（30582072）资助。

Analysis of Crop Variety Regional Trials Based on Selection of Covariance Structures

HU Xi-Yuan,YOU Hai-Lei,REN Chang-Hong,WU Dong,LI Jian-Ping   

  1. College of Agronomy，Northwest A & F University，Yangling 712100，China
  • Received:2009-02-23 Revised:2009-06-25 Published:2009-11-12 Published online:2009-09-07
  • Contact: E-mail:xiyuanhu@yahoo.com.cn;Tel:029-87081390

PDF

1872

被引次数

7

摘要：

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。

关键词: 作物品种, 区域试验, 线性混合模型, 方差协方差

Abstract:

The method mainly used for analyzing crop variety regional trials is based on analysis of variance (ANOVA), which requires a homogenous variance-covariance of data. Now, other models different from the ANOVA model are available. However, the problems that how the models should be assessed and that which model is more suitable for given trial data are not solved and hence restrict the applicability of the models in practices. This paper tried to solve these problems on the basis of liner mixed models. Relations between various variance-covariance structures of linear mixed model and models available for analyzing crop regional trials were discussed. Then on the basis of analyses of the corn regional trials in northeast and north China, using the information criterion and likelihood-ratio-test, the characteristics of variance-covariance structures of regional trial data and the performance difference between the ANOVA model and the linear mixed model with optimal variance-covariance structure were assessed. The results showed that the variance-covariance of variety effect over environments was not homogeneous as defined in the ANOVA model, but heterogeneous in all the considered trials. The ratio of the same variety contrast with significant difference between the ANOVA model and the optimal linear mixed model averagely reached 86%. Also, there was obvious difference in the yield ranking of varieties between the two models. The error of variety effect estimation in the optimal linear mixed model was smaller than that in the ANOVA model.

Key words: Crop variety, Regional trial, Linear mixed model, Covariance

[1] Wu T-X(吴天侠), Gai J-Y(盖钧镒), Ma Y-H(马育华). An inquiring into the nearest neighbouring analysis for trails with large number of lines in plant breeding. Acta Agron Sin (作物学报), 1995, 21(1): 9-16 (in Chinese with English abstract)

[2] Mo H-D(莫惠栋). Statistics for Agricultural Experiments (农业试验统计), 2nd edn. Shanghai: Shanghai Scientific and Technical Publishers, 1992. pp 260-278 (in Chinese)

[3] Hu X-Y(胡希远), Spilke J. Spatial variability and its statistical control in field experiment. Acta Agron Sin (作物学报), 2007, 33(4): 620-624 (in Chinese with English abstract)

[4] Kempthorne O. The Design and Analysis of Experiments. New York: John Wiley & Sons, 1952. pp 86-156

[5] Zhang Q-Y(张群远), Kong F-L(孔繁玲). Models and methods for estimating variety means in regional crop trials: Comparisons of arithmetic mean, weighted least squares estimates and BLUPs. Acta Agron Sin (作物学报), 2003, 29(6): 884-891 (in Chinese with English abstract)

[6] Wu Y-Q(吴元奇), Pan G-T(潘光堂), Rong T-Z(荣廷昭). Study progress in crop stability. J Sichuan Agric Univ (四川农业大学学报), 2005, 23(4): 482-489 (in Chinese with English abstract)

[7] Shukla G K. Some statistical aspects of partitioning genotype
environmental components of variability. Heredity, 1972, 29: 237-245

[8] Finly K W, Wilkinson G N. The analysis of adaptation in a plant breeding program. Aust J Agric Res, 1963, 14: 742-754

[9] Eberhart S A, Russell W A. Stability parameters for comparing varieties. Crop Sci, 1966, 6: 36-40

[10] Gauch H G. Model selection and validation for yield trials with interaction. Biometrics, 1988, 44: 705-715

[11] Wang L(王磊), Yang S-H(杨仕华), Xie Y-X(谢英贤). AMMI model and its application to the regional crop trial analysis. J Appl Fund Eng Sci (应用基础与工程科学学报), 1997, 5(1): 39-46 (in Chinese with English abstract)

[12] Oman S D. Multiplicative effects in mixed model analysis of variance. Biometrika, 1991, 78: 729-739

[13] Zhang Q-Y(张群远), Kong F-L(孔繁玲). Comparison of statistical models for regional crop trial analysis. Sci Agric Sin (中国农业科学), 2002, 35(4): 365-371 (in Chinese with English abstract)

[14] Little R C, Milliken G A, Stroup W W, Wolfinger R D. SAS System for Mixed Models. Cary, NC: SAS Institute, Inc. 1996. pp 303-326

[15] Verbeke G, Molenberghs G. Linear Mixed Models in Practice. New York: Springer-Verlag, 1997. pp 185-210

[16] Hartley H O, Rao C R. Maximum likelihood estimation for the mixed analysis of variance model. Biometrica, 1967, 54: 93-108

[17] Harville D A. Maximum likelihood approaches to variance component estimation and to related problems. J Am Stat Assoc, 1977, 72: 320-338

[18] Zhu J(朱军). Principles of Linear Model Analysis (线性模型分析原理). Beijing: Science Press, 2000. pp 160-166 (in Chinese)

[19] Wolfinger R. Covariance structure selection in general mixed models. Commun Stat Simula Comput, 1993, 22: 1079-1106

[20] Piepho H P. Stability analysis using the SAS system. Agron J, 1999, 91: 154-160

[21] Bozdogan H. Akaike’s information criterion and recent developments in information complexity. J Math Psychol, 2000, 44: 62-91

[22] Piyepho H P, Mohring J. Best linear unbiased prediction of cultivar effects for subdivided target region. Crop Sci, 2005, 45: 1151-1159
Smith A B, Cullis B R, Thompson R. The analysis of crop cultivar breeding and evaluation trials: An overview of current mixed model approaches. J Agric Sci, 2005, 143: 449-462
[1] 张久权, 闫慧峰, 褚继登, 李彩斌. 运用广义线性混合模型分析随机区组重复测量的试验资料[J]. 作物学报, 2021, 47(2): 294-304.
[2] 张毅,许乃银,郭利磊,杨子光,张笑晴,杨晓妮. 我国北部冬麦区小麦区域试验重复次数和试点数量的优化设计[J]. 作物学报, 2020, 46(8): 1166-1173.
[3] 许乃银，金石桥，李健. 我国棉花品种区域试验重复次数和试点数量的设计[J]. 作物学报, 2016, 42(01): 43-50.
[4] 许乃银,李健. 棉花区试中品种多性状选择的理想试验环境鉴别[J]. 作物学报, 2014, 40(11): 1936-1945.
[5] 王春平,胡希远,沈琨仑. 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[J]. 作物学报, 2013, 39(03): 449-454.
[6] 许乃银，张国伟，李健，周治国. 基于HA-GGE双标图的长江流域棉花区域试验环境评价[J]. 作物学报, 2012, 38(12): 2229-2236.
[7] 吴存祥，李继存，沙爱华，曾海燕，孙石，杨光明，周新安，常汝镇，年海，韩天富. 国家大豆品种区域试验对照品种的生育期组归属[J]. 作物学报, 2012, 38(11): 1977-1987.
[8] 关荣霞，方宏亮，何艳琴，常汝镇，邱丽娟. 国家大豆区域试验品种(系) SSR位点纯合度分析[J]. 作物学报, 2012, 38(10): 1760-1765.
[9] 王洁, 廖琴, 胡小军, 万建民. 北方稻区国家水稻品种区域试验精确度分析[J]. 作物学报, 2010, 36(11): 1870-1876.
[10] 濮绍京;金文林;白琼岩;张连平;陈立军;赵波;钟连全. 基于玉米区试的籽粒产量抽样方法研究[J]. 作物学报, 2008, 34(06): 991-998.
[11] 张群远;孔繁玲;杨付新. 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[J]. 作物学报, 2003, 29(06): 884-891.
[12] 张群远;孔繁玲;杨付新. 品种区域试验中算术平均值、 BLUP和AMMI估值的精度比较[J]. 作物学报, 2001, 27(04): 428-433.
[13] 金文林. 作物区试中品种稳定性评价的秩次分析模型[J]. 作物学报, 2000, 26(06): 925-930.
[14] 金文林;白琼岩. 作物区试中品种产量性状评价的秩次分析法[J]. 作物学报, 1999, 25(05): 632-638.
[15] 郭银燕;张云康;胡明训;胡秉民;陈昆荣. 浙江省早籼稻蒸煮品质的品种、地点、品种×地点互作效应研究[J]. 作物学报, 1999, 25(04): 499-503.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备15008789号-2