Badanie struktury interakcji genotypowo-środowiskowej — przegląd metod

Aastveit A. H., Martens, H. 1986. ANOVA interactions interpreted by partial least squares regression. Biometrics 42: 829 — 844.
Google Scholar

Aastveit A. H., Mejza S. 1992. A selected bibliography on statistical methods for the analysis of genotype × environment interaction. Biul. Oc. Odm. 25: 83 — 97.
Google Scholar

Abdalla O. S., Crossa, J. P. L. 1997. Cornelius. Results and biological interpretation of shifted multiplicative model clustering of durum wheat cultivars and testing sites. Crop Sci. (37):88 — 97.
Google Scholar

Abou-El-Fittouh H. A., Rawlings J. O., Miller P. A. 1969. Classification of environments to control genotype by environment interactions with an application to cotton. Crop Sci. 9: 135 — 140.
Google Scholar

Adugna, W., Labuschagne M. T. 2003. Cluster and canonical variate analyses in multilocation trials of linseed. J. Agric. Sci. 140: 297 — 304.
Google Scholar

Aggoun A., Benmahammed K., Dekhili, M. 2006. Effects of environmental factors on kernel weight in five Algerian durum wheat populations (Triticum durum Desf.). Cahiers Agriculture 15: 425 — 431.
Google Scholar

Alberts M. J. A. 2004. A comparison of statistical methods to describe genotype × environment interaction and yield stability in multi-location maize trials. Master’s thesis, University of Free State, South Africa.
Google Scholar

Annicchiarico P. 2002. Genotype × environment interactions — challenges and opportunities for plant breeding and cultivar recommendations. FAO. Plant Production and Protection Papers.
Google Scholar

Annicchiarico P. 1992. Cultivar adaptation and recommendation from alfalfa trials in northern Italy. Journal of Genetics and Breeding 46: 269 — 278.
Google Scholar

Basford K., McLachlan G. J. 1985. The mixture method of clustering applied to three-way data. J. Classif., (2): 109 — 125.
Google Scholar

Basford K. E., Kroonenberg P. M., DeLacy I.H. 1991. Three-way methods for multivariate genotype × environment data: an illustrated partial survey. Field Crops Res. 27: 131 — 157.
Google Scholar

Berger J. D., Robertson L. D., Cocks, P. S. 2002. Genotype × environment interaction for yield and other plant attributes among undomesticated Mediterranean Vicia species. Euphytica 126: 421 — 435.
Google Scholar

Bradu D., Gabriel K. R. 1978. The biplot as a diagnostic tool for models of two-way tables. Technometrics 20: 47 — 68.
Google Scholar

Bull J. K., Basford K. E., Cooper M., Delacy I. H. 1994. Enhanced interpretation of pattern analysis of environments — The use of blocks. Field Crops Res. 37: 25 — 32.
Google Scholar

Calinski T., Czajka S., Kaczmarek Z. 1979. On some methods for studying the genotype × environment interaction. Quaderni di Epidemiologia 1: 11 — 30.
Google Scholar

Calinski T., Czajka S., Kaczmarek Z. 1987. A model for the analysis of a series of experiments repeated at several places over a period of years. II. Example. Biul. Oc. Odm. 17–18: 35 — 71.
Google Scholar

Calinski T., Czajka S., Kaczmarek Z.1987. A model for the analysis of a series of experiments repeated at several places over a period of years. I. Theory. Biul. Oc. Odm. 17–18: 7 — 33.
Google Scholar

Calinski T., Czajka S., Kaczmarek Z. 1989. A model for the analysis of a series of variety trials repeated at places subject to grouping. II. Example. Biul. Oc. Odm. 21–22: 45 — 64.
Google Scholar

Calinski T., Czajka S., Kaczmarek Z. 1989. A model for the analysis of a series of variety trials repeated at places subject to grouping. I. Theory. Biul. Oc. Odm. 21–22: 27 — 44.
Google Scholar

Campbell B. T., Baenziger P. S., Eskridge K. M., Budak H., Streck N. A., Weiss A., Gill K. S., Erayman M. 2004. Using environmental covariates to explain genotype × environment and QTL × environment interactions for agronomic traits on chromosome 3A of wheat. Crop Sci. 44: 620 — 627.
Google Scholar

Cattell R. F.1965. Factor analysis: An introduction to essentials. I. The purpose and underlying models. Biometrics 21: 190 — 215.
Google Scholar

Ceretta S., van Eeuwlik F. 2008. Grain yield variation in malting barley cultivars in Uruguay and its consequences for the design of a trials network. Crop Sci. 48: 167 — 180.
Google Scholar

Cliff N., Krus D. J. 1976. Interpretation of canonical variate analysis: Rotated vs. Unrotated solutions. Psychometrika 41: 35 — 42.
Google Scholar

Cooper M., Stucker R. E., DeLacy I. H., Harch B. D. 1997. Wheat breeding nurseries, target environments, and indirect selection for grain yield. Crop Sci. 37: 1168 — 1176.
Google Scholar

Cormack R. M. 1971. A review of classification. Journal of the Royal Statistical Society A, 134: 321 — 367.
Google Scholar

Cornelius P. L., Seyedsadr M., Crossa J. 1992. Using the shifted multiplicative model to search for “separability” in crop cultivar trials. Theor. Appl. Gen. (84): 161 — 172.
Google Scholar

Cornelius P. L., Van Sanford D. A., Seyedsadr M. S. 1993.Clustering cultivars into groups without rank-change interactions. Crop Sci. (33): 1193 — 1200.
Google Scholar

Corsten L. C. A., Denis J. B. 1990. Structuring interaction in two-way tables by clustering. Biometrics 46: 207 — 215.
Google Scholar

Corsten L. C. A., van Eijnsbergen A. C. 1972. Multiplicative effects in two-way analysis of variance. Statistica Neerlandica 26: 61 — 68.
Google Scholar

Cox D. R. 1984. Interaction. International Statistical Review 52: 1 — 31.
Google Scholar

Crossa J. 1988. A comparison of results obtained with two methods for assessing yield stability. Theor. Appl. Gen. 75: 460 — 467.
Google Scholar

Crossa J. 1990. Statistical analyses of multilocation trials. Advances in Agronomy 44: 55 — 85.
Google Scholar

Crossa J., Cornelius P. L. 1997. Site regression and shifted multiplicative model clustering of cultivar trials site under heterogeneity of error variances. Crop Sci. (37): 406 — 415.
Google Scholar

Crossa J., Cornelius P. L., Seyedsadr M., Byrne P. 1993. A shifted multiplicative model cluster analysis for grouping environments without genotypic rank change. Theor. Appl. Gen. (85): 577 — 586.
Google Scholar

Crossa J., Cornelius, P. L., Yan W. 2002. Biplots of linear-bilinear models for studying crossover genotype × environment interaction. Crop Sci. 42: 619 — 633.
Google Scholar

Crossa J., Gauch H. G., Zobel R. W. 1990. Addictive main effects and multiplicative interaction analysis of two international maize cultivar trials. Crop Sci. (6): 36 — 40.
Google Scholar

Crossa J., Vargas M., van Eeuwijk F. A., Jiang C., Edmeades G. O., Hoisington D. 1999. Interpreting genotype × environment interaction in tropical maize using linked molecular markers and environmental covariates. Theor. Appl. Gen. 99: 611 — 625.
Google Scholar

Crossa J., Westcott B., Gonzalez C. 1988. Analyzing yield stability of maize genotypes using a spatial model. Theor. Appl. Gen. 75: 863 — 868.
Google Scholar

de la Vega A. J., Chapman S. C. 2001. Genotype by environment interaction and indirect selection for yield in sunflower II. Three-mode principal component analysis of oil and biomass yield across environments in Argentina. Field Crops Res. 72: 39 — 50.
Google Scholar

de la Vega A. J., Chapman S. C. 2006. Multivariate analyses to display interactions between environment and general or specific combining ability in hybrid crops. Crop Sci. 46: 957 — 967.
Google Scholar

de la Vega, A. J.,Kroonenberg, P. M. 2002. Investigating the physiological bases of predictable and unpredictable genotype by environment interactions using three-mode pattern analysis. Field Crops Res. 78: 165 — 183.
Google Scholar

Dehghani H., Ebadi A., Yousefi A. 2006. Biplot analysis of genotype by environment interaction for barley yield in Iran. Agronomy J. 98: 388 — 393.
Google Scholar

Denis, J. B., Vincour T. P. 1982. Panorama des méthodes statistiques d’analyse des interactions génotype × milieu. Agronomie 2: 219 — 230.
Google Scholar

Digby P. G. N. 1979. Modified joint regression analysis for incomplete variety × environment data. J. Agric. Sci. 93: 81 — 86.
Google Scholar

Everitt B. S., Landau S., Leese M. 2001. Cluster analysis. Oxford University Press Inc., New York, 2001.
Google Scholar

Finnie S. M., Bettge A. D., Morris C. F. 2006. Influence of cultivar and environment on water-soluble and water-insoluble arabinoxylans in soft wheat. Cereal Chem. 83: 617 — 623.
Google Scholar

Fisher R. A. 1936. The use of multiple measurements in taxonomic problems. Annals of Eugenics 7: 179 — 188.
Google Scholar

Fisher R. A., Mackenzie W. A. 1971. Studies in crop variation. II. The manorial response of different potato varieties. J. Agric. Sci. 23:311 — 320.
Google Scholar

Fox P. N., Rosielle A. A. 1982. Reference sets of genotypes and selection for yield in unpredictable environments. Crop Sci. 22: 1171 — 1175.
Google Scholar

Franco J., Crossa J. 2002. The modified location model for classifying genetic resources: I. Association between categorical and continuous variables. Crop Sci. (42): 1719 — 1726.
Google Scholar

Franco J., Crossa J., Taba S., Eberhart S. A. 2002. The modified location model for classifying genetic resources: Ii. unrestricted variance covariance matrices. Crop Sci. (42): 1727 — 1736.
Google Scholar

Franco J., Crossa J., Taba S., Shands H. 2003. A multivariate method for classifying cultivars and studying group × environment x trait interaction. Crop Sci. (43): 1249 — 1258.
Google Scholar

Franco J., Crossa J., Villasenor J., Taba S., Eberhart S. A. 1998. Classifying genetic resources by categorical and continuous variables. Crop Sci. 38: 1688 — 1696.
Google Scholar

Franco J., Crossa J., Villasenor J., Taba S., Eberhart S. A. 1999. A two-stage, three-way method for classifying genetic resources in multiple environments. Crop Sci. (39): 259 — 267.
Google Scholar

Freeman G. H. 1972. Statistical methods for the analysis of genotype-environment interactions. Heredity 31: 339 — 254.
Google Scholar

Freeman G. H., Dowker B. D. 1973. The analysis of variation between and within genotypes and environments. Heredity 30: 97 — 109.
Google Scholar

Gabriel K. R. 1971. The biplot graphic display of matrices with application to principal component analysis. Biometrika 58: 453 — 467.
Google Scholar

Gallacher D. J. 1997. Optimised descriptors recommended for Australian sugarcane germplasm (Saccharum spp. hybrid). Australian J. Agric. Res. 48: 775 — 779.
Google Scholar

Garbuglio D. D., Gerage A. C., de Araujo P. M., Fonseca N. D., Shioga P. S. 2007. Factor analysis and bissegmented regression for studies about environmental stratification and maize adaptability. Pesquisa Agropecuaria Brasileira 42: 183 — 191.
Google Scholar

Gauch, H. G. 1988. Model selection and validation for yield trials with interaction. Biometrics 44: 705 — 715.
Google Scholar

Gauch H. G. 1992. Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs. Elsevier, Amsterdam.
Google Scholar

Gauch H. G. 2006. Statistical analysis of yield trials by AMMI and GGE. Crop Sci. 46: 1488 — 1500.
Google Scholar

Gauch H. G., Zobel R. W. 1997. Identifying mega-environments and targeting genotypes. Crop Sci. 37: 311 — 326.
Google Scholar

Gollob H. F. 1968. A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33: 73 — 116.
Google Scholar

Gordon A. D. 1999. Classification. Chapman and Hall/CRC, Boca Raton, FL.
Google Scholar

Gower J. C. 1999. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53: 325 — 338.
Google Scholar

Gower J. C. 1967. Multivariate analysis and multidimensional geometry. The Statistician 17: 13 — 28.
Google Scholar

Gower J. C., Digby P. G. N. 1981. Three dimensional biplots. Biometrika 77: 773 — 785.
Google Scholar

Gower J. C., Hand D. J. 1996. Biplots. Chapman and Hall, United Kingdom.
Google Scholar

Grafius J. E., Kiesling R. L. 1960. The prediction of the relative yields of different oat varieties based on known environmental variables. Agron. J. 52: 396 — 399.
Google Scholar

Gruneberg W. J., Manrique K., Zhang, D., Hermann M. 2005. Genotype × environment interactions for a diverse set of Sweetpotato Clones evaluated across varying ecogeographic conditions in Peru. Crop Sci. 45: 2160 — 2171.
Google Scholar

Hill J., Goodchild N. A. 1981. Analysing environments for plant breeding purposes as exemplified by multivariate analyses of long term wheat yields. Theor. Appl. Gen. 58: 317 — 325.
Google Scholar

Hotelling H. 1936. Relations between two sets of varieties. Biometrika 28: 321 — 337.
Google Scholar

Huhn M., Truberg B. 2002. Contributions to the analysis of genotype × environment interactions: Theoretical results of the application and comparison of clustering techniques for the stratification of field test sites. Journal of Agronomy and Crop Sci. 188: 65 — 72.
Google Scholar

Ibanez M. A., Di Renzo M. A., Samame S. S., Bonamico N. C., Poverne M. M. 2001. Genotype-Environment interaction of Lovegrass forage yield in the semi-arid region of Argentina. The Journal of Agricultural Science 137: 329 — 336.
Google Scholar

Isik, K., Kleinschmit J. 2005. Similarities and effectiveness of test environments in selecting and deploying desirable genotypes. Theor. Appl. Gen. 110: 311 — 322.
Google Scholar

Jackson, P. A., Byth D. E., Fischer K. S., Johnston R. P. 1994. Genotype × environment interaction in progeny from a barley cross. 2. Variation in grain-yield, yield components and dry-matter production among lines with similar times to an thesis. Field Crops Res. 37: 11 — 23.
Google Scholar

Johnson D. E., Graybill F. A. 1972. An analysis of a two-way model with interaction and no replication. Statistica Neerlandica 67: 862 — 868.
Google Scholar

Johnson G. R. 1977. Analysis of genotypic similarity in terms of mean yield and stability of environmental response in a set of maize hybrids. Crop Sci. 17: 837 — 842.
Google Scholar

Johnston J. A., Wesselingh R. A., Bouck A. C., Donovan L. A., Arnold M. L. 2001. Intimately linked or hardly speaking? The relationship between genotype and environmental gradients in a Louisiana iris hybrid population. Molecular Ecology 10: 673 — 681.
Google Scholar

Jolliffe I. T. 2002. Principal component analysis. Second ed. Springer Verlag, New York Inc.
Google Scholar

Kang M. S., Gauch, H. G. Jr. (ed.). 1996. Genotype-by-environment interaction. CRC Press, New York.
Google Scholar

Kang M. S. (ed.). 1990. Genotype-by-environment interaction and plant breeding. Louisiana State University Agricultural Centre, Baton Rouge, Louisiana.
Google Scholar

Kempton R. A. 1984. The use of biplot in interpreting variety by environment interactions. The Journal of Agricultural Science 103: 123 — 135.
Google Scholar

Knigh R. 1970. The measurement and interpretation of genotype environment interactions. Euphytica 19: 225 — 235.
Google Scholar

Kroonenberg P. M. 1983. Three-mode Principal Component Analysis: Theory and Applications. DWO Press, Leiden, The Netherlands.
Google Scholar

Kroonenberg P. M., Basford K. E. 1989. An investigation of multi-attribute genotype response across environments using 3-mode principal component analysis. Euphytica 44:109 — 123.
Google Scholar

Laurentin H., Montilla, D., Garcia V. 2007. Interpreting genotype × environment interaction in sesame (Sesamum indicum L.). The Journal of Agricultural Science 145: 263 — 271.
Google Scholar

Lefkovitch L. P. 1985. Multi-criteria clustering in genotype-environment interaction problems. Theor. Appl. Gen. 70: 585 — 589.
Google Scholar

Lejeune M., Calinski, T. 2000. Canonical analysis applied to multivariate analysis of variance. Journal of Multivariate Analysis 72: 100 — 119.
Google Scholar

Lin C. S. 1982. Grouping genotypes by a cluster method directly related to genotype-environment interaction mean square. Theor. Appl. Gen. 62: 277 — 280.
Google Scholar

Lin C. S., Binns, M. R., Lefkovitch L. P. 1986. Stability analysis: Where do we stand? Crop Sci. 5: 894 — 900.
Google Scholar

Mandel J. 1969. A method for fitting empirical surfaces to physical or chemical data. Technometrics 3: 411 — 429.
Google Scholar

Mandel J. 1971. A new analysis of variance model for non-additive data. Technometrics 13: 1 — 18.
Google Scholar

Martinez M. C., Boursiquot J. M., Grenan S., Boidron R. 1997. Ampelometric study of adult leaves from somaclones of the Grenache — N grapevine (Vitis vinifera L.). Canadian Journal of Botany — Revue Canadienne de Botanique 75: 333 — 345.
Google Scholar

Oliveira G. V., Carneiro P. C., Dias L. A., Carneiro J. E., Cruz, C. D. 2005. Factor analysis in the environment stratification for the evaluation of common bean cultivars. Crop Breeding and Applied Biotechnology 5: 166 — 173.
Google Scholar

Ortiz R., Crossa, J., Vargas, M., Izquierdo J. 2007. Studying the effect of environmental variables on the genotype x environment interaction of tomato. Euphytica 153: 119 — 134.
Google Scholar

Padi F. K. 2007. Genotype × environment interaction and yield stability in a cowpea-based cropping system. Euphytica 158: 11 — 25.
Google Scholar

Perkins J. M. 1972. The principal component analysis of genotype-environmental interactions and physical measures of the environment. Heredity 29: 51 — 70.
Google Scholar

Peterson C. J., Pfeiffer W. H. 1989. International winter wheat evaluation: Relationships among test sites based on cultivar performance. Crop Sci. 29: 276 — 282.
Google Scholar

Ramey T. B., Rosielle A. A. 1983. Hass cluster analysis: a new method of grouping genotypes or environments in plant breeding. Theor. Appl. Gen. 66: 131 — 133.
Google Scholar

Reynolds M. P., Trethowan R., Crossa J., Vargas, M., Sayre K. D. 2004. Physiological factors associated with genotype by environment interaction in wheat. Field Crops Research 85: 253 — 274.
Google Scholar

Robert N. 1997. Structuring genotype × environment interaction for quality traits in bread wheat, in two multi-location series of trials. Euphytica 97: 53 — 66.
Google Scholar

Romagosa I., Ullrich S. E, Han F., Hayes P. M. 1996. Use of the additive main effects and multiplicative interaction model in QTL mapping for adaptation in barley. Theor. Appl. Gen. 93: 30 — 37.
Google Scholar

Salehuzzaman M. Joarder, O. I. 1979. Genotype × environment interaction, diversity estimates and application of discriminated function selection in soybean. Genet. Pol. 20: 89 — 101.
Google Scholar

Sanchez-Dominguez S., Munoz-Orozco A., Gonzalez-Hernandez V. A., Martinez-Garza A. 2006. Characterization and classification of Mexican peanut (Arachis hypogaea L.) germplasm. Agrociencia 40: 171 — 182.
Google Scholar

Santini A., Camussi A. 2000. The environmental effect on crown shape of common cypress clones in the Mediterranean countries. Annals of Forest Science 57: 277 — 286.
Google Scholar

Seif E., Evans J. C., Calaam L. N. 1979. A multivariate procedure for classifying environments according to their interaction with genotypes. Australian Journal of Agricultural Research 30: 1021 — 1026.
Google Scholar

Seiler G. P., Stafford R. E. 1985. Factor analysis of components of yield in guar. Crop Sci. 25: 905 — 908.
Google Scholar

Shukla G. K. 1972. Some statistical aspects of partitioning genotype-environment components of variability. Heredity 29: 237 — 245.
Google Scholar

Signor C. E., Dousse S., Lorgeou J., Denis J. B., Bonhomme R., Carolo P., Charcosset C. 2001. Interpretation of genotype × environment interactions for early maize hybrids over 12 years. Crop Sci. 41: 663 — 669.
Google Scholar

Spearman C. 1904. General intelligence objectively determined and measured. American Journal of Psychology 15: 201 — 293.
Google Scholar

Tai G. C. C., Levy D., Coleman W. K. 1994. Path analysis of genotype-environment interactions of potatoes exposed to increasing warm-climate constraints. Euphytica 75: 49 — 61.
Google Scholar

Truberg B. Huhn M. 2002. Contributions to the analysis of genotype × environment interactions: Experimental results of the application and comparison of clustering techniques for the stratification of field test sites. Journal of Agronomy and Crop Science (188): 113 — 122.
Google Scholar

Van Eeuwijk F. A., Denis J. B., Kang M. S. 1996. Incorporating additional information on genotypes and environments in models for two-way genotype by environment tables. In: Genotype by environment interaction. CRC Press, Boca Raton, Kang, F. L., Gauch, H. G. Jr. (ed.): 15 — 49.
Google Scholar

Van Eeuwijk F. A., Kroonenberg P. M. 1998. Multiplicative models for interaction in three-way ANOVA, with applications to plant breeding. Biometrics 54: 1315 — 1333.
Google Scholar

Varela M., Crossa J., Rane J., Joshi A. K., Trethowan R. 2006. Analysis of a three-way interaction including multi-attributes. Australian Journal of Agricultural Research 55: 1185 — 1193.
Google Scholar

Vargas M., Crossa, J., Van Eeuwijk, F. A., Ramirez, M. E., Sayre K. 1999. Using partial least squares regression, factorial regression, and AMMI models for interpreting genotype × environment interaction. Crop Sci. 39: 955 — 967.
Google Scholar

Vargas M., Crossa J., Van Eeuwijk F. A., Sayre, K., Reynolds M. P. 2001. Interpreting treatment x environment interaction in agronomy trials. Agron. J. 93: 949 — 960.
Google Scholar

Walton P. D. 1972. Factor analysis of yield in spring wheat (Triticum aestivum L.). Crop Sci. 12: 731 — 733.
Google Scholar

Ward J. H. 1963. Hierarchical grouping to optimize an objective function. J. Am. Statist. Assoc. (58): 236 — 244.
Google Scholar

Westcott B. 1986. Some methods of analysing genotype-environment interactions. Heredity 56: 243 — 253.
Google Scholar

Westcott B. 1987. A method of assessing the yield stability of crop genotypes. The Journal of Agricultural Science 108: 267 — 274.
Google Scholar

Williams E. J. 1952. The interpretation of interactions in factorial experiments. Biometrika 39: 65 — 81.
Google Scholar

Williams W. T. 1976. Pattern Analysis in Agriculture Science. CSIRO, Melbourne and Elsevier, Amsterdam.
Google Scholar

Wold H. 1966. Estimation of principal components and related models by iterative least squares. In: Multivariate analysis. Krishnaiah P. R. (ed.). Academic Press, New York: 391 — 420.
Google Scholar

Wold H. 1975. Soft modelling by latent variables: The nonlinear iterative partial least squares approach. In: Perspectives in probability and statistics: Papers in honour of Bartlett, M. S., Gani, J. (ed.). Academic Press, London: 117 — 142.
Google Scholar

Wood J. T. 1976. The use of environmental variables in the interpretation of genotype-environment interaction. Heredity 37: 1 — 7.
Google Scholar

Yan W. 1999. Methodology of cultivar evaluation based on yield trial data with special reference to winter wheat in Ontario. PhD thesis, University of Guelph, Guelph, ON, Canada.
Google Scholar

Yan W. 2001. GGE biplot - a Windows application for graphical analysis of multienvironment trial data and other types of two-way data. Agron. J. 93: 1111 — 1118.
Google Scholar

Yan W., Cornelius P. L., Crossa J., Hunt L. A.2001. Two types of GGE biplots for analyzing multi-environment trial data. Crop Sci. 41: 656 — 663.
Google Scholar

Yan W., Falk D. E. 2002. Biplot analysis of host-by-pathogen data. Plant Dis. 86: 1396 — 1401.
Google Scholar

Yan W., Hunt L. A. 2001. Interpretation of genotype × environment interaction for winter wheat yield in Ontario. Crop Sci. 41: 19 — 25.
Google Scholar

Yan W., Hunt L. A. 2002. Biplot analysis of diallel data. Crop Sci. 42: 21 — 30.
Google Scholar

Yan W., Kang M. S. 2003. GGEbiplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton.
Google Scholar

Yan W., Rajcan I. 2002. Biplot evaluation of test sites and trait relations of soybean in Ontario. Crop Sci. 42: 11 — 20.
Google Scholar

Yan W., Tinker N. A. 2005. An integrated biplot analysis system for displaying, interpreting, and exploring genotype × environment interaction. Crop Sci. 45: 1004 — 1016.
Google Scholar

Yang R.-C.2002. Likelihood-based analysis of genotype × environment interactions. Crop Sci. 42: 1434 — 1440.
Google Scholar

Yang R.-C., Beker R. J. 1991. Genotype-environment interactions in two wheat crosses. Crop Sci. 31: 83 — 87.
Google Scholar

Yang R. C., Stanton D., Blade S. F., Helm J., Spaner D., Wright S., Domitruk D. 2006. Is yield analysis of barley cultivar trials in the Canadian prairies. Journal of Agronomy and Crop Science 192: 184 — 294.
Google Scholar

Yochmowitz M. G., Cornell R. G. 1978. Stepwise tests for multiplicative components of interaction. Technometrics 20: 79 — 84.
Google Scholar

Zobel R. W., Wright M. J., Gauch H. G. 1988. Statistical analysis of yield trial. Agron. J. 80: 388 — 393.
Google Scholar