Multivariate analysis of genotypic diversity of agronomic traits in orchardgrass (Dactylis glomerata L.) germplasm collection

Amirouche N., Misset M.T. 2007. Morphological variation and distribution of cytotypes in the diploid-tetraploid complex of the genus Dactylis L. (Poaceae) from Algeria. Plant Systematics and Evolution 264: 157 — 174.
Google Scholar

Annese V., Cazzato E., Corleto A. 2006. Quantitative and qualitative traits of natural ecotypes of perennial grasses (Dactylis glomerata L., Festuca arundinacea Schreb., Phalaris tuberosa L., Brachypodium rupestre (Host) R. et S.) collected in Southern Italy. Genetic Res. Crop Evol. 53: 431 — 441.
Google Scholar

Casler M.D. 1991. Genetic variation and covariation in a population of tetraploid Dactylis L. accessions. Theor. Appl. Genet. 81: 253 — 264.
Google Scholar

Casler M.D., Fales S.L., McElroy A.R. 2000. Genetic progress from 40 years of orchardgrass breeding in North America measured under hay management. Crop Sci. 40: 1019 — 1025.
Google Scholar

Casler M. D., van Santen E. 2000. Patterns of variation in a collection of meadow fescue accessions. Crop Sci. 40: 248 — 255
Google Scholar

Crossa J., Franco J. 2004. Statistical methods for classifying genotypes. Euphytica 153: 19 — 37.
Google Scholar

Di Giorgio G., Graziano D., Ruisi P., Amato G. Giambalvo D. 2009. Pheno-morphological and agronomic diversity among Scorpiurus muricatus (Fabaceae) natural populations collected in Sicily. J. Agric. Sci. 147: 411 — 422.
Google Scholar

Federer W.T. 1956. Augmented (or hoonuiaku) designs. Hawaiian Planters Record 2:191 — 208.
Google Scholar

Federer W.T. 1961. Augmented designs with one-way elimination of heterogeneity. Biometrics 17:447 — 473.
Google Scholar

Federer W.T., Reynolds M., Crossa J. 2001. Combining results from augmented designs over sites. Agron. J. 93: 389 — 395.
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

Gauthier M.F., Lumaret R. 1999. Genetic introgression on between tetraploid Dactylis glomerata sp. reichenbachii and glomerata in the French Alps. Insight from morphological and isoenzyme variation, plant systematic and evolution. Plant Systematics and Evolution 241: 219 — 234.
Google Scholar

Gutierrez L., Franco J., Crossa J, Abadie T. 2003. Comparing a preliminary racial classification with a numerical classification of the maize landraces of Uruguay. Crop Sci. 43: 718 — 727.
Google Scholar

Hair J. F., Anderson R.E., Tatham R. L. 1987. Multivariate data analysis with readings. 2nd ed. Macmillan Publ. Co., New York, USA.
Google Scholar

Hartung K. 2006. Biometrical approaches for analysing gene bank evaluation data on barley (Hordeum spec.). Ph.D Dissertation, Hohenheim University, Stuttgart, Germany.
Google Scholar

Hartung K., Piepho H.P. 2005. A threshold model for multiyear genebank data based on different rating scales. Crop Sci. 45:1045 — 1051.
Google Scholar

Hartung K., Piepho H.P., Knupffer H. 2006. Analysis of genebank evaluation data by using geostatistical methods. Genetic Res. Crop Evol. 53: 737 — 751
Google Scholar

Hooks T., Pedersen J.F., Marx D. B., Vogel K. P. 2006. Variation in the U.S. photoperiod insensitive sorghum collection for chemical and nutritional traits. Crop Sci. 46:751 — 757.
Google Scholar

Hu J., Zhu J., Xu H. 2000. Methods of constructing core collections by stepwise clustering with three sampling strategies based on the genotypic values of crops. Theor. Appl. Genet. 101:264 — 268
Google Scholar

Humpreys M.O. 1991. A genetic approach to the multivariate differentiation of perennial ryegrass (Lolium perenne L.) cultivars. Heredity 66:437 — 443.
Google Scholar

Islam M. R., Hamid A., Khaliq O. A., Ahmed J. U., Haque M. M., Karim M. A. 2007. Genetic variability in flooding tolerance of mungbean (Vigna radiata L. Wilczek) genotypes. Euphytica 156: 247 — 255.
Google Scholar

Jahufer M. Z. Z., Cooper M., Harch B. D. 1997. Pattern analysis of the diversity of morphological plant attributes and herbage yield in a world collection of white clover (Trifolium repens L.) germplasm characterised in a summer moisture stress environment of Australia. Genet. Res. Crop Evol. 44: 289 — 300.
Google Scholar

Jaynes D. B., Kaspar T.C., Colvin T.S., James D.E. 2003. Cluster analysis of spatiotemporal corn yield patterns in an Iowa field. Agron. J. 95: 574 — 586.
Google Scholar

Johnson R.A., Wichern D. W. 2002. Applied multivariate statistical analysis. Prentice-Hall, Inc. Upper Saddle River, New York, USA.
Google Scholar

Kempton R.A., Gleeson A.C. 1997. Unreplicated trials. In: Kempton R.A., Fox P.N. Statistical methods for plant variety evaluation. Chapman & Hall, London, UK: 86 — 100.
Google Scholar

Khattree R., Naik D. N. 2000. Multivariate data reduction and discrimination with SAS software. SAS Institute Inc., Cary, USA.
Google Scholar

Kölliker R., Stadelmann F. J., Reidy B., Nösberger J. 1999. Genetic variability of forage grass cultivars: A comparison of Festuca pratensis Huds., Lolium perenne L. and Dactylis glomerata L. Euphytica 106: 261 — 270.
Google Scholar

Krzanowski W.J. 1988. Principles of multivariate analysis: a users’s perspective. Oxford University Press, Oxford, UK.
Google Scholar

Li C.T., Shi, C.H., Wu, J. G., Xu, H.M., Zhang, H. Z., Ren, Y.L. 2004. Methods of developing core collections based on the predicted genotypic value of rice (Oryza sativa L.). Theor. Appl. Genet. 108: 1172 — 1176.
Google Scholar

Liakat A. M., McClung A. M., Jia M. H., Kimball J. A., McCouch S. R., Eizenga G. C. 2011. A rice diversity panel evaluated for genetic and agro-morphological diversity between subpopulations and its geographic distribution. Crop Sci. 51: 2021 — 2035.
Google Scholar

Littell R. C., Milligan G. A., Stroup W. W., Wolfinger R. D., Schabenberger O. 2006. SAS for mixed models. SAS Institute Inc. Cary, USA.
Google Scholar

López I. F., Balocchi O.A., Kemp P.D., Valdés C. 2009. Phenotypic variability in Holcus lanatus L. in southern Chile: a strategy that enhances plant survival and pasture stability. Crop & Pasture Sci. 60: 768 — 777.
Google Scholar

Mahalakshmi V., Ng Q., Atalobhor J., Ogunsola D., Lawson M., Ortiz R. 2007. Development of a West African yam Dioscorea spp. core collection. Genetic Res. Crop Evol. 54: 1817 — 1825.
Google Scholar

Mohammadi S. A., Prasanna M. 2003. Analysis of genetic diversity in crop plants -salient statistical tools and considerations. Crop Sci. 43: 1235 — 1248.
Google Scholar

Majtkowski W., Żurek G., Schmidt J., Majtkowska G. 2003. Collections of grass genetic resources in Poland – source of information for distribution of species. In: Problem of grass biology Frey L. (ed.). W. Szafer Institute of Botany, Polish Academy of Sciences, Kraków: 219 — 227.
Google Scholar

Padilla G., Cartea M. E., Ordas A. 2007. Comparison of several clustering methods in grouping kale landraces. Journal of American Society for Horticultural Science 132: 387 — 395.
Google Scholar

Padilla G., Cartea M. E., Rodriguez V. M., Ord A. 2005. Genetic diversity in a germplasm collection of Brassica rapa subsp. rapa L. from northwestern Spain. Euphytica 145: 171 — 180.
Google Scholar

Pecetti L., Annicchiarico P., Porqueddu C., Khedim A., Abdelguerfi A. 2009. Fitting germplasm types of tall fescue and orchardgrass to different cropping environments of the Mediterranean region. Crop Sci. 49: 2393 — 2399.
Google Scholar

Peng Y., Zhang X. 2003. Progress in studies on genetic diversity of Dactylis glomerata L. Journal of Plant Genetic Resources 02: 19 — 41.
Google Scholar

Peng Y., Zhang X.Q., Deng Y.L., Ma X. 2008. Evaluation of genetic diversity in wild orchardgrass (Dactylis glomerata L.) based on AFLP markers. Hereditas 145: 174 — 181.
Google Scholar

Petersen R. G. 1985. Augmented designs for preliminary yield trials. ICARDA Barley Wheat and Triticale Newsletter 4: 27 — 32.
Google Scholar

Piepho H. P., Mohring J. 2005. Best Linear Unbiased Prediction of cultivar effects for subdivided target regions. Crop Sci. 45: 1151 — 1159.
Google Scholar

Pswarayi A., Eeuwijk van F.A., Ceccarelli S., Grando S., Comadran J., Russell J.R., Stanca A.M., Francia E., Pecchioni N., Akar T., Al-Yassin A., Benbelkacem A., Choumane W., Karrou M., Ouabbou H., Bort J., Araus J. L., Molina-Cano J. L., Thomas W. T. B., Romagosa I. 2008. Barley adaptation and improvement in the Mediterranean basin. Plant Breeding 127: 554 — 560.
Google Scholar

Rencher A.C. 1992. Interpretation of canonical discriminant functions, canonical varieties, and principal components. Journal of the American Statistical Association 46: 217 — 225.
Google Scholar

Rencher A.C. 2002. Methods of multivariate analysis. J. Wiley & Sons, New York.
Google Scholar

Robinson G.K. 1991. That BLUP is a good thing: the estimation of random effects. Statistical Science 6:15 — 51.
Google Scholar

Rojas W., Barriga P., Figueroa H. 2000. Multivariate analysis of the genetic diversity of Bolivian quinoa germplasm. Plant Genetic Resources Newsletter 122: 16 — 23.
Google Scholar

SAS Institute Inc. 2004. SAS on-line Doc 9.1.3 Cary, USA.
Google Scholar

Schmidt J. 1985. Analiza zmienności ekotypów kupkówki pospolitej (Dactylis glomerata L.) na podstawie materiałów z kolekcji traw. Biul. IHAR 158: 117 — 121.
Google Scholar

Schmidt J. 1987. Zmienność kupkówki pospolitej (Dactylis glomerata L.). Biul. IHAR 162: 83 — 88.
Google Scholar

Schmidt J. 2002. The evaluation of orchardgrass (Dactylis glomerata L.) from Bieszczady Mountains. In: Święcicki W., Naganowska B., Wolko B. (Eds.). Broad Variation and Precise Characterization — Limitation for the Future. Eucarpia Section Genetic Resources, Institute of Plant Genetics, Polish Academy of Sciences, Poznań: 268.
Google Scholar

Scott R.A., Milliken G. A. 1993. A SAS program for analysing augmented randomized complete block design. Crop Sci. 33: 865 — 867.
Google Scholar

Searle S. R. 1987. Linear models for unbalanced data. Wiley, New York, USA.
Google Scholar

Sebolai B., Pedersen J. F., Marx D.B., Boykin D.L. 2005. Effect of grid size, control plot density, control plot arrangement, and assumption of random or fixed effects on non-replicated experiments for germplasm screening. Crop Sci. 45: 1978 — 1984.
Google Scholar

Upadhyaya H. D., Dwivedi S. L., Nadaf H.L., Singh S. 2011 a. Phenotypic diversity and identification of wild Arachis accessions with useful agronomic and nutritional traits. Euphytica 118: 103 — 115.
Google Scholar

Upadhyaya H.D., Gowda C.L.L., Reddy K.N., Singh S. 2009. Augmenting the pearl millet core collection for enhancing germplasm utilization in crop improvement. Crop Sci. 49: 573 — 580.
Google Scholar

Upadhyaya H. D., Ravishankar C.R., Narasimhudu Y., Sarma N.D.R.K., Singh S. K., Varshney S.K., Reddy V.G., Singh S., Parzies H.K., Dwivedi S.L., Nadaf H.L., Sahrawat S., Gowda C.L.L. 2011 b. Identification of trait-specific germplasm and developing a mini core collection for efficient use of foxtail millet genetic resources in crop improvement. Field Crops Res. 124: 459 — 467.
Google Scholar

Upadhyaya H.D., Reddy K.N., Gowda C. L. L., Singh S. 2011c. Development of pearl millet minicore collection for enhanced utilization of germplasm. Crop Sci. 51:217 — 223.
Google Scholar

van SantenE., Sleper D.A. 1996. Orchardgrass. p. 503–534. In: L .E . Moser et al (ed.) Cool-season forage grasses. Agron. Monogr. 34. ASA, CSSA, and SSSA, Madison, WI.
Google Scholar

Vaylay R., van Santen E. 2002. Application of canonical discriminant analysis for the assessment of genetic variation in tall fescue. Crop Sci. 42: 534 — 539.
Google Scholar

Wang J. C., Hu J., Xu H. M., Zhang S. 2007. A strategy on constructing core collections by least distance stepwise sampling. Theor. Appl. Genet. 115: 1 — 8.
Google Scholar

Ward J. 1963. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 38: 236 — 244.
Google Scholar

Wolfinger R.D., Federer W.T., Cordero-Brana O. 1997. Recovering information in augmented designs, using SAS PROC GLM and PROC MIXED. Agron. J. 89: 856 — 859.
Google Scholar

Xie W.G., Zhang XQ., Cai H.W., Liu W., Peng Y. 2010 a. Diversity comparison and phylogenetic relationships of cocksfoot (Dactylis glomerata L.) germplasm as revealed by SSR markers. Canadian J. Plant Sci. 90: 13 — 21.
Google Scholar

Xie W.G., Zhang XQ., Cai H.W., Liu W., Peng Y. 2010 b. Genetic diversity analysis and transferability of cereal EST-SSR markers to orchardgrass (Dactylis glomerata L.). Biochemical Systematics and Ecology 38: 740 — 749.
Google Scholar

Xu Y. 2010. Molecular plant breeding. CAB International, Wallingford, UK.
Google Scholar

Yeater K. M., Bollero G.A, Bullock D.G, Rayburn A.L., Rodriguez-Zas S. 2004. Assessment of genetic variation in hairy vetch using canonical discriminant analysis. Crop Sci. 44: 185 — 189.
Google Scholar

Zeng B., Zhang X.Q., Lan Y. 2008. Evaluation of genetic diversity and relationships in orchardgrass (Dactylis glomerata L.) germplasm based on SRAP markers. Canadian J. Plant Sci. 88: 53 — 60.
Google Scholar

Żurek G., Majtkowski W. 2003. European Dactylis and Festuca database. In: Boller B., Willner E., Maggioni L., Lipman E. Report of a Working Group on Forages. Eight meeting, 10–12 April 2003, Linz, Austria. International Plant Genetic Resources Institute, Rome: 26 — 28.
Google Scholar