Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Biometrical Letters

The Journal of Polish Biometric Society

2 Issues per year

Open Access
Online
ISSN
1896-3811
See all formats and pricing
More options …

Principal component analysis for functional data on grain yield of winter wheat cultivars

Mirosław Krzyśko
  • Corresponding author
  • Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska 87, 61-614 Poznan, Poland
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Adriana Derejko
  • Corresponding author
  • Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences, Poland
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Tomasz Górecki
  • Corresponding author
  • Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska 87, 61-614 Poznan, Poland
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Edward Gacek
Published Online: 2013-12-10 | DOI: https://doi.org/10.2478/bile-2013-0019

Summary

The aim of this paper is to present a statistical methodology to assess patterns of cultivars' adaptive response to agricultural environments (agroecosystems) on the basis of complete Genotype x Crop Management x Location x Year (GxMxLxY) data obtained from 3-year multi-location twofactor trials conducted within the framework of the Polish post-registration trials (PDOiR), with an illustration of the application and usefulness of this methodology in analyzing winter wheat grain yield. Producing specific varieties for each subregion of a target region, from widely adapted varieties, may exploit positive genotype x location (GL) interactions to increase crop yields. Experiments designed to examine combinations of environment (E), management practices (M) and cultivars (G) also provide evidence of the relative importance of each of these factors for yield improvement. The evidence shows that variation due to E far outweighs the variation of grain yield that can be attributed to M or G, or the interactions between these factors, and between these factors and E (Anderson, 2010). This statistical method involves the use of functional PCA and cluster analysis. A total of 24 cultivars were evaluated over 3 years in 20 environments using randomized incomplete split-block designs with two replications per trial. The methodology proved an efficient tool for the reliable classification of 24 winter wheat cultivars, distinguishing cultivar groups that exhibited homogeneous adaptive response to environments. It enables the identification of cultivars displaying wide or specific adaptation. The remaining cultivars were locally adapted to some testing environments, or some of them were not relatively adapted to the environments because they always yielded substantially below the environmental means. Performing earlier specific selection, or adopting distinct genetic bases for each agro-ecosystem, may further increase the advantage of specific breeding.

Keywords: cultivar adaptation; functional data; grain yield; postregistration trials; principal component analysis; winter wheat

  • Abramowitz M., Stegun I.A. (1965): Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Dover Publications.Google Scholar

  • Allard R.W., Bradshaw A.D. (1964): Implications of genotype-environmental in­teractions in applied plant breeding. Crop Sci. 4: 503-508.CrossrefGoogle Scholar

  • Anderson W.K. (2010): Closing the gap between actual and potential yield of rainfed wheat. The impacts of environment, management and cultivar. Field Crops Research 116: 14-22.Web of ScienceGoogle Scholar

  • Annicchiarico P. (2002a): Defining adaptation strategies and yield stability tar­gets in breeding programmes. In: Kang M.S. (Ed.). Quantitative genetics, genomics and plant breeding. CABI, Wallingford, UK: 165-183.Google Scholar

  • Annicchiarico P. (2002b): Genotype-environment interactions: challenges and op­portunities for plant breeding and cultivar recommendations. FAO Plant Pro­duction and Protection Paper No. 174. Food and Agriculture Organization, Rome.Google Scholar

  • Annicchiarico P., Bellah F., Chiari T. (2006a): Repeatable genotype-location in­teraction and its exploitation by conventional and GIS-based cultivar recom­mendation for durum wheat in Algeria. Eur. J. Agron. 24: 70-81.CrossrefGoogle Scholar

  • Annicchiarico P., Russi L., Piano E., Veronesi F. (2006b): Cultivar adaptation across Italian locations in four turfgrass species. Crop Sci. 46: 264-272.CrossrefGoogle Scholar

  • Annicchiarico P., Iannucci A. (2008): Adaptation strategy, germplasm type and adaptive traits for field pea improvement in Italy based on variety responses across climatically contrasting environments. Field Crops Res. 108: 133-142.Web of ScienceGoogle Scholar

  • Annicchiarico P., Chiapparino E., Perenzin M. (2010): Response of common wheat varieties to organic and conventional production systems across Italian loca­tions, and implications for selection. Field Crops Res. 116: 230-238.Google Scholar

  • Basford K.E., Cooper M. (1998): Genotype x environment interactions and some considerations of their implications for wheat breeding in Australia. Austr. J. Agric. Res. 49: 153-174.Google Scholar

  • Braun H.J., Rajaram S., van Ginkel M. (1996): CIMMYT’s approach to breeding for wide adaptation. Euphytica 92: 175-183. Google Scholar

  • de la Vega A.J., Chapman S.C. (2006): Defining sunflower selection strategies for a highly heterogeneous target population of environments. Crop Sci. 46: 136-144.CrossrefGoogle Scholar

  • Dencic S., Mladenov N., Kobiljski B. (2011): Effects of genotype and environment on breadmaking quality in wheat. Int. J. Plant Prod. 5: 71-82.Google Scholar

  • Gauch H.G. (1992): Statistical analysis of regional yield trials. AMMI analysis of factorial designs. Elsevier Science, New York.Google Scholar

  • Gauch H.G. (2006): Statistical analysis of yield trials by AMMI and GGE. Crop Sci. 46: 1488-1500.Web of ScienceCrossrefGoogle Scholar

  • Gauch H.G., Piepho H.P., Annicchiarico P. (2008): Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Sci. 48: 866-889.CrossrefWeb of ScienceGoogle Scholar

  • Gauch H.G., Zobel R.W. (1997): Identifying mega-environments and targeting genotypes. Crop Sci. 37: 311-326.CrossrefGoogle Scholar

  • Ghaderi A., Adams M.W., Saettler A.W. (1982): Environmental response patterns in commercial classes of common bean (Phaseolus vulgaris L.). Theor. Appl. Genet. 63: 17-22.CrossrefGoogle Scholar

  • Górecki T., Krzysko M. (2012): A kernel version of functional principal compo­nents analysis, Statistics in Transition, 13(3): 559-568.Google Scholar

  • Karonski M. (1973): On a definition of cluster and pseudocluster for multivariate normal populations. In: Proceedings of the 39th Session of the International Statistical Institute. Vienna: 523-528.Google Scholar

  • Kozak M. (2010a): Use of parallel coordinate plots in multi-response selection of interesting genotypes. Commun. Biometry Crop Sci. 5: 83-95.Google Scholar

  • Kozak M. (2010b): Comparison of three types of G x E performance plot for showing and interpreting genotypes’ stability and adaptability. Int. J. Plant Prod. 5: 71-82.Google Scholar

  • Kruskal J.B. (1956): On the shortest spanning subtree of a graph and the traveling salesman problem. In: Proceedings of American Mathematical Society 7: 48­50.Google Scholar

  • Lillemo M., van Ginkel M., Trethowan R.M., Hernandez E., Crossa J. (2005): Differential adaptation of CIMMYT bread wheat to global high temperature environments. Crop Sci. 45: 2443-2453.CrossrefGoogle Scholar

  • Prim R.C. (1957) Shortest connection networks and some generalizations. Bell System Technical Journal 36: 1389-1401.Google Scholar

  • R Core Team (2013): R: A language and environment for statistical comput­ing. R Foundation for Statistical Computing, Vienna, Austria. http://www. <http://www.R-project.org>R-project.org <http://www.R-project.org>.Google Scholar

  • Ramsay J.O., Wickham H., Graves S., Hooker G. (2012): fda: Functional Data Analysis. R package version 2.3.2. http://CRAN.R-project.org/package= <http://CRAN.R-project.org/package=fda>fda <http://CRAN.R-project.org/package=fda>.Google Scholar

  • Ramsay J.O., Silverman B.W. (2005): Functional Data Analysis, Second Edition, Springer.Google Scholar

  • Rane J., Pannu R.K., Sohu V.S., Saini R.S., Mishra B., Shoran J., Crossa J., Vargas M., Joshi A.K. (2007): Performance of yield and stability of advanced wheat genotypes under heat stress environments of the Indo-Gangetic Plains. Crop Sci. 47: 1561-1573.CrossrefWeb of ScienceGoogle Scholar

  • Rodriguez M., Rau D., Papa R., Attene G. (2008): Genotype by environment interactions in barley (Hordeum vulgare L.): different responses of lan- draces, recombinant inbred lines and varieties to Mediterranean environment. Euphytica 163: 231-247.Web of ScienceGoogle Scholar

  • Singh R.P., Huerta-Espino J., Sharma R., Joshi A.K.,Trethowan R. (2007): High yielding spring bread wheat germplasm for global irrigated and rainfed pro­duction systems. Euphytica 157: 351-363.Web of ScienceGoogle Scholar

  • Sivapalan S., O’Brien L., Ortiz-Ferrera G., Hollamby G.J., Barclay I., Martin P.J. (2000): An adaptation analysis of Australian and CIMMYT/ICARDA wheat germplasm in Australian production environments. Aust. J. Agric. Res. 51: 903-915.Google Scholar

  • Trethowan R., Crossa J. (2007): Lessons learnt from forty years of international spring bread wheat trials. Euphytica 157: 385-390.Web of ScienceGoogle Scholar

  • Trethowan R.M., van Ginkel M., Rajaram S. (2002): Progress in breeding wheat for yield and adaptation in global drought affected environments. Crop Sci. 42: 1441-1446.CrossrefGoogle Scholar

  • Ulukan H. 2008. Agronomic adaptation of some field crops: a general approach. J. Agron. Crop Sci. 194: 169-179.CrossrefWeb of ScienceGoogle Scholar

  • Yan W., Hunt L.A. (1998): Genotype by environment interaction and crop yield. Plant Breed. Rev. 16: 135-179.Google Scholar

  • Yan W., Kang M.S., Ma B., Woods S., Cornelius P.L. (2007): GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 47: 643-655.CrossrefWeb of ScienceGoogle Scholar

  • Yang R.C., Crossa J., Cornelius P.L., Burgueno J. (2009): Biplot analysis of genotype x environment interaction: Proceed with caution. Crop Sci. 49: 1564-1576.CrossrefGoogle Scholar

  • Zhang Y., He Z., Zhang A., van Ginkel M., Ye G. (2006): Pattern analysis on grain yield of Chinese and CIMMYT spring wheat cultivars grown in China and CIMMYT. Euphytica 147: 409-420. Google Scholar

About the article

Published Online: 2013-12-10

Published in Print: 2013-12-01


Citation Information: Biometrical Letters, Volume 50, Issue 2, Pages 81–94, ISSN (Print) 1896-3811, DOI: https://doi.org/10.2478/bile-2013-0019.

Export Citation

This content is open access.

Comments (0)

Please log in or register to comment.
Log in