Pełnotekstowe zasoby PLDML oraz innych baz dziedzinowych są już dostępne w nowej Bibliotece Nauki.
Zapraszamy na https://bibliotekanauki.pl

PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników

Czasopismo

2013 | 50 | 2 | 81-94

Tytuł artykułu

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

Treść / Zawartość

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
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.

Wydawca

Czasopismo

Rocznik

Tom

50

Numer

2

Strony

81-94

Opis fizyczny

Daty

wydano
2013-12-01
online
2013-12-10

Twórcy

  • Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska 87, 61-614 Poznan, Poland
  • Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences, Poland
  • Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska 87, 61-614 Poznan, Poland
autor
  • The Research Centre for Cultivar Testing, Slupia Wielka, Poland

Bibliografia

  • Abramowitz M., Stegun I.A. (1965): Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Dover Publications.
  • Allard R.W., Bradshaw A.D. (1964): Implications of genotype-environmental in­teractions in applied plant breeding. Crop Sci. 4: 503-508.[Crossref]
  • 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.[WoS]
  • 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.
  • 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.
  • 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.[Crossref]
  • Annicchiarico P., Russi L., Piano E., Veronesi F. (2006b): Cultivar adaptation across Italian locations in four turfgrass species. Crop Sci. 46: 264-272.[Crossref]
  • 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.[WoS]
  • 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.
  • 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.
  • Braun H.J., Rajaram S., van Ginkel M. (1996): CIMMYT’s approach to breeding for wide adaptation. Euphytica 92: 175-183.
  • 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.[Crossref]
  • Dencic S., Mladenov N., Kobiljski B. (2011): Effects of genotype and environment on breadmaking quality in wheat. Int. J. Plant Prod. 5: 71-82.
  • Gauch H.G. (1992): Statistical analysis of regional yield trials. AMMI analysis of factorial designs. Elsevier Science, New York.
  • Gauch H.G. (2006): Statistical analysis of yield trials by AMMI and GGE. Crop Sci. 46: 1488-1500.[WoS][Crossref]
  • 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.[Crossref][WoS]
  • Gauch H.G., Zobel R.W. (1997): Identifying mega-environments and targeting genotypes. Crop Sci. 37: 311-326.[Crossref]
  • 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.[Crossref]
  • Górecki T., Krzysko M. (2012): A kernel version of functional principal compo­nents analysis, Statistics in Transition, 13(3): 559-568.
  • 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.
  • Kozak M. (2010a): Use of parallel coordinate plots in multi-response selection of interesting genotypes. Commun. Biometry Crop Sci. 5: 83-95.
  • 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.
  • 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.
  • 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.[Crossref]
  • Prim R.C. (1957) Shortest connection networks and some generalizations. Bell System Technical Journal 36: 1389-1401.
  • 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>.
  • 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>.
  • Ramsay J.O., Silverman B.W. (2005): Functional Data Analysis, Second Edition, Springer.
  • 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.[Crossref][WoS]
  • 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.[WoS]
  • 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.[WoS]
  • 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.
  • Trethowan R., Crossa J. (2007): Lessons learnt from forty years of international spring bread wheat trials. Euphytica 157: 385-390.[WoS]
  • 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.[Crossref]
  • Ulukan H. 2008. Agronomic adaptation of some field crops: a general approach. J. Agron. Crop Sci. 194: 169-179.[Crossref][WoS]
  • Yan W., Hunt L.A. (1998): Genotype by environment interaction and crop yield. Plant Breed. Rev. 16: 135-179.
  • 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.[Crossref][WoS]
  • 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.[Crossref]
  • 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.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

bwmeta1.element.doi-10_2478_bile-2013-0019
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.