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Czasopismo

2013 | 50 | 2 | 137-149

Tytuł artykułu

Check plots in field breeding experiments

Treść / Zawartość

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
This paper deals with the problems of selection in the early stages of a breeding program. During the improvement process, it is not possible to use an experimental design that satisfies the requirement of replicating all the treatments, because of the large number of genotypes involved, the small amount of seed and the low availability of resources. Hence unreplicated designs are used. To control the real or potential heterogeneity of experimental units, control (check) plots are arranged in the trial. There are many methods of using the information resulting from check plots. All of the usually applied adjusting methods for unreplicated experiments are appropriate for some specific structure of soil fertility. Their disadvantage is the fact that, before and also after the experiment, we usually do not know what a kind of soil structure is present in the experiment. Hence we cannot say which of the existing methods is appropriate for a given experimental situation. The method of inference presented below avoids this disadvantage. It is always appropriate, because of the fact that a trend of soil variability is identified and estimated. In the paper the main tool used to explore this information will be based on a response surface methodology. To begin with we will try to identify a response surface characterizing the experimental environments. We assume that observed yield (or another trait) results directly from two components, one of them due to soil fertility and the other due to the genotype effect. This means that difference between observed yield and forecast can be treated as the estimate of a genotype effect. The obtained response surface will then be used to adjust the observations for genotypes. Finally, the data so adjusted are used for inferences concerning the next stage of the breeding program. The theoretical considerations are illustrated with an example involving yields of spring barley.

Wydawca

Czasopismo

Rocznik

Tom

50

Numer

2

Strony

137-149

Opis fizyczny

Daty

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

Twórcy

  • Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Poland
autor
  • Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Poland

Bibliografia

  • Baker R.J., McKenzie R.I.H. (1967): Use of control plots in yield trials. Crop. Sci. 7:335-337.
  • Box G.E.P., Draper N. (2007): Response Surfaces, Mixtures, and Ridge Analyses, Wiley, New York.
  • Bradley N. (2007): The response surface methodology. Indiana University South Bend. Briggs K.G., Shebeski L.H. (1967): Implications concerning the frequency of control plots in wheat breeding nurseries. Can. J. Plant Sci. 48: 149 - 153.
  • Cullis B.R., Warwick J.A., Fisher B.J., Read B.J., Gleeson A.C. (1989): A new procedure for the analysis of early generation variety trials. Appl. Statist. 38: 361-375.
  • Brownie C., Gumpertz M.L. (1997): Validity of Spatial Analyses for Field Trials. Journal of Agricultural, Biological, and Environmental Statistics 2(1): 1-23. Golaszewski J. (1999): Application of geostatistical methods to analysis of the data from a pea breeding trial. Biometrical Letters 36(2): 145-157.
  • Golaszewski J. (2002): Geostatistical approach to data from field experiments with check plots - Electronic Journal of Polish Agricultural Universities, EJPAU, Ser. Agronomy 5(2).
  • Holtsmark G., Larsen B.R. (1905): Om multigheder for at indskraenke de fejl, som ved marksforsog betinges af jordens uensartethed. Tidsskrift for Landbrugets Planteavl, 12: 330-351.
  • Kempton R.A. (1984): The design and analysis of unreplicated field trials. Vortr. Pflanzenzuchtg 7: 219-242.
  • Kempton R.A., Fox P.N. (1997): Statistical methods for plant variety evaluation. Chapman & Hall.
  • Khuri A.I., Cornell J.A. (1987): Response Surfaces. Designs and Analysis. Marcel Dekker, Inc. New York
  • Khuri A.I. (2006): Response Surface Methodology and Related Topics. World Scientific, Singapore.
  • Martin R.J. (1986): On the design of experiments under spatial correlation. Biometrika 73: 247-277.
  • Mejza S., Marczynska K. (2011): Check plot density in estimation of soil fertility. Proceedings: Biometric Methods and Models in Current Science and Research, D. Hampel, J. Hartmann, J. Michalek , (eds) Central Institute of Supervising and Testing in Agriculture, Brno: 167-172.
  • Myers an R.H., Montgomery D.C. (2001): Response Surface Methodology, John Wiley and Sons, 2nd edition.
  • Sebolai B., Pedersen J.F., Marx D.B., Boykin D.L. (2005): Effect of control plot density, control plot arrangements, and assumption of random or fixed effects on nonreplicated experiments for germplasm screening using spatial models. Crop Sciences: 1978-198.
  • Utz H.F. (1997): PLABSTAT. A computer program for statistical analysis of plant breeding experiments. Version 2N. Institute of Plant Breeding, Seed Science and Population Genetics. University of Hohenheim, Germany.
  • Zimmerman D.L., Harville D.A. (1991): A Random Field Approach to the Analysis of Field - Plot Experiments and Other Spatial Experiments. Biometrics 47: 223-239.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

bwmeta1.element.doi-10_2478_bile-2013-0024
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