A series of search designs for 2m factorial designs of resolution V which permit search of one or two unknown extra three-factor interactions

1992 ◽  
Vol 44 (1) ◽  
pp. 185-196 ◽  
Author(s):  
Teruhiro Shirakura ◽  
Shinsei Tazawa
Keyword(s):  
Factorial Designs ◽  
Search Designs ◽  
Factor Interactions ◽  
Three Factor

scholarly journals A Graphical Assessment of Data from Intercropping Factorial Experiments

1983 ◽  
Vol 19 (1) ◽  
pp. 23-31 ◽  
Author(s):  
B. Gilliver ◽  
S. C. Pearce
Keyword(s):  
Graphical Method ◽  
Bivariate Analysis ◽  
Factorial Experiments ◽  
Yield Data ◽  
Crop Species ◽  
Factor Interactions ◽  
Three Factor

SUMMARYA graphical method, based on bivariate analysis, is used to present yield data from intercropping experiments involving two crop species. The method is used to demonstrate two- and three-factor interactions in factorial experiments.

Effects of varying variety and spacing on yeids and composition of mixtures of lucerne and tall fescue

1965 ◽  
Vol 64 (3) ◽  
pp. 351-359 ◽  
Author(s):  
J. L. Fyfe ◽  
H. H. Rogers
Keyword(s):  
Tall Fescue ◽  
Dry Matter ◽  
Factorial Experiment ◽  
Annual Total ◽  
Factor Interactions ◽  
Three Factor

1. In a 3 x 3 x 3 factorial experiment of two replications, with three-factor interactions partly confounded with block effects, the effects of lucerne variety (du Puits or one of two Institute synthetics), tall fescue variety (S.170 or one of two Institute bulks) and spacing (mixed drills 6 in. apart, alternate grass and lucerne drills 6 in. or 12 in. apart) and their two-factor interactions were studied on yield of dry matter and proportion of lucerne. Data are reported for the total of fourteen cuts, for the annual total for three harvest years and for spring, summer and autumn cuts.

Recent developments in the design of field experiments. IV. Lattice squares with split plots

1947 ◽  
Vol 37 (2) ◽  
pp. 156-162 ◽  
Author(s):  
O. Kempthorne
Keyword(s):  
Field Experiments ◽  
Factorial Designs ◽  
Split Plot ◽  
Trial Testing ◽  
Recent Developments ◽  
Factor Interaction ◽  
Three Factor

The testing of a large number of varieties or treatments can generally be most conveniently made by the use of the quasi-factorial designs devised by Yates. The value of such designs is enhanced by the possibility of introducing further treatments on parts of the plots. The present paper describes a lattice square trial testing 25 organic treatments (actually 22 different treatments with a control represented three times) in which all combinations of nitrogen, phosphate and potash were also tested by splitting the plots and confounding the three-factor interaction with whole plots, the total number of split-plots being 300. Both the design and analysis are comparatively simple and straight-forward, and will serve as an example of the use of split-plot confounding in most types of quasi-factorial designs.

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