USE OF MOVING MEANS IN WHEAT YIELD TRIALS

1973 ◽  
Vol 53 (3) ◽  
pp. 447-450 ◽  
Author(s):  
T. F. TOWNLEY-SMITH ◽  
E. A. HURD
Keyword(s):  
Experimental Error ◽  
Wheat Yield ◽  
Error Variance ◽  
Analysis Of Covariance ◽  
Test Plant ◽  
Lower Error ◽  
Yield Trials

The efficiency of adjustments employing repeated controls was compared with the efficiency of moving mean adjustments in yield of wheat. Results reported show the moving mean of adjacent hybrid plots to give superior control of the experimental error. The best number of adjacent controls was tested and found to vary widely from test to test. Plant breeders may have to run several analyses to obtain the most accurate adjustment. An analysis of covariance was made on some trials to see if this technique would avoid overadjustment and give lower error variance. With few exceptions neither the use of covariance nor control plots gave as great a reduction in error variance as the optimum adjustment obtained by using the moving mean of adjacent plots.

USE OF MOVING MEANS IN GRASS YIELD TRIALS

1975 ◽  
Vol 55 (2) ◽  
pp. 587-592 ◽  
Author(s):  
T. LAWRENCE ◽  
T. F. TOWNLEY-SMITH
Keyword(s):  
Experimental Error ◽  
Block Design ◽  
Test Plant ◽  
Optimum Number ◽  
Growth Type ◽  
Intermediate Wheatgrass ◽  
Random Block ◽  
Random Block Design ◽  
Yield Trials ◽  
Type Intermediate

Analyses of data from three grasses of contrasting growth-type (intermediate wheatgrass, Agropyron intermedium (Host.) Beauv.; Russian wild ryegrass, Elymus junceus Fisch.; and Altai wild ryegrass, Elymus angustus Trin.), seeded in different row spacings, showed that the moving mean adjustment of plot yields prior to analysis gave improved control of the experimental error as compared to variance analysis of unadjusted values. The optimum number of adjacent plot yields to include in the moving mean varied widely from test to test as well as from year to year in the same test. Plant breeders may have to run several analyses to obtain the most accurate adjustment. However, this can be easily accomplished in one computer run by inserting appropriate values into the program. A lattice analysis made on several trials suggested that the lattice analysis and the use of the moving mean adjustment in a random block design were comparable in relative efficiency, but the moving mean adjustment in a random block design is more flexible in terms of number of lines that can be assessed.

Using height to adjust for interplot interference in spring wheat yield trials

1999 ◽  
Vol 79 (2) ◽  
pp. 169-174 ◽  
Author(s):  
F. R. Clarke ◽  
R. J. Baker ◽  
R. M. Depauw
Keyword(s):  
Historical Data ◽  
Final Height ◽  
Wheat Yield ◽  
Analysis Of Covariance ◽  
Data Sets ◽  
Average Height ◽  
Test Plot ◽  
Height Difference ◽  
Interplot Interference ◽  
Yield Trials

Interplot interference can occur in yield trials when genotypes vary for height. We investigated the possibility of adjusting yield for interplot interference by using height measurements in an analysis of covariance. Computer simulation showed that genetic correlation between height and yield would have no impact on analysis of covariance, as well as showing that analysis of covariance would reduce precision in the absence of interference due to height difference. Sixty-five data sets from yield trials conducted in Saskatchewan in 1992, 1993 and 1994 were analyzed to see if analysis of covariance would improve precision of yield estimates. Forty percent of the historical data sets showed evidence of significant interplot interference. In those trials in which interference occurred, yield decreased an average of 0.39% for every centimetre by which the average height of the flanking plots exceeded that of the test plot. Using final height to adjust for interplot interference is effective when genotypes vary for height and when the regression coefficient is significant. Key words: Interplot interference, yield adjustment, height difference, covariate

The Use of a Small Computer in Agricultural Research

1977 ◽  
Vol 13 (3) ◽  
pp. 257-264 ◽  
Author(s):  
S. C. Pearce
Keyword(s):  
Analysis Of Variance ◽  
Agricultural Research ◽  
Error Variance ◽  
Analysis Of Covariance ◽  
Research Worker ◽  
Small Computer ◽  
Disturbing Factor

SUMMARYIt is suggested that quite a small computer, in association with a minimal program for the analysis of variance, can be used to calculate quantities of use to the agronomic research worker beyond what he usually obtains. For example, without further programs it is possible to calculate an analysis of covariance, in which adjustment is made for some disturbing factor. It is also possible to find how much of the error variance arises from a particular plot, and to deal with situations in which data are incomplete or the yields from two plots have become mixed.

Field experimentation on rough land: the method of Papadakis reconsidered

1998 ◽  
Vol 131 (1) ◽  
pp. 1-11 ◽  
Author(s):  
S. C. PEARCE
Keyword(s):  
Correct Answer ◽  
Iterative Procedure ◽  
Experimental Error ◽  
Field Experiments ◽  
Analysis Of Covariance ◽  
Mathematical Basis ◽  
Alternative Approach ◽  
Special Weight ◽  
Field Experimentation ◽  
Fertility Pattern

Much of the error in field experiments comes from environmental differences, which traditional methods (e.g. blocks or rows and columns) sometimes fail to control, especially if the fertility pattern is complex or unexpected. In 1937 Papadakis proposed an alternative approach in which the performance of each plot was judged by that of its neighbours. If a plot was situated in an area of generally high cropping, its own high crop was partly discounted but if surrounding plots were yielding poorly, it was given special weight. Adjustment was by the analysis of covariance. The method has not been much used, partly because it still lacks a mathematical basis and partly because it is suspected of bias in the estimation of contrasts between treatments.It has been studied here by simulation, i.e. bodies of data have been generated on the computer with known characteristics and then analysed by Papadakis's method to see if a correct answer was returned. Also, an improvement in the covariate is suggested and an iterative procedure developed to ensure more consistent results.It emerges that the suspicion of bias was unjustified. Also, the method can be extremely effective in reducing experimental error. In all its variant forms it has difficulty with outside plots, and for that reason is more effective on compact areas than on strips. Also, interference between plots is especially serious, because the neighbours affected are those needed to form the adjusting covariate. Discontinuities arising from past use of the land can cause problems but a method is suggested for minimizing their effect.

scholarly journals Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early-Generation Wheat Yield Trials

Crop Science ◽  
2019 ◽  
Vol 59 (2) ◽  
pp. 491-507 ◽  
Author(s):  
Brian P. Ward ◽  
Gina Brown-Guedira ◽  
Priyanka Tyagi ◽  
Frederic L. Kolb ◽  
David A. Van Sanford ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Wheat Yield ◽  
Early Generation ◽  
Selection Models ◽  
Yield Trials

scholarly journals Genetic Gains for Grain Yield in CIMMYT's Semi‐Arid Wheat Yield Trials Grown in Suboptimal Environments

Crop Science ◽  
2018 ◽  
Vol 58 (5) ◽  
pp. 1890-1898 ◽  
Author(s):  
L. A. Crespo-Herrera ◽  
J. Crossa ◽  
J. Huerta-Espino ◽  
M. Vargas ◽  
S. Mondal ◽  
...  
Keyword(s):  
Grain Yield ◽  
Wheat Yield ◽  
Genetic Gains ◽  
Yield Trials ◽  
Semi Arid

Analyzing pre-post designs using the analysis of covariance models with and without the interaction term in a heterogeneous study population

2019 ◽  
Vol 29 (1) ◽  
pp. 189-204 ◽  
Author(s):  
Fei Wan
Keyword(s):  
Treatment Effect ◽  
Treatment Strategies ◽  
Interaction Model ◽  
Error Variance ◽  
Ordinary Least Squares ◽  
Conditional Variance ◽  
Analysis Of Covariance ◽  
Parallel Group ◽  
Interaction Term ◽  
Variable Bias

Pre-post parallel group randomized designs have been frequently used to compare the effectiveness of competing treatment strategies and the ordinary least squares (OLS)-based analysis of covariance model (ANCOVA) is a routine analytic approach. In many scenarios, the associations between the baseline and the post-randomization scores could differ between the treatment and control arms, which justifies the inclusion of the treatment by baseline score interaction in ANCOVA. This heterogeneity may also cause heteroscedastic errors in ANCOVA. In this study, we compared the performances of the ANCOVA models with and without the interaction term in estimating the marginal treatment effect in a heterogeneous two-arm pre-post design. We explored the relationship between the two nested ANCOVA models from the perspective of an omitted variable bias problem and further revealed the reasons why the usual ANCOVA may fail in heterogeneous scenario through the discussion of the three types of variances associated with the ANCOVA estimators of the marginal treatment effect: the target unconditional variance, the conditional variance allowing unequal error variances, and the OLS conditional variance derived under the assumption of constant error variance. We demonstrated analytically and with simulations that the proposed heteroscadastic-consistent variance estimators provide valid unconditional inference for ANCOVA, and the ANCOVA interaction model is more powerful than the ANCOVA main effect model when a design is unbalanced.

Removing Spatial Variation from Wheat Yield Trials: A Comparison of Methods

Crop Science ◽  
1994 ◽  
Vol 34 (1) ◽  
pp. 62-66 ◽  
Author(s):  
Walter W. Stroup ◽  
P. Stephen Baenziger ◽  
Dieter K. Mulitze
Keyword(s):  
Spatial Variation ◽  
Wheat Yield ◽  
Comparison Of Methods ◽  
Yield Trials
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