Analysis of variety yield trials using two-dimensional separable ARIMA processes
Material type:
ArticleLanguage: En Publication details: 1996Subject(s):
In:
Biometrics v. 52, no. 2, p. 763-770622189Summary: The two-dimensional spatial analysis procedure based on separable ARIMA processes, proposed by Cullis and Gleeson (1991 Biometrics 47, 1449-1460), is used to analyze 35 cereal yield trials with incomplete block designs. Models with different large-scale variation components and diverse small-scale variation processes, modeled as one-dimensional and two-dimensional (separable) ARIMA processes, were compared. Nineteen spatial models were considered and two criteria were used to assess spatial model adequacy: (a) the average standard error of the pairwise variety differences (SED) and (b) the mean squared error of prediction (MSE) based on a cross-validation approach. Spatial analysis is more efficient in reducing residual variation than incomplete block analysis. Although there was no one model that best fit all the trials, the two-dimensional first-order autoregressive model was the most efficient in terms of the SED and MSE criteria (in 21 and 14 trials, respectively)
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|---|
| Article | CIMMYT Knowledge Center: John Woolston Library | CIMMYT Staff Publications Collection | CIS-1994 (Browse shelf(Opens below)) | 1 | Available | 622189 |
The two-dimensional spatial analysis procedure based on separable ARIMA processes, proposed by Cullis and Gleeson (1991 Biometrics 47, 1449-1460), is used to analyze 35 cereal yield trials with incomplete block designs. Models with different large-scale variation components and diverse small-scale variation processes, modeled as one-dimensional and two-dimensional (separable) ARIMA processes, were compared. Nineteen spatial models were considered and two criteria were used to assess spatial model adequacy: (a) the average standard error of the pairwise variety differences (SED) and (b) the mean squared error of prediction (MSE) based on a cross-validation approach. Spatial analysis is more efficient in reducing residual variation than incomplete block analysis. Although there was no one model that best fit all the trials, the two-dimensional first-order autoregressive model was the most efficient in terms of the SED and MSE criteria (in 21 and 14 trials, respectively)
Genetic Resources Program
English
R96ANALY|EE|3
CCJL01
CIMMYT Staff Publications Collection