Analysis of Variety Yield Trials Using Two-Dimensional Separable ARIMA Processes
Abstract
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 firstorder autoregressive model was the most efficient in terms of the SED and MSE criteria (in 21 and 14 trials, respectively).
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Empirical investigations of lattice-based fertility models by Cullis and Gleeson (1991), Kempton et al. (1994) and Grondona et al. (1996) are restricted to separable autoregressive integrated moving average processes, which excludes the authors' specific proposal.
“Empirical investigations of lattice-based fertility models are described by Cullis and Gleeson (1991), Kempton et al. (1994) and Grondona et al. (1996) but with a restriction to separable autoregressive integrated moving average processes, which excludes our speci®c proposal.”
From Bayesian Analysis of Agricultural Field Experiments (1999) · cites this paper as Grondona et al., 1996
Cites
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Cullis, B. R., & Gleeson, A. C. (1991) · Biometrics · doi:10.2307/2532398 · Cited by 325 · see citing works
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Besag, J., & Kempton, R. (1986) · Biometrics · doi:10.2307/2531047 · Cited by 246 · see citing works
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Accounting for natural and extraneous variation in the analysis of field experiments
Gilmour, A. R., Cullis, B. R., Verbyla, A. P., & Verbyla, A. P. (1997) · Journal of Agricultural, Biological, and Environmental Statistics · doi:10.2307/1400446 · Cited by 752 · see citing works
Bayesian Analysis of Agricultural Field Experiments
Besag, J., & Higdon, D. (1999) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00201 · Cited by 183 · see citing works
Evaluation of experimental designs and spatial analyses in wheat breeding trials
Qiao, C. G., Basford, K. E., DeLacy, I. H., & Cooper, M. (2000) · Theoretical and Applied Genetics · doi:10.1007/s001220050002 · Cited by 91 · see citing works
- Authors
- Grondona, M. O., Crossa, J., Fox, P. N., & Pfeiffer, W. H.
- ORCID
- Crossa, J.
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1996-06
- Volume
- 52
- Issue
- 2
- Pages
- 763
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W2327583954
- Cited by
- 66
- References
- 3
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 2 · 2022: 2 · 2021: 3 · 2020: 3 · 2019: 3 · 2018: 4 · 2017: 1 · 2015: 5 · 2013: 2 · 2012: 4
- FWCI
- 2.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biometrics (publishedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Genetics and Plant Breeding
- Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Agriculture, Soil, Plant Science
- Keywords
- cereal yield, incomplete block designs, spatial modeling, model adequacy, mean squared prediction error, cross-validation, first-order autoregressive model
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