Enhanced diagnostics for the spatial analysis of field trials

Stefanova, Katia; Smith, Alison; Cullis, Brian R

doi:10.1198/jabes.2009.07098

Cited by 81 publications

(107 citation statements)

References 13 publications

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“…This suggests that adjacent plots have a negative effect on each other. Figure 1 (panels (a) and (d)) present the diagnostic plots suggested by Stefanova, Smith, and Cullis (2009). These are the row and column faces of the sample values of the empirical semi-variogram of the residuals from model 1 in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…These plots are augmented with the mean and 95 % point-wise coverage intervals of the faces of the empirical semivariogram from a parametric bootstrap sample of size 100. This procedure is fully described in Stefanova, Smith, and Cullis (2009), essentially the current model is simulated 100 times using the current variance components and the sample variogram is calculated for each simulation. The 2.5 % and 97.5 % percentiles are obtained and included in Figure 1.…”

Section: Resultsmentioning

confidence: 99%

“…Our approach uses the enhanced spatial modeling ideas found in Stefanova, Smith, and Cullis (2009). They describe an approach to the analysis of individual trials using a "hybrid" approach which includes terms in the linear mixed model to account for spatial variation and randomization processes used in the design.…”

Section: Excluding Information On Pedigreesmentioning

confidence: 99%

“…Table 1 presents the summary of the sequence of models fitted to the PYTM trial. Our analysis commenced by fitting a base-line model following the approaches recommended by Gilmour, Cullis, and Verbyla (1997) and recently modified by Stefanova, Smith, and Cullis (2009). This model included direct (D) effects for both additive and non-additive genetic effects, as well as a Block term to respect the resolvability of the design, and lastly used a separable first order autoregressive variance model for the residuals.…”

Section: Including Information On Pedigrees and Competitionmentioning

confidence: 99%

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Predicting Additive and Non-additive Genetic Effects from Trials Where Traits Are Affected by Interplot Competition

Hunt

Smith

Jordan

et al. 2012

JABES

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There are two key types of selection in a plant breeding program, namely selection of hybrids for potential commercial use and the selection of parents for use in future breeding. Oakey et al. (in Theoretical and Applied Genetics 113, 809-819, 2006) showed how both of these aims could be achieved using pedigree information in a mixed model analysis in order to partition genetic effects into additive and non-additive effects. Their approach was developed for field trial data subject to spatial variation. In this paper we extend the approach for data from trials subject to interplot competition. We show how the approach may be used to obtain predictions of pure stand additive and non-additive effects. We develop the methodology in the context of a single field trial using an example from an Australian sorghum breeding program. There are two key types of selection in a plant breeding program, namely selection of hybrids for potential commercial use and the selection of parents for use in future breeding. Oakey et al. (in Theoretical and Applied Genetics 113, 809-819, 2006) showed how both of these aims could be achieved using pedigree information in a mixed model analysis in order to partition genetic effects into additive and non-additive effects. Their approach was developed for field trial data subject to spatial variation. In this paper we extend the approach for data from trials subject to interplot competition. We show how the approach may be used to obtain predictions of pure stand additive and non-additive effects. We develop the methodology in the context of a single field trial using an example from an Australian sorghum breeding program.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Excluding Information On Pedigreesmentioning

confidence: 99%

Section: Including Information On Pedigrees and Competitionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Additive and Non-additive Genetic Effects from Trials Where Traits Are Affected by Interplot Competition

Hunt

Smith

Jordan

et al. 2012

JABES

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, the use of a spatial design adding an independent residual variance component allowed the removal of spatially dependent residual noise from the model, which was expected according to the theory of models that include both sources of non-genetic variation (Stefanova et al, 2009). This finding was important evidence of the need to separate spatial from the independent residual variances, in order to capture the random tree-to-tree variation.…”

Section: Variance-covariance Structures Comparisonmentioning

confidence: 72%

Use of Analytic Factor Structure to Increase Heritability of Clonal Progeny Tests of Pinus taeda L.

Zapata-Valenzuela

2012

Chilean J. Agric. Res.

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Advanced variance-covariance structures are commonly used in genetic evaluation of crops to account for micro-site variability and achieve higher accuracy of predictions to increase selection efficiency. Various genetic variance-covariance structures were explored to predict best linear unbiased genetic merits of 453 loblolly pine (Pinus taeda L.) cloned progeny tested at 16 different locations in the southern U.S. Statistical models were compared using model fit statistics, variance components and genetic parameters. Among the models explored, spatial autoregressive error correlation with independent residual term for the R side with a factor analytic structure for the G side of the mixed model was superior. The model produced one of the smallest fit statistics (LogL equal to -2694), a small error variance (12.72), and the highest broad-sense heritability (0.45), compared with the default homogeneous error and genetic variance-covariance structure (statistical significance at P < 0.05). We concluded that the combination of specific structure for error and genetic design was effective to remove spatial-related variance, and to increase the accuracy of predictions of clonal genetic values, which could be used as analytical tool for increasing the selection efficiencies in forest genetic trials.

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