An improvement on the papadakis covariate to account for spatial variation

Taye, Girma; Njuho, Peter Mungai

doi:10.1198/108571107x227946

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…When spatial correlation is comparable in two directions, it may be preferable to employ two-dimensional extensions of the linear variance and random walk models considered here (Kempton et al, 1994;Besag and Higdon, 1999;Williams et al, 2006;Piepho and Williams, 2007;Lee and Piepho, 2007). Also, there have been numerous suggestions how to extend the Papadakis method in two dimensions (Wilkinson et al, 1983;Taye and Njuho, 2007).…”

Section: Discussionmentioning

confidence: 99%

Nearest Neighbour Adjustment and Linear Variance Models in Plant Breeding Trials

2008

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This paper reviews methods for nearest neighbour analysis that adjust for local trend in one dimension. Such methods are commonly used in plant breeding and variety testing. The focus is on simple differencing methods, including first differences and the Papadakis method. We discuss mixed model representations of these methods on the scale of the observed data. Modelling observed data has a number of practical advantages compared to differencing, for example the facility to conveniently compute adjusted cultivar means. Most models considered involve a linear variance-covariance structure and can be represented as state-space models. The reviewed methods and models are exemplified using three datasets.

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Section: Discussionmentioning

confidence: 99%

Nearest Neighbour Adjustment and Linear Variance Models in Plant Breeding Trials

2008

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“…But the results show that an incomplete block analysis without a spatial component is competitive compared with a complete block analysis with a spatial error component. Different approaches to the analysis of field trials with adjustment for spatial variation can be found for example in Stringer and Cullis (2002), in Taye and Njuho (2007), Yang et al. (2004) or in Cullis et al.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

The extent and prevailing shape of spatial relationships in Polish variety testing trials on wheat

Pilarczyk

2009

Plant Breeding

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Independence of observations is one of the basic assumptions of the analysis of variance. Performed randomizations prevent results from being biased in cases when independence is violated. The objective of the present paper is to find out the predominant shape of spatial relationships in Polish wheat variety testing trials. One of the possibilities is to apply some geo-statistical method (Cressie Noel 1993, Grondona andCressie 1991). Such an approach is used in this paper. Using the results of nearly 200 trials on wheat varieties, conducted either in generalized lattice (GL) designs (Patterson and Hunter 1983) or in incomplete split-block designs, the empirical values of semivariance have been calculated. Residuals were computed from a model with fixed effects for varieties and replicates, ignoring incomplete blocks, then 10 different geo-statistical models have been fitted to empirical semivariances. With spatial models convergence problems occurred in some cases. The linear and bounded-linear models were the ones most often successfully fitted. Inclusion of revealed relationships into classical models for incomplete blocks did not improve substantially the effectiveness of analyses.

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“…The Papadakis method (Papadakis 1937(Papadakis , 1984 is perhaps the simplest means of incorporating spatial dependence in model estimation. The Papadakis method uses the average model residual for trees in spatial proximity as a covariate in the model, and has been confirmed as being approximately valid (Bartlett 1978, Taye andNjuho 2007). Residual correlograms were examined to identify the scale of spatial dependence, and based on this the average residual was generated for all trees less than 10 metres from the subject.…”

Section: Papadakis Methodsmentioning

confidence: 99%

Modelling spatial dependence in an irregular natural forest

Fox

Bi²,

Ades³

2008

Silva Fenn.

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The spatial dependence present in a natural stand of Eucalyptus pilularis (Smith) dominated mixed species forest was characterised and modelled. Two wildfires imposed a significant spatial dependence on the post disturbance stand. It was hypothesised that spatial variation in the intensity of the wildfires generated the observed structures. The influence of patch formation, micro-site variability and competitive influences were also noted in the residuals of a distance-dependent individual-tree growth model. A methodology capable of modelling these complicated patterns of observed dependence was sought, and candidates included the spatial interaction, direct specification and Papadakis methods. The spatial interaction method with a moving average autoregression was identified as the most appropriate method for explicitly modelling spatial dependence. Both the direct specification and Papadakis methods failed to capture the influence of competition. This study highlights the possibility that stand disturbances such as natural and artificial fires, insect and fungal attacks, and wind and snow damage are capable of imposing powerful spatial dependencies on the post disturbance stand. These dependencies need to be considered if individual tree growth models are to provide valid predictions in disturbed stands.

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An improvement on the papadakis covariate to account for spatial variation

Cited by 3 publications

References 15 publications

Nearest Neighbour Adjustment and Linear Variance Models in Plant Breeding Trials

Nearest Neighbour Adjustment and Linear Variance Models in Plant Breeding Trials

The extent and prevailing shape of spatial relationships in Polish variety testing trials on wheat

Modelling spatial dependence in an irregular natural forest

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