2008
DOI: 10.1017/s0014479708006698
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Accounting for Spatial Variability in Field Experiments on Tea

Abstract: Spatial variability among experimental units is a common problem in field experiments on tree crops such as tea (Camellia sinensis). Spatial variability is partly accounted for by blocks, but a substantial amount remains unaccounted for and this may lead to erroneous conclusions. In order to capture spatial variability in field experiments on tea, six commonly used spatial analysis techniques were investigated: Covariate method with pre-treatment yield as the covariate, Papadakis and the Modified Papadakis nea… Show more

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Cited by 5 publications
(5 citation statements)
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“…Assim, o método de Papadakis, realizado com base na análise de covariância, em que a covariável é determinada como a média das estimativas dos erros nas parcelas vizinhas (variação espacial), mostrou-se eficiente para a comparação da produtividade de grãos de genótipos de trigo (Benin et al, 2013) e de soja (Storck et al, 2008). Estudos relacionados a métodos de análise espacial (Duarte & Vencovsky, 2005), para as culturas de chá (Peiris et al, 2008), feijoeiro (Souza et al, 2000), feijoeiro e milho (Costa et al, 2005) e laranjeira (Maia et al, 2013) foram realizados e, de maneira geral, relataram a melhoria da eficiência na seleção de genótipos.…”
Section: Introductionunclassified
“…Assim, o método de Papadakis, realizado com base na análise de covariância, em que a covariável é determinada como a média das estimativas dos erros nas parcelas vizinhas (variação espacial), mostrou-se eficiente para a comparação da produtividade de grãos de genótipos de trigo (Benin et al, 2013) e de soja (Storck et al, 2008). Estudos relacionados a métodos de análise espacial (Duarte & Vencovsky, 2005), para as culturas de chá (Peiris et al, 2008), feijoeiro (Souza et al, 2000), feijoeiro e milho (Costa et al, 2005) e laranjeira (Maia et al, 2013) foram realizados e, de maneira geral, relataram a melhoria da eficiência na seleção de genótipos.…”
Section: Introductionunclassified
“…These spatial trends can be eliminated with postdata treatment. Different strategies exist, including model variance-covariance matrixes, row-columns, and moving-means ( Cullis et al 1998 ; Peiris et al 2008 ; Müller et al 2010 ; Leiser et al 2012 ).…”
mentioning
confidence: 99%
“…Strategies to eliminate field spatial variation from phenotypic data include modeling variancecovariance matrixes, row-columns design, and moving means (Cullis et al, 1998;Lado et al, 2013;Leiser et al, 2012;B. U. Müller et al, 2010;Peiris et al, 2008). Yield prediction models accounting for spatial field variation decreased the error variance and increased the accuracy of yield selection in early generations (Sun et al, 2015).…”
Section: Core Ideasmentioning
confidence: 99%
“…Field spatial variation can be measured as the trait correlation among plots, which can affect the yield ranks and impact the accuracy of selection (Gilmour et al., 1997; Sun et al., 2015); however, appropriate spatial adjustment of the phenotypic data can help to remove these field spatial variation trends and increase selection accuracy (Lado et al., 2013). Strategies to eliminate field spatial variation from phenotypic data include modeling variance–covariance matrixes, row–columns design, and moving means (Cullis et al., 1998; Lado et al., 2013; Leiser et al., 2012; B. U. Müller et al., 2010; Peiris et al., 2008). Yield prediction models accounting for spatial field variation decreased the error variance and increased the accuracy of yield selection in early generations (Sun et al., 2015).…”
Section: Introductionmentioning
confidence: 99%