Improving the precision of cotton performance trials conducted on highly variable soils of the southeastern USA coastal plain

Campbell, B. Todd; Bauer, Philip J.

doi:10.1111/j.1439-0523.2007.01397.x

Cited by 7 publications

(10 citation statements)

References 32 publications

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“…Field design and spatial methods are important for minimizing the effect of spatial trend on the increasing precision of mean estimates (Basford et al, 1996; Brownie and Gumpertz, 1997, Federer, 1998; Qiao et al, 2000; Campbell and Bauer, 2007). Different field designs and spatial methods have been developed and discussed in the last thirty years (Bartlett, 1978; Wilkinson et al, 1983; Schwarzbach, 1984; Besag and Kempton, 1986; Williams, 1986; Gilmour et al, 1997; Gleeson, 1997; Edmondson, 2005; Williams et al, 2006), but few of them concentrated on field trials of sugar beet and barley in Europe.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Spatial Models for Sugar Beet and Barley Trials

et al. 2010

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Several spatial methods exist for the adjustment of local trend in one dimension. The aim of this study was to evaluate and compare the precision of different spatial methods. For this purpose, 293 sugar beet (Beta vulgaris) and 64 multienvironment barley (Hordeum vulgare) trials of two German plant breeding companies were analyzed using a baseline model, which comprised a block and replicate effect, and different one‐dimensional spatial models augmenting the baseline model. Model fit was assessed using the Akaike Information Criterion (AIC), the phenotypic correlation of the adjusted genotype means between two environments, and the relative efficiency. For the sugar beet and barley trials the baseline model outperformed the spatial models in the majority of cases, while in some cases the addition of a spatial component proved beneficial. Based on these results we propose a conservative approach to spatial modeling that starts with a baseline model and then checks whether adding a spatial component improves the fit. Among the alternative models studied, the linear variance and the first‐order autoregressive models were the most promising candidates.

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Section: Discussionmentioning

confidence: 99%

Comparison of Spatial Models for Sugar Beet and Barley Trials

et al. 2010

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“…In agricultural experimentation, factors such as soil heterogeneity, agricultural practices, and environmental conditions influence the genotypic performance of lines and contribute to the spatial variability (Arnold and Kempton, 1979; Gilmour et al, 1997). Therefore, modeling spatial correlations might be necessary to improve genotypic effect estimation even after a good experimental design is used (Federer, 1998; Qiao et al, 2000; Campbell and Bauer, 2007; Casler, 2015; Borges et al, 2019). Several approaches have been proposed to control spatial variability such as nearest‐neighbor adjustment (Katsileros et al, 2015), smoothing techniques including penalized splines analysis (Stefanova et al, 2009; Piepho and Williams, 2010; Velazco et al, 2017), modeling the variance–covariance matrix of spatial correlations using geostatistical components (Williams, 1986; Williams et al, 2006; Piepho and Williams, 2010), or using mixed models (Smith et al, 2005).…”

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Mega‐Environmental Design: Using Genotype × Environment Interaction to Optimize Resources for Cultivar Testing

2019

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The efficient use of testing resources is one of the key factors for successful plant breeding programs. Controlling micro‐ and macro‐environmental variability is an effective way of improving the testing efficiency and the selection of superior genotypes. Common experimental designs in genotypic testing usually use replicated or augmented experiments at each location, but they are balanced across locations. Some studies suggest that the increase in population size even at the expense of balanced experiments might be beneficial if genotype × environment interaction (GEI) is modeled. The objective of this study was to compare strategies for micro and macro‐environmental variability control that include GEI information to optimize resource allocation in multi‐environment trials (METs). Six experimental designs combined with four spatial correction models were compared for efficiency under three experimental sizes using simulations under a real yield variability map. Additionally, six resource allocation strategies were evaluated in terms of accuracy and the expected response to selection. The α‐lattice (ALPHA) experimental design was the best one at controlling micro‐environmental variability. The moderate mega‐environmental design (MED) strategy had the largest response to selection. This strategy uses historical mega‐environments (MEs) to unbalance genotypic testing within MEs while modeling GEI. The MED was the best resource allocation strategy and could potentially increase selection response up to 43% in breeding programs when genotypes are evaluated in METs.

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“…1985, Besag and Kempton 1986, Kempton et al. 1994, Besag and Higdon 1999, Watson 2000, Edmondson 2005, McCullagh and Clifford 2006, Campbell and Bauer 2007, Piepho et al. 2008), and with the advent of powerful statistical packages, fully‐fledged REML‐based mixed model analyses with spatial covariance structures can conveniently be used for crop variety trials and plant breeding trials (Smith et al.…”

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Linear variance models for plant breeding trials

Piepho

Williams

2010

Plant Breeding

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Most plant breeding trials involve a layout of plots in rows and columns. Resolvable row-column designs have proven effective in obtaining efficient estimates of treatment effects. Further improvement may be possible by postblocking or by inclusion of spatial model components. This study reviews options for augmenting a baseline row-column model by the addition of spatial components. The models considered are different variants of the linear variance model in one and two dimensions. Usefulness of these options is assessed by analysing a number of field trials from plant breeding and variety testing.Key words: field trial -linear variance -residual maximum likelihood -resolvable row-column designs -separabilityspatial analysis Spatial modelling of field trials has received considerable attention in the past (Wilkinson et al. 1983, Green et al. 1985, Besag and Kempton 1986, Kempton et al. 1994, Besag and Higdon 1999, Watson 2000, Edmondson 2005, McCullagh and Clifford 2006, Campbell and Bauer 2007, Piepho et al. 2008, and with the advent of powerful statistical packages, fullyfledged REML-based mixed model analyses with spatial covariance structures can conveniently be used for crop variety trials and plant breeding trials (Smith et al. 2005).A large number of spatial models have been proposed for field trial data, so spatial analysis requires a number of choices to be made. Thus, model selection and precautions against overfitting become a major issue. The danger of over-fitting increases with the number of candidate models being tried on the data, so it is advisable to limit the number of model choices at the design stage. In some applications of spatial modelling for field trials, spatial models have been treated as alternatives to the use of blocking and classical design principles. Recently, interest has increased in finding optimal designs with a particular spatial analysis in mind (Cullis et al. 2006, Martin et al. 2006, Butler et al. 2008. Taking a somewhat intermediate stance, Williams et al. (2006) proposed resolvable spatial row-column designs based on linear variance (LV) models, which provide randomization protection via the option of reverting to classical analysis for row-column designs when the spatial component does not lead to an improved fit. Thus, analysis may proceed by fitting a baseline model with row and column effects and checking if addition of a spatial component is worthwhile. This approach, while allowing spatial correlation to be exploited, uses only a very limited set of candidate models. Also, users of a spatial rowcolumn design are protected if a particular data set demands a complex variance-covariance structure while paying only a small premium in reduced precision if several of the spatial variance-covariance parameters turn out to be relatively small.This study describes two-dimensional LV models for resolvable row-column designs. It investigates the model proposed by Williams et al. (2006) and proposes a new model, which meets the separability condition (Martin 1979). ...

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Improving the precision of cotton performance trials conducted on highly variable soils of the southeastern USA coastal plain

Cited by 7 publications

References 32 publications

Comparison of Spatial Models for Sugar Beet and Barley Trials

Comparison of Spatial Models for Sugar Beet and Barley Trials

Mega‐Environmental Design: Using Genotype × Environment Interaction to Optimize Resources for Cultivar Testing

Linear variance models for plant breeding trials

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