Benefit of spatial analysis for furrow irrigated cotton breeding trials

Liu, S M; Constable, G. A.; Cullis, Brian R; Stiller, Warwick N.; Reid, Peter

doi:10.1007/s10681-014-1205-2

Cited by 20 publications

(30 citation statements)

References 31 publications

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“…Although post‐blocking is not recommended for routine analysis, it is frequently used to investigate the efficiency of alternative experimental designs (Qiao et al, 2000; Robinson et al, 1988). Similar gains in efficiency using RC designs compared with RCB have been reported in cotton yield trials (Liu et al, 2015; Williams and Luckett, 1988). Genotypic effects for NDVI were significant (α = 0.05) in Trial 1 in 2014 across all models (Table 1).…”

Section: Resultssupporting

confidence: 74%

Improving the Precision of NDVI Estimates in Upland Cotton Field Trials

Hugie¹,

Bauer²,

Stone³

et al. 2018

The Plant Phenome Journal

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Core Ideas Estimates of NDVI in upland cotton were influenced by field heterogeneity. Row–column design and spatial analysis improved the precision of NDVI estimates. Extensive rank changes in genotype mean estimates were observed across models. Controlling for experimental error attributable to field heterogeneity is important in high‐throughput phenotyping studies that enable large numbers of genotypes to be evaluated across time and space. In the current study, we compared the efficacy of different experimental designs and spatial models in the analysis of canopy spectral reflectance data collected on upland cotton (Gossypium hirsutum L.). Canopy spectral reflectance, as measured by normalized difference vegetation index (NDVI), was measured at first bloom on three upland cotton performance trials conducted in Florence, SC, during 2014 and 2015. The relative efficiency and estimates of genotype effects were compared among randomized complete block, an α‐lattice incomplete block, row–column incomplete block, nearest neighbor adjusted, and spatially correlated error models. The row–column model provided the greatest improvement in the precision of genotype effect estimates compared with the randomized complete block model. Genotype rankings based on NDVI varied substantially between the randomized complete block and alternative models, particularly at 5 and 10% selection intensities. These results suggest that the use of more complex experimental designs and spatial analyses should be routinely considered to minimize experimental error due to field heterogeneity and improve the precision and reliability of traits measured using high‐throughput phenotyping systems. These findings also indicate that further research into the effects of field heterogeneity on the relationship between NDVI and lint yield in upland cotton is warranted.

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Section: Resultssupporting

confidence: 74%

Improving the Precision of NDVI Estimates in Upland Cotton Field Trials

Hugie¹,

Bauer²,

Stone³

et al. 2018

The Plant Phenome Journal

View full text Add to dashboard Cite

show abstract

“…As several empirical and simulation studies have indicated, trial layout plays an important role in determining the effectiveness of one model over the others. In analysis of spatial variation in Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton (Gossypium hirsutum L.) trials, Liu et al (2015) observed a higher frequency of spatial models in rectangular trials compared with square trials. The current results are in accordance with previous studies that indicated the plausibility of higher correlations along the dimension of short-distance plots (Cullis and Gleeson, 1989;Piepho et al, 2008;Piepho and Williams, 2010).…”

Section: Precision and Relative Efficiency Of Spatial Modelsmentioning

confidence: 99%

“…The NNA and correlated error (CE) models are some of the most commonly used methods to account for fine-scale spatial variation (Brownie et al, 1993). While some of these methods can substantially increase the efficiency compared with classical design-based models, their relative efficiency is often determined by several factors-for example, plot size (Casler, 2013) and trial layout (Liu et al, 2015). While some of these methods can substantially increase the efficiency compared with classical design-based models, their relative efficiency is often determined by several factors-for example, plot size (Casler, 2013) and trial layout (Liu et al, 2015).…”

mentioning

confidence: 99%

“…While the NNA indirectly uses covariates to model spatial variation, the CE models directly account for the spatial variation using covariance structures among residuals based on distance (isotropic models) and both distance and direction (anisotropic models) (Littell et al, 2006). While some of these methods can substantially increase the efficiency compared with classical design-based models, their relative efficiency is often determined by several factors-for example, plot size (Casler, 2013) and trial layout (Liu et al, 2015). Therefore, a spatial model that is adequate for one trial may not be adequate for other trials, hence comparison of different spatial models to determine the best plausible model for each trial is recommended (Gilmour et al, 1997;Richter and Kroschewski, 2012).…”

mentioning

confidence: 99%

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Spatial Variability Effects on Precision and Power of Forage Yield Estimation

Sripathi

Conaghan

Grogan³

et al. 2017

Crop Science

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Spatial analyses of yield trials allow adjustment of cultivar means for spatial variation, improving the statistical precision of yield estimation. While the relative efficiency of spatial analysis has been frequently reported in several yield trials, its application to long‐term Lolium spp. forage yield trials has not been characterized. The objective of this study was to evaluate the trend analysis, nearest‐neighbor analysis (NNA), and correlated error (CE) models for their ability to account for spatial variability in 138 Lolium spp. forage yield trials. This case study was performed on data from five locations and 11 yr (2001–2011) using randomized complete block design (RCBD) trials conducted by the Department of Agriculture, Food and Marine (DAFM) in Ireland. The relative efficiencies of trend, NNA, and CE models compared with RCBD models were 129, 143, and 193% for analysis by trial × year, and 121, 125, and 171% for analysis by trial, respectively. When the top one, two, three, four, or five cultivar(s) were compared between CE and RCBD models, the agreement between two models to find common cultivars varied from 66% for the top cultivar to 28% for the top five cultivars. Using CE models, four replicates were sufficient to detect mean yield differences between cultivars of 7% of the mean and 80% power. Spatial analysis should be added to the routine DAFM testing programs, not only to improve the precision of yield estimates, but also to reduce the risk of missing potential candidate cultivars, given the existence of spatial variation.

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“…The model without any reduction in resources for a given sowing year was considered a full model, whereas the models with reduced resources based on different schemes for harvest year, location, and replicates were considered reduced models. The average variance of genotypic differences of both full and reduced models (AVD full and AVD red , respectively) were compared to calculate relative efficiency (RE) as follows (Liu et al, 2015):

RE = 100 ({AVD}_{full} / {AVD}_{red})

…”

Section: Methodsmentioning

confidence: 99%

Modeling Genotype × Environment Correlation Structures in Long‐term Multilocation Forage Yield Trials

Sripathi

Conaghan

Grogan³

et al. 2018

Crop Science

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Genotype × environment interactions are a critical aspect of field experiments to evaluate yield of forage cultivars. The objectives of this study were (i) to model genotypic effects across establishment years, locations, and harvest years of forage yield trials using variance‐covariance structures, (ii) to predict cultivar performance across different environments, and (iii) to compare the relative efficiency of different cost reduction scenarios according to locations, harvest years, and replicates per sowing year. This study is focused on long‐term ryegrass (Lolium spp.) yield trials conducted by the Department of Agriculture, Food and Marine (DAFM) in Ireland. Log likelihood responses of different models indicated that unstructured variance‐covariance models were superior in fit to heterogeneous variance structure models. The unstructured variance‐covariance models also improved predictability compared with heterogeneous variance structure models to estimate genetic effects. Among locations, Athenry (representing western Ireland) was consistently clustered with other locations in both first and second harvest years of late‐ and intermediate‐maturity trials. For resource allocation, cost reduction by reducing the number of harvest years was not effective for the DAFM trials. A 20% reduction in cost by excluding one location or four replicates per sowing year resulted in only an 8% reduction in efficiency. Modeling genetic effects using unstructured variance‐covariance models allowed us to group locations on the basis of genetic correlations to improve prediction accuracy of cultivar effects across different locations and to reduce costs associated with future cultivar trials.

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Benefit of spatial analysis for furrow irrigated cotton breeding trials

Cited by 20 publications

References 31 publications

Improving the Precision of NDVI Estimates in Upland Cotton Field Trials

Improving the Precision of NDVI Estimates in Upland Cotton Field Trials

Spatial Variability Effects on Precision and Power of Forage Yield Estimation

Modeling Genotype × Environment Correlation Structures in Long‐term Multilocation Forage Yield Trials

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