Evaluation of experimental designs and spatial analyses in wheat breeding trials

Qiao, C. G.; Basford, K. E.; DeLacy, I. H.; Cooper, Mark

doi:10.1007/s001220050002

Cited by 76 publications

(108 citation statements)

References 17 publications

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“…In the agricultural field, studies have reported greater effectiveness of balanced incomplete block and a designs compared to RCB designs (Patterson et al, 1978;Patterson and Silvey, 1980;Patterson and Hunter, 1983;Kempton et al, 1994;Yau, 1997;Qiao et al, 2000), and in the area of forestry similar results have been obtained, for example by Fu et al (1998Fu et al ( , 1999 and Gezan et al (2006).…”

Section: Introductionsupporting

confidence: 60%

Experimental designs for evaluation of genetic variability and selection of ancient grapevine varieties: a simulation study

2009

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Classical methodologies for grapevine selection used in the vine-growing world are generally based on comparisons among a small number of clones. This does not take advantage of the entire genetic variability within ancient varieties, and therefore limits selection challenges. Using the general principles of plant breeding and of quantitative genetics, we propose new breeding strategies, focussed on conservation and quantification of genetic variability by performing a cycle of mass genotypic selection prior to clonal selection. To exploit a sufficiently large amount of genetic variability, initial selection trials must be generally very large. The use of experimental designs adequate for those field trials has been intensively recommended for numerous species. However, their use in initial trials of grapevines has not been studied. With the aim of identifying the most suitable experimental designs for quantification of genetic variability and selection of ancient varieties, a study was carried out to assess through simulation the comparative efficiency of various experimental designs (randomized complete block design, a design and row-column (RC) design). The results indicated a greater efficiency for a and RC designs, enabling more precise estimates of genotypic variance, greater precision in the prediction of genetic gain and consequently greater efficiency in genotypic mass selection.

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Section: Introductionsupporting

confidence: 60%

Experimental designs for evaluation of genetic variability and selection of ancient grapevine varieties: a simulation study

2009

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“…The CV after using alpha lattice design was still too high suggesting that experiment was not carried out efficiently. The efficiency of alpha design in second experiment could be improved by using neighbor or spatial analysis or row and column analyses [4,6,11,[16][17][18][19][20]. The rank orders of mean based on Alpha design (least square means) and RCBD (simple means) also change, which is relevant when selecting genotypes for the purpose of recommendations for the farmers.…”

Section: Resultsmentioning

confidence: 99%

Efficiency of Alpha Lattice Design in Rice Field Trials in Pakistan

et al. 2010

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Two rice trials were conducted from 2005 to 2006 in rice research institute, Kala Shah Kako Pakistan to evaluate the efficiency of alpha lattice design in field experiments. The average standard error of difference between genotypes mean is used to calculate relative efficiency of alpha lattice design. Both experiments clearly identified the advantages of small blocks. The average gain in efficiency was 119% with maximum 128%. Mean ranks comparison for both randomized complete block and alpha lattice design were performed. It was observed that the ranks were not constant across the experiments. The results emphasize that the traditional randomized complete block designs (RCBD) should be replaced by alpha lattice in the agricultural field experiments when number of varieties to be tested in an experiment increases to more than five or ten. In such a situation finding a homogeneous block is quite difficult in field experiments.Keywords: Rice; Alpha lattice design; RCBD; Pakistan.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.4773 J. Sci. Res. 3 (1), 91-95 (2011)

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“…Experimental design factors that were found significant are usually recommended to be retained in the spatial model (Dutkowski et al 2002(Dutkowski et al , 2006Federer 1998;Qiao et al 2000;Ye and Jayawickrama 2008). In most of the northern Swedish Norway spruce trials, a simple experimental CRD was usually used in the field experiments, and all trees were usually randomized in the field trials.…”

Section: Discussionmentioning

confidence: 99%

“…When there is significant independent error, Costa e Silva et al (2001) and Dutkowski et al (2002) indicated that separation of global and local trends was unnecessary. In agricultural trials, however, an extended model without a non-significant independent error term is recommended by Gilmour et al (1997) and others (Cullis and Gleeson 1989;Qiao et al 2000). Therefore, in this study, the extended model was used to detect the improvement for parental and offspring ranking correlations and selection gain in these nine variables.…”

Section: Discussionmentioning

confidence: 99%

“…Spatial analysis can capture both local gradient variations within blocks (patches) and a global gradient trend along the row and column of the trial, so it is becoming a popular method to use for both agricultural and forestry field trials (Anekonda and Libby 1996;Brownie and Gumpertz 1997;Cullis et al 1998;Cullis and Gleeson 1989;Cullis and Gleeson 1991;Fox et al 2007a, b;Gilmour et al 1997;Qiao et al 2000;Yang et al 2004;Ye and Jayawickrama 2008;Chen et al 2017). In forestry, there are several spatial analysis methods used, such as post-blocking (Dutkowski et al 2002;Ericsson 1997), nearest-neighbor adjustment (Anekonda and Libby 1996;Joyce et al 2002;Wright 1978), and kriging (Hamann et al 2002;Zas 2006).…”

Section: Introductionmentioning

confidence: 99%

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Efficiency of using spatial analysis for Norway spruce progeny tests in Sweden

Chen

Helmersson

Westin

et al. 2017

Annals of Forest Science

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Key message Spatial analysis could improve the accuracy of genetic analyses, as well as increasing the accuracy of predicting breeding values and genetic gain for Norway spruce trials. Context Spatial analysis has been increasingly used in genetic evaluation of field trials in tree species. However, the efficiency of spatial analysis relative to the analysis using the conventional experimental designs or pre- and post-blocking method in Swedish genetic trials has not been systematically evaluated. Aims This study aims to examine the effectiveness of spatial analysis in improving the accuracy of predicting breeding values and genetic gain. Methods Spatial analysis, using separable first-order autoregressive processes of residuals in rows and columns, was used in nine types of trait classes from 145 field trials of Norway spruce (Picea abies (L.) Karst.) in Sweden. Results Ninety-six percent of variables (traits) were converged for the spatial model. Large trials with a large block variance tend to have a larger improvement from the model of experimental design to spatial model in accuracy. Growth and Pilodyn measurement traits showed greater improvements in log likelihood, accuracy, and genetic gain. Block variance was reduced by more than 80% for trait height and diameter using spatial analysis, indicating that it is more effective using both pre-blocking and post-blocking analyses in Swedish Norway spruce trials. The prediction accuracy for diameter and height for progeny breeding values showed an increase of 3.6 and 3.4%, respectively. The improvement of efficiency for growth traits is also related to the geographical location of test sites, tree age, number of survival trees, and the spacing of the trial. Conclusion The spatial analysis approach is more efficient in Swedish Norway spruce trials than the conventional methods using models based on the experimental design.

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Evaluation of experimental designs and spatial analyses in wheat breeding trials

Cited by 76 publications

References 17 publications

Experimental designs for evaluation of genetic variability and selection of ancient grapevine varieties: a simulation study

Experimental designs for evaluation of genetic variability and selection of ancient grapevine varieties: a simulation study

Efficiency of Alpha Lattice Design in Rice Field Trials in Pakistan

Efficiency of using spatial analysis for Norway spruce progeny tests in Sweden

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