Mixed spatial models for data analysis of yield on large grapevine selection field trials

Mixed spatial models for data analysis of yield on large grapevine selection field trials

In large field trials, it may be desirable to adjust for spatial correlation due to variation in soil fertility and in other environmental factors. Spatial correlation within a field trial can mask differences in the genotypic values of clones, consequently reducing the possibility of identifying su...

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Bibliographic Details
Published in:Theoretical and applied genetics Vol. 115; no. 5; pp. 653 - 663
Main Authors: Gonçalves, Elsa, St.Aubyn, António, Martins, Antero
Format: Journal Article
Language:English
Published: Heidelberg Berlin/Heidelberg : Springer-Verlag 01-09-2007
Berlin Springer
Springer Nature B.V
Subjects:
Biological and medical sciences
Classical genetics, quantitative genetics, hybrids
Evaluation Studies as Topic
Fundamental and applied biological sciences. Psychology
Genetics of eukaryotes. Biological and molecular evolution
Genotype
Models, Biological
Pteridophyta, spermatophyta
Selection, Genetic
Vegetals
Vitaceae
Vitis - genetics
Vitis - growth & development
Genetics
Agriculture
Models
Yield
Selection
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Summary:In large field trials, it may be desirable to adjust for spatial correlation due to variation in soil fertility and in other environmental factors. Spatial correlation within a field trial can mask differences in the genotypic values of clones, consequently reducing the possibility of identifying superior genotypes. This paper describes a strategy to improve the precision of statistical data analysis of grapevine selection trials through the use of mixed spatial models. The efficiency of mixed spatial models was compared with that of a classical randomized complete block model (with independent and identically distributed errors). The comparisons were based on yield data from three large experimental populations of clones of the Arinto, Aragonez (Tempranillo) and Viosinho grapevine varieties. The fit of the spatial mixed models applied to yield data was significantly better than that of the classical approach, resulting in a positive impact on selection decisions and increasing the accuracy of genetic gain prediction.
Bibliography:http://dx.doi.org/10.1007/s00122-007-0596-z
ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0040-5752
1432-2242
DOI:10.1007/s00122-007-0596-z