Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/42361
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dc.creatorAndrade, Mario Henrique Murad Leite-
dc.creatorFernandes Filho, Claudio Carlos-
dc.creatorFernandes, Maiara Oliveira-
dc.creatorBastos, Abel Jamir Ribeiro-
dc.creatorGuedes, Marcio Lisboa-
dc.creatorMarçal, Tiago de Souza-
dc.creatorGonçalves, Flavia Maria Avelar-
dc.creatorPinto, Cesar Augusto Brasil Pereira-
dc.creatorZotarelli, Lincoln-
dc.date.accessioned2020-08-12T12:31:03Z-
dc.date.available2020-08-12T12:31:03Z-
dc.date.issued2020-
dc.identifier.citationANDRADE, M. H. M. L. et al. Accounting for spatial trends to increase the selection efficiency in potato breeding. Crop Science, Madison, 2020. DOI: https://doi.org/10.1002/csc2.20226.pt_BR
dc.identifier.urihttps://acsess.onlinelibrary.wiley.com/doi/abs/10.1002/csc2.20226pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/42361-
dc.description.abstractA crucial point in agricultural experimentation is to compare treatments with high accuracy. However, agricultural experimentation is prone to field heterogeneity, and a common source of error is the spatial variation between the plots used in an experiment. With plant breeding experiments, the high number of tested genotypes requires breeders to use large areas, which invariably increases the likelihood of spatial variation. The use of models that do not address this variation can lead to errors in selecting the best genotypes. Our goal was to evaluate the effects of two spatial models—first‐order autoregressive (AR1) and spatial analysis of field trials with splines (SpATS)—to control the spatial variation in 30 experiments from potato (Solanum tuberosum L.) breeding programs. Specifically, we sought to control for three traits: total tuber yield (TTY), marketable tuber yield (MTY), and tuber specific gravity (SG). The results obtained with the use of spatial models were compared with the base model (independent errors) based on precision, heritability, and the impact on the selection of the best clones. Spatial models were effective in controlling local and global errors and achieved greater accuracy and efficiency over the base model. The spatial approach also showed greater heritability for all analyzed traits. The spatial models led to differences in the clone ranking and consequently in the selection of the best clones. Thus, spatial analysis has the power to make more precise analyses, which leads to more accurate selections and should be used to analyze phenotype data of potato breeding programs.pt_BR
dc.languageen_USpt_BR
dc.publisherCrop Science Society of Americapt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceCrop Sciencept_BR
dc.subjectPotatoe - Genetic improvementpt_BR
dc.subjectAgricultural experimentationpt_BR
dc.subjectBatata - Melhoramento genéticopt_BR
dc.subjectExperimentação agropecuáriapt_BR
dc.titleAccounting for spatial trends to increase the selection efficiency in potato breedingpt_BR
dc.typeArtigopt_BR
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