Do Spatial Designs Outperform Classic Experimental Designs?

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorHoefler, Raegan-
Autor(es): dc.creatorGonzález-Barrios, Pablo-
Autor(es): dc.creatorBhatta, Madhav-
Autor(es): dc.creatorNunes, Jose Airton Rodrigues-
Autor(es): dc.creatorBerro, Ines-
Autor(es): dc.creatorNalin, Rafael S.-
Autor(es): dc.creatorBorges, Alejandra-
Autor(es): dc.creatorCovarrubias, Eduardo-
Autor(es): dc.creatorDiaz-Garcia, Luis-
Autor(es): dc.creatorQuincke, Martin-
Autor(es): dc.creatorGutierrez, Lucia-
Data de aceite: dc.date.accessioned2026-02-09T11:54:42Z-
Data de disponibilização: dc.date.available2026-02-09T11:54:42Z-
Data de envio: dc.date.issued2021-09-03-
Data de envio: dc.date.issued2021-09-03-
Data de envio: dc.date.issued2020-08-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/48048-
Fonte completa do material: dc.identifierhttps://doi.org/10.1007/s13253-020-00406-2-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1150172-
Descrição: dc.descriptionControlling spatial variation in agricultural field trials is the most important step to compare treatments efficiently and accurately. Spatial variability can be controlled at the experimental design level with the assignment of treatments to experimental units and at the modeling level with the use of spatial corrections and other modeling strategies. The goal of this study was to compare the efficiency of methods used to control spatial variation in a wide range of scenarios using a simulation approach based on real wheat data. Specifically, classic and spatial experimental designs with and without a two-dimensional autoregressive spatial correction were evaluated in scenarios that include differing experimental unit sizes, experiment sizes, relationships among genotypes, genotype by environment interaction levels, and trait heritabilities. Fully replicated designs outperformed partially and unreplicated designs in terms of accuracy; the alpha-lattice incomplete block design was best in all scenarios of the medium-sized experiments. However, in terms of response to selection, partially replicated experiments that evaluate large population sizes were superior in most scenarios. The AR1 × AR1 spatial correction had little benefit in most scenarios except for the medium-sized experiments with the largest experimental unit size and low GE. Overall, the results from this study provide a guide to researchers designing and analyzing large field experiments.-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer Nature-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceJournal of Agricultural, Biological, and Environmental Statistics-
Palavras-chave: dc.subjectExperimental design-
Palavras-chave: dc.subjectAutoregressive process-
Palavras-chave: dc.subjectPrediction accuracy-
Palavras-chave: dc.subjectResponse to selection-
Palavras-chave: dc.subjectSpatial correction-
Palavras-chave: dc.subjectRandomization-based experimental designs-
Palavras-chave: dc.subjectDesign experimental-
Palavras-chave: dc.subjectProcesso autorregressivo-
Palavras-chave: dc.subjectPrecisão de predição-
Palavras-chave: dc.subjectCorreção espacial-
Palavras-chave: dc.subjectRandomização-
Título: dc.titleDo Spatial Designs Outperform Classic Experimental Designs?-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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