Aplicação da análise espacial na avaliação de experimentos de seleção de clones de laranjeira Pêra

Abstract

Em experimentos de competição de cultivares de citros, geralmente são utilizados muitos tratamentos, o que requer o emprego de grandes blocos e parcelas com poucas plantas. Tem sido debatido que, nessas condições, pode ocorrer a correlação entre parcelas vizinhas, violando assim a pressuposição de erros independentes da análise de variância. O presente trabalho teve por objetivo avaliar diferentes parametrizações de modelos, considerando ou não a dependência espacial entre parcelas, em dois experimentos de competição de clones de laranjeira Pêra (Citrus sinensis L. Osbeck). Foi utilizada a estrutura auto-regressiva separável de primeira ordem (AR1 x AR1) como modelo de dependência espacial entre os erros. Os resultados encontrados apontam que a modelagem espacial dos erros utilizando modelos auto-regressivos separáveis de primeira ordem para experimentos de seleção de clones de laranjeira Pêra, normalmente trazem pequenos ganhos em termos de qualidade de ajuste. A análise desconsiderando o fator blocos mais o ajuste espacial auto-regressivo separável de primeira ordem apresentou melhor qualidade de ajuste entre os modelos avaliados.

Spatial modeling increases accuracy of selection for Phytophthora infestans‐resistant tomato genotypes

“…This type of analysis is even more important in breeding trials where only checks are repeated within the experimental area, as happens in the replicated check design we used in this study. The results found in analyses considering autoregressive spatial adjustment in two directions (AR1 ⊗ AR1) were superior to those found in analysis assuming independency of errors (Casanoves et al, 2005;Cullis & Gleeson, 1991;Campos et al, 2016;Duarte et al, 2007;Gilmour et al, 1997;Grondona et al, 1996;Maia et al, 2013) that always opted for using models with spatial structure (AR1 ⊗ AR1) to Crop Science perform genotype selection. Autoregressive models ensure increased experimental precision because they form homogeneous blocks with no variations between plots within them (Resende et al, 2014), which guarantees that the P. infestans resistance response found via spatial analysis is solely attributed to genetic causes.…”

“…This means that spatial models are more efficient even when experiments are arranged in a complete block design. Adopting models that consider spatial dependency between adjacent plots could be beneficial for breeding programs, especially when blocks are poorly allocated, or when it is not possible to use more accurate experimental designs such as complete randomized blocks (Maia et al, 2013).…”

“…ysis. In addition, several other studies pointed out the superiority of spatial analysis to attenuate environmental effects as well as to increase experimental accuracy (Costa et al, 2005;Duarte & Vencovsky, 2005;Maia et al, 2013). Late-blight, caused by Phytophthora infestans (Mont.)…”

Spatial dependence in experiments of progeny selection for bean ( Phaseolus vulgaris L.) yield

“…In selection trials of clones of orange cv. Pera, autoregressive models to describe the spatial dependence of errors provided small gains in quality of fit in comparison with the randomized blocks analysis (Maia et al, 2013). The authors explained the results were probably due to the absence of spatial dependence in the evaluated trials.…”

“…Several studies in plant breeding have evaluated the efficiency of analyses that consider spatial dependence of errors in both annual and perennial plants. In most of these studies, the spatial analysis was more efficient or similar to traditional analyses that assume independence of errors and neglect the location of the observations used in the analyses (Zimmerman & Harville, 1991;Yang et al, 2004;Costa et al, 2005;Duarte & Vencovsky, 2005;Resende et al, 2006;Candido et al, 2009;Yang & Juskiw, 2011;Maia et al, 2013;Negash et al, 2014).…”