Classificação de famílias do feijoeiro sob diferentes cenários de dependência espacial e precisão experimental

Abstract

Resumo -O objetivo deste trabalho foi avaliar a eficiência da análise estatística espacial, em comparação a análises usuais em blocos ao acaso e em látice, na classificação de famílias de feijoeiro (Phaseolus vulgaris), sob diferentes cenários de dependência espacial e de precisão experimental. Foram considerados 12 cenários, formados por quatro classes de dependência espacial entre erros (nula, baixa, média e alta) e três classes de precisão experimental (moderada, alta e muito alta). Para as três classes de precisão experimental, foram definidos os valores de acurácia seletiva de: 0,60, 0,80 e 0,95, respectivamente. Já as quatro classes de dependência espacial entre erros assumiram, respectivamente, os seguintes valores de alcance do modelo geoestatístico exponencial: 0, 10, 20 e 40 m. Para experimentos com precisão experimental muito alta ou ausência de dependência espacial, a eficiência da análise espacial para classificação de famílias do feijoeiro é semelhante à eficiência das análises usuais. Para experimentos com alta e, principalmente, com moderada precisão experimental, a análise espacial é mais eficiente que as análises usuais para classificação de famílias de feijoeiro, quando os erros apresentam dependência espacial.Termos para indexação: Phaseolus vulgaris, acurácia seletiva, análise espacial, autocorrelação, erros dependentes, modelos geoestatísticos. Classification of common bean families under different scenarios of spatial dependence and experimental precisionAbstract -The objective of this work was to evaluate the efficiency of spatial statistical analysis, compared to the usual analyzes in randomized blocks and in lattice, for the classification of common bean (Phaseolus vulgaris) families under different scenarios of spatial dependence and experimental precision. Twelve scenarios were considered, formed by four classes of spatial dependence between errors (null, low, medium, and high) and three classes of experimental precision (medium, high, and very high). For the three classes of experimental precision, the following values of selective accuracy were defined: 0.60, 0.80, and 0.95, respectively. The four classes of spatial dependence between errors assumed, respectively, the following range values for the exponential geostatistical model: 0, 10, 20, and 40 m. In experiments with very high experimental precision or with the absence of spatial dependence, the efficiency of the spatial analysis for the classification of common bean families is similar to that of the usual analyses. In experiments with high and, especially, with medium experimental precision, the spatial analysis is more efficient than the usual analyses for the classification of common bean families, when the errors show spatial dependence.

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.…”

“…crop, when doweling spatial trends were included. Campos et al (2016) demonstrated that the use of spatial analysis resulted in more accurate family ranking in common beans as it considered the spatial trends within the field area in the anal-…”

Spatial variability in evaluation experiments of corn genotypes in the state of Mato Grosso do Sul, Brazil

“…Comparing the accuracy values obtained by the experiments (Table 1) with the spatial autocorrelation coefficient (Table 4), we observed that Experiment 11 presented moderate accuracy (𝑟̂𝑔 ̂𝑔=0.65), and spatial analysis had greater efficiency when errors showed spatial dependence. Campos et al (2016) assessed the efficiency of spatial analysis using geostatistics to classify common bean families and concluded that in experiments with moderate experimental precision, spatial analysis presents higher efficiency in the classification of common bean families.…”

“…Other authors in literature have used spatial statistical analysis involving first-order autoregressive models which are separable in two dimensions (Resende & Sturion, 2003;Maia, Siqueira, Carvalho, Peternelli, & Latado, 2013), geostatistical models (Campos et al, 2016;Silva et al, 2016), Papadakis methods, and moving averages (Candido, Perecin, Landell, & Pavan, 2009). However, ANOVA-AR is yet to be used.…”

Genetic parameters and performance of papaya genotypes to black spot resistance (Asperisporium caricae)

“…Nevertheless, only the coefficient of experimental variance did not show the quality of the experiment. Recently, some works have been adopted the selective accuracy (Storck and Silva, 2014;Campos et al, 2016). The selective accuracy was presented greater than 81% for this characteristic.…”