Tamanhos de amostra e de parcela para variáveis de crescimento e produtivas de tomateiro

Abstract

Palavras-chave: Solanum licopersicum, precisão experimental, planejamento experimental, ensaio de uniformidade. ABSTRACTSample size and plot size for growth and productivity characteristics of tomatoThe aim of this study was to estimate the sample size and the plot size for growth and productivity characteristics in different moments of development of tomato grown under high tunnel and in field during the seasons of spring-summer and autumn-winter. The growth variables studied were plant height, number of leaves, stem diameter in the collar and stem diameter at the first inflorescence; and, the productivity variables were the number of inflorescences/ infructescences and the number of fruits per plant, evaluated at different moments of the crop development. When the variances among the rows of the crop were heterogeneous, the sample size was calculated with the highest coefficient of variation (CV) among the rows of the crop, and when they were homogenous, the sample size was calculated with the average CV. The plot size was calculated by the methods of the maximum curvature modified and maximum curvature of coefficient of variation and the differences in percentage of average by the Hatheway method. For a single recommendation of the sample size, considering the semi-amplitude of confidence interval of 10%, the sample size in the crop row is 9 and 22 plants, respectively, for the growth and productivity characteristics in high tunnel, and 7 and 16 plants in the crop row, respectively, for the growth and productivity characteristics in the field. The plot size for the growth characteristics is 3 plants in the crop row and, for the productive characteristics, plots of 7 plants in the crop row.

Planning and implementing experiments and analyzing experimental data in vegetable crops: problems and solutions

“…Therefore, in addition to the plot size, the sample size should also be very well defined and described in the experimental plan, to prevent problems in collecting and, later, in the statistical analysis of the data. Fortunately, several studies on the adequate size of plot samples, for different vegetable crops, are available (Souza et al, 2002;Lúcio et al, 2003Lúcio et al, , 2012Lorentz et al, 2004;Santos et al, 2010;Haesbaert et al, 2011). The characteristic assessed in the experimental unit that expresses the effect of the randomized treatment is called the observed variable, response variable or dependent variable.…”

“…The most effective and also most used strategies to improve the quality of experiments with vegetables with multiple harvests are, namely, identification of crop-specific plot (Table 1) and sample sizes; determining the variability behavior between rows and between harvests, and; the study of data transformation and the use of Papadakis method to minimize the effects of excess zeros causing overdispersion in the database (Lúcio et al, 2011(Lúcio et al, , 2016Santos et al, 2012Santos et al, a,b, 2014Benz et al, 2015;Lúcio & Benz, 2017).…”

“…Studies in several vegetables have shown that harvest clustering reduces the variability between plants, decreasing data dispersion, and mitigating the negative effect of excess zeros in the database (Carpes et al, 2008(Carpes et al, , 2010Lúcio et al, 2011Lúcio et al, , 2016Santos et al, 2012Santos et al, a,b, 2014Benz et al, 2015;Lúcio & Benz, 2017). However, even with harvest clustering, variability may go on increasing, as the cumulative value of harvest "n" remains the same as that observed in harvest "n-1", when there are no products to be harvested in a plant, while in another, in which there are harvestable products, the value increases.…”

“…A consequence of the high variability observed among plants when harvests are analyzed individually is the need of using larger plots, with several plants (Lorentz & Lúcio, 2009;Lúcio et al, 2010Lúcio et al, , 2012Lúcio et al, , 2016Santos et al, 2012bSantos et al, , 2014. It results in underutilizing the experimental area, with likely reduction in the number of treatments and/or of experiments in the same area.…”

Nonlinear regression and plot size to estimate green beans production

“…The relationship between the observed variables in trials with vegetables and their behavior during the productive cycle of the species is important, as it produces information on how the multiple harvests should be planned and carried out, in order to reduce the number of plants with zero values for variables number and weight of fruits or pods harvested. An interesting strategy in this regard is to estimate the optimum plot size that provides the smallest variance between the different plots evaluated (Lorentz et al, 2005;Lúcio et al, 2008Lúcio et al, , 2012 The regression models classified as nonlinear are useful to describe the growth of individuals over time, since they facilitate the researcher to make a decision as they show parameters having a biological interpretation. According to Seber & Wild (1989) and Smyth (2002) the nonlinear models are generally adopted when the researcher observes and assumes as hypothesis that the relationship between the dependent variable and the predictors follows a certain function.…”