Relations between zero-inflated variables in trials with horticultural crops

Abstract

Certain characteristics of some vegetable crops allow multiple harvests during the production cycle; however, to our knowledge, no study has described the behavior of fruit production with progression of the production cycle in vegetable crops with multiple harvests that present data overdispersion. We aimed to characterize the data overdispersion of zero-inflated variables and identify the behavior of these variables during the production cycle of several vegetable crops with multiple harvests. Data from 11 uniformity trials were used without applying treatments; these comprise the database from the Experimental Plants Group at the Federal University of Santa Maria, Brazil. The trials were conducted using four horticultural species grown during different cultivation seasons, cultivation environments, and experimental structures. Although at each harvest, a larger number of basic units with harvest fruit was observed than units without harvest fruit, the basic unit percentage without fruit was high, generating an overdispersion within each individual harvest. The variability within each harvest was high and increased with the evolution of the production cycle of Capsicum annuum, Solanum lycopersicum var. cerasiforme, Phaseolus vulgaris, and Cucurbita pepo species. However, the correlation coefficient between the mean weight and number of harvest fruits tended to remain constant during the crop production cycle. These behaviors show that harvest management should be done individually, at each harvest, such that data overdispersion is reduced.

Variability and Experimental Desing for Trials with Cucurbita pepo and Capsicum annuum

“…The increase in plot size shows itself as a simple and efficient strategy to reduce the variability among rows and columns, agreeing with the results found by Lúcio et al (2016). These results show that if the plot size is sufficiently large, the completely randomized design might be used in the experiments with C. pepo and C. annuum.…”

“…The heterogeneity of variances between rows in the trials with horticultural crops has been attributed, among other causes, to lateral openings of the greenhouse that might provide differentiated conditions of cropping on the sides of the greenhouse. Lorentz et al (2005), and Lúcio et al (2008Lúcio et al ( , 2016 reported that for horticultural crops, factors such as the subjective point of harvest, occurrence of zero values in a particular harvest, multiple harvests and use of protected environments, are additional sources of variability, which might be influencing the variability among rows.…”

“…When the size of the plot is increased, the chances of these areas being diluted among the plots increases, reducing the variability. In addition, the increase in plot size also reduces zero value cases, which contributes to the reduction of variance in the row (Lúcio et al, 2016), tending to homogenize variances among rows.…”

“…For the experimental error to be minimized, it is important to identify the factors that generate variability in the experiment. For example, studies by Webb (1978), Raji, Jannatizadeh, Fattahi, and Esfahlani (2014), Correa et al (2011), Lúcio, Nunes, Rego, andPasini (2016), Lindemann-Zutz, Fricke, and Stützel (2016), and Searle, Johnstone, and Reid (2016), have reported different factors that might generate variability in the experiments inflating the experimental error.…”

Variability of production and the planning of experiments in snapbeans

“…When we increase the plot size, we also increase the chances of diluting these areas within the plot, reducing the variance between plots. Besides, by increasing plot size, we also reduce the frequency of null values, contributing to the reduction of the variance within the row (Lúcio et al, 2016) and also to the homogenization of variances between rows. However, too large plots in experiments in a limited area restrict the number of replications.…”