Non-parametric statistical methods and data transformations in agricultural pest populations studies.

Alcides Cabrera Campos1*, Caridad Walkiria Guerra Bustillo2, Magaly Herrera Villafranca3, and Moraima Suris Campos4

Analyzing data from agricultural pest populations regularly detects that they do not fulfill the theoretical requirements to implement classical ANOVA. Box-Cox transformations and nonparametric statistical methods are commonly used as alternatives to solve this problem. In this paper, we describe the results of applying these techniques to data from Thrips palmi Karny sampled in potato (Solanum tuberosum L.) plantations. The X2 test was used for the goodness-of-fit of negative binomial distribution and as a test of independence to investigate the relationship between plant strata and insect stages. Seven data transformations were also applied to meet the requirements of classical ANOVA, which failed to eliminate the relationship between mean and variance. Given this negative result, comparisons between insect population densities were made using the nonparametric Kruskal-Wallis ANOVA test. Results from this analysis allowed selecting the insect larval stage and plant middle stratum as keys to design pest sampling plans.

Keywords: Kruskal-Wallis test, negative binomial distribution, Box-Cox transformations, Thrips palmi, Solanum tuberosum.
1Universidad de las Ciencias Informáticas, Autopista Novia del Mediodía, km 2½, Torrens, Boyeros, La Habana, Cuba. *Corresponding author (alcides@uci.cu).
2Centro Universitario Municipal de Güines, Calle 86, N° 7312, entre 73 y 77, Güines, Mayabeque, Cuba.
3Instituto de Ciencia Animal, Apartado Postal 24, San José de las Lajas, Mayabeque, Cuba.
4Centro Nacional de Sanidad Agropecuaria, Autopista Nacional y Carretera de Tapaste, San José de las Lajas, Apartado Postal 10, Mayabeque, Cuba.