Non-Parametric Statistical Methods and Data Transformations in Agricultural Pest Population Studies
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 th...
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| Autores principales: | , , , |
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| Lenguaje: | English |
| Publicado: |
Instituto de Investigaciones Agropecuarias, INIA
2012
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| Materias: | |
| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392012000300020 |
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| Sumario: | 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. |
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