Methodological considerations in discriminating olive-orchard management type using olive-canopy arthropod fauna at the level of order

  1. Jerez-Valle, Carlos 1
  2. García-López, Pedro A. 2
  3. Campos, Mercedes 3
  4. Pascual, Felipe 1
  1. 1 Universidad de Granada, Dept. Zoología. Campus de Fuentenueva s/n, 18071 Granada
  2. 2 Universidad de Granada, Dept. Estadística e Investigación Operativa. Campus de Fuentenueva s/n, 18071 Granada
  3. 3 Estación Experimental del Zaidín (CSIC), Dept. Protección Ambiental. Profesor Albareda 1, 18008 Granada
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Zeitschrift:
Spanish journal of agricultural research

ISSN: 1695-971X,  2171-9292

Datum der Publikation: 2015

Ausgabe: 13

Nummer: 4

Art: Artikel

DOI: 10.5424/SJAR/2015134-6588 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Andere Publikationen in: Spanish journal of agricultural research

Zusammenfassung

The cultivation of the olive tree (Olea europaea L.) has great importance in the entire Mediterranean basin, so that the implementation of organic practices in their management directly affects the sustainability of the agricultural system. Bioindication with arthropods can help to detect the different agricultural practices. In this work, we analyse the most appropriate methodology for discriminating between management using arthropods at the taxonomic level of order, with the novelty of taking into account the weather conditions to select the sampling dates. Between 12 and 15 sampling stations (depending on the year) were selected from olive orchards belonging to organic, conventional non-tillage, and strict conventional management, being sampled by beating the canopy fortnightly in the spring-summer period of 2007, 2008 and 2009. Organic management was more abundant and richer than the rest for the three years. Most groups with significant differences in terms of relative abundance were more abundant in organic orchard, except Neuroptera. Finally, different discriminant methods were evaluated (Linear Discriminant Analysis, Multiple Discriminant Analysis, and Support Vector Machine) with several different data sets. The discriminant analysis with interannual variability reached 97.9% accuracy in differentiating between organic and non-organic management using the LDA method, considering the taxa with significant differences from the abundance, excluding pests, and using samples with more uniform and stable weather patterns (late summer).

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