Spatio-temporal population dynamics and area-wide delineation of Bactrocera oleae monitoring zones using multi-variate geostatistics

Spatio-temporal population dynamics and area-wide delineation of Bactrocera oleae monitoring... Area-wide integrated pest management requires an understanding of insect population dynamics and definition of suitable techniques to quantify spatio-temporal variability to make better pest management decisions. However, the viability of area-wide integrated pest management has often been questioned because of the high monitoring costs. The present study aimed to: (i) analyse the spatial and temporal dynamics of the olive fruit fly over a large olive growing area (Ormylia, Greece), and (ii) define a methodology to determine monitoring zones to optimize the monitoring effort over space and time in area-wide integrated pest management programmes. Data from an olive fruit fly monitoring network based on McPhail traps were utilized. The multi-variate spatial (elevation) and temporal (6 periods) data of olive fruit fly population density were analysed by principal component analysis, co-kriging and factor kriging to produce thematic maps and to delineate monitoring zones. Olive fruit fly density was spatially correlated from 200 to 4 000 m. The spatial pattern changed over the monitoring season. Areas with high density of olive fruit flies shifted from high altitudes in summer to lower altitudes towards autumn. Three recommended levels of monitoring intensity were defined, thus delineating homogeneous monitoring zones for summer (July to September) and October. It was concluded that delineating monitoring zones through multi-variate geostatistics is a suitable approach for optimising the monitoring effort, because population density distribution is spatially structured over large areas and changes over time. Precision Agriculture Springer Journals

Spatio-temporal population dynamics and area-wide delineation of Bactrocera oleae monitoring zones using multi-variate geostatistics

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Springer US
Copyright © 2012 by Springer Science+Business Media, LLC
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
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