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Geostatistical analysis of the spatial variation of the berry borer and leaf miner in a coffee agroecosystem

Geostatistical analysis of the spatial variation of the berry borer and leaf miner in a coffee... The advent of geostatistics and geographical information systems has made it possible to analyze complex spatial patterns of ecological phenomena over large areas in applied insect ecology and pest management. The objective of this study was to use geostatistics to characterize the spatial structure and map the spatial variation of damage caused by the berry borer (Hypothenemus hampei) and leaf miner (Leucoptera coffeella) in a coffee agroecosystem planted with the cultivar Catuai Vermelho IAC-99. Infestations of berry borer and leaf miner were evaluated in fruits and leaves, respectively. The pests were monitored at 67 georeferenced points in an area of 6.6 ha in 2005, 2006 and 2007. Variograms estimated by the method of moments (MoM) and residual maximum likelihood REML were compared. The latter were generally better in terms of the kriging error coefficients. Spherical variograms estimated by REML for berry borer infestation in fruits had ranges of spatial dependence of 34.62–118.4 m and for the leaf miner they were 53.93–133.7 m. For models fitted by weighted ordinary least squares (OLS) to the MoM experimental variogram, the ranges varied between 37.22 and 68.67 m for the berry borer and 100 and 155.4 m for leaf miner infestation. The variogram model parameters were used with the data for ordinary kriging to map the spatial variation of coffee pests for different monitoring periods. If there was no suitable variogram, inverse distance weighting was used to map the variation. The maps enabled visualization of the intensity of infestation of the insect pests for the different periods evaluated. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Geostatistical analysis of the spatial variation of the berry borer and leaf miner in a coffee agroecosystem

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References (55)

Publisher
Springer Journals
Copyright
Copyright © 2009 by Springer Science+Business Media, LLC
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
DOI
10.1007/s11119-009-9151-z
Publisher site
See Article on Publisher Site

Abstract

The advent of geostatistics and geographical information systems has made it possible to analyze complex spatial patterns of ecological phenomena over large areas in applied insect ecology and pest management. The objective of this study was to use geostatistics to characterize the spatial structure and map the spatial variation of damage caused by the berry borer (Hypothenemus hampei) and leaf miner (Leucoptera coffeella) in a coffee agroecosystem planted with the cultivar Catuai Vermelho IAC-99. Infestations of berry borer and leaf miner were evaluated in fruits and leaves, respectively. The pests were monitored at 67 georeferenced points in an area of 6.6 ha in 2005, 2006 and 2007. Variograms estimated by the method of moments (MoM) and residual maximum likelihood REML were compared. The latter were generally better in terms of the kriging error coefficients. Spherical variograms estimated by REML for berry borer infestation in fruits had ranges of spatial dependence of 34.62–118.4 m and for the leaf miner they were 53.93–133.7 m. For models fitted by weighted ordinary least squares (OLS) to the MoM experimental variogram, the ranges varied between 37.22 and 68.67 m for the berry borer and 100 and 155.4 m for leaf miner infestation. The variogram model parameters were used with the data for ordinary kriging to map the spatial variation of coffee pests for different monitoring periods. If there was no suitable variogram, inverse distance weighting was used to map the variation. The maps enabled visualization of the intensity of infestation of the insect pests for the different periods evaluated.

Journal

Precision Agriculture – Springer Journals

Published: Dec 12, 2009

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