Risk mapping of redheaded cockchafer ('Adoryphorus couloni') (Burmeister) infestations using a combination of novel k-means clustering and on-the-go plant and soil sensing technologies

Cosby, Amy; Falzon, Gregory; Trotter, M; Stanley, John; Powell, Kevin; Lamb, David

doi:10.1007/s11119-015-9403-z

Author(s)

Cosby, Amy

Falzon, Gregory

Trotter, M

Stanley, John

Powell, Kevin

Lamb, David

Publication Date

2016

Abstract

The ability to identify areas of pasture that are more likely to support damaging levels of the soil-borne, redheaded cockchafer ('Adoryphorus couloni') (Burmeister) (RHC) would allow farmers to target expensive control measures. This study explored soil properties, measured via electromagnetic surveys (EM38), pasture biomass via active optical sensors (CropCircle™) and topography via GPS elevation survey as potential indicators of RHC population density. A combination of these variables was used to produce risk maps with an accuracy of 88% at predicting likely RHC density-categories on a dairy property in the Gippsland region of Victoria, Australia. This risk mapping protocol could be used to improve sampling programs and direct site-specific pest management.

Citation

Precision Agriculture, 17(1), p. 1-17

ISSN

1573-1618

1385-2256

Link

https://hdl.handle.net/1959.11/20450

Language

en

Publisher

Springer New York LLC

Title

Risk mapping of redheaded cockchafer ('Adoryphorus couloni') (Burmeister) infestations using a combination of novel k-means clustering and on-the-go plant and soil sensing technologies

Type of document

Journal Article

Entity Type

Publication

Author(s)	Cosby, Amy Falzon, Gregory Trotter, M Stanley, John Powell, Kevin Lamb, David
Publication Date	2016
Abstract	The ability to identify areas of pasture that are more likely to support damaging levels of the soil-borne, redheaded cockchafer ('Adoryphorus couloni') (Burmeister) (RHC) would allow farmers to target expensive control measures. This study explored soil properties, measured via electromagnetic surveys (EM38), pasture biomass via active optical sensors (CropCircle™) and topography via GPS elevation survey as potential indicators of RHC population density. A combination of these variables was used to produce risk maps with an accuracy of 88% at predicting likely RHC density-categories on a dairy property in the Gippsland region of Victoria, Australia. This risk mapping protocol could be used to improve sampling programs and direct site-specific pest management.
Citation	Precision Agriculture, 17(1), p. 1-17
ISSN	1573-1618 1385-2256
Link	https://hdl.handle.net/1959.11/20450
Language	en
Publisher	Springer New York LLC
Title	Risk mapping of redheaded cockchafer ('Adoryphorus couloni') (Burmeister) infestations using a combination of novel k-means clustering and on-the-go plant and soil sensing technologies
Type of document	Journal Article
Entity Type	Publication

Risk mapping of redheaded cockchafer ('Adoryphorus couloni') (Burmeister) infestations using a combination of novel k-means clustering and on-the-go plant and soil sensing technologies

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