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Precision agriculture ’13 pp 585–592Cite as

Mapping redheaded cockchafer infestations in pastures – are PA tools up to the job?

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Abstract

The redheaded cockchafer (Adoryphorus couloni) (Burmiester) (RHC) is a serious pest of improved pastures in south-eastern Australia and current detection relies on pasture damage becoming visible to the naked eye. Various precision agriculture sensors are able to delineate spatial variability in soil texture and moisture content as well as numerous contributing factors to the photosynthetic ‘vigour’ of pastures, namely biomass, canopy architecture and species composition. The aim of this paper is to seek to determine whether the same technologies can be used to identify paddock zones prone to RHC infestation. This study investigates the association between data generated by a CropCircle™ (an active optical plant canopy sensor (AOS)), an EM38, (an electromagnetic induction soil sensor), and third instar RHC larvae counts. Results indicate that the red wavelength reflected component of the AOS from the pasture canopies offered the most accurate model of third instar RHC larvae count (residual mean square error = 1.04).

Keywords

  • Redheaded cockchafer
  • Adoryphorus couloni
  • active optical sensors
  • soil electrical conductivity
  • insect pest management

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  • DOI: 10.3920/978-90-8686-778-3_72
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References

  • Arnold, T.B. and Emerson, J.W. 2011. Nonparametric Goodness-of-Fit Tests for Discrete Null Distributions. The R Journal 3 34–39.

    Google Scholar 

  • Conover, W.J. 1972. A Kolmogorov Goodness-of-Fit Test for Discontinuous Distributions. Journal of American Statistical Association 67 591–596.

    CrossRef  Google Scholar 

  • Cosby, A., Trotter, M., Lamb D., Falzon G., Stanley J., Powell, K. and Bruce, R. 2012. Detection of pasture pests using proximal PA sensors: a preliminary study investigating the relationship between EM38, NDVI, elevation and redheaded cockchafer in the Gippsland region. In: (Ed.) I. Yunusa, Capturing Opportunities and Overcoming Obstacles in Australian Agronomy, Proceedings of 16th Australian Agronomy Conference. Online Community Publishing.

    Google Scholar 

  • Dixon, W.J. 1950. Analysis of extreme values. Journal of Annals of Mathematical Statistics21 488–506.

    Google Scholar 

  • Douglas, M.H. 1972. Red-headed cockchafers can be controlled by pasture management. Victorian Journal of Agriculture 70 61–63.

    Google Scholar 

  • Flynn, E.S., Dougherty C.T. and Wendroth, O. 2008. Assessment of pasture biomass with normalised difference vegetation index from active ground-based sensors. Agronomy Journal 100 114–121.

    CrossRef  Google Scholar 

  • Geonics Limited, 2003. EM38-Ground Conductivity Meter Operating Manual. Geonics Limited, Mississauga, Ontario, Canada.

    Google Scholar 

  • Gleser, L.J. 1985. Exact Power of Goodness-of-Fit Tests of Kolmogorov Type for Discontinuous Distributions. Journal of American Statistical Association 80 954–958.

    CrossRef  Google Scholar 

  • Hardy, R. and Tandy M. 1971. Redheaded pasture cockchafer. Tasmanian Journal of Agriculture 42 263–267.

    Google Scholar 

  • Hastie, T. 2011. gam: Generalized Additive Models. R package version 1.06.2. http://CRAN.R-project.org/package=gam.

    Google Scholar 

  • Holland, K.H., Schepers, J.S., Schanahan, J.F, Horst, G.L., 2004. Plant canopy sensor with modulated polychromatic light source. In: (Ed.) D.J. Mulla, Proceedings of the 7th International Conference on Precision Agriculture and other Precision Resources Management. Precision Agriculture Centre, Minneapolis, MN, USA. CD-ROM.

    Google Scholar 

  • Holland K.H., Lamb D.W. and Schepers J.S. 2012. Radiometry of proximal active optical sensors (AOS) for agricultural sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5 1–10.

    CrossRef  Google Scholar 

  • Hossain, M.B., Lamb, D.W., Lockwood, P.V. and Frazier, P. 2010. EM38 for volumetric soil water content estimation in the root zone of deep vertisol soils. Computers and Electronics in Agriculture 74 100–109.

    CrossRef  Google Scholar 

  • Komsta, L. 2011. Outliers: Tests for outliers. R package version 0.14. http://CRAN.R-project.org/package=outliers.

    Google Scholar 

  • JMP, Version 9. SAS Institute Inc., Cary, NC, USA. 1989–2007.

    Google Scholar 

  • Matthews, E.G. 1984. A Guide to the Genera of Beetles of South Australia Part 3 Polyphaga: Eucinetoidea, Dascilloidea and Scarabaeoidea. South Australian Museum, Adelaide.

    Google Scholar 

  • McNeill, J.D., 1980. Electrical Conductivity of Soils and Rocks. In: Technical Note TN-5. Geonics Limited, Mississauga, Ontario, Canada.

    Google Scholar 

  • McQuillan, P.B. and Webb, W.R. 1994. Selective soil organic matter consumption by larvae of Adoryphorus couloni (Burmeister) (Coleoptera: Scarabaeidae). Journal of the Australian Entomological Society 29 75–79.

    CrossRef  Google Scholar 

  • Mickan, F. 2008. The Redheaded Pasture Cockchafer, Department of Primary Industries, Melbourne, Victoria, Australia, Agnote 1358.

    Google Scholar 

  • Pinheiro, J., Bates. D., DebRoy, S., Sarkar, D. and the R Development Core Team. 2012. nlme: Linear and Nonlinear Mixed Effects Models. R package 3 1–104.

    Google Scholar 

  • Plant, R.E. 2001. Site specific management: the application of information technology to crop production. Computers and Electronics in Agriculture 30 9–29.

    CrossRef  Google Scholar 

  • R Development Core Team, 2012. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. www.R-project.org/.

    Google Scholar 

  • Rath, A.C. and Pearn, S.G. 1993. Development of economic control of the root-feeding cockchafer, Adoryphorus couloni (Coleoptera: Scarabaeidae) with the fungus Metarhizium anisopliae. In: (Ed.) E.S. Delfosse, Pests of Pastures. Weed, Invertebrate and Disease Pests of Australian Sheep Pastures, (CSIRO Information Services: Melbourne) pp. 332–336.

    Google Scholar 

  • Rorabacher, D.B. 1991. Statistical Treatment for Rejection of Deviant Values: Critical Values of Dixon Q Parameter and Related Subrange Ratios at the 95 percent Confidence Level. Journal of Analytical Chemistry 83 139–146.

    Google Scholar 

  • Rouse, J.W., Haas, J.R., Schell, J.A. and Deering, D.W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. In: (Eds.) Freden, S., Mercanti, E., Becker, M., Third Earth Resources Technology Satellite- 1 Symposium. NASA US Government Printing Office: Washington, DC, pp. 309–317.

    Google Scholar 

  • Serrano, J.M., Peca, J.O., Marques da Silva, J.R. and Shaidian, S. 2010. Mapping soil and pasture variability with an electromagnetic induction sensor. Computer and Electronics in Agriculture 73 7–16.

    CrossRef  Google Scholar 

  • Trotter M.G., Lamb D.W., Donald G.E. and Schneider D.A. 2010. Evaluating an active optical sensor for quantifying and mapping green herbage mass and growth in a perennial grass pasture. Crop and Pasture Science 61 389–398.

    CrossRef  Google Scholar 

  • Venables, W. N. and Ripley, B. D. 2002. Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0.

    CrossRef  Google Scholar 

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Authors and Affiliations

  1. Precision Agriculture Research Group, University of New England, Armidale, NSW, 2351, Australia

    A. Cosby, M. Trotter, G. Falzon, J. Stanley, D. Schneider & D. Lamb

  2. Department of Primary Industries, Biosciences Research Division, Rutherglen, Victoria, 3685, Australia

    K. Powell

  3. The Centre for 4 Dimensional Data Analysis, University of New England, Armidale, NSW, 2351, Australia

    G. Falzon

Authors
  1. A. Cosby
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  2. M. Trotter
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  3. G. Falzon
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  4. J. Stanley
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  5. K. Powell
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  6. D. Schneider
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  7. D. Lamb
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Correspondence to A. Cosby .

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    © 2013 Wageningen Academic Publishers The Netherlands

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    Cosby, A. et al. (2013). Mapping redheaded cockchafer infestations in pastures – are PA tools up to the job?. In: Stafford, J.V. (eds) Precision agriculture ’13. Wageningen Academic Publishers, Wageningen. https://doi.org/10.3920/978-90-8686-778-3_72

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