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.

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).

The redheaded cockchafer ('Adoryphorus couloni') (Burmeister) (RHC) is an important, native soil-borne pest of improved pastures in South Eastern Australia. The aim of this preliminary investigation was to determine whether commonly used Precision Agriculture (PA) sensors could identify landscape attributes that correlate with RHC population density. Soil apparent electrical conductivity (soil ECa) measurements were derived from EM38, relative photosynthentically-active biomass via the normalised difference vegetation index (NDVI) derived from an Active Optical Sensor (AOS) and elevation measurements derived from dGPS (differential global positioning system) mapping. Eight paddocks across seven properties in the Gippsland region of Victoria were surveyed using a Geonics EM38, CropCircle™ AOS and a dGPS. Eighteen to twenty sample sites in each paddock were selected based on different zones of soil ECa, and the RHC (and other cockchafer species) populations were assessed at each of these sites. No RHC were found in East Gippsland confirming that the damage to pasture observed by farmers at this time was caused by a different cockchafer species. Few RHC were found across all sites, probably due to high rainfall, however correlations tended to suggest that RHC were more likely to establish or survive in areas of high elevation and low soil ECa. On one property RHC were associated with low NDVI values and at one other high NDVI suggesting more complex relationships may exist between AOS data and RHC densities. Threshold-level relationships were apparent between RHC density and elevation and ECa to suggest that a useful indicator of pest risk could be developed, at least for some areas of Gippsland, however the relationships are complex and need to be investigated further.

Precision agriculture (PA) technologies and applications have largely been targeted at the cropping and horticultural industries. Little research has been undertaken exploring the potential for PA in grazing systems. This paper reports on the results of five studies examining PA technologies and techniques in grazing systems including: spatial variability in soil nutrients and fertiliser response across the grazing landscape; spatial landscape utilisation in relationship to individual animal productivity and health; spatial variability in pasture pests; and the development of a sensor network for monitoring spatial soil moisture, soil temperature and ambient temperature across a grazing landscape. The large variability exhibited in our trials suggests there is an enormous opportunity for precision agriculture in grazing systems. Sensing and responding to this variability will require careful application of modern PA technology and a substantial investment in research to better understand spatial variability in our grazing landscapes.