The redheaded cockchafer ('Adoryphorus couloni') (Burmeister) (RHC) is an important pest of semi-improved and improved pastures of south-eastern Australia. The third instar larvae of the RHC feed on the organic and root matter found in the soil causing reduced pasture growth and in severe cases death of plants. The control of the RHC is complicated by its lifecycle which involves the insect spending the majority of its life underground with only a brief time as an adult beetle flying. The RHC is particularly hard to control as there are no insecticides registered for use against the pest or any effective cultural control methods. ... This thesis aims to identify possible relationships between third instar RHC larvae with environmental variables which can be measured using precision agriculture sensors.

'Farmscapes' are farming landscapes that comprise combinations of forests and scattered remnant vegetation (trees), natural and improved grasslands and pastures and crops. Scattered eucalypt trees are a particular feature of Australian farmscapes. There is a growing need to assess carbon and biomass stocks in these farmscapes in order to fully quantify the carbon storage change in response to management practices and provide evidence-based support for carbon inventory. Since tree trunk diameter, more formally known as diameter at breast height (DBH), is correlated with tree biomass and associated carbon stocks, DBH is accepted as a means inferring the biomass–carbon stocks of trees. On ground measurement of DBH is straightforward but often time consuming and difficult in inaccessible terrain and certainly inefficient when seeking to infer stocks over large tracts of land. The aim of this research was to investigate various avenues of estimating DBH using synoptic remote sensing techniques. Tree parameters like crown projected area, tree height and crown diameter are all potentially related to DBH. This thesis first uses on–ground measurements to establish the fundamental allometric relationships between such parameters and DBH for scattered and clustered Eucalyptus trees on a large, ~3000-ha farm in north eastern part of New South Wales, Australia. The thesis then goes on to investigate a range of remote sensing techniques including very high spatial resolution (decicentimetre) airborne multispectral imagery and satellite imagery and LiDAR to estimate the related parameters. Overall, the research demonstrated the usefulness of remote sensing of tree parameters such as crown projection area and canopy volume as a means of inferring DBH on a large scale.

An opportunity exists to infer the physiological and physical state of an animal from changes in their behaviour. As resting, eating, walking and ruminating are the predominant daily activities of ruminant animals, monitoring these behaviours could provide valuable information for monitoring individual animal health and welfare status. Conventional animal monitoring methods have relied on visual observations of animals by human labour. This can only provide information on an animal's behaviour for the period in which they are being observed. Historically, observations could be made for long periods where shepherds were employed to observe their flocks nearly constantly. This is obviously no-longer feasible in the current livestock industry. Recently, with the advent of small, low power accelerometer technology, the ability to remotely monitor animal movement continuously has arisen. This is achieved through the application of on-animal inertia monitoring unit (IMU) sensors. This movement data might potentially lead to continuous behavioural monitoring of livestock. These devices have been developed for higher value livestock such as dairy cattle but little research or development has been directed towards their use in sheep. Previous work has evaluated collar and leg deployments however the sheep industry demands these devices be in an eartag form factor to align with current industry practices. Therefore, this thesis aims to evaluate the potential for using ear-borne accelerometer devices to detect and categorise key behaviours expressed by sheep. Deviation from normal patterns of behaviour may be used as an indicator of changes in individual health status. If behaviour can be categorised using the data collected by these body worn devices and radio telemetry incorporated, animal health could be monitored in near real time allowing early treatment intervention when necessary, ultimately improving on-farm productivity. Scoping work in this thesis identified the difference in acceleration signals between the basic sheep behaviours: grazing, walking and resting, giving potential for discrimination between behaviours with classification algorithms. Subsequently a successful behaviour classification algorithm was developed based on accelerometer data obtained from the ear deployment, yielding activity predictions similar to those obtained through visual observation. To apply this technology to a commercial application, a simulated lameness experiment was designed, where lame walking behaviour was discriminated from sound walking events successfully using the ear and leg modes of deployment. The final experiment investigated the application of ear deployed accelerometer devices to detect behavioural changes associated with increased infection by internal parasites, a disease of extreme economic importance within Australia. Animals with a higher faecal worm egg count were shown to have a lower probability of engaging in longer periods of activity, however this experiment was limited by a very mild level of infection. Overall this thesis demonstrates that sheep behaviour can be classified using an ear-mounted tri-axial accelerometer sensor, the first of its kind to date. It also explored the suitability of using time-series behavioural classification data as an early indicator of health and welfare issues. This work aims to link a previous "research only" technology in sheep, to a commercial application, a stepping stone towards bridging the gap between research and industry adoption.

The ability to quantify pasture biomass and growth rate is of prime importance to the sustainability and profitability of extensive livestock industries, specifically as it relates to provide information for better farm management decisions. Assessment of pasture growth rate (PGR, kg/ha.day) using remote sensing has gained considerable interest to the farm managers for livestock grazing management. The context of this research is to investigate the use of in situ sensors and a light use efficiency (LUE) model to estimate PGR. A key parameter in this model is the light interception by the canopy, or fAPAR. Measuring fAPAR using active optical sensors (AOS) introduces new challenges hitherto not appreciated using traditional passive optical sensors and so a considerable portion of this work focusses on the derivation of fAPAR from a widely used optical reflectance index, the normalized difference vegetation index (NDVI). Therefore this research project comprises of two main components: (i) investigating an AOS to infer the fraction of absorbed photosynthetically active radiation (fAPAR) by the plant, a key variable in LUE model; and (ii) evaluating the LUE model using in situ sensors for estimating of PGR (kg/ha.day) at the sub field scale.