Forest health surveillance methodology in hardwood plantations in Queensland, Australia

Forest health surveillance (FHS) of hardwood plantations commenced in Queensland in 1997 as plantations expanded following a state government planting initiative arising from the national 2020 forest policy vision. The estate was initially characterised by a large number of small plantations (10-50 ha), although this has changed more recently with the concentration of larger plantations in the central coast and South Burnett regions. Due to the disparate nature of the resource, drive- and walkthrough surveys of subsets of plantations have been undertaken in preference to aerial surveys. FHS has been effective in detecting a number of new hardwood pests in Queensland including erinose mites (Rhombacus and Acalox spp.), western white gum plate galler (Ophelimus sp.), Creiis psyllid and bronzing bug (Thaumastocoris sp.), in evaluating their potential impact and assisting in focussing future research efforts. Since 2003 there has been an increased emphasis on training operational staff to take a greater role in identifying and reporting on forest health issues. This has increased their awareness of forest health issues, but their limited time to specifically survey and report on pests and diseases, and high rates of staff turnover, necessitate frequent ongoing training. Consequently, common and widespread problems such as quambalaria shoot blight (Quambalaria pitereka), chrysomelid leaf beetles (mainly Paropsis atomaria) and erinose mites may be under-reported or not reported, and absence data may often not be recorded at all. Comment is made on the future directions that FHS may take in hardwood plantations in Queensland.

Estimating crop area using seasonal time series of enhanced vegetation index from MODIS satellite imagery

Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state, and national scales. There is now an increasing drive from industry for more accurate and cost-effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. Multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April-November) were investigated to estimate crop area for wheat, barley, chickpea, and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period; (ii) harmonic analysis of the time series (HANTS) of the EVI values; and (iii) principal component analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea, and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at pixel scale (>98% correct classification) for identifying overall winter cropping. However, discrimination among crops was less accurate. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire scale, the result contradicted the poor pixel-scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped-area estimates before harvest.

Spelling strategies for recovery of pasture condition

This work will progess and extend recommendations and guidelines for use of integrated wet season spelling in Queenslands savannahs and woodlands. The research will generate a greater ecological and pasture production understanding of pastures and soils that exist in C land condition areas (major landtypes), and their recovery. Practical, cost-effective spelling regimes will be developed. Research will be conducted on-property with small plot exclosures and plots with controlled utilisation levels, examining ecological responses to different spelling regimes. This information will improve bio-economic modelling capacity. Industry consultations with producers and field staff will drive implementation of the recommendations arising.

CRC50089 Grain Insect Ecology

Resistance to phosphine in target pests threatens market access for Australian grain. While the grains industry is now attempting to develop an effective and sustainable strategy to manage this resistance, action is severely limited by significant gaps in our knowledge of the key ecological factors that influence the development of resistance. There is a need to research this information as a foundation for a rational approach to managing phosphine resistance in the Australian grains industry. Research outcomes: The project has provided critical research methodologies and preliminary data to fill the large gaps in our knowledge of the ecology of two key pests, Rhyzopertha dominica and Tribolium castaneum, and how this may drive the development of phosphine resistance. This information will contribute to the groundwork for future research needed to provide a scientific basis for a rational resistance management strategy.