MOE and MOR assessment technologies for improving graded recovery of exotic pines in Australia.

This project was designed to provide the structural softwood processing industry with the basis for improved green and dry grading to allow maximise MGP grade yields, consistent product performance and reduced processing costs. To achieve this, advanced statistical techniques were used in conjunction with state-of-the-art property measurement systems. Specifically, the project aimed to make two significant steps forward for the Australian structural softwood industry: • assessment of technologies, both existing and novel, that may lead to selection of a consistent, reliable and accurate device for the log yard and green mill. The purpose is to more accurately identify and reject material that will not make a minimum grade of MGP10 downstream; • improved correlation of grading MOE and MOR parameters in the dry mill using new analytical methods and a combination of devices. The three populations tested were stiffness-limited radiata pine, strength-limited radiata pine and Caribbean pine. Resonance tests were conducted on logs prior to sawmilling, and on boards. Raw data from existing in-line systems were captured for the green and dry boards. The dataset was analysed using classical and advanced statistical tools to provide correlations between data sets and to develop efficient strength and stiffness prediction equations. Stiffness and strength prediction algorithms were developed from raw and combined parameters. Parameters were analysed for comparison of prediction capabilities using in-line parameters, off-line parameters and a combination of in-line and off-line parameters. The results show that acoustic resonance techniques have potential for log assessment, to sort for low stiffness and/or low strength, depending on the resource. From the log measurements, a strong correlation was found between the average static MOE of the dried boards within a log and the predicted value. These results have application in segregating logs into structural and non-structural uses. Some commercial technologies are already available for this application such as Hitman LG640. For green boards it was found that in-line and laboratory acoustic devices can provide a good prediction of dry static MOE and moderate prediction for MOR.There is high potential for segregating boards at this stage of processing. Grading after the log breakdown can improve significantly the effectiveness of the mill. Subsequently, reductions in non-structural volumes can be achieved. Depending on the resource it can be expected that a 5 to 8 % reduction in non structural boards won’t be dried with an associated saving of $70 to 85/m3. For dry boards, vibration and a standard Metriguard CLT/HCLT provided a similar level of prediction on stiffness limited resource. However, Metriguard provides a better strength prediction in strength limited resources (due to this equipment’s ability to measure local characteristics). The combination of grading equipment specifically for stiffness related predictors (Metriguard or vibration) with defect detection systems (optical or X-ray scanner) provides a higher level of prediction, especially for MOR. Several commercial technologies are already available for acoustic grading on board such those from Microtec, Luxscan, Falcon engineering or Dynalyse AB for example. Differing combinations of equipment, and their strategic location within the processing chain, can dramatically improve the efficiency of the mill, the level of which will vary depending of the resource. For example, an initial acoustic sorting on green boards combined with an optical scanner associated with an acoustic system for grading dry board can result in a large reduction of the proportion of low value low non-structural produced. The application of classical MLR on several predictors proved to be effective, in particular for MOR predictions. However, the usage of a modern statistics approach(chemometrics tools) such as PLS proved to be more efficient for improving the level of prediction. Compared to existing technologies, the results of the project indicate a good improvement potential for grading in the green mill, ahead of kiln drying and subsequent cost-adding processes. The next stage is the development and refinement of systems for this purpose.

Strength and stiffness assessment technologies for improving grading effectiveness of radiata pine wood.

This work was designed to provide the Australian structural radiata pine processing industry with some indications for improving stress grading methods and/or technologies to give an increase in structural grade yields, and significantly reduce processing costs without compromising product quality. To achieve this, advanced statistical techniques were used in conjunction with state-of-the-art property measurement systems applied to the same sample of sawn timber. Acoustic vibration analyses were conducted on green and dry boards. Raw data from existing in-line systems was captured on the same boards. The Metriguard HCLT stress rating system was used as the "reference" machine grading because of its current common use in the industry. A WoodEye optical scanning system and an X-ray LHG scanner were also able to provide relevant information on knots. The data set was analyzed using classical and advanced statistical tools to provide correlations between data sets, and to develop efficient strength and stiffness prediction equations. Reductions in non-structural dry volumes can be achieved..

Molecular tools for studying puberty in the grey mullet, Mugil cephalus: cloning of cDNAs that regulate reproductive function

The cDNAs coding for the brain GnRHs (AY373449-51), pituitary GH, SL and PRL, and liver IGFs (AY427954-5) were isolated. Partial cDNA sequences of the brain (Cyp19b) and gonadal (Cyp19a) aromatases have also been obtained. These tools would be utilized to study the endocrine regulation of puberty in the grey mullet.

GIS-based tools for management of pine plantations, Queensland, Australia

Two examples of GIS-based multiple-criteria evaluations of plantation forests are presented. These desktop assessments use available topographical, geological and pedological information to establish the risk of occurrence of certain environmentally detrimental processes. The first case study is concerned with the risk that chemical additives (i.e. simazine) applied within the forestry landscape may reach the drainage system. The second case study assesses the vulnerability of forested areas to landslides. The subject of the first multiple-criteria evaluation (MCE) was a 4 km2 logging area, which had been recently site-prepared for a Pinus plantation. The criteria considered relevant to the assessment were proximity to creeks, slope, soil depth to the restrictive layer (i.e. potential depth to a perched water table) and soil erodability (based on clay content). The output of the MCE was in accordance with field observations, showing that this approach has the potential to provide management support by highlighting areas vulnerable to waterlogging, which in turn can trigger overland flow and export of pollutants to the local stream network. The subject of the second evaluation was an Araucaria plantation which is prone to landslips during heavy rain. The parameters included in the assessment were drainage system, the slope of the terrain and geological features such as rocks and structures. A good correlation between the MCE results and field observations was found, suggesting that this GIS approach is useful for the assessment of natural hazards. Multiple-criteria evaluations are highly flexible as they can be designed in either vector or raster format, depending on the type of available data. Although tested on specific areas, the MCEs presented here can be easily used elsewhere and assist both management intervention and the protection of the adjacent environment by assessing the vulnerability of the forest landscape to either introduced chemicals or natural hazards.

New tools for management of phosphine resistance

Phosphine is the primary fumigant used to protect the majority of the world' s grain and a variety of other stored commodities from insect pests. Phosphine is playing an increasingly important role in the protection of commodities for two primary reasons. Firstly, use of the alternative fumigant, methyl bromide, has been sharply curtailed and is tightly regulated due to its role in ozone depletion, and secondly, consumers are becoming increasingly intolerant of contact pesticides. Niche alternatives to phosphine exist, but they suffer from a range of factors that limit their use, including: 1) Limited commercial adoption due to expense or slow mode of action; 2) Poor efficacy due to low toxicity, rapid sorption, limited volatility or high density; 3) Public health concerns due to toxicity to handlers or nearby residents, as well as risk of explosion; 4) Poor consumer acceptance due to toxic residues or smell. These same factors limit the prospects of quickly identifying and deploying a new fumigant. Given that resistance toward phosphine is increasing among insect pests, improved monitoring and management of resistance is a priority. Knowledge of the mode of action of phosphine as well as the mechanisms of resistance may also greatly reduce the effort and expense of identifying synergists or novel replacement compounds.

Annual forecasting of the Australian macadamia crop - integrating tree census data with statistical climate-adjustment models

To facilitate marketing and export, the Australian macadamia industry requires accurate crop forecasts. Each year, two levels of crop predictions are produced for this industry. The first is an overall longer-term forecast based on tree census data of growers in the Australian Macadamia Society (AMS). This data set currently accounts for around 70% of total production, and is supplemented by our best estimates of non-AMS orchards. Given these total tree numbers, average yields per tree are needed to complete the long-term forecasts. Yields from regional variety trials were initially used, but were found to be consistently higher than the average yields that growers were obtaining. Hence, a statistical model was developed using growers' historical yields, also taken from the AMS database. This model accounted for the effects of tree age, variety, year, region and tree spacing, and explained 65% of the total variation in the yield per tree data. The second level of crop prediction is an annual climate adjustment of these overall long-term estimates, taking into account the expected effects on production of the previous year's climate. This adjustment is based on relative historical yields, measured as the percentage deviance between expected and actual production. The dominant climatic variables are observed temperature, evaporation, solar radiation and modelled water stress. Initially, a number of alternate statistical models showed good agreement within the historical data, with jack-knife cross-validation R2 values of 96% or better. However, forecasts varied quite widely between these alternate models. Exploratory multivariate analyses and nearest-neighbour methods were used to investigate these differences. For 2001-2003, the overall forecasts were in the right direction (when compared with the long-term expected values), but were over-estimates. In 2004 the forecast was well under the observed production, and in 2005 the revised models produced a forecast within 5.1% of the actual production. Over the first five years of forecasting, the absolute deviance for the climate-adjustment models averaged 10.1%, just outside the targeted objective of 10%.

Tools to manage climate risk in cropping (Australia).

Because of the variable and changing environment, advisors and farmers are seeking systems that provide risk management support at a number of time scales. The Agricultural Production Systems Research Unit, Toowoomba, Australia has developed a suite of tools to assist advisors and farmers to better manage risk in cropping. These tools range from simple rainfall analysis tools (Rainman, HowWet, HowOften) through crop simulation tools (WhopperCropper and YieldProphet) to the most complex, APSFarm, a whole-farm analysis tool. Most are derivatives of the APSIM crop model. These tools encompass a range of complexity and potential benefit to both the farming community and for government policy. This paper describes, the development and usage of two specific products; WhopperCropper and APSFarm. WhopperCropper facilitates simulation-aided discussion of growers' exposure to risk when comparing alternative crop input options. The user can readily generate 'what-if' scenarios that separate the major influences whilst holding other factors constant. Interactions of the major inputs can also be tested. A manager can examine the effects of input levels (and Southern Oscillation Index phase) to broadly determine input levels that match their attitude to risk. APSFarm has been used to demonstrate that management changes can have different effects in short and long time periods. It can be used to test local advisors and farmers' knowledge and experience of their desired rotation system. This study has shown that crop type has a larger influence than more conservative minimum soil water triggers in the long term. However, in short term dry periods, minimum soil water triggers and maximum area of the various crops can give significant financial gains.

Tree growth relationships and silvicultural tools to assist stand management in private native spotted gum dominant forests in Queensland and northern New South Wales.

Spotted gum dominant forests occur from Cooktown in northern Queensland (Qld) to Orbost in Victoria (Boland et al. 2006) and these forests are commercially very important with spotted gum the most commonly harvested hardwood timber in Qld and one of the most important in New South Wales (NSW). Spotted gum has a wide range of end uses from solid wood products through to power transmission poles and generally has excellent sawing and timber qualities (Hopewell 2004). The private native forest resource in southern Qld and northern NSW is a critical component of the hardwood timber industry (Anon 2005, Timber Qld 2006) and currently half or more of the native forest timber resource harvested in northern NSW and Qld is sourced from private land. However, in many cases productivity on private lands is well below what could be achieved with appropriate silvicultural management. This project provides silvicultural management tools to assist extension staff, land owners and managers in the south east Qld and north eastern NSW regions. The intent was that this would lead to improvement of the productivity of the private estate through implementation of appropriate management. The other intention of this project was to implement a number of silvicultural experiments and demonstration sites to provide data on growth rates of managed and unmanaged forests so that landholders can make informed decisions on the future management of their forests. To assist forest managers and improve the ability to predict forest productivity in the private resource, the project has developed: • A set of spotted gum specific silvicultural guidelines for timber production on private land that cover both silvicultural treatment and harvesting. The guidelines were developed for extension officers and property owners. • A simple decision support tool, referred to as the spotted gum productivity assessment tool (SPAT), that allows an estimation of: 1. Tree growth productivity on specific sites. Estimation is based on the analysis of site and growth data collected from a large number of yield and experimental plots on Crown land across a wide range of spotted gum forest types. Growth algorithms were developed using tree growth and site data and the algorithms were used to formulate basic economic predictors. 2. Pasture development under a range of tree stockings and the expected livestock carrying capacity at nominated tree stockings for a particular area. 3. Above-ground tree biomass and carbon stored in trees. •A series of experiments in spotted gum forests on private lands across the study area to quantify growth and to provide measures of the effect of silvicultural thinning and different agro-forestry regimes. The adoption and use of these tools by farm forestry extension officers and private land holders in both field operations and in training exercises will, over time, improve the commercial management of spotted gum forests for both timber and grazing. Future measurement of the experimental sites at ages five, 10 and 15 years will provide longer term data on the effects of various stocking rates and thinning regimes and facilitate modification and improvement of these silvicultural prescriptions.

Scoping genetic tools, their limitations and applications for wild fisheries management

FRDC has commissioned a review of the role that existing and future genetic technologies may play in addressing critical challenges facing the exploitation of wild fisheries. Wild fisheries management has been assisted by genetic research for over 50 years and in Australia, this research has been largely funded by FRDC. Both fisheries management and the methods of genetic analysis have changed significantly during this time. Given these dynamics, as well as perceptions that communication between fisheries managers and geneticists has been poor in some cases, there is a strong need to reassess the ways in which genetic research can contribute to fisheries and for all stakeholders to critically examine each other's needs and capabilities.

Development of a Multiplexed Bead-Based Suspension Array for the Detection and Discrimination of Pospiviroid Plant Pathogens

Efficient and reliable diagnostic tools for the routine indexing and certification of clean propagating material are essential for the management of pospiviroid diseases in horticultural crops. This study describes the development of a true multiplexed diagnostic method for the detection and identification of all nine currently recognized pospiviroid species in one assay using Luminex bead-based suspension array technology. In addition, a new data-driven, statistical method is presented for establishing thresholds for positivity for individual assays within multiplexed arrays. When applied to the multiplexed array data generated in this study, the new method was shown to have better control of false positives and false negative results than two other commonly used approaches for setting thresholds. The 11-plex Luminex MagPlex-TAG pospiviroid array described here has a unique hierarchical assay design, incorporating a near-universal assay in addition to nine species-specific assays, and a co-amplified plant internal control assay for quality assurance purposes. All assays of the multiplexed array were shown to be 100% specific, sensitive and reproducible. The multiplexed array described herein is robust, easy to use, displays unambiguous results and has strong potential for use in routine pospiviroid indexing to improve disease management strategies.

Status, challenges and tools for identification and diagnosis of Peronosclerospora and Sclerophthora of regulatory concern for graminicolous crops

Graminicolous Downy Mildew (GDM) diseases caused by the genera Peronosclerospora (13 spp.) and Sclerophthora (6 spp. and 1 variety) are poorly studied but destructive diseases of major crops such as corn, sorghum, sugarcane and other graminoids. Eight of the 13 described Peronosclerospora spp. are able to infect corn. In particular, P. philippinensis (= P. sacchari), P. maydis, P. heteropogonis, and S. rayssiae var. zeae cause major losses in corn yields in tropical Asia. In 2012 a new species, P. australiensis, was described based on isolates previously identified as P. maydis in Australia; this species is now a pathogen of major concern. Despite the strong impact of GDM diseases, there are presently no reliable molecular methods available for their detection. GDM pathogens are among the most difficult Oomycetes to identify using molecular tools, as their taxonomy is very challenging, and little genetic sequence data are available for development of molecular tools to detect GDM pathogens to species level. For example, from over 15 genes used in identification, diagnostics or phylogeny of Phytophthora, only ITS1 and cox2 show promise for use with GDM pathogens. Multiplex/multigene conventional and qPCR assays are currently under evaluation for the detection of economically important GDM spp. Scientists from the USA, Germany, Canada, Australia, and the Philippines are collaborating on the development and testing of diagnostic tools for these pathogens of concern.

Assessment of functional forms of crop yield loss models of invasive plant species applied in decision support tools and bioeconomic modelling

AbstractObjectives Decision support tools (DSTs) for invasive species management have had limited success in producing convincing results and meeting users' expectations. The problems could be linked to the functional form of model which represents the dynamic relationship between the invasive species and crop yield loss in the DSTs. The objectives of this study were: a) to compile and review the models tested on field experiments and applied to DSTs; and b) to do an empirical evaluation of some popular models and alternatives. Design and methods This study surveyed the literature and documented strengths and weaknesses of the functional forms of yield loss models. Some widely used models (linear, relative yield and hyperbolic models) and two potentially useful models (the double-scaled and density-scaled models) were evaluated for a wide range of weed densities, maximum potential yield loss and maximum yield loss per weed. Results Popular functional forms include hyperbolic, sigmoid, linear, quadratic and inverse models. Many basic models were modified to account for the effect of important factors (weather, tillage and growth stage of crop at weed emergence) influencing weed–crop interaction and to improve prediction accuracy. This limited their applicability for use in DSTs as they became less generalized in nature and often were applicable to a much narrower range of conditions than would be encountered in the use of DSTs. These factors' effects could be better accounted by using other techniques. Among the model empirically assessed, the linear model is a very simple model which appears to work well at sparse weed densities, but it produces unrealistic behaviour at high densities. The relative-yield model exhibits expected behaviour at high densities and high levels of maximum yield loss per weed but probably underestimates yield loss at low to intermediate densities. The hyperbolic model demonstrated reasonable behaviour at lower weed densities, but produced biologically unreasonable behaviour at low rates of loss per weed and high yield loss at the maximum weed density. The density-scaled model is not sensitive to the yield loss at maximum weed density in terms of the number of weeds that will produce a certain proportion of that maximum yield loss. The double-scaled model appeared to produce more robust estimates of the impact of weeds under a wide range of conditions. Conclusions Previously tested functional forms exhibit problems for use in DSTs for crop yield loss modelling. Of the models evaluated, the double-scaled model exhibits desirable qualitative behaviour under most circumstances.

Investigation of an emerging bacterial disease in wild Queensland groper, marine fish and stingrays with production of diagnostic tools to reduce the spread of disease to other states of Australia : final report

This project describes how Streptococcus agalactiae can be transmitted experimentally in Queensland grouper. The implications of this research furthers the relatedness between Australian S. agalactiae strains from animals and humans. Additionally, this research has developed diagnostic tools for Australian State Veterinary Laboratories and Universities, which will assist in State and National aquatic animal disease detection, surveillance, disease monitoring and reporting

Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials

Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.