16 resultados para Low threshold current densities

em eResearch Archive - Queensland Department of Agriculture


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The seed-feeding jewel bug, Agonosoma trilineatum (F.), is an introduced biological control agent for bellyache bush, Jatropha gossypiifolia L. To quantify the damage potential of this agent, shadehouse experiments were conducted with individual bellyache bush plants exposed to a range of jewel bug densities (0, 6 or 24 jewel bugs/plant). The level of abortion of both immature and mature seed capsules and impacts on seed weight and seed viability were recorded in an initial short-term study. The ability of the jewel bug to survive and cause sustained damage was then investigated by measuring seed production, the survival of adults and nymph density across three 6-month cycles. The level of seed capsule abortion caused by the jewel bug was significantly affected by the maturity status of capsules and the density of insects present. Immature capsules were most susceptible and capsule abortion increased with jewel bug density. Similarly, on average, the insects reduced the viability of bellyache bush seeds by 79% and 89% at low and high densities, respectively. However, sustaining jewel bug populations for prolonged periods proved difficult. Adult survival at the end of three 6-month cycles averaged 11% and associated reductions in viable seed production ranged between 55% and 77%. These results suggest that the jewel bug has the potential to reduce the number of viable seeds entering the soil seed bank provided populations can be established and maintained at sufficiently high densities.

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In classical weed biological control, assessing weed response to simulated herbivory is one option to assist in the prioritization of available agents and prediction of their potential efficacy. Previously reported simulated herbivory studies suggested that a specialist herbivore in the leaf-feeding guild is desirable as an effective biological control agent for cat's claw creeper Macfadyena unguis-cati (Bignoniaceae), an environmental weed that is currently a target for biological control. In this study, we tested (i) whether the results from glasshouse-based simulated herbivory can be used to prioritise potential biological control agents by evaluating the impact of a leaf-sucking tingid bug Carvalhotingis visenda (Drake & Hambleton) (Hemiptera: Tingidae) in quarantine; and (ii) the likely effectiveness of low- and high-densities of the leaf-sucking tingid after its release in the field. The results suggest that a single generation of C. visenda has the potential to reduce leaf chlorophyll content significantly, resulting in reduced plant height and leaf biomass. However, the impact of one generation of tingid herbivory on below-ground plant components, including the roots and tuber size and biomass, were not significant. These findings are consistent with results obtained from a simulated herbivory trial, highlighting the potential role of simulated herbivory studies in agent prioritisation.

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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.

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We investigate the role of plant species in crops, pasture and native vegetation remnants in supporting agronomic pests and their predators. The study was conducted in three Australian States and across 290 sites sampled monthly for two years. Pastures played a key role in harbouring pest species consistent across States, while native vegetation hosted relatively more predators than other habitat types within each State. Furthermore, native plant species supported the lowest pest density and more predators than pests; in contrast, 75 of the exotic weed species surveyed hosted more pests than predators. Despite the role of pasture in harbouring pests, we found in NSW that pasture also supported the highest proportion of juvenile predators, while native vegetation remnants had the lowest. Our results indicate that non-crop habitat (native remnants or pasture) with few exotic weeds supports high predator and low pest arthropod densities, and that weeds are associated with high pest densities. By linking broad response variables such as ‘all pests’ with specific predictors such as ‘plant species’, our study will inform on-farm management actions of which weeds to control and which natives to plant or regenerate. This study shows the importance of knowing the function of habitats and plants species in supporting pests and predators in agricultural landscapes across multiple regions.

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Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

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Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

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Data on seasonal population abundance of Bemisia tabaci biotype B (silverleaf whitefly (SLW)) in Australian cotton fields collected over four consecutive growing seasons (2002/2003-2005/2006) were used to develop and validate a multiple-threshold-based management and sampling plan. Non-linear growth trajectories estimated from the field sampling data were used as benchmarks to classify adult SLW field populations into six density-based management zones with associated control recommendations in the context of peak flowering and open boll crop growth stages. Control options based on application of insect growth regulators (IGRs) are recommended for high-density populations (>2 adults/leaf) whereas conventional (non-IGR) products are recommended for the control of low to moderate population densities. A computerised re-sampling program was used to develop and test a binomial sampling plan. Binomial models with thresholds of T=1, 2 and 3 adults/leaf were tested using the field abundance data. A binomial plan based on a tally threshold of T=2 adults/leaf and a minimum sample of 20 leaves at nodes 3, 4 or 5 below the terminal is recommended as the most parsimonious and practical sampling protocol for Australian cotton fields. A decision support guide with management zone boundaries expressed as binomial counts and control options appropriate for various SLW density situations is presented. Appropriate use of chemical insecticides and tactics for successful field control of whiteflies are discussed.

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Surface losses of nitrogen from horticulture farms in coastal Queensland, Australia, may have the potential to eutrophy sensitive coastal marine habitats nearby. A case-study of the potential extent of such losses was investigated in a coastal macadamia plantation. Nitrogen losses were quantified in 5 consecutive runoff events during the 13-month study. Irrigation did not contribute to surface flows. Runoff was generated by storms at combined intensities and durations that were 20–40 mm/h for >9 min. These intensities and durations were within expected short-term (1 year) and long-term (up to 20 years) frequencies of rainfall in the study area. Surface flow volumes were 5.3 ± 1.1% of the episodic rainfall generated by such storms. Therefore, the largest part of each rainfall event was attributed to infiltration and drainage in this farm soil (Kandosol). The estimated annual loss of total nitrogen in runoff was 0.26 kg N/ha.year, representing a minimal loading of nitrogen in surface runoff when compared to other studies. The weighted average concentrations of total sediment nitrogen (TSN) and total dissolved nitrogen (TDN) generated in the farm runoff were 2.81 ± 0.77% N and 1.11 ± 0.27 mg N/L, respectively. These concentrations were considerably greater than ambient levels in an adjoining catchment waterway. Concentrations of TSN and TDN in the waterway were 0.11 ± 0.02% N and 0.50 ± 0.09 mg N/L, respectively. The steep concentration gradient of TSN and TDN between the farm runoff and the waterway demonstrated the occurrence of nutrient loading from the farming landscapes to the waterway. The TDN levels in the stream exceeded the current specified threshold of 0.2–0.3 mg N/L for eutrophication of such a waterway. Therefore, while the estimate of annual loading of N from runoff losses was comparatively low, it was evident that the stream catchment and associated agricultural land uses were already characterised by significant nitrogen loadings that pose eutrophication risks. The reported levels of nitrogen and the proximity of such waterways (8 km) to the coastline may have also have implications for the nearshore (oligotrophic) marine environment during periods of turbulent flow.

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Each Agrilink kit has been designed to be both comprehensive and practical. As the kits are arranged to answer questions of increasing complexity, they are useful references for both new and experienced producers of specific crops. Agrilink integrates the technology of horticultural production with the management of horticultural enterprises. REPRINT INFORMATION - PLEASE READ! For updated information please call 13 25 23 or visit the website www.deedi.qld.gov.au (Select: Queensland Industries – Agriculture link) This publication has been reprinted as a digital book without any changes to the content published in 1999. We advise readers to take particular note of the areas most likely to be out-of-date and so requiring further research: see detailed information on first page of the kit. Even with these limitations we believe this information kit provides important and valuable information for intending and existing growers. This publication was last revised in 1998. The information is not current and the accuracy of the information cannot be guaranteed by the State of Queensland. This information has been made available to assist users to identify issues involved in the production of low chill stonefruit. This information is not to be used or relied upon by users for any purpose which may expose the user or any other person to loss or damage. Users should conduct their own inquiries and rely on their own independent professional advice. While every care has been taken in preparing this publication, the State of Queensland accepts no responsibility for decisions or actions taken as a result of any data, information, statement or advice, expressed or implied, contained in this publication.

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To experimentally investigate the effect of vertical artificial substrate and different densities of the banana prawn Penaeus (Fenneropenaeus) merguiensis on nutrient levels in prawn pond effluent, a time series experiment was conducted in a replicated tank system supplied periodically with discharge from a prawn production pond. Few differences (P>0.05) were detected between tanks without prawns, and tanks with low densities (5 prawns in 1700 litres) of prawns (10-12 g), in terms of nitrogen and phosphorus in the water column over the 28-day experimental period. Higher densities of prawns (starting at 25 or 50 per tank) caused an elevation of these macronutrients in the water column. This was partly due to prawn biomass losses from mortalities and weight reductions in the tank system. The survival and condition of prawns was significantly (P<0.05) reduced in tanks at these higher densities. The presence of artificial substrate (2 m2 tank-1) did not affect (P>0.05) the levels of nutrients in tank water columns, but significantly (P<0.05) increased the amount of nitrogen in tank residues left at the end of the trial when no prawns were present. The prawns had obviously been grazing on surfaces inside the tanks, and their swimming actions appeared to keep light particulate matter in suspension. Higher prawn densities increased microalgal blooms, which presumably kept ammonia levels low, and it is suggested that this association may provide the means for improved remediation of prawn farm effluent in the future.

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Non-Technical Summary Seafood CRC Project 2009/774. Harvest strategy evaluations and co-management for the Moreton Bay Trawl Fishery Principal Investigator: Dr Tony Courtney, Principal Fisheries Biologist Fisheries and Aquaculture, Agri-Science Queensland Department of Agriculture, Fisheries and Forestry Level B1, Ecosciences Precinct, Joe Baker St, Dutton Park, Queensland 4102 Email: tony.courtney@daff.qld.gov.au Project objectives: 1. Review the literature and data (i.e., economic, biological and logbook) relevant to the Moreton Bay trawl fishery. 2. Identify and prioritise management objectives for the Moreton Bay trawl fishery, as identified by the trawl fishers. 3. Undertake an economic analysis of Moreton Bay trawl fishery. 4. Quantify long-term changes to fishing power for the Moreton Bay trawl fishery. 5. Assess priority harvest strategies identified in 2 (above). Present results to, and discuss results with, Moreton Bay Seafood Industry Association (MBSIA), fishers and Fisheries Queensland. Note: Additional, specific objectives for 2 (above) were developed by fishers and the MBSIA after commencement of the project. These are presented in detail in section 5 (below). The project was an initiative of the MBSIA, primarily in response to falling profitability in the Moreton Bay prawn trawl fishery. The analyses were undertaken by a consortium of DAFF, CSIRO and University of Queensland researchers. This report adopted the Australian Standard Fish Names (http://www.fishnames.com.au/). Trends in catch and effort The Moreton Bay otter trawl fishery is a multispecies fishery, with the majority of the catch composed of Greasyback Prawns (Metapenaeus bennettae), Brown Tiger Prawns (Penaeus esculentus), Eastern King Prawns (Melicertus plebejus), squid (Uroteuthis spp., Sepioteuthis spp.), Banana Prawns (Fenneropenaeus merguiensis), Endeavour Prawns (Metapenaeus ensis, Metapenaeus endeavouri) and Moreton Bay bugs (Thenus parindicus). Other commercially important byproduct includes blue swimmer crabs (Portunus armatus), three-spot crabs (Portunus sanguinolentus), cuttlefish (Sepia spp.) and mantis shrimp (Oratosquilla spp.). Logbook catch and effort data show that total annual reported catch of prawns from the Moreton Bay otter trawl fishery has declined to 315 t in 2008 from a maximum of 901 t in 1990. The number of active licensed vessels participating in the fishery has also declined from 207 in 1991 to 57 in 2010. Similarly, fishing effort has fallen from a peak of 13,312 boat-days in 1999 to 3817 boat-days in 2008 – a 71% reduction. The declines in catch and effort are largely attributed to reduced profitability in the fishery due to increased operational costs and depressed prawn prices. The low prawn prices appear to be attributed to Australian aquacultured prawns and imported aquacultured vannamei prawns, displacing the markets for trawl-caught prawns, especially small species such as Greasyback Prawns which traditionally dominated landings in Moreton Bay. In recent years, the relatively high Australian dollar has resulted in reduced exports of Australian wild-caught prawns. This has increased supply on the domestic market which has also suppressed price increases. Since 2002, Brown Tiger Prawns have dominated annual reported landings in the Moreton Bay fishery. While total catch and effort in the bay have declined to historically low levels, the annual catch and catch rates of Brown Tiger Prawns have been at record highs in recent years. This appears to be at least partially attributed to the tiger prawn stock having recovered from excessive effort in previous decades. The total annual value of the Moreton Bay trawl fishery catch, including byproduct, is about $5 million, of which Brown Tiger Prawns account for about $2 million. Eastern King Prawns make up about 10% of the catch and are mainly caught in the bay from October to December as they migrate to offshore waters outside the bay where they contribute to a large mono-specific trawl fishery. Some of the Eastern King Prawns harvested in Moreton Bay may be growth overfished (i.e., caught below the size required to maximise yield or value), although the optimum size-at-capture was not determined in this study. Banana Prawns typically make up about 5% of the catch, but can exceed 20%, particularly following heavy rainfall. Economic analysis of the fishery From the economic survey, cash profits were, on average, positive for both fleet segments in both years of the survey. However, after the opportunity cost of capital and depreciation were taken into account, the residual owner-operator income was relatively low, and substantially lower than the average share of revenue paid to employed skippers. Consequently, owner-operators were earning less than their opportunity cost of their labour, suggesting that the fleets were economically unviable in the longer term. The M2 licensed fleet were, on average, earning similar boat cash profits as the T1/M1 fleet, although after the higher capital costs were accounted for the T1/M1 boats were earning substantially lower returns to owner-operator labour. The mean technical efficiency for the fleet as a whole was estimated to be 0.67. That is, on average, the boats were only catching 67 per cent of what was possible given their level of inputs (hours fished and hull units). Almost one-quarter of observations had efficiency scores above 0.8, suggesting a substantial proportion of the fleet are relatively efficient, but some are also relatively inefficient. Both fleets had similar efficiency distributions, with median technical efficiency score of 0.71 and 0.67 for the M2 and T1/M1 boats respectively. These scores are reasonably consistent with other studies of prawn trawl fleets in Australia, although higher average efficiency scores were found in the NSW prawn trawl fleet. From the inefficiency model, several factors were found to significantly influence vessel efficiency. These included the number of years of experience as skipper, the number of generations that the skipper’s family had been fishing and the number of years schooling. Skippers with more schooling were significantly more efficient than skippers with lower levels of schooling, consistent with other studies. Skippers who had been fishing longer were, in fact, less efficient than newer skippers. However, this was mitigated in the case of skippers whose family had been involved in fishing for several generations, consistent with other studies and suggesting that skill was passed through by families over successive generations. Both the linear and log-linear regression models of total fishing effort against the marginal profit per hour performed reasonably well, explaining between 70 and 84 per cent of the variation in fishing effort. As the models had different dependent variables (one logged and the other not logged) this is not a good basis for model choice. A better comparator is the square root of the mean square error (SMSE) expressed as a percentage of the mean total effort. On this criterion, both models performed very similarly. The linear model suggests that each additional dollar of average profits per hour in the fishery increases total effort by around 26 hours each month. From the log linear model, each percentage increase in profits per hour increases total fishing effort by 0.13 per cent. Both models indicate that economic performance is a key driver of fishing effort in the fishery. The effect of removing the boat-replacement policy is to increase individual vessel profitability, catch and effort, but the overall increase in catch is less than that removed by the boats that must exit the fishery. That is, the smaller fleet (in terms of boat numbers) is more profitable but the overall catch is not expected to be greater than before. This assumes, however, that active boats are removed, and that these were also taking an average level of catch. If inactive boats are removed, then catch of the remaining group as a whole could increase by between 14 and 17 per cent depending on the degree to which costs are reduced with the new boats. This is still substantially lower than historical levels of catch by the fleet. Fishing power analyses An analysis of logbook data from 1988 to 2010, and survey information on fishing gear, was performed to estimate the long-term variation in the fleet’s ability to catch prawns (known as fishing power) and to derive abundance estimates of the three most commercially important prawn species (i.e., Brown Tiger, Eastern King and Greasyback Prawns). Generalised linear models were used to explain the variation in catch as a function of effort (i.e., hours fished per day), vessel and gear characteristics, onboard technologies, population abundance and environmental factors. This analysis estimated that fishing power associated with Brown Tiger and Eastern King Prawns increased over the past 20 years by 10–30% and declined by approximately 10% for greasybacks. The density of tiger prawns was estimated to have almost tripled from around 0.5 kg per hectare in 1988 to 1.5 kg/ha in 2010. The density of Eastern King Prawns was estimated to have fluctuated between 1 and 2 kg per hectare over this time period, without any noticeable overall trend, while Greasyback Prawn densities were estimated to have fluctuated between 2 and 6 kg per hectare, also without any distinctive trend. A model of tiger prawn catches was developed to evaluate the impact of fishing on prawn survival rates in Moreton Bay. The model was fitted to logbook data using the maximum-likelihood method to provide estimates of the natural mortality rate (0.038 and 0.062 per week) and catchability (which can be defined as the proportion of the fished population that is removed by one unit of effort, in this case, estimated to be 2.5 ± 0.4 E-04 per boat-day). This approach provided a method for industry and scientists to develop together a realistic model of the dynamics of the fishery. Several aspects need to be developed further to make this model acceptable to industry. Firstly, there is considerable evidence to suggest that temperature influences prawn catchability. This ecological effect should be incorporated before developing meaningful harvest strategies. Secondly, total effort has to be allocated between each species. Such allocation of effort could be included in the model by estimating several catchability coefficients. Nevertheless, the work presented in this report is a stepping stone towards estimating essential fishery parameters and developing representative mathematical models required to evaluate harvest strategies. Developing a method that allowed an effective discussion between industry, management and scientists took longer than anticipated. As a result, harvest strategy evaluations were preliminary and only included the most valuable species in the fishery, Brown Tiger Prawns. Additional analyses and data collection, including information on catch composition from field sampling, migration rates and recruitment, would improve the modelling. Harvest strategy evaluations As the harvest strategy evaluations are preliminary, the following results should not be adopted for management purposes until more thorough evaluations are performed. The effects, of closing the fishery for one calendar month, on the annual catch and value of Brown Tiger Prawns were investigated. Each of the 12 months (i.e., January to December) was evaluated. The results were compared against historical records to determine the magnitude of gain or loss associated with the closure. Uncertainty regarding the trawl selectivity was addressed using two selectivity curves, one with a weight at 50% selection (S50%) of 7 g, based on research data, and a second with S50% of 14 g, put forward by industry. In both cases, it was concluded that any monthly closure after February would not be beneficial to the industry. The magnitude of the benefit of closing the fishery in either January or February was sensitive to which mesh selectivity curve that was assumed, with greater benefit achieved when the smaller selectivity curve (i.e., S50% = 7 g) was assumed. Using the smaller selectivity (S50% = 7 g), the expected increase in catch value was 10–20% which equates to $200,000 to $400,000 annually, while the larger selectivity curve (S50% = 14 g) suggested catch value would be improved by 5–10%, or $100,000 to $200,000. The harvest strategy evaluations showed that greater benefits, in the order of 30–60% increases in the tiger annual catch value, could have been obtained by closing the fishery early in the year when annual effort levels were high (i.e., > 10,000 boat-days). In recent years, as effort levels have declined (i.e., ~4000 boat-days annually), expected benefits from such closures are more modest. In essence, temporal closures offer greater benefit when fishing mortality rates are high. A spatial analysis of Brown Tiger Prawn catch and effort was also undertaken to obtain a better understanding of the prawn population dynamics. This indicated that, to improve profitability of the fishery, fishers could consider closing the fishery in the period from June to October, which is already a period of low profitability. This would protect the Brown Tiger Prawn spawning stock, increase catch rates of all species in the lucrative pre-Christmas period (November–December), and provide fishers with time to do vessel maintenance, arrange markets for the next season’s harvest, and, if they wish, work at other jobs. The analysis found that the instantaneous rate of total mortality (Z) for the March–June period did not vary significantly over the last two decades. As the Brown Tiger Prawn population in Moreton Bay has clearly increased over this time period, an interesting conclusion is that the instantaneous rate of natural mortality (M) must have increased, suggesting that tiger prawn natural mortality may be density-dependent at this time of year. Mortality rates of tiger prawns for June–October were found to have decreased over the last two decades, which has probably had a positive effect on spawning stocks in the October–November spawning period. Abiotic effects on the prawns The influence of air temperature, rainfall, freshwater flow, the southern oscillation index (SOI) and lunar phase on the catch rates of the four main prawn species were investigated. The analyses were based on over 200,000 daily logbook catch records over 23 years (i.e., 1988–2010). Freshwater flow was more influential than rainfall and SOI, and of the various sources of flow, the Brisbane River has the greatest volume and influence on Moreton Bay prawn catches. A number of time-lags were also considered. Flow in the preceding month prior to catch (i.e., 30 days prior, Logflow1_30) and two months prior (31–60 days prior, Logflow31_60) had strong positive effects on Banana Prawn catch rates. Average air temperature in the preceding 4-6 months (Temp121_180) also had a large positive effect on Banana Prawn catch rates. Flow in the month immediately preceding catch (Logflow1_30) had a strong positive influence on Greasyback Prawn catch rates. Air temperature in the preceding two months prior to catch (Temp1_60) had a large positive effect on Brown Tiger Prawn catch rates. No obvious or marked effects were detected for Eastern King Prawns, although interestingly, catch rates declined with increasing air temperature 4–6 months prior to catch. As most Eastern King Prawn catches in Moreton Bay occur in October to December, the results suggest catch rates decline with increasing winter temperatures. In most cases, the prawn catch rates declined with the waxing lunar phase (high luminance/full moon), and increased with the waning moon (low luminance/new moon). The SOI explains little additional variation in prawn catch rates (~ <2%), although its influence was higher for Banana Prawns. Extrapolating findings of the analyses to long-term climate change effects should be interpreted with caution. That said, the results are consistent with likely increases in abundance in the region for the two tropical species, Banana Prawns and Brown Tiger Prawns, as coastal temperatures rise. Conversely, declines in abundance could be expected for the two temperate species, Greasyback and Eastern King Prawns. Corporate management structures An examination of alternative governance systems was requested by the industry at one of the early meetings, particularly systems that may give them greater autonomy in decision making as well as help improve the marketing of their product. Consequently, a review of alternative management systems was undertaken, with a particular focus on the potential for self-management of small fisheries (small in terms of number of participants) and corporate management. The review looks at systems that have been implemented or proposed for other small fisheries internationally, with a particular focus on self-management as well as the potential benefits and challenges for corporate management. This review also highlighted particular opportunities for the Moreton Bay prawn fishery. Corporate management differs from other co-management and even self-management arrangements in that ‘ownership’ of the fishery is devolved to a company in which fishers and government are shareholders. The company manages the fishery as well as coordinates marketing to ensure that the best prices are received and that the catch taken meets the demands of the market. Coordinated harvesting will also result in increased profits, which are returned to fishers in the form of dividends. Corporate management offers many of the potential benefits of an individual quota system without formally implementing such a system. A corporate management model offers an advantage over a self-management model in that it can coordinate both marketing and management to take advantage of this unique geographical advantage. For such a system to be successful, the fishery needs to be relatively small and self- contained. Small in this sense is in terms of number of operators. The Moreton Bay prawn fishery satisfies these key conditions for a successful self-management and potentially corporate management system. The fishery is small both in terms of number of participants and geography. Unlike other fisheries that have progressed down the self-management route, the key market for the product from the Moreton Bay fishery is right at its doorstep. Corporate management also presents a number of challenges. First, it will require changes in the way fishers operate. In particular, the decision on when to fish and what to catch will be taken away from the individual and decided by the collective. Problems will develop if individuals do not join the corporation but continue to fish and market their own product separately. While this may seem an attractive option to fishers who believe they can do better independently, this is likely to be just a short- term advantage with an overall long-run cost to themselves as well as the rest of the industry. There are also a number of other areas that need further consideration, particularly in relation to the allocation of shares, including who should be allocated shares (e.g. just boat owners or also some employed skippers). Similarly, how harvesting activity is to be allocated by the corporation to the fishers. These are largely issues that cannot be answered without substantial consultation with those likely to be affected, and these groups cannot give these issues serious consideration until the point at which they are likely to become a reality. Given the current structure and complexity of the fishery, it is unlikely that such a management structure will be feasible in the short term. However, the fishery is a prime candidate for such a model, and development of such a management structure in the future should be considered as an option for the longer term.

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In wheat, tillering and water-soluble carbohydrates (WSCs) in the stem are potential traits for adaptation to different environments and are of interest as targets for selective breeding. This study investigated the observation that a high stem WSC concentration (WSCc) is often related to low tillering. The proposition tested was that stem WSC accumulation is plant density dependent and could be an emergent property of tillering, whether driven by genotype or by environment. A small subset of recombinant inbred lines (RILs) contrasting for tillering was grown at different plant densities or on different sowing dates in multiple field experiments. Both tillering and WSCc were highly influenced by the environment, with a smaller, distinct genotypic component; the genotypeenvironment range covered 350750 stems m(2) and 25210mg g(1) WSCc. Stem WSCc was inversely related to stem number m(2), but genotypic rankings for stem WSCc persisted when RILs were compared at similar stem density. Low tilleringhigh WSCc RILs had similar leaf area index, larger individual leaves, and stems with larger internode cross-section and wall area when compared with high tilleringlow WSCc RILs. The maximum number of stems per plant was positively associated with growth and relative growth rate per plant, tillering rate and duration, and also, in some treatments, with leaf appearance rate and final leaf number. A common threshold of the red:far red ratio (0.390.44; standard error of the difference0.055) coincided with the maximum stem number per plant across genotypes and plant densities, and could be effectively used in crop simulation modelling as a ocut-off' rule for tillering. The relationship between tillering, WSCc, and their component traits, as well as the possible implications for crop simulation and breeding, is discussed.

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Choy sum (Brassica rapa subsp. parachinensis) is a dark green leafy vegetable that contains high folate (vitamin B9) levels comparable to spinach. Folate is essential for the maintenance of human health and is obtained solely through dietary means. Analysis of the edible portion of choy sum by both microbiological assay and LC-MS/MS indicated that total folate activity remained significantly unchanged over 3 weeks storage at 4 degrees C. Inedible fractions consisted primarily of outer leaves, which showed signs of rotting after 14d, and a combination of rotting and yellowing after 21 d, contributing to 20% and 40% of product removal, respectively. Following deconjugation of the folate present in choy sum to monoglutamate and diglutamate derivatives, the principal forms (vitamers) of folate detected in choy sum were 5-methyltetrahydrofolate and 5-formyl tetrahydrofolate, followed by tetrahydrofolate (THF), 5,10-methenyl-THF, and 10-formyl folic acid. During storage, a significant decline in 5-formyl-THF was observed, with a slight but not significant increase in the combined 5-methyl-THF derivatives. The decline in 5-formyl-THF in relation to the other folate vitamers present may indicate that 5-formyl-THF is being utilised as a folate storage reserve, being interconverted to more metabolically active forms of folate, such as 5-methyl-THF. Although folate vitamer profile changed over the storage period, total folate activity did not significantly change. From a human nutritional perspective this is important, as while particular folate vitamers (e.g. 5-methyl-THF) are necessary for maintaining vital aspects of plant metabolism, it is less important to the human diet, as humans can absorb and interconvert multiple forms of folate. The current trial indicates that it is possible to store choy sum for up to 3 weeks at 4 degrees C without significantly affecting total folate concentration of the edible portion. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.

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Context. The feral pig (Sus scrofa) is a widespread pest species in Australia and its populations are commonly controlled to reduce damage to agriculture and the environment. Feral pigs are also a resource and harvested for commercial export as game meat. Although many other control techniques are used, commercial harvesting of feral pigs is often encouraged by land managers, because it carries little or no cost and is widely perceived to control populations. Aims. To use feral-pig harvesting records, density data and simple harvest models to examine the effectiveness of commercial harvesting to reduce feral-pig populations. Methods. The present study examined commercial harvest off-take on six sites (246-657 km2) in southern Queensland, and 20 large blocks (~2-6000 km2) throughout Queensland. The harvest off-take for each site was divided by monthly or average annual population size, determined by aerial survey, to calculate monthly and annual harvest rates.Asimple harvest model assuming logistic population growth was used to determine the likely effectiveness of harvesting. Key results. Commercial harvest rates were generally low (<~20%) and are likely to provide only modest reductions in population size. Additionally, harvest rates capable of substantial reductions (>50%) in long-term population size were isolated occurrences and not maintained across sites and years. High harvest rates were observed only at low densities. Although these harvest rates may be sufficiently high to hold populations at low densities, the population is likely to escape this entrapment following a flush in food supply or a reduction in harvest effort. Implications. Our results demonstrated that, at current harvest rates, commercial harvesting is ineffective for the landscape-scale control of feral-pig populations. Unless harvest rates can be significantly increased, commercial harvesting should be used as a supplement to, rather than as a substitute for, other damage-control techniques.

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Methane is a potent greenhouse gas with a global warming potential ∼28 times that of carbon dioxide. Consequently, sources and sinks that influence the concentration of methane in the atmosphere are of great interest. In Australia, agriculture is the primary source of anthropogenic methane emissions (60.4% of national emissions, or 3260kt-1methaneyear-1, between 1990 and 2011), and cropping and grazing soils represent Australia's largest potential terrestrial methane sink. As of 2011, the expansion of agricultural soils, which are ∼70% less efficient at consuming methane than undisturbed soils, to 59% of Australia's land mass (456Mha) and increasing livestock densities in northern Australia suggest negative implications for national methane flux. Plant biomass burning does not appear to have long-term negative effects on methane flux unless soils are converted for agricultural purposes. Rice cultivation contributes marginally to national methane emissions and this fluctuates depending on water availability. Significant available research into biological, geochemical and agronomic factors has been pertinent for developing effective methane mitigation strategies. We discuss methane-flux feedback mechanisms in relation to climate change drivers such as temperature, atmospheric carbon dioxide and methane concentrations, precipitation and extreme weather events. Future research should focus on quantifying the role of Australian cropping and grazing soils as methane sinks in the national methane budget, linking biodiversity and activity of methane-cycling microbes to environmental factors, and quantifying how a combination of climate change drivers will affect total methane flux in these systems.