960 resultados para Agriculture -- Accidents -- Australia
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This project covered the 2006-2011 operations of the Northern Node of Barley Breeding Australia (BBA-North). BBANorth collaborated with the Southern and Western nodes and all BBA participants to deliver improved barley varieties to the Australian grains industry. BBA-North focused on the northern region and was the national leader in breeding high yielding, disease resistant barleys with grain quality that enhanced the crop's status as a preferred feed grain. Development of varieties for the malting and brewing industries was also targeted. This project incorporated coordination, breeding, regional evaluation, foliar and soil-borne disease tests, molecular marker screens and grain and malt quality analyses.
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Enhance productivity of peanuts in Papua New Guinea and Australia. Also the application of remote sensing technologies to enhance profitability in peanut systems.
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We used an established seagrass monitoring programme to examine the short and longer-term impacts of an oil spill event on intertidal seagrass meadows. Results for potentially impacted seagrass areas were compared with existing monitoring data and with control seagrass meadows located outside of the oil spill area. Seagrass meadows were not significantly affected by the oil spill. Declines in seagrass biomass and area 1 month post-spill were consistent between control and impact meadows. Eight months post-spill, seagrass density and area increased to be within historical ranges. The declines in seagrass meadows were likely attributable to natural seasonal variation and a combination of climatic and anthropogenic impacts. The lack of impact from the oil spill was due to several mitigating factors rather than a lack of toxic effects to seagrasses. The study demonstrates the value of long-term monitoring of critical habitats in high risk areas to effectively assess impacts.
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Background: The Queensland East Coast Otter Trawl Fishery (ECOTF) for penaeid shrimp fishes within Australia's Great Barrier Reef World Heritage Area (GBRWHA). The past decade has seen the implementation of conservation and fisheries management strategies to reduce the impact of the ECOTF on the seabed and improve biodiversity conservation. New information from electronic vessel location monitoring systems (VMS) provides an opportunity to review the interactions between the ECOTF and spatial closures for biodiversity conservation. Methodology and Results: We used fishing metrics and spatial information on the distribution of closures and modelled VMS data in a geographical information system (GIS) to assess change in effort of the trawl fishery from 2001-2009 and to quantify the exposure of 70 reef, non-reef and deep water bioregions to trawl fishing. The number of trawlers and the number of days fished almost halved between 2001 and 2009 and new spatial closures introduced in 2004 reduced the area zoned available for trawl fishing by 33%. However, we found that there was only a relatively minor change in the spatial footprint of the fishery as a result of new spatial closures. Non-reef bioregions benefited the most from new spatial closures followed by deep and reef bioregions. Conclusions/Significance: Although the catch of non target species remains an issue of concern for fisheries management, the small spatial footprint of the ECOTF relative to the size of the GBRWHA means that the impact on benthic habitats is likely to be negligible. The decline in effort as a result of fishing industry structural adjustment, increasing variable costs and business decisions of fishers is likely to continue a trend to fish only in the most productive areas. This will provide protection for most benthic habitats without any further legislative or management intervention.
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On-going, high-profile public debate about climate change has focussed attention on how to monitor the soil organic carbon stock (C(s)) of rangelands (savannas). Unfortunately, optimal sampling of the rangelands for baseline C(s) - the critical first step towards efficient monitoring - has received relatively little attention to date. Moreover, in the rangelands of tropical Australia relatively little is known about how C(s) is influenced by the practice of cattle grazing. To address these issues we used linear mixed models to: (i) unravel how grazing pressure (over a 12-year period) and soil type have affected C(s) and the stable carbon isotope ratio of soil organic carbon (delta(13)C) (a measure of the relative contributions of C(3) and C(4) vegetation to C(s)); (ii) examine the spatial covariation of C(s) and delta(13)C; and, (iii) explore the amount of soil sampling required to adequately determine baseline C(s). Modelling was done in the context of the material coordinate system for the soil profile, therefore the depths reported, while conventional, are only nominal. Linear mixed models revealed that soil type and grazing pressure interacted to influence C(s) to a depth of 0.3 m in the profile. At a depth of 0.5 m there was no effect of grazing on C(s), but the soil type effect on C(s) was significant. Soil type influenced delta(13)C to a soil depth of 0.5 m but there was no effect of grazing at any depth examined. The linear mixed model also revealed the strong negative correlation of C(s) with delta(13)C, particularly to a depth of 0.1 m in the soil profile. This suggested that increased C(s) at the study site was associated with increased input of C from C(3) trees and shrubs relative to the C(4) perennial grasses; as the latter form the bulk of the cattle diet, we contend that C sequestration may be negatively correlated with forage production. Our baseline C(s) sampling recommendation for cattle-grazing properties of the tropical rangelands of Australia is to: (i) divide the property into units of apparently uniform soil type and grazing management; (ii) use stratified simple random sampling to spread at least 25 soil sampling locations about each unit, with at least two samples collected per stratum. This will be adequate to accurately estimate baseline mean C(s) to within 20% of the true mean, to a nominal depth of 0.3 m in the profile.
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Despite international protection of white sharks (Carcharodon carcharias), important conservation parameters such as abundance, population structure and genetic diversity are largely unknown. The tissue of 97 predominately juvenile white sharks sampled from spatially distant eastern and southwestern Australian coastlines was sequenced for the mitochondrial DNA (mtDNA) control region and genotyped with six nuclear-encoded microsatellite loci. MtDNA population structure was found between the eastern and southwestern coasts (FST = 0.142, p < 0.001), implying female natal philopatry. This concords with recent satellite and acoustic tracking findings which suggest the sustained presence of discrete east coast nursery areas. Furthermore, population subdivision was found between the same regions with biparentally inherited microsatellite markers (FST = 0.009, p <0.05), suggesting that males may also exhibit some degree of reproductive philopatry. Five sharks captured along the east coast had mtDNA haplotypes that resembled western Indian Ocean sharks more closely than Australian/New Zealand sharks, suggesting that transoceanic dispersal or migration resulting in breeding may occur sporadically. Our most robust estimate of contemporary genetic effective population size was low and below the threshold at which adaptive potential may be lost. For a variety of reasons, these contemporary estimates were at least one, possibly two orders of magnitude below our historical effective size estimates. Further population decline could expose these genetically isolated populations to detrimental genetic effects. Regional Australian white shark conservation management units should be implemented until genetic population structure, size and diversity can be investigated in more detail. Reference: Blower, D. C., Pandolfi, J. M., Gomez-Cabrera, M. del C., Bruce, B. D. & Ovenden, J. R. (In press - April 2012). Population genetics of Australian white sharks reveals fine-scale spatial structure, transoceanic dispersal events and low effective population sizes. Marine Ecology Progress Series.
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Microsatellite genotypes of individual scallops
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There is a large gap between the refined approaches to characterise genotypes and the common use of location and season as a coarse surrogate for environmental characterisation of breeding trials. As a framework for breeding, the aim of this paper is quantifying the spatial and temporal patterns of thermal and water stress for field pea in Australia. We compiled a dataset for yield of the cv. Kaspa measured in 185 environments, and investigated the associations between yield and seasonal patterns of actual temperature and modelled water stress. Correlations between yield and temperature indicated two distinct stages. In the first stage, during crop establishment and canopy expansion before flowering, yield was positively associated with minimum temperature. Mean minimum temperature below similar to 7 degrees C suggests that crops were under suboptimal temperature for both canopy expansion and radiation-use efficiency during a significant part of this early growth period. In the second stage, during critical reproductive phases, grain yield was negatively associated with maximum temperature over 25 degrees C. Correlations between yield and modelled water supply/demand ratio showed a consistent pattern with three phases: no correlation at early stages of the growth cycle, a progressive increase in the association that peaked as the crop approached the flowering window, and a progressive decline at later reproductive stages. Using long-term weather records (1957-2010) and modelled water stress for 104 locations, we identified three major patterns of water deficit nation wide. Environment type 1 (ET1) represents the most favourable condition, with no stress during most of the pre-flowering phase and gradual development of mild stress after flowering. Type 2 is characterised by increasing water deficit between 400 degree-days before flowering and 200 degree-days after flowering and rainfall that relieves stress late in the season. Type 3 represents the more stressful condition with increasing water deficit between 400 degree-days before flowering and maturity. Across Australia, the frequency of occurrence was 24% for ET1, 32% for ET2 and 43% for ET3, highlighting the dominance of the most stressful condition. Actual yield averaged 2.2 t/ha for ET1, 1.9 t/ha for ET2 and 1.4 t/ha for ET3, and the frequency of each pattern varied substantially among locations. Shifting from a nominal (i.e. location and season) to a quantitative (i.e. stress type) characterisation of environments could help improving breeding efficiency of field pea in Australia.
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This book provides for the first time a detailed host list for all the fruit fly species (Tephritidae) known from Australia. It includes available distribution, male lure and host plant information for the 278 species currently recorded from Australia (including Torres Strait Islands but excluding Christmas and Cocos (Keeling) islands in the Indian Ocean). This total includes 269 described species plus nine undescribed species of Tephritinae. Thirteen fruit fly specialists from throughout Australia collaborated with QDPI in the production of this book. It provides an invaluable reference source for anyone involved in fruit fly research, ecological studies, pre- and post-harvest control, regulation, quarantine and market access.
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Fruit size and quality are major problems in early-season stonefruit cultivars grown in Australia and South-East Asia. In Australia, Thailand and Vietnam, new training and trellising systems are being developed to improve yield and fruit quality. Australian trials found that new training systems, such as the Open Tatura system, are more productive compared with standard vase-trained trees. We established new crop-loading indices for low-chill stonefruit to provide a guide for optimum fruit thinning based on fruit number per canopy surface and butt cross sectional area. Best management practices were developed for low-chill stonefruit cultivation using growth retardants, optimizing leaf nitrogen concentrations and controlling rates and timing of irrigation. Regulated deficit irrigation (RDI) improved fruit sugar concentrations by restricting water application during stage II of fruit growth. New pest and disease control measures are also being developed using a new generation of fruit fly baits. Soft insecticides such as (Spinosad) are used at significantly lower concentrations and have lower mammalian toxicity than the organophosphates currently registered in Australia. In addition, fruit fly exclusion netting effectively eliminated fruit fly and many other insect pests from the orchard with no increase in diseases. This netting system increased sugar concentrations of peach and nectarine by as much as 30%. Economic analyses showed that the break-even point can be reduced from 12 to 6 years Open Tatura trellising and exclusion netting.
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Development of new agricultural industries in northern Australia is often perceived as a solution to changes in water availability that have occurred within southern Australia as a result of changes to government policy in response to and exacerbated by climate change. This report examines the likely private, social and community costs and benefits associated with the establishment of a cotton industry in the Burdekin. The research undertaken covers three spatial scales by modelling the response of cotton and to climate change at the crop and farm scale and linking this to regional scale modelling of the economy. Modelling crop growth as either a standalone crop or as part of a farm enterprise provides the clearest picture of how yields and water use will be affected under climate change. The alternative to this is to undertake very costly trials in environmental chambers. For this reason it is critical that funding for model development especially for crops being crop in novel environments be seen as a high priority for climate change and adaptation studies. Crop level simulations not only provide information on how the crop responds to climate change, they also illustrate that that these responses are the result of complex interactions and cannot necessarily be derived from the climate information alone. These simulations showed that climate change would lead to decreased cotton yields in 2030 and 2050 without the affect of CO2 fertilisation. Without CO2 fertilisation, yields would be decreased by 3.2% and 17.8%. Including CO2 fertilisation increased yields initially by 5.9%, but these were reduced by 3.6% in 2050. This still represents a major offset and at least ameliorates the impact of climate change on yield. To cope with the decreased in-crop rainfall (4.5% by 2030 and 15.8% in 2050) and an initial increase in evapotranspiration of 2% in 2030 and
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This study presents the use of a whole farm model in a participatory modelling research approach to examine the sensitivity of four contrasting case study farms to a likely climate change scenario. The newly generated information was used to support discussions with the participating farmers in the search for options to design more profitable and sustainable farming systems in Queensland Australia. The four case studies contrasted in key systems characteristics: opportunism in decision making, i.e. flexible versus rigid crop rotations; function, i.e. production of livestock or crops; and level of intensification, i.e. dryland versus irrigated agriculture. Tested tactical and strategic changes under a baseline and climate change scenario (CCS) involved changes in the allocation of land between cropping and grazing enterprises, alternative allocations of limited irrigation water across cropping enterprises, and different management rules for planting wheat and sorghum in rainfed cropping. The results show that expected impacts from a likely climate change scenario were evident in the following increasing order: the irrigated cropping farm case study, the cropping and grazing farm, the more opportunistic rainfed cropping farm and the least opportunistic rainfed cropping farm. We concluded that in most cases the participating farmers were operating close to the efficiency frontier (i.e. in the relationship between profits and risks). This indicated that options to adapt to climate change might need to evolve from investments in the development of more innovative cropping and grazing systems and/or transformational changes on existing farming systems. We expect that even though assimilating expected changes in climate seems to be rather intangible and premature for these farmers, as innovations are developed, adaptation is likely to follow quickly. The multiple interactions among farm management components in complex and dynamic farm businesses operating in a variable and changing climate, make the use of whole farm participatory modelling approaches valuable tools to quantify benefits and trade-offs from alternative farming systems designs in the search for improved profitability and resilience.
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Predicting which species are likely to cause serious impacts in the future is crucial for targeting management efforts, but the characteristics of such species remain largely unconfirmed. We use data and expert opinion on tropical and subtropical grasses naturalised in Australia since European settlement to identify naturalised and high-impact species and subsequently to test whether high-impact species are predictable. High-impact species for the three main affected sectors (environment, pastoral and agriculture) were determined by assessing evidence against pre-defined criteria. Twenty-one of the 155 naturalised species (14%) were classified as high-impact, including four that affected more than one sector. High-impact species were more likely to have faster spread rates (regions invaded per decade) and to be semi-aquatic. Spread rate was best explained by whether species had been actively spread (as pasture), and time since naturalisation, but may not be explanatory as it was tightly correlated with range size and incidence rate. Giving more weight to minimising the chance of overlooking high-impact species, a priority for biosecurity, meant a wider range of predictors was required to identify high-impact species, and the predictive power of the models was reduced. By-sector analysis of predictors of high impact species was limited by their relative rarity, but showed sector differences, including to the universal predictors (spread rate and habitat) and life history. Furthermore, species causing high impact to agriculture have changed in the past 10 years with changes in farming practice, highlighting the importance of context in determining impact. A rationale for invasion ecology is to improve the prediction and response to future threats. Although our study identifies some universal predictors, it suggests improved prediction will require a far greater emphasis on impact rather than invasiveness, and will need to account for the individual circumstances of affected sectors and the relative rarity of high-impact species.
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Loss of nitrogen in deep drainage from agriculture is an important issue for environmental and economic reasons, but limited field data is available for tropical crops. In this study, nitrogen (N) loads leaving the root zone of two major humid tropical crops in Australia, sugarcane and bananas, were measured. The two field sites, 57 km apart, had a similar soil type (a well drained Dermosol) and rainfall (∼2700 mm year -1) but contrasting crops and management. A sugarcane crop in a commercial field received 136-148 kg N ha -1 year -1 applied in one application each year and was monitored for 3 years (first to third ratoon crops). N treatments of 0-600 kg ha -1 year -1 were applied to a plant and following ratoon crop of bananas. N was applied as urea throughout the growing season in irrigation water through mini-sprinklers. Low-suction lysimeters were installed at a depth of 1 m under both crops to monitor loads of N in deep drainage. Drainage at 1 m depth in the sugarcane crops was 22-37% of rainfall. Under bananas, drainage in the row was 65% of rainfall plus irrigation for the plant crop, and 37% for the ratoon. Nitrogen leaching loads were low under sugarcane (<1-9 kg ha -1 year -1) possibly reflecting the N fertiliser applications being reasonably matched to crop requirements and at least 26 days between fertiliser application and deep drainage. Under bananas, there were large loads of N in deep drainage when N application rates were in excess of plant demand, even when applied fortnightly. The deep drainage loss of N attributable to N fertiliser, calculated by subtracting the loss from unfertilised plots, was 246 and 641 kg ha -1 over 2 crop cycles, which was equivalent to 37 and 63% of the fertiliser application for treatments receiving 710 and 1065 kg ha -1, respectively. Those rates of fertiliser application resulted in soil acidification to a depth of 0.6 m by as much as 0.6 of a unit at 0.1-0.2 m depth. The higher leaching losses from bananas indicated that they should be a priority for improved N management. Crown Copyright © 2012.
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In current simulation packages for the management of extensive beef-cattle enterprises, the relationships for the key biological rates (namely conception and mortality) are quite rudimentary. To better estimate these relationships, cohort-level data covering 17 100 cow-years from six sites across northern Australia were collated and analysed. Further validation data, from 7200 cow-years, were then used to test these relationships. Analytical problems included incomplete and non-standardised data, considerable levels of correlation among the 'independent' variables, and the close similarity of alternate possible models. In addition to formal statistical analyses of these data, the theoretical equations for predicting mortality and conception rates in the current simulation models were reviewed, and then reparameterised and recalibrated where appropriate. The final models explained up to 80% of the variation in the data. These are now proposed as more accurate and useful models to be used in the prediction of biological rates in simulation studies for northern Australia. © The State of Queensland (through the Department of Agriculture, Fisheries and Forestry) 2012. © CSIRO.