15 resultados para stochastic load factor

em eResearch Archive - Queensland Department of Agriculture


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Decision-making in agriculture is carried out in an uncertain environment with farmers often seeking information to reduce risk. As a result of the extreme variability of rainfall and stream-flows in north-eastern Australia, water supplies for irrigated agriculture are a limiting factor and a source of risk. The present study examined the use of seasonal climate forecasting (SCF) when calculating planting areas for irrigated cotton in the northern Murray Darling Basin. Results show that minimising risk by adjusting plant areas in response to SCF can lead to significant gains in gross margin returns. However, how farmers respond to SCF is dependent on several other factors including irrigators’ attitude towards risk.

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BACKGROUND Control of pests in stored grain and the evolution of resistance to pesticides are serious problems worldwide. A stochastic individual-based two-locus model was used to investigate the impact of two important issues, the consistency of pesticide dosage through the storage facility and the immigration rate of the adult pest, on overall population control and avoidance of evolution of resistance to the fumigant phosphine in an important pest of stored grain, the lesser grain borer. RESULTS A very consistent dosage maintained good control for all immigration rates, while an inconsistent dosage failed to maintain control in all cases. At intermediate dosage consistency, immigration rate became a critical factor in whether control was maintained or resistance emerged. CONCLUSION Achieving a consistent fumigant dosage is a key factor in avoiding evolution of resistance to phosphine and maintaining control of populations of stored-grain pests; when the dosage achieved is very inconsistent, there is likely to be a problem regardless of immigration rate. © 2012 Society of Chemical Industry

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Dry-season weight loss in grazing cattle in northern Australia has been attenuated using a number of strategies (Hunter and Vercoe, 1987, Sillence et al. 1993, Gazzola and Hunter, 1999). Furthermore, the potential to improve efficiency of feed utilisation (and thus, dry-season performance) in ruminants through conventional modulation of the insulin-like growth factor (IGF) axis (Oddy and Owens, 1997, Hill et al., 1999) and through immunomodulation of the IGF axis (Hill et al., 1998a,b) has been demonstrated. The present study investigated the use of a vaccine directed against IGFBP-1 in Brahman steers which underwent a period of nutritional restriction followed by a return to wet-season grazing.

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Globalisation is set to have a major impact on world horticultural production and distribution of fruit and vegetables throughout the world. In contrast to developing countries such as China, production and consumption of fresh fruit and vegetables in most developed countries is relatively static. For developed countries, we are starting to see consolidation in the number of farms producing fruit and vegetables with falling or static prices and real farm incomes. Global supply chains are now dominated by a few large multi-national retailers supplied by preferred trans-national distribution companies. The major competitive advantages that are emerging are consistency of supply of high quality product over an extended season and the control of genetic resources and their marketing. To capture these new competitive advantages, new strategic analyses and planning processes must be implemented. In the past, strategic analyses and planning has been undertaken on an ad hoc basis without accurate global intelligence. In the future, working ‘on the supply chain’ will become equally, if not more important, than working ‘in the supply chain’. A revised approach to strategic planning, which encompasses and adjusts for the changes caused by globalisation, is urgently needed. A new 6-step strategic analyses process is described.

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Modeling of cultivar x trial effects for multienvironment trials (METs) within a mixed model framework is now common practice in many plant breeding programs. The factor analytic (FA) model is a parsimonious form used to approximate the fully unstructured form of the genetic variance-covariance matrix in the model for MET data. In this study, we demonstrate that the FA model is generally the model of best fit across a range of data sets taken from early generation trials in a breeding program. In addition, we demonstrate the superiority of the FA model in achieving the most common aim of METs, namely the selection of superior genotypes. Selection is achieved using best linear unbiased predictions (BLUPs) of cultivar effects at each environment, considered either individually or as a weighted average across environments. In practice, empirical BLUPs (E-BLUPs) of cultivar effects must be used instead of BLUPs since variance parameters in the model must be estimated rather than assumed known. While the optimal properties of minimum mean squared error of prediction (MSEP) and maximum correlation between true and predicted effects possessed by BLUPs do not hold for E-BLUPs, a simulation study shows that E-BLUPs perform well in terms of MSEP.

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1. Many organisms inhabit strongly fluctuating environments but their demography and population dynamics are often analysed using deterministic models and elasticity analysis, where elasticity is defined as the proportional change in population growth rate caused by a proportional change in a vital rate. Deterministic analyses may not necessarily be informative because large variation in a vital rate with a small deterministic elasticity may affect the population growth rate more than a small change in a less variable vital rate having high deterministic elasticity. 2. We analyse a stochastic environment model of the red kangaroo (Macropus rufus), a species inhabiting an environment characterized by unpredictable and highly variable rainfall, and calculate the elasticity of the stochastic growth rate with respect to the mean and variability in vital rates. 3. Juvenile survival is the most variable vital rate but a proportional change in the mean adult survival rate has a much stronger effect on the stochastic growth rate. 4. Even if changes in average rainfall have a larger impact on population growth rate, increased variability in rainfall may still be important also in long-lived species. The elasticity with respect to the standard deviation of rainfall is comparable to the mean elasticities of all vital rates but the survival in age class 3 because increased variation in rainfall affects both the mean and variability of vital rates. 5. Red kangaroos are harvested and, under the current rainfall pattern, an annual harvest fraction of c. 20% would yield a stochastic growth rate about unity. However, if average rainfall drops by more than c. 10%, any level of harvesting may be unsustainable, emphasizing the need for integrating climate change predictions in population management and increase our understanding of how environmental stochasticity translates into population growth rate.

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Stochastic growth models were fitted to length-increment data of eastern king prawns, Melicertus plebejus (Hess, 1865), tagged across eastern Australia. The estimated growth parameters and growth transition matrix are for each sex representative of the species' geographical distribution. Our study explicitly displays the stochastic nature of prawn growth. Capturing length-increment growth heterogeneity for short-lived exploited species such as prawns that cannot be readily aged is essential for length-based modelling and improved management.

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Parasitoid survival and fecundity is generally enhanced with access to carbohydrate food sources. In many agricultural ecosystems, there is often a scarcity of suitable carbohydrates for parasitoids. This study compared the suitability of aphid honeydew and buckwheat nectar as diet for the aphid parasitoid Lysiphlebus testaceipes. Wasp lifespan and egg load were both increased with access to carbohydrates, but no differences were detected between the various carbohydrates diets tested. As aphid honeydew is a sufficient source of nutrition and L.testaceipes is a short-lived species, adding additional sources of carbohydrates like floral nectar strips to the agricultural landscape is unlikely to significantly increase the biological control exerted by L.testaceipes. © 2012 Australian Entomological Society.

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Maize is one of the most important crops in the world. The products generated from this crop are largely used in the starch industry, the animal and human nutrition sector, and biomass energy production and refineries. For these reasons, there is much interest in figuring the potential grain yield of maize genotypes in relation to the environment in which they will be grown, as the productivity directly affects agribusiness or farm profitability. Questions like these can be investigated with ecophysiological crop models, which can be organized according to different philosophies and structures. The main objective of this work is to conceptualize a stochastic model for predicting maize grain yield and productivity under different conditions of water supply while considering the uncertainties of daily climate data. Therefore, one focus is to explain the model construction in detail, and the other is to present some results in light of the philosophy adopted. A deterministic model was built as the basis for the stochastic model. The former performed well in terms of the curve shape of the above-ground dry matter over time as well as the grain yield under full and moderate water deficit conditions. Through the use of a triangular distribution for the harvest index and a bivariate normal distribution of the averaged daily solar radiation and air temperature, the stochastic model satisfactorily simulated grain productivity, i.e., it was found that 10,604 kg ha(-1) is the most likely grain productivity, very similar to the productivity simulated by the deterministic model and for the real conditions based on a field experiment. © 2012 American Society of Agricultural and Biological Engineers.

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Measurement of individual emission sources (e.g., animals or pen manure) within intensive livestock enterprises is necessary to test emission calculation protocols and to identify targets for decreased emissions. In this study, a vented, fabric-covered large chamber (4.5 × 4.5 m, 1.5 m high; encompassing greater spatial variability than a smaller chamber) in combination with on-line analysis (nitrous oxide [N2O] and methane [CH4] via Fourier Transform Infrared Spectroscopy; 1 analysis min-1) was tested as a means to isolate and measure emissions from beef feedlot pen manure sources. An exponential model relating chamber concentrations to ambient gas concentrations, air exchange (e.g., due to poor sealing with the surface; model linear when ≈ 0 m3 s-1), and chamber dimensions allowed data to be fitted with high confidence. Alternating manure source emission measurements using the large-chamber and the backward Lagrangian stochastic (bLS) technique (5-mo period; bLS validated via tracer gas release, recovery 94-104%) produced comparable N2O and CH4 emission values (no significant difference at P < 0.05). Greater precision of individual measurements was achieved via the large chamber than for the bLS (mean ± standard error of variance components: bLS half-hour measurements, 99.5 ± 325 mg CH4 s-1 and 9.26 ± 20.6 mg N2O s-1; large-chamber measurements, 99.6 ± 64.2 mg CH4 s-1 and 8.18 ± 0.3 mg N2O s-1). The large-chamber design is suitable for measurement of emissions from manure on pen surfaces, isolating these emissions from surrounding emission sources, including enteric emissions. © © American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.

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In irrigated cropping, as with any other industry, profit and risk are inter-dependent. An increase in profit would normally coincide with an increase in risk, and this means that risk can be traded for profit. It is desirable to manage a farm so that it achieves the maximum possible profit for the desired level of risk. This paper identifies risk-efficient cropping strategies that allocate land and water between crop enterprises for a case study of an irrigated farm in Southern Queensland, Australia. This is achieved by applying stochastic frontier analysis to the output of a simulation experiment. The simulation experiment involved changes to the levels of business risk by systematically varying the crop sowing rules in a bioeconomic model of the case study farm. This model utilises the multi-field capability of the process based Agricultural Production System Simulator (APSIM) and is parameterised using data collected from interviews with a collaborating farmer. We found sowing rules that increased the farm area sown to cotton caused the greatest increase in risk-efficiency. Increasing maize area also improved risk-efficiency but to a lesser extent than cotton. Sowing rules that increased the areas sown to wheat reduced the risk-efficiency of the farm business. Sowing rules were identified that had the potential to improve the expected farm profit by ca. $50,000 Annually, without significantly increasing risk. The concept of the shadow price of risk is discussed and an expression is derived from the estimated frontier equation that quantifies the trade-off between profit and risk.

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Hendra virus causes sporadic but typically fatal infection in horses and humans in eastern Australia. Fruit-bats of the genus Pteropus (commonly known as flying-foxes) are the natural host of the virus, and the putative source of infection in horses; infected horses are the source of human infection. Effective treatment is lacking in both horses and humans, and notwithstanding the recent availability of a vaccine for horses, exposure risk mitigation remains an important infection control strategy. This study sought to inform risk mitigation by identifying spatial and environmental risk factors for equine infection using multiple analytical approaches to investigate the relationship between plausible variables and reported Hendra virus infection in horses. Spatial autocorrelation (Global Moran’s I) showed significant clustering of equine cases at a distance of 40 km, a distance consistent with the foraging ‘footprint’ of a flying-fox roost, suggesting the latter as a biologically plausible basis for the clustering. Getis-Ord Gi* analysis identified multiple equine infection hot spots along the eastern Australia coast from far north Queensland to central New South Wales, with the largest extending for nearly 300 km from southern Queensland to northern New South Wales. Geographically weighted regression (GWR) showed the density of P. alecto and P. conspicillatus to have the strongest positive correlation with equine case locations, suggesting these species are more likely a source of infection of Hendra virus for horses than P. poliocephalus or P. scapulatus. The density of horses, climate variables and vegetation variables were not found to be a significant risk factors, but the residuals from the GWR suggest that additional unidentified risk factors exist at the property level. Further investigations and comparisons between case and control properties are needed to identify these local risk factors.

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Veterinarians have few tools to predict the rate of disease progression in FIV-infected cats. In contrast, in HIV infection, plasma viral RNA load and acute phase protein concentrations are commonly used as predictors of disease progression. This study evaluated these predictors in cats naturally infected with FIV. In older cats (>5 years), log10 FIV RNA load was higher in the terminal stages of disease compared to the asymptomatic stage. There was a significant association between log10 FIV RNA load and both log10 serum amyloid A concentration and age in unwell FIV-infected cats. This study suggests that viral RNA load and serum amyloid A warrant further investigation as predictors of disease status and prognosis in FIV-infected cats.

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Exposure to hot environments affects milk yield (MY) and milk composition of pasture and feed-pad fed dairy cows in subtropical regions. This study was undertaken during summer to compare MY and physiology of cows exposed to six heat-load management treatments. Seventy-eight Holstein-Friesian cows were blocked by season of calving, parity, milk yield, BW, and milk protein (%) and milk fat (%) measured in 2 weeks prior to the start of the study. Within blocks, cows were randomly allocated to one of the following treatments: open-sided iron roofed day pen adjacent to dairy (CID) + sprinklers (SP); CID only; non-shaded pen adjacent to dairy + SP (NSD + SP); open-sided shade cloth roofed day pen adjacent to dairy (SCD); NSD + sprinkler (sprinkler on for 45 min at 1100 h if mean respiration rate >80 breaths per minute (NSD + WSP)); open-sided shade cloth roofed structure over feed bunk in paddock + 1 km walk to and from the dairy (SCP + WLK). Sprinklers for CID + SP and NSD + SP cycled 2 min on, 12 min off when ambient temperature >26°C. The highest milk yields were in the CID + SP and CID treatments (23.9 L cow−1 day−1), intermediate for NSD + SP, SCD and SCP + WLK (22.4 L cow−1 day−1), and lowest for NSD + WSP (21.3 L cow−1 day−1) (P < 0.05). The highest (P < 0.05) feed intakes occurred in the CID + SP and CID treatments while intake was lowest (P < 0.05) for NSD + WSP and SCP + WLK. Weather data were collected on site at 10-min intervals, and from these, THI was calculated. Nonlinear regression modelling of MY × THI and heat-load management treatment demonstrated that cows in CID + SP showed no decline in MY out to a THI break point value of 83.2, whereas the pooled MY of the other treatments declined when THI >80.7. A combination of iron roof shade plus water sprinkling throughout the day provided the most effective control of heat load.