51 resultados para ENVIRONMENTAL STATISTICS

em eResearch Archive - Queensland Department of Agriculture


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There are two key types of selection in a plant breeding program, namely selection of hybrids for potential commercial use and the selection of parents for use in future breeding. Oakey et al. (in Theoretical and Applied Genetics 113, 809-819, 2006) showed how both of these aims could be achieved using pedigree information in a mixed model analysis in order to partition genetic effects into additive and non-additive effects. Their approach was developed for field trial data subject to spatial variation. In this paper we extend the approach for data from trials subject to interplot competition. We show how the approach may be used to obtain predictions of pure stand additive and non-additive effects. We develop the methodology in the context of a single field trial using an example from an Australian sorghum breeding program.

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Fisheries management agencies around the world collect age data for the purpose of assessing the status of natural resources in their jurisdiction. Estimates of mortality rates represent a key information to assess the sustainability of fish stocks exploitation. Contrary to medical research or manufacturing where survival analysis is routinely applied to estimate failure rates, survival analysis has seldom been applied in fisheries stock assessment despite similar purposes between these fields of applied statistics. In this paper, we developed hazard functions to model the dynamic of an exploited fish population. These functions were used to estimate all parameters necessary for stock assessment (including natural and fishing mortality rates as well as gear selectivity) by maximum likelihood using age data from a sample of catch. This novel application of survival analysis to fisheries stock assessment was tested by Monte Carlo simulations to assert that it provided unbiased estimations of relevant quantities. The method was applied to the data from the Queensland (Australia) sea mullet (Mugil cephalus) commercial fishery collected between 2007 and 2014. It provided, for the first time, an estimate of natural mortality affecting this stock: 0.22±0.08 year −1 .

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The Brix content of pineapple fruit can be non-invasively predicted from the second derivative of near infrared reflectance spectra. Correlations obtained using a NIRSystems 6500 spectrophotometer through multiple linear regression and modified partial least squares analyses using a post-dispersive configuration were comparable with that from a pre-dispersive configuration in terms of accuracy (e.g. coefficient of determination, R2, 0.73; standard error of cross validation, SECV, 1.01°Brix). The effective depth of sample assessed was slightly greater using the post-dispersive technique (about 20 mm for pineapple fruit), as expected in relation to the higher incident light intensity, relative to the pre-dispersive configuration. The effect of such environmental variables as temperature, humidity and external light, and instrumental variables such as the number of scans averaged to form a spectrum, were considered with respect to the accuracy and precision of the measurement of absorbance at 876 nm, as a key term in the calibration for Brix, and predicted Brix. The application of post-dispersive near infrared technology to in-line assessment of intact fruit in a packing shed environment is discussed.

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Many statistical forecast systems are available to interested users. In order to be useful for decision-making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and their statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of `quality’. However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what ‘quality’ entails and how to measure it, leading to confusion and misinformation. Here we present a generic framework to quantify aspects of forecast quality using an inferential approach to calculate nominal significance levels (p-values) that can be obtained either by directly applying non-parametric statistical tests such as Kruskal-Wallis (KW) or Kolmogorov-Smirnov (KS) or by using Monte-Carlo methods (in the case of forecast skill scores). Once converted to p-values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. Our analysis demonstrates the importance of providing p-values rather than adopting some arbitrarily chosen significance levels such as p < 0.05 or p < 0.01, which is still common practice. This is illustrated by applying non-parametric tests (such as KW and KS) and skill scoring methods (LEPS and RPSS) to the 5-phase Southern Oscillation Index classification system using historical rainfall data from Australia, The Republic of South Africa and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. We found that non-parametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system or quality measure. Eventually such inferential evidence should be complimented by descriptive statistical methods in order to fully assist in operational risk management.

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Grain samples from a combined intermediate and advanced stage barley breeding trial series, grown at two sites in two consecutive years were assessed for detailed grain quality and ruminant feed quality. The results indicated that there were significant genetic and environmental effects for “feed” traits as measured using grain hardness, acid detergent fibre (ADF), starch and in-sacco dry matter digestibility (ISDMD) assays. In addition, there was strong genotypic discrimination for the regressed feed performance traits, namely Net Energy (NE) and Average Daily Gain (ADG). There was considerable variation in genetic correlations for all traits based on variance from the cultivars used, sites or laboratory processing effects. There was a high level of heritability ranging from 89% to 88% for retention, 60% to 80% for protein and 56% to 68% for ADF. However, there were only low to moderate levels of heritability for the feed traits, with starch 30–39%, ISDMD 55–63%, ADF 56–68%, particle size 47–73%, 31–48% NE and ADG 44–51%. These results suggest that there were real differences in the feed performance of barleys and that selection for cattle feed quality is potentially a viable option for breeding programs.

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In this study, we assessed a broad range of barley breeding lines and commercial varieties by three hardness methods (two particle size methods and one crush resistance method (SKCS—Single-Kernel Characterization System), grown at multiple sites to see if there was variation in barley hardness and if that variation was genetic or environmentally controlled. We also developed near-infrared reflectance (NIR) calibrations for these three hardness methods to ascertain if NIR technology was suitable for rapid screening of breeding lines or specific populations. In addition, we used this data to identify genetic regions that may be associated with hardness. There were significant (p<0.05) genetic effects for the three hardness methods. There were also environmental effects, possibly linked to the effect of protein on hardness, i.e. increasing protein resulted in harder grain. Heritability values were calculated at >85% for all methods. The NIR calibrations, with R2 values of >90%, had Standard Error of Prediction values of 0.90, 72 and 4.0, respectively, for the three hardness methods. These equations were used to predict hardness values of a mapping population which resulted in genetic markers being identified on all chromosomes but chromosomes 2H, 3H, 5H, 6H and 7H had markers with significant LOD scores. The two regions on 5H were on the distal end of both the long and short arms. The region that showed significant LOD score was on the long arm. However, the region on the short arm associated with the hardness (hordoindoline) genes did not have significant LOD scores. The results indicate that barley hardness is influenced by both genotype and environment and that the trait is heritable, which would allow breeders to develop very hard or soft varieties if required. In addition, NIR was shown to be a reliable tool for screening for hardness. While the data set used in this study has a relatively low variation in hardness, the tools developed could be applied to breeding populations that have large variation in barley grain hardness.

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Pastoralists from 37 beef cattle and sheep properties in western Queensland developed and implemented an environmental management system (EMS) over 18 months. The EMS implemented by them was customised for the pastoral industry as part of a national EMS pilot project, and staff from this project encouraged and assisted pastoralists during this trial. The 31 pastoralists surveyed at the end of the pilot project identified few benefits of EMS implementation, and these were largely associated with environmental management and sustainability. In terms of the reasons for uptake of an EMS, these pastoralists identified drivers similar to those reported in other primary industry sectors. These included improving property and environmental management, financial incentives, a range of market benefits, assistance with red tape issues, access to other training opportunities and assistance and support with the development of their EMS. However, these drivers are weak, and are not motivating pastoralists to adopt an EMS. In contrast, barriers to adoption such as the time involved in developing and implementing EMS are tangible and immediate. Given a lack of effective drivers and that pastoralists are under considerable pressure from ongoing rural adjustment processes, it is not surprising that an EMS is a low priority. It is concluded that widespread uptake and on-going use of an EMS in the pastoral industry will not occur unless pastoralists are required or rewarded for this by markets, governments, financiers, and regional natural resource management bodies.

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BACKGROUND: Field studies of diuron and its metabolites 3-(3,4-dichlorophenyl)-1-methylurea (DCPMU), 3,4-dichlorophenylurea (DCPU) and 3,4-dichloroaniline (DCA) were conducted in a farm soil and in stream sediments in coastal Queensland, Australia. RESULTS: During a 38 week period after a 1.6 kg ha^-1 diuron application, 70-100% of detected compounds were within 0-15 cm of the farm soil, and 3-10% reached the 30-45 cm depth. First-order t1/2 degradation averaged 49 ± 0.9 days for the 0-15, 0-30 and 0-45 cm soil depths. Farm runoff was collected in the first 13-50 min of episodes lasting 55-90 min. Average concentrations of diuron, DCPU and DCPMU in runoff were 93, 30 and 83-825 µg L^-1 respectively. Their total loading in all runoff was >0.6% of applied diuron. Diuron and DCPMU concentrations in stream sediments were between 3-22 and 4-31 µg kg^-1 soil respectively. The DCPMU/diuron sediment ratio was >1. CONCLUSION: Retention of diuron and its metabolites in farm topsoil indicated their negligible potential for groundwater contamination. Minimal amounts of diuron and DCMPU escaped in farm runoff. This may entail a significant loading into the wider environment at annual amounts of application. The concentrations and ratio of diuron and DCPMU in stream sediments indicated that they had prolonged residence times and potential for accumulation in sediments. The higher ecotoxicity of DCPMU compared with diuron and the combined presence of both compounds in stream sediments suggest that together they would have a greater impact on sensitive aquatic species than as currently apportioned by assessments that are based upon diuron alone.

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Synthetic backcrossed-derived bread wheats (SBWs) from CIMMYT were grown in the north-west of Mexico (CIANO) and sites across Australia during 3 seasons. A different set of lines was evaluated each season, as new materials became available from the CIMMYT crop enhancement program. Previously, we have evaluated both the performance of genotypes across environments and the genotype x environment interaction (G x E). The objective of this study was to interpret the G x E for yield in terms of crop attributes measured at individual sites and to identify the potential environmental drivers of this interaction. Groups of SBWs with consistent yield performance were identified, often comprising closely related lines. However, contrasting performance was also relatively common among sister lines or between a recurrent parent and its SBWs. Early flowering was a common feature among lines with broad adaptation and/or high yield in the northern Australian wheatbelt, while yields in the southern region did not show any association with the maturity type. Lines with high yields in the southern and northern regions had cooler canopies during flowering and early grain filling. Among the SBWs with Australian genetic backgrounds, lines best adapted to CIANO were tall (>100 cm), with a slightly higher ground cover. These lines also displayed a higher concentration of water-soluble carbohydrates in the stem at flowering, which was negatively correlated with stem number per unit area when evaluated in southern Australia (Horsham). Possible reasons for these patterns are discussed. Selection for yield at CIANO did not specifically identify the lines best adapted to northern Australia, although they were not the most poorly adapted either. In addition, groups of lines with specific adaptation to the south would not have been selected by choosing the highest yielding lines at CIANO. These findings suggest that selection at CIMMYT for Australian environments may be improved by either trait based selection or yield data combined with trait information. Flowering date, canopy temperature around flowering, tiller density, and water-soluble carbohydrate concentration in the stem at flowering seem likely candidates.

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We examine the microchemistry of otoliths of cohorts of a fished shed population of the large catadromous fish, barramundi Lates calcarifer from the estuary of a large tropical river. Barramundi from the estuary of the large, heavily regulated Fitzroy River, north eastern Australia were analysed by making transects of 87Sr/86Sr isotope and trace metal/Ca ratios from the core to the outer edge. Firstly, we examined the Sr/Ca, Ba/Ca, Mg/Ca and Mn/Ca and 87Sr/86Sr isotope ratios in otoliths of barramundi tagged in either freshwater or estuarine habitats that were caught by the commercial fishery in the estuary. We used 87Sr/86Sr isotope ratios to identify periods of freshwater residency and assess whether trace metal/Ca ratios varied between habitats. Only Sr/Ca consistently varied between known periods of estuarine or freshwater residency. The relationships between trace metal/Ca and river flow, salinity, temperature were examined in fish tagged and recaptured in the estuary. We found weak and inconsistent patterns in relationships between these variables in the majority of fish. These results suggest that both individual movement history within the estuary and the scale of environmental monitoring were reducing our ability to detect any patterns. Finally, we examined fish in the estuary from two dominant age cohorts (4 and 7 yr old) before and after a large flood in 2003 to ascertain if the flood had enabled fish from freshwater habitats to migrate to the estuary. There was no difference in the proportion of fish in the estuary that had accessed freshwater after the flood. Instead, we found that larger individuals with each age cohort were more likely to have spent a period in freshwater. This highlights the need to maintain freshwater flows in rivers. About half the fish examined had accessed freshwater habitats before capture. Of these, all had spent at least their first two months in marine salinity waters before entering freshwater and some did not enter freshwater until four years of age. This contrasts with the results of several previous studies in other parts of the range that found that access to freshwater swamps by larval barramundi was important for enhanced population productivity and recruitment.

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This special issue of Continental Shelf Research contains 20 papers giving research results produced as part of Australia's Torres Strait Co-operative Research Centre (CRC) Program, which was funded over a three-year period during 2003-2006. Marine biophysical, fisheries, socioeconomic-cultural and extension research in the Torres Strait region of northeastern Australia was carried out to meet three aims: 1) support the sustainable development of marine resources and minimize impacts of resource use in Torres Strait; 2) enhance the conservation of the marine environment and the social, cultural and economic well being of all stakeholders, particularly the Torres Strait peoples; and 3) contribute to effective policy formulation and management decision making. Subjects covered, including commercial and traditional fisheries management, impacts of anthropogenic sediment inputs on seagrass meadows and communication of science results to local communities, have broad applications to other similar environments.

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The ability to predict phenology and canopy development is critical in crop models used for simulating likely consequences of alternative crop management and cultivar choice strategies. Here we quantify and contrast the temperature and photoperiod responses for phenology and canopy development of a diverse range of elite Indian and Australian sorghum genotypes (hybrid and landrace). Detailed field experiments were undertaken in Australia and India using a range of genotypes, sowing dates, and photoperiod extension treatments. Measurements of timing of developmental stages and leaf appearance were taken. The generality of photo-thermal approaches to modelling phenological and canopy development was tested. Environmental and genotypic effects on rate of progression from emergence to floral initiation (E-FI) were explained well using a multiplicative model, which combined the intrinsic development rate (Ropt), with responses to temperature and photoperiod. Differences in Ropt and extent of the photoperiod response explained most genotypic effects. Average leaf initiation rate (LIR), leaf appearance rate and duration of the phase from anthesis to physiological maturity differed among genotypes. The association of total leaf number (TLN) with photoperiod found for all genotypes could not be fully explained by effects on development and LIRs. While a putative effect of photoperiod on LIR would explain the observations, other possible confounding factors, such as air-soil temperature differential and the nature of model structure were considered and discussed. This study found a generally robust predictive capacity of photo-thermal development models across diverse ranges of both genotypes and environments. Hence, they remain the most appropriate models for simulation analysis of genotype-by-management scenarios in environments varying broadly in temperature and photoperiod.

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Climate variability and change are risk factors for climate sensitive activities such as agriculture. Managing these risks requires "climate knowledge", i.e. a sound understanding of causes and consequences of climate variability and knowledge of potential management options that are suitable in light of the climatic risks posed. Often such information about prognostic variables (e.g. yield, rainfall, run-off) is provided in probabilistic terms (e.g. via cumulative distribution functions, CDF), whereby the quantitative assessments of these alternative management options is based on such CDFs. Sound statistical approaches are needed in order to assess whether difference between such CDFs are intrinsic features of systems dynamics or chance events (i.e. quantifying evidences against an appropriate null hypothesis). Statistical procedures that rely on such a hypothesis testing framework are referred to as "inferential statistics" in contrast to descriptive statistics (e.g. mean, median, variance of population samples, skill scores). Here we report on the extension of some of the existing inferential techniques that provides more relevant and adequate information for decision making under uncertainty.

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Environmental heat can reduce conception rates (the proportion of services that result in pregnancy) in lactating dairy cows. The study objectives were to identify periods of exposure relative to the service date in which environmental heat is most closely associated with conception rates, and to assess whether the total time cows are exposed to high environmental heat within each 24-h period is more closely associated with conception rates than is the maximum environmental heat for each 24-h period. A retrospective observational study was conducted in 25 predominantly Holstein-Friesian commercial dairy herds located in Australia. Associations between weather and conception rates were assessed using 16,878 services performed over a 21-mo period. Services were classified as successful based on rectal palpation. Two measures of heat load were defined for each 24-h period: the maximum temperature-humidity index (THI) for the period, and the number of hours in the 24-h period when the THI was >72. Conception rates were reduced when cows were exposed to a high heat load from the day of service to 6 d after service, and in wk -1. Heat loads in wk -3 to -5 were also associated with reduced conception rates. Thus, management interventions to ameliorate the effects of heat load on conception rates should be implemented at least 5 wk before anticipated service and should continue until at least 1 wk after service. High autocorrelations existed between successive daily values in both measures, and associations between day of heat load relative to service day and conception rates differed substantially when ridge regression was used to account for this autocorrelation. This indicates that when assessing the effects of heat load on conception rates, the autocorrelation in heat load between days should be accounted for in analyses. The results suggest that either weekly averages or totals summarizing the daily heat load are adequate to describe heat load when assessing effects on conception rates in lactating dairy cows.

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Sorghum ergot, caused by Claviceps africana, has remained a major disease problem in Australia since it was first recorded in 1996, and is the focus of a range of biological and integrated management research. Artificial inoculation using conidial suspensions is an important tool in this research. Ergot infection is greatly influenced by environmental factors, so it is important to reduce controllable sources of variation such as inoculum concentration. The use of optical density was tested as a method of quantifying conidial suspensions of C. africana, as an alternative to haemocytometer counts. This method was found to be accurate and time efficient, with possible applications in other disease systems.