Search

Abstract: This paper presents an innovative method for tourist market segmentation-based on dominant movement patterns of tourists; that is, the travel sequences or patterns used by tourists most frequently. There were three steps to achieve this goal. In the first step, general log-linear models were adopted to identify the dominant movement patterns, while the second step was to discover the characteristics of the groups of tourists who travelled with these patterns. The Expectation–Maximisation algorithm was then used to partition tourist segments in terms of socio-demographic and travel behavioural variables. The third step was to select target markets based upon the earlier analysis. These methods were applied to a sample of tourists, over the period of a week, on Phillip Island, Victoria, Australia. A significant outcome of this research is that it will assist tourism organisations to identify tourism market segments and develop better tour packages and more efficient marketing strategies aligned to the characteristics of the tourists. [Copyright &y& Elsevier]

Because seasonal rainfall is the largest driver of grain production in Australia, forecasts of can increase the profitability of grain farms when used to inform on-farm decision making. Seasonal forecasting in the grainbelt of Western Australia (WA) is difficult because climate drivers that affect the Australian continent have little influence on the south-west corner. The Statistical Seasonal Forecast system was developed to provide skilful forecasts to assist on-farm decision making in the south-west of WA. It has now been operational from 2013 to 2018. This article describes the models and methods used to create forecasts, the results of forecast validation, and verification of the accuracy of six years of operational seasonal forecasts. Forecast validation was performed using leave-one-out cross-validation. It showed positive skill, lower for late spring and summer forecasts that rises in autumn and is highest for forecasting winter rainfall. Skill for forecasting growing season (May to October) is good for lead times of up to three months. Verified skill of the operational forecasts is lower than estimated using cross-validation. The operational system performed reasonably well at forecasting 3-month periods, and for most years it showed better skill than climatology. Verified skill for growing season forecasts is higher than that of forecasting any 3-month period, with higher skill in the south. With only a small sample of six years of forecasts, longer-term accuracy may be better or worse. To test whether declining accuracy is a risk, use of a less generous measure of forecast validation is recommended. • The SSF was developed to provide skilful forecasts of seasonal rainfall to assist grain farmers in south-west Australia. • It uses partial least squares regression to derive indices relating global SST and SLP with rainfall. • Accuracy assessment shows positive skill for 3-month periods and higher skill for forecasting growing season rainfall. [ABSTRACT FROM AUTHOR]

Yield prediction is a major determinant of many management decisions for crop production. Farmers and their advisors want user-friendly decision support tools for predicting yield. Simulation models can be used to accurately predict yield, but they are complex and difficult to parameterise. The goal of this study is to build a simple and parsimonious model for predicting wheat yields that can be implemented in a decision tool to be used by farmers at a paddock level. A large yield data set accumulated from trials on commonly grown varieties in Western Australia is used to build and validate a generalised additive model (GAM) for predicting wheat yield. Explanatory variables tested included weather data and derivatives, geolocation, soil type, land capability, and wheat varieties. Model selection followed a forward stepwise approach in combination with cross-validation to select the smallest set of explanatory variables. The predictive performance is also evaluated using independent data. The final model uses seasonal water availability, location and year to predict wheat yield. Because the GAM model has minimal inputs, it can be easily employed in a decision tool to predict yield throughout the growing season using rainfall data up to the prediction date and either climatological averages or seasonal forecasts of rainfall for the remainder of the growing season. It also has the potential to be used as an input to agronomic models that predict the effect on yield of various management choices for fertiliser, pest, weed and disease management. • Wheat yields in Western Australia are driven by seasonal rainfall. • Yields can be predicted using a simple model with few, easily obtained inputs. • The model can be easily implemented in an online decision tool or mobile app. [ABSTRACT FROM AUTHOR]

Natural radionuclides and stable elements were measured in weaver ants, leaves and soils collected from three sites in tropical northern Australia. Radionuclide concentration ratios for ants relative to soil were derived from the measurements and used to refine the current environmental radiological assessment for remediation of Ranger uranium mine. Use of site-specific concentration ratios for weaver ants gave a more conservative estimate of environmental exposure to the arthropod wildlife group than use of default concentration ratios in the ERICA Tool. This was primarily because the 226 Ra concentration ratio for weaver ants was more than 7 times greater than for generic arthropods. [ABSTRACT FROM AUTHOR]

The National Health and Medical Research Council and Natural Resource Management Ministerial Council of Australia developed the current Australian Drinking Water Guidelines which recommend an annual radiation dose value of 1 mSv year −1 . One of the potential major contributors to the radiation dose from drinking water is radium-228, a naturally occurring radionuclide arising from the thorium decay series. Various methods of analysing for radium-228 in water have been established and adapted by analytical radiochemistry laboratories. Seven laboratories in Australia participated in analysing radium-228 spiked water samples with activity concentrations ranging from 6 mBq L −1 to 20 Bq L −1 . The aim of the exercise was to compare and evaluate radium-228 results reported by the participating laboratories, the methods used and the detection limits. This paper presents the outcome of the exercise. [ABSTRACT FROM AUTHOR]

With increasing interest in on-farm experiments, there is a pressing need to develop rigorous statistical methods for analysing these experiments. The adoption of advanced technologies such as yield monitors and variable-rate fertilizer applicators has enabled farmers and researchers to collect biophysical data linked to spatial information at a scale which allows them to investigate the role of spatial variability in the development of optimum management practices. A relevant topic for investigation could be: "what are the optimum rates of nitrogen and how/why do these differ across the field"? Although it has been recently understood that traditional statistical methods that are appropriate for analysing small-plot experiments are inappropriate for answering these questions, a unifying approach to inference for on-farm experiments is still missing and this limits the adoption of the technique. In this paper we propose a unifying approach to the analysis of on-farm strip experiments adapting the core ideas of local likelihood or geographically weighted regression. We propose a statistical model that allows spatial nonstationarity in modelled relationships and estimates spatially-varying parameters governing these relationships. A crucial step is bandwidth selection in implementing these models, and we develop bandwidth selection methods for two important scenarios relevant to the modelling of yield monitor data in on-farm experiments. Local t -scores have been introduced for inferential purposes and the associated problem of multiple testing has been described in the context of analysing on-farm experiments. We demonstrate in this paper how local p -values can be adjusted to overcome this problem. To illustrate the applicability of our proposed method, we analysed two publicly available datasets. Graphical displays are created to guide practitioners to make informed decisions on optimal management practices. [ABSTRACT FROM AUTHOR]