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Twelve large-scale climate drivers are employed to investigate their spatio-temporal influence on the variability of seasonal wheat yield in five major wheat-producing states across Australia using data for the period 1983–2013. Generally, the fluctuations in the Indian Ocean appear to have a dominant effect on the Australian wheat crop in all states except Western Australia, while the impact of oceanic conditions in the Pacific region is much stronger in Queensland. The results show a statistically significant negative correlation between the Indian Ocean Dipole (IOD) and the anomalous wheat yield in the early growing stage of the crop in the eastern and southeastern wheat belt regions. This correlation suggests that the wheat yield can be skillfully forecast 3–6 months ahead, supporting early decision-making in regard to precision agriculture. In this study, we use vine copula models to capture climate-yield dependence structures, including the occurrence of extreme events (i.e., the tail dependences). The co-occurrence of extreme events is likely to enhance the impacts of climate mode and this can be quantified probabilistically through conditional copula-based models. Generally, the developed D-vine quantile regression model provide greater accuracy for the forecasting of wheat yield at given different confidence levels compared to the traditional linear quantile regression (LQR) method. A five-fold cross-validation approach is also used to estimate the out-of-sample accuracy of both copula-statistical forecasting models. These findings provide a comprehensive analysis of the spatio-temporal impacts of different climate mode indices on Australian wheat crops. Improved quantification of the impacts of large-scale climate drivers on the wheat yield can allow a development of suitable planning processes and crop production strategies designed to optimize the yield and agricultural profit.

Research examining the prevalence, physical activity (PA) and sedentary behaviour (SB) in shift workers show mixed results. This systematic review and meta-analysis aimed to compare PA and SB in shift workers with non-shift workers following the PRISMA guidelines. Ebscohost megafile ultimate (CINHAL, E-journals, Academic search ultimate, health source consumer edition, SPORT Discus), PubMed, Scopus, Web of Science, and Science Direct databases were searched up to April 2021. Cross-sectional and baseline data from longitudinal studies reporting PA and SB in full time workers were eligible. Data on participants characteristics and time spent in PA and SB and/or prevalence of workers meeting PA guidelines were extracted and pooled with random effects model. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) 10-item checklist was adapted and used. A total of 49 studies met inclusion criteria and 21 studies included for meta-analysis. The prevalence of meeting physical activity guidelines (OR 0.84, 95% CI: 0.68, 1.03) and standardized mean difference (SMD) of time spent in moderate-to-vigorous physical activity (SMD −0.1, 95% CI: −0.4, 0.20) were similar in shift and non-shift workers. Time spent in sedentary behaviour was lower in shift workers than non-shift workers (SMD −0.2, 95% CI: 0.50, −0.001). While the differences in PA are not so evident between shift and non-shift workers, the prevalence of sufficient PA was low in both groups. These preliminary findings provide support for inclusion of physical activity and sedentary behaviour in health promotion interventions targeted at shift workers.

Vine copulas are employed to explore the influence of multi-synoptic-scale climate drivers – El Nino Southern Oscillation (ENSO) and Inter-decadal Pacific Oscillation (IPO) Tripole Index (TPI) – on spring precipitation forecasting at Agro-ecological Zones (AEZs) of the Australia’s wheat belt. To forecast spring precipitation, significant seasonal lagged correlation of ENSO and TPI with precipitation anomalies in AEZs using data from Australian Water Availability Project (1900–2013) was established. Most of the AEZs exhibit statistically significant dependence of precipitation and climate indices, except for the western AEZs. Bivariate and trivariate copula models were applied to capture single (ENSO) and dual predictor (ENSO & TPI) influence, respectively, on seasonal forecasting. To perform a comprehensive evaluation of the developed copula-statistical models, a total of ten one- and two-parameter bivariate copulas ranging from elliptical to Archimedean families were examined. Stronger upper tail dependence is visible in the bivariate model, suggesting that the influence of ENSO on precipitation forecasting during a La Nina event is more evident than during an El Nino event. In general, while the inclusion of TPI as a synoptic-scale driver into the models leads to a notable reduction in the mean simulated precipitation, it depicts a general improvement in the median values. The forecasting results showed that the trivariate forecasting model can yield a better accuracy than the bivariate model for the east and southeast AEZs. The trivariate forecasting model was found to improve the forecasting during the La Nina and negative TPI. This study ascertains the success of copula-statistical models for investigating the joint behaviour of seasonal precipitation modelled with multiple climate indices. The forecasting information and respective models have significant implications for water resources and crop health management including better ways to adapt and implement viable agricultural solutions in the face of climatic challenges in major agricultural hubs, such as Australia’s wheat belt.

This article examines an improved alternative to the random effects (RE) model for meta-analysis of heterogeneous studies. It is shown that the known issues of underestimation of the statistical error and spuriously overconfident estimates with the RE model can be resolved by the use of an estimator under the fixed effect model assumption with a quasi-likelihood based variance structure - the IVhet model. Extensive simulations confirm that this estimator retains a correct coverage probability and a lower observed variance than the RE model estimator, regardless of heterogeneity. When the proposed IVhet method is applied to the controversial meta-analysis of intravenous magnesium for the prevention of mortality after myocardial infarction, the pooled OR is 1.01 (95% CI 0.71-1.46) which not only favors the larger studies but also indicates more uncertainty around the point estimate. In comparison, under the RE model the pooled OR is 0.71 (95% CI 0.57-0.89) which, given the simulation results, reflects underestimation of the statistical error. Given the compelling evidence generated, we recommend that the IVhet model replace both the FE and RE models. To facilitate this, it has been implemented into free meta-analysis software called MetaXL which can be downloaded from www.epigear.com.

This paper proposes the bivariate noncentral chi-square (BNC) distribution by compounding the Poisson probabilities with the bivariate central chi-square distribution. The probability density and cumulative distribution functions of the joint distribution of the two noncentral chi-square variables are derived for arbitrary values of the correlation coefficient, degrees of freedom(s), and noncentrality parameters. Computational procedures to calculate the upper tail probabilities as well as the percentile points for selected values of the parameters, for both equal and unequal degrees of freedom, are discussed. The graphical representation of the distribution for different values of the parameters are provided. Some applications of the distribution are outlined.

Beyond the customary analysis through the estimation and hypothesis testing about the parameters of the multiple regression models, often a natural interest is to predict the responses for a given set of values of the predictors. The main objective of this article is to obtain the prediction distribution for a set of future responses from a multiple linear regression model which follow equicorrelation structure. It derives the marginal likelihood estimate for the equicorrelation parameter, @r, and then uses the invariant differentials to compute the joint distribution of the unobserved but realized future errors. The prediction distribution is derived by using the structural relation of the model. The main finding of this paper is that the prediction distribution turned out to be a Student-t which depends only on the estimated @r and is invariant to the degrees of freedom of the original Student-t distribution.