The dynamics of phase farming in dryland salinity abatement

Farm Management, Land Economics/Use,

Modeling digital camera monitoring count data with intermittent zeros for short-term prediction

Digital camera monitoring has revolutionised survey designs in many fields, as an important source of information. The extended sampling coverage offered by this monitoring scheme makes it preferable compared to other traditional methods of survey. However, data obtained from digital camera monitoring are often highly variable, and characterized by sparse periods of zero counts, interspersed with missing observations due to outages. In practice, missing data of relatively shorter duration are mostly observed and are often imputed using interpolation techniques, ignoring long-term trends leading to inherent estimation biases. In this study, we investigated time series forecasting methods that adequately handle intermittency and produced plausible estimates for imputation and forecasting purposes. The study utilised a yearlong digital camera monitoring data set of hourly counts of powerboat launches at three boat ramps in Western Australia. Several time series forecasting methods were evaluated and the accuracies of their point estimates of forecasts for various lead times in hours of up to one week were assessed using cross-validation techniques. Intermittent demand forecasting techniques, including Croston\u27s method and Syntetos-Boylan Approximation (SBA) models, and count data forecasting methods including autoregressive conditional Poisson (ACP) models, integer-valued moving average (INMA) models, and integer-valued autoregressive (INAR) models were evaluated. ACP and INAR models performed better than intermittent demand forecasting techniques for short forecast horizons and provided some evidence of their sufficiency in predicting the dynamics in recreational boating activities. This result established that, in as much as intermittency may be a key feature for a given dataset, it should not override the systemic characteristics of data in the application of forecasting techniques. Our results provide plausible estimates for short-term missing data and forecasts for monitoring events, with applications in supporting proper tracking of usage of facilities, guiding resource allocations and providing insightful perspectives for management decisions

Spatio-temporal modelling of malaria incidence for evaluation of public health policy interventions in Ghana, West Africa

Malaria is a major challenge to both the public health and the socio-economic development of Ghana. Major factors which account for this situation include poor environmental conditions and the lack of prevention services. In spite of the numerous intervention measures, the disease continues to be the most prevalent health problem in the country. The risk assessment reports for Ghana were based on household surveys which provide inadequate data for accurate analysis of incidence cases. This poses a serious threat to planning and management for the health care delivery system in Ghana. Malaria transmission varies with geographical location and time (or season). Spatio-temporal modelling coupled with adequate data has shown to better define the public burden of the disease, providing risk maps to describe the incidence variation in space and time and also identifying high risk areas for health policy implementation. Geostatistics contributes immensely to the prediction of the random processes distributed in space or time in epidemiological studies. In this study, we conduct spatial statistical analysis of malaria incidence to produce evidence-based monthly maps of Ghana illustrating the patterns of malaria risk over space and time. This is achieved using monthly morbidity cases reported on the disease from public health facilities at district level and population data over the period 1998-2010 to compute the malaria incidence rates, being the number of reported cases per unit resident population of 10,000. Lognormal ordinary kriging is used to model the spatial and temporal correlations, and then back-transformed to estimate the monthly malaria risk at local level. The space-time experimental variogram describing the correlations structure is modelled with nested spherical and exponential-cosine functions coupled with nugget effect. The modelled variogram indicate both short and long spatial and temporal dependence of the malaria incidence rates at local level with the temporal component exhibiting an increasing seasonal pattern of period of 12 months. The results also indicate varied spatial distribution of malaria incidence across the country, the highest risk being observed in the northern most and several locations in central and western parts of the country, and lowest in some areas in the north and south along the coast. This statistical-based model approach of malaria epidemiology will be useful for short-term prediction and also provide a basis for resource allocation for the disease’s control in the country

Trade-off assessments between reading cost and accuracy measures for digital camera monitoring of recreational boating effort

Digital camera monitoring is increasingly being used to monitor recreational fisheries. The manual interpretation of video imagery can be costly and time consuming. In an a posteriori analysis, we investigated trade-offs between the reading cost and accuracy measures of estimates of boat retrievals obtained at various sampling proportions for low, moderate and high traffic boat ramps in Western Australia. Simple random sampling, systematic sampling and stratified sampling designs with proportional and weighted allocation were evaluated to assess trade-offs in terms of bias, accuracy, precision, coverage rate and cost in estimating the annual total number of powerboat retrievals in 10,000 jackknife resampling draws. The relative standard error (RSE ± standard deviations) obtained by the sampling designs for sampling proportions from 0.4 onwards were below a 20 % threshold for three of the sampling designs across the three boat ramps. Coverage rates of over 90 % were observed for the confidence intervals for the estimated annual number of powerboat retrievals, with low relative standard errors (RSE \u3c 20 %). Interpreting 40 % of camera footage within a year provided the minimum level to obtain sufficient accuracy measures for all sampling designs considered. The stratified random sampling design with weighted allocation consistently resulted in the smallest variance for estimates of annual powerboat retrievals across the various sampled proportions. These findings have the potential to considerably reduce the cost of manual data interpretation, since operating cost increased linearly with increasing sampling proportion

Using surface regolith geochemistry to map the major crustal blocks of the Australian continent

Multi-element near-surface geochemistry from the National Geochemical Survey of Australia has been evaluated in the context of mapping the exposed to deeply buried major crustal blocks of the Australian continent. The major crustal blocks, interpreted from geophysical and geological data, reflect distinct tectonic domains comprised of early Archean to recent Cenozoic igneous, metamorphic and sedimentary rock assemblages. The geochemical data have been treated as compositional data to uniquely describe and characterize the geochemistry of the regolith overlying the major crustal blocks across Australia according to the following workflow: imputation of missing/censored data, log-ratio transformation, multivariate statistical analysis, multivariate geospatial (minimum/maximum autocorrelation factor) analysis, and classification. Using cross validation techniques, the uniqueness of each major crustal block has been quantified. The ability to predict the membership of a surface regolith sample to one or more of the major crustal blocks is demonstrated. The predicted crustal block assignments define spatially coherent regions that coincide with the known crustal blocks. In some areas, inaccurate predictions are due to uncertainty in the initial crustal boundary definition or from surficial processes that mask the crustal block geochemical signature. In conclusion, the geochemical composition of the Australian surface regolith generally can be used to map the underlying crustal architecture, despite secondary modifications due to physical transport and chemical weathering effects. This methodology is however less effective where extensive and thick sedimentary basins such as the Eromanga and Eucla basins overlie crustal blocks

Imputation of missing data from time-lapse cameras used in recreational fishing surveys

While remote camera surveys have the potential to improve the accuracy of recreational fishing estimates, missing data are common and require robust analytical techniques to impute. Time-lapse cameras are being used in Western Australia to monitor recreational boating activities, but outages have occurred. Generalized linear mixed effect models formulated in a fully conditional specification multiple imputation framework were used to reconstruct missing data, with climatic and some temporal classifications as covariates. Using a complete 12-month camera record of hourly counts of recreational powerboat retrievals, data were simulated based on ten observed camera outage patterns, with a missing proportion of between 0.06 and 0.61. Nine models were evaluated, including Poisson and negative binomial models, and their associated zero-inflated variants. The imputed values were cross-validated against actual observations using percent bias, mean absolute error, root mean square error, and skill score as performance measures. In 90% of the cases, 95% confidence intervals for the total imputed estimates from at least one of the models contained the total actual counts. With no systematic trends in performance among the models, zero-inflated Poisson and its bootstrapping variant models consistently ranked among the top 3 models and possessed the narrowest confidence intervals. The robustness and generality of the imputation framework were demonstrated using other camera datasets with distinct characteristics. The results provide reliable estimates of the number of boat retrievals for subsequent estimates of fishing effort and provide time series data on boat-based activity

Biplots for compositional data derived from generalized joint diagonalization methods

Biplots constructed from principal components of a compositional data set are an established means to explore its features. Principal Component Analysis (PCA) is also used to transform a set of spatial variables into spatially decorrelated factors. However, because no spatial structures are accounted for in the transformation the application of PCA is limited. In geostatistics and blind source separation a variety of different matrix diagonalization methods have been developed with the aim to provide spatially or temporally decorrelated factors. Just as PCA, many of these transformations are linear and so lend themselves to the construction of biplots. In this contribution we consider such biplots for a number of methods (MAF, UWEDGE and RJD transformations) and discuss how and if they can contribute to our understanding of relationships between the components of regionalized compositions. A comparison of the biplots with the PCA biplot commonly used in compositional data analysis for the case of data from the Northern Irish geochemical survey shows that the biplots from MAF and UWEDGE are comparable as are those from PCA and RJD. The biplots emphasize different aspects of the regionalized composition: for MAF and UWEDGE the focus is the spatial continuity, while for PCA and RJD it is variance explained. The results indicate that PCA and MAF combined provide adequate and complementary means for exploratory statistical analysis

Using intervention analysis to evaluate the trends in release rates of recreational fisheries following extensive management changes

Changes to management of a fisheries resource are often required to ensure ongoing sustainability. However, such changes can sometimes lead to unintended effects such as increased release rates and associated post-release mortality. These effects may be highly variable between species and areas. Recreational fishing management changes were introduced on the west coast of Australia in 2009/10 to recover stocks of demersal scalefish. Key changes included reducing mixed species bag limits across management zones and increasing the minimum size limit for one species in some management zones. The restrictive catch limits resulted in increased release rates of key demersal species. However, whether such increases are significant and sustained over time, and thus of management concern, have not been evaluated. We carried out intervention time series analysis to evaluate the impact of management changes on release rates of four key demersal species for the recreational sector in metropolitan and regional management zones covering ∼8° latitude using an 18-year time series of charter recreational fishery data from July 2002 to January 2020. We observed varying responses in release rates by species and zones, the most common of which were a step increase, a ramp and a temporary increase that decayed. These responses may be related to targeted management changes which influenced fisher behaviour, perceived recreational value of some species and recruitment variation. Our study demonstrates that intervention analysis, which has seen limited use in this context, can assist in evaluating the impact of management changes on different species for recreational fisheries

Improving processing by adaption to conditional geostatistical simulation of block compositions

Exploitation of an ore deposit can be optimized by adapting the beneficiation processes to the properties of individual ore blocks. This can involve switching in and out certain treatment steps, or setting their controlling parameters. Optimizing this set of decisions requires the full conditional distribution of all relevant physical parameters and chemical attributes of the feed, including concentration of value elements and abundance of penalty elements. As a first step towards adaptive processing, the mapping of adaptive decisions is explored based on the composition, in value and penalty elements, of the selective mining units. Conditional distributions at block support are derived from cokriging and geostatistical simulation of log-ratios. A one-to-one log-ratio transformation is applied to the data, followed by modelling via classical multivariate geostatistical tools, and subsequent back-transforming of predictions and simulations. Back-transformed point-support simulations can then be averaged to obtain block averages that are fed into the process chain model. The approach is illustrated with a \u27toy\u27 example where a four-component system (a value element, two penalty elements, and some liberable material) is beneficiated through a chain of technical processes. The results show that a gain function based on full distributions outperforms the more traditional approach of using unbiased estimates