The big pond debate.

Over recent decades, Australian piggeries have commonly employed anaerobic ponds to treat effluent to a standard suitable for recycling for shed flushing purposes and for irrigation onto nearby agricultural land. Anaerobic ponds are generally sized according to the Rational Design Standard (RDS) developed by Barth (1985), resulting in large ponds, which can be expensive to construct, occupy large land areas, and are difficult and expensive to desludge, potentially disrupting the whole piggery operation. Limited anecdotal and scientific evidence suggests that anaerobic ponds that are undersized according to the RDS, operate satisfactorily, without excessive odour emission, impaired biological function or high rates of solids accumulation. Based on these observations, this paper questions the validity of rigidly applying the principles of the RDS and presents a number of alternate design approaches resulting in smaller, more highly loaded ponds that are easier and cheaper to construct and manage. Based on limited data of pond odour emission, it is suggested that higher pond loading rates may reduce overall odour emission by decreasing the pond volume and surface area. Other management options that could be implemented to reduce pond volumes include permeable pond covers, various solids separation methods, and bio-digesters with impermeable covers, used in conjunction with biofilters and/or systems designed for biogas recovery. To ensure that new effluent management options are accepted by regulatory authorities, it is important for researchers to address both industry and regulator concerns and uncertainties regarding new technology, and to demonstrate, beyond reasonable doubt, that new technologies do not increase the risk of adverse impacts on the environment or community amenity. Further development of raw research outcomes to produce relatively simple, practical guidelines and implementation tools also increases the potential for acceptance and implementation of new technology by regulators and industry.

Changes in phosphine sorption in wheat after storage at two temperatures.

BACKGROUND: Wheat can be stored for many months before being fumigated with phosphine to kill insects, so a study was undertaken to investigate whether the sorptive capacity of wheat changes as it ages. Wheat was stored at 15 or 25C and 55% RH for up to 5.5 months, and samples were fumigated at intervals to determine sorption. Sealed glass flasks (95% full) were injected with 1.5 mg L-1 of phosphine based on flask volume. Concentrations were monitored for 11 days beginning 2 h after injection. Some wheat samples were refumigated after a period of ventilation. Several fumigations of wheat were conducted to determine the pattern of sorption during the first 24 h. RESULTS: Phosphine concentration declined exponentially with time from 2 h after injection. Rate of sorption decreased with time spent in storage at either 15 or 25C and 55% RH. Rate of sorption tended to be lower when wheat was refumigated, but this could be explained by time in storage rather than by refumigation per se. The data from the 24 h fumigations did not fit a simple exponential decay equation. Instead, there was a rapid decline in the first hour, with phosphine concentration falling much more slowly thereafter. CONCLUSIONS: The results have implications for phosphine fumigation of insects in stored wheat. Both the time wheat has spent in storage and the temperature at which it has been stored are factors that must be considered when trying to understand the impact of sorption on phosphine concentrations in commercial fumigations.

Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials

Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.