Influence of biochars on flux of N2O and CO2 from Ferrosol

Biochars produced by slow pyrolysis of greenwaste (GW), poultry litter (PL), papermill waste (PS), and biosolids (BS) were shown to reduce N₂O emissions from an acidic Ferrosol. Similar reductions were observed for the untreated GW feedstock. Soil was amended with biochar or feedstock giving application rates of 1 and 5%. Following an initial incubation, nitrogen (N) was added at 165 kg/ha as urea. Microcosms were again incubated before being brought to 100% water-filled porosity and held at this water content for a further 47 days. The flooding phase accounted for the majority (<80%) of total N₂O emissions. The control soil released 3165 mg N₂O-N/m², or 15.1% of the available N as N₂O. Amendment with 1 and 5% GW feedstock significantly reduced emissions to 1470 and 636 mg N₂O-N/m², respectively. This was equivalent to 8.6 and 3.8% of applied N. The GW biochar produced at 350°C was least effective in reducing emissions, resulting in 1625 and 1705 mg N₂O-N/m² for 1 and 5% amendments. Amendment with BS biochar at 5% had the greatest impact, reducing emissions to 518 mg N₂O-N/m², or 2.2% of the applied N over the incubation period. Metabolic activity as measured by CO₂ production could not explain the differences in N₂O emissions between controls and amendments, nor could NH₄⁺ or NO₃^– concentrations in biochar-amended soils. A decrease in NH₄⁺ and NO₃^– following GW feedstock application is likely to have been responsible for reducing N₂O emissions from this amendment. Reduction in N₂O emissions from the biochar-amended soils was attributed to increased adsorption of NO₃^–. Small reductions are possible due to improved aeration and porosity leading to lower levels of denitrification and N₂O emissions. Alternatively, increased pH was observed, which can drive denitrification through to dinitrogen during soil flooding.

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Early detection surveillance programs aim to find invasions of exotic plant pests and diseases before they are too widespread to eradicate. However, the value of these programs can be difficult to justify when no positive detections are made. To demonstrate the value of pest absence information provided by these programs, we use a hierarchical Bayesian framework to model estimates of incursion extent with and without surveillance. A model for the latent invasion process provides the baseline against which surveillance data are assessed. Ecological knowledge and pest management criteria are introduced into the model using informative priors for invasion parameters. Observation models assimilate information from spatio-temporal presence/absence data to accommodate imperfect detection and generate posterior estimates of pest extent. When applied to an early detection program operating in Queensland, Australia, the framework demonstrates that this typical surveillance regime provides a modest reduction in the estimate that a surveyed district is infested. More importantly, the model suggests that early detection surveillance programs can provide a dramatic reduction in the putative area of incursion and therefore offer a substantial benefit to incursion management. By mapping spatial estimates of the point probability of infestation, the model identifies where future surveillance resources can be most effectively deployed.