Evaluating sewage associated JCV and BKV polyomaviruses for sourcing human fecal pollution in a coastal river in Southeast Queensland Australia

In this study, the host-sensitivity and -specificity of JCV and BKV polyomaviruses were evaluated by testing wastewater/fecal samples from nine host groups in Southeast Queensland, Australia. The JCV and BKV polyomaviruses were detected in 48 human wastewater samples collected from the primary and secondary effluent suggesting high sensitivity of these viruses in human wastewater. Of the 81 animal wastewater/fecal samples tested, 80 were PCR negative for this marker. Only one sample from pig wastewater was positive. Nonetheless, the overall host-specificity of these viruses to differentiate between human and animal wastewater/fecal samples was 0.99. To our knowledge, this is the first study in Australia that reports the high specificity of JCV and BKV polyomaviruses. To evaluate the field application of these viruses to detect human fecal pollution, 20 environmental samples were collected from a coastal river. Of the 20 samples tested, 15% and 70% samples exceeded the regulatory guidelines for E. coli and enterococci levels for marine waters. In all, 5 (25%) samples were PCR positive for JCV and BKV indicated the presence of human fecal pollution in the studied river. The results suggest that JCV and BKV detection using PCR could be a useful tool for the identification of human sourced fecal pollution in coastal waters.

Greenhouse Gas Emissions Calculator for Grain and Biofuel Farming Systems

Greenhouse gas markets, where invisible gases are traded, must seem like black boxes to most people. Farmers can make money on these markets, such as the Chicago Climate Exchange, by installing methane capture technologies in animal-based systems, no-till farming, establishing grasslands, and planting trees.

Spatial variability of soil carbon in forested and cultivated sites: Implications for change detection

The potential to sequester atmospheric carbon in agricultural and forest soils to offset greenhouse gas emissions has generated interest in measuring changes in soil carbon resulting from changes in land management. However, inherent spatial variability of soil carbon limits the precision of measurement of changes in soil carbon and hence, the ability to detect changes. We analyzed variability of soil carbon by intensively sampling sites under different land management as a step toward developing efficient soil sampling designs. Sites were tilled crop-land and a mixed deciduous forest in Tennessee, and old-growth and second-growth coniferous forest in western Washington, USA. Six soil cores within each of three microplots were taken as an initial sample and an additional six cores were taken to simulate resampling. Soil C variability was greater in Washington than in Tennessee, and greater in less disturbed than in more disturbed sites. Using this protocol, our data suggest that differences on the order of 2.0 Mg C ha(-1) could be detected by collection and analysis of cores from at least five (tilled) or two (forest) microplots in Tennessee. More spatial variability in the forested sites in Washington increased the minimum detectable difference, but these systems, consisting of low C content sandy soil with irregularly distributed pockets of organic C in buried logs, are likely to rank among the most spatially heterogeneous of systems. Our results clearly indicate that consistent intramicroplot differences at all sites will enable detection of much more modest changes if the same microplots are resampled.

Environmental factors controlling soil respiration in three semiarid ecosystems

Previous research suggests that soil organic C pools may be a feature of semiarid regions that are particularly sensitive to climatic changes. We instituted an 18-mo experiment along an elevation gradient in northern Arizona to evaluate the influence of temperature, moisture, and soil C pool size on soil respiration. Soils, from underneath different free canopy types and interspaces of three semiarid ecosystems, were moved upslope and/or downslope to modify soil climate. Soils moved downslope experienced increased temperature and decreased precipitation, resulting in decreased soil moisture and soil respiration las much as 23 acid 20%, respectively). Soils moved upslope to more mesic, cooler sites had greater soil water content and increased rates of soil respiration las much as 40%), despite decreased temperature. Soil respiration rates normalized for total C were not significantly different within any of the three incubation sites, indicating that under identical climatic conditions, soil respiration is directly related to soil C pool size for the incubated soils. Normalized soil respiration rates between sites differed significantly for all soil types and were always greater for soils incubated under more mesic, but cooler, conditions. Total soil C did not change significantly during the experiment, but estimates suggest that significant portions of the rapidly cycling C pool were lost. While long-term decreases in aboveground and belowground detrital inputs may ultimately be greater than decreased soil respiration, the initial response to increased temperature and decreased precipitation in these systems is a decrease in annual soil C efflux.

Analysis of productive performance of crop and animal production systems : an integrated analytical framework

This article presents a two-stage analytical framework that integrates ecological crop (animal) growth and economic frontier production models to analyse the productive efficiency of crop (animal) production systems. The ecological crop (animal) growth model estimates "potential" output levels given the genetic characteristics of crops (animals) and the physical conditions of locations where the crops (animals) are grown (reared). The economic frontier production model estimates "best practice" production levels, taking into account economic, institutional and social factors that cause farm and spatial heterogeneity. In the first stage, both ecological crop growth and economic frontier production models are estimated to calculate three measures of productive efficiency: (1) technical efficiency, as the ratio of actual to "best practice" output levels; (2) agronomic efficiency, as the ratio of actual to "potential" output levels; and (3) agro-economic efficiency, as the ratio of "best practice" to "potential" output levels. Also in the first stage, the economic frontier production model identifies factors that determine technical efficiency. In the second stage, agro-economic efficiency is analysed econometrically in relation to economic, institutional and social factors that cause farm and spatial heterogeneity. The proposed framework has several important advantages in comparison with existing proposals. Firstly, it allows the systematic incorporation of all physical, economic, institutional and social factors that cause farm and spatial heterogeneity in analysing the productive performance of crop and animal production systems. Secondly, the location-specific physical factors are not modelled symmetrically as other economic inputs of production. Thirdly, climate change and technological advancements in crop and animal sciences can be modelled in a "forward-looking" manner. Fourthly, knowledge in agronomy and data from experimental studies can be utilised for socio-economic policy analysis. The proposed framework can be easily applied in empirical studies due to the current availability of ecological crop (animal) growth models, farm or secondary data, and econometric software packages. The article highlights several directions of empirical studies that researchers may pursue in the future.

Inherent errors in pollutant build-up estimation in considering urban land use as a lumped parameter

Stormwater quality modelling results is subject to uncertainty. The variability of input parameters is an important source of overall model error. An in-depth understanding of the variability associated with input parameters can provide knowledge on the uncertainty associated with these parameters and consequently assist in uncertainty analysis of stormwater quality models and the decision making based on modelling outcomes. This paper discusses the outcomes of a research study undertaken to analyse the variability related to pollutant build-up parameters in stormwater quality modelling. The study was based on the analysis of pollutant build-up samples collected from 12 road surfaces in residential, commercial and industrial land uses. It was found that build-up characteristics vary appreciably even within the same land use. Therefore, using land use as a lumped parameter would contribute significant uncertainties in stormwater quality modelling. Additionally, it was also found that the variability in pollutant build-up can also be significant depending on the pollutant type. This underlines the importance of taking into account specific land use characteristics and targeted pollutant species when undertaking uncertainty analysis of stormwater quality models or in interpreting the modelling outcomes.

Annual nitrogen dynamics and urea fertilizer recoveries from a dairy pasture using 15N; effect of nitrification inhibitor DMPP and reduced application rates

Direct nitrogen (N) losses from pastures contribute to the poor nitrogen use efficiency of the dairy industry, though the exact fate of applied N and the processes involved are largely unknown. Nitrification inhibitors such as DMPP can potentially increase fertilizer N use efficiency (NUE), though few studies globally have examined the effectiveness of DMPP coated urea in pastures. This study quantified the NUE of DMPP combined with reduced application rates, and the effect on N dynamics and plant–soil interactions over an annual ryegrass/kikuyu rotation in Queensland, Australia. Labeled 15N urea and DMPP was applied over 7 winter applications at standard farmer (45 kg N ha−1) and half (23 kg N ha−1) rates. Fertilizer recoveries and NUE were calculated over 13 harvests, and the contribution of fertilizer and soil N estimated. Up to 85% of the annual N harvested was from soil organic matter. DMPP at the lower rate increased annual yields by 31% compared to the equivalent urea treatment with no difference to the high N rates. Almost 40% of the N added at the conventional fertilizer application rate as urea was lost to the environment; 80 kg N ha−1 higher than the low DMPP. Combining the nitrification inhibitor DMPP with reduced fertilizer application rates shows substantial potential to reduce N losses to the environment while sustaining productivity in subtropical dairy pastures.

Legumes or nitrification inhibitors to reduce {N2O} emissions from subtropical cereal cropping systems in Oxisols?

The DAYCENT biogeochemical model was used to investigate how the use of fertilizers coated with nitrification inhibitors and the introduction of legumes in the crop rotation can affect subtropical cereal production and {N2O} emissions. The model was validated using comprehensive multi-seasonal, high-frequency dataset from two field investigations conducted on an Oxisol, which is the most common soil type in subtropical regions. Different N fertilizer rates were tested for each N management strategy and simulated under varying weather conditions. DAYCENT was able to reliably predict soil N dynamics, seasonal {N2O} emissions and crop production, although some discrepancies were observed in the treatments with low or no added N inputs and in the simulation of daily {N2O} fluxes. Simulations highlighted that the high clay content and the relatively low C levels of the Oxisol analyzed in this study limit the chances for significant amounts of N to be lost via deep leaching or denitrification. The application of urea coated with a nitrification inhibitor was the most effective strategy to minimize {N2O} emissions. This strategy however did not increase yields since the nitrification inhibitor did not substantially decrease overall N losses compared to conventional urea. Simulations indicated that replacing part of crop N requirements with N mineralized by legume residues is the most effective strategy to reduce {N2O} emissions and support cereal productivity. The results of this study show that legumes have significant potential to enhance the sustainable and profitable intensification of subtropical cereal cropping systems in Oxisols.

Non-linear response of soil N2O emissions to nitrogen fertiliser in a cotton-fallow rotation in sub-tropical Australia

Nitrogen fertiliser is a major source of atmospheric N2O and over recent years there is growing evidence for a non-linear, exponential relationship between N fertiliser application rate and N2O emissions. However, there is still high uncertainty around the relationship of N fertiliser rate and N2O emissions for many cropping systems. We conducted year-round measurements of N2O emission and lint yield in four N rate treatments (0, 90, 180 and 270 kg N ha-1) in a cotton-fallow rotation on a black vertosol in Australia. We observed a nonlinear exponential response of N2O emissions to increasing N fertiliser rates with cumulative annual N2O emissions of 0.55 kg N ha-1, 0.67kg N ha-1, 1.07 kg N ha-1 and 1.89 kg N ha-1 for the four respective N fertiliser rates while no N response to yield occurred above 180N. The N fertiliser induced annual N2O EF factors increased from 0.13% to 0.29% and 0.50% for the 90N, 180N and 270N treatments respectively, significantly lower than the IPCC Tier 1 default value (1.0 %). This non-linear response suggests that an exponential N2O emissions model may be more appropriate for use in estimating emission of N2O from soils cultivated to cotton in Australia. It also demonstrates that improved agricultural N management practices can be adopted in cotton to substantially reduce N2O emissions without affecting yield potential.