Odour emissions from anaerobic piggery ponds. 2: Improving estimates of emission rate through recognition of spatial variability

Odour emission rates were measured for seven different anaerobic ponds treating piggery wastes at six to nine discrete locations across the surface of each pond on each sampling occasion over a thirteen month period. Significant variability in emission rates were observed for each pond. Measurement of a number of water quality variables in pond liquor samples collected at the same time and from the same locations as the odour samples indicated that the composition of the pond liquor was also variable. The results indicated that spatial variability was a real phenomenon and could have a significant impact on odour assessment practices. Considerably more odour samples would be required to characterise pond emissions than currently recommended by most practitioners, or regulatory agencies.

Sustainable grazing management for temporal and spatial variability in north Australian rangelands – a synthesis of the latest evidence and recommendations

Rainfall variability is a major challenge to sustainable grazing management in northern Australia, with management often complicated further by large, spatially-heterogeneous paddocks. This paper presents the latest grazing research and associated bio-economic modelling from northern Australia and assesses the extent to which current recommendations to manage for these issues are supported. Overall, stocking around the safe long-term carrying capacity will maintain land condition and maximise long-term profitability. However, stocking rates should be varied in a risk-averse manner as pasture availability varies between years. Periodic wet-season spelling is also essential to maintain pasture condition and allow recovery of overgrazed areas. Uneven grazing distributions can be partially managed through fencing, providing additional water-points and in some cases patch-burning, although the economics of infrastructure development are extremely context-dependent. Overall, complex multi-paddock grazing systems do not appear justified in northern Australia. Provided the key management principles outlined above are applied in an active, adaptive manner, acceptable economic and environmental outcomes will be achieved irrespective of the grazing system applied.

Large-chamber methane and nitrous oxide measurements are comparable to the backward Lagrangian stochastic method

Measurement of individual emission sources (e.g., animals or pen manure) within intensive livestock enterprises is necessary to test emission calculation protocols and to identify targets for decreased emissions. In this study, a vented, fabric-covered large chamber (4.5 × 4.5 m, 1.5 m high; encompassing greater spatial variability than a smaller chamber) in combination with on-line analysis (nitrous oxide [N2O] and methane [CH4] via Fourier Transform Infrared Spectroscopy; 1 analysis min-1) was tested as a means to isolate and measure emissions from beef feedlot pen manure sources. An exponential model relating chamber concentrations to ambient gas concentrations, air exchange (e.g., due to poor sealing with the surface; model linear when ≈ 0 m3 s-1), and chamber dimensions allowed data to be fitted with high confidence. Alternating manure source emission measurements using the large-chamber and the backward Lagrangian stochastic (bLS) technique (5-mo period; bLS validated via tracer gas release, recovery 94-104%) produced comparable N2O and CH4 emission values (no significant difference at P < 0.05). Greater precision of individual measurements was achieved via the large chamber than for the bLS (mean ± standard error of variance components: bLS half-hour measurements, 99.5 ± 325 mg CH4 s-1 and 9.26 ± 20.6 mg N2O s-1; large-chamber measurements, 99.6 ± 64.2 mg CH4 s-1 and 8.18 ± 0.3 mg N2O s-1). The large-chamber design is suitable for measurement of emissions from manure on pen surfaces, isolating these emissions from surrounding emission sources, including enteric emissions. © © American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.

Quantifying nitrous oxide emissions from sugarcane cropping systems : Optimum sampling time and frequency

Nitrous oxide (N2O) emissions from soil are often measured using the manual static chamber method. Manual gas sampling is labour intensive, so a minimal sampling frequency that maintains the accuracy of measurements would be desirable. However, the high temporal (diurnal, daily and seasonal) variabilities of N2O emissions can compromise the accuracy of measurements if not addressed adequately when formulating a sampling schedule. Assessments of sampling strategies to date have focussed on relatively low emission systems with high episodicity, where a small number of the highest emission peaks can be critically important in the measurement of whole season cumulative emissions. Using year-long, automated sub-daily N2O measurements from three fertilised sugarcane fields, we undertook an evaluation of the optimum gas sampling strategies in high emission systems with relatively long emission episodes. The results indicated that sampling in the morning between 09:00–12:00, when soil temperature was generally close to the daily average, best approximated the daily mean N2O emission within 4–7% of the ‘actual’ daily emissions measured by automated sampling. Weekly sampling with biweekly sampling for one week after >20 mm of rainfall was the recommended sampling regime. It resulted in no extreme (>20%) deviations from the ‘actuals’, had a high probability of estimating the annual cumulative emissions within 10% precision, with practicable sampling numbers in comparison to other sampling regimes. This provides robust and useful guidance for manual gas sampling in sugarcane cropping systems, although further adjustments by the operators in terms of expected measurement accuracy and resource availability are encouraged. By implementing these sampling strategies together, labour inputs and errors in measured cumulative N2O emissions can be minimised. Further research is needed to quantify the spatial variability of N2O emissions within sugarcane cropping and to develop techniques for effectively addressing both spatial and temporal variabilities simultaneously.

Assessing the relationship between shire winter crop yield and seasonal variability of the MODIS NDVI and EVI images

Australian researchers have been developing robust yield estimation models, based mainly on the crop growth response to water availability during the crop season. However, knowledge of spatial distribution of yields within and across the production regions can be improved by the use of remote sensing techniques. Images of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, available since 1999, have the potential to contribute to crop yield estimation. The objective of this study was to analyse the relationship between winter crop yields and the spectral information available in MODIS vegetation index images at the shire level. The study was carried out in the Jondaryan and Pittsworth shires, Queensland , Australia . Five years (2000 to 2004) of 250m resolution, 16-day composite of MODIS Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images were used during the winter crop season (April to November). Seasonal variability of the profiles of the vegetation index images for each crop season using different regions of interest (cropping mask) were displayed and analysed. Correlation analysis between wheat and barley yield data and MODIS image values were also conducted. The results showed high seasonal variability in the NDVI and EVI profiles, and the EVI values were consistently lower than those of the NDVI. The highest image values were observed in 2003 (in contrast to 2004), and were associated with rainfall amount and distribution. The seasonal variability of the profiles was similar in both shires, with minimum values in June and maximum values at the end of August. NDVI and EVI images showed sensitivity to seasonal variability of the vegetation and exhibited good association (e.g. r = 0.84, r = 0.77) with winter crop yields.

Spatial model of site index based on Y-ray spectrometry and a digital elevation model for two Pinus species in Tuan Toolara State Forest, Queensland, Australia

Site index prediction models are an important aid for forest management and planning activities. This paper introduces a multiple regression model for spatially mapping and comparing site indices for two Pinus species (Pinus elliottii Engelm. and Queensland hybrid, a P. elliottii x Pinus caribaea Morelet hybrid) based on independent variables derived from two major sources: g-ray spectrometry (potassium (K), thorium (Th), and uranium (U)) and a digital elevation model (elevation, slope, curvature, hillshade, flow accumulation, and distance to streams). In addition, interpolated rainfall was tested. Species were coded as a dichotomous dummy variable; interaction effects between species and the g-ray spectrometric and geomorphologic variables were considered. The model explained up to 60% of the variance of site index and the standard error of estimate was 1.9 m. Uranium, elevation, distance to streams, thorium, and flow accumulation significantly correlate to the spatial variation of the site index of both species, and hillshade, curvature, elevation and slope accounted for the extra variability of one species over the other. The predicted site indices varied between 20.0 and 27.3 m for P. elliottii, and between 23.1 and 33.1 m for Queensland hybrid; the advantage of Queensland hybrid over P. elliottii ranged from 1.8 to 6.8 m, with the mean at 4.0 m. This compartment-based prediction and comparison study provides not only an overview of forest productivity of the whole plantation area studied but also a management tool at compartment scale.

Reassessing spatial and temporal dynamics of kangaroo populations

Long-running datasets from aerial surveys of kangaroos (Macropus giganteus, Macropus [uliginosus, Macropus robustus and Macropus rufus) across Queensland, New South Wales and South Australia have been analysed, seeking better predictors of rates of increase which would allow aerial surveys to be undertaken less frequently than annually. Early models of changes in kangaroo numbers in response to rainfall had shown great promise, but much variability. We used normalised difference vegetation index (NDVI) instead, reasoning that changes in pasture condition would provide a better predictor than rainfall. However, except at a fine scale, NDVI proved no better; although two linked periods of rainfall proved useful predictors of rates of increase, this was only in some areas for some species. The good correlations reported in earlier studies were a consequence of data dominated by large droughtinduced adult mortality, whereas over a longer time frame and where changes between years are less dramatic, juvenile survival has the strongest influence on dynamics. Further, harvesting, density dependence and competition with domestic stock are additional and important influences and it is now clear that kangaroo movement has a greater influence on population dynamics than had been assumed. Accordingly, previous conclusions about kangaroo populations as simple systems driven by rainfall need to be reassessed. Examination of this large dataset has permitted descriptions of shifts in distribution of three species across eastern Australia, changes in dispersion in response to rainfall, and an evaluation of using harvest statistics as an index of density and harvest rate. These results have been combined into a risk assessment and decision theory framework to identify optimal monitoring strategies.

Large-scale characterization of drought pattern: a continent-wide modelling approach applied to the Australian wheatbelt – spatial and temporal trends

* Plant response to drought is complex, so that traits adapted to a specific drought type can confer disadvantage in another drought type. Understanding which type(s) of drought to target is of prime importance for crop improvement. * Modelling was used to quantify seasonal drought patterns for a check variety across the Australian wheatbelt, using 123 yr of weather data for representative locations and managements. Two other genotypes were used to simulate the impact of maturity on drought pattern. * Four major environment types summarized the variability in drought pattern over time and space. Severe stress beginning before flowering was common (44% of occurrences), with (24%) or without (20%) relief during grain filling. High variability occurred from year to year, differing with geographical region. With few exceptions, all four environment types occurred in most seasons, for each location, management system and genotype. * Applications of such environment characterization are proposed to assist breeding and research to focus on germplasm, traits and genes of interest for target environments. The method was applied at a continental scale to highly variable environments and could be extended to other crops, to other drought-prone regions around the world, and to quantify potential changes in drought patterns under future climates.