Fumigant toxicity of essential oils from the Myrtaceae family and 1,8-cineole against 3 major stored-grain insects

Six out of 42 essential oils extracted from species of the family Myrtaceae found in Australia were shown to have potent fumigant toxicity against three major stored-grain insects: Sitophilus oryzae, Tribolium castaneum and Rhyzopertha dominica. These were the essential oils from Eucalyptus nicholii, E. codonocarpa, E. blakelyi, Callistemon sieberi, Melaleuca fulgens and M. armillaris. The LD₅₀ and LD₉₅ of the selected essential oils against S. oryzae adults were between 19.0–30.6 and 43.6–56.0 μl/l air, respectively. Also, these oils were approximately twice as toxic to T. castaneum and R. dominica at the LD₉₅. Fumigant effects of the essential oils rich in 1,8-cineole were considered to warrant further research into their potential for commercial use.     

Statistical analysis of decay of organophosphorus and pyrethroid insecticides and carbaryl on paddy rice, maize, sunflowers and field peas

A model is developed to describe the decay of seven insecticides on stored paddy rice, maize, sunflowers and peas, and is shown to be more precise than each of six literature models on pesticide decay. It relates residues (R), as a ratio of applied concentration (R(0)), to time after insecticide application (t), modifying the usual first-order kinetics equation lnR=lnR(0)-kt by assuming that the rate constant k has a mixture of values according to a gamma distribution with mean K. The resulting equation is lnR=lnR(0)-Kcln(1+t/c), where c is a constant determined empirically. For large c, this model reduces to the first-order model with K=k. The parameter K is further modelled as linear in temperature for maize and rice. The rate of loss of protectants was found not to be seriously affected by co-application with other insecticides, and this is a useful finding for future studies on rates of loss of grain protectants. The apparent rate of loss was also not seriously affected by the method of analysis, whether by gas chromatography or high-performance liquid chromatography, and this finding supports previous validation of the methods.     

A “mixed” small gain and passivity theorem in the frequency domain

We show that the negative feedback interconnection of two causal, stable, linear time-invariant systems, with a “mixed” small gain and passivity property, is guaranteed to be finite-gain stable. This “mixed” small gain and passivity property refers to the characteristic that, at a particular frequency, systems in the feedback interconnection are either both “input and output strictly passive”; or both have “gain less than one”; or are both “input and output strictly passive” and simultaneously both have “gain less than one”. The “mixed” small gain and passivity property is described mathematically using the notion of dissipativity of systems, and finite-gain stability of the interconnection is proven via a stability result for dissipative interconnected systems.     

Broad-scale geomorphology and benthic habitats of the Perth coastal plain and Rottnest Shelf,Western Australia,identified in a merged topographic and bathymetric digital relief model

A digital relief model (DRM) of the Swan Coastal Plain and Rottnest Shelf (7400 km²) was built with a range of topographic and high-resolution bathymetric datasets, gridded to a 50 m cell size. The DRM enabled the delineation of relict coastal landforms, benthic habitats and development of a regional morphostratigraphic framework. Well-defined features include: (1) limestone ridges on the coastal plain that sit subparallel to the modern shoreline and were largely formed as coastal dune barriers during or shortly after Quaternary interglacial periods of high sea level; (2) rocky reefs on the inner shelf that rise up to 10 m above the adjacent seafloor, which are remnants of coastal dune barriers that formed when the sea level was 20–30 m lower than present and (3) a discontinuous ridge 3–10 m high along much of the outer shelf, which likely represents a coastal barrier that formed when the sea level was around 60 m lower than present. The DRM provides a useful regional perspective of the distribution and form of these extensive reefs.     

The use of LiDAR in applied interpretive landform mapping for natural resource management,Murray River alluvial plain,Australia

High resolution Light Detection and Ranging (LiDAR) Digital Elevation Models (DEMs) have proved an invaluable tool for landform mapping along the alluvium of the Murray River in southern Australia. They are a key part of an integrated approach to addressing salinity, land management and groundwater resource issues on the Murray River alluvial plain when used in conjunction with satellite imagery, airborne and ground geophysics, and surface and borehole information on soils and sediments. This article describes the processing and application of LiDAR DEMs to the Murray River alluvial plain and how this has informed concepts of landform architecture in the region and confirmed and elaborated on earlier studies. In particular the LiDAR data show how the landform architecture of the Murray River alluvial plain changes dramatically downstream, from the unconfined depositional landscapes of the Riverine Plain to being increasingly confined to an incised valley downstream.     

Non-destructive estimation of wheat leaf chlorophyll content from hyperspectral measurements through analytical model inversion

Optimizing nitrogen (N) fertilization in crop production by in-season measurements of crop N status may improve fertilizer N use efficiency. Hyperspectral measurements may be used to assess crop N status indirectly by estimating leaf and canopy chlorophyll content. This study evaluated the ability of the PROSAIL canopy-level reflectance model to predict leaf chlorophyll content of spring wheat (Triticum aestivum L.) during the growth stages between pre-tillering (Zadoks Growth Stage (ZGS 15)) to booting (ZGS50). Spring wheat was grown under different N fertility rates (0–200 kg N ha^?1) in 2002. Canopy reflectance, leaf chlorophyll content, N content and leaf area index (LAI) values were measured. There was a weakly significant trend for the PROSAIL model to over-estimate LAI and under-estimate leaf chlorophyll content. To compensate for this interdependency by the model, a canopy chlorophyll content parameter (the product of leaf chlorophyll content and LAI) was calculated. The estimation accuracy for canopy chlorophyll content was generally low earlier in the growing season. This failure of the PROSAIL model to estimate leaf and canopy variables could be attributed to model sensitivity to canopy architecture. Earlier in the growing season, full canopy closure was not yet achieved, resulting in a non-homogenous canopy and strong soil background interference. The canopy chlorophyll content parameter was predicted more accurately than leaf chlorophyll content alone at booting (ZGS 45). A strong relationship between canopy chlorophyll content and canopy N content at ZGS 45 indicates that the PROSAIL model may be used as a tool to predict wheat N status from canopy reflectance measurements at booting or later.     

Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery

This Letter evaluates several narrow-band indices from EO-1 Hyperion imagery in discriminating sugarcane areas affected by ‘orange rust’ (Puccinia kuehnii) disease. Forty spectral vegetation indices (SVIs), focusing on bands related to leaf pigments, leaf internal structure, and leaf water content, were generated from an image acquired over Mackay, Queensland, Australia. Discriminant function analysis was used to select an optimum set of indices based on their correlations with the discriminant function. The predictive ability of each index was also assessed based on the accuracy of classification. Results demonstrated that Hyperion imagery can be used to detect orange rust disease in sugarcane crops. While some indices that only used visible near-infrared (VNIR) bands (e.g. SIPI and R800/R680) offer separability, the combination of VNIR bands with the moisture-sensitive band (1660?nm) yielded increased separability of rust-affected areas. The newly formulated ‘Disease–Water Stress Indices' (DWSI-1=R800/R1660; DSWI-2=R1660/R550; DWSI-5=(R800+R550)/(R1660+R680)) produced the largest correlations, indicating their superior ability to discriminate sugarcane areas affected by orange rust disease.     

Sharing the floodplain: Mediated modeling for environmental management

Complex ecosystems, such as the Upper Mississippi River (UMR), present major management challenges. Such systems often provide a range of ecosystem services that are differentially valued by stakeholders representing distinct interests (e.g., agriculture, conservation, navigation) or institutions (e.g., federal and state agencies). When no single entity has the knowledge or authority to resolve conflicts over shared resource use, stakeholders may struggle to jointly understand the scope of the problem and to reach reasonable compromises. This paper explores mediated modeling as a group consensus building process for understanding relationships between ecological, economic and cultural well-being in the UMR floodplain. We describe a workshop structure used to engage UMR stakeholders that may be extended to resource use conflicts in other complex ecosystems. We provide recommendations for improving on these participatory methods in structuring future efforts. In conclusion, we suggest that tools which facilitate collaborative learning, such as mediated modeling, need to be incorporated at an institutional level as a vital element of integrated ecosystem management.     

A method for operational calibration of AVHRR reflective time series data

We have developed an automatic method to monitor the AVHRR instrument sensitivity over time in the short-wave reflective channels to ensure that trends in the data series obtained by this instrument are real and not sensor artefacts.Our radiometric calibration method uses the Multivariate Alteration Detection (MAD) algorithm to statistically select invariant features over land areas from multiple image-pairs that are compared to assess changes in the instrument's calibration. This method requires no a priori regional knowledge and is globally applicable. A calibrated time series from Pseudo-Invariant Features located in central Australia are shown to have long-term trends removed. The resulting MAD-based calibration has a root mean squared error of ∼ 5-6% for both channels 1 and 2 and is in alignment with other approaches.