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.

Greenhouse Gas Fluxes in Tropical and Temperae Agriculture: The Need for a Full-Cost Accounting of Global Warming Potentials

Agriculture's contribution to radiative forcing is principally through its historical release of carbon in soil and vegetation to the atmosphere and through its contemporary release of nitrous oxide (N2O) and methane (CHM4). The sequestration of soil carbon in soils now depleted in soil organic matter is a well-known strategy for mitigating the buildup of CO2 in the atmosphere. Less well-recognized are other mitigation potentials. A full-cost accounting of the effects of agriculture on greenhouse gas emissions is necessary to quantify the relative importance of all mitigation options. Such an analysis shows nitrogen fertilizer, agricultural liming, fuel use, N2O emissions, and CH4 fluxes to have additional significant potential for mitigation. By evaluating all sources in terms of their global warming potential it becomes possible to directly evaluate greenhouse policy options for agriculture. A comparison of temperate and tropical systems illustrates some of these options.

A study of environmental and management drivers of non-co2 greenhouse gas emissions in Australian agro-ecosystems

Australian climate, soils and agricultural management practices are significantly different from those of the northern hemisphere nations. Consequently, experimental data on greenhouse gas production from European and North American agricultural soils and its interpretation are unlikely to be directly applicable to Australian systems.

The application of neural networks to induction machine control

Neural networks (NNs) are discussed in connection with their possible use in induction machine drives. The mathematical model of the NN as well as a commonly used learning algorithm is presented. Possible applications of NNs to induction machine control are discussed. A simulation of an NN successfully identifying the nonlinear multivariable model of an induction-machine stator transfer function is presented. Previously published applications are discussed, and some possible future applications are proposed.

Identification and control of induction machines using artificial neural networks

The use of artificial neural networks (ANNs) to identify and control induction machines is proposed. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics, and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Both systems are inherently adaptive as well as self-commissioning. The current controller is a completely general nonlinear controller which can be used together with any drive algorithm. Various advantages of these control schemes over conventional schemes are cited, and the combined speed and current control scheme is compared with the standard vector control scheme

Identification and control of induction machines using artificial neural networks

This paper proposes the use of artificial neural networks (ANNs) to identify and control an induction machine. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics; and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Various advantages of these control schemes over other conventional schemes are cited and the performance of the combined speed and current control scheme is compared with that of the standard vector control scheme

Coal Seam Gas Water from Maramarua, New Zealand: Characterisation and Comparison to United States Analogues

Groundwater from Maramarua has been identified as coal seam gas (CSG) water by studying its composition, and comparing it against the geochemical signature from other CSG basins. CSG is natural gas that has been produced through thermogenic and biogenic processes in underground coal seams; CSG extraction requires the abstraction of significant amounts of CSG water. To date, no international literature has described coal seam gas water in New Zealand, however recent CSG exploration work has resulted in CSG water quality data from a coal seam in Maramarua, New Zealand. Water quality from this site closely follows the geochemical signature associated with United States CSG waters, and this has helped to characterise the type of water being abstracted. CSG water from this part of Maramarua has low calcium, magnesium, and sulphate concentrations but high sodium (334 mg/l), chloride (146 mg/l) and bicarbonate (435 mg/l) concentrations. In addition, this water has high pH (7.8) and alkalinity (360 mg/l as CaCO3), which is a direct consequence of carbonate dissolution and biogenic processes. Different analyte ratios ('source-rock deduction' method) have helped to identify the different formation processes responsible in shaping Maramarua CSG water

Characterisation of Coal Seam Gas Waters in New Zealand

Coal seam gas (CSG) exploration and development requires the abstraction of significant amounts of water. This is so because gas desorbtion in coal seams takes place only after aquifer pressure has been reduced by prolonged pumping of aquifer water. CSG waters have a specific geochemical signature which is a product of their formation process. These waters have high bicarbonate, high sodium, low calcium, low magnesium, and very low sulphate concentrations. Additionally, chloride concentrations may be high depending on the coal depositional environment. This particular signature is not only useful for exploration purposes, but it also highlights potential environmental issues that can arise as a consequence of CSG water disposal. Since 2002 L&M Coal Seam Gas Ltd and CRL Energy Ltd, have been involved in exploration and development of CSG in New Zealand. Anticipating disposal of CSG waters as a key issue in CSG development, they have been assessing CSG water quality along with exploration work. Coal seam gas water samples from an exploration well in Maramarua closely follow the geochemical signature associated with CSG waters. This has helped to identify CSG potential, while at the same time assessing the chemical characteristics and water generation processes in the aquifer. Neutral pH and high alkalinity suggest that these waters could be easily managed once the sodium and chloride concentrations are reduced to acceptable levels.

Alleviating ‘overfitting’ via genetically-regularised neural network

A hybrid genetic algorithm/scaled conjugate gradient regularisation method is designed to alleviate ANN `over-fitting'. In application to day-ahead load forecasting, the proposed algorithm performs better than early-stopping and Bayesian regularisation, showing promising initial results.

Exploring retinal and functional markers of diabetic neuropathy

Diabetic peripheral neuropathy (DPN) is one of the most debilitating complications of diabetes. DPN is a major cause of foot ulceration and lower limb amputation. Early diagnosis and management is a key factor in reducing morbidity and mortality. Current techniques for clinical assessment of DPN are relatively insensitive for detecting early disease or involve invasive procedures such as skin biopsies. There is a need for less painful, non-invasive and safe evaluation methods. Eye care professionals already play an important role in the management of diabetic retinopathy; however recent studies have indicated that the eye may also be an important site for the diagnosis and monitoring of neuropathy. Corneal nerve morphology has been shown to be a promising marker of diabetic neuropathy occurring elsewhere in the body, and emerging evidence tentatively suggests that retinal anatomical markers and a range of functional visual indicators could similarly provide useful information regarding neural damage in diabetes – although this line of research is, as yet, less well established. This review outlines the growing body of evidence supporting a potential diagnostic role for retinal structure and visual functional markers in the diagnosis and monitoring of peripheral neuropathy in diabetes.