Yield-factor volatility models

The term structure of interest rates is often summarized using a handful of yield factors that capture shifts in the shape of the yield curve. In this paper, we develop a comprehensive model for volatility dynamics in the level, slope, and curvature of the yield curve that simultaneously includes level and GARCH effects along with regime shifts. We show that the level of the short rate is useful in modeling the volatility of the three yield factors and that there are significant GARCH effects present even after including a level effect. Further, we find that allowing for regime shifts in the factor volatilities dramatically improves the model’s fit and strengthens the level effect. We also show that a regime-switching model with level and GARCH effects provides the best out-of-sample forecasting performance of yield volatility. We argue that the auxiliary models often used to estimate term structure models with simulation-based estimation techniques should be consistent with the main features of the yield curve that are identified by our model.

Cultivar-specific effects of pathogen testing on storage root yield of sweet potato, Ipomoea batatas (L.) Lam.

The accumulation and perpetuation of viral pathogens over generations of clonal propagation in crop species such as sweet potato, Ipomoea batatas,inevitably result in a reduction in crop yield and quality. This study was conducted at Bundaberg, Australia to compare the productivity of field-derived and pathogen-tested (PT)clones of 14 sweet potato cultivars and the yield benefits of using healthy planting materials. The field-derived clonal materials were exposed to the endemic viruses, while the PT clones were subjected to thermotherapy and meristem-tip culture to eliminate viral pathogens. The plants were indexed for viruses using nitrocellulose membrane-enzyme-linked immunosorbent assay and graft-inoculations onto Ipomoea setosa. A net benefit of 38% in storage root yield was realised from using PT materials in this study.Conversely, in a similar study previously conducted at Kerevat, Papua New Guinea (PNG), a net deficit of 36% was realised. This reinforced our finding that the response to pathogen testing was cultivar dependent and that the PNG cultivars in these studies generally exhibited increased tolerance to the endemic viruses present at the respective trial sites as manifested in their lack of response from the use of PT clones. They may be useful sources for future resistance breeding efforts. Nonetheless, the potential economic gain from using PT stocks necessitates the use of pathogen testing on virus-susceptible commercial cultivars.

Manufacturing Yield Improvement by Clustering

Dealing with product yield and quality in manufacturing industries is getting more difficult due to the increasing volume and complexity of data and quicker time to market expectations. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large databases. Growing self-organizing map (GSOM) is established as an efficient unsupervised datamining algorithm. In this study some modifications to the original GSOM are proposed for manufacturing yield improvement by clustering. These modifications include introduction of a clustering quality measure to evaluate the performance of the programme in separating good and faulty products and a filtering index to reduce noise from the dataset. Results show that the proposed method is able to effectively differentiate good and faulty products. It will help engineers construct the knowledge base to predict product quality automatically from collected data and provide insights for yield improvement.

Evaluation of coronary angiographic projections to balance the clinical yield with the radiation risk

Objective: Radiation safety principles dictate that imaging procedures should minimise the radiation risks involved, without compromising diagnostic performance. This study aims to define a core set of views that maximises clinical information yield for minimum radiation risk. Angiographers would supplement these views as clinically indicated. Methods: An algorithm was developed to combine published data detailing the quality of information derived for the major coronary artery segments through the use of a common set of views in angiography with data relating to the dose–area product and scatter radiation associated with these views. Results: The optimum view set for the left coronary system comprised four views: left anterior oblique (LAO) with cranial (Cr) tilt, shallow right anterior oblique (AP-RAO) with caudal (Ca) tilt, RAO with Ca tilt and AP-RAO with Cr tilt. For the right coronary system three views were identified: LAO with Cr tilt, RAO and AP-RAO with Cr tilt. An alternative left coronary view set including a left lateral achieved minimally superior efficiency (,5%), but with an ,8% higher radiation dose to the patient and 40% higher cardiologist dose. Conclusion: This algorithm identifies a core set of angiographic views that optimises the information yield and minimises radiation risk. This basic data set would be supplemented by additional clinically determined views selected by the angiographer for each case. The decision to use additional views for diagnostic angiography and interventions would be assisted by referencing a table of relative radiation doses for the views being considered.

Agronomic and economic evaluation of irrigation strategies on cotton lint yield in Australia

Cotton is one of the most important irrigated crops in subtropical Australia. In recent years, cotton production has been severely affected by the worst drought in recorded history, with the 2007–08 growing season recording the lowest average cotton yield in 30 years. The use of a crop simulation model to simulate the long-term temporal distribution of cotton yields under different levels of irrigation and the marginal value for each unit of water applied is important in determining the economic feasibility of current irrigation practices. The objectives of this study were to: (i) evaluate the CROPGRO-Cotton simulation model for studying crop growth under deficit irrigation scenarios across ten locations in New South Wales (NSW) and Queensland (Qld); (ii) evaluate agronomic and economic responses to water inputs across the ten locations; and (iii) determine the economically optimal irrigation level. The CROPGRO-Cotton simulation model was evaluated using 2 years of experimental data collected at Kingsthorpe, Qld. The model was further evaluated using data from nine locations between northern NSW and southern Qld. Long-term simulations were based on the prevalent furrowirrigation practice of refilling the soil profile when the plant -available soil water content is<50%. The model closely estimated lint yield for all locations evaluated. Our results showed that the amounts of water needed to maximise profit and maximise yield are different, which has economic and environmental implications. Irrigation needed to maximise profits varied with both agronomic and economic factors, which can be quite variable with season and location. Therefore, better tools and information that consider the agronomic and economic implications of irrigation decisions need to be developed and made available to growers.

Adapting wheat sowing dates to projected climate change in the Australian subtropics: analysis of crop water use and yield

Projected increases in atmospheric carbon dioxide concentration ([CO2]) and air temperature associated with future climate change are expected to affect crop development, crop yield, and, consequently, global food supplies. They are also likely to change agricultural production practices, especially those related to agricultural water management and sowing date. The magnitude of these changes and their implications to local production systems are mostly unknown. The objectives of this study were to: (i) simulate the effect of projected climate change on spring wheat (Triticum aestivum L. cv. Lang) yield and water use for the subtropical environment of the Darling Downs, Queensland, Australia; and (ii) investigate the impact of changing sowing date, as an adaptation strategy to future climate change scenarios, on wheat yield and water use. The multimodel climate projections from the IPCC Coupled Model Intercomparison Project (CMIP3) for the period 2030–2070 were used in this study. Climate scenarios included combinations of four changes in air temperature (08C, 18C, 28C, and 38C), three [CO2] levels (380 ppm, 500 ppm, and 600 ppm), and three changes in rainfall (–30%, 0%, and +20%), which were superimposed on observed station data. Crop management scenarios included a combination of six sowing dates (1 May, 10 May, 20 May, 1 June, 10 June, and 20 June) and three irrigation regimes (no irrigation (NI), deficit irrigation (DI), and full irrigation (FI)). Simulations were performed with the model DSSAT4.5, using 50 years of daily weather data.Wefound that: (1) grain yield and water-use efficiency (yield/evapotranspiration) increased linearly with [CO2]; (2) increases in [CO2] had minimal impact on evapotranspiration; (3) yield increased with increasing temperature for the irrigated scenarios (DI and FI), but decreased for the NI scenario; (4) yield increased with earlier sowing dates; and (5) changes in rainfall had a small impact on yield for DI and FI, but a high impact for the NI scenario.