Investigation of plasmodiophora brassicae (clubroot disease) in vegetable brassica using arabidopsis thaliana as a model system

Clubroot, caused by Plasmodiophora brassicae, is the most devastating soil-borne disease of vegetable brassicas. It occurs all over the world and is responsible for crop losses of up to 10% every year. In Australia, the disease is being managed effectively with chemicals and cultural practices, but ideally control can be improved in the long term by the introduction of resistant cultivars. The life cycle ofP. brassicae and mode of action of plant resistance has not been fully elucidated because of the technical difficulties of working with an obligate, soil-borne plant pathogen. However, Arabidopsis thaliana, which is a host ofP. brassicae, has great potential as a model system for studying the life cycle, the infection process and development of resistance. We have developed a sand-liquid-culture system for growing Arabidopsis that allows easy observation of all life stages and, most importantly, the primary plasmodial stages within the root hair. The method was first optimised for observations of the lifecycle of the pathogen in a susceptible Arabidopsis ecotype (Col-3) where all stages of the lifecycle have now been observed and characterised. Further screening of Arabidopsis ecotypes for disease resistance has utilised one of the most virulent Australian pathotypes of brassica (ECD number 16/19/31). To date, Arabidopsis ecotype Ta-0 has shown a level of tolerance to the disease even though the roots get infected. It has been reported earlier that resistance toP. brassicae in Arabidopsis is due to one or a small number of genes. To examine changes in gene expression during the early, critical stages of infection, RNA was extracted from the susceptible and resistant ecotypes at two time points, 4 days and 17 days after inoculation. Microarray analysis will be used to investigate genome wide changes in gene expression during infection but also to identify candidate genes that may confer resistance to Australian isolates of the pathogen.r />

Evaluation of a heat pump system for greenhouse heating

Greenhouse heating costs for some commercial growers in southern Australia are now a significant production cost. This is particularly the case for those operators who installed heating systems using liquefied petroleum gas (LPG) when this fuel was relatively inexpensive. Heat pump systems used in various configurations have been suggested as an option for reducing energy use and costs for greenhouse heating, particularly if off-peak electricity is used. This paper investigates the financial and environmental viability of an air-to-water heat pump system for a 4000 m2 greenhouse, located 120 km north of Melbourne, Victoria. The simulation software, TRNSYS, was used to predict the performance of the system. The heat pump system was found to have a simple payback period of approximately six years and reduce LPG consumption by 16%. Greenhouse gas emissions were 3% higher using the heat pump system, compared to the existing LPG boiler.r />

Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling

Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) is a Takagi-Sugeno-type fuzzy inference system for online learning which can be applied for dynamic time series prediction. To the best of our knowledge, this is the first time that DENFIS has been used for rainfall-runoff (R-R) modeling. DENFIS model results were compared to the results obtained from the physically-based Storm Water Management Model (SWMM) and an Adaptive Network-based Fuzzy Inference System (ANFIS) which employs offline learning. Data from a small (5.6 km2) catchment in Singapore, comprising 11 separated storm events were analyzed. Rainfall was the only input used for the DENFIS and ANFIS models and the output was discharge at the present time. It is concluded that DENFIS results are better or at least comparable to SWMM, but similar to ANFIS. These results indicate a strong potential for DENFIS to be used in R-R modeling.