Monitoring Infection Risk for Air and Soil Borne Fungal Plant Pathogens using Antibody and DNA Techniques and Mathematical Models describing Environmental Parameters

Economic losses resulting from disease development can be reduced by accurate and early detection of plant pathogens. Early detection can provide the grower with useful information on optimal crop rotation patterns, varietal selections, appropriate control measures, harvest date and post harvest handling. Classical methods for the isolation of pathogens are commonly used only after disease symptoms. This frequently results in a delay in application of control measures at potentially important periods in crop production. This paper describes the application of both antibody and DNA based systems to monitor infection risk of air and soil borne fungal pathogens and the use of this information with mathematical models describing risk of disease associated with environmental parameters.

Defining Spatial and Temporal Hydromorphological Sampling Strategies for the Leigh Brook River Site

Detailed surveys of depth and velocity are undertaken to describe hydro-ecological status of rivers. Fieldwork for these surveys is time consuming and expensive. This paper aims to describe the methodology applied in order to determine the most suitable depth sampling strategy for effective field data collection and river representation in time and space at the Leigh Brook river site, Worcester, UK. The accuracy of three different sampling strategies for predicting depth at non-measured points has been compared and the mesohabitats that better characterise depth changes due to variations in discharge have been identified. The results show that depth changes due to discharge change are mainly located at shallow and deep glide mesohabitat types. The analysis for the comparison of sampling strategies indicates that grid sampling strategies give better results than regular transects. Since the results also show that higher errors in predictions are obtained in the deepest areas, higher sampling densities should be applied in these locations.

Forecasting Methodologies for Ganoderma Spore Concentration Using Combined Statistical Approaches and Model Evaluations

High concentration levels of Ganoderma spp. spores were observed in Worcester, UK, during 2006–2010.These basidiospores are known to cause sensitization due to the allergen content and their small dimensions. This enables them to penetrate the lower part of the respiratory tract in humans. Establishment of a link between occurring symptoms of sensitization to Ganoderma spp. and other basidiospores is challenging due to lack of information regarding spore concentration in the air. Hence, aerobiological monitoring should be conducted, and if possible extended with the construction of forecast models. Daily mean concentration of allergenic Ganoderma spp. spores in the atmosphere of Worcester was measured using 7-day volumetric spore sampler through five consecutive years. The relationships between the presence of spores in the air and the weather parameters were examined. Forecast models were constructed for Ganoderma spp. spores using advanced statistical techniques, i.e. multivariate regression trees and artificial neural networks. Dew point temperature along with maximumtemperature was the most important factor influencing the presence of spores in the air of Worcester. Based on these two major factors and several others of lesser importance, thresholds for certain levels of fungal spore concentration, i.e. low (0–49 s m−3), moderate(50–99 s m−3), high (100–149 s m−3) and very high (150and predicted values varied from rs=0.57 to rs=0.68).