The environmental fate of diuron under a conventional production regime in a sugarcane farm during the plant cane phase

BACKGROUND: Field studies of diuron and its metabolites 3-(3,4-dichlorophenyl)-1-methylurea (DCPMU), 3,4-dichlorophenylurea (DCPU) and 3,4-dichloroaniline (DCA) were conducted in a farm soil and in stream sediments in coastal Queensland, Australia. RESULTS: During a 38 week period after a 1.6 kg ha^-1 diuron application, 70-100% of detected compounds were within 0-15 cm of the farm soil, and 3-10% reached the 30-45 cm depth. First-order t1/2 degradation averaged 49 ± 0.9 days for the 0-15, 0-30 and 0-45 cm soil depths. Farm runoff was collected in the first 13-50 min of episodes lasting 55-90 min. Average concentrations of diuron, DCPU and DCPMU in runoff were 93, 30 and 83-825 µg L^-1 respectively. Their total loading in all runoff was >0.6% of applied diuron. Diuron and DCPMU concentrations in stream sediments were between 3-22 and 4-31 µg kg^-1 soil respectively. The DCPMU/diuron sediment ratio was >1. CONCLUSION: Retention of diuron and its metabolites in farm topsoil indicated their negligible potential for groundwater contamination. Minimal amounts of diuron and DCMPU escaped in farm runoff. This may entail a significant loading into the wider environment at annual amounts of application. The concentrations and ratio of diuron and DCPMU in stream sediments indicated that they had prolonged residence times and potential for accumulation in sediments. The higher ecotoxicity of DCPMU compared with diuron and the combined presence of both compounds in stream sediments suggest that together they would have a greater impact on sensitive aquatic species than as currently apportioned by assessments that are based upon diuron alone.

Issues of Digital Ecosystems Approach to Biological Ecosystem Simulation Model Fit Optimization

This study examines the application of digital ecosystems concepts to a biological ecosystem simulation problem. The problem involves the use of a digital ecosystem agent to optimize the accuracy of a second digital ecosystem agent, the biological ecosystem simulation. The study also incorporates social ecosystems, with a technological solution design subsystem communicating with a science subsystem and simulation software developer subsystem to determine key characteristics of the biological ecosystem simulation. The findings show similarities between the issues involved in digital ecosystem collaboration and those occurring when digital ecosystems interact with biological ecosystems. The results also suggest that even precise semantic descriptions and comprehensive ontologies may be insufficient to describe agents in enough detail for use within digital ecosystems, and a number of solutions to this problem are proposed.