POPULATION DYNAMICS OF PLANT PARASITIC NEMATODES AND THEIR RELATIONSHIPS WITH SOIL PHYSICO-CHEMICAL PARAMETERS DURING THE FALLOW PERIOD AFTER SUGARBEET AND LEADING INTO TOMATO CULTIVATION

The main objective of this research was the study of the soil nematode community, and in particular plant parasitic nematodes (PPN), from a field located in Portugal’s southern region, used for sugarbeet production. The study was performed from February to July 2003, covering part of the fallow period previous to tomato cultivation, the alternative crop in the rotation. The end of the fallow period in March and the soil preparation period in May were marked by a significant reduction in the numbers of PPN, whereas their numbers increased on the following tomato crop. The genus Helicotylenchus stood out as the most representative group, forming 90% of all PPN counted each month. The genus Heterodera was relatively abundant in the months following the previous sugarbeet crop, and numbers of the genus Meloidogyne increased during the tomato crop. The correlations between these group and environmental parameters show that, apart from the direct influence of the host, pH, organic matter, temperature and soil moisture significantly influenced nematode abundance and community composition.

An Assessment to Toxicological Risk of Pesticide Exposure

On the one hand, pesticides may be absorbed into the body orally, dermally, ocularly and by inhalation and the human exposure may be dietary, recreational and/or occupational where toxicity could be acute or chronic. On the other hand, the environmental fate and toxicity of the pesticide is contingent on the physico-chemical characteristics of pesticide, the soil composition and adsorption. Human toxicity is also dependent on the exposure time and individual’s susceptibility. Therefore, this work will focus on the development of an Artificial Intelligence based diagnosis support system to assess the pesticide toxicological risk to humanoid, built under a formal framework based on Logic Programming to knowledge representation and reasoning, complemented with an approach to computing grounded on Artificial Neural Networks. The proposed solution is unique in itself, once it caters for the explicit treatment of incomplete, unknown, or even self-contradictory information, either in terms of a qualitative or quantitative setting.