期刊名称：International Journal of Environmental Monitoring and Analysis
印刷版ISSN：2328-7659
电子版ISSN：2328-7667
出版年度：2019
卷号：7
期号：6
页码：128-136
DOI：10.11648/j.ijema.20190706.13
语种：English
出版社：Science Publishing Group
摘要：Vegetables are grown world-wide in almost 200 countries, but they are regularly subject to pest pressure. To cope with the multiple pests, farmers resort to pesticides whose use in developing countries carries health and environmental risks. This study aimed to investigate the practices of vegetable farmers from Ouagadougou when using pesticides, and to examine the potential for contamination of ground and surface water. Based on the use of questionnaires and field observations, this study investigated farmers’ practices on vegetable pest management using pesticides. The physicochemical properties of the active ingredients of pesticide were analysed, and Goss and GUS algorithms were applied to estimate the risk of surface and ground water contamination, respectively. The majority of producers were male (58%), illiterate (80%) and use pesticide in their vegetable crops (97.72%). The products used by the farmers in the study areas were insecticides (28), herbicides (5), fungicides (1), and nematicide (1), altough more than 50% of these pesticides were registered for the treatment of cotton crops but not for vegetables. Depending on the crop, 88% of the farmers applied pesticides up to 5 times or more per cropping season. Among active ingredients from pesticides used by farmers, eight are highly solubles, nine are readily degradables, six are moderately mobiles, and five are imobiles. Five have high potential to contaminate surface water while one has high potential to contaminate ground water. These results can be used for the development of tool to predict water contamination by pesticides in pest management by vegetable farmers. This could contribute to the reinforcement of pesticides policy for advance their health, environmental and economic consequences.
关键词：Vegetable; Farmer Practices; Pesticides; Goss Algorythm; GUS Index; Water Contamination
