Abstract

Introduction: Herbicide application is a vital strategy of weed control. The effects of these chemicals on the non-target soil microorganisms are very intense; have adverse impact on physicochemical parameters of the soil, which in turn affect soil fertility and plant growth.
 Research Gap: There are insufficient literatures on extensive monitoring of the effects of prolonged herbicides use. Existing literatures concerning analysis of effect of prolonged herbicides application on soil are not comprehensive with respect to number of soil characteristics analyzed.
 Aim: This study assessed the effects of Atrazine and Glyphosate on physicochemical properties and microbial population of carrot and maize farm soils, exposed to prolonged use at Songhai Delta.
 Place and Duration of Study: The study was conducted in Songhai Delta farms and the Department of Environmental Management and Toxicology, Federal University of Petroleum Resources, Effurun, from April to July, 2019.
 Methodology: These pesticides were applied according to the manufacturers’ instructions at sublethal concentrations. Their effects on soil pH, cation exchange capacity, total organic carbon, nitrates, phosphates, sulphates and microbial populations at two depths (0-15 and 15-30 cm) were assessed using standard methods. Microbial counts were carried out for total heterotrophic bacteria and fungi using the pour plate method.
 Results: There were variations in the different properties of the carrot and maize farm soils. Soil pH was higher in maize farm (5.91±0.10) than in carrot farm (5.88±0.06) at the depth of 15-30 cm. The pH, total organic carbon, nitrate content had no significant difference while phosphate and microbial counts were significantly different at P=.05.
 Conclusion: This assessment has shown that the herbicides had no influence on pH, total organic carbon, nitrate but a negative one on bacterial and fungal populations with prolonged use. A modification in physicochemical and microbiological characteristics of soil could be used to predict the fertility and health status of soils.

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