Despite a changing world in terms of data sharing, availability, and transparency, there are still major resource issues associated with collating datasets that will satisfy the requirements of comprehensive pesticide risk assessments, especially those undertaken at a regional or national scale. In 1996, a long-term project was initiated to begin collating and formatting pesticide data to eventually create a free-to-all repository of data that would provide a comprehensive transparent, harmonized, and managed extensive dataset for all types of pesticide risk assessments. Over the last 20 years, this database has been keeping pace with improving risk assessments, their associated data requirements, and the needs and expectations of database end users. In 2007, the Pesticide Properties DataBase (PPDB) was launched as a free-to-access website. Currently, the PPDB holds data for almost 2300 pesticide active substances and over 700 metabolites. For each substance around 300 parameters are stored, cover...

/pdf/an-international-database-for-pesticide-risk-assessments-and-3hhcvcpuyp.pdf

An international database for pesticide risk assessments and management

The hydraulic conductivity function, which is required to solve the Richards equation, is difficult to measure. Therefore prediction methods are frequently used where the shape of the conductivity function is estimated from the more easily measured water retention characteristic. Errors in conductivity estimations can arise either from an invalidity of the prediction model for a given soil, or from an incorrect description of the retention data. This second error source is particularly important for soils with heterogeneous pore systems that cannot be adequately described by the usually used retention functions. To describe the retention characteristics of such soils, a flexible θ(ψ) function was formed by superimposing unimodal retention curves of the van Genuchten (1980) type. By combining this retention model with the conductivity prediction model of Mualem (1976), conductivity estimations for soils with heterogeneous pore systems are obtained. Estimated conductivities by this model and the classical van Genuchten-Mualem method can differ by orders of magnitude. Thus reported disagreements between measured and estimated conductivities may in some cases be due to an inadequate description of the retention data rather than due to a failure of the prediction model.

Hydraulic conductivity estimation for soils with heterogeneous pore structure

The application of agricultural pesticides in Africa can have negative effects on human health and the environment. The aim of this study was to identify African environments that are vulnerable to the accumulation of pesticides by mapping geospatial processes affecting pesticide fate. The study modelled processes associated with the environmental fate of agricultural pesticides using publicly available geospatial datasets. Key geospatial processes affecting the environmental fate of agricultural pesticides were selected after a review of pesticide fate models and maps for leaching, surface runoff, sedimentation, soil storage and filtering capacity, and volatilization were created. The potential and limitations of these maps are discussed. We then compiled a database of studies that measured pesticide residues in Africa. The database contains 10,076 observations, but only a limited number of observations remained when a standard dataset for one compound was extracted for validation. Despite the need for more in-situ data on pesticide residues and application, this study provides a first spatial overview of key processes affecting pesticide fate that can be used to identify areas potentially vulnerable to pesticide accumulation.

/pdf/mapping-geospatial-processes-affecting-the-environmental-47fhu0ytlm.pdf

Mapping Geospatial Processes Affecting the Environmental Fate of Agricultural Pesticides in Africa.

https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=2294&context=cee_facpub

Leland B. Baskin

Papers

The Pesticide Root Zone Model (PRZM): A procedure for evaluating pesticide leaching threats to groundwater