A simpler, quicker way to track pesticide emissions from agricultural fields has been devised by Agricultural Research Service scientists.
Using low-cost laboratory tests and mathematical models, research leader Scott Yates and colleagues at the U.S. Salinity Laboratory in Riverside, Calif., are able to evaluate — and even predict — fumigant emissions.
With a ban looming on methyl bromide, a pre-plant soil fumigant widely used by fruit and vegetable growers, Environmental Protection Agency regulators are evaluating various emissions-lowering fumigation alternatives. Each potential methyl bromide replacement will require its own set of regulations, based on findings from complex field studies.
According to Yates, such studies can take up to a year to complete, cost hundreds of thousands of dollars and, in the process, bring research teams in contact with toxic chemicals. Since a variety of factors must be considered in each EPA evaluation, such as variations in soil and environmental conditions, as many as 60 costly, long-term field experiments may be necessary.
Yates' team has shown that lab tests can yield some of the same results as those painstakingly obtained from outdoor field studies. To collect their data, the ARS scientists designed elaborate soil columns and soil cell equipment with which to observe pesticide movement through soil. They combine data collected this way with numerous mathematically driven models.
These laboratory studies are ideally suited for helping pinpoint information such as the total fraction of a given fumigant that ends up leaving the soil after it's applied. This measurement, known as "cumulative emissions," is one of the EPA's critical data requirements for obtaining approval for soil fumigants.
Yates is quick to acknowledge that field studies will always be needed to tie lab-based findings to real-world agricultural landscapes. That's because laboratory methods cannot provide certain emissions data that are linked to prevailing weather conditions, agricultural practices and other factors.