Appendix A:
Description of the National Pesticide Loss Database

Don W. Goss and Joaquin Sanabria

Texas Agricultural Experiment Station, Temple, Texas

 Pesticide leaching and runoff losses were estimated for a variety of soils and climates using the pesticide fate and transport model GLEAMS (Groundwater Loading Effects of Agricultural Management) ⁷. GLEAMS is a process model that uses as inputs soil parameters, field characteristics (such as slope, slope length, slope shape, and slope position), management practices, pesticide properties, and climate to estimate pesticide leaching and runoff losses.

GLEAMS leaching and runoff estimates were generated for 243 pesticides applied to 120 soils for 20 years of daily weather from each of 55 climate stations distributed throughout the United States. This resulted in 1,603,800 runs of 20 years each, or 32,076,000 years of data. Pesticide runoff was movement beyond the edge of the field, including both pesticides in solution and pesticides adsorbed to soil material and organic matter. Pesticide leaching was movement beyond the bottom of the root-zone. Separate GLEAMS estimates were made for irrigated and nonirrigated conditions. The crop in these simulations was a generic row crop behaving similar to corn, soybeans, cotton, or sorghum, planted in straight rows.

The daily mass of pesticide that was removed by leaching or runoff in solution or runoff with sediment were recorded for each model run. The daily values were summed for each year. The daily volume of water that leached below the rootzone or flowed beyond the edge of the field was recorded and summed over the year, and the daily mass of sediment loss was recorded and summed over the year. The actual total mass applied of each pesticide was also recorded. Final pesticide loss results are reported as 1) the percentage of total mass of pesticide applied, and 2) the annual concentration of pesticide leaving the field, expressed as the percentage of total mass of pesticide applied per million parts of water or sediment.

For the SMP analysis reported in the main body of this paper, concentration loss estimates for each of the five pesticides were imputed to about 170,000 NRI sample points (representing 13 crops) according to the soil characteristics and proximity to one of the 55 climate stations. These data, together with pesticide use data, comprised the analytical framework for estimating Threshold Exceedence Units on a watershed basis.

Soil Parameters

The number of soil types associated with the NRI sample points is too large to run GLEAMS simulations for each type. Instead, 120 generalized soils were constructed that had a wide range of properties known to be sensitive to pesticide loss. Soils of the NRI database were categorized into specific groups that would behave similar to one of the 120 constructed soils.

The primary selected properties were soil texture and organic matter content. Twelve selected textures (table A-1) were combined with four organic matter contents-0.5 percent, 1.0 percent, 2.0 percent, and 4.0 percent-for surface horizons. The soil contained two horizons. Textures with an asterisk in table A-1 provided four subsurface horizons. Organic matter content of a subsurface horizon was 20 percent of the surface horizon. The subsurface horizon was not coarser in texture than the surface horizon (table A-2). The thirty horizon combinations in table A-2 times four organic matter contents made up 120 soils. The hydrologic group (HG) was assigned according to subsurface texture. Using tables found in the GLEAMS manual and relations in the USDA Soil Survey Handbook, the other soil parameters required by GLEAMS were estimated.

The Curve Number (CN) is a value used in many hydrological models, and is required to run GLEAMS. The CN value is dependent on the soil hydrologic value, soil surface conditions, and the amount of moisture stored in the soil. The CN for each soil (table A-2) was assigned relative to values adapted from Knisel et al⁸ as a basis. The CN was one of the primary factors used to distribute the 120 soils to NRI points.

The model estimates irrigation timing and amounts depending on soil moisture.

Climate Parameters

GLEAMS includes a climate generator that simulates daily rainfall and temperature. Climate for a specific weather station was simulated by the program CLIGEN⁹. The program generates daily weather using the mean, standard deviation, and skew for monthly precipitation, maximum temperature, minimum temperature, and the mean and standard deviation of monthly solar radiation. Two other monthly values required are: 1) the probability of having a wet day after a wet day, and 2) the probability of having a dry day after a wet day. A twenty year record was simulated with the climate generator from a 40-year frequency distribution to produce a distribution of pesticide loss estimates that would reasonably represent most weather conditions.

Fifty-five climatic stations across the United states were chosen from the CLIGEN database. The distribution of the 55 stations provided a uniform spatial coverage for climate input. The location of the 55 stations with latitude and longitude are given in table A-3.

Planting and harvest dates, also shown in table A-3, were estimated for each of the 55 climate stations based on mean and standard deviation of monthly low temperatures from the climatic record. This calculation was accomplished in a spreadsheet by:

1. Subtracting the monthly minimum temperature form the monthly mean temperature.
2. Calculating the daily rate of change of this value over a two month period.
3. Determining month by month if this value crosses 37 degrees Fahrenheit during the month. This date in the spring is the planting date. This date in the fall is the harvesting date, or date growth stops.

Pesticide Application Parameters

GLEAMS results were simulated for the 243 pesticides in the SCS/ARS/CES pesticide properties database¹⁰. The pesticide database is included in the GLEAMS model pesticide parameter editor, including foliar characteristics constructed using the procedure by Willis and McDowell¹¹. The Insect Control Guide¹² and the Weed Control Guide ¹³ were used to define the action of each compound, when applied, how frequently applied, and recommended rates and methods of application.

Pesticide application timing was based on the planting date, harvest date, and purpose. Pesticide application method was based on planting date and purpose. Some herbicides were designated only for pre-plant application and some only for post-plant application. Those herbicides with applications designated as "all methods" were included with the pre-emergent herbicides. Preplant pesticides were simulated with application seven days before planting. Pre-emergent pesticides were simulated with application on the planting date. Post-plant pesticides were simulated with application fourteen days after the planting date. Over the top insecticides, fungicides, and miticides were simulated in 3 repeat applications commencing after one third of the growing season was completed. For example, an insecticide with a recommended repeat application every five days was first applied one-third of the way through the growing season, and then repeated every five days for a total of three applications. Growth regulators were applied after one fourth of the growing season was completed. Defoliants were applied 5 days before harvest date.

Some soil insecticides and nematicides were incorporated in the soil; some surface applied, and some applied over-the-top of foliage. The SCS/ARS/CES pesticide properties database also includes growth regulators and defoliants, both of which are applied on foliage. The insecticides used as foliar applications were applied at label recommended frequency. The Insect Control Guide included recommendations on the frequency of application, i.e. 3-5 days, 5 days, or 7 days. In GLEAMS, insecticides were applied every 3 days for the 3-5 day recommendation, every 5 days for the 5 day recommendation, and every 7 days for the 7 day recommendation.

Estimating the 95th Percentile Concentration

Output from the GLEAMS simulations included annual estimates of:

1. Mass loss in leachate as a percent of the amount applied.
2. Mass loss dissolved in runoff as a percent of the amount applied.
3. Mass loss adsorbed to eroded soil as a percent of the amount applied.
4. Volume of water percolation in centimeters.
5. Volume of water runoff in centimeters.
6. Sediment loss in kilograms.

The mass loss was expressed as a percentage of the amount applied so that loss estimates could be derived for any application rate. This is necessary because, in practice, application rates often vary from the label rates, which were used to derive the loss estimates. This also provides the flexibility to apply different application rates across the country to reflect differences in use, or to simulate the effects of proposed policies on pesticide loss. Similarly, the annual concentration was expressed as a ratio of concentration to the mass of pesticide applied so that, when multiplied by the application rate in kilograms per hectare, the concentration in ppm would be obtained.

The equation for concentration relative to the application rate is thus:

Relative concentration = (proportion pesticide loss)/(million liters of percolate or runoff per hectare)

where:

proportion pesticide loss = (Kg of pesticide loss per hectare)/(Kg of pesticide applied per hectare)

million liters per hectare = (cm of percolate or runoff)*(0.1 billion cm² per hectare)

The actual concentration is then obtained by multiplying by the application rate in Kg/ha:

Concentration in ppm = (relative concentration)*(application rate in Kg/ha) = Kg/million liters = mg/l.

Separate estimates of concentration were obtained for each of the 20 years of simulated weather data. The 20-year distribution of relative concentrations were used to derive a prediction equation for the concentration corresponding to any percentile. Using this function, the concentration corresponding to the 95th percentile was calculated for use in this study. The 95th percentile is the concentration that would be expected to be exceeded only five percent of the time; equivalently, 95 percent of the time the concentration would be expected to be equal to or less than the 95th percentile concentration.

The prediction equation for the relative concentration corresponding to a particular percentile is:

Relative concentration = exp(a+b1x+b2x²+b3*x³+b4*x⁴) - 1

where x is one minus the desired percentile (e.g., x=.05 for the 95th percentile).

The parameter estimation was made using the least squares method after linearizing the function with a log transformation. The selection of the best polynomial for each case was made by a step-wise technique having the maximum R², and at least 0.05 significance level for each parameter as criteria for the selection of the best model in each of the 1,603,800 cases. The transformed variable produced better fits than a linear model. One was added to every variable to avoid zeros for the calculations of the natural logarithm. Models were fitted for cases with five or more pairs of points that produced an R² of 0.85 or greater. No estimate of loss was made for the few cases that did not meet these criteria. Zeros were retained for all cases where there was no pesticide loss.

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