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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.

Table A-1. Soil Textures Simulated in GLEAMS
Texture Clay Silt Sand
Sand 5 5 90
Loamy Fine Sand 6 12 82
Fine Sandy Loam * 15 25 60
Loam 20 35 45
Silt Loam < 15% clay 10 70 20
Silt Loam > 15% clay * 20 60 20
Sandy Clay Loam 30 15 55
Clay Loam 35 30 35
Silty Clay Loam * 37 55 8
Silty Clay 45 45 10
Clay < 70% clay 50 20 30
Clay > 70% clay * 75 15 10

 

Table A-2. 120 Basic Soils Represented By Surface And Subsurface Texture
    CN by OM percent
Surface Texture Subsurface Texture HG 0.5 1.0 2.0 4.0
1 Sand Fine Sandy Loam A 66 65 63 58
2 Loamy Fine Sand Fine Sandy Loam A 69 68 67 64
3 Fine Sandy Loam* Fine Sandy Loam A 72 71 70 67
4 Sand Silt Loam < 15% clay B 74 74 72 69
5 Loamy Fine Sand Silt Loam < 15% clay B 78 77 75 72
6 Fine Sandy Loam* Silt Loam < 15% clay B 80 79 78 75
7 Sand Silty Clay Loam C 86 81 80 79
8 Loamy Fine Sand Silty Clay Loam C 88 85 83 82
9 Fine Sandy Loam* Silty Clay Loam C 91 87 86 85
10 Sand Clay > 70% clay D 91 90 89 88
11 Loamy Fine Sand Clay > 70% clay D 92 91 90 89
12 Fine Sandy Loam* Clay > 70% clay D 93 92 91 90
 
13 Loam Silt Loam < 15% clay B 74 74 72 69
14 Silt Loam < 15% clay Silt Loam < 15% clay B 78 77 75 72
15 Silt Loam < 15% clay* Silt Loam < 15% clay B 80 79 78 75
16 Loam Silty Clay Loam C 86 81 80 79
17 Silt Loam < 15% clay Silty Clay Loam C 88 85 83 82
18 Silt Loam < 15% clay* Silty Clay Loam C 91 87 86 85
19 Loam Clay > 70% clay D 91 90 89 88
20 Silt Loam < 15% clay Clay > 70% clay D 92 91 90 89
21 Silt Loam < 15% clay* Clay > 70% clay D 93 92 91 90
 
22 Sandy Clay Loam Silty Clay Loam C 86 81 80 79
23 Clay Loam Silty Clay Loam C 88 85 83 82
24 Silty Clay Loam* Silty Clay Loam C 91 87 86 85
25 Sandy Clay Loam Clay > 70% clay D 91 90 89 88
26 Clay Loam Clay > 70% clay D 92 91 90 89
27 Silty Clay Loam* Clay > 70% clay D 93 92 91 90
 
28 Silty Clay Clay > 70% clay D 91 90 89 88
29 Clay < 70% clay Clay > 70% clay D 92 91 90 89
30 Clay > 70% clay* Clay > 70% clay D 93 92 91 90

 

Table A-3. Climate Station Locations and Plant and Harvest Dates
Station Number Location Date Plant Date Harvest Latitude Longitude
831 Birmingham AL 4/1 10/27 86.75W 33.57N
7435 Saint Johns AZ 6/13 9/16 109.37W 34.50N
9660 Yuma AZ 1/1 12/31 114.60W 32.67N
3734 Jonesboro AR 4/4 10/23 90.70W 35.83N
2319 Death Valley CA 2/3 11/26 116.87W 36.47N
5114 Los Angeles CA 1/1 12/31 118.38W 33.93N
7668 Salinas CA 2/28 11/20 121.60W 36.67N
9684 Willits CA 5/25 9/20 123.33W 39.42N
130 Alamosa CO 5/19 8/28 105.85W 37.43N
3553 Greeley CO 5/13 9/19 104.70W 40.40N
1641 Clermont FL 1/1 12/31 81.78W 28.48N
8703 Tifton GA 3/13 11/11 83.53W 31.47N
6152 Moscow ID 5/31 9/12 117.00W 46.73N
9303 Twin Falls ID 5/25 9/11 114.35W 42.55N
5751 Moline IL 5/3 10/2 90.52W 41.45N
8723 Terre Haute IN 4/26 10/5 87.30W 39.35N
2110 Decorah IA 5/11 9/16 91.80W 43.30N
7147 Rock Rapids IA 5/13 9/20 96.70W 43.43N
3218 Great Bend KS 4/22 10/14 98.77W 38.35N
5078 Lake Charles LA 3/1 11/26 93.22W 30.12N
6937 Presque Isle ME 5/29 9/9 68.00W 46.65N
7325 Rumford ME 5/26 9/15 70.57W 44.52N
9923 Worchester MA 5/10 10/3 71.87W 42.27N
2784 Fife Lake State Park MI 6/5 9/12 85.35W 44.55N
4026 International Falls MN 6/1 9/4 93.38W 48.57N
1094 Brookhaven MS 3/21 11/3 90.43W 31.57N
5671 Moberly MO 4/23 10/12 92.42W 39.47N
2173 Cut Bank MT 6/5 9/1 112.37W 48.60N
3581 Glendive MT 5/19 9/12 104.72W 47.10N
3395 Grand Island NE 5/5 9/29 98.32W 40.97N
507 Austin NV 6/12 9/14 117.07W 39.50N
1515 Carrizozo NM 5/7 10/5 105.88W 33.65N
8383 Syracuse NY 5/11 10/3 76.27W 43.07N
7079 Raleigh NC 4/7 10/27 78.63W 35.78N
5754 McLeod ND 5/22 9/13 97.23W 46.40N
8357 Toledo OH 5/12 9/29 83.80W 41.58N
6638 Okemah OK 4/4 10/28 96.30W 35.43N
1862 Corvallis OR 5/8 10/8 123.28W 44.57N
6426 Paisley OR 6/12 9/2 120.55W 42.70N
3699 Harrisburg PA 4/24 10/14 76.85W 40.22N
6527 Orangeburg SC 3/30 10/26 80.83W 33.47N
4007 Hot Springs SD 5/23 9/12 103.47W 43.43N
4609 Jefferson City TN 4/21 10/11 83.50W 36.12N
786 Big Spring TX 4/1 10/30 101.50W 32.23N
958 Borger TX 4/19 10/19 101.45W 35.65N
2015 Corpus Christi TX 2/2 12/31 97.43W 27.77N
5618 Marshall TX 3/25 11/1 94.37W 32.55N
8910 Temple TX 3/17 11/13 97.35W 31.05N
3611 Hanksville UT 5/11 9/24 110.68W 38.42N
7201 Richmond VA 4/17 10/16 77.33W 37.50N
6624 Port Angeles WA 4/30 10/19 123.43W 48.12N
1570 Charleston WV 4/26 10/9 81.60W 38.37N
6859 Prentice WI 6/7 9/1 90.40W 45.53N
7380 Powell WY 5/23 9/11 108.77W 44.75N
7845 Rock Springs WY 6/7 9/6 109.07W 41.60N

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