The USDA-Natural Resources Conservation Service , specifically
(National NAPRA Team)
Indiana Dept. of Environmental Management (IDEM)
Office of the Indiana State Chemist (OISC).
The Indiana State Conservationist
Purdue University Cooperative Extension Service
Conservation Technology Information Center
Purdue University Agronomy Department
Purdue University Agricultural and Biological Engineering Department
This model, called NAPRA, uses 30 to 50 years of climate and rainfall data along with soil and crop characteristics specific to the local area to generate a 30 year range of probabilities for pesticide loss using specific practices on that soil.
The approach which the model takes is to run a standard scientific leaching model, GLEAMS, over and over, using each year's weather for the last 30 years, and all the other listed input data, to calculate the chance that a particular pesticide would have leached into groundwater from this soil, had conditions been all the same. This is not an attempt to say that this specific farm produced some contaminant in a certain year. The answers are in probabilities. For example, it might report that for this field one tillage practice with corn has a 2 % chance of atrazine leaching in detectable amounts over the next 20 years, but a different tillage practice with corn would have a 0.05% chance of leaching. The county agent and farmer could compare yields, labor, and equipment costs of the two methods against the modeled probability of contamination when picking a management strategy. The model can calculate probability of exceeding any specified limit for a wide range of agricultural chemicals, making it a very flexible tool.
Pesticides have been found in surface and groundwater nationwide. USDA continues to be very concerned about nonpoint source contamination due to normal agricultural practices. Our goal is to minimize pesticide risk to the environment. To accomplish this, we must help producers to understand all of the environmental risks associated with their pesticide management decisions.
NRCS Three-Tiered Pesticide Environmental Risk Screening
Tier One decision aid support is provided by the NRCS Soil/Pesticide Interaction Screening Procedure (SPISP).
SPISP evaluation is based on pesticide and soil properties that influence environmental fate. It screens pesticide/soil combinations into two classes:
SPISP has two main functions:
Tier Two decision aid support is provided by the automated National Agricultural Pesticide Risk Analysis (NAPRA) process. NAPRA utilizes the USDA-ARS environmental fate computer model GLEAMS (Groundwater Loading Effects of Agricultural Management Systems, Knisel, 1994).
Probabilities of exceeding pesticide mass loadings and concentrations are developed by running 30 to 50 years of climate data. Annual pesticide concentrations in runoff and percolate, as well as yearly four-day maximum percolate and runoff concentrations are developed.
Tier Two NAPRA has been designed to provide further evaluation when it is called for by Tier One SPISP screening. NAPRA results are designed to help quantify the potential environmental benefits of management alternatives.
Tier Two NAPRA analysis is focused on generic management scenarios and how pesticide losses are affected by management under the climatic conditions of the region being studied. Results are probability based and consider both the off-site movement of pesticide and its toxicity to non-target species.
Tier Three analysis is site specific. It can also be performed with NAPRA software. At this Tier Three level, generic (Tier Two) inputs are replaced by individual producers' filing records and field measured soils data. The cost of acquiring this detailed data may limit the implementation of Tier Three analysis. It should be targeted to situations where there is a high level of environmental concern and Tier Two generic scenario analysis indicates a high level of risk for all viable alternatives.
The NRCS three-tiered pesticide risk analysis process is summarized in Table 1.
SPISP and NAPRA technology cannot be effectively implemented without close cooperation with the Cooperative Extension Service, independent crop consultants and agribusiness representatives. Their technical expertise in alternative management practices, efficacy, and economic considerations, will all be essential to the development of sound pesticide environmental risk analysis. They can also provide outlets for the risk analysis information and reinforcement of its value.
Without academic and private sector "buy-in" growers will be reluctant to accept and utilize the new technology. In addition, strong educational programs must accompany this three-tiered environmental risk screening process to prevent its misuse. An example of this process is presented for Lancaster County, Pennsylvania.
Table 2 provides pesticide parameter data and SPISP II ratings for four common corn herbicides. All of these herbicides have physical and biochemical properties that make them relatively mobile in water. For these particular pesticides, differences in Koc and solubility are not as sensitive as soil 1/2 life. Atrazine and Metolachlor have higher mobility ratings than Cyanazine and Alachlor primarily due to differences in their soil 1/2 lives.
Pesticide |
Koc |
Solubility (PPM) |
Soil 1/2 life (days) |
Leaching (LCH)* |
Solution (SOL)* |
Adsorbed (ADS)* |
---|---|---|---|---|---|---|
Atrazine |
100 |
33 |
60 |
1 |
1 |
2 |
Cyanazine |
190 |
170 |
14 |
2 |
2 |
3 |
Alachlor |
170 |
240 |
15 |
2 |
2 |
3 |
Metolachlor |
200 |
530 |
90 |
1 |
1 |
2 |
*1= large, 2= medium, 3= small, 4 = extra small (leaching only)
Soil & Texture Class |
K factor |
O.M. % |
depth of first layer |
Hydro Group |
Leaching (LCH)* |
Solution (SOL)* |
Adsorbed (ADS)* |
---|---|---|---|---|---|---|---|
Chester SIL |
0.32 |
3 |
10 |
B |
2 |
2 |
2 |
Colonie VFSL |
0.24 |
2 |
10 |
A |
1 |
3 |
3 |
Hagerstown SIL |
0.32 |
3 |
10 |
C |
3 |
1 |
1 |
*1= high, 2 = intermediate, 3 = low, 4 = very low (leaching only)
Pesticide |
Chester SIL |
Colonie VFSL |
Hagerstown SIL |
||||||
---|---|---|---|---|---|---|---|---|---|
LCH |
SOL |
ADS |
LCH |
SOL |
ADS |
LCH |
SOL |
ADS |
|
Atrazine |
1/2 |
1/2 |
2/2 |
1/1 |
1/3 |
2/3 |
1/3 |
1/1 |
2/1 |
Cyanazine |
2/2 |
2/2 |
3/2 |
2/1 |
2/3 |
3/3 |
2/3 |
2/1 |
3/1 |
Alachlor |
2/2 |
2/2 |
3/2 |
2/1 |
2/3 |
3/3 |
2/3 |
2/1 |
3/1 |
Metolachlor |
1/2 |
1/2 |
2/2 |
1/1 |
1/3 |
2/3 |
1/3 |
1/1 |
2/1 |
Pesticide |
Chester SIL |
Colonie VFSL |
Hagerstown SIL |
||||||
---|---|---|---|---|---|---|---|---|---|
LCH |
SOL |
ADS |
LCH |
SOL |
ADS |
LCH |
SOL |
ADS |
|
Atrazine |
E |
E |
E |
E |
E |
P |
E |
E |
E |
Cyanazine |
E |
E |
P |
E |
P |
P |
P |
E |
E |
Alachlor |
E |
E |
P |
E |
P |
P |
P |
E |
E |
Metolachlor |
E |
E |
E |
E |
P |
P |
E |
E |
E |
An E rating indicates that there is potential for off-site pesticide movement.
By design, a P rating indicates a low probability of significant offsite pesticide movement with a high degree of confidence. Further environmental risk screening (Tier Two) is not necessary, except in cases where a pesticide is widely applied over an entire watershed and/or its toxicity very high. The mix of P's and E's can vary greatly with different soil and pesticide combinations. However, it is unlikely that a Soil/Pesticide combination will be rated P for both surface runoff and leaching.
Our goal is to refine the SPISP II evaluation with NAPRA. For this example, we will focus on. a single soil, Chester silt loam, and one resource concern: a drinking water reservoir that receives water runoff from a hypothetical field (pesticide losses associated with sediment are treated separately). NAPRA analysis requires the specification of additional variables: climate, slope, slope length, tillage method, tillage direction, amount of residue, pesticide application date, rate and method and pesticide toxicity. Pesticide toxicity's used in this analysis are shown in Table 6.
Pesticide |
Percolate (human) MCL/HA PPB |
Percolate (fish) MATC PPB |
Runoff (Human) MCL/HA PPB |
Runoff (fish) MATC PPB |
Sediment (fish) STV PPB |
---|---|---|---|---|---|
Atrazine |
3 |
848 |
3 |
848 |
84,800 |
Cyanazine |
1 |
288 |
1 |
288 |
54,720 |
Alachlor |
2 |
37 |
2 |
37 |
6,290 |
Metolachlor |
100 |
253 |
100 |
253 |
50,600 |
NAPRA results in Table 7 are based on Lancaster, PA climate, 6% slope for 200 ft, moldboard/disk tillage up and down the slope, no residue and a pre-emergent surface herbicide application on May 1. Pesticide application rates used are the 1993 national averages in pounds of active ingredient per acre: atrazine 1. 16, cyanazine 1. 80, alachlor 2.00 and metolachlor 1.90. These rates are used only for illustrative purposes. Actual comparisons between pesticides must be based on field specific efficacious rates.
Pesticide |
SPISP II Solution Loss Potential |
NAPRA Off-site Movement Potential |
NAPRA Toxicity Exceedence Probability |
---|---|---|---|
Atrazine |
E |
38 % |
38 % |
Cyanazine |
E |
23 % |
47 % |
Alachlor |
E |
25 % |
35 % |
Metolachlor |
E |
79 % |
<2 % |
NAPRA results are expressed as the percent probability of exceeding a specific concentration at the edge of the field or below the bottom of the rootzone, in any one year. NAPRA Off-site Movement Potential is based on the percent probability of each pesticide alternative to exceed the same concentration. In Table 7, the "NAPRA Off-site Movement Potential" values are all based on atrazine's 3 PPB MCL.
The "NAPRA Off-site Movement Potential" is an intermediate result that provides a more refined off-site movement potential than SPISP II, however, it does not consider variation in toxicity between pesticides. The final step in the NAPRA process involves the use of each pesticide's toxicity to arrive at a final NAPRA result.
The "NAPRA Toxicity Exceedence Probability" in Table 7 is based on each pesticide's individual chronic human toxicity (MCL/HA) from Table 6.
Figure 1 illustrates the relationship between off-site movement potential and off-site risk based on values reported in Table 7.
Table 8, "NAPRA Hazard Summary: Annual Pesticide Loss", is based on the same variable inputs as Table 7, and additionally includes multiple management scenarios. Results are reported for percolate, runoff and sediment, and hazards to both humans and fish are considered.
The NAPRA final results are " % Probability of Exceeding " MCL/HA, MATC or STV (the chance of exceeding a specific concentration in any one year) and are specific to the following variables: Slope = 6%, Organic Matter = 3.0 %, Soil= Chester Silt Loam, Climate = Lancaster Pa., Resource= surface water, and Crop = corn, and all of the management practices listed.
Management practices: M/D is moldboard/disk, NT is no-till, RT is reduced tillage, LTD is up and down the slope and PRE is pre-emergent. MCL, HA, MATC and STV are all acronyms for specific toxicity values that are chosen based on the resource concern. Reading down the table through the four blocks of four herbicides, notice how the relative risks (in the columns on the right) change as a function of management alternatives (in the columns on the left).
Table 8 variables in the "Management Alternatives" descriptions are different in their details. Differences in the results section between alternatives can be attributed to these variables.
Differences between individual pesticides under each management option (on Table 8) are linked to each pesticide's physical and biochemical properties, and the application rates and toxicity's we chose for this example. Application rates in this report are 1993 average application rates. Actual application rates are linked to soil type, climate, pest pressure, etc. and vary widely.
In this example, there are minimal off-site pesticide risks through percolation movement and relatively low off-site pesticide risks through sediment movement. There are, however, relatively high off-site pesticide risks in solution runoff, with some scenarios approaching a fifty percent chance of exceeding a pesticide MCL.
Exceedence probabilities are based on concentrations at the edge of the field and the bottom of the rootzone. Exceedence probabilities in a water body, even if it was located at the edge of the field, would likely be considerably smaller. Additional pesticide degradation and dilution may occur as the distance between the field and the water body increases. However, even with the limitation that NAPRA does not model direct effects all the way into a water body, reducing relative hazard at the edge of the field will usually result in similar decreases to hazard within the water body.
Figure 2 illustrates management practice effects on the probability of annual pesticide solution runoff to exceed each pesticide's MCL at the edge of the field. In this example, the best management alternative to protect a drinking water reservoir from solution pesticide losses, is reduced tillage: residue management with shallow herbicide incorporation as the only soil disturbance.
This alternative results in a two-thirds reduction in off-site pesticide risk compared to conventional tillage with pre-emergent surface applied herbicide. In some cases, this reduced tillage alternative may conflict with residue requirements that are in place for erosion control. Erosion can lead to sediment which impacts both water quality and aquatic habitat.
The optimum solution for a particular ecosystem will change with different soils, climates and resource concerns. The goal is to tailor the Resource Management System (RMS) components or Best Management Practices (BMP's) to address all identified environmental risks to ground and surface water, without adversely affecting other resource concerns including soil and air quality.
NAPRA adds local climate, management practices and pesticide toxicity, to the pesticide and soil properties also considered by SPISP II. NAPRA evaluates the effects of alternative management strategies on off-site pesticide risks. The goal is to develop RMS's and/or BMP's that are specific to the variability of our natural resource concerns. To be effective, water quality management practices must be differentially applied based on natural resource sensitivity.
Endquote from National NAPRA Team.
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theller@purdue.edu