Cessna, A.J.

Farenhorst, A.

Leeson, Y.L.

McQueen, R.

Sheedy, C.

Thomas, A.G.

Wolf, T.M.

English

eCommons@USASK

Predicting air-borne droplet drift from agricultural areas

University of Saskatchewan Research Archive

 1  Predicting Air-Borne Droplet Drift from Agricultural Areas   Thomas M. Wolf1, Julia Y. Leeson1, Allan J. Cessna1, A. Gordon Thomas1, Ross McQueen2, Claudia Sheedy3 and Annemieke Farenhorst2  1Agriculture and Agri-Food Canada, Research Centre, Saskatoon, SK; 2University of Manitoba, Winnipeg, MB; 3Agriculture and Agri-Food Canada, Research Centre, Lethbridge, AB   Introduction  Application spray drift represents an important pathway for pesticides to enter the environment, where they can contaminate air, water, and soil, and harm off-target organisms1.  Although drift reduction strategies have been researched and are documented in the scientific literature, the overall impact of their adoption on overall drift losses remains unclear.  This study was initiated to quantify spray drift losses across western Canada by linking spray applicator surveys with empirical spray drift loss data. Under the National Agri-Environmental Health Analysis and Reporting Program (NAHARP)2, Agriculture and Agri-Food Canada is developing a number of agri-environmental indicators. These indicators assess:  i) how well agriculture and agri-food systems manage and conserve natural resources and  ii) how compatible these systems are with natural systems and processes in the broader environment.  As part of this program, the Indicator of Risk of Water Contamination by Pesticides (IROWC-Pest) is being developed to provide information on spatial and temporal changes in the risk of pesticide contamination of ground and surface waters on a national basis.  Two main factors are used to calculate this risk:  i) how much pesticide is applied and  ii) what proportion moves into the surrounding environment.  The proportion of pesticide transported in surface runoff as well as pesticide leached through soil to a 1-m depth is estimated using the Pesticide Root Zone Model3. However, a portion of each pesticide application is lost to the atmosphere as application drift that may subsequently impact water quality via atmospheric deposition. Consequently, the incorporation of the effect of application drift from ground sprayers on the risk of water contamination by pesticides is required.  Objective The objectives of this study were to (a) develop models that can be used to calculate air-borne drift in agricultural areas from survey data in order to improve estimation of the risk of water contamination by pesticides using IROWC-Pest.     2 Methods Survey Data Sprayer configuration data were extracted from the Prairie Weed Management Surveys of spring-seeded cereal, oilseed and pulse crops in 2001 in Alberta, 2002 in Manitoba and 2003 in Saskatchewan.  In each of these surveys, ground sprayer configuration was identified for herbicide applications.  In total, this information for all three surveys was available for 1,563 fields and included nozzle type (from which spray quality was inferred), boom height, travel speed and presence of shrouds or cones.  Categorization within these four model variables resulted in a total of 225 possible unique combinations.  Of these, 73 combinations were present in the survey data.  Model Variables Each of the four variables were divided into several categories as determined by the questionnaire used in the weed surveys.  Spray Quality (Q) had five categories (Very Fine, Fine, Medium, Coarse and Very Coarse).  Boom Height (H) had three categories (30 to 50 cm, 51 to 100 cm and >100 cm).  Travel Speed (T) had five categories (5 to 8 km h-1, 9 to 16 km h-1, 17 to 24 km h-1, 25 to 32 km h-1 and >32 km h-1).  Boom Shrouding (S) had three categories (none, cone and shroud).   Spray Drift Data Spray drift data were taken from trials conducted in Saskatchewan from 1986 to 2004 with “initial airborne drift” as the dependent variable.  “Initial airborne drift” is the amount of airborne drift 5 m downwind of the downwind end of the boom.  For each trial, application parameters were assigned to categories used for survey data.  Additionally, wind speed (W) was included as a variable.  Model Development Simple multiple regression analysis was conducted on the drift data with spray quality, boom height, travel speed, boom shrouding and wind speed as independent variables.    Results Pesticide labels differ in which wind speeds they permit application.  As a result, two example wind speed ranges were used to develop the models, representing low wind speeds (10 to 20 km/h) and  intermediate wind speeds (15 to 25 km/h).  Other models, for wind speed ranges that fall within the 10 to 35 km h-1 boundaries of the original dataset, can be developed in the future.   Low wind speed Model (10 to 20 km h-1, n = 62, r2 = 0.62)  Drift (%) = 0.20W - 1.63Q + 1.57H + 1.03T - 0.63S + 0.35 Equation 1  Intermediate wind speed Model (15 to 25 km h-1, n = 53, r2 = 0.59)  Drift (%) = 0.10W - 1.78Q + 2.12H + 1.06T - 1.09S + 1.86  Equation 2  3  The models accounted for 62 and 59% of the spray drift variation in the original dataset for the low and medium wind speed models, respectively.  Based on the p-values for the estimated coefficients, spray quality was the most important variable in predicting the percent drift during pesticide applications using ground sprayers at either low or intermediate wind speeds.  For the low wind speed model, wind speed and travel speeds also had significant effects.  For the intermediate wind speed model, the presence of shrouds and the travel speed had significant effects.    For both wind speeds, the relationship between observed and predicted drift was linear, with a tendency to under-estimate drift (Figures 1 and 2).    Model Application – An Example Predicted airborne drift (as a percent of the applied amount) was calculated based on application data reported in the Prairie Weed Management Surveys.  These data were used to predict the drift in each intensively farmed ecoregion4 within each province.  Ecodistricts with survey data but relatively low cultivated area were combined with adjacent ecoregions5.  Maps were produced with ArcView GIS 3.2 from Environmental Systems Research Institute, Inc. based on ecodistict and ecoregion boundaries available from Agriculture and Agri-Food Canada6.   Using the intermediate wind speed model, the predicted drift at the time the surveys were conducted is illustrated in Map 1.  Using the low wind speed model, predicted drift was lower with a similar distribution, as expected (map not shown).  0 2 4 6 8 10 12Observed airborne drift (% of applied)024681012Predicted airborne drift (% of applied)Predicted = 1.204 + 0.618 * ObservedCorrelation: r = 0.786Figure 2.  Relationship between observed and predicted drift values for medium wind speed model 0 2 4 6 8 10 12Observed airborne drift (% of applied)024681012Predicted airborne drift (% of applied)Predicted = 1.398 + 0.591 * ObservedCorrelation: r = 0.769Figure 1.  Relationship between observed and predicted drift values for low wind speed model  4  The high risk of drift for the Peace River Ecoregion in northwest Alberta was related to the proportionately higher use of Very Fine and Fine spray qualities in that region (Map 2).    Discussion These models demonstrate how experimental spray drift data from ground application of herbicides and analogous sprayer configuration survey data can be integrated to predict pesticide application drift. The robustness of these models will allow the quantification of air-borne drift with sprayer configuration data sets from areas other than the Canadian Prairies. In addition, these models can be used to predict the impact of the adoption of new application technology (nozzle type and use of shrouds or cones) and changes in practices (boom height, travel speed, and wind speed at spraying time) by regulators, policy makers, and producers on 3.7 - 4.0 % Drift 1.9 - 2.1 2.2 - 2.4 2.8 - 3.0 2.5 - 2.7 Map 1. Predicted airborne drift with 20 km h-1 wind Map 2. Use of Very Fine and Fine spray quality nozzle types 35.0 - 36.0 % Area 1.0 - 3.0 4.0 - 6.0 10.0 - 12.0 7.0 - 9.0  5 the magnitude of application drift. In Canada, greater than 90% of agricultural pesticides are applied with ground sprayers. Pesticides that enter the atmosphere via application drift increase the risk of water contamination through the processes of wet (precipitation) and dry (particulate) atmospheric deposition that remove pesticides from the atmosphere. Consequently, these models, which predict application drift at low and intermediate wind speeds, will be incorporated into the IROWC-Pest agri-environmental indicator to improve estimates of risk of water contamination by agricultural pesticides. This will ensure that estimates of risk of water contamination by pesticides using IROWC-Pest will also be responsive to changes in application technology and spray practices. A similar methodology could be employed to estimate the magnitude of drift from aerial, orchard and vineyard applications of pesticides.  References 1. Cessna, A.J., Wolf, T.M., Stephenson, G.R., and Brown, R.B. 2005. Pesticide Movement to Field Margins: Routes, Impacts and Mitigation. Pages 69-112 in A. G. Thomas, ed. Field Boundary Habitats: Implications for Weed, Insect, and Disease Management. Topics in Canadian Weed Science, Volume 1. Sainte-Anne-de-Bellevue, Québec: Canadian Weed Science Society - Société canadienne de malherbologie. 2. Lefebvre, A., Eilers, W. and Chunn, B. (eds.). 2005. Environmental Sustainability of Canadian Agriculture: Agri-Environmental Indicator Report Series - Report #2. Agriculture and Agri-Food Canada, Ottawa, Ontario. 3. Carsel, R.F., Imhoff, J.C., Hummel, P.R., Cheplick, J.M. and Donigan, A.S.J. 1998. PRZM-3. A model for predicting pesticide and nitrogen fate in the crop root and unsaturated soil zones: User manual for release 3.0, National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Athens, Georgia, U.S.A. [Online] Available at http://www.epa.gov/ceampubl/gwater/przm3/index.htm 4. Ecological Stratification Working Group.  1995.  A National Ecological Framework for Canada.  Agriculture and Agri-Food Canada, Research Branch, Centre for Land and Biological Resources Research and Environment Canada, State of the Environment Directorate, Ecozone Analysis Branch, Ottawa/Hull.  Report and national map at 1:7 500 000 scale. 5. Leeson, J. Y., A. G. Thomas, L. M. Hall, C. A. Brenzil, T. Andrews, K. R. Brown and R. C. Van Acker. 2005. Prairie weed surveys of cereal, oilseed and pulse crops from the 1970s to the 2000s.  Weed Survey Series Publication 05-1CD.  Agriculture and Agri-Food Canada, Saskatoon Research Centre, Saskatoon, Saskatchewan. 6. Agriculture and Agri-Food Canada.  2003.  A national ecological framework for Canada: GIS data.  [Online] Available: http://sis.agr.gc.ca/cansis/nsdb/ecostrat/gis_data.html  [25 November 2003].   

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Predicting air-borne droplet drift from agricultural areas

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