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Drift from the use of handheld knapsack pesticide sprayers in Boyacá (Colombian Andes) Glenda García-Santos, Giuseppe Feola, David Nuyttens, and Jaime Diaz J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.5b03772 • Publication Date (Web): 19 Oct 2015 Downloaded from http://pubs.acs.org on October 25, 2015

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Journal of Agricultural and Food Chemistry

1

Drift from the use of handheld knapsack pesticide sprayers in Boyacá

2

(Colombian Andes)

3 4

Glenda García-Santos1,2 *, Giuseppe Feola1,3, David Nuyttens 4, Jaime Diaz5

5 6

1

Department of Geography, University of Zurich, Switzerland

7

2

Department of Geography, Alpen-Adria-University Klagenfurt, Austria (current

8

address)

9

3

Department of Geography and Environmental Science, University of Reading,

10

United Kingdom

11

4

12

Agricultural and Fisheries Research (ILVO), Belgium

13

5

14

Boyacá, Colombia

Agricultural Engineering, Technology and Food Science Unit, Institute for

Department of Sanitary and Environmental Engineering, Universidad de

15 16

* Corresponding author at: Department of Geography, Alpen-Adria-University

17

Klagenfurt, Universitaetstrasse 65-67, 9020, Austria. E-mail address:

18

[email protected] (G. García-Santos)

19 20

1 ACS Paragon Plus Environment


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Abstract

23

Offsite pesticide losses in tropical mountainous regions have been little studied.

24

One example is measuring pesticide drift soil deposition, which can support

25

pesticide risk assessment for surface water, soil, bystanders, off target plants

26

and fauna. This is considered a serious gap, given the evidence of pesticide-

27

related poisoning in those regions. Empirical data of drift deposition of a

28

pesticide surrogate, Uranine tracer, within one of the highest potato producing

29

regions in Colombia, characterized by small plots and mountain orography, is

30

presented. High drift values encountered in our study reflect the actual spray

31

conditions using handled knapsack sprayers. Comparison between measured

32

and predicted drift values using three existing empirical equations showed

33

important underestimation. However, after their optimization based on

34

measured drift information, the equations showed a strong predictive power for

35

this study area and the study conditions. The most suitable curve to assess

36

mean relative drift was the IMAG calculator after optimization.

37 38

Keywords: drift curve, knapsack sprayer, “Manual Técnico Andino”, developing

39

countries, mountain region, Andean region, tropical soils, potato production


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40 41

Introduction

42

Tropical mountainous regions in developing countries are often neglected in

43

research and policy, but represent key areas to be considered, if a sustainable

44

agricultural and rural development is to be promoted.1 These mountain

45

ecosystems are fragile sources of ecosystem services to communities living in

46

the lowlands (e.g. water resources) and are often directly threatened by human

47

activities, e.g. land use competition between forest and agriculture.2-3 Most

48

importantly, despite often being geographically marginal, tropical mountainous

49

regions are relevant both in terms of contribution to the agricultural production

50

and for sustaining a significant part of the population’s livelihoods, despite low

51

productivity.4 This is observed, for example, in the Andean countries (Peru,

52

Ecuador, Bolivia and Colombia). In the highlands of Colombia, for instance, the

53

mountainous department of Boyacá contributes to about 26% of the national

54

potato,5 which relies mainly on smallholders (more than the 95% of the

55

workforce), who are tenants of more than the 56% of the potato cultivated land,

56

and supply 45% of the regional production .5

57

Due to their relative marginality and low degree of mechanization, agricultural

58

research has only had a limited impact on the communities and crops in those

59

regions, which has also been recognised in the context of the so called Green

60

Revolution.6 One of the issues yet to be properly addressed is that of measuring

61

pesticide drift in typical field conditions using handheld application techniques,

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knapsack sprayers, and developing accurate pesticide drift prediction tools,

63

which can support pesticide risk assessment. A recent review on drift mass

64

balance in pesticide application omitted the handheld techniques and focused



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on boom sprayers, possibly due to the lack of knapsack sprayer studies. In this

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regard, few drift studies based on knapsack technique were found by the

67

authors .8-17 This is considered a serious gap, given the evidence of significant

68

presence of pesticide-related environmental and health risks e.g. in Ecuador,18

69

Bolivia19 and Colombia .14, 20-23

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Pesticide risk after boom sprayers is commonly evaluated by empirical or

71

probabilistic approaches.24 Several empirical curves or models have been

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developed for this technique in, among others, Germany,25 The Netherlands,26

73

and Belgium.27,

74

regulatory bodies in the respective countries.

75

The Ganzelmeier et al.25 and Holterman and van de Zande26 equations

76

estimate drift deposition as a function of the distance from the field border and

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crop type, whereas Nuyttens et al.28,

78

equation for a reference spray boom application including drift distance as well

79

as meteorological conditions (wind speed, temperature and humidity).

80

In Colombia, drift is included in the assessment of risk to terrestrial ecosystems,

81

specifically to earthworms, as part of the control and registration process

82

applicable to agrochemicals. Drift is evaluated through table-based values

83

(worst case scenario) given by the Andean technical handbook/manual,

84

“Manual técnico Andino”, in function of distance from the applied field border.30

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The origin of the table-based values is not quoted and is not known by the

86

authors. However, values seem to be derived by the Ganzelmeier et al.

87

equation 25.

88

One of the major advantages of empirical models compared with mechanistic

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models is that they are easy to use and limited data are needed,31 which makes

28, 29

Most of these curves were designed to be used by

29

developed a non-linear drift prediction


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90

them especially appealing for pesticide risk assessment purposes in developing

91

countries, where low data availability is often an issue. A major disadvantage of

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empirical models is that they produce single drift values and the evaluation of

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probability is usually insufficiently considered.24 In addition, evidence exists that

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empirical models do not perform well when used in contexts different from those

95

they were developed in.32 It is therefore sensible to verify whether existing table-

96

based values and curves are appropriate for use elsewhere.

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In the Andean region, pesticides are mainly applied using knapsack sprayers,

98

and orography and micro-meteorology are complex. 90% of the farmers are

99

smallholders, who mainly apply pesticides using knapsack sprayers with old

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and sometimes obstructed nozzles .16 In this region, there is poor maintenance

101

of equipment, no monitoring of meteorological conditions and no respect for

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buffer zones (observed by the authors).

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It is expected that the use of existing drift curves, designed under European

104

conditions, might produce an important discrepancy between the actual and the

105

expected pesticide drift deposition. In addition, the risk for rural residents might

106

also be inaccurately estimated .16,

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assessment based on these drift models might be seriously flawed. Local scale

108

studies will allow the creation of datasets from which the future distribution

109

functions can be interpolated

110

pesticide risk more realistically.

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The framework of this study is the project entitled “Reducing human health and

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environmental risks from pesticide use: Integrating decision-making with spatial

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risk assessment models: The case of Vereda la Hoya, Colombia”.34 Under the

114

umbrella of the project our working hypothesis was that there would be

24

33

As a consequence, pesticide risk

and used to assess environmental and human



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115

differences between measured and predicted drift depositions using existing

116

drift curves due to important differences in environmental and meteorological

117

conditions encountered in the studied area (agricultural and anthropogenic

118

effects kept constant) and differences in spray application practices. The

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objectives of this study were i) to present for the first time an extended drift

120

dataset in marginal potato production located in the second biggest potato

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production region of Colombia and ii) to assess the suitability of the existing

122

empirical drift worst case scenario curve suggested for the Andean region and

123

of the empirical mean drift curves used in Europe for risk assessment.

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Materials and methods

125

Study area and sprayed plot. The study area is within the potato crop

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producing region of Boyacá, the second highest potato production area after

127

Nariño in Colombia.60 The experimental catchment and plot selected to

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measure off-target drift deposition on downwind soil is in the district La Hoya

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community (Tunja, Boyacá). La Hoya basin covers an area of 840 ha in the

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altitude range between 2600 and ~3000 m a.s.l. and mainly features small

131

agricultural plots ( 100 m) and outside of the sprayed plot.

152

153

Spraying technique. For all the 25 spray replications, a farmer from La Hoya

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sprayed the plot using the conventional application technique in low-

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mechanised potato production, i.e. a knapsack sprayer equipped with one high

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discharge hollow cone nozzle at a height above the canopy between 0.2 – 0.35

157

m, with a solution of a green fluorescent tracer dye (Uranine) (further details in

158

the next section). The nozzle used in the study was selected from among the

159

most used in potato production by 10 local farmers. Droplet size spectrum

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information, missing in García-Santos et al.,35 was measured by the Institute for

161

Agricultural and Fisheries Research (ILVO) in Belgium, using a Phase Doppler

162

Particle Analyser (PDPA) laser-based measuring set-up and protocol as

163

described by Nuyttens et al.38, 39 at 2.75 bar and at a distance of 0.40 m below

164

the nozzle. Other relevant measured sprayer related parameters as described

37

, 16 HAPs were



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in García-Santos et al.35 were distance of nozzle to edge of crop (~1 m), liquid

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pressure (2.75 bar), average spray volume (530 L ha-1), application speed of

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the farmer (100 min ha-1), tracer concentration (7 mg in a 20 L tank), number of

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20 L tanks per trial (n= 1), application time (15 minutes), and nozzle diameter (1

169

mm; no manipulation was observed).

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Sprayed solution. Uranine (sodium fluorescein, C20H10Na2O5) was used to

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quantify drift by dry deposition on the horizontal and vertical collectors as

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surrogate of agrochemicals. Though there might be an effect of formulation on

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pesticide drift, this effect is limited compared with the effect of application

174

technique and environmental conditions. It is therefore generally accepted to

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use artificial tracers instead of pesticides for spray drift studies. Uranine is

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generally accepted as a tracer and was used before by many others.

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tracer is highly soluble. If released into the atmosphere, Uranine has an

178

estimated vapour pressure of 4.0 10-14 mm Hg at 25 oC, which indicates

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Uranine exist solely in the particulate-phase in the atmosphere and may be

180

removed by wet and dry deposition. Dry deposition on HAPs of Uranine is not

181

expected to volatilize based upon its low vapour pressure (Hazardous

182

Substances Data Bank, HSDB). This tracer is inexpensive, toxicologically safe

183

(HSDB) and has an extremely low detection limit of ~0.005 µg L-1. Uranine was

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mixed with 20 L of water in a tank. Samples of the solution in the tank were

185

collected before spraying, to measure the initial tracer concentration per trial.

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The HAPs were collected from the field and the vertical mesh immediately after

187

the trial, dried in an oven, placed in plastic bags and stored in a dark place until

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their analysis. Details of the tracer extraction protocol, recovery (99%) and drift

189

calculation are described in detail in García-Santos et al.35

35, 42

This


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Meteorological conditions and border effect. Drift experiments were

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conducted in 2008 and 2009 out of the rainy season, when farmers are more

192

likely to spray agrochemicals. The area has prevailing south east winds with an

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average wind speed of 1.8 ± 1.39 m s-1 and a maximum of 7.6 m s-1 (1 year

194

meteorological

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meteorological station, Davis Vantage Pro-2, was installed at 10 m distance

196

from the experimental plot to measure the meteorological variables during the

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trials. Sensors such as a thermo-hygrometer (± 3% accuracy or error), a wind

198

speed sensor (± 5% accuracy), a rain gauge (± 4% accuracy) and a weather

199

vane (± 7o accuracy) were located in a mast 3 m above ground. The variables

200

considered in the study were ambient temperature (T, oC), relative humidity

201

(RH, %), dew point (Td, oC), vapour pressure deficit (VPD, kPa), wind speed (u,

202

m s-1) and wind direction (wd, o). All variables were registered every 1 minute

203

and averaged per trial (average trial duration 12 minutes). Relative drift

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measurements at 7 and 11 m distances from the corner of the plot were studied

205

statistically (Kolmogorov-Smirnov Z test) to evaluate the existence of a possible

206

border effect on the results.

207

Spray drift deposition curves. We selected and tested three accepted drift

208

curve types. One of these equations is derived from the table base drift

209

information to assess risk to earthworms, suggested by the Andean technical

210

manual (“Manual técnico Andino”) and used for the Andean region (no

211

information on field datasets were found). The other two are used in different

212

regions of Europe (more details below). Mean values and 90th percentile values

213

for each drift distance and wind speed from our dataset (25 trials), were

214

compared to the predicted values. The goodness of fit of measured data was

measurements;

data

from

this

study).

An

automatic



Page 10 of 34

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studied through the coefficient of determination (r2), predicting efficiency (EF) as

216

the Nash–Sutcliffe model efficiency coefficient43 and the root mean square error

217

(RMSE). EF and r2 close to 1 and low RMSE express a good agreement

218

between measured and predicted data.

219

The first equation tested predicts a worst case scenario of drift as a function of

220

distance and is used to estimate terrestrial risk for earthworms in a tiered

221

approach. We obtained this equation from the table-based values in the Andean

222

technical handbook/manual for pesticide registration and control in the Andean

223

region (Annex 7 E- Table 1 in Gaceta Oficial30). The origin of the table-based

224

values or reference to previous studies are not mentioned in the official

225

document. We observed that the derived equation from the table follows the

226

same mathematical expression as that developed by Ganzelmeier et al.25 in

227

Germany i.e. a single exponential function

228

%D = a x b

229

where %D is relative drift expressed as the percentage of the applied dose and

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x (m) is the distance from the border to a point downwind from the treated field,

231

and a and b are 3.7839 and -1.1219, respectively. Note that the equation in

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Ganzelmeier et al.25 had the following coefficients 2.7593 (a) and -0.9778 (b).

233

The second equation tested, IMAG drift calculator (v 1.1), was developed in the

234

Netherlands26 for conventional boom sprayers in function of distance as the

235

sum of two exponential functions,

236

%D = a e-xb + c e-xd

237

where x (m) is the downwind distance from the last nozzle to field edge and the

238

values of a (%), b (m-1), c (%) and d (m-1) depend on the crop and application

(1)

(2)


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technique. In case of an extensive crop (0.5 m height) and conventional

240

standard horizontal boom sprayers (1 m height), a, b, c, d are 114, 1.74, 1.29

241

and 0.139, respectively. For bare soil ( 0.5 m, so it might be a combination of other factors, like for

411

example how well the lance is directed downwards (not investigated here).

412

Some recommendations to decrease drift while using a knapsack sprayer point



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out to lower the sprayer tip and use of drift shield attached to the lance of the

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knapsack sprayer,17 not observed in the study area.

415

We found that meteorological variables alone explained 45% of the measured

416

drift variance. This low prediction power is explained by the complexity of drift

417

as measured under field conditions, also observed by Snelder et al..13 Though

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high correlations were found by Nuyttens et al.28,

419

this is explained because of the use of wind tunnels. Thereby, drift potential is

420

found to increase with wind speed e.g. double spray drift deposition is found

421

with an increase from 3 m s-1 to 5 m s-1.53, 54 In our study and despite the low

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prediction power of wind speed, very high wind speed increased the amount of

423

drift (Fig. 3). The studied wind conditions do not follow recommendations by

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good agricultural practices55 (1 – 1.8 m s-1) to minimize the potential for drift, but

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rather represent higher wind velocities, actual conditions, in which farmers

426

eventually or accidentally might spray. This might happen often in marginal

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mountainous regions.36 Low relative humidity and higher temperatures increase

428

the potential of drift.44,

429

humidity data do not represent a sufficiently wide range to make a sound

430

analysis. Nevertheless, we observed that trials with a higher temperature

431

combined with low relative humidity tended to yield higher drift.

432

To conclude, factors like the use of high pressure cone nozzles producing a

433

high volume of small droplets, high distance between the nozzle and crop

434

canopy and high wind speed were found to affect drift positively. Other factors

435

like farmers’ mode of spraying were not considered in our study i.e. only one

436

farmer sprayed, but it is contemplated the use of different farmers to add

437

variability to the presented results as also observed in rice production studies.13

54, 56

29, 44

and Holterman et al.,53

However, our measured temperature and relative


Page 19 of 34


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Performance of the drift curves. Drift deposition on soil was estimated

439

according to the table-based information provided by the Andean technical

440

manual (“Manual técnico Andino”). The worst drift scenario at 1 m distance from

441

the field edge underestimated the measured data (90th percentile) by 74%

442

(derived from Table 2, Andean technical manual). These results need to be

443

treated with caution because table-based drift values might correspond to boom

444

spray methods instead of knapsack spraying. Information on spray technique,

445

original data, geographical information of the study, theoretical background of

446

the table-based drift values are not mentioned in the Andean technical manual.

447

As mentioned before, we observed that the table information follows the

448

Ganzelmeier et al.25 equation. In this respect, in the Catania plain (Italy)57 as

449

well as in Adana-Turkey58 a low performance of the equation as applied to

450

agricultural areas was found. However, its prediction power significantly

451

improved after optimization of the coefficients i.e. overestimation of measured

452

drift of 3%. An optimized Andean curve as in Ganzelmeier et al.25, may be used

453

to assess risk characterisation using conservative data (realistic worst case

454

condition) under good agricultural practices (low wind speed, < 2.5 m s-1) for the

455

highlands of marginal potato production in Boyacá using knapsack sprayers.

456

The IMAG calculator and Belgian curve44 had low performance. Low

457

performance of the IMAG calculator was also found by others.58 One could

458

explain this because they were designed for boom sprayers. Another source of

459

error might be due to the limited trial number (n = 25), although, this number

460

greatly varies from 2,59 1235 to hundreds.25 We found that optimized curves by

461

IMAG calculator and by Nuyttens et al.44 performed well. The best prediction



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came from the optimized IMAG calculator in function of distance-only with an

463

overestimation of measured drift of 9% at the first meter from the border edge.

464

We found that the inclusion of meteorological variables did not result in a higher

465

predictive power of the measured drift values. This might also reflect the lower

466

correlations encountered before. However, the curve in function of wind speed

467

and other meteorological conditions by Nuyttens et al.44 performed well and

468

therefore it may be used to assess actual drift deposition under different wind

469

speeds up to 6.5 m s-1 (measured in our study) and drift risks.

470

The results showed that the existing empirical equations derived from boom

471

sprayer technique were insufficient to predict handled spray drift for tropical

472

mountainous environments. Existing equations generally underestimated the

473

spray drift and hence pesticide risk would also be underestimated. However, the

474

existing equations could be optimised with application of relevant newly

475

collected data from the local region i.e. the optimized IMAG calculator was

476

suitable to predict drift deposition and the optimized curve derived from the

477

Andean technical manual is suggested to assess the highest tier for risk

478

assessment. In that regard, these equations with revised coefficients may have

479

greater predictive capacity for broader tropical mountainous regions, although

480

this would need to be confirmed with further study.

481

Acknowledgement. This research was funded by the Swiss National Science

482

Foundation (SNSF) through the project number 110807. We thank Prof. C.R.

483

Binder and Prof. H. Barnard for their revision of a previous version of the

484

manuscript; the students D. Scheiben, S. Karrer, P. Muñoz, F. Pui and the

485

farmer who collaborated during the drift trials. Writing of the manuscript was

486

possible thanks to A. Santos-Martin and M. Boudet-Garcia.


Page 21 of 34


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Supporting information description.

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Table 1 includes the Kolmogorov-Smirnov Z test of the difference of relative drift

489

expressed as a percentage of the applied dose (%) at 7 m and 11 m distance

490

from the field corner. Exact significance: *0.01 level.

491

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679 Figure captions Figure 1. Experimental set up to measure drift deposition on soil from edge border up to 20 m from the sprayed field (right site) and in one vertical transect at 1 and 2 m height at 8 m distance from the field border (□: Horizontal High Absorbent Paper (HAP), ∆: Vertical HAPs). Figure 2. Relative drift deposition expressed as the percentage of the applied dose from the field border at 0 m up to 20 m distance after 25 trials (n= 134 samples) and mean relative drift calculated with the mean of the points (wind speed between 1 and 6.5 m s-1). Figure 3. Mean relative drift horizontal deposition from the field border at 0 m up to 20 m distance at different wind speed. Vertical lines denote one standard deviation from the mean. Inside figure: Airborne relative drift (%) at 1 and 2 m height from the ground and 8 m distance from the treated field as a function of wind speed (m s-1) measured at the nozzle height. Vertical lines represent one standard deviation from the mean.



Page 30 of 34

Tables Table 1. Left: Measured mean relative drift for wind speed 1 -2.5 m s-1 and 3 – 6.5 m s-1 from the field edge to 20 m distance, the 90th percentile drift values and the mean values of the meteorological conditions during the spray trials (T: temperature; Td: dew point; RH: relative humidity (%); u: wind speed; VPD: vapour pressure deficit). Right: Calculated mean drift after the Andean curve, the IMAG calculator and the curve by Nuyttens et al. (2006). Table on the left shows drift values using the original equations and table on the right shows drift values after optimization of the coefficients. Below part shows performance of the curves as r2 (coefficient of determination), RMSE (root mean squared error) and EF (efficiency) and curve coefficients.

VPD (kPa)

1 - 2,5 m s -1 61,0 ± 22,2 11,3 -10,9 ± 4,5 6,8 ± 4,0 4,0 ± 2,3 2,7 ± 2,1 2,1 ± 2,2 1,6 ± 0,9 0,7 ± 0,8 0,6 ± 0,7 0,4 ± 0,2 0,2 ± 0,4 0,2 ± 0,4 0,2 ± 0,2 14,4 ± 0,2 13,1 ± 0,7 91,9 ± 4,8 2,5 ± 0,6 0,1 ± 0,1

82,6 11,3 14,6 10,8 5,8 4,7 4,3 2,4 1,5 1,2 0,6 0,6 0,6 0,4

3 - 6,5 m s -1 73,3 ± 26,9 38,2 ± 15,5 15,6 ± 9,0 9,4 ± 5,1 6,5 ± 4,0 4,8 ± 3,5 2,9 ± 2,1 3,7 ± 1,6 1,3 ± 1,1 2,1 ± 0,5 1,1 ± 0,8 0,6 ± 0,4 0,4 ± 0,3 0,5 ± 0,6 15,0 ± 0,1 12,3 -84,1 ± 1,3 5,0 ± 0,39 0,30

--

Relative drift (%) 90th precentile

Calculated drift (original curve coefficients)

Calculated drift (optimized curve coefficients)

Andean curve (90th IMAG percentile) (potato)

Andean curve (90th percentile)

IMAG

f(x)

f(x)

--15,0 8,6 6,2 4,9 4,1 3,4 2,8 2,5 2,2 1,8 1,6 1,4

--11,8 7,6 4,8 3,1 2,0 1,0 0,5 0,3 0,1 0,0 0,0 0,0

0,97 0,93 2,0

1,00 0,98 0,3

0,97 0,93 1,1

15 -0,8

29,00 -6,82 18,35 -0,44

-1,06 -1210,96 -71,60 3,74 12,57 68,77

IMAG Nuyttens et al. (bare soil) (2006)

f(x)

f(x)

f(x)

--3,8 1,7 1,1 0,8 0,6 0,5 0,4 0,3 0,3 0,2 0,2 0,1

--21,1 4,5 1,5 0,8 0,7 0,5 0,4 0,3 0,3 0,2 0,1 0,1

--6,9 2,4 1,3 1,0 0,8 0,7 0,5 0,4 0,4 0,2 0,2 0,1

2

0,92 -0,13 10,9

0,80 -0,04 0,05

0,91 0,60 4,5

0,98 0,13 6,2

a b c d e d

3,7839 -1,1219

114,00 1,74 1,29 0,139

25,00 1,50 1,54 0,133

-1,03 48,89 1,08 0,5 -0,45 -1,41

100,0 55,5 22,2 16,3 12,1 7,3 5,9 4,8 3,0 2,5 1,8 1,0 0,7 1,1 r EF RSME

f(x , u, T, Td, RH) 2,5 m s -1 5 m s -1 ----3,1 5,6 1,5 2,8 1,0 1,8 0,8 1,4 0,6 1,1 0,5 0,8 0,37 0,66 0,31 0,55 0,27 0,48 0,21 0,37 0,17 0,30 0,14 0,26 0,97 0,16 9,5

Optimized coefficients

0 0,5 1 2 3 4 5 6,5 8 9,5 11 14 17 20 o T ( C) o Td ( C) RH (%) -1 u (m s )

Measured drift Relative drift (%)

Performance

Meteorological variables

x (distance, m)

Relative drift (%) 90th precentile

Coefficients

Measured drift Relative drift (%)

Nuyttens et al. (2006) f(x , u, T, Td, RH) 2,5 m s -1 5 m s -1 ----9,6 18,9 4,6 9,1 3,0 5,9 2,2 4,3 1,7 3,4 1,3 2,6 1,1 2,1 0,9 1,7 0,8 1,5 0,6 1,1 0,5 0,9 0,4 0,8 0,96 0,93 1,0

30

ACS Paragon Plus Environment

Page 31 of 34


Figure graphics Figure 1. Experimental set up to measure drift deposition on soil from edge border up to 20 m from the sprayed field (right site) and in one vertical transect at 1 and 2 m height at 8 m distance from the field border (□: Horizontal High Absorbent Paper (HAP), ∆: Vertical HAPs).



Page 32 of 34

Figure 2. Relative drift deposition expressed as the percentage of the applied dose from the field border at 0 m up to 20 m distance after 25 trials (n= 134 samples) and mean relative drift calculated with the mean of the points (wind speed between 1 and 6.5 m s-1).


Page 33 of 34


Figure 3. Mean relative drift horizontal deposition from the field border at 0 m up to 20 m distance at different wind speed. Vertical lines denote one standard deviation from the mean. Inside figure: Airborne relative drift (%) at 1 and 2 m height from the ground and 8 m distance from the treated field as a function of wind speed (m s-1) measured at the nozzle height. Vertical lines represent one standard deviation from the mean.



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