Comparison and evaluation of eight pesticide ...

Agriculture, Ecosystems and Environment 90 (2002) 177–187

Comparison and evaluation of eight pesticide environmental risk indicators developed in Europe and recommendations for future use J. Reus a , P. Leendertse a,∗ , C. Bockstaller b , I. Fomsgaard c , V. Gutsche d , K. Lewis e , C. Nilsson f , L. Pussemier g , M. Trevisan h , H. van der Werf i , F. Alfarroba j , S. Blümel k , J. Isart l , D. McGrath m , T. Seppälä n a

Centre for Agriculture and Environment, Utrecht, The Netherlands Association pour la Relance Agronomique en Alsace, Colmar, France c Danish Institute of Agricultural Sciences, Slagelse, Denmark d BBA Institute of Technology Assessment in Plant Protection, Kleinmachnow, Germany e University of Hertfordshire, Hatfield, UK f Swedish University of Agricultural Sciences, Alnarp, Sweden g Veterinary and Agrochemical Research Centre, Tervuren, Belgium h Università Cattolica del Sacro Cuore, Piacenza, Italy i Institut National de la Recherche Agronomique, Rennes, France j Ministério da Agricultura do Desenvolvimento Rural e das Pescas, Oeiras, Portugal k Bundesamt und Forschungszentrum für Landwirtschaft, Wien, Austria l Centre of Research and Development, Barcelona, Spain m TEAGASC, Johnstown Castle Research Centre, Wexford, Ireland n Finnish Environment Institute, Helsinki, Finland b

Received 3 May 2000; received in revised form 21 February 2001; accepted 22 February 2001

Abstract Information is required on pesticide risk indicators used in Europe in order to optimise their use to reduce the environmental impact of pesticides. The present study was performed to compare and evaluate eight pesticide risk indicators developed in Europe and to give recommendations for further use and harmonisation of these risk indicators. This paper is based on the results of the CAPER project (concerted action on pesticide risk indicators). First the indicators and their methodologies were characterised. The eight indicators differed with regard to the compartments, effects and methods used to calculate environmental impact scores. Subsequently, the environmental risk of 15 individual pesticide applications was assessed with the eight indicators and the outcomes were compared. The rankings of the 15 applications by the indicators differed when the score for the environment as a whole was concerned. This was caused by the large variety in environmental compartments taken into account by the indicators. However, for the individual compartments surface water, groundwater and soil the indicators gave similar rankings of the 15 pesticide applications. The ranking based on the indicator ‘kilograms of active ingredient’ did not correlate with most of the rankings by the risk indicators. Indicators are regarded as useful tools for the reduction of

∗ Corresponding author. Tel.: +31-30-244-1301; fax: +31-30-244-1318. E-mail address: [email protected] (P. Leendertse).

0167-8809/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 8 8 0 9 ( 0 1 ) 0 0 1 9 7 - 9

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J. Reus et al. / Agriculture, Ecosystems and Environment 90 (2002) 177–187

the environmental impact of pesticides. The development of a scientific framework for indicators in farmer decision tools is needed to harmonise and increase the use of pesticide risk indicators in the European Union. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Pesticides; Risk indicators; Environment; Toxicity; Europe

1. Introduction There is an increasing need among users of pesticides, consumers and policy makers to get more information about the risk of pesticides to human health and the environment. There is also a need for information on how to quantify the effects of measures to reduce these risks. For this purpose various indicators, which are able to describe the environmental risk of pesticides, have been developed. Obviously, sampling and monitoring can contribute to the assessment of the environmental impact of pesticides, but this is very costly. Therefore methodologies have been developed to predict the environmental impact of pesticides. One of the earliest systems was proposed by Higley and Wintersteen (1992) which extended the concept of economic injury levels. Hornsby (1992) presented a screening procedure to minimise the risk of water pollution from pesticides. Kovach et al. (1992) developed a scoring system for agricultural pesticide use known as the environmental impact quotient (EIQ) which considered a range of impacts to farm workers, consumers and the environment. Reus and Pak (1993) presented the ‘Environmental Yardstick for Pesticides’. This approach assigns environmental impact points to pesticides according to their potential to pollute groundwater and their ecotoxicological impacts on aquatic and soil organisms. The different environmental impact assessment methods, which have been developed often, differ in purpose, environmental compartments and effects, and methodology. Several pesticide risk indicators have policy applications. These indicators aim to help governments describe risk trends over time and to describe national ecosystem health. For example, in Germany the Biologische Bundesanstalt uses the SYNOPS indicator (Gutsche, 1995) to calculate risk potentials in order to identify pesticides posing an unacceptably high environmental risk. Trevisan et al. (1993) describe a method for assessing the risk of pesticide contamination to groundwater considering

the vulnerability of the aquifer. New developments are also emerging from the work of the OECD Pesticide Forum which aims to develop risk indicators for both human health and the environment at policy level (OECD, 1998). Indicators also differ with respect to the compartments and effects taken into account. The screening procedure to minimise the risk of water pollution from pesticides of Hornsby (1992) specifically addresses the surface water compartment, while the environmental yardstick considers the compartments surface water, groundwater and soil (Reus and Pak, 1993; Reus and Leendertse, 2000). It is clear that the indications of risk given by the indicator can strongly depend on the compartment and effects considered. Although the risk ratio approach is favoured by many researchers, different methodologies have been used. For example, van der Werf and Zimmer (1998) have developed an expert system using fuzzy logic to assess the environmental impact of a single pesticide application in order to rank various alternatives. Lewis and Tzilivakis (1998) have developed a whole-farm approach to environmental management part of which is dedicated to assessing agricultural pesticide use using scoring and ranking techniques to assess regulatory compliance as well as environmental performance. van der Werf (1996), Levitan (1997), Hart (1997) and Falconer (1998) give overviews of pesticide risk indicators known from literature. They give a short description of each indicator, including a summary of the methodology behind each indicator and the way the indicators are used in practice. From these literature sources it is often difficult to fully understand the assumptions behind the indicators and it is also not possible to compare the outcomes when a same set of pesticides or crop protection scenarios is evaluated. Therefore, in the concerted action on pesticide risk indicators (CAPER) several additional steps have been taken. In CAPER the indicators are fully described including information about the methodology behind each indicator and the assumptions which have been made. Furthermore, an analytic comparison of the


indicators was made using a standardised data set. This gave additional information about the strengths and weaknesses of each indicator and provided information on the differences in outcome between the indicators. In CAPER, eight indicators which have been developed throughout the European Union were compared, including both those with policy applications and those intended for farm level use. Main objective was to evaluate the various methodologies and to identify the strengths and weaknesses of the indicators. This paper gives a summary of the study. The full study is described in Reus et al. (1999) which is also available on line at www.clm.nl. 2. Methods 2.1. Characterisation of the eight indicators In this study the purpose, environmental compartments and effects, methodology (including calculations, models, etc.), data source(s) and stage of development of eight risk indicators (Table 1) were compared. The eight pesticide risk indicators differed considerably with regard to purpose, compartments and effects taken into account, methodology used, and presentation. The acronyms of the indicators used in this paper are explained in Table 1. 2.1.1. Purpose, scale and stage of development of the indicators Purpose, scale and stage of development of the indicators differ. Most indicators have been developed as a tool for farmers and advisers to select pesticides with the least environmental impact (Table 2) (advise

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before use). One indicator (Hasse diagram, HD) has been developed for scientific purposes. Two indicators (SYNOPS and SyPEP) have been developed to advise policy makers about the risks related with the use of pesticides in a certain region or in a certain crop (evaluation after use). The scale varies from pesticide to national level. Most indicators are developed for crop or farm level (Table 2). Three of the eight indicators are already used in practice. 2.1.2. Compartments and effects taken into account in the eight indicators All indicators include the risk to water organisms (Table 3). Seven indicators take into account the risk of groundwater contamination. Five give information about risk to soil organisms (mainly earthworms). Only three include risk of pesticides to human health. Emission to air is taken into account by three indicators. Bioaccumulation and effects on bees are included in two indicators. Risk to birds, natural enemies and indirect effects to the ecosystem (biodiversity) are not included in any of the indicators. 2.1.3. Methodologies of the eight indicators The indicators differ with respect to the chemical properties of pesticides, which are taken into account, the application factors, which are included, and the environmental conditions influencing the outcomes of the calculations. Persistence in soil (DT50 ), mobility in soil (expressed in Koc or Kom ) and toxicity to water and soil organisms (LC50 and NOEC) are used in most indicators. Some of the indicators use complicated computer models to estimate risks, others make use of more simple algorithms (Table 4). Four indicators (EYP, SYNOPS, SyPEP and EPRIP) use a risk ratio approach, i.e.

Table 1 The pesticide risk indicators evaluated in the CAPER including acronyms of the indicators used and country in which the indicator was developed Number

Risk indicator

Acronym

Country

1 2 3 4 5 6 7 8

Environmental Yardstick HD SYNOPS 2 Environmental performance indicator of pesticides Pesticide environmental impact indicator Environmental potential risk indicator for pesticides System for predicting the environmental impact of pesticides Pesticide environmental risk indicator

EYP HD SYNOPS 2 p-EMA Ipest EPRIP SyPEP PERI

The Netherlands Denmark Germany United Kingdom France Italy Belgium Sweden

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Table 2 Purpose, scale and stage of development of the eight indicators which have been evaluated and compared in the studya Indicator

EYP

Purpose Advice to farmers Advice to extension Policy makers Food industry/consumers Water companies

HD

SYNOPS

p-EMA ∗

∗

∗

(∗ )

∗

∗

∗

∗ ∗

Scale Pesticide level Crop level Farm level Regional level National level

(∗ ) (∗ ) (∗ )

∗

∗

∗

∗

∗

∗

∗

EPRIP

∗

(∗ ) (∗ )

(∗ ) (∗ )

(∗ ) (∗ )

Stage of development Under development Pilot/testing Used in practice

Ipest

SyPEP

PERI

(∗ )

∗

∗ ∗

∗ ∗

(∗ )

∗

∗

∗

∗

∗

∗

∗

(∗ )

∗ ∗

(∗ ) ∗

(∗ ) ∗ ∗

∗ ∗

∗

∗

∗

∗

∗

∗ ∗

∗

a The acronyms used is explained in Table 1. * means that the indicator is developed for the purpose and scale indicated; (*) means that the indicator is not developed for this purpose and scale, but in practice it is used in that respect.

Table 3 Environmental compartments and effects which are included in the eight pesticide risk indicatorsa Indicator Compartments Groundwater Surface water Soil Air Effects Human health Aquatic organisms Soil organisms Bio-accumulation Bees

EYP

HD

∗

∗

∗

∗

∗

∗

SYNOPS

Ipest

EPRIP

SyPEP

PERI ∗

∗

∗

∗

∗

∗

∗

∗

∗

∗

∗

(∗ ) (∗ )

∗

∗

∗

∗

∗

∗

∗

∗

∗

∗

∗

(∗ ) (∗ )

p-EMA

∗

∗

∗

∗

∗

∗

(∗ ) ∗

∗

∗b ∗b

∗

∗b

∗

∗b

a The acronyms used is explained in Table 1. * means that compartment/effect is taken into account; (*) means that compartment/effect is partly or rudimentarily taken into account. b PERI takes into account the effects on non-target organisms in general, not the risk for specific groups of organisms.

Table 4 The general methodology used by each of the eight risk indicatorsa Methodology used Relative scoring tables Relative ranking Fuzzy expert system Risk ratios a

EYP

HD

SYNOPS

p-EMA

Ipest

EPRIP

SyPEP

∗

∗

∗ ∗ ∗

∗

PERI

∗

The acronyms used is explained in Table 1. * indicates the general methodology used in the indicator.

∗


the ratio between exposure (usually the concentration in a certain environmental compartment) and toxicity for relevant organisms. Concentrations are usually determined by (computer) model calculations, which take into account differences in environmental conditions and application factors (detailed descriptions are given in Reus et al., 1999 and also in Reus and Pak, 1993; Bernaerts and Pussemier, 1997; Trevisan et al., 1993). Two indicators (p-EMA and PERI) use a scoring table and produce a total score for environmental risk. Pesticides are classified according to certain chemical properties. Scores for each property are combined in a scoring table or algorithm to assess the relative overall environmental impact of pesticides (Lewis and Tzilivakis, 1998). One indicator (Ipest) makes use of an intermediate approach. The score of a pesticide is based on chemical properties, environmental conditions, application factors and a set of decision rules using a fuzzy expert system (van der Werf and Zimmer, 1998). One indicator (HD) produces a relative ranking of pesticides according to different aspects, like chemical properties (e.g. half life (DT50 ) and sorption (Koc )) and application rate (cf. Brüggeman and Halfon, 1995). 2.1.4. Presentation of outcome All indicators produce a score to reflect environmental risk or performance. For two indicators (EYP and SYNOPS) the score is directly based on concentrations of a pesticide in the environment or on the ratio between concentration and toxicity. This means that the scores vary with a factor of 1000 or more. Most other indicators come up with scores between two extremes, e.g., between 0 and 1, or between 1 and 5, for example by log-transforming ratios between concentration and toxicity. One indicator (p-EMA) produces negative scores to indicate detrimental effects on the environment (the pesticide with the highest risk gets the most negative score), while all the other indicators produce positive scores (the pesticide with the highest risk gets the highest score). Two indicators (p-EMA and PERI) give only one total score for environmental risk or performance. Three indicators (HD, Ipest and EPRIP) give separate scores for different environmental compartments that are also combined in a total score. Three indicators (EYP, SYNOPS and SyPEP)

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only produce separate scores for environmental compartments. 2.2. Experimental methodology used to analyse indicators The eight indicators were compared using an assessment of the environmental risk of 15 individual pesticide applications. Also the influence of environmental conditions on the outcomes was evaluated. In this paper, the main methods and results are presented. Full information is presented in Reus et al. (1999). For the assessment of the environmental impact of individual pesticide applications, 15 active ingredients were identified (Table 5). The pesticides belong to various chemical groups and include some older pesticides and some new products. For each pesticide, application rate (according to label recommendations and crop), date and method of application were defined. The type of crop and the method of application determined the percentage of spray drift to surface water and the interception of the pesticide by the crop, i.e., the percentage not reaching the soil surface. Furthermore, a standard database for the 15 pesticides was established in order to avoid introducing additional variability into the comparison exercise. Priority was given to regulatory data. The database is presented in Reus et al. (1999). Subsequently, scores for the individual pesticide applications were calculated with each indicator using the database. The Hasse diagram (HD) was not included in the detailed evaluation of individual pesticide applications, as the number of pesticides was too low to make a sensible ranking. The results of the other seven indicators were compared with each other by testing the level of correlation using the Spearman rank correlation test. For this purpose only the rankings (from low to high risk) of pesticide applications by each indicator were compared as the large differences in format of output values prevented a direct comparison of scores. Four different aspects were taken into account: risk for the environment as a whole, risk for water organisms, risk for groundwater and risk for soil organisms. Finally, for each of the pesticide applications and for each indicator, the scores were calculated under different environmental conditions, such as distance to

182


Table 5 Overview of the 15 pesticide applications (including active ingredient, type of pesticide, crop, application rate, spray drift and interception by crop) which have been used in the study to compare the environmental risks between the eight indicators Active ingredient

Type of pesticide

Cropa

Application rate (g active ingredient/ha)

Percentage spray drift (standard situation) (%)

Interception by crop (%)

Chlorothalonil Diflubenzuron Dimethoate Epoxiconazole Esfenvalerate Glyphosate Imidacloprid Isoproturon Kresoxim-methyl Mancozeb MCPA Metribuzin Pirimicarb Rimsulfuron Tolylfluanid

Fungicide Insecticide Insecticide Fungicide Insecticide Herbicide Insecticide Herbicide Fungicide Fungicide Herbicide Herbicide Insecticide Herbicide Fungicide

Potato Apple Potato Winter Winter Apple Apple Winter Winter Potato Winter Potato Apple Potato Apple

1500 100 200 125 5 2200 60 1500 125 1500 800 750 400 12.5 750

0.7 10.1 0.7 0.7 0.7 0.7 10.1 0.5 0.7 0.7 0.5 0.5 10.1 0.5 10.1

50 80 50 70 80 20 80 2 70 50 30 10 80 10 80

a

wheat wheat

wheat wheat wheat

Potato (Solanum tuberosum L.), wheat (Triticum spp.), apple (Malus spp.).

surface water, soil type and weather. Full information on these conditions is presented in Reus et al. (1999).

3. Results 3.1. Comparison of indicators based on ranking of individual pesticide applications 3.1.1. Total environmental risk The total environmental risk was calculated by p-EMA, Ipest, EPRIP, PERI, EYP, SYNOPS and SyPEP. For the latter three indicators the total score was calculated only for the CAPER project to allow a comparison between all indicators. The scores of the 15 pesticide applications as calculated by the indicators clearly showed that the format and magnitude of the output values differed considerably (Table 6). For EYP and SYNOPS the differences between scores were large (a factor of several thousands). For the other indicators the differences were less extreme. The level of correlation between the rankings of the 15 pesticide applications differed (Table 7). The majority of the correlation’s had a significance level of more than 95%. There were, however, some exceptions. The ranking by PERI deviated from the

rankings by all other indicators due to the importance of the leaching potential of pesticides in this indicator. The other differences can be explained by the fact that indicators take into account different environmental compartments (e.g. occupational hazard is included in p-EMA and not in most other indicators; SYNOPS does not take into account the risk of groundwater contamination, etc.). Another explanation is that some indicators take into account the environmental effects of metabolites (e.g. SyPEP), while others do not (e.g. EPRIP). The rankings by the indicators were also compared with the ranking by application rate. It appeared that the ranking of pesticides by application rate was correlated with the rankings of p-EMA, Ipest and EPRIP. There was no correlation with the rankings by the other four indicators (Table 7). Fig. 1 gives a graphical presentation of the correlation’s with a significance level of more than 99%. This graph shows that on one hand, the rankings by SyPEP, SYNOPS and EYP were highly correlated and on the other hand, the rankings by EPRIP, Ipest and p-EMA were quite similar. Ipest was also well correlated with EYP and seemed to build a bridge between the two groups. Although the ranking of pesticides by the indicators were not always correlated, some pesticides among the 15 active ingredients had


183

Table 6 Scoresa for the total environmental risk for 15 pesticide applications as indicated by seven indicatorsb Active ingredient

EYP

SYNOPS

p-EMA

Ipest

EPRIP

SyPEP

PERI

Chlorothalonil Diflubenzuron Dimethoate Epoxiconazole Esfenvalerate Glyphosate Imidacloprid Isoproturon Kresoxim-methyl Mancozeb MCPA Metribuzin Pirimicarb Rimsulfuron Tolylfluanid

812 918 17 16 66 4 45 7474 22 193 343 5419 1096 338 606

1.60480 0.73791 0.00615 0.03772 0.90443 0.02210 0.10731 6.77248 0.17244 0.00504 0.03545 0.74610 2.81476 0.00026 0.21487

−39 −14 −39 −12 −27 −29 −10 −45 −13 −40 −38 −40 −30 −9 −36

0.5715 0.4503 0.3384 0.2570 0.2800 0.4567 0.3263 0.7152 0.2750 0.4774 0.5328 0.6001 0.5942 0.3109 0.6674

6 6 12 3 3 2 2 135 6 49 49 135 9 2 9

6 3 3 5 5 3 2 10 5 4 2 9 9 3 4

2.9 4.2 4.3 4.8 3.9 1.1 5.3 5.3 2.8 4.8 5.3 5.3 4.3 5.2 2.6

a b

The higher the score (or the more negative in the case of p-EMA), the higher the risk for the environment. The acronyms used are explained in Table 1.

a high predicted impact on the environment with all indicators.

differences in toxicity (i.e. toxicity to algae, daphnia and fish) between the pesticides, as was shown by the high correlation with ranking based only on toxicity. Fig. 2 shows a graphical presentation of the correlation’s with a significance level of more than 99%. Similar rankings were found for SyPEP, EYP and SYNOPS given and for EYP, SYNOPS, EPRIP and Ipest.

3.1.2. Surface water The environmental risk for surface water was described by EYP, SYNOPS, Ipest, EPRIP and SyPEP. For all five indicators the rankings of the 15 pesticide applications with respect to surface water were significantly correlated (Table 8). For all five indicators the ranking of pesticide applications based on the application rate was not correlated with the rankings based on the surface water scores. This was due to the large

3.1.3. Other compartments For groundwater the rankings of the 15 pesticide applications were significantly correlated, except for

Table 7 Correlation between rankings of the 15 pesticide applications for total environmental risk by seven indicators (Spearman correlation test)a,b,c

EYP SYNOPS p-EMA Ipest EPRIP SyPEP PERI Application rate (kg) a

EYP

SYNOPS

p-EMA

Ipest

EPRIP

SyPEP

PERI

Application rate (kg)

1.00 0.66∗∗ 0.49∗ 0.76∗∗ 0.60∗ 0.51∗ 0.34 0.20

1.00 0.30 0.46∗ 0.29 0.73∗∗ −0.04 0.08

1.00 0.76∗∗ 0.87∗∗ 0.45∗ 0.17 0.74∗∗

1.00 0.68∗∗ 0.38 0.15 0.68∗∗

1.00 0.42 0.38 0.50∗

1.00 −0.01 0.25

1.00 −0.08

1.00

The acronyms used is explained in Table 1. The HD was not included in this evaluation, as the number of pesticides was too low to make a sensible ranking compartment/effect is taken into account. c Application rate was included in the analysis to show how the indicators correlate with this parameter. ∗ P < 0.05. ∗∗ P < 0.01. b

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Fig. 1. Graphical presentation of the correlation’s between the rankings of the 15 pesticide applications for total environmental risk (indicators within an ellipse or circle show a correlation with a significance level of 99%, the acronyms used are explained in Table 1, kg stands for the application rate expressed in kilogram).

the rankings by EPRIP and SyPEP. One explanation is the relative importance of metabolites in SyPEP, while EPRIP does not include metabolites. The ranking by application rate is not correlated with the rankings by the indicators. This is due to the large differences in leaching potential between the different pesticides. There was a moderate to high correlation with the ranking based on the GUS index. For soil, the rankings of the 15 pesticide applications by SYNOPS were significantly correlated with the rankings by EPRIP and EYP. The rankings by EYP and EPRIP were not correlated. This may be due to the inclusion of chronic exposure in EYP, based on toxicity data extrapolated from toxicity data for water organisms. Because of the small differences between soil scores of the various pesticides, coincidence can play a major role as well. The ranking of pesticides

Fig. 2. Graphical presentation of the correlation’s between the rankings of the 15 pesticide applications for risk to surface water (indicators within an ellipse or circle show a correlation with a significance level of 99%, the acronyms used are explained in Table 1, kg stands for the application rate expressed in kilogram).

by application rate is not correlated with the rankings by the indicators, except for EPRIP. This is due to the differences in degradation and toxicity between the different pesticides. 3.2. Reaction of indicators to different environmental conditions The indicators evaluated in CAPER differed in the way they react to differences in environmental conditions. The ranking of the 15 pesticide applications was barely influenced by the different environmental conditions. Only different distances to surface water changed the ranking of pesticides substantially for two indicators. This could be explained by the shift in relative importance from drift (important at short

Table 8 Correlation between rankings of the 15 pesticide applications for risk to surface water (Spearman correlation test)a,b

EYP SYNOPS Ipest EPRIP SyPEP Application rate (kg) Aquatox a

EYP

SYNOPS

Ipest

EPRIP

SyPEP

Application rate (kg)

Aquatox

1.00 0.80∗∗ 0.85∗∗ 0.84∗∗ 0.66∗∗ 0.18 0.76∗∗

1.00 0.78∗∗ 0.64∗∗ 0.84∗∗ 0.11 0.77∗∗

1.00 0.64∗∗ 0.60∗ −0.23 0.77∗∗

1.00 0.60∗ 0.30 0.73∗∗

1.00 0.12 0.78∗∗

1.00 −0.18

1.00

The acronyms used is explained in Table 1. Application rate and aquatoxicity were included in this analysis to show how the indicators correlate with these parameters. ∗ P < 0.05. ∗∗ P < 0.01. b


distances) to run-off (relatively more important at large distances). The scores of most indicators were influenced by different environmental conditions. Not surprisingly, smaller distances to surface water increased the score for risk to water organisms, while higher organic carbon contents in soil decreased the risk of groundwater contamination. Higher temperatures decreased the risk to soil organisms, as degradation of pesticides increased.

4. Discussion 4.1. Comparison of indicators Differences between indicators (i.e. their purpose, compartments, methodology, presentation of results) sometimes led to differences in ranking of the 15 individual pesticide applications that were evaluated. Still there was a certain degree of agreement in this ranking. Especially, the ranking of the pesticide applications for separate compartments and effects was well correlated. Edward-Jones et al. (1998) arrived at similar conclusions in their study on indicators. This shows that the different methodologies used to rank the pesticide applications do not lead to large differences. Where there was no significant correlation, this can be explained by the fact that some indicators do no take into account the leaching potential of metabolites or by the fact that some indicators include two routes of emission to surface water (spray drift, run-off), while others consider only spray drift. Some of the indicators, like EPRIP, use complicated computer models to estimate risks, others, like PERI, make use of simpler algorithms. More particularly, there is a clear difference between indicators using a ‘risk ratio approach’ and indicators that aggregate input data in a different way, e.g., in a scoring table. The first group estimates exposure (often expressed as concentrations in a certain compartment) and relates exposure to (laboratory) data on toxicity. This approach is consistent with the risk assessment procedures used for the authorisation of pesticides (Reus and Leendertse, 2000). Estimating exposure is often complex, as it requires computer modelling and detailed research. The second group of indicators has the advantage that input data on pesticide properties

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can be combined based on expert judgement. However, the results are more subjective. For the comparison exercise in CAPER a common database was constructed to avoid differences in output due to differences in input data. This exercise revealed that many data on chemical properties of pesticides were lacking or showed large differences. Others have identified this problem as well. Especially, data on properties of formulated products, adjuvants and metabolites are barely available. The creation of a EU-database using the information from the registration dossiers is therefore needed. It is essential that the data are checked by an independent organisation since most of the information comes directly from the pesticide industry. 4.2. Requirements of indicators A conference on the results of CAPER made clear that indicators are seen as important tools to reduce the environmental impact of pesticides by many stakeholders throughout the European Union (Reus et al., 1999). In the conference a number of requirements which pesticide risk indicators should meet were discussed. The ideal indicator should deal with real risks of a pesticide application, rather than hazard and include application rate, application factors and environmental conditions. However, including all these variables would make the indicator too complex to use. Moreover, field data on environmental effects under different conditions are lacking for most pesticides. Therefore, an indicator should strike a balance between completeness and applicability and be flexible enough to include field data if they are available. An indicator should give separate scores for different environmental effects (including human health), rather than producing only one overall score for environmental risk. On the other hand, individual scores on a large number of different effects would be confusing for users. Therefore, an indicator should give users guidance on how they can interpret the scores for different effects, for example by embedding the indicator in a decision support system. The judgement of the environmental effects of an individual pesticide application by an indicator should be consistent with the judgement by the registration authorities, in order to avoid confusion among users.

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This consistency is best guaranteed if the indicator follows the risk assessment methodologies of the registration procedure. On the other hand, not all possible environmental effects are yet included in the registration procedures. One example is the emission of pesticides to air (Boland and Leendertse, 1999) or the effects on non-target beneficial arthropods. Indicators could be used to fill this gap. Indicators should produce reliable information. This makes validation necessary. Indicators can be validated by comparing outcomes with environmental effects in the field, but this kind of validation is extremely complicated and can only be carried out if indicators produce output which can be measured in the field, like concentrations in surface water or groundwater. Another way to validate an indicator and to increase its reliability is to make the calculations behind an indicator transparent and subject to expert judgement and peer review (Reus et al., 1999). Indicators should be flexible in the output they deliver, i.e., it should be possible to aggregate and disaggregate information, for example on crop, regional and national level. This makes it possible to identify problem areas and to better target policy instruments. For use as a farmers tool indicators are preferably embedded in a decision support system which gives information about environmental effects of farm management as a whole and which guides users with respect to the measures they can take to minimise environmental risk. 4.3. Recommendations for indicator use It is recommended to develop a harmonised scientific framework for an EU pesticide indicator. With such a framework the monitoring and evaluation of pesticide policies can be harmonised and farmers’ decision tools can be based on the same principles. As the results of the present indicators for individual compartments and effects largely agree, the present methodologies can be used as a basis combined with the latest state of the art from literature and the registration process. A modular structure leaves room for indicator developers to select the environmental effects they want to consider, to take into account specific conditions at the national or regional level and to select the most appropriate indicator (simple or advanced).

It is also recommended to produce a handbook describing the steps to set up a pesticide risk indicator. As OECD is already developing indicators for policy evaluation, this handbook should focus on indicators at farm level. The handbook should describe how to develop an indicator using the various environmental modules and it should give indicator developers information on how to present the outcomes of the indicators to farmers, e.g. by embedding the information in a decision support system or by presenting the outcomes in simple formats. 5. Conclusions 1. The rankings of the 15 pesticide applications by the indicators differ when the score for the environment as a whole is concerned. This is caused by the large differences in environmental compartments taken into account. However, some pesticides among the 15 active ingredients, which have been evaluated, have a high ranking (higher impact on the environment) with all indicators. 2. In general, the pesticide risk indicators give a similar rankings of the 15 pesticide applications for the individual compartments surface water, groundwater and soil. 3. The ranking based on the indicator ‘kilograms of active ingredient’ did not correlate with most of the rankings by the pesticide risk indicators. The scores for surface water were largely determined by the toxicity to water organisms. The scores for groundwater were largely determined by DT50 and Koc . An exception was toxic or mobile pesticides, which are used at extreme low application rates. In those cases application rate has a large influence. 4. The indicators evaluated in CAPER differ in the way they react to differences in environmental conditions. The scores of most indicators were influenced by different environmental conditions, but these conditions barely influenced the rankings of the pesticides. Acknowledgements The concerted action CAPER was financially supported by the Commission of the European


Communities within the framework of the FAIR programme (contract number CT97-3510). The official title is: ‘Evaluation of methods to compare or rank pesticides according to their environmental impact’. The project does not necessarily reflect the Commission’s views and in no way anticipates the Commission’s future policy in this area. The Centre for Agriculture and Environment has co-ordinated the concerted action, but most of the work has been carried out in workshops and discussions with the other participating organisations. References Bernaerts, S., Pussemier, L., 1997. Estimation of pesticide emissions to surface and groundwater in Belgium using the SEPTWA95 model. Med. Fac. Landbouww. Univ. Gent 62 (2a), 157–170. Boland, D., Leendertse, P.C., 1999. Minder bestrijdingsmiddelen de lucht in—maatregelen in praktijk en beleid. Centre for Agriculture and Environment, Utrecht. Brüggeman, R., Halfon, E., 1995. Theoretical base of the program ‘Hasse’. GSF-forschungszentrum für Umwelt und Gesundheit, Germany. Edward-Jones, G., Quin, F., Maud, J., Davis, K., Tait, J., 1998. Pesticide use optimisation: review of risk indicators. Final Report to MAFF CSA 3943. Falconer, K., 1998. Classification of pesticides according to environmental impact. Centre for Rural Economy, University of Newcastle-Upon-Tyne, Newcastle. Gutsche, V., 1995. The influence of pesticides and pest management strategies on wildlife. Integrated Crop Protection: Towards Sustainability. BCPC Symposium No. 63. Hart, A., 1997. Key characteristics of pesticide risk indicators used as policy tools: a comparison of 11 indicators. In: OECD Workshop on Pesticide Risk Indicators, Copenhagen, April 21–23, 1997. Higley, L.G., Wintersteen, W.K., 1992. A novel approach to environmental risk assessment of pesticides as a basis for

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